Repository: TwistedHardware/mltutorial
Branch: master
Commit: 684ed020a3c6
Files: 58
Total size: 13.6 MB

Directory structure:
gitextract_2qeygc7v/

├── .gitignore
├── LICENSE
├── README.md
└── notebooks/
    ├── ARSSF/
    │   ├── 1.Introduction.ipynb
    │   └── 2. OCR.ipynb
    ├── IPython-Tutorial/
    │   ├── .ipynb_checkpoints/
    │   │   └── 4 - Numpy Basics-checkpoint.ipynb
    │   ├── 1 - Notebooks & Cells.ipynb
    │   ├── 2 - Markdown & LATEX.ipynb
    │   ├── 3 - Basic Python.ipynb
    │   ├── 4 - Numpy Basics.ipynb
    │   ├── 5 - Plotting Charts.ipynb
    │   ├── 6 - IPython Widgets.ipynb
    │   ├── 7 - Pandas.ipynb
    │   ├── 8 - SymPy.ipynb
    │   ├── BLEGH.md
    │   ├── Indian General Elections 2014.ipynb
    │   └── data/
    │       ├── 2014_indian_election_turnout.csv
    │       └── 2014_indian_elections_results.csv
    ├── Lesson 1 - Understanding the tools.ipynb
    ├── Lesson 2 - Intoduction to ML.ipynb
    ├── Lesson 3 - Regression.ipynb
    ├── Lesson 4 - Clustering.ipynb
    ├── Lesson 5 - Preprocesing.ipynb
    ├── Lesson 6 - Features Extraction.ipynb
    ├── data/
    │   └── temp_data_features.csv
    ├── data-mining/
    │   ├── 1. Web Scraping.ipynb
    │   ├── 2. YouTube Data.ipynb
    │   ├── 3. Twitter API.ipynb
    │   ├── 4. Google Custom Search.ipynb
    │   ├── 5. MongoDB.ipynb
    │   └── 6. Twitter Streaming API.ipynb
    ├── ipython_widgets/
    │   ├── 2. Events.ipynb
    │   ├── 3. Styling Widgets.ipynb
    │   ├── 4. Widges Layout.ipynb
    │   └── Widgets Containers.ipynb
    ├── jupyter/
    │   ├── 1. Introduction.ipynb
    │   ├── 2. Markdown & LaTeX.ipynb
    │   ├── 3. Python Basics.ipynb
    │   ├── 4. Numpy.ipynb
    │   └── BibTex/
    │       ├── Compile Article.ipynb
    │       ├── citations.tplx
    │       ├── mynotebook.ipynb
    │       └── ref.bib
    ├── live/
    │   ├── .ipynb_checkpoints/
    │   │   └── 1. painting-checkpoint.ipynb
    │   └── 1. painting.ipynb
    ├── parallel_computing/
    │   └── 1. Load Balancing.ipynb
    ├── quick_tips/
    │   ├── 2. Pickle Data and Model.ipynb
    │   ├── 3. Sentiment Analysis.ipynb
    │   ├── 4. XPath.ipynb
    │   └── 5. The largest Prime.ipynb
    ├── tf/
    │   ├── .ipynb_checkpoints/
    │   │   ├── 1. Machine Learning Basics-checkpoint.ipynb
    │   │   └── 2. Tensors-checkpoint.ipynb
    │   ├── 1. Machine Learning Basics.ipynb
    │   ├── 2. Tensors.ipynb
    │   ├── 3. Variables.ipynb
    │   └── tf_board/
    │       └── events.out.tfevents.1526725038.tatooine
    ├── vlogs/
    │   └── 4th dem debate.ipynb
    └── youtube.csv

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]

# C extensions
*.so

# Distribution / packaging
.Python
env/
bin/
build/
develop-eggs/
dist/
eggs/
lib/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.cache
nosetests.xml
coverage.xml

# Translations
*.mo

# Mr Developer
.mr.developer.cfg
.project
.pydevproject

# Rope
.ropeproject

# Django stuff:
*.log
*.pot

# Sphinx documentation
docs/_build/



================================================
FILE: LICENSE
================================================
GNU GENERAL PUBLIC LICENSE
                       Version 2, June 1991

 Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
 Everyone is permitted to copy and distribute verbatim copies
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================================================
FILE: README.md
================================================
Machine Learning Tutorial
=========================

View Notbooks here:
http://nbviewer.ipython.org/github/twistedhardware/mltutorial/tree/master/notebooks/

For video tutorial check:
https://www.youtube.com/user/roshanRush


================================================
FILE: notebooks/ARSSF/1.Introduction.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:f8d7e6885b3c2c66d6cd4b3d36c57815afb126577254e011442dd7ee1423ad81"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Automated Referee System for Saber Fencing (ARSSF)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://upload.wikimedia.org/wikipedia/commons/thumb/f/ff/Final_2013_Fencing_WCH_SMS-IN_t202316.jpg/800px-Final_2013_Fencing_WCH_SMS-IN_t202316.jpg)\n",
      "\u00a9 Marie-Lan Nguyen / Wikimedia Commons / CC-BY 3.0\n",
      "\n",
      "\n",
      "**These are the basic rules:**\n",
      "- You score a point once your saber touches the other player upper body.\n",
      "- If both players hit with in 120ms from each other, the player with the Right of Way (RoW) gets the point. In simpler terms the attacker gets the point.\n",
      "- If both attacked at the same time and and hit each other within 120ms no point is awarded to neither one of them.\n",
      "- Players loose the RoW once they are blocked or if they miss.\n",
      "- There are other more rules...\n",
      "\n",
      "![](rules.png)\n",
      "\n",
      "References:\n",
      "\n",
      "- [Fencing on Wikipedia](http://en.wikipedia.org/wiki/Fencing)\n",
      "- [Fencing Rules on Wikipedia](http://en.wikipedia.org/wiki/Fencing_practice_and_techniques)\n",
      "- [International Fencing Federation](http://fie.org/)\n",
      "- [Sydney Sabre](http://www.sydneysabre.com/)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The Goal"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a system that can detect scoring points and keep track of the results."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "What is the Approach"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Feature Extraction\n",
      "- Tracking Lights\n",
      "- OCR (Optical Character Recognition)\n",
      "- Tracking Players Location\n",
      "\n",
      "##Pre-Processing\n",
      "- Split the game into rounds using OCR on the score\n",
      "- Split the rounds into segments \"bouts\"\n",
      "\n",
      "##Image Processing & Computer Vision\n",
      "- OCR\n",
      " - Score\n",
      " - Player Names\n",
      "- Motion Tracking\n",
      " - Location of Left and Right Players\n",
      "- Template Matching\n",
      " - Detect Red/Green Lights\n",
      "\n",
      "##Machine Learning\n",
      "- Features:\n",
      " - Players' Motion\n",
      " - Lights\n",
      "- Result of reach segment \"bout\" is the result"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Computer Vision & Image Processing Libraries"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be working with OpenCV and Scikit-Image. OpenCV is a computer vision library and it has capabilites to process video and extract frames. Scikit-Image is an image procesing library that might come in handy for some advanced image processing."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Features Extraction"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Import Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import datetime\n",
      "\n",
      "import cv2\n",
      "from cv2 import cv\n",
      "\n",
      "import skimage\n",
      "import skimage.color\n",
      "import skimage.feature\n",
      "import skimage.filter\n",
      "\n",
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Open Video"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cap = cv2.VideoCapture(\"video/SabreFinal.mp4\")\n",
      "if not cap.isOpened():\n",
      "    print \"Unable to Open Video\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Reading Frames"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret, image = cap.read()\n",
      "if ret:\n",
      "    plt.imshow(image)\n",
      "else:\n",
      "    print \"Unable to Read Frame\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f327e56cd10>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for counter in range(130):\n",
      "    ret, image = cap.read()\n",
      "if ret:\n",
      "    plt.imshow(image)\n",
      "else:\n",
      "    print \"Unable to Read Frame\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+3lqasqKJFQKFUoJ6PKI8maJ1yvZmRlNVgZ3jBVo9yoyRbWEP5+nlHU4Oj8jy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Yr3ZgAPG/s/DfP0n1EBpWNSrpTmhroWrL8rfnarnO5fXde0rpWOSK2pm7rfr+CDcm26lVEXtMfj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\nFpcuBvAQvedwvE9/OKQpRSudGMkTvakoC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spqitaaL37pC3zzqSf5xV/4D7h89SrnXn6Z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XIwGoEwrWiM61SjWTDcW7juKsyVDUMsSYTeBLi6Dq6MWRXEecJrYxIro1uNA7SunQ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XUkEkkjcRgYxoGhLBLdOCAlIpqEoofBRHTzkOk0It6TJeClJmk0nK3WqECKgmNV+Ewyqkfs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\n/X9H+k4BD8tQGgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f32114f6390>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Note:\n",
      "**OpenCV uses in BGR (Blue,Green,Red) color schema as default. But almost every other library (including Matplotlib) uses RGB as default.**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
      "plt.imshow(image_rgb)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f3213671c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VDSpuUAiEUhyd1kyOS1KtydY2qKoGsAgvkEo/rIzxslWDQCfvsb93TC/PGPQ6\nlEXNweE+UeRIopTx+JTFYoH3hrIs2sK3Q6mI09PTIDe+d4+msWxsbDCdTnnuued47cZ13v/+9/Ph\nD3+Yvb09Dg/3+dznPsf58+c5OTmh3+9yeDQmiiJ2d3cDwJ+egLdUVUGWZYzHoaFoNBgS6YjHr12j\naRruvHGTNE3pdvvUtaHbjel0epTVAmsti8UMJYM2vq4b8m4Qi3scURxRLEpQksFgQJrmjGfT+w1p\nzoMKaybNMrTzbZa3fNQO5R1WWr5bc/2PLJhbC53Bdz/u72rZ3+M9B+dgcC4H7ne+meYqr7x0nZPT\nmiTOyNIenXQDITokSUYnH/HXX3uVxjf0e0N6eYdOlnN+5wrnr1zh5xoPEdh5w6Kcc3BwwPHBIftH\nhxhjMKZGSk23NyRSEVnU4cojVzmdxpyc7DObTzDOcnRyjPYh8sjyHMQSCCx+2RCDWIGzSFN63R6V\nqairGtdKvZy3VE2NqStUJInjFKlC9CeExDQNjQtwuwQQYxxZJvDSMp/OiQTEcczOzjbdTsZkUhEp\nTRpr+v1e6Hy0Do9qVQiOuigCZUXgXtM0oygKpNBIodHacfNm2GydTifwp1W10pbXxqC0ZnM0ZHp6\ngnOabre7ojm01vT6HfCSTr/HYu+AKI5IfChmmsYjNcADss4Hst0gFRcYlsB6f8M550JmIkNNI1Bn\niul0ujpm2ZWrVHBSy9ct/+/Bjkwp7zsCpRSidRrGmLYoHX7Osmx1/x+WtIkHlFU1TdO02YNsAX+Z\nXcDe7lGgG9YKxuNAFQBBu+8aalMjjKFMJJ1+xnBzE+fCejgeTx9yXsvuTaU1ZVkSac/TT72N6eEh\nAJ18A6ksddHQ2+qu7smyS7dpmlUjTqfT4fLlyzzyyFVeeuklfvVXf5Vbt26R5znPPPMMf/iHf4gx\nodcgAPSYw8NDev0+vX5ndT7dXg4uOISlc9M6Jooirl+/ztHREYfHoUEwabtjsyxrNeehe3nZZVzX\n9arjVFgRukU3RqtalzGGOApZ8tJpN02DlLJ95p4kSdtGsbCCgnzSg3DUTd02GX5nJuJHFsy1in+o\nv1pDR4qn3vU4L/zZmEF/ndmkQes+6xvXGI4y/oN/9BxprvAuRGV12TCeTHjj3l0Wi+DxlVKsj9YY\nDAacv/gIF7Yv8jRQVtNVh1uxqJlPS5z4U0Ybmxgxp9fLQ2NSq4ypipLj42Nu3dplc3OTXj8jjnNc\nqxYIrdsBWg0FAAAgAElEQVQG65oVRymJiJxCRQKtS5xw5D27asNvqhppHM4INDHGhO5ZpUOE4pxD\nKIHwHmc8WZySJhHDYQ/nDePxCbGW6CQhiTRaCjpZircGLQQiSSjqqm0GMUgVkUQRdRk6JIUMXZRx\nnLYbxtHr9ahlyWKxAGfRMnD/ZV2Q0ydKNU1TM10Y4kSSdsNohtmixDjPziOX+Kuvf41YRzTWkvbX\nEVojXI0T0Qpcl5OFPH7V7Sh9UBy5QKCjVFCPNI1Fa4FwNWkUs1gseNtjj3Nvf48kS5nNZvzcz/0c\nf/EXf4kxlslkQmMNWipkq7v3rcNQYukwBE1TU7SjEAKFEiFl6E1Y8uNKRauGOGNMy0HbVcQuCNTc\nUkPtPfi26UfHwYmcTBcIpZE6AhzO2FDHwAc5tLLUxlFNxihCs9f5zfWgIKkNi6Kgqhqch0G/T1VV\n9Podbr35Or/8oQ/z+c/9W4S3COfpdFIAkjijk6e8+JdfobGOjY0NdnZ2yLKM09NTbt++3dYZPB//\n+Edb5yf5xMc+ThQp6roEBccnx2xtbbFmQxd5XZlWmhhUKZPTMa+/fkJRFIxGa8znC7rdLqenE+Io\nxTRh1MXGhQsAZN2M08lpUEvJAO7WQVHVnI6nbO+cx9iaRb1gMBpivSNLc4pFeB6x1ngNrrbMixmj\n9SHFZAFSBOGDafDOYlzoAE6jODz3KCKJ9LdVNq0w5/uGXn9HO7j3Jr6tWnfyHt4Lvva1l3n3s+9F\nqO+O0kJ89yaVHwbLsy5JklHVirx7jp969hk2toakeSvjkoI4iYmTmO6gw/alC6vX1nVDOa85PZlw\n883blItJq3dVbXt5l35vwMWdS+RZn3e+520Ui8tY22BsEeROi4KjoyMGozUecY75fEZRlEDIGIQG\nbx22qcB4pAgAbI1Ax6ClQCcxZVNgbUMSt0U1L7HWkCqPdw5BkNFZ45Aq8OvSB9UBQG84IM8SokiR\nZh20FHTzlNlsgpaCpqlCc5BSCAHOheaLzc0tTk9PV91+QoSIL4rjULArm9Xskfl8jrUNg8GA2WwW\nmkryDsNB1Ebz4XmItohXm4a1tQ3KssaXJUfHx1y6dIlivmBehkKiEG106R3iwZreA3ywEAKh49Ac\n1Mr/Fovpima4P76gptvtsr+7x/bFbV59/TU+8IEP8NWvfpVz587xV3/1VZxz7OzsYBvD9HT80Ocs\nI3YhQvTX64XrvM+132+GCg1n5epcl9LE5ZybsgjA4LnPbYexB/qh61tmG65tUBPOo6RESkjicN91\nOJDaGpJYU1eGn37uOQ6OjvnGq6+RpF2MNUitybIMZ2twNePxmMViQTrqYEzF/v4+SiniKOXC+XM8\n+eSTzIuSsiyZTsccHx/inCNNY0ajUPDM87xt3Ik5ORlT11XQp0vH2toak8mklSYWYCFWoR4SOssL\nNjc3USqi2+1jbSgmDwYDikVoCFquo0CFhXumlKLf7/PIlUdQSrG7uxtomLzD3NQURUGe5xjjw32p\nmzBaQUCig+M9ODhge3ub1yevBQWUbIUIrg7FYhmy36ap8DhsWeO/qXnsm+0HBuZ2cYDWmr27B2T5\nAC8kO1s9Xn3pS2xubYPUpGmyejhSSmSchi46L1jMDX+brsO/q/kaRPzdj/vbWt7rE6ddGudwXnKw\nX/Cef7D5t3ptHEfEcUR/1OHyo/dB3hnL7du3kU4ymxYc7E3ZOn+Zm29MWZT7ZN2Mbi+nP+igNyQX\nrzyKgFVqXZYlu7u7NE3D8fExRV0gvUYIzXQyaeWOdgUCWmucAedE4POEQmoRdO8u8MgOiVARzjX4\nNvJBsKI/sjwljXXQrztHlHXaxhiJsw2DYY+mqrDWkGQ5s3lBnne4d+9eGCjWAlVd1VRN3aqMFFHE\nKmJJ05SqgcqUrK+vM5lMqMqGyliiOEYClWmIlSLvDNCpZjJf0NSWMOYAGmuJ04Q4TVBZl6IsybL+\nA3z1w/bgMKwwv0dRlveVEstUXAgPKg5t4WXBoCy5du0aZVnyi7/4i3z9G6/yz3791/lnv/ZrTKdT\n5tMZnTRQNMsZI0mSMhqN6Ha7vPTSSytlx9bmufAsT0OUubwfy7R8Neit7Su47yAciPuD4IQQ7ZgH\nuWpskn4J7CFTlFqhtURrgRaQaB1GVggBi4pMS2xtyZKIRy5d5N9+7nNcvnKNJNYh0vWglKc0DZ/5\nk0/z+LXHqBZzhoM1sjQUIeu65s03bhHHMf3+MCivrCTLRqGwH8dhyB6OSEvu3b1NsWiI4zQ02yE4\nOjlcadD7/T4AVVPSVDVYRxwn9Dt9Ep0Q9SOaslmNDhFCMZlMGK2vURQFWsUoFai/5Qwf7z3nNs+t\nqJIoipgXC2SkESi0jHDeUM4r0jhHR5Zer8Ph8QGDwYDd3V2efvppvmG/gUCC0lhHUFYp1XaztsPw\nlnvxh1XN8tpffZGjkyOODw85nSx48sknOb99IaRTnGAMNBMwTcNkDI0LPJ8UGqljIr3JrVcdFy6f\nRyffP1WLbUB/H8Fcx2Fq4ZVzG5weKqz93h2Q1IrLVx556He79464/LYe0MMbmExmnJyMmc5OqMoS\nKcJ8kzzP6feG5FmXsix54oknwqTIyhBJxeH+ATdu3ODGG69jnSWJE+IkwfuEulrgvSXRcYgULFjr\ngvLDh6aZpmnwLTe4sdknSRJ6vaD9xkuEZDWhzxlWzqIsa3wbeS8WFSDAwubGFtPpFKE04/GUK488\nStnUFFWIgNbW1rDWBp2wjEjTvOWl52xsnAMR8er113FecPniDt5ZDvf3mM1mdNUQQRSGX7kwAc+5\n20gJ1nm08yuq6r5ufBnp3o92wxRMQ20sRWFWipplFKyUQjgw3qwGdh0fHzMajfjSl77EM888w61b\nt3n55Zd55pln+PSnPsX/85v/N//df/Pfsrm5yeWLYQjpzdvXmc1mzGYzPvShD3Ht2jVu3brFl//y\nRfb390Heb8n/dg0mD0b3S1teh3tIx2xRhKzN+0DlZHmCsCHzUkqgVdCui9YRZkmyKvymaUxRN6Rp\n6LPIYo2RBmcKlBL0O12E8zz77LN84hMfBxEK8Mv2+SzJV5M9jRErKmPV2SkCfYQI5/bYtZ9gNBoB\nYepk9XJFYxydPCPWITOLogjjwuvn8znGBOlinud4B1GU4L1o11FJFEWMRiPKRUGn02lb/LPV2IBu\nr4t1ZtV4p2REEofP8t5zYXsbaxvW1vrcvneT3qAbFGEygig8CymCbl4mKRKBVFFQBHlP0wRKywaZ\nOYn6znD9AwNzIRb0csXaYxeRKqEoCo4P9zg+PMLUlvHpmMl0EnSpiWZ9+xyXLl5mbXQOnWgaM0Hp\nlNPDXRCCKA46TtWORPVtIeH46IgvfOEL/PzPf4CNc989Itbp9/c6pQxFjSiS4AVve/Kt+Z6OD//H\nP7v6t9AwWOsyWOvy4PeCOOuoiprT0wlHh6fMF3PquiBJsjCjpNej3+nx9ne9m7e/51047zCmQUpF\nMZkynZ4yGZ9wcLDHRByvuNNECJSE2fwEIsWiKImUXM2TEc6jpSDSYfCTVhJvDYNBzqIwNJWlrgyx\nlkRRkCM2tWEw2kAmmnltyLMuTsSU1lA1hkjHFL7idDzBWEOed1ksSlzt6Pc7bO1scO/eLlUZiqE6\nTjg4PiKSYbqhxTOdBxVDmmTESbLSGntvqYqCThLfBzgv0ZF4QCfezs9oW7A9BtG2ltvahWFaLTVh\n23ksS5BNtcY7h6kb4jjmN37jN9ja2uZLL7zARz7yEbYvXODXfu1/4Sd/8kkODw+ZzYLk7t/7wM/T\n6/Z49cbr/PGffJov/PnzQdbmHZ1+3o7CBS9VGAVsQ7HT47GrImqgMIUIk0LvqyoABNJbvDdBkikl\naSxbXXWKxxCnEfgA8BKQLih2JJLaB+WQwBFFgnll0UqhhMW7Gq0C3++9pZhXaKl47bXXQhHXhVZ8\nRRggVlRhDLaOI5q6onEeJyRRHLG+vs7p8QmPXn2Uc+c2qOuaqqqYzCbhvKRERgnjkyPWRgNqY5Eq\nCvfBGWQUYZsGGQWHlnU7NE1DJ++yWCyQUrG/v8/R0RHee7qdjJ7K0DpaUS/z+RytI9Io4/hoEiaz\neo9SMUVxirPQ1DVVPSc1mkhppuMZaZwxn85DU9a8oj9c4/DgIPR4oECAFhLvPMZ7vJeB4kNg9Q9p\nZB4KLm3hRQUP66iJtKCuCjbP9biwPcJYSxQnWGm4+cYr3L11k9m0QEUZdQ21cVy8+AiD0TprG5sr\nSqZxDc55pG/46WffSVNOuHMjcJBZlpFmeRgWFUcIKUFK9nYPSVSX4Wb2fbvO0Frugg7cw8mx4Qd1\n26WSZN2UrJty/mKQWC6mNXGqsSa0Lp8eHjOdTjmdnq50uXmeM+oPGKyvs7a5wbXHH8eYkuPjQw73\ndzm4e4+jo12KsmjnwWgeu/YozrYVfufD9Drf4L3FOYsQkqq26CjBtVy9TtrCqYC8m1EbQ7fXYXvn\nInu7R2RZFyEjpIKyatg4twV4jo4OkUrTGwyQUcTpyTHzRYGOIox1TOZzYg/nL5xnfHxE7Wq6/T5d\nHbdFyqaduOhWqo6802F7ezusIRkFKqmdl//gMKqlFPDBSBce5tSXevLl75dp+vh0TEMYwvbMM2/n\n6OiY3//93+fOnTuMj0/Y2ljn2rVrPHr1Ki+88AJ//OnPgAChNIPBKNAFUYRxlijWYWpfkjBdhMhw\nKZO07v6Apm8+z291rmEEbaCxslgRRQopHUrqoDkQAlwbLeFXzk06UEudtJKMx6dYZ1eqm9UsJARK\nhIFbL774Ik8//SSHx0f00hRbNy3Nd39UQ7fb5dHtC2xvbzObzZhOpxTz0D06mUxWyqUw11y0TjnG\nOEfWydHCB5WUc3ghoQ00bGNW6p6qqhCotp7Uqoya0K+R5hn94YCqKKmqKgwoA5y1lG1Px3Q6xZiI\nXjddtfJPp9PVdSyHdfU7XY6Ojuj3uly/fp33/+zP83u/96/aESAWnME/RHuFTuNQe/khlSaGBR6a\nBIwJbeGxUmhlcTSgIlCOui6R3ofRIiY0CvXyBOcV3VSTJAmz8S4Cw+T0kMl0GopCdUXeCzr0Sxcv\nsra2RZqmKKVo6gJjKqzzbVFN0DSW4/GM6cTS63Z4/G2PMhh2H2pk+/vYUh2S52mYmDb84RIQ5b3A\nKelIkmQRw/WHtftVaZhMJ4wPj9jdP6BYzJlMThAuDBlLdMTmhW36ox5rm0O8hZe+/BJCgRIaYy15\nGqOj4DCEB4QkSSIqYxmMengniXWMUIrxdEKed5BeEklHUTQoJdjYusDu7h7dOAORUNUTTscLkjSi\n11+jKEMBV0vHaH2D4+Nj6rohzTqkZY31npt37pBEml6nQ20alI7RcUSnFyKyk5OTMIRJSZwxTCYT\nkjhqN1gUClUPfFHGkiIKUroKWPUP/Q37ZhC1rZRROoEzlo/9wb9pKQVLkoQvU0jyjOFoxCc//Sny\nvMNo4xyz2QxrQciYKII4iiknY5ywWASzoryvVQ99hHghkC2V8s3n9CCQe+8RypN3Qt+DQBALiY6W\n4wRcK2N1WG9bsGnfW4ASHvz9GfKHR0fgYbGYE0Vhjne322UxnYWRx42hWhQ89baf4HN/+nlm8wla\nKsqqwnjH9qWLPPnkk5TzBcYY9u/t4gREMhQgl3Tdkrd+UGOf57226UpSNxVKKoqmwiPxq9ES9xVf\nrpXVRnHouN7Y2ODw+IhOv7ca6Fa2s+a11kEG2zRUjaGqQtdzv99dSTKttRwfn/LYY0/w5ptvsvzC\njNFohIojyrKmrk/4pV/6ZX77X/w2zhisdaGm1K4zvQwEvWr7IZrvuJd/gMjisLYBdJii1wK797ad\nIBamhjnX4Jxuecxm1S69bE+0DXhb4ZpZAAnVMNgaYDzMW57saP8Ou7fuEMcxi6Lg+OiY+XzOzqWL\nDEdDHnnkCmknZ3t7RHxlE2fA1p7xyZwsT4nTvz/PbY1tNcACjyH5PvLx/y4sSTWb6Rqbm2sP/0fT\ncLi3zysv/zW3bt7jjRsv8xNPXEFG4V7leU5Vec5tbpJEMXU7nKsqizCLPIpxxlCUhnPnzzObLKiN\n5eLlR2la3W6ehwLg7r1D8m7M5UeucXh4FOZJb16gKEqm0wn9foeuzsnzLvf27oLwSBUzn08ARRQn\nuKZGekmWd3n02qPs7u4hRKAjptMZSgWlx2w2I08TxuMx/bVz7bA0Hoj8lhPsAm0RhiU9jOABuO83\nCIXI9f5wrAdb+lece3tsHCcrQPriF78Ygo+mwR0cIKVsv5SjLYrGiqaetxmo/xtZwurfIozf/U5R\nuRSCXjenkyq0BHRL1FpDUxs8oLXHt2N1l1pwIQRRyyEvr8vaIH907TczWWNXTnA6nYYCeQt6cRyz\nt7fH8fExG6M1OlmOms+ZVaEeMpvNUNz/0pEHRxeHgnPUTkdULTCDlAK/f7IqGjopyLIEiyVKUubT\n2UrmuLW1xWw2I03T0GmcZRwfnRJFUegSzTKaKhSVO3nOstN2c3OTpml444032dvb4+rVR/HecXR0\nRBRFPP/886yvb7C2tsb58+fx3vOVr3ylHfcbajFJHPP883/G1StXGERhDIFo15pSuqXIggRWSYn6\nLgq+H3iY6L0PHJG3KDxYixAWMHhnwTUIH2Gtaj2ob/nJhsaAVhnOzfCmbaAoC9BhaBXFgkTlRMJR\n+IpUdtGJpbPVJU03aDz4esLurVdoGkujY8qFRIuE+aJguL7O2556ko2NjTDDQ+kwY9uCUiF5+M4X\nB01RYsopsXQsZqf476wu+tGxKGLj4g75oM/BwRGb53YQWpHHOZHS9LsD4s0hRVEx6PTRSeAUL15d\n49atW6T9Dv1YM5nPsPTorvUxTdAsD0broCYsyoK812XtfMrh3j5KZcRJjrEwmRZkWYfuKGZRlThn\nqeYzzm1vc/vOTZwVXHs8cM6LkyPSLNAm/X6fvf077B+e8Pijj5OmKa+99gpeCCaTU3q9Lt46srzT\nbjqJikJEa1wTqBhxP/qCJSC2W0kEzvlB8z7o0QN4C9LO/0/dmwfblt31fZ+19nymO8/33Tf2qNbU\nkgFZEpJshJKg4BgXVOEUISI4KKTshCA8FEMMVChCpRIltpK4UgmGlBhUWIgYHCMhCyEkoZa6Wz2p\n5/feffe+Ow9nPntYQ/5Ye59z3utuQcCVJrvq1h3PcM/Ze63f7/v7DjXi2HHMq8Xh5OQEYyxJUmO6\nHayq56riy/Mc8FB5zqA7EUcVRYae8iSvqmMhxDjpalqEJISPMAqBi0ps1BNC38MTTtVLiaH7wmK9\nKsXIjhdyB8U4mMpgKcyE3y7wkMJ3nuTWUGvU6fZ7hH4wziAAJwCKagnPvvA83/qt38rBwQHZKEVG\nAYkviGsuHlJYx+N3jB4nz240GtRqNeqJ8zMXfvk+WPe+aKNIohDfE0RhUmo3LL4240V6OjoxSRJG\nWerYOmGA5/sEcTSmIfb7feIwKg27JKZQnJ+fs7q8RKtZZ9CvogwdbXo4TBkOd7l9ew9rLeubF3jw\noTeMYb328Snts3MOdnd54tHHeM+3vB1hcoQB7bnXWimN9D0atUaZrnWXNP2u43WFWe6uIsZVggXP\njXTdm1g5+VmNNQVGlx7cWmNMgDGqVDBalM7RpnDxY7YAm2NN4fB5FAgNojRvKisZVWQUeU7kGYLQ\nB2lo1EKsX/CNZ77K7Mw8xhgGaYb0w5KqFFCr11heWabVbBKU5vd56ixYu92u8y9Phxib05oNKXSH\nw8OcSw/9/6w8/yZHrdl07W754QU+cwuL5IVlZnkJLzQks/NIKdk/OKZvIu5/yzvYvvUyQVTnyuY9\nXH/5BSI/IApi0myE6gyZnV0kp0N3WNBszjC/6hggceizcfEa7XaXk5MjFpYXkWFIr9dhkI7IdU5z\ndoZhd8jt27e5cOEiaVZgrGZnd49+/3mKfMTy8iq3dt2F1mo0GY0G44xPz/eplbayQWnoVMnSi0I7\nb29MmXJUJcBUFfar+JIwOb9tWUFX84jKvvbKlSs0m02SpM7BgatUR6NRibV7Jd+9snp1wqVpyAcM\nVk7YM9MWvNMVuS1hEM8T+L6kliQu/lA6jnw51h3/vfRACL98bPezqqNwj2HGdgW+72BPei6bN83S\nctAq0Qb6/S4AzWaT2dnZsYeO1gUrKyscHBw4cY8qkHlO4PkkUUwg3evveZ7jkBtD57xNPamVEvkQ\nyrCVrPQRmp2dp33eHb8ebh4SjN/jaoZRVfVJkpArN5CulLij0Yi9vT1ajRph6I9dL8MgZG9vj729\nPdYvbPLud78TIUQ5ND0vOeY5cVxjbs5paLIsY29vb3z/i4uLrCwtY62mFscoVaCU01csr28xPz+P\n8JxzZTosPdf/ouEUQoj/A/gu4Mha+8byZ/PAbwIXgZvA91lr2+Xv/hHwQ7j01r9nrf30n3L/E8zK\nVo59YIQbOBkhMaV5u7Rm/GHLOKWqtTQOk5lcNLrAUmBtiMUp4IxyG4GUFmsz55vseUhrELZACg9l\nRngEFAoC28SOMojd4CdQiiRweKpnAnRPs9PpjY17uv0+WZaOzXyazaazIcWA0Kiiy3PPP8LVN7yd\nxdV/y7SZ1/FY3bhAHAdk2XnJG4cwqdGYWSZOZmi3e6ysrWO8JmmaMhxZVlYvcePGixjrcfHifezv\n7ZC05olbcHZ2xmkv5cqV+zg6OWF/75itrS1acyucHO9z3O7QqLdoGTjv9EvfGY/1tS32dm+hMoUf\nxdRqDU7OTkmznDD0WVpdozVskWcj1tec5/Rzz72AQZI0mkQdV8EJ6Uy8RBgSRw0qqbEwlkJnCOGN\nF0hZLpyTxVqitS2VoFPHqyzuFX8c4OjoyNELcS12o9Hg4Ycf5qtf/SrLy8ucnJwghDce2AkLsqRH\nVqpQM8VWufuxpr4jqEzQAh9pFZgChCgFQ5NBbQVpuMO5NmKr+3c6A0RlTRuiCu4YDmdZhsVBbkLW\n0IWzuxCeC0/P83wMn1TCqqIM8QikIAkDGkk8hpeiKCo3Pzivn7K4uMjS0oK73uLEFVyDAUZLmvXb\n4+4kiiIGg+HEJbGkJhpj6HSc0V273SbNnRnacDgkTV1gRqPRYGVlyQnmooh2+4y9wz086ZNlKanO\nWFhYYGFhgeZMg7mFeTqdDr2ecl78pa116Hvu3MgyjNLs3D5gaXaem8Mu3/nvfgfDszOsSDEIVtfX\nODg4wEqBICAJo7HK95sdf5bK/JeBfwL86tTP/iHwGWvtLwkh/kH5/T8UQjyIC3B+EMeJ+wMhxL3W\nvhJc0FiUdWmUouRpVnzNyjkOSi6mxeGE2hDiLhRRXTxoLBpPOhksQrlFHI2xZbqLEUhh8KQuNw2c\nixsKWTndaRckLK3B6gytHE5lVIpRAdYKrFKgBEVWQBCADJ1BkYkQ1tKIDK2ZVqlEHNLtpQz6XaSE\n4eCEND/j5OxlnnzC41L/AvPz89TqIWH0uqNdf7FDeHhhhE5B+AGzC4s055Yp/AThNZCNhNunQ+6/\n90EODg45PT1lbX2BjYsPkKdD6jOLtIaKfuZEMEtrW5yfn/PMize5du0aeHX2T46YmWmyvLrC+fk5\n27duOY58r+YMmLKU9nmfwI/K9x2UBGVB+D7tXp9hlvPQ/fdT5CmdTmdsqWuMIgxDbqau+gk8F4zR\n7fbQ2jK9plXhGXdUu0wCqrUCz5NYUw2rJtj53Zj19MJXfXbYrwuxfuyxx2i1WpycnIzNsqIocote\n7nxzqioZbGms8sqj2mh836ceJ87FL5B4ZUUvhMATgNUoMxmySW/Cq6+eY7UvTOYBAq/kP1eeLxUE\nVeTOUsAIgSc9tHX2ET6TQA23scRsb287H5QoIstGDPt9mq06rZkGSRRTq7u8X1dlWwa9Hmtra6ys\nLDlminXMlyRJGA4UzdbMuJIfjUZorej3+2MYSpWpVpX/yzTenyTOiXNlZcX5BRkz3gRarRaBHxD4\noUskKwY8/cyTjEYjVldXue+BN9BoOlgkDEL67fPydfIRQiI9H89YVHdAb6SZqa3z0ssvcu/WRSIv\nQRmFEbC06ggbAheekmUZ7dOzb3oZ/qmriLX2C0KIS3f9+LuB95Rf/wrwh7gF/W8Av26tLYCbQoiX\ngG8B/uTu+xXGQSjSgtIVZU2VJ5Ytq+/JZ2sFwkyqeN/NdRBGY5VzEXTmUQVG5a+4cCZtYYlhmgJr\ncqzRzitY5WBiirKy9qUEm2HMCKvcBaQKQxEIlM7x/AirRoxGQ3y/hbUujNg3EpulBDIAI8mGPZqt\nOioryIYdCARpd4mj25qTwx38MMZYj1wp8iIlCUOWl1wqUr0Zlyevu2iMwQkMfEkY/OUxpkmShCBY\nRBd9B0F4BbOLC8ikgQgT7r1vmZu3Tjk97+EFCUFY47zdZ21lFbTh9t4e6xsXwJPs7Owwu7hOrbXA\nwcEuN7dv02q1WFvfLCvXiCCqU2vOsrt/RKPeolCwceESh4eHpEoyv7BInqacnp6ysrbG8UmHldUL\nDEd9dg/2WZidZWFpmTRNOT4+xhjXbqe5wfMihrkiiBKGWY4VpdeHMAgCPOGDthirp9wRwZMCpIeR\nhqJwoRKGSTvvczeTpFwgy0VRlx1qUeRjm1uAfl+Vnisp1sqxutMYcxfRSuIU0YJJda1dFwHEYUCz\nnhB4EilFqdl1/PNyOynvc1L9WeNR1WHTC/nkf3Ceyka7ayotctJcEQSORTIcpc4qOi1ozNUQWjmj\nKcs4ycgxeAz7+/u86aE3cnhwQBImyDqEQcziwjJx6PzZZ2Zm8H2X6mWMcFDF6qrrDGRInit6vR67\nbU0z7CJ4ZBzX6GiVlqiecH5+Tr2eENcSVF5QT2p02m0CKclGI+6/7wFGhSJTBbf398i1wgjDaeeI\nQmScn50SRzH94QArhKMiehY/CXjk0a+Qpxn3XL3K2vI6Ekmj1nBsmDR1sJEnaOBz8Px1HrrvXo7P\nbnE6ODgAACAASURBVOOtKvANyhR4gRwPc5VS+DLA8wIngvsmx5+3JFyx1h6WXx8CK+XX69y5cO8y\nrVqZOqYtQE2ZGGJKCbE73GdXARg0oHklZjSNRVZtFeCmlNq4NHdj8WSFF+qxxeR0wCtVh6AMyNJC\nSUiEUmjtKjalFUYHGF1gNGgjUVmGTQp3ousCYyWmyBFVYLLOsNbt4lophqqLNkPykcMATd4vJ6k+\nrXoDk2s6J2f0Oz3SIieKfPIiJR3lCOGR1GqsrK+xvLhIEP4FeZP/lg7Xjpc+Hb6hUBlJ3GDznmtc\nv9lmZ7fH1sYCL718xtzcDJsbS7z08nXSUeFYAdoyyhSrG/NkuWb39iFLS4s8+OCbeOrrj3N8fEyr\n1eDatXu5efMWh4fnzM8vEscxJ0fHBGHM7t4BGxsbvPzi8xwetVleXGB5OcYa9z70ej2uXrvk7Aa0\n5sXrL3Pt2jUaM7NI4ZMOhnR7A+JYgrYIPPJco5VFFRohtasutSY3BspB4d2V9WT2M8GVq3NzWn3p\n3yXNFnKyUFdHha/DhM9+x2PJiodcBozfxXZwjy9IooQo9Bz1V7rzTqBdmITxy+qe8rHcY1R4vjB3\nreBTxzSGO30d5mVFnuc5ngyJIhe4TAmjBkFwRycSByGh5zM/O8fpkbP5iIKQmeYsm5ubNGr1EmZx\nwp0gcIERM7OzrKytEoURUWNmLHefrQ/4V//3lzFagi3nCtaSZym+NfSHA2ZnZ+n1evjSo/ALGrUa\nkeczMJZGHFFgsNKxVm7t3MTzC4bFkFjHZDp3dEFfgBTUWjV0T3N0eoTONbVawvLSout+rAsA6ff7\nDhZaWGRv+xbZKKXpRxwNusR42KIgtzlKWoZZSt2vk+WK9kkbYSW9Xo/5+flXfR+q4y/c31trrajK\njNf4k9f6xd2ezmNTIl1BLtbRE+308MdV6maqYnD35W5TnfDWGoxRVDFZ2kwGQV45AHGMToO0Aq0y\ncpNhpEGhy79RTuBiXVCEB2AUwiqEdZWK0RlaZWNoSHg+6ahPHDs2RJoNWfBnGPR6GF0glCLCoLIB\nhbXESQwM0VpgreOxWt9DKw9V5IS2BkpT852U1GQ9zvY0h7duEcVx2eK6NBk/Cmg268zOLBD8fwjJ\nW2mJw6S0cnWt6PbuLaLWOisrc3S6mmFquHZ1npu3+gwHmqtXr3B4+5SDg1OMdV3VU0++xJUrl+n3\nUw6Ojul2u1y6eh+7u7fQKuO822OUFiS1BnsHx8zPz1FvzqCV4vBwwFm7w0Nvegu7uzscHh2yML9Q\ntt51smzEjZdusL6+SuBLNtc3OTs5Q2tDcyahXlvEhT8oQs9BAMvLJTUxcC0yeKhCOTgPSxD4+L7n\nYs0wCGGRQmJLQhll9amNGZ+v8EopPVD6hPt3DjRd7+le47Kgmb6dEHdW+xID1tEMg8An8P0SBpH4\npcmWlBanvy87itLUTqmsxL8n91lRgL+ZPcCrcderxCRjLEHpHhnHEVk6HN/v9AZXaEXg+QwGQ3zP\nJ44TarU69XqTpcVVakmNWi2h0Wji15rIIGD58rXXPB+vPFxHpbMsL69x1r7tDOvSIfVahMpzLm1e\nYHlxmU67DYXjnI+GI877Z6giJ/ECBn5BrlMKldHtdlhYbCCkxEpBmMR4YUBjpsXZ2dmYbmixNGea\nREHAea/NxsoGSZBQZI6wcXR0RJplzC3Oc897NlHtPucioxClCVcQIjxLq9UaryfDNGVlYZmZmZlX\nnYdMH3/exfxQCLFqrT0QQqwBR+XPbwMXpv5us/zZK45f+a0/pChcTt8b7r/A/ddW7vqLiTe0KFvA\nakjqXjh3VFWQo8Tqydc4ObLGlGI1VdLGHCYoRTnW0s5sSJYbCcKlx1RUdolb1K1RWGvQxnmJB4Hn\nqJMUGJOhtEYgsBpUlmI8iSnhm875KcdHZ2hVIBUOCsotRT4kCecotHMb1IETn0hCjPFQeYbyLHnh\nVILWpI5NYTVKabCKIIxQqqDRaJGNjNvF2wdjB7iiKAhCjzD0CSKPVqtJnPh/YTHU9DFKRwR1l5ST\n5+7iX1lZo93uETebhLEgVQIzgjCSpKOU9nnK3HyLW7f2iZOQq/dscnDQ5dbOLm94w1VeePE6WZbx\n/AsvceXKJQb9Lvv7+ywsLuJ5HsPRiH5/hKx7dDpdGs1ZBoMRjz/+BBcvbhFuuDnG6f4uhXI2qp4Q\njIYpfeWUkjOtGYQQHJ4cU+Sa1bU1916mI9Is5fHHH3f/oCfd++vsYjDaDRo9fAw+2mqKIncGVBI8\nX2LNhLF19zLoBpgV3U+MT3cppeNfl8N/F4BR8kuMQeuJH8zd14g7py2h74abYRi6qDWdIawtcyWn\nR1dVR1HK/D3h2B5Tk1tjjPNnmVrIq6Kr+pgYiU06k0a9Ph5qEk6UnxWzpLpN9fqYsqjZ39tjcWEB\nz/O4dOkab3zozdz7xrdyx9Diz3BYawhiRSxjlvwVhJQc7txiJvQ5OTyifdbn4JmX8ISkFSWcnZ7S\nmp0hT0cEYYBfFW4YrNWEYeCSq0TLXavW0O52EJ4cW1VXnUycxKg85+Bgn62NLdd9e2bshV4Yzf7x\nEVmQsDg7z3JrHjxJEIdIqTCmGF+3tVqNzmDEH/7x7zEajag3vnkYz593Mf+/gB8E/tvy86emfv5r\nQoj/Hgev3AM88mp38APf815GoxHNZoNCFxR5irRuN58esrgPFzTs6v9yoHRHW+sk4EIKVFY64BmH\n57mLyrWPxjiYxZZDFGsNCFBlGyaFG4Ti+a66KhN1rC6wukD4wlEjiwIRxy7CDYs2jtfqCQ+DKumT\nzqLAGsX83ALGSKR5Dq0UKk8x2jIcdWk0a2hVuMGvSinSAZ5w8IBVChM4m9fAq6OVQhcaVV6UyhTY\nImCUZ2ALlDLovI61IUhJFNWcCb4XMhpmFLlP97yLMZa8UKUPhSgHOw1arRnqzdBlK2pDXmQY7eCE\nKImJXkXxpLKMdNDDsxlR7EIqhv2UOE5ItYuEazYke3sZ1qSsrc1wezfl/LzD/JUVti5u8uQTT+NJ\nj9n5WZrNBt945kVWVpdZWJjhya8/zfPPvUgY+LzxjW/hsUcfpdvt8s53vpPd3V167TZCMKZ77Wwr\nDg4OaTYaZKOUxYVldnZv0G63qScxnpTMzToxyu3dA6I4crxiL8CRpgxhFCI9yYc+9Hd46ztO+dJX\nvsiLLz6LLVv2vd1dgiAcDyUnBYVEysBt6mikFE7OPrVzvhYd1wrGbCx3iHGn6SwEpOOFu87e/V54\nJZYv8QTMNhvU6zUnmrEucjDwfATWhUxbtyk4eb3LLLVYt5GIkKlGGVNi6dNVefXcXzl/Ku1xhVtI\np1kwTjSkqNVirA7Js/yOCrO6v6LIUVnG/Vevcvmee/me/+hDr7Zs/JkOKQX33D/H7376Fu3OCe3O\nOYv1hE6nzcr8IsPhgFkR0mw0SPtD5oIaNRHgSUUYxcRRhLQu1zbwHY///LyDEZZMZzQbTUyhif0I\n2WiNh8v9ThdVin18L8T3AiwG3/cQwt1P4vlYYRFpztHBHmEnohZH6DyDQKO1wrfCaW+wvPmh+7j/\n0iWKQlGv1/it3/3Xr/l//1moib+OG3YuCiF2gJ8BfhH4hBDiP6GkJgJYa78hhPgE8A1AAT9qX6M/\n0yajitXCTpgr1jrs2mrn4SBK7q4VFivMBDssr4/Jgu8GqNVuag0oZbBU4bLiDtzRPZ6TOiuryI0m\nLn/nJt5yzCc21nHTJc5kSKvC5Y0b5/mANgjt/EWMMM6Fz1YtrwtJOD3pIIxb6LPRCE2OsY6RYJUq\nq/oUJz91gimjclTmoTKFCQPnZ2zU1IYnsYWPyjW68BimBUJUNE+BLhKGAxcGUOQ5URyVHYzFCwLQ\nqRtWaUX3vEu/00cIN3TpDXv0B5NEnPn5WZJ6jfn5BaIoxJeCYa/H2ekheTbgeP8GQZgSECCFR783\n4NJ9l9k/ytjcjFiYC9nZ7XCw3yUKY5JGk689+hy1Wsy1ey4z6Kdcf/k6p6eHPPzwwxweHnK4f8ji\n4jKdTo+T0zbf+MYLLC0tIYTgySefpNls0pppktRidnZ2ODo6YnF+1qX7WLh93nGDrjjCk5oglJyc\nnGIszM3N4YeRE+6YnIWFOTI1dLajRY7Smk9/5nOI2iZ5PuI7v/MD/Hsf/G76/T5zM7PEccw//Sf/\nE4997asURcjJyRnGaGq1asObdJDTQ8RxNW7vrHilJ+/AyN2C75UiFAchVud7NRvy8LFSkgQJK8uL\nCJtijMIr+dAS7YybPG8M1pRr99Qm4rjyFeXQUtoX4xYowWTDme4k7NS1okVFD3ZFzbS61fd9wsBn\nNBwgtMAT3h2bgSvo6ngSfCFYmp39f7eQu30Uqx2s+p99+MOc3e6jrUeaFVy4eInec0O0geXWLKKf\nQu587ruHRwRBQOL7FIMBukjJpQEJyiisNrTPO06sleU0WnU86zPbmEEqy/mxSyLKsozFxUXm52bZ\n2tpkZmaO2Zk5Ij8ilM4DJk2dfUC304Vcs//CCzAYgmfp9fv8xx/624yKIaEIQBs8IbFC0Ot1GXSd\n1cR5p/1NX4o/C5vl+1/jV9/xGn//C8Av/Gn3CxMDounhiTspqom9nmDf7s6nTqbqpPfH1cP0x9Tz\nKSuK8V284ndSSteKWuf+Jq0ofdMnOL0xBl9ahKkwekd7FBYH2BuN9XDKU2PdtL4cyARBwCgdOMzc\nSqTnwhW00WjtBE15kRLFDbR29gXGaJTOiW2CURlWR46OZgyiEoeYwNkyWo3KLcUoQ3kKkFgjKExG\nNhwSyhFZrtC5C3SoqrN0OAQVo4wGO1E1hmGIylJm6lEZCycoRh1GvVP6Z4ccHR0hpaRzds5w1KXZ\nSuh1T9nammN/bx9Jjax/wrPfeI5cx5y3Iy5eXOPBB5Z57PHrbF3Yot/tkSRR6R+9RK0ec3ySceXK\nFScOiicL9ObmJlJKev0ORT7knnvu4datW1y/fp16EjEzM8P6+jp7e3v0ej03eEOwtLREmo/KoIGA\nLMtZW93g+PiEbrdLq9Vy+avNmFarxWiY0mrW6eduBnLp0mX+5IlbXL58mU6nw8c+9jE+8pGP8FM/\n+ZN89H/4KL//+7/P5voaWmt+5Ed+hK2trbFH/Oc+9we02+1xwk8l+hjngJo7z9OKZ12JYxxu/tpU\nxup7D9ja3AA7whpDZYFhjSH0w7JKd/Rdax2mf9f16j6q4ayszOHurMGq5z6ZUU3YKKlKx4u3EGKM\n79py0xmlKXEcjiPgsszBXJW/Sa/XY4glLimZpEOIa7zaUZic/f19Xn7k67zvgx/kc7/1Kd73/X+L\nX/y7f58f/Im/x/rSJte/8cesX9hE0WT71nU2NjY4vP4y1J2tbrPZROUFM3NzJEmCynPO2m3iJKGf\njsbc+arDWFxc5OzsBE9KVOaIFZcvXmJjY4P5pbnS71+X0JILBUn7I/pFH2k8akmCUs4eOgkjAh+6\nxwN8owl9j2bqUbMeKggQ1vLZz/wBSA9lDYGfEAQRkR8wW2u96mtSHa8rwXm8kJtSgFAeVuCqcKux\nJZaotHKmQVOY+d2CI5gaLgmFL3HURlU45oFxOLMxpdBDFwgbOqVpuWC7SsM4m1arx5hiVQ1Zq10g\ng1WUCCqVz4zzuPYmm4QVCOv8nvM8Y5qpY60TMmnjhp5au68t2uHzRiOsU74izPgxnS1tJSPXeHiY\nPAffIkyK0ZJKUqyKHGsyjPEo8iGCsHp0EJK8GCHkZJGZyMUjRv0OHjVGpW+FsSEqU0gT4lPgCY8k\n9qjXZzk7PaAZh0jtQOVu74SnHv8aa1ffxOblB+mlfZ555glazVlWV5bZv73N8vIy9YZT/W1vb7O0\ntMCb3/IQX/mTrxEEHvMLs7z5LW/ky1/+Mjdv3mRlZYVaPeTk6Jgnn3yS5eVlLly4gMpTZ4gVRWxs\nbCCsZm9vj36vz9DzEZ5kZmaObueEKPToascKyPPcqevSFGUabN/codsZ0u913XkioN/vc3Z2xvLK\nZT796U/zn374R/nUpz6F0pp//HM/y0/+9M/wax//OOcHR/zK//lxrJ04Imrl1JvLa2usr6zwQz/0\nQ1y/fp3Pf/7zHB0d0Wo071josyIff+0WRm9cjQPjhaVSMEopCQQ8cP9VfFmgtUUV0XiRzbLCebII\niy51GpNxKnd0tkKI8Zk5HTpdDfXGBc9dm0n18zh203ZrBL4Xjh0CdSlw8kqTKc+66y5Vzn2w4m67\nZJ4YL/Bpt9v8xIc/zLd967t457e/mydefIZ22gNPM7u0yI0b2+BJ8pf2ed+7vp29Gy8DkPQKlpZW\neObJp1hdXeZrj32Vh//K28lefqkMeXBWDDMzMxydn7I4v8AwdR4w9Xqd+myL2kyd7s4twjKyr1ar\ncc8997C1telmTl5JitCGPMvoD7rsvnydw8ND8lFKfzRkMBqSJHXWV9a5fPkqKtXj908phZUCpTWx\ngtAIhHE0jCzP0b57vWZnahhcKLS2AcJG1BsNVha/uYX362eBa+0rTtjXmphP1KFyqiJwk/g7KI5T\nVb6jVJmy8tXYMiG8OmvdglcON6sWFl3CNWA94VgWTPlvaOO6A2OdWRCOosZUK+oqdidcMtYrfTwE\np6dnWK0R0mJM7iCXsvKX0iWNKAsqz9C+w/RBkmUjMLnbdEzuYKfyuVvhThCjFUZ5GFWAytCFxpMB\nVvqYfIjJBSrt4osaFuPoekj0aIixsfObMQYZBJDnaJOhs54byKZ9FArhObc5bXyEGuIZDzNsE9dq\niCzFDxJMNiId9ihGGak+ZueFJxi0u1y8do20e042GNCqxURRwPXrL5FEEevr6+zu3uL09Jjzs1Pu\nvXaVm9vbnJyccHR0xDve8Q4ef+wJdnZ2WFicYXV1lcPDQ3Z3d0mShPlZl+94fnrGzvYtkiTE9yXz\n87OcnZxiCsv8wizYnJlWne3tbXRe0Jydca996YB3/fp1pOfhexaVq7FFc7vd5vT0lB/+4R/m4Ycf\n5ld/9VdZXVnnox/9KD//cz9HFCZOlOZPRDBCCPzEZTb2221e7HT4yEc+gtaaK1eujAMPqkDlWq2G\n9D2+7/u+j8/8m8+68GLrAXLcaaSDYalcngwgNzbn8ALl2OUixJQe4+ASk4y2aG3GbBopBMhXVuZG\nKcYjP+OUn175GIY7B57VMQ2jVKtInimkB/V6gtYFraaLg7NYlMoZDJ3CMteKxcVFVldXXfi3wEny\nreXRJ79O0mjy2S/9G57aexqakmCmgY0Tek+/RD7KuXVzm3tHCQQeX/7jL/Af2p/gxssvsP3Yk5yc\nnDA7O8t/8De/m0/+zqfwfMni/CxitEL38Ig867J2YYNep02tHrO4vMSFe646WwGr+Jb3/lWORgPW\n1tZQQqCss9ntdE/pnZ/R77SdlUCtiU4z6n7kwmykRmpNq9Fkbm6B2zu32VjexFLlwJZUTwRFlmOl\nICjcbAI/ZICPwseLfZIgwPdCrBRoLSiUz2mnw0B3vuma+rp6s7hzw4yhCYSzoayUodXnsWhIlK6K\nuADXCmMcL7TajKvoycIugEr6X1XxujSFm2B9blG3GKsQZeVqjUCUuCVI8BxjxkqLEeUFIgFhMFYh\nZeSGkCWtSPrOVx1h6XbP0TolSeoIFMbmpVpVs7V1ievXb77q8zdmwlOuDqtK4FMCRmCVxTrxKxQW\nqwqUp5FWUqgBRWadklWLsTeyE25k6LzqZAxWGnSRgvEw+QhbSHTWB9+S5SWXmQBM7obGZoRSljzv\nIWQISLrdPkYpojigd7qNzlJ8mVNvzqFNzv72y6xsroPN2d3ZIx31aDbr1Go1tm/cRNqC87NDlldW\nODw85Ctf/hJbW1sMGjG3b9/G6oJr166VFrc5R0f7KKVYnF8AmpyeHmNxUWHLS/MEUcit3VsMh32U\nLbh4+TIqU3T7fYbZkMuXL+OFAW9981v53Gf3CHyfRt1VzS+88CI//l/+GD/7cz+NKSy/+Ruf5KGH\nHqLX7fMLv/CLjIYZcRCzOLfAaDQsK2AN1qI8iwh9wpLV4Xk+SRjyzAsv8ra3vY20fYbA4BG5AiDX\nfPzXf41P/PYn2dvbQ4qI2fller0O//x//+f83m9/knrLufZ1uwOiIODi1Q1q4QzHpx2MmaThoBVW\ngNLO1dEgENZzIiZd2V6UFrbSooxx7CxrS69xgYcYFz5iCl5hKj5PiNK7xZa3lT5ow/WXv8Fo1OY7\n/vr7uHbtAdrtU55++imGw6Fb/MsIwFqtRpIkeGGA9D0Egps3brC2fpmb+zvsHw2IkgZe+4TQC7l+\n/SZXL13FxpYbL9+AZkhPd7G6QAeKQBoWV+ewwuPzX/hDarUInTsR4bV77+ENH3g/6BHWm3TYuVa0\ns5Tu8SFZp0u33eXSQw/R77Xp9occn7bZ27uFHxpMVrDcmsHPcrKsx+rCKmmng+9btE1ZqiVkObT3\nDtlcWSvdDifrh5Q+nnVd08gTzHgeGpBxyAd/8MN89vOPcP36TbZ3t3nve9/L0888y8bGBa49eI0v\nfvELLC1OEwVfebyuMMv0JH+qQHec21LsI8sTqtAaVQ327hgSTeCWO9rDqn0sB6O+HwJONSesJC8r\n32nO6/TtgVI4NIFRDJEDVaqNQuuyUnaLr9I5xkyd+MaOq/0iHVGogrp0AzFb4u1aa46Pj+n3+wRh\n8w4DesfznVT7DsoRmNJTxppqWKVL/5kcayPHpHCUHjcwtQptCrAhpiiNhyhpaRTl48g7QgEc7g95\nWhCHLgDZ8wVKCPI8J4o9tFXocnZA+boURYFRijRNSdMMwyHHezBqzDK3uEx72OHsfJ/19XVmmiFn\n5/vMti6h8gEbG6s89dRTbGxuobVmc3OT5557DiEErVaLS5cusX97hxdeeIHZ2Vnq9Tpzcy2eeOIJ\nBr0+GxsbrK6ukhcpOzs7REiSesLKygrdbsTp2TFFlhP5EZ7vs7y8jMDj+ss3kSpztDS/FJAh+MhP\n/H3+1Wce4QMf+Gu8+c1v513f/l6uXLnCF7/8J9x7772MBkM+9S8+6Xw/wgDr+ai8QHguHq+aqYwG\nQ7zAWdu+//3v5/z83Jlb+RKs438jPYTvcf+9D3DfvQ84T+vyPPjiH32RRqPG0el+ictCPy/47Ge/\nSpoOMAa2Llxj80JElqfoQo2HnM6nmzuuE6aujwpiq2CcV2MyVx7e09X53fi5te6c9KOQ0aDDc8/2\neP7GTTY3H+fixU3W19dZXFxESkm/0+X8/NypbstQEE/45EXBsMiI6jWXBNXpwK0eUvr02h2Wl1b5\nype+wsXVTfr9HngCbS2f/GcfI1ie5cbRLuvr6yT1Jm9404OuKCoUEkHRG3Dr+g12XnyB7rDLYNBz\nZAPpszw/hy4UgedxenLCvQ8/jNQFjVaT/ZMzVlZWSPMuyxuznNza5aGH3kivn7HQmCdvzIBMGag2\n+zdu448ks8JnabM2HtBZa13yExXVebIhBp7PUbfPabtPa36FB5JlXt4+IamtcnHLwUJ7t0/QyuPG\n9b3XWEnd8brCLNU/pa3jgmsrKPSdtENwrYiEcetXsVmmF2FXZWskclzhViehWyBdG2ysk2OAoNCK\nUAQufUROhj7TcM20M101pFJKjX9WeUy421WiJtc9aGswuOdY5AVYg+dJtFGI0idGAHt7e6ysrKF0\nOubkggtgDoIQo7QbqBqL9CTaKipamSnFUA531+NupRpmuk3H3dapWAsQZaCCzktevSw3DIk1Gl04\nCqdTvpZWCzp3CkPPc1JwGeA6JPf/5XlGHAZopZBCUBQZfmDwvYLh4BhrMoRQ1Frz5NpyfnqIH8Zs\nbW6wt3ubRlLj9PyM0PcYDfv0hylxHHPt2jUGg8EYblhaWqLT6XB+fk6n06FWi7hy5QrpcMTOzg5B\n4LGyusS1a9cQuWL/cJ8bN26wtLTI1oVLnJ2cAIbhqM9594xLFxskSZMQnzD0kZ5GIAmSmJ/66Z/m\np/7rX+L+hx7kpeee5+F3vAOAf+e7PjA+j3/7E59gZWmRfrfH3sFtAi9wmanSEAUBhSqYabZQVpAX\n+XigPMGgqxg3y/f/wN92XzNZyHOl+djHPsqgfTo2pXJCpQKBod4IGGWC/cMzsiKn3gycaRzC2akq\n7ZSRtuqGHU3R2gnmXZ3DEtyIRkqsmCqW5PQ86s7ZlOtm3bmchAFWZ2QUKBXTbNZZXl6kXq/R6XSc\neZS1zDZbpduhc0BMi5xcOadTX3oszi9QjAoGWZe8rUmSiFYYE6WWqwsbLCZzLL15hZ//0f+ce+69\nnyefeYbG/Cz/8vd+l7Nej9vPPEumMobDAaZQHO4fUMdjrdYk7g+JQkEkQxQ+Eg+/V2CVYqh6SFUi\nAtYwyguG3R7FcICUmlF2xlxSZ9Tp4skAzw/o9Qa0j/cJZIrfy2n5TbzcEhuBory2ynWqmk1UGaHG\nGPAFrUaLGy9f5+nnttndPWB9bYlvPPMEq6srFMWIZjNidq7Ogw/eDx997TX1L4XBxyuQ8ldhpuiS\no1XR7qoX524VafXZILBCOt8VXCWeG43Clu2mRRuD0k60YO56zDur+6k3w1rsmK7oMHNrFVYXzqzI\nOGxSWItRZeqKhTwflRJw7mDrGO0iv97zXd/NcJBiSwVfZZFaLapgKIoMY9Q4zEO4KxStc6wtsOQY\nm5Ww0TRn2WV5Wq2dF42pZNuOOeSGuYZqoGtRWFQ5M3AfRZHhRFMu3kqXHGZPCnxf0Gg4oYgqxQ6+\nHxBID4lBWINRI7rtI0bdY5abEWbYoX2wy/HONrEn6XY6FFnGTKNOICX1OMEUitOTE5IoptlocLC3\nz9NPPoVRmiSKCf2Aw8NDjo+PqdfrbG5usrS4SLfd4fjgEGudM2Gr1eLs9Izbu7dpNpoM+wPSUcri\n0hpJY4YHH3wj1grSNKUoVOm5PWR7+xa1WsJ3/rX38ea3vY3f+cS/uOM0febRJxEG7rt4hRd225yu\n7QAAIABJREFUX6ZXjPiu93+A//GX/jt+6h/8Iz7127+DKQzzc3Mopdjc3OR7v/d7qcKTjZ6I06wR\nqMKdy7/zL3+HCxcvIUXA3Mw8vf4pooQ3pkMg8swS+HWCQCKDIeedI/b390lTF3id50XZLU02+uqo\nFvHJ+a1LGu/0ZTgpDO5WH1ZQYlEUYxsC3/cdi0oLCpVRFM7Mquqkq3BvIeG8fcbe3i77+w4mC8OQ\nra1LvO1tf4VOu0Or0eTBK/fz/ne+j3e84W08fO8beWDzKvetXSbKoXPe4Q0PPMTN7VtsrG/wJ195\nhKtXr/K1rzzCzMyMc94sgyKCIMACvhEEViByjSwMvhF42jIajiiyHE9DhOdCrIFABIQ5zA9htquJ\n+znp4Skn27fYfellnvjqlzg+3CHxBDE+NQJiDYGyTpn9GjNAnReEY7NhN+yem5llc2ONK5e3QKYc\nHW/jh4rbe9fZP9xmZXWW3dvXX/X+quN1q8yVATwfjTPQklbiRBICV4e7DyFwgQ7lWT+ugsWdH1Z6\naCGx1iAFzkBI+GgDhTL4keOqG+u451o4kyHp/hiPSr6v8PBcGK6ypYFS6cBonUJUOqvFyWJfVr3O\nPkBgrQJhJqZCwo5Ndnw/cEZdBK5DkAZPWD7+z/5n/uq738fTjz+CNoY0HVGv19x9WoVAI4UBWw1O\ndUlvMyWtUkxV8K4St+VnbRSaAmMLjCnACqSQSMpcRFvSsKzAoNzvpAWmNhJTEIoQUS781UJQDcyi\nKHImUcp9BI7kiU+A7zs1K76i3zUIMjwRI2TA0X6fOKkTxDVqUUT3/Ji5hWV8IQmjmOPzU7LhkPWN\nDVaWFrjeO2Nvb4dWs0UYBiwtLeF7Htvb21hruffqFeZnmxzu7/PCC88R12p4QrK0sECjlmCtZZiO\nWFndojW3ig1bpLllONJgPawqnQQ9SRD4/Pt/44PoLGdjcYEwSvjN3/gNPvQDH+L+Nz5Aq9FCFIr/\n5X/7X/n4L/1Tfv6/+Vm2Ll3ii3/wB1hP86nf/E3qYUSz0aQ1TDnY2+c973kPH//4xykKiwgdZBIE\nPmEY87nPfI5//DM/S5IkCCOYbc4hPYGxfax0FqrVYa0liD3SzEnIjAZZioxcR+dyS531li01G67N\nR1TiH1ddjzs4K13cn3QDGWt12dk6pbMQAmFkKYJyRY0rbiRWaCe2K/JS4CcQShB6AWAR0l1LQpaF\nlnQZoEKAKjRpmrO9c5swTpifXyCIYtJ+wbNPPE/WH5KOBgwGA8IgwpMeLREzemvB6KyPzSEdZaSD\njDe/+a088ujXiBJX9Wdl1JqHwNcKKzK056x+pfIo+ToA+DLAWI2Pj9Au0k/is5QFCAOGwoWMGEUt\n8LEYPKvxrKbop/jWG7N/olpMUQZ3COk5mmaZH2vSnFCD8NzaEictimJAqxmidULSWsLoebZ3nkcA\n5+c9zs72mZ2Z+aZr6usKs4x9J+7ewaYq42rxfi2my/gmuOrd8zzyoiCO6wRBgtEGZQRKW4QMEFJg\nhY/RCmscTIExr36fr3he5QDJCrAOmvA8AWJiOjSNRRaFq4ykgLzISMIYIWTJ6XWVsQvI0LRaTV58\n9lksDl+r1JZ3Y/pVW6tK/FwwMXOq/t5tSlWjrh0YYjXGGhds69sxZOTUs5KK7WNF1Q25aq1S9Wml\n0Uo775Gysq/iwUTp8aG1S1EpigIpPeIwBgyqyPE8l/tZmJxht0OcKIQMkUZQpO6iEDVB7AnaxwdY\nEaCtYG1jjaIoeOaprzMzO8PGxgaddhurFO1+H+G7YIEkjgk8n8cfe4xWq0ajVuPatWt0ez329vfB\naFaXFul2e6ysrpLlBW946C3c3Dsmjn0HV5Xno1KKRlLjxZd2CGs1GknEfOJERk98/Qk+8vh/hSkU\nqiiQSN5270PIPOfC1gUeuHiNH/+xH6exNkcUhWzdc43Oc88Sxs6qt91uj6G6yugtDEPiKCEdpNSi\nGkWeO9dN6dgoQkqs9bi7h7UYdz4rUb5f7v2ufMkFPq72m0CSQohysS7ZXNrcwVKZnEMTWm5FyQXK\nOZB1/i44goH0JKLsOu00FAqEYYARGt/3xo/vxjwOcnQCKokvnJXul7/8FS5eusTR6SF5liN8wWy9\ngVGGRqNJo9FgeXmZwxe2ydIcr5sjraBz0qbb6fJHn/88iyvL7B8dMj8/z/7ubaQV1GU4fvmstQjL\n2LHSMIGbdJ6PXwdjJla9wjjb4cZMwtAqdJHjWZxneaGxWU4gavgIEJKoVmMYADiLBuseGIRA5wV+\nuQFa3Pvf7rR5/qUbKGtZ2VwiihJW1uZc53noxEnv/vZ3veo6VR2v6wD07sFlxUj58xwTuEXy9FPf\n4LHHnmf79gHglKCNWkKj0eDChQs88MADzLQiZlpuKOqeiwWtXHSd0VBCFTAZYLiBqDuRnYe1dick\nYrzYVhi2e04KpXTZkhqEhCgKHAdcVfi2Y8L4IqcoDMakIKIxs6fy46gWgEpCDtUGogk8zy3UJd3x\nzr+zY7wfbFnZh5PnaDV+KUKy1oKseMfu8aXnsler92ma5zy5OCdMB2MVo9GIsJ6MZwAAw1GBH3gE\n5eA27XUIkxphXMdQUKQ9rMqozywQhQH9QUquDL3zcxcQsDDvcPLTI9bW1vC9kNnZFrt7e2hPsra2\nxmNfexSswhN19g8POT49ZXNzHd+XLC0sko2GjFRK1tV4UYO9o2PSwZAkcEZcxrqurOo6Ll9aRUYz\naOU44IN0gOcFdEc5RepCgrGWMPYI6gnnasC//tLn+PT3fJ5s1KOwkNRreGGAlYK3v/2t/K2/+T18\n27d9G5cvXuLRRx+l3enghQH5yRkf/js/yhNP/JTzAKcawONog1NedtO2dhV/eXI9lbWJLoiCYGw1\nK4Uz3HL+RJW5lqIywAWBsaC0QktAOaaKV2Z+VjOqCjoQTCxx4zhGC+eUmKkMJONzNghCcpPeAelM\nQ5jV9aWMJleKUZbR7fXo9IfOnrZVwwsi3vWtb2dra4ujoyO01txSL9FLR4w6XTec1VALE5q1Otdf\neolarc59V65Re+hN/PEffYFQSqRWYEsml7UoFJ71EGWnWs3ZxNSGNKZFA56ArNOn3mogIzd3GvT6\naGUIvQCfAGEN1pMkSUzHFu71nRZaVT7uSIQnUViwhuXNdaKZOb7wh5/n9Nh53qyuLnFhY5MH73kQ\nGfh4/l/yDNBXO+7Ee19ZIb9alV79TEpJv5eysnoPmZmlKAoXDhy7/Lxnn93l2Wd32Vyd44Pf/Q7q\nNQ9rqnCMVz6OEGKCp5fslarynnQOpnT+u1P6XC3A4KTKWiukJ8YXHVAGKSgMKYEfkeU5EKBU4RbM\nErNUSo0ZPs650UOWC7XwZVl5546dYgqs8dwFLN0AtNpolAZvqoswxmClq7JcfmPVDUwYKlLKEmZS\nTjgkfVRR4Es59u/wPA+TlZRK7eAeVb5WFQyTj1Li0PllWyT5aOiq+1qNfJhjwohOr8/y8jqD/jlJ\nvcHx0W36PWdStLjQYm+vz/HhPkEQEUYR87OzJHHCre0bWDRrq8v4vs/W1hZ5nnNwcMBw1MfYFnme\nksQxnf4QYQPS3F24o9GICxcu8OTjX6BZb6BNQRjFZcUpyJUT9dQbTUASxhFCtPCkj9EWP3A2xYM8\nx59rIIQgafj42qKNLecMhp2dHaSUPPLII2PRUq1WYzgccmnzCu9817vRWlD6UI3b/292zM7OcnBw\nMMUFd+dKEnp4vo+e6uhcQWHu6OREhYVbOVVtG2cJYBwUWZ3fnue5OMfycJu+ZZANsEYQhfVSOFcN\nSg1xHKHS/M7b2DsTkapF9NKlS8S1BpevXcWLQ7LhyNEZrSCWPifHZy5bVCmk5znhTr9Po9VEKEMk\nQwLp8WN/97/gl3/5l1lbWqbf7+Mbl+VqtXWsITPxghcWEFWwthlDT8A4TLuyXwi1wLcC0xlQMXoD\nKxDlINUTLp8hle5/8j2fXLmNwP2/bpHOsgzPgpVuE9ncuoAIA2bnIt7znveQKucuqXXOyckJO/1d\nhsMhRTZ5HV/teN0W8+mKTll3gilrUNbglR7j037J0t458JRWIq1EGIEvywXCCp5++kXCaIaz20co\nLUGEKJ3iR82S++38hpdWV9DakuUCqzUOI/RKzL5806UG4XxTHF44oUt6CJQ2+IFjjQjjMkrd/zZR\neFYXjpASrc248ncUJTs+kZMoQdjSuwJKKMNVx2meIzwPZQoCggmPXd5ZTUC1EZa3LXnPAs+5nmo9\nHjZV9gWiLOVsuUGFQiK0QZjJLENlavx3RhmkD0o7HxplXMdiceGzURBQi8M7qq7J+6hJy2Hc3OwC\nRW5JB0MGgwFzcwtYoQk9Qfd8nySMCaVGC4NnFceHt4mjGnMt9z4O8px2t00cxzRqNazOWJhr0umc\nY4xzSVRKoVXGwtxMyT+3zLTmaTZmOB/kdHp9rFZkw4Kd7W3qcYJA4gcOz/eDkGFWUOSaWs3Jy7XW\nGIHjD1vl5jDGsbH8KByf11IGGK2QSILxYlFBIdDvS+r1ejnQzHjxxRf569/5HXiBxFDaV4jJhgog\nRdltjbneAqSPFZ4rOKggM4d/p3kOhcbi5ibWajxpsCai1ajheaKssEuIzgDCx5MenjRjmA0kmND9\n3hG/yg7V3d7znLd3FAWkqSyN5wTKegRRgsxGpa98iRkL52OEqGwLJKGXEImQRPqY/giGGWqYEVIa\nd/miRCmcqtrzI8gtBo+BVSAtJpaQ5eSjIbUg4sUnnuHi1csIIfG1wDMWl3JadReem1lJWeYdCMIg\nBCmwMqAQmUtj8gWhEhgrEQRIbcdbmkvO8wiReOVr4gvQwhAlEaSAFSivZBJ5Hr70ENZ5T0nfJx30\n2Lu1w87uPjeefZHZxRa+7xNEoeustEEKn1D+JQ10dmKeKdHP1DGN4N09Rb8bQ3exTyFau6n92Vkb\nY2vs/j/MvXmwbelZ3vf7hjXs6Ux37tvdt9XdUmtALbVkCUgTA0EGVSlAhTIYiDEVUlSRVIUpSdlg\nYTupxI6TKgpSdpEidmKwQREmNhALy4gpDCULIyka6W7R6lZ337733HvPtMc1fEP+eL+19j7n3m6U\nwSWtUuvcs8/ea6/hW+/3fs/7vM/z8k2cNz2Gy7FBaxEceuTR1zAe5rSNfP98Pqcsh2S6K2D4xOvu\nCkOnmTId1LB57Gsd6tNuRtrkoCLSTmwTnzcQwpqRsxn47r0KWWf/Z9+3+bPbT4eHr18PqOjRRBQe\nlbpcu+unMGib6IS66CeKe3Lvu/ONpyUUuutSFAV5UpojrvnHRVHQ1Ct8K5z2arliOBzTtHOMsiwW\nMwYjcVuvGkfwSw7vzNne2sPVFXs725yczFjM5mhrwGbpuzTT6XFvGVYUBefO7zKbzTg8PMQYxdHR\njMnWGJAMbTo75vBkyejSEhsjQTVkuRyjUgavFJiMw4NjsoGIamkNwW9mndx17p3RiVIKr8H7iLFr\nyqsxpltx9+7yi4WYDpNbKncsE1/c4ITfNRZ4xdcksZZuy/ncY5RFhUhZFmxtbaE1DMqczz11i8m4\nwJi1DlK3SpNVWMAoderZs2mJr7VQFbvv7eiS8reNBjdtcVFjMqkTGaMkiVCKZbWidi0vvnydZ5/9\nPDev7+OawI/88A+jUTRVLc1EKJr+GTytlxKBm7duMVSWWbVkaDKapsL6yNDkvO7aNW68dJ3isdei\ntcKi5Bmwiug7enPo74vWGh8DRltUnqGaRlYQSMA2KLQycufV+hk0qahsjMYoLarzPhKtRuc5xkm/\nhk7wrOjprJuy2rri9v4t/vD3fx9bDLh8+SJbkxF5WXDtNY9grWUyGjAoRxTFgPf+nb951xjo79Er\n/uXf8nYaM1N0LfWy7JcMWZgSHVwhmt4qJAbGBqYVvPzu2sAjj7yW3/rtj/B1X/fvobIS7z0XL15k\nMBiT55rjw1t87I//iCIbo9QebeuZTudkWUHjZbBEJU38p7Fp4cEHNNrmOOnsPxXQpbK/pjNqrYla\nk2fZqQc6EvvKdp8d0+HanV6N8H61AqM9WrnElnGnvm8tdbCGdbrmn81ibIeZN03T+0h21ztEhw5a\nVigqpCzPp4EbMFb1D7rzK2ymadv196qNB79tW0JWiOQB6+Oaz+e9AbE1qdV9PhUoyCp0DMyOj1jl\nBZOdXZxXZBncObiFMpp5NWc4GONqx3IxZ7C1JTAAmmGZ4b3hpRsvMRmO8O2KyWSbve0dtIKbqwV3\n9vfZ3t5myZS2aji3tUW7mrJsW3RaUdV1xWAwoWnqnuNflqVoe/iuEO1RplPUXI9lYfhIr0EIDk2S\nfLBdhqvXPG6l8Kg1fKYUxgYCTTKxkI7NU+A49wjmWuM2ivdShNSMxgJLRQdt62lcxZ2jYzKraZqS\nSElQNS5Is5B3rocClFJpbELHdj/dzi9o/qmVdSrmeu8THCMUNBVroq+I0fHB3/g9Du8cyGeUEe9f\nLRN9npVYFZlMtrlxY5/GebIgndbaQXQez5pKKQMX2qYlbA+ojmcS5H2gNBlew9WrV/jCs3+KNUaC\nrVZYH4Sp0huIdOqTsloOUbp2+5UQCuUCeYJKbBS6M0HIGx25SEWhPSqhC1GWYvcozDeTGG6tMFvC\naYip9Z53vu0JvvWv/zjzVcvNF2+ynE9pvGNZrcgzResXqNnyFFJxr+1LFsx9F8zTbL55gusZPiLw\nx+mB3GXBRVHgfUuWWWIQxxeF4bHXvYHv//7v58oDD1Fub/PsZz/LP//n/5Tj6YpqIcJRFy5cIEYR\nRhqNRskQINnOqZ4JeRd2vwmdeIJY2SkFRuOjotNzlkaahrzIeq6r81JQVCn710oe9M39dz+7cwwb\nBeGzAVopRdu2PX1ss7mpC6KbTIWuC22zuNOdj1Jtn/l3E5QxpncE7/bfNI2oKqbv2iwWde+Xz4Mm\n9k0uWmvapmI0GvXt8k1bEUPEZBabaVzQrFYrXIhkxRCl1oyepmmIcYYGzl/YI6KZTqcsgxjy5uWQ\nCxcuCGZZLdm/cZNz585Bygi7e7ZarRgUJQfTY6pZzflzF/nCS1/gvt2cpmnJcxG/Ojw8ZHd3h2Wz\nvuZXr17FGMPTn3smZdnrbsjN5rHN+2WSvJXQSQXCMzoHL0JtnQy0j3f3TJwhmZzaugaUs+MzRs/J\n8YyjgwZju2P32ExR1ZGy3AZ9C++3MNoIVCmUpY3kKvSZ8F2r5tRM1I0xSTwUTeNwTqR0lXapsB4J\nrsEHBzGK2XYI+GhQyYc0eCnOOtdSliUxRhaLGeNJ2Xdfh+iJLqZrnCaguqV1LeVoyGpesTMY41tH\nmeXgA5cuXJRVu9FoIpmx6FYw52R/LSsXIjpKRh1j7BuZjDFk2hBaR+YVKkFSxPW6TKWJWTyoFFEp\nNEr0ZqzUHARO0nQ2Ud1Y6oqqWWbxPvDsn36On/+FX+XJr/xaxmOB4OpqyjNP/ym180ynx3zd1/35\nVx4QfCkx85g60bzvMeiuk9EqA5g0qMSUdp29rzmwkoHKRXQ+gIpYG7ly9QL/6B/8fZZ1y2q14s7B\nHbZGQ4IKjAcl91/eI7RL6jaS5TkutGg9FPZIEGOTGDvq3jqIxlQENMoQY4Jh0vPX/QyxswkTUoyP\n6yKoKNh56Qr1DmO7TrwOP19nypswhsYkmzr5r+vu7B4qOnPsbpLx4krTSQN0QWITGtqc5b33Ionb\nNBQpaFutcK5BVgwOoxUxeJxr+85Qoy1tvYIoovoAVV0zGOfElMkqrWmbundjqeua1fI2RVFQDkrq\nRhpc9m8f4Hzkyn1XJegu5pisYDDcAiWwznw+J3hPUAFjc7RRDEcTFosFq8WcohxgM0teTPAusKqX\nOCcBoywHxBgYj8YYY8kWLcPJgNs3nqe0ENqWq1fux/nA7oULTOcLomArfZC7ceMmb3jDGyiLgXQa\npwmxGJS4pk2GAqw9OTeUBsW4wtBJJ2sigyLnqL9vkgkK7W8N5WxCWUqfgdWIIqucGBF02XJU/UQg\nxyDHWhQZq3qOKUQnJKQ6yiZLBY1kkt2jll7uglAHFclHEk2XjkCQgluMlKYQA3bfYIxluQq4IGG0\nk+7oJwrVKX1KgbBqK0J6nyfgY8TrSOEDKkCmLbGuwHu2Luyxv7/P0GRM5zOyYcFsNuPq+fNSACai\ntMLEdXG3uzZRxTS2hLariOSFmEdEL2JrNoA3YJRHB+lT6SZSJSfRJ4BSJ42Uu1vSQyKLrMTvl2sZ\ntcIUOd5U0s9hc5rgcAHuHBzy4ktHPPzwLjf3v8Boa8Sb3vxmUEbqG39Gi+eXLpgn5UG94UrSZXri\nRC9a5rJtqhLKz6TQLE1DHU6HZzguMK3Fu5ItXxDCkPsubvWfHxS5VNjdiuEgW0MOSiPTZ1pC9SsD\n1Q/k7nsEV+wGokqBPLmru5YQNzDkdH6dHZjzPmVUILlr6m5N5981YnSbMFpEJKyTEeiy8T5QbGTI\nIXVotl447sqsM/QOfpHsPbFlElc+wimIxmrTP5wQadoGFCmYS1C3hehT+yThG4LcE52WmHXdkNsC\nXzlQiHNLlrFaSnfrYlkRtaJeLYgIK6RqG8bDIXmuqFrH8cltirJEa8N4NAA0bfDUywV5IXrr29vb\nGGNYrVYcHN4W6MLIiuLczjlCCCwWC4rCcufgDk3tiNowVBMu7E6YTqfMpjNa5/HB08znhBBpmqQN\nHyNlOeDy5Svs7u5x/tx57ty5I3AC9JltDzsBkdOYcwdFSVCOKA+ZFf9WeoY7PY3tVOFo409nM+Xg\nXcok+nwRoR7G9D9Z6jvn2d7eYrE8pNc4j4ITd9iwUhGUtPJ3e9MbkOE6U1eJEikFV09q3EvH17Zt\ncrZy4rylIotVI/CNjtB79abVgBKNFR8DNjeoFggxicEFovNY78mMxcZIFjz1akluLSslqoZbuztU\nbcOlnXO88MILXNndFbquRmCVtk3XqJscWT/r6TWjFXkunZuZVtSuJYsKpwJWB3R6zsPmbeomCegD\nu9kaynUl9qsArTSd67CxhpjiVuMcpsxxBP7K9/5lhsVFfJhz4colPvGJj/PgtWtpHAZe3Wr5S6ya\neJaeFLvGjf5KqT7w3b3cO2NIgQQx0YmIVNWcc+fP4VrHbF6zu3uearlAAYPSUNemXzKKy3r6HqDr\nPt3EpO+19ctSNpeo8vB0mW/f7BMCJt9keQBRGohiojeiPJ3lVwyiyui9w8dkKh06XRiP8/QFt+57\nVcKHlHxxX8i0CRJYFzWTUP5g0MMyWSafMUrROodDYKwu1BR5LpOTzTAoat9BROvluFKKvb09Qiur\nhd3dPZSC5WKWcEM53sFQDH998NR1KzBJnlPVLXVVoTp+PJrgoW1AacNiMaPIB5w7d57aeeq6pm0a\n9k+O+1WJtuJAv1wtKIsBL7/8Mru7u2xNJjhXE4w8fHlmqVYLjNGc2xtzeLDi8M6c4XBAVVecTOeE\n4NHaMizHjEZb7O/f4WMf+wR3Du8wmUy49sBVnGspspz5xn3oW+CVTVdPEgFpGJNjhJCcfLrgL/8n\nHcOvPNY2oY/1Y7LBFQ8xpQgRY3NElkGejbIsODxsGZR5D6N0UVuCtQQlSVhVP8XI87EurnfnKWQa\ng9nYhzUZ2k5oKnGxMsagTUMINZFM7OvYxN8VRhussWRohllBG5dkjaPIcprMc3B4i9svXKdaLGlX\nFRevXiXOVuAj0+mUVsGDW9scHx1RjkfcuHOTeaK9qkiytTNnrtXmliAtBcPRSPI6c48+ivT5dfhH\n4KKNXXZjUOjM9DGgL95u9KF0N/HKffehCQzHGcrX5FnB0dEdRiORbN7b22MwKF/h2Nfbl0UBtPtd\nKy1wQsKfOn2Vrhh5r5PpOc5RU2Ql1mR45RmPpPVcK09uFb5dobXM9kb0otY6KWo9u8pDJfK3m9j1\nZrDufvZB/kys3ywuGlSPN+d53h9zDN1EJPTHyDr4ArgEZZzFYbvs3ZxycpfXM2NOH0sIfZfb5jUX\nGMafugddVt83Jym70QIuGVKmpUHFGMESQ+FT1icQS8fL1a08RKu6pshsv4oIROq2ReHFjaYoWN66\nRZ7njEYjhiNNJ9pltKIoh5zb2eXw8JiisAQXCL7h9q2bFOWghyZya2iaBu89g0yadIwxbG/vcmv/\nDvVqybRZMRoNKLIMSsiKDJ1povPUqwWL+QnGigmKQtG2jUAKGH7/Dz/MX/iGd/Gh3/wQ05Npwu8j\n7373NzGZjIR5YToZWN1fC5mUAYTZo5VolKvEeMrznCLLEqOIZLy8DrCd3uFmQDmNY5/258zzUuoi\n6b1BpdWtAm0V1oq29qBYZ9kmGUZ3qApdxrwRzLtCbMcqWQ8o8M4REh3SqIhRmrZRKPKUgUNRWKq6\nZVjmgpHrjk0iZijaK6wL/JO/97PEtqZZLJgMcgZFgVGaIgRGQVGkleJ41cKqZjIUaufSNWQX76Na\nnnB5b4/ls89Q1XUSG4u9i9LZrUt0YxISi0qR5TnRaHTj+3H9aptSAuN0HgMheKwVCYMYpccgBKFz\ndlz2zTimc4vOM6EHEzg8vM2HfvN3eOdXvpX777+P5XLJYjFjOCzvYvad3b4YD9AHgJ8HLsrt42dj\njP+jUmoPeD9wjeQDGmM8Tp/5MeD7ENziB2OMv/FK+z8VFP+M95xqdsBAlOzH6IxoxRigzAtyG8g0\njEYjqqpie1wSYsR5T3CBsjBoSlTmMUahfQAljThEQ4hSGMntEEKGdwGiJXjEiCBh1mv1xK6ooWhT\npklYU6nqugatU8E2nJnEVO+05F1E55wK0rDGws9OgN216d5/tq1faJtZz5g49be6QQ9Hqbq+niw3\n78fmqsQlZUsXA23wLOuK4dYY50Mq/Ir7u4okHXi1FoWKmqIoGY5KlssVy8UKrTW184yGE7TWVKsG\nm8v925qMaduGZdVw8+WXE8YMo2EOiWs9n50AiswmKqU1TMZbLBYLuSZKc/36i1hT9lYw0VxRAAAg\nAElEQVRh0bXE1Hnb1g2xqRIsUEtziJZsM88zplNhSFSrhv/yP/8v+Kmf+il+4sffKwXzyZi2bfnw\nH/0Rf+6JJ3jwwQeBdQDvfp7N6E4lAlqRD8peG9+q/3d5Vfc9zjmuXDknyoRBoU2b6JQmdTx2K2Fh\nJ3WFuzWVVoynlZZsU1aKShKbV1gVd2OzszEUtyMpOilrZFz4DJvJpPGWtzzBuZ1dRlujBLPNZCLP\nS37rn/4y18qS2jliOULFQN4CBLIAWRrfioitGtyq5tzFCxz+8Yexec6wKJkd3uCR+x5gOp3ig8dk\nlug8eXJZskr3UmIq0strd12wNmq2hhNcBGtzVB0oCeQxstVoWhtPJUtKmVSvUIRu5aIl+dRBoZTo\nJJnUzu+99JcopXBGodGYYFBZDomw6L1nuai4cP4K3rcsFiuuXLnUr6xfbftiRlAL/EiM8f9SSo2B\njyqlPgT8R8CHYoz/vVLqrwJ/DfhrSqk3An8JeCNwFfhNpdTr4hoAByAoTdL9wzsRHXLR4wUJX2et\naSkWtaINYlwhF3LN5wZQeGJoIWYoYi8QJY0MFcPBCO8dbduS55YYPVkuJHzfajKToeyA/VvH/ML7\nfp62DTx4/1W+8zu/PVGMmv77Ojs5m1r1Nh/c1kVsZsAEWS5nGctlhfYiOqS1UNciHqWFJnUquAfB\n5IgahVTEVVcgUzKhKAyuDZgiMU9MV1gN6UFcB3mBoJJoQRQjggi9+UcXoFUqdnZiYy4adJAGGc86\nQK3PNyNEi3eO5aJCfEcd0TXgZUBHF4ShoDSrpk24dUGe54LLW0vb1qKkpxSBwNH0EDWTYJHlOVW1\nIktUx+VyKSuQLIPYonRG3VQobXA+4hZzhmUu2Z4WvZnZ7IjBYMRqPmN3eytBVwIzxZQhahWZz04w\nRLZ2dlk1jroRp6iHHn4YrTWf+sTH+Tf/+g94y9ueYLU44dprHuYDv/Yr/Nh7f4wPf/jD8AUrQlOp\n/mKSWJlzjWTYJuKVxyvf01I7phCAshlBGWG9peDSj4oEyaHSJB872dyEj2uLMoG6bVAmSvZrAoEW\nFW0aq8loWgWiCtIQJ2uQhOMnrvQGbCbv7xIWgfWEby72iFJrkvdfu/YA8/lchPO0p/FLom5Y1TWT\nYQkYHnvkMa7fvMHTH/sYEc/h0SGr5YrQ1LymGDFaLBjUK+ogzJ9cr4vPxICORuo2lRTdtYGDw9vs\nnjuHUorpwRHlVwwkyULW2qKfg1jWGeRJ0AqzUZxUabWhTU5hBgRvqAj4oymjaMh8YKHFI6CXLEaC\neUeZDEiyGIcldjLBuVQQNgIYOpdWvD4yKEouP/5atgYD/HDI+KEH8EGjTEZRDJiMtnjh+Rc5f2GL\n0WiEzTOyPGd7d5dX274YQ+ebwM3077lS6k+QIP0twNemt/0c8LtIQP9W4H0xxhZ4Xin1p8A7gX99\nZr/9T6U7rZNA5wwky5SOuXI35Uud+X1ziQtrutim4UKMG1xafXqm08ry4ot3+Ll/8stiLqAKPv/8\nLf763/g7/PAP/QCToSbLNMJAt302tBmIBbKRrs3NGbyDJjqvxLPHvXk9ZD/rhp/N2sLZ87xXBr+5\nr27lwJnPd/IEHTe4M3HuPreJi55dEXQNFt3+61o42c458kIyEJNWLk2TcPnkatPpxTjnGI/H1Em3\nZrlcyjXKbI/ra60xWrOzsyPwTdWiUoEqxoC1mrIoyMsxi/mKxrUEJ/sqy5K69T3sMx4bQhATEO89\ng9Fwze82BowSHnwMnDt/gfmqIXjRGnnbE+9kd3eb4+Njdna3ePsTb+ahhx9hOpuhTUaczfmmb/om\nxuMxH/rQh9je3ia69RJ9XRA9fX+1lialfjz3TJRXX6We3ToIzxgjdnMpuJg4xHtH1LJiMkZYQMpk\nwmTxabyo0+PvXqtkuedrJkuHeW9CP3XdyvmEBW1coW2DNkvy0tO6BqLj/b/0PnRm8aHmySe/ihs3\nbqCU1GO2hkIP3nyyQ4wJY19vSmk5r4RNL6uKy0UhyUlVExu57oNyQIRkApJRBI21Uvjt2F/BqHWG\nnuDHbFCwCoFoIvuHh1yimzjvoYsSNdEY8sGAnUsXOH/1MtnuhOlAURmIRqPSiivL5LkpjGV3d5ei\nFZ0nrwyvf+cTfO75G3ivGE+2+Pa/9B1AkLZ/I3Ij589d5XWvv/9Vx8L/o7WdUuoh4AngI8ClGON+\n+tM+cCn9+z5OB+6XkOB/+jpsLOXvhUefDVSbgSUdy6nPdPva3GefmUbJTWP0iSkT8MGRsV5C6gx+\n8f2/ijajpIRoQUUGA8t/+9/8D/z0T/5tlJpLVh0tXQerBMcOolACxcR19d9qCSguBvIzRYx7BfPN\nc97E6zchj83X7rUfabdeG1RrNAQwVhpX1EbWrrXgqCL7KzIJoZW0pYNJpDNSnfr+7rM+yETgnGNn\nZw/QKGMT7cqsefkhgrGEGBlOtlg1bT/ZdIE+T99jrWW5XFJVFdY6mhjRyQxYMEkrhdX5jPl8gXMR\nbfI08APRB5pK9C2a1RI3HLG7vY1r6rQCANer/2mIcPn8ZYxVzGZL2lZWcKtVS9NUXLnyRq5dPccT\nb3kDq2pGWy/Jcmn+qpslzjW8851/jne/+xu5ffuAn/3Zn2XVJD362BmDqz7B6MZvx1W/F3z2Spuw\nm9bjp2lO63WoJN/QBp86nhWowGA4FFOU9L3rBpz1ZC3PXcfISQbSKqBQd8FAveCaUhgFe9s7HB4e\noiLkuqRRltxsEdscZSyZCdhM6H8XLl9gOCxp24osK4lBM85LTIi9REyMsiJUMXG1u2cmBJqqBrUe\nh6PRqE8ouvFW5AIvDgdDKczajCwIq6oNLV4BUWNixKZmtuAjWVkKJRVPubXNtUcfZJxnWAsqEyZK\nt6qy5YA2XUenIrWKHKtOekTa9ZXeLLoGsrplmSuc2eilUZrH3/xW2hYmkwm3bk8BWQkVRcZ8OsWF\nwCc/9cKrjo0vOpgniOV/B34oxjg7E5SienXezF1/++Vf/zBFLrTA1z50ibc9/to+iJ/53nvvsMsy\nz7BNTheI1oH1bIYZo9p4zdA2iqY1DIsJLjRYJS3uuzvneeKtj/P+X/plvus73404FgEoYtAJDhEm\nACTYIxn8GiUNDicnJ8QgBcRXOpdu21QjPPu3s/++K8j704ndZgfrvQqpa9aO6+sPxBQsYux1IVQ4\nfXzd59u2pakbBoMBGnjphdODzVpL6z3RSZu/SSyfuq77e+W9T+3yUgyrqqovFMuyXlZBvpFsPS8L\nVqsVIQS2trY5PJnL+dGgTc7WluDmg8GA5aKSe9u2zBYLVJp0d3akRb9pHNoaGu9EW3+Dq62UQamW\nn/iv/ga//msf4MatfQbDjKgMtrC4dA7WGm7evMFwMKaqpGj63d/93Tz22GP86I/+8Dr7T92ZHTRH\nVL35858VyPsVZIJa7jUeuuuJUp0QaArIomqeZZkUlo1BK0vUVlhYm7BKFGhIgnQaq+n7RMNFWCHd\n2OtMiqWG1CYf0Zz5fElTWbwbUTdgcpueNTE+uXHjOu94x9vxPpDncpQZCqs0DtFcisgk2/HutbbE\nhOG3STaidtLNfO7cOVahFdGL3GByUZ2sncMOCmrnWGWRIjHlmgRFZlEybjss2Ltwia2LF2gHFq3k\nWOzWEHtpB8YlziqcEt/QnmIKOAWm09BB4opRyZtBwdk769WasSRaLZbf/q3fYvvcVZoGFovVmnyh\nAs8881leeOFZiiLH+VcvyH5RwVwplSGB/B/HGH8lvbyvlLocY7yplLoC3EqvXwc2nUfvT6+d2r7t\n3V/JeDzGe09VVcBpHZbNTLzHCTfE+e/1PjidnQte7DH23iwYkCUbKudzz9xkZzjhyXd8NbPlkheu\nX2dVNTzw4P1MJjnjQsS4us8qpWladyozCkHamVEBojAqjFEc3z6C6ES5Del+7ZgmcuxrxgspcxTe\n7VqDunvgNouZ/fXx4oxeFIluicG79cS4+fkucNzVrUjbC4uF2IjOtTF4PG1IZr1ojMnoGrW6oLta\nraRWkJf4oGRlYDShdf2k4JqWcjRAq8Dx9ES6blPhtaqqvmtWRXBNQ9PUgCLLJEArYyTwpuud57nU\nQoqCRbukdS1tDBwd1GS5wZghly5d4eB4xnh7hxg85aDEaM10cYLRGVtbO2JioBSf/sxn8aHlypX7\nGOUlJ7MF9913P//rP/iH5HlOXc+4ePGcBHQXe+x9NBoyHIwxJktQxwm7u9u88MIL/OAP/iBVveBn\nfuZ/Is9ygldolaWgFpGJVHjnLki7t0DmKcBv3D8QOQmDwiTLQd1n1YmNlWBJo5O5SwoyMQRynbNc\nLmXl1a/oUuEuriUyVGJEBbrGJyMTQAxCINgofBqbekJQ3Dk6lAk7L7ClQB66hFYt8eRorcBI8Cz1\nkOl0ns4tYJU0/AVtyO0AHxoR44qk40umGhGiMSzqliZG5nVLOZ6Q5SWzesl4e8S0nvPom15HMIb/\n7Ed/lFvHR3zb93wX+api6KQRSafnslGOxWqFHZV4F6i9Z2UDlZX7kk1KpoNIk0WpFfgAJu+aOQEw\nUqnq6aSKKF3tIUWLIHe1mxtbI0J60txuUMEQfSTLDIPhgKK0qKgp8hwXPefPfRVveP3rGA1LxqMB\n73vfz90Vy7rti2GzKOAfAp+NMW460P0a8L3A300/f2Xj9V9USv0kAq+8Fvijs/vt+M0iLKSExsRp\nd3pY47fd+09n16cz1bOQxWbA28SfZXLoCqdaugUXNe/6mq+hXrVcmEw4zC2veeB+Wt8wPzzkm7/5\nXbThIPGzxZw3z7JUxJMiUlexjhtBWGvNYrG4J6/+nj/vcQ+6iWkTXule21yJbFa8NwN4p/fSvb55\nDN1nxK1JSVE0yqPsEmbX0+ziWsRfay08b9dKR6TW1E3d71c4xzYZVcjKqAva4/GYEAJlOVhLjKZj\nGw6HnJwc99IASkk2v6pWKKXY3d1Ntm4rtDIobQnRU5Q51lgmwzFtdMyXiv/jf/tVpqsTvuZrnkyG\nwhcTV32HQTmibT3jyYTrN24yGA7Jc5uKZ4qmrnnxxRfY2RnTtjVVFTmZzvGhQGspAjrniR72dvbw\nMVJVDbs751EqMpkMmM+n5DHjJ977E/ytv/VfY0xBhzd3ENOrMRROaZHQrTrFhpAQu9zi1HOQZRnx\nrgROrMmqqj4z/tTdE37399Cn98LCiAGbGYzgBinDFx8jIBlNe5QKlENNtvSM85I8z8jzLNERBdYp\nioLr16/TnVqIqTCvoigYGkPnJxA1KG2pVGS4t8O1h65x/v6rHOpIpeE93/zvp2sUuO/aAwQij7z+\ndTgFB9MjtIos2hWViZxoj1aql9HwOmAmGR6HyhQxMyitsUqaqbpVYdyYwF5ppSwLc2ELyXvvTpi6\n63x6k31YrVkt5nz605/h7W9/B1W1IgZPvVxRaEOm9Cma8b22LyYzfxL4y8AnlVIfT6/9GPDfAb+k\nlPqPSdTEdIKfVUr9EvBZwAH/abxHVUWkOjtWSmpnT0HHO8kyYlCnoINNqOQsoX/zb6eCeegZtGcg\nmSRLmin8qmVrVNKcOPAN+wcH2AixrrhzsM/r3/QwTVPhOS1y1R1HRwHcvE3yIBqU0sznC3rWSjyN\nhUdMamqQoq/MM10RWLwiu2OX63bm/OJpyua9frat6/HZswOy2+fmvnvmS8IfO3y0O47uunfZdEcZ\n822LkB0SXz1KkbXMCxFjSnK54qRUCIVsY9/dNRVlvCg2YbmIFkUvAmD7N26SZVnigMfkRB/xTY2L\nFdViRTEc8Ik/vYUebWGV5vc/8lG2t5/h27/tPyC0LYPCcPvOPq4NeJQwBqylqoSqaPOCqm5417vf\nTdPUtG2DDy2utbz44gGTyYSyLCQ7NRl5bjiZzZlOp8BMxtzlCwn799zav81P/uRP82M/9uMYs17Z\nZVnW3w8xzhZI4fS2DhAmQnTCRy6Lgqg8rXNiRZsCWmYUVdv2HPmYMGdrc5rG9zo50q9giEntUStE\nvC49j1KLjTSp2zhqBTo1QaX3JCmq/vi67s56VYu7VF5iVYF3EatzgU6CZjze5uTkRCAnH3AEXq6m\nvPZNX0FcVkzynIuTCds7O1hjyAcjpsrRqkhjFddVg1NaNFsUQliInZEHBB9xyqXCpsAZnvWql5Q9\no1QnJyeEoYSdqyjBVceAjiJbrWN4hedmnRSSXIVO3b3u1/RRTzfetchSk7Rp2oqnn36asshwrkEb\nhc0yyiITTXrlMfb/IzUxxvgH9DnAXdu7XuEzfxv426++Y7E8i3FdTHMuCEf5Hrj32WIonH5PB8NE\nSTHv+rrNbFb2ETi4c4df/bVf5pFrr+Xa1TcSC8NHP/ppfNDktqRtG7Z3hgxKC8r3jT6J1dQfw/rY\nFD5AxPZshhjh4PDwLpijn3he4fL071OaqpGgarIicboVpx2N0iSYbNy6jO50AXgd4OVz6q6miC6Q\nxihY6uYqo1vNdPszWiaq4FN2GdkIzAl3Tqp03eqqYxeFXsWRU38XhULfs14mkwmdeXdf92Bz9dL5\nsUpDUoyB6BWu9ejcEPMB06MjwHBuMGa5qtkqC2JsCTFy7dpDYHJu3LqZxNAEX/dBzv0v/sXv4Omn\nnxYZ5cazuzNkNptxfHzCZDxmNB6htZgQCOyzJMtKBuWImzdv8LrXPczhwW3apuVPPv0ZHn30UZ5/\n/vMpmIvIWy/Sle6N6KSvWTBn12odK8oYw2wxRZtEW9XSTeyc6++FRtG6liLPqKoG7wOTyYhLFy8x\nHg+4dfM2q6WjrmpQQiNVuSFZ3PZyzUDC+hWdQEgIogGUFzkdXU8bqKtWFCaDRgUDQXN4cCzJG7q/\nt4899hjb29u88NzzhBgZXz7P+NH70YiUhFOaE0hCVS2OSDSaYDeF8FSyfFvTC2OUZyZ0c2KHd9yr\nrZaIConp1dUP4ppZ1OkJvVJNY32PursUziR09/zYXdtXfdVXExRcve8BwHBwfMJisWB7dw/vBcrd\n2p5w+fLFV93Pl9RpqAtAmwHjXgEG6OmL0hAhNlyCLasU4KArTsaoQGvBnRPCpcI6ix4WA7Z3h7ix\n4fv+w2+hbT2f+cR1BuV91MGxmDsIK5R2fN+3fBchqwiqJhBoXSQGI3oqWrBtFwJFwilFT8IRDBSZ\nobCG+WxKaQuc82R5RtITFKYDTjIaRC/FWpsYJ4HoW6JvMZkhBodRkehbOvMLadz2fTEyy7K7mn8E\n3lgymUyEOtmxeqzBx07a1/fXuhvEnvXne5lfY/AhMX3QKCxKZ5gsR1vLKhUFU+9NMgKQzK0rdsr+\nc9rGUw4FZtGpst+0nXhYpCgL4YLH2BfwYoy4JmJsxmRri9l0IdfFGFQXKKxGG0M7m7J/6yY6NUU9\nfPVBchU5OTlmOrvDbLriYNpStYpz53aZLVYUpUkm3xmuDTz11GdFF0PJauK5575ACIHhcEBwkbZu\nBXawhkFecPHiFWkqKktGA5v48MLAqauaq5cvsf/y9R5uWq1WRBVAmVR0lgnSu7Zf6UUaCCmTDooi\nM9TVnKIogcjx8TSh75Yyt5y/MOIr3vQOzu/dx9belkyg7QrXtrQrRdtoxpOcRx9+kOODBYOxJrMT\nfFQou6QcwP7+dRaLBdP5TZ769J8yO17iGouxonxYlDlWQdO0Pa10tVrhXCM8+syh1JjM5EQdWVZT\ngqqIfkQxynjkoddQLeYc3r4lPQ3eUUxGNFoybK8VoYuEWqNMTie4x8bYJj1JBN9PeX3C0k3+fZ61\nTqJ057IRxByxTx4TNJLFTsnR9ePWWktHI+pjEptzREgQ1D26TeOaNNDz/dNnRHOppWoCi8WKqm4x\nZUFW5CxWC87vncM3LSpGPvupT71CJJXty8I27iybY/Nmnf1793vX8LF+TYJUF7B6yCDxaSMyYYxG\nE37hH7+PP//kW3jkkS3yvCK3JW/4ikf5e3//l2m95db+Ied29vihH/kByrFlUS0Sv3y9Slj/7CYS\n2fpiVa8+mNFUDYPtAW3wZN0+kGJM8KHPhk9jbKJEF8I6qHbn1Omqd52jQD8JSuDt8EmZYLyPp36H\nNRbbF1YTHz0gy+0QI62X7jnne4JEbw4A0iKuNWTG0jSN8JxDSKqSSedDrW33usmim3DquibPc6y1\nHB0drYugG4F/e2fCYr7qAztG2ArHs7k0uqRMtq+LAE1T8cZHrvEVb3k7s5Mpi9mMwmief+7zTCbS\nVDIcj9i9cIWTRYO2BYPBiCxPRfgogXg5X6DM+kHMc8sf/uGHefLJJyWLVRHnHaGWCWd3d8zt2zVF\nYbFWegpi6DqE4c1vfhMf+chHUpMJNE2zUcdpUgE6kBVemFIYfKiJLkN5hSk0999/hfvuu8igLJns\nbGFsho4aoyxtqKgWA7St0NmMuqpR2mM05LlhMAw4v8RYRTkZcGU4BGWYrfZpXY1vYVp7VF4wUiWD\n4R5XvvYNZMaCakA5mpXj/e//RXKrU+1gXSMRHH2M1lvULmCyDOfh6GhO3QRav2B2e8b+7+2zszXh\ngQce4B3veAdFbrlw4QJVtYTYUtqcoKWBabMGFPW9M+TN7VQW3dUB1EYMifd4v1b96xJ//Kl6xlnG\n3OkdbFCJY7h30Suu+17u+lNs2d7b4fMf/yQvvPAF7ty+g44iBb1aSac0IZLlFmNe4RjS9iXVZukD\nYqeBGOOpguf6Aq5hAjEr1riwdnZxTuQqN+GEs0XDqCImy3jq6efZv33Iax69n8X8Bv/iA7/LH/zB\nPg+/9j5+4Af/E37rN3+H9/7Nb2M+m+Gd64tGpycO0WDots3vQhtEeVbambPUuNIViLr3b+6z+7kJ\ni2wG781rJfSzjYCqdToe6FcmSvX/CSNGujrFHDot5xMrRTxYJFNRSTskxnUWE2PiMqvYTxhdF+am\n+a1kZg6jVC/fGzu4QJ+eODp6ns1lJXF8fHyqw7TLwpVSLOarftLsvkvco1K2pLUwX0KUlYmSY2y9\nJx6+hGs8pfZY7dAm0tZLjFZCU4yB3e0tnG/SaqeDiiL/zpPvRCeXoL7AHSNXr17BOdFmca0oSJaD\nAahO+MzTNBVZJlIJklCsNe6n02PG4zFVXYuJtBMdm+X8hOArtrZ2OHd+j3O7D3L58mV0NsMywjee\nVVOTaYVOVmeubcC3GKtT8dCQlVPaKvDyzUNmsxOadimZqHJYnTEaZixncz7xO59CZYqawJsffyvX\nb9wihpzBaMTe7mW2d0pG4wHaR4IThcXlokED48kEFUQlc5M9prUizyIojy0i57d3aFdLHn7oCm95\n/PtoW2FdhehEQrkrrjuHbxdkVhMwa4lcvb7n4a6mnXDm52mFys60GeiFwyBxLYKseVQyjjgdY1R6\nBtYF6DXkmfbPvbcECCRWVtcpavpEUmst3bdx3cSnULz3x/8qw1JcswwKYyNGwUgrjPX9JKZe8Ztl\n+7LIzDe3zQAGp4OoFENl+bNs6l7M/mwm75zrl2PdVjeOzz79LP/qN/6Yd7/r3+UjH/sTfu4f/SvK\nYUaxfZ4Xbzv+7k//DF/9lU9wvDok0oh7tmtRUTSxURrdDxoty2IEF1TBoOPpiWQzIOdJdfBem7xX\n9frjm6+3bUtZFqeuR6fS6KMUpXzKSH2MdxUUN7e1gcXaYCMq6ZTsqZ9JRkAhS3/Rkg/iXh5MUsIT\n6du8LECp1BzUsFwuGQ0GQtbaYNNET5+deu85f36Xk5MTlsvlegUVAuOxmCE3TXMqgG5mZyGEJKq1\nLvyGEBK3V2zalFGEVlYnAxWIISMEgUSMEo0OtCI4x8Gdmzzxjie4/txT6KiZzWaUoyFPPvkks1Xi\n0ic6ZF3X/UR0cnIiFEhrKAYlOp3vZDJhfjJlOCr7Y1dA21QcHx5xbnePxrWcP38e7z2PPvooh4eH\nPPbYGzBqiAsVOgvoOGExb8kHnibOMVHw67ppWC6mrJZLZrOF0OVii7WiUU7U5ANhWRW5ZzgoaGuP\nNUOi0+gmw7iG3LQMhzsUO7s88fg7+eCv/ySNqxhNthgMh+zu7rC9M2FSjFERFosFzzzzDHs7Wxij\nmUy2yfOiX0XJWI8pU3XcOjjmwoVHufbQRep2TuNaTIQ8+WCqVCuJ0feu8x2kZozBpWfJq5j0T04X\n6s/WzM6uNNfPXlew7Yr3iJhWXHPlu+e1M28ROOTeobEjMbwSjn7vZ3u9Ku26cXR6XnOr2S4UWgmU\nItrrCquEshqToGq30n217UsYzNf4diea1QUZOM3a6MSplBbutW8DebHDZGuXxWJO1IbAEq00Xmmw\nWU+t6gZbOZjwwd/+QzwF5y+d59d+5Z+hy/M4O6ZVwhiYTadMVxWLxYzSGnJr0BaatkWrbM0oiaJT\nLJrfnT64yKWmFiJhGShDG6ANYLMS59dNQX2WnswmQtT4cC8uuQTUkBgvMpik6bmznOvgpbNdot0W\nkPqBZPBJSEw1/cPjfUOXkXdZflQabTPJRrThpZu3OL+7h7GKrBDT227QS0dhSV4MMLYguiaxcCxa\n277QJ4euOD6eCsMms6nl3mNMLk41CL16MtmVjr4g+GfbVugIbVMTQmAwGPX3tpu4m6aRTkUXiIgA\nmzI68ZtLrDXkeUYMjqaFYZ7BVs6nP/5RGWeIPv5stqRqWrxXuLamc7yqqorZbMbuzi5ED6pMYlxL\nRuMR1XJOYTQrLYG/LFzqShWmRNCBb3zPe/jgB/6FMC280Ax3t85jVUYINbFtODw44fbtp9L4EHcm\nq6WwbRUQWzSaSZZhck2IMrlanZqEggenMBl4tyLPMpSKjIcFKuZU04DORbystGNsNuBkeQjK4+ee\n2XzJnTvHuDawu7PFe97zjZS5ZT67gyVSZCOMFv2XGAO5zQlaWB9BZSwbS9NYdDYQ7Z+wNl+BIPWm\nsLEiVakmo0SZTyvQgdTeL0FfK4uxVky0g2jthxBSDSqN8+DkGPRaFdK1kaZZcetdsNkAACAASURB\nVHIy48aN6zjnuHX7JgCr1YL5fM5jjz5CWZY8+uijnDt3jqC0OIclzAAtrBcTu0amACr0wbmXDmka\nCPL8kxKnql6BVoxGE1lNeXleDw4PuX1zH9fWXL18HuXFEEZ3qpopCepimFbqVPPevbYvq8y8v7kI\njCJBSDLQ3gZVtfio+MJz1/mND74PbWKyY/NoBTs7O2xvb/Pg/Ve59uB9bG9vs7O9xe2TGUGNqauG\nW7dO+PxLx2TDa7Sqo1d5bF6wu3eZf/PHn+RdX/9V+KbBGoNbuR6PBeHFhhhFOlMH2uCSDd6arigc\neqnMD8tB3xJ/9ly7f/eFYFIn2cbPs7P7XfWEaDG6IHhwPmB0aufe6OgUqid9ttJRNmUySufV4YRK\n4VVk1Tp2hxM++ZlnUcrw+U89xRtf9xpGWxOUWlf6uwk4z0XkyiSmjQsOrTtIR9gmnXZ5nueMt7ao\n64bbtw+48fKzHBzeIcaA0RmrVSMt2qlr8+u/4euZbI3669vpVkhBcohJ7ByBeAIBd4rOqZXGeU8z\na/B1TVaM2L91neChDskmTCl2JlssKrHGO57OIIqOSgiBGzdu8NCD17h9+zbn9nZwdcVyabCZZXtn\nm+WyQkXRA2mqmjAOlEXGcfQYbdje2mW8tc1sNmOxnDGbim7IYrakLAqyzIgdXwwMSotSnizbQilF\nRnIMCk5qEVF4bQGP0pJY2C7IoLHRy8pKW4zOMSqnbQOKBTZfQtBYsyKqfcgijdsmM9tEr9E2maQQ\nKIcDonY4FzEmQ4Wa3ChQJJtBKcLrqEAJHGZsQYgGbcok7WAJXqipMmhPP/ehh6xjj2V0Y8qanMF4\nQIyR4XCINnByPOfOnQO+8IUv8MwzzzCdTvmev/JdySzk9M739/c5OjqhrmuWywV5nrOzs8Pe3h7j\n8ZDFYoEhMpvN+uQgqHU/xWamLwweWU0sV3OOjo6oqoqbN2+yXC6hdf3z0GX8Pji+4S+8a52kGo0K\nka2tLcaDIZqGerVExRalHNZYefI3nnOVwvT/H6qJ/1a2TSjCR4UL9D/zjeAFXcDaxI49LmiC6gYq\nKOWIMXB07Dg6PuC5528T+SigsNZQ147WG2qv+finPo/OtwnJMVtsEBTRBZQyPPvs83zTu76WNtQE\n3RnyyjFIl2bEtQ1a5VI1P1XA7iRlpRDZNA3W2p5p0gW1s1BIh6t1GUe3+TQxbL62SefrrmVPT3Qe\nW6xdiDqmS9sko4qketdNIJ1ORwclWGvJ8wJNBjEHO+L24ZKAweoJL9+c4lrH9jgnhMBqtUqu6wI9\nECJWKYqyZFgMaBrHjZevc3x8xGw2o6qq3ii5qhq0suR5hlKawpaiBZMPWExrymxE1LBcLvDOMz2a\n4kOdmonS0l5r6qYRzm4I2ExjrWT5mxNwf72Dpw0efIsPYhgS2pq6qolFJh2P1vZQj9GAUkynMw4P\nD9nb2eVtb3krrauZzU6oncBB0+mULCsYDYeEELh58yZadd3Lcn+db3n22efwbaDMB1g0uZGGGKLD\nuTZR5MAoJUqeUeSErZGGIc+6Wazv8Ezxq/Etw0GSQtCGKniCN7SxYTCwtK4lNoFAIW37wZDlmqZZ\nMCotmVXUdUVbibnCcLDFaLiFYoBRBSjfc8ld8GiTOOlsQBzBU9dyjFIkVhKbokr0wbuzyzUjRKVf\nJIs3xvCBD/xLXn75ZUg0V60VxpZrhzIDKjMMh0OapurjhNAEtUBRwPHxMY888jDHxyds72yzWi65\nceMGOzs7qYvVcHR0LKugYg3fds9X9/ODH/wgSkVsJn0hIvyVaLZWMHjBxmVFqVTXcSt1qeA8VV1T\nLZfkNiO6CgNCsVWS1cuKeS051iMUfwbQ8mWRmZ9mh9yjPT8Kp9W5zgFccevOoVDkdIbVBqXWWRhA\nV1YNIeCB3d0hF0ZDPv2pp/jUZ57l0n1XOJ4uQEVU8GhlpJ05Kp77/As0tZflTiCxJVSS1e1wcUA7\nKWaoRJuMBpNods61aC2ZqlLr1ve7zt0HssxuBOvT+D8b1yHPc+qqQecG155WNgT6jtrNbKIbaMaY\nXhq2g2S6z0Ds1SU7mdo7B8f85u/8Hg9c26duFcZkPPjQw8TY8NLLzzF45BxDJea1bdv2WPawHAgM\npTW/+3/+PpPxLovpPGX8BYP0oOTZiNnhLbJMpyJty3gyRhVCDbzjjsAIvTEGaOoGazVFmfeTlLCU\nkudqb0xt+nNsmgZtDIPhEBD2iGtFC8ZkOXXjODk6IiqxlTuZQVEMuHT5Kr4z4fAtTduSZblIvHrP\npz71Kc6d3+WBB64y2dni4OBOf4/m8znj8bi/9iGILPFisaQsx1glFMbp0R2Go5FgSrEhhnWw0lqC\nt0kAq0qmD847cWHSKkEQ0vHQZYNHBzUv3pzimiXaOu6/+jBVNSfPNdoERoMJL738ElvDkgAYk0PU\nuLomeseb3/oIJveslo7PfOYpYMVgcJEQak5mAWsyoqs3xp0YEptOlMuLkJkx0h08GA5k/CaqqoqK\nHjTe2Da4bPJbKkbLf5o8z8XaMHYECEubdOljENhG/GVTR3MQaYIY4eDgoB+fW1vbvPDCixwfO/I8\nZ7Va9ecyGAwYjUYcHx8zGE8Yj8en4pNzjmGqEUjNYIy1hpOT6fp6pOfVpWdORUBLJBLYPmCsJifH\naE1TVYy3Jqym0wQj6+6kE7yq1q9/EduXLpgraZgJURyG5EpI2zHpJnUzvWRghph4FzEEHn/z69nb\n3mN//04qpgn+RQrgLiS9hCiz56xxZF5U07AW3zq2hiXT6ZQQGnKbo7Tlg//yd6ga2L99yKVzIzoH\noEiiIHauqvG05Oyaq2qISovDvIp9V+HZzst1primMwk8EzboiBKoYtcMlbbWBbJiSNM2RJXRRqic\nJ1Mah+qXrUHJf8polFb4EGha6VZFbS4fPcZasmLI1vYOPgQ+88wLvPnxt/HSywcEXaJQzBYr9vdv\nUBihLSoFrWux1nB0NAWiYNLJqciSMT9eoNLxF4VodLRtyyAr2dveRWlFpguWVUWsWwbDkqAgMxrl\nDSEq8nzAalmTF5a8yPBeimwdm0l3zB3vaRqVdFJkteG8Jy8K8WAMAW0sWZbjo7SVny9LMIpLly7h\nImRZTt0EmkaYKgSVCrPCdS8HJQaNMZqnn36aZbViMplw/wMPEH3Ee0dZ5gwGJXVd9fd9e2cHdGB7\ne8z3fe/38FM/9ZOSWatIWRRorZIDfBofUf6tUjIr46uzIJRERSHnnmVyHfb2zvHAzi4xOLKiIoaC\nEKIYsKRAOZvP2ZmMibHBGosPmrYVTH+yPeGl6y/x3d/9HXziE59kULaMRpBngZNqiqtbdDJSDzHS\nNlHukXUJSpN+kCyPaAzGDojqNIOre3bkJOX/bJTms5BWQXKOjrwoWCyXgMUFL89DVDRtS9cZHZEk\nQrTh5VnvNceNpm1FR/81r3kNzz33HMZoQsr6O2aUdP+uiESMVQzKnDqpbq7rYZplVSOwUmS1qlLN\nKMM5qduECFqtu7IJyQ+3y7a1Jjh46k+eEhYT8KbXP4zJpBNWeOsKZVV3aYiRnnRxltBwdvuyCOY9\n97nTMQ8i/KONhhhSf19GCCrJkwauv/Q5louG8dAwKLYZj69ijCbPCy5dukg53GY4HDIaj8is5dOf\nfpr/+X/5ORSK0Hq+9T3vIdcSEG7dusn7/9mvYu02rQfvLIPxNhFHcB7nktN9NFKM9GJvF7zcPO8j\nIapT7tnOOaw2LJYzUAFtSEEozbhnukf7bDoIS8ZmljYqZsuare2IykQ1rg1iMbaoQemMwWBANsjZ\nKydUVUU5GdGGGmM0bSrUuuiS0bRkidqKqUQbHFopGh+4/74r7J67j+efe4GjkynZYBcbNavmAM+K\nYmDYv3kDm2lmi0OsvUDUGptnNL4FJYPO+0Ce5SwWYriLWeukkzjXvQCRCoTWYwtF8Cu0zgltQ4sn\napHjVSESjeXg6ITBYEDrHGUhMMtwWEoik1Z0Xceic01PYX3b299O03hefPFFymIouK9gWVSV4O5N\nags3eUHbBoaDMa7xOCcZn/eep576E8nmqorJYMRyucQYw+72Nt47brz0IhcuXCLqyOHhHba3d9NK\nSDK5ZV1xdPOIxx9/HOcb4XX7InWVi7mBjusJPnq5Bh1Ns1tBnVIA1dC4Bh8l4cmKbaQhLOJ9sfGc\naeL/zdybxtp2nvd9v3dYw977zOfOlyIvSXGWZNKkSZmWrNmObEcyPDQu4tZR4gJtigSuC7j2l6Ao\ngqDulwJtUSMJGitRjCRCbddO1MbWYEuKTIkUSVHiJIq8JO98xnuGPa213qEfnnetvc/lYKUNoCzi\n4J6zec4e1nrX8z7Df0BaiNOqEnNx78lySx0Nk8aBVYyrKc8++wwh/EIyUwgyiwmayXRIRDgcWkNu\nekzGgbrSWDMlOEHamDwymtbozJEXpEHxPEIt0pJojDY88cQT7G7t8FMf/ynqUKeMWlqRPggxqW4c\nTWy6+6Sp684lCDNDoai5do1KG+BoNOLYsWO8+uqrDAY9Tp48yfbOJhHfVU4i4dygbGRSTbGH+0yn\nU3Q6721FW1UNKJFZbmo5D84HlDb41MYkBXNp3gbyvBCwQFRiNKMs99x9L8FJdRDcWPgmqX0rFcgs\nNsYoMMX2c73d8QML5vNU9FY7uWv439hPTicHZHFrpXjnbbeKTnhys2nd5qWfPGU6aZiMIxcvyA7r\nnSF4D2RYraGpyKw446wsL5IrTdkfMBlXHE4jg7JP9Hvp3SazYBKatR38xEhwLmXSggKZ/1xRwd7B\nPqQb+kZ263xrqc3yfQw03rG4ukrVeAKWSe3R0aEp0crSOMeXv/IEr52/IlKvtYhOaZ2s6ZqKxk3k\nBs8ylpYWOH3qGGfOnGFl2bK45OkPVrh8+TIHfkjeW2bt5DleOX+JoEoubl5gMmmwyjAZjSl6JeCx\nOmM6HXHs5HEmTWBy9TpXrmxybWOf1dVVmmipGwVRU+QLeKewWqqL+eFNO2gy0YgjURD7t6g8LjYy\n4AuJhKFV6q9PMUYwuAI2kKqnLPvdIFSGTqJF3raXtra3qSvXCX557zHpXLXY/6qpqF2DshW9csDQ\nT/i///RLfPLnfpGmaXjppZd5+umnuffeu1lcXJS2nhaFSqMVxksVsLOzw9LqEpPJhF6v1diWecZo\nNGJpaYnNzU0KKwiMVjpW1resHzNvmmKksiLMsj3n5s6liuSFFbtBA2WZw3xPnTmsNEfJZWFuA9zb\n2yOGyOrqKuvJtaeF5q2vrwOB4XAfrTWXL2+I5sxohFEZBwcjlI1Mx5rMLmBzuOW2WzHZywwWa0b7\nzKDgNxxaSwvl7rvupCxLmokkBUSNUjKUds5J69AchSz7tI40BvUWlnvtZz44OOiu9cbGBiG6Tl65\n1R+yc3OSToM/Ux1HRGtN1VSpIpL2VowSQNfX17l69eqR5Kxd59KGnN337XXUc0YtoqJ5NC6kD9D9\n/P1AIf+j6JnDbHG1Gar0i2aEmFbkPTcFGIHNNUahlQwXh1PfOfmInnWJtTk2k/L6ysUNgffpjMwY\nbHDE6RjQeC0l1+HhIVrllL2BuJxIfYtSyXHdSBnkQqBO4lpmLsueD8rtUHJnZyfpPpsjm5RIqPru\nc7eHTjCop7/9PDobMB3X3HnHaTY2XqPMFc5BEyOTaQRdElVBv78wmzcgrYn3//gH8d6zubnJzs4W\n51/b4YXvXuH69es4J/3kX/3VX2U4DPyVD3+M57/3MkqJSNP6+nFeu3CZy5cucvvN57i6cZnRdEJ9\nsM+jH3g/0VeMh3u4xpH3V6jqms3tAyIl4ykc+jH14QhMLn1WL3OD6OUmaI2qpb+tEiRR+tx5sl4L\nQUwFjTYdk9Q5R91YTOYZ9ErqKmCN7zatLMsQpKL01A8PD7l6+TKLy8tCrEn3mU8MvxAj7YjUWovJ\nc8nytMXFwP/5h38gVO4kivXSSy/zrne9C2NVCsJiAZeVBUW/h3NB3OKbhldffYU77riL7e2dLiC0\nG4oyoos+G2S2LNlkKGFSy6iKZJkizzR1ctcRlmWLtGj1eHSyq3OpDTh348dWy0QlHRuE/h4jKs13\nxKEI+v0+586d60hcAL1eTyClNHJ9yh5F3uP4idM0dcU77xqANli9wHg8xfsFlMkYHX6XWGeUecn+\naIzIT4QjxJcYI6+99hqlLbjnnnuT0BWJ0xAJ0XEwPOTMqTNs7m5ijOHee+/lmW9/m6w1CQ9z6qtz\nRzunan+nKMSCMITAiZPHmEwmHVFNlE8D3gkMOM9KCbQpu3/ttdfw3nPq1BlZt4np2VZJdV2ztLTE\nwcFBxwFp5xitjrz3EWNmpL2uWxsFm2+tJtOgUhZ/46bwZgJ5Nx4/ODSLh6BJWiuzC3wjIaDtHWs1\n/5hk2DEK6SVEhY4RfGIkBi+tmVBDbDBa4ZpIK6wfvGdzY5PjCzmFzehl8CP338+3XrrMtHYohwSY\nKJZ2mEjUASdNMQCaUJMpI64kWiy5WobmbEMy7O8dCt5ZHf2MN7K5uhtbri1nbr6FD330p7l66Qr7\ne9tzFzJlZsExHO0xrTKUzlFGEKoyHKpYXBaRqqXlNc7ddhuHh4eA3Jx1XVMUGdf3Dzl16hRPP/0M\nWa+PNpG6qbiyscHB3j7VtGJ5oQfNGovH1jh19gw7O9eFAUkgNJoQLJPxCKMiKuHkVbT0BysMzT65\nsSjTMitNQgbliH9ilM3j5AluPncLr770EqGe0Di5vsZYGh/JyPAqp4kWN61YO7GGQVTl5j1Ub4Ru\n9no9tM0TQ5huXbUZlPetJlDEZJKpmSLHeyHdhNQnx8+EviaTCUpHBr2SiOocbgC0tmRZwcrKGpPJ\nhEuXLnHy2El2tq8Tm0h/ccDi4iK7OwcdrFMpqdgEzm9SSyUQQ+wywnkbP8HVR3SQdhJItdpm0vOB\nRP5otuY6Sd2UMEStyIqCres7XRX5/g98mMbT9Z3zMgPEcSm3OYsLy7Kxpo1pMqmIWBT7KB0JqsLH\nirwA11hiTEPNG9Z7+z7vvPNOelkp6yJqIokxnBifhc2555572H5sWyCp4wkn14+xd3iA1prBYND5\nIrTVcvtaIt8xwHtPM61TYK07xcb77ruPZ599lhgjKyurgCSHw8m0G7QWRcHJkycZjUZSAWkr0wol\nCVkXz8KM1d3242VYat7wueeDcp7n4CMKh1biR+tbscHUfpHq4z/izHz+QwkxRXYjYwwRTeNkeOB8\nxIWINTohWVpNb6Goa90iQTwhtIs4SFsiavA1C4uLjCZTQox4LYpqTz/zbT70Iw+iQyS4Kbfffitf\nffJ5it4iXnmq6RTbj4RkMS0u5TP6uqBsEjQrfRbvYtfuCAFU0MTGoXzojJgjWtoEoRWiOtpuiUGI\nUS+//hqHX/hTnvzGU/ynv/ALeEpB6MSAqz233XqWO++5nazos7p+kqWVFYzRUj04zwvPf5eXX73A\nY1//JjfffAubmxucPn1aBr7O88EPfpCtrQ1uvf2d7O7vEScj1tfXERd3xbETJxjvHUCyhcszxbUr\nF7qA4hEcvVGaiKVxQWRIARUi+9d3wStiptBYpM5RmBRUmhCoguOTv/SL7FQjKu84Z+/h/AsvkFuF\nD/toK821g4MhB/uRE+vLnFhbFAaoERLFUn+Rsig42N9PZgbt+tJkWZHcXyIoLagX77FG0x/0mE5q\nvIfQRMRUKTAcH9JUktWNpg0uNFid4YPHuZqi6KFiYDycQAwSfIxk9ouLy1hree6557h69Sr4wPLi\nEsdPnGL/+nUuvn4Bd8c7qSa1bH4qJgirJ6Bw9YyrIJuUJAjWKPBBBNEiuBjJbIYtyg6543CUuovd\nHdrH6LQRKN0N+LTWImerLFZb9ne2MUrzpS98iT/9ky90rUulAsqWxCDG1GVuMDbdpzYFThUFjZMU\nUPPM4E0gswO0zpiMDomq7iCNkrBIhh6c55677sZYxbSZpBisZVOICmtyjAl86+nHuePcLeIx6gPL\nC316gz6HwyGDwYDz58/PslfvpJKIct0HgwGj0YhoRWraWtsxx++8806++tWvMhqNGI/H9Ps96rri\n2LETaWYwZWlpiRZmOZlMqJtxUiYV6QtlDb2FAb0FmaNYlWFNO5jX5JkYmBsbASemFFGn4bZK8sDJ\nVEbLJm6NMEykjevR+UxA7+2OH5w2i4KWmtB6UsIssLVHm63+ZSXGfItj/rGmaZhOp5w+fQLvHeic\ngOLazh6jyuFdZNDL0NFz/MQ6ewdDskzJxS0FH5zrXDIZ5zFz/blWwKc7ySrMZULSxxwOh90Q7cZd\nun2P7b9Kqa6cVRG2rm5y05lTxOCELBJNt9vfdcctAByORvRtw9ULL5LnGePxmMYrynKBRx7+Ub7x\n+FPsD4doY6VfW8hA5ulnvsXu9jb33Xcf2sDqyjKvv/46eVZSVRXVxFH2Sl698Do6QrFYULsEzfSB\nOjZSumctIWjGBci0oak9Fk2ICo9ca5ToPStjaJqpkJJOnuLbX/8Ldq7vcnphCWm3pDYAEpTq6Klr\nx3g6JYSBOAz1Z1jgKmH5gdncIBlFk/qhMQaaSjLZ6WTM9evXGfQXu1Lbew/eM51MmE5JmjzzNHCx\nkvPeE9OQtywLQoBJU3P5yhX+3dee6CBtg/4i2xub/PzP/gQvvvQ9FhcDz734Xb751NN86lOf6tZ2\nu25lyc8ZOyPDstn3KhFKpEo12qKUWP1lhdjStaqCb3Z0EmtzbUAQCYCrVzcgWogKYyLWSmtzaWkx\n9a/lOVuc88y7dKYiGBPv3LlIRM5L8I6mqY7wMNp1In+YmJ++vW/jkd+bNzV57ZXznDpxkoV+n51r\nm1RahOoefvhBXjn/8pHrLzMZyZDX1tY4efIk+7vXGVdj9vf3OTw8pGkafv/3f5+bbrqJ8XjMcDjk\n8cef4J3vvI0TJ05RVRVFkc169koxnU66nnpZlp3t33A47Prj0dVdshdj7Fo9bXWl0ILeYzY78k0j\nejs2JEu5mbxANW2Ik+o/7mAOs8UsGWdsDYeOvOn5AN1N8eezY6CdsLRU+fZoL0KWZZy96RjON5AF\ngtKMg+eFV17l7ttuxtSBGDOKzKB0nRablGs6tXBMFOyqNjJpb+Uwg4fMZOLv6UOaZgcZ1mUzka02\nkCstIl2mzea97y6qUiptboGbz55lc3/C7t4OrhoSXHvjGDSeMs8YVxPWVpdQFtZXehRFyZXxkNUT\nJ7l69YAmDXAP9g/EoMBYQph0G+YnPvFXpZxUmqb2LPYWGY/HTIYyNF5YGlDoRXp9IfOoI5tq0qLv\nhnOCgzSoroeZIRDNsav5b3791yHA7/zvv0Oklbs1PPaYqAguDpZYXz/Bhe+9Aihh1yKKjCFJwpa5\nbDQLi8ti0pBL26OqKso8ByuSs3medygQa5SgPZLR9vrqKlmRM51Wkn15T6ynFGWJKXJWV3O2tw+l\nB90GPqQ33uv12Nra4tSpU6yvr3HLzbdQFD0whuFoxN7eAdeuXGU8HjIcHvJD73mQ7d0DbrvjTp5+\n+ls8+r730xtIhnfmzBkO9/ckg1Yq4ct1N8hv14y05EBn7VlvlSuFYet9hQmQ5aklc0PSM7uXjpb/\n8wPQ/f1DfJRQ0LKEIbK3t0deyFBbHtNJO6UdplZkWS6kIBJRRlmcA+9iMowICbo3G17ODxBnG0G6\n31RAx4jWM9Npk6qU5UEPN21QXhButsz58z//Ev1egVTjotEjnrCCgb9w4UIyCRl2DNS2avHes7Cw\nwGQy4dixY5w9e4aNjWs89thj3HPPPWkG47uNpTX6LooiJWkabVdw47GcT+fRcabH35rWtJvSjcE4\nhMB4OsIQhD+AnC+txONUa03Zy1NXYIbaeavj+5RP/w9/zH+wBDHvHp9HtdyYqcMsqB89QUlTpJOk\nVUe+rLUERI9CIbj158+/xsQFGjwq1Dxw7zvRdYOvaurKAUagdMqgWwq81+hoUFEEcRQiwxpDIkek\n4VQIopY3WFhAm+QmpNTsg6bjRlEuhSKGwGKZs1xaHnj3Pezt7sz9XsBHhydlPTEQXIPVKpVqcu7E\nlgyWl5epm4b1tXVuv/12ptO6Y6U+/PDDnaDVvJhUv99ncWGRtbU1jLUJIpoqpJAUGgNJR0Ili7d8\ntmEphbFWMPJA0zgmVcX3XjkvgmBBGLftgh+PxqyurnZzkfZchLnrLLrnKcBFESRq8dfiJ9qStRx7\ne3vs7u52ayV4R+NqWinkqppS1VPZQLXCJnSJ9wEfInVdMTkcEasG5cDXDdeuXmXn+i5Xrlzh0Ucf\n5ZZzt2KzTLANUbO8vMbi8ipnzr6DW269g8XFVbI8Z3l1hV6vT1EUFEXJ4tIyJ06f5MLlS7Me9tzx\nZo+1R2ug3L7X1iDi+8naZNCahnZzVbC1OYeHw+5n+XcmQwxe2iLaoHVM7ZWWcdy2M26Y/3gvzj7a\nsrGxRYwzWC43bDbpLiAxI5jPzjWKIsvo5wUnF1eo9kdo51nuDVhaXGRhMGBlZYX1Y6u4anokLsQo\n0L7Nzc00pGxk3ab32mqUHxwcdHyFpnEcO3aMRx55pCPRtedO4pCn3++T53nHbh2nQD6LUSnCpL8V\n846jpt1C828IcYa88jF02HmdZjXzhtlvty7a420zc6VUCXwZKIAc+KMY428ppdaAfwXcQrKMizHu\npb/5LeBvIuihvxtj/NM3f+5WLAdyjDhyR9X1wGXvbdmcipYd1mbf8/rcMUJwAbJENootEibQ2tM1\nTSMDTaVSmappYkXIIpXfx5iC5aLH7adPcHlzh34+gChZQyDgo0k9egAhCERE1UwCuSyYvCgJwTFY\nWuBH3/8o33vpPBcvXeTOu+/m+ReeZ7GFjlmNj65rNQUEXRAcRBeJvmJlKSO6IQaDio1Yp3UORu0m\nF9HWEp0jRi9GtfU0oTIaPvGJT7C7u9u1E+6++w4uX74set3tQvSwtLzIqlw7fgAAIABJREFU5sZW\n1xaqasfa6irDwwN29w548bkXUMCDDz2E97VwAlSgaWRjkPJQJZ15yR6dNqJt7iMqy7m0s8m0qSnQ\nqCDqjT/2/vexuSPDra9/8c8wUdaFwaC8sBxjjDhJGeVGDA5RK2nNCjzWpiwu0mVGIXh8IzeaUQbX\nNOzs7KKtwhrDpPJ4FxlNK0xmWVlbxyBIino8ZnowolxYQmloJg2Xrl/k9z7zL7j0+gXZqHIjWZVy\nhKixWUaxuEDuapHPRdPrL2KsIS8Nu7vXuHTpPGdvOoNJazoEMEp3ktpaGzHEZk5eNkaMj/IZjQGX\nQADKYLQhLyyu8aierO35itUn8pHwcTTKpE1WGyaTKesJuVO0xWg6ZK1JpqlCSGtwisaJKiaapnLU\nVU1eiI2fTz3epnaCpQ8107ri8NI2J0+ewGYiE0C6N2dVd9ebTIEzCVgpxdrKKstZjzw6bFR4Fwil\nxUcYj2ucrrh+/ZCgNKFDwYAj4nzgzE1nKYqM3uAU+/v7XWB2zuPqWiwqU1wpipwYA9PpmH6/7H63\nvXfquuladlqDtVIR4CVJDDGijRXOiREodAt7bO9ZFUWALyRkTyQSXEDh0cqD0gR0h22Xwws0+82M\nL+aOtw3mMcapUupDMcaxEjDnv1NKvQ/4BPD5GOP/pJT674DfBH5TKXUv8NeAexEz5y8ope6MMb7t\nu5iH7P37Hmoum7vxa/5oJWizIEO16B0+jJlUu6g8kOUGq+G2W2/h6tbOTPBmxsmj8UEMGdDEKFZf\nbWYqvUBwjWdpecCPvPcRptOaX/xrv0gMkf3hISurK3PvS0rZ+RZiW3qGRBrR2uDTkG3umnRvqc0e\nun5cKo9BqOuT6oDHPv91lFJ8/OMf55vf/Cbj8ZiDgwN+5md+hu3tbZaWlsQgeTxlZ2eH8XjMsWPH\n6PUy9g8PZIMh8nM//wucPXWKrz/+uNxsWs5NK+wlixZ8FNy0NhZUlYKteDBu7e7wn/+NX+Gf/+N/\ngoky7NYotjc3eeX8K+xsb7NSDuYvrtC1vccYgaAWRUZmZrC0uhbf0bwsj8C3vPed+qQgE3ISRg9j\nLHmWYbIeMSpsUdOk1lCMEZtZkTNGqo/r13dx3tE0kvW7KI7rPkpQE3KJQxuNNpqFgRDWptOpVDUq\nsrKyznQ65cTJE7jEAbhhIaMA712ip2eQNmwNWJvUA3XS70jIqhbyOJlMxCj7Le6TLsvUitCItZxz\nIgAmm8PRqrGF0EUC0fcZDRti4dBKAnVZ9ChLg9aFnId0rYgG76VKDD5w7733srN1hRh9Qn/MwXO7\niuLNcdRGa0bX96n8PjY0ECJ1cExVpEbTqIAuoapnlcb8s/gQ2NzcZGlpoXt+YYm37Ur5myzPKIqC\nXq88wgVpE0fnXHLqarHvM9Rai/qRJ33j2TcJkjxfPQlMUw5rNarxGK3IrMBrtdFth6+7x2/M7t/s\n+H48QMfp2xwwwHUkmH8gPf5PgT9HAvongX8RY2yA15RSLwMPA19/k+c90rubP2bQvqNO9HADLRiO\nPN4Najrd7tlJHw6HInYV5eKYMOK2W9Y4dqLHztYGjBxrSyUr6wu846aTeD8hhplmSodYSQsytllE\nSytOQdVHuOee+3AuUFVi8db4mn6/4Ifufw9PPfHNVDKJp2LUOhFn5oY3UVANMXh0uoHFrRxgjmjS\n3hB6dh7nL3grPRtj5I/+6I9EXyIFmU9/+tP8nf/6byf2m+uGtEJy8Dgnm0lUmqpxfOGLf8b1nV3e\n++jDQmaJrS2XRhsxZWjPU+c6k1ZkXhQUWU5TVXzuj/91d436vZLP/MN/JMO76OhlYhzsfRBUStK+\nFvSMQOoCM8xtBGirgLmKpc2G2vK5KApAzUkppx4mgdFwiMNgTI+6afAuUmYZlWuILlLv7+Ojwuqc\n06eXuxt8XlEPUlvLBSwR30EGc7S2eO8wxsqmHRVZ1usyvvYIQToAPiJIDz9Llbu1l+B3rkniXSrK\npq+sQHy1wBzb5OIGxHlaKxbwqChwzKWlJcFrRX8kMCnVXsNACH1sXqCso64mxAh1MwsypKraJhmL\n4BUqKHSEQdljR1u8D2TaMhgImUqeR4h1OlXXkW5ohrYieDacjullBVvVmMXBgEklyJJYV/zIQw9S\nDix/+qW/wLkaFVSX3RM9MQS2trbY2togxshdd93F0tJSt957vQHD4ZB+b4G6rplOa/r9Pr1ej729\nPRk6W3nPLTFN1mMb8CUWtOtBa03jncguR50qRjsjbqmZ3Leg7yJra6uUSqGUR6kZBFVQTUYgxYkx\nq99CY709/tJgruQKPwXcDvxOjPE5pdTJGONG+pUN4GT6/gxHA/clJEP//3S81U70VlKQ88H8xscv\nXnwdm2kcHk3g/Y8+wp13rFG7PY4f7xOdwWbifvPAA+/G2nYwc+SVMcqiYkza0UePEAEP3ksmPhj0\naKYNwsoTht5kMknBZUZzn995O72XhAaQV53Hrt+QpXN0MAyzDe/06dN86c//rEN3tL3n8XjMiRMn\naJqG4XDIZDKhvzDg3LlzvPLKKxJotGJtbY2rV6+SZRn3P/jDFDZja+uaZOZqtoHV7cbra3EaShup\nlJeBoij47Gc/y8bWJnFcYXSkiQEbIuggxWaMmGTR1jLwFNJ7aJmCb+wvzz7vfObSCVzNnbPW4owo\nUEoXI95NybKMw4MReWEpByW6sFhTMK0riqwUlyBriMHz3ve+l8uXL8+1+mLSBJHXbL09FWLwPCgH\nqW0lRJ+6rgihh3dNh7Lq3ic6tVl06mu3g2U6wS8VUt86zWSUPjpEf2PCc+NaEXRFW3XMhu6Bo1l5\n+7wNPk4gLuBdJBgAw3g8JrOOPMuRGZVLLQfhcwhAQEpOaRsZNBknT53l0//HP+GB++/l1OkT8pqx\ni1Wpax7Rik4yenlpmcHyEmfX7uDSpUsU/ZLlpWUWXMXm1jXCZsXJtYKvfeVrSNtK5JGdimhlueWW\nWzBGLAqvXbuGc461tTVa0try8jIaIQ6tr693w/PxeCwa5MYwGCwcWXtKvUXr/02ON4th3Wws9fYb\n71HRo7XIfoj1JZ242PyM4+2O7yczD8D9Sqll4E+UUh+64f9H1dZkb/EUb/bgH/7JYyglw7H77rqF\nu++4ucuqgosQlMg6RJ1A863tWqI9m5BUDFP2rCVzU0ZjMjFFEDMHQU289MIr6GApi4Kf/JmPcHD9\nGn/0b/+MrY19ygJuPXuKH7v/JDkFZaYEDlRmHa5dPquA7AKhc24LBLSR91dPHbfddTtbO9c5cfo4\nzjmaMEZnouesbcaHf/JjfPGLX2QQI9EHVIgiOh8EEeJE8IWoZVMw6bPPW7m1np7twFeyOIGZRcFL\nEqNnd3cXpYSmnOc5e3t7aK05d+6cVCkoRpMxWmumlQwhjTEUucUay87WJgRPkVl2dja7jAEAH6l8\nQyDS1A6bBs3aSvYcAmJXpoVyvX1lExtj5y9qY0QHyRBnqySADTSuwQVPZmYon/FEIKMtacpaizYq\nzTMENmhVQq4Qk+uT9PFVaksoEkEt+IRljvjUrnAxkPdKIrC5uyHiUYOcPJZMfQNB8bM/+/NSdaQb\nsZlWVONJl2HHGMELsSVEh7LSzz88HDGejGiamqbxbFzdpFcOMLYkVA0heIIW8Eyel1iT00wrNI4s\nqSc2CS0EAZXnaOdxCd1gjEmoEdHaCemcNk4nqVaRRrCmRPs0MLca34gSpYmRzAhDNqg2OfCJbFOA\nOqSwGhsjjQsMen2szZhOK7T2eCdD9Tzro5B7BiW2iVGJ6qWKmk//s88wmjQcDqeciNIxVkq1anAJ\nex0xyvIHf/ivMSqjv7qGJ7J/OGJhaQWlghhXmCwhbwqWFo+zs7cvG3Ua8noUIUxRStEE2N3bT+xg\nw+bO/o0xDoBvP/ftRGqT65tpGXT2ej3Onj3L9tauJGKxXe1p4RpSQqKT3ETEhwbvZ4bQbaImyqxg\nvHAuQlVRNbWgWFLymOVJupqG1y5ucv7KZtdff7vj+4Ymxhj3lVKfAx4ENpRSp2KM15RSp4HN9GuX\ngXfM/dlN6bE3HD/78fcBJB2LZItmUukKIkyDxgchVKAtAYeKb26LNp+ZGWPACHIhahnKTaoaosU3\nkT/4g3+D0gFjG7QtqJ3m1YvXWS9e4j3vuR+bFQz3D7CLKzNyxA2v17VfYgRluoxAa02/3xe8d+2Z\nTCp8dCwtrRCV4dLVi0yqKTEudzj1G3t98rxKkEpWdZ9v/vXbFotI/M7KcSn1JAs/dvxYKg0VDz30\nEKPRiGeffZYQPB/60E/wiU98grqueeKJb3DlyhUG/UUpg6uKyVjkQauqSs/dViLt54YQfEqnAj5K\nMGpRC22vt3tfcZaxCyX6jQuzzTJbyVJlJTCFxuGS5orKiiPXXNQ1kxdsQljIrZV6nmm9tIzbECJa\ny2A8yzJsYVk0GT5mHOwfoIziwuXXufeBu9i6MsKqPnEyZqEs+NYz3+HhBx5EG7qMvKqqzpi5JZUd\nHkpn0vkaE023Jsqyx8LCMs1azblztzGZ1Jw4flagc25IXY/Ji5wiH/DMU0+zvjJIJTxkKiMrB/gw\nQmlD0csY1lVHUmvX/uyczJBg7fdN05BlBVmRNgWdU/aWZHrjIlFJmqzSEFsbqKbQ70eEBerIbIE2\nntaoIYQgc5U60DQebbOjFWYUU4e6rvnUpz5FnpU89eQTR+7ZNyyF2HJGTKoa5hFsISHL0gA1GrSy\nqf0pmzdK2hxKzVPgDTG67nWP3GvpZ20zjBKpaq1F5K12DfVhw+T8FN84jh8/3rVc5H2lNadaffNZ\nNX9jJd0FdJc06wPUwRG1kAVbLfvMZAQlQmfnzhzj7KnVLmH46pPffcN90x5vC01USh1TSq2k73vA\nx4CngT8GfiX92q8A/1f6/o+BX1JK5UqpW4E7gMff7Lnbm7rtdbYf2kVhuMVEOfbETkkNEDunqPAo\nojbMO9GHQPfV9qYUhhiENlzVNU0ykrU2Q1HgVUmjSiax5DuvbPD8+au8evkaFze3uL43m37HOMPB\ntygayYwjID3C/YMhedFjPK2oXUBnlsHCCoP+MlleorXl2sYuuBTA46w90PaZ36wsm58ZzAf1G2/Y\nrr2URIq2tzf58Ac/xAM/dD+Zkf7oAw88wDvecTNF0Wc68YxHDWfP3JbMHBRF0cP7mVrcrHR3tDro\nIYSZLR/qSBBuMeYCjZzBLmc/J4inNqCMVFPdTe1RwRND0+mHBAVZWXD8+PHO5KNl8VkjUgLRy8DY\nJ0KH2M2FI5tHm9XMt2a897i6wUbBxhRZxlJ/wN7eIQ+/936UbkCLO1JmCx764QdnG2bTsLW1dcTx\nyHvPpUuXGI1G5MZKBp9W8cHBHi+++AK/+7v/iEcffgD8hAU/xZrAKF6nMTUxtyzpHkUtrUAfAyoo\nGh9pQmRrZ4eqVlS142A4IaoMldAo89fIKNXd2PMJQDsQrqoq9dQVigUCGY14FuEVoA3aZjgf8crT\nNBUqqyhKhTY+VYYSMCVoZvTKwREtlDzPpTJK90cIsL97yMHeIZktCErjo8wI2qRG1kfAWAsYIrJZ\nNO1amJufBQLv/bFHOo2cN4ld3fddq80K8VXrKO0qw5GvLCvIsoI8L8nzkqLodd+D7v6/tTm93oCy\n7KNVTmZ7aFsSlMYFjbYl2uSiGZ9bFpYWyYseNitEdiPTRK2SBAhzcQTR1p+IbaFLRirt/fX/t81y\nGvinqW+ugc/EGL+olHoa+KxS6m+RoInppD2vlPos8DzggL8d3+IdnD59ugPuKzUP/3FUlae/sNgN\n5UKQGyqqIbk11LUs2hZYL88zE7qaTZ1dNxR6748+xD/+9GdZKI/LR0mIhKDEZYQQGDaBJ7/9Au+6\n+3asVbz02rOsrCzykY985A3vv31d52RXzW2BVuJn2YwbbB3I8wwSnvX69SGHF67wysuvUpo3ZuM3\nTqvb4GCMwRsIRhG0Ipqj6oMxxiQVAESN0ZYmSKYmov6aPE8T9dQbnE6nLPQHjIejbjO96eytXL5y\nkdtuu41Bv8f51Du/UeBnHo3TZnuJM9/10eU9+e7f0GZI7RDvhiUx26A6Xcp0EiKNd9KqsSZRrB21\nN2R1RZZplhYWyeYHicwYivJsiW2X/ptnGrdDVO8j2ipUjAwPx2hlOXliHZS0I0CkY0EgrlXtGY9H\n3TUTS7Ixe3s73Hzzzbz00ksdE9Q5WadXrlzhO9/5DkpHFpThnbHg1PIK31ppuPu+OxgPG5776nf4\n2Dvu4YVXX+E7mUlZ74zo89f/s19mcXHAv/yXn0UhJb1GEXToTFDa3ni7htq+ulRwshbxVRItG9M0\nmsXBIkE5AhatBEGlksWfd4qgClydoWOJD2NCrGllB7TWaJMdOa/7+/sEIuPxCJfmPlLBVBweDtnf\n38f5jKYadfdrVVVp7jDGu6yLDTEKemtWCZPmXhFrDSHJdrStJbps/Gg1bYxhcWHQrasbk6L2sygl\nvIXNzU36hQj3vfv+H+JTn/oUJiXdVVWxsrKSrOYsv/Ebv8FwuM9oNJIVmIhPEPnn/+xfESLY5MrU\n65UcO3aMwWCZU2uLvOued1JXM5MMafNIXz6o2Xzwxsr8zY6/DJr4HeCH3+TxXeCjb/E3/wD4B2/7\nqsBTTzzVTXqtUQnCY5j6SO01yuagNCbTKd1uiCFy+623sLZWiuGCB52U65SRfnPjPCeOezSS/TWt\n76DSPPLIAzzz/Mso3ZbqerZYUDgMo8bz1HMv8c5bT/HIw+/FaLmCTS3wtuBip+AYg6ZxYCw0wbN+\nYp3DgyGDhSUUlhgMPgSaylE1DZevbidjWqhdQ24NAUVUGh8DVs3QGB1aAiCIv6n3gjWfL2MlI3ep\n2hCKt/cRZbOuR2dVJsG1E4yCyXQKWpGXPQKK3sIiZVkmOdTEZCXNCyJ0DjFJrtNjQBsibqYAF0Tj\nxESPSZllREpR0s+hvdGMiBWRSvoEhBbrQJcGqlrORZZ64m2m3riGSa2ADJO36BQZ4omKpeSc3jl8\n9DKAU6mnrw0ojXeuayd4HYhURBfRaHSjuPzaRSbTfXpLx9CZJveSaX/tscd4zw+9m36/n9BREecd\nWVnQBE3lAnfccTfWChS2yEuqesqpU6f4rd/6LZ5/7nm2L21ye1hhujchWy556ZlX2Docsu4tt7uS\nr7z8OmY9lw0MBFuOYWdrm83tLSGgBU8gYLVUnyGQ/HIPMHGA1YJEUSTsugJ8hVKOYMBYjckh2jGT\nasLSyiLBG4IXuGVmLQvlErGZoqzFmIhvpigdhVMQxD0nxkhwTWp1Bc6eOs3P/fwvURQF42TO8YUv\nfZEnnvwm/8kv/CLXrl3hpZfO45woKXZCaVElVFYkBI0tCwKSpaiEuI1z7US6nntbcUmrRSthx6oY\n0zJT8pmkd5vu+qPP11XXURQbJ6MDPvbRD/DY1x4nhIgJcLBzHV83XZJ0bXSFEAJlMYAQCUEhCLWG\n1ESXlpDV7dJOcSRy6eIGxm7z3brmrjtvE45BFHSVmWshqwiGGfqqe5K3OH5gdP6yEJlJ7zzRR6Ei\nhIat4Yh8sAKNwuYlmTFiS5Uy1a89/k2WCynPdYArL1/g+rVNsJrBYl8slkLkzJmT3HrnXVTao4xm\nMpkwGPQlmOmQnHvmpAJAMl8iTRN4/wc/yI89+h50nHYuJ21rpCW8hajwTUXQhvFkxJrVNM5x8cJF\n7r7vXYzHY1xQVFXN9vY2uzsHnDpxmr39TZyLOF8nooERo4Q8S8FvLrNSaZF3lUScOQhZg7IGP54e\nacVIWdkSalI6kbKPLBOCR17kAomMCmUyjh07xs7OFgf715mMD5lMxqI1ERNBqw3mqq0CUsYXYzt7\nltfQkOlM3HjKGdLDAj5G1s+ss7u7K6QIC3mW2muuhpjBXPaslJSjOiBYjxhpnCOS0dQVmYqMfSQk\nspS1tjOxtplspCZtAj4ktIyWQFDXDf1BiW88dT2lKGVImuXiSrR1bYPlZbBa7qF6XBFDzSMPP9Qp\nZ1pr5X96ha8ja+vrGJNT5JIsdO5GGl566UVWVlZ4/oXn+cbnv8y7l0/T7B3w+MU9nHesHF+hdoZP\nX/w8z482yc+epkhOOTGJzuEDvUFPgocLggTKDNpY9odDrmxsCtkHgS4GJRTxEKRqsihOH1sHFcm0\nxViDsh6dRxo3QYUcqywxeJx3lPkCmoCihuiIqknytCFhxiM+eLxvsEbu5xdeeIHB8jp1XbO7d53x\neMKVzQ2yTBBVMoNRQKuoqIhREgyNtBobV6OC6yC3uu1HM6tetQpogryvYEXR1GhCaiUZrbFaGNyN\nd0TfYCkBWbMyPZ3vb5M2RM+g3+Pl7z5HaKZYZXnsq1/mm1//C7GUn2uFOhfJbI7OrCRcJGZydyTX\ntLlHog8QI64SS0NX15jYYFTAJB/QG+ec9kZy1VscP7BgLnA7uenaXqoMxgy+8niliDiZTseAr5NN\nk7FYa7B5TjOecnBlkzUybDAsxR69vGRrY4uN668y3B7xjvfcgSNQVSLO5FyNyXs3DF7mBqko0LC3\nv8NXv/JloIYo+F4QVpo2CmOzpJKnwRhC9AwPKvYPLnP+ldfoLy5x7tw5tnd3aZqGhYUFPve5z/E3\n/8YvUxRw8tga1ibBpHQRBVo9m3y3g7Wg6NTbYoyMx9OEk+0dwVTPY8211jPMLUeHMUoptre3efrp\np3jk4UfltZqKU8dP4Ooxp06sceHCK/T7PSl1VbtRiC1Xe6ZIAykxVpCSfzqtqL0gNKajyZHF6UJg\nd3dXsP7eUQVo6rrreQo+W55LdChEdCtE6aASAxaFRZFpQ6a1+ISqmfaNJOiCsHGNkI2kwaIhKLTJ\nUFqxtLgiJgVK00wO0N6QmYx6UnO4f4293UX+ysc+wuc/9yJOWSGwTEYUWUnjZ/jhiKAvVNOwtLTE\n4sIi+NCRnXxSndzY3ODxxx/n9PGTZP2c5/wO/R6sFItk/Qw07GztMSksnDvGZDwiK3u4qsa5Bu8j\nuTVMDg8prCUCmRXGpzKGIssJeY53lphFogrSXkurWylFjmd1ZZHSGPq9HJP3iFXOw+95iExbYrDJ\nYk/o5Hmes7x4DNeMsHoAsSHGqWj8KNkojc0IRgaRkh5FvvH1r0nQS4Gvn+fkS0u8/N0XAcWgFN15\n0V+ZzTB0qs4Xenlqb9Fp1GTpMwNYY9HRsbexyb233Y7RObktuxlcqynuk7SH1kKQyjKbEE2ySXbB\nPGXpKgre3vuGslewlvVpPWXb631EBDDh4jvzZqDFireHb0EC6Qi+IfhU3TrHd599hugkxugEy3yz\nkN3e+293/MCCuUvDtRCkzxm8TJM1Gtc4dJ7jfEQ52f1d4wnRSQ/WG6ajmsneEFykqSqMtUyrfabs\no7zHAJPrhxivqF2gl+Xcfus5sq88LqScufeiulZL2v2NoigLFhZzXGNEjB6F1jJYjQg56GB/KBdf\nBaEQV5rReMJgMODv//3/kc985ncJIbC9vc3nPvc5bjp7luefe57Dg20unQdURCGyokVvIOYaoaaq\nZippRZZz/OQJfMIIZ1nG4uIiIGYCxhialRQMvWhHXNneZ2dv98gAsl0IbR/+ypWLuEtXOHHiFGur\nxwSSWBQ0VcV2JZvFaDRMg9msC7jteoqh1ZNIrRMfmIzGQlqJntI7DocHtHIBIKSH4XhEv99nf3+f\nXm7RoWF5pYfCiGFzUbC2viRVRF6ggCo4DusGpRUmeqYjxzQ69psKrcVbsXYNNsvolYtYm1MUIj26\nvX2Fqqq6Dc6aYqbFEiMmUau3JnuoqIjBce7MSaDihaeeY623yqipCTZjsegxqqaYTNBXxhi00QQv\nQ9ciH+CDyNNG16RNcsrGtQ2++9zznD5xnJ6Cxf6ARtVYDSUQQk2vt8jiiVVCjJ3pST0asba8Iq0z\nF/jG41+laRwxBBE0m8pQ3k0b1lbWOMgqPv7Tn+T3fu/T3HzuJmgCeE9e5BhtRLBMeZRz1OMGP234\n8//nj1nQYJWniUOaqeBHphPBiE8PXxesdR6Edq4KQsgSLb+tIjOMkcH02vI6IRG9OnCDzbr2ofee\nk6vLHcNREowZMc8IJErsE9PRrt951E50Gj+aMLAKqNH1tBPokvmAIWAIEYKryZXBpCrVV44sRPAt\n+IJ0f8sQPkSPqSZYJRBEgkc7L76dccYJaROIdugOoM0s2Lfvff4wUUS1YowYwCt5To2W1hUxtQTn\n4lNKPP+SWP4DlMCN0uuNUZx7YlBUIQCaouhhiwE6t2SlITpPyDOqqmJ7b59YV5QqZ//qDgt2QOE8\nVhsJdsbiVCTHszMd4SbCwsx6JccHixilRASImShUO8xrIWxuOmV1eZk8C5j5TFcrXC1wRuopaC0s\nSdd0cLCiKDA646d+6if47d/+bX7mE5/g83/yJxxbWSbXir3d65SFpSwzUPK588LSK3tStkfFdCyO\nKDq5nO9f36PtdNR1DWnBtl9NGv66SvQc9iqP1ksJfy7HPNFlMplIe6apGJQFWS4332uvn+exx/6C\n973/Ufr9HqPRiMFggHP13IArLbRoRe3SC+kn1DOrtnZwVZaltAla85E0CwjOs9AfYAlkyrCy1Gdl\naVF6hHo29GkaQdQs9HJOrSwJpr2pQDlUzFHBCBHMOZraim51AGcszTARkYj00wCtqRqaaGhUkiA0\nGhMNTWzRHQaiI3MNKIebTLn9ZEbIeownga988d/yyAc/KplTO4iOhsyIC/zy0nq3pqLRBFeTGcvm\npQv81Y+8Dzc9pJfl5ASqOKVnS1wTqGlYKBeoEzLGO0EEqWP9Toairmsq1whiRFuKoqSuhHdxMKnY\nPXTcd+fdXHn5WX78Rx9iNKlYKUosCmVNQuSA1lNsjORaUUcPyV3n0tYm3372vGSS6A45tFnJYNWo\niCUSknSDD0G8RK3p0E+5zVAYahpcEzp8tUrSvSLmprFt0Ez3jFZQyASNAAAgAElEQVQxZbVpoIqG\nKAlEm0S0609rLX3lGLj13BkW+z2y3NJa62XaSCYeNT4adDaQ2YCL1BOBvMa2TakUqIAlQWW9I+AJ\nnZWfRmFpaun952UphtDeC2ooU6wuLwpIw5ME4I5m5jcy1lu012zGpUA5SBT/Gxk7KgiSqf1bnrrw\nljH1ByyBK2WWtuIS7nzD4WhCGTMKpvSLAWWWk/UKUEFEcRQc7mygMGxd2+J0KKiHU2JZYjOFd55G\nQ+VqhqMh4+uHbOzucM+D9+CjBIa9qQCxtNGEKMMlFWZ4ZK2kRZAFjU79decCQSX/xBp01NRNTVDy\nmiiPCgajEEXDac2PvfdH2dvdpV/kWK04cXydMrMo7YQFmTQZgvOMhgfdLu6cqPmp1HoZDoeyYCrJ\nADJboKyh7PWIAYxyOO9k6BsirnaYLBCUweOkxx5TFue9/Nw0eN9w6fIF7lte5sUXX+DH3/d+Thxf\n5tvPfiu9TkZItG2ZG8wyEh9dIpgEdGzdckR6IMRA0AHrBDY6G1wl/8SYFqYCZWFQaEzcE9JECJ3+\ntYmKXNX0TA8dpY2URU+YN8b2EasgM4o6TFhfERRJawcXlRISGgqtemIQrVVSl5TBbDtclefMiBQo\npblw4TIrpcLFCf2e4pOf/Gk2h46ADJaBNMSboWlURAhLRhOjxVjPxsXzqHqTfu7JYoZVYE1EhRqU\npcgyCjslx+HciNIqvEoaN42mNIbcVBShQVPiyXGu5vDwUNxzxg34ZUZ71zg42OPJ711g0gT+57/3\nP6Crit/87/8eylh+4qPvxU+2yKwmM4JscgEyE+mXGePqUCjxUdMoRSxLeqnCifhuQA+6G2b7oAha\nkymbDDc8pY4ErVCIHk5A5hkuN9SVw7spZb83a1V5CaaNc7I+QyQEh1KRPNk+tpLI4JOYXqTXs9TV\nIaGaKRy6xMtAZUyD5jsvPsP+eIzx8PC7H+DX/ttf41P/xX9J5QP5oGB5aYGbjg8olMeqiMEnxKwi\nBmkT+WpK0e+z0IPR6JDRwT55YegXCyg/QQekBagylBHEU7eeWtmLlBQa5VBaRLZUNF1PRYa6CWbc\nVr/t37UzmjeSzo8cP7Bg3u5MRmdUiOaxCoZvP/s8CwsrTKuK4WRE5RqG4ynTacV0OmVxUPCTH3k/\nzeEhA1MyOZzIze88uQeTWTwB2+sx3t7iya89Sa9XoO6/l7oZs766yPXL22il8fWUqPKE+tAJuiY4\n2sxmTKsx1iYtbFSnKU0USvU8/jNGyVSsMjSNlI5LS0s8+a0nWVlZEdPoPMMa6U8LyuAozF/6h6l8\naxPgubKu03lA+uF1su7Sc+SIloyzu7uLw+CSOl0rpN9+r7WmMRXnX36F+9/9AA8++CDOR2xWcvdd\n9+Fdxde//vVuYQYlAyJICpS0LYbA8PAQFQPWzPDCQYXuM3YD3fk6MTFeaWWJE/27Q9BAaoCLumJU\nPm0MThicaeMNUfqVUSnp7ZM0qxOBx0SF804Gy1pUGK3K0a2DFCQUQvveRBhL4HCie6LjFO0DKgqr\nLy8XqFzToQsMiqWlJVQM6HmLwBAJdYWvR9jYUCpDFhVEJ6QwvOjM2wIQbQ6bIfaHSSseFWToG2az\nDx0d3pt03jxaBfEp1WPyQqo5FwxPfusZnnrsGzSIcbaxq4S4JZWkTiqgKWuuJtILV0oTgqzBXm/A\noNejV5i0aYlkrlIG39T0BgOWT90EZY9XvvsivcaDgSZUqKTVAuDSutE2QQOjE+azmqkSCnJLc/aW\nWzh7863UUfHNxx6jOjhIPAHJeK21ktknOdpmUglqJbU8tBI1TxQQLDtDh897qLrmv/o7v8a/+dzn\nWVg8xuhwn8nEsX2wxenjAwELIIiRqETkCyv33cD0UEqje5Yw1uwejliMPcq+8GKiF6MWF90bZJyl\nL5+8TbXq7qE2KVJKUbkmqWjO7vn2X4GX8n0dP7Bg3rljey1ZlZvS1J7D6ZQnvvOY9LrS7wpeWFHm\nPUZhytbOPuzssxoMVdWAMkRtOXb8OOO6YjQasr60io+XMbUmLw3NtGHkh/zw/e/i4tUvc3z9GDff\ndIZJJSD9ra0txsMR0+kIo0Q+NTpHCA2ZzSVwuAZH5NjqGsHVFL0eIQT2Dw4JsSE46YXkue20PkbT\nEa+++BK33XYbRgXoF8LqVAkBHUI3pdcJppWbnLqpIdLBlowyeC0SmdoL3EpKXQOZEhOHINDF6BoW\nF1cYuYBNq8fHQFPXhFTChxDQEZppChiNx+iMLF9A1zULgwV83VKbO9QuMcjP03oibaHQwqrmtD5C\nQKnZgvZJCrTtmQYlE3ql2yykHdwGgT6muDqvudJicGP0wphLw9jGG3wjN7GPWULY1On1IWiNMgm+\nqoQB2gYGee75dptUGkESeZpQE3QgBI1XDXvXLlAsv4NpytR8nTYO37C+tEJmFJkS9IpGzut0PESb\nBu09yoLyCo0YnRgidRNxpSNLQ3Dvo8xSFCKgFRtB9ZB0rRFdm8nUo2KgMArdVwz6DXjpdz/08Pv4\n8Q//BPs72zzysY/xgcGA/+V/+18JWUZmlIBxgli/+QQtvb6zRZnnNJVsuq6RitQgsq1WRZRzEBRK\nBWwM9PKC2267g4lWPPvMkyzYElDYoocJgVygWXijuqDUKk7qLImdJSkM5xzeOVEWzS3WlNTOU/QG\nQgjzcq611iQdK2xmqcctXyAxw5UMPIO2hKAJJqeJIm/7d3/9N/joBz7M3sGYJkBtcpTJQGdkeipG\nNEoJqSjBHqOS87R5MGbj0DEceybTHjHvM4glTCpKRLYiKvG/PdpaCR3vRdpGsxlWCCGRIOXvW4RQ\nq1XUtaHeXnR2FlP/fQLwf8ij8R7XBCrvcErRNJ6DkWhp33HLTfT7fVZWVlhbWWdlZY2l5TW01rz6\n6ss00yFN7WlChs77nH7HWQbLq8TCsre9he332d27zukTJznY3UeFyPhwSGMDq0tL/K1f/uuS6R8c\nEqJhOp1yeExEpT7wwfdjrcZVI2IMWEsiZBjW11dpkuqi9Ald1xv23jD1dUIOCPvUGMODDz7I9pVN\nFv5f5t402LLruu/77eGcO7z7hn7v9YjG1MQMkBRJcDLFwZJVlEhJFiVVVC6XS04Uf5AjWc6gxFYq\n3yJV4g9JJanom8qRJVuTldAWRVHmBEqRRIYkCJAACYAE0OhGoxvd73W/fu9O55y998qHtc+593U3\nQKasFHxYYL/hvnvPsPfaa//Xf/3/o9HC2gqDkUW7c6vCpk0+uVnEaDuyySwSkVwYMYr2W6MTI6VE\nPYuZOdHodnWe8CtetbbRdv+UI9RyMdQYw2h1SAyG3qDE+pK/+qu/4q1veRgRQ1Mv8D/9rJZ1YHNr\neJENJHI2S8RKNnkwacHrvWEH4lpdp6weB/rnwmH7QGNMp10jSbPZdKjWb5jNJvyDf/AL/OZv/otD\nn7HcgNVy6wGdsNbqxLvFuNRmI0PRHyHFiCMn7uTRR9/OJ/7N7+NcAc4x6g85mE4ZDYY0TYAQGRY9\npdYVXmnG0UBMXLr0CjYEer6AFJgkIcZ9hn4FMQWJSBkNwWqR04gaoRhRfXpDjlxkDNo5nBUK0yit\nUwQvgMwg74Ke+OpX+ORnP8vPfOyniSnyR3/0SdbW1jQ5aK9TFvzqxZjwYALWCpNZxYXLl7hiDYaG\ne46f4vZRH2cNprTYlSH7acq/+YPf5cB4TJry4Ol1vvjk08wlsjkoePQtD1FVU1yyGPEU1lF6z+XJ\nNWbTMSlBVTWEqJoqdRJ+4zd+m+OnjmNsweZgyPX968pSkaxrHwKSAjE03HnHqfzMnI69rkCq4731\n1vjZv/8fs7ezy8BavvncC/zIT/4089jwmS98nun1AxZDRcdEq+fTjpPkHHXsc3HXMpkon/3guvDy\ntV2+777b8b19PAsZi2XmWPfOSz9b/rqFVVrtoGUi43Ii870cb1gwn84qitIBQmwS83nNeDrlyNo6\n5dYW8/mU6fiA/at7xPCdXPQTptMD7n/T3RxMD1gpNzGFodxe588e/yrj2YSiLBkOhvQRbIjYFEnG\n0YRIIxHXE2Ko8CQGvR7WFvQdlCbhj29h4xxEsCZmnYeC4coAa2FWh6xrrd2ATtSDUeEGljQidOvo\nMKyvr7O6MeLg4Drb20eISmaFnME6W9Ib9DpWyLxq1MosJTCLtuxlWlRDljh1FustJappbY2h8B6z\ndx2cVxoU2Yg6T+QoyoQIeYEYH0x54okn+PAPf4QrV6/xgQ/8TZpqSjWfaJYIh1qq28FlpMCZHomK\nSMBZbZKyS3tC41qZXG2A6CApdBtrklG7LNGCkmWh5yI5q2szlJYtsIBGHMYJznpefPElqqbGuMxI\nMjqu2vdZTArlXtusYtkyKRZF3Uw/dYar1w74v796kZ/7xR/kwt4Ou+PAl7/8Zd7zw3exNxtjfcH1\n2ZxBUSp1MiUoPI2NlE7lHcRbJtMDRMBR0DSG43ffxYc++qP85Wc+x+7z32SeOfhro21++u/+PS5f\neZWP/+7v0DMzkHnuBQhd01eQgHUlEvVO2CxkJpKIeMYH1ykHfW7vr/CZxz5H2SvZOLapZiBhRpTY\nbflF9DxBn49JOkbEWor+gJWVNSRVNPMZ1y5e4ZF778BLTa+uMWGObRq2C0tyBZg+GyS2jp/kIDXE\nyVW2p2NkPgdP1mT32GAZJRjbHgGhGfS5PJ1SGYUQ+/2CEsPdR44wr+ZIYWhqhXdCCFBamsZggjAY\njKgmOzirzmF6aKZgTCLEOaZ0fPqzn8N7bU6czyq++ZlPcerkSazP/Srk52WFaNT60OSivTOOlAyD\noeeYHfGd6ipVKEnSQ0JDOTyCkesZ227ZYzGfg0XlHHQ3IgLG5jltVKCvW17b5MUqqwXIdR01im/Z\nPK93vGHBXDsoOURNnIynDAcD5VtmDWyDy3ZeJgu1B8hC+jEKbtDnG89/m1g6fDlSs2Cvqhj90mHG\nlUqYtpmZtYSUqGZzVnt9/bl3IJH+oNS2W6OZd1uA1JuoOJxzjqpqmSSGGGrKXk+3d85qMppiJ8Tk\nvefuM2coQE1gnW6hjdhMmWur/HpPYt5aiTEqLIWu5N77BZc6/9cGR+d1EGiXqGYlWi9SqVpnFJsL\nQavoguAwOYuOHN0+xtlzL2GdY319g6Yq+ZM///PD20EOZ7vtz51TdUpNtRc89ltlJ8t/2z6L9rXt\n/xZBeLGQpJRICC4vSCKiUEuMlIXnL//8MQa9glBPdYNgFn+/eK+FkUB7bovtcDYi6RYrWF0dcWyz\nzx/8zu9TpzGFBJ7+xtd5y4d+lBMnbmPe1MyqhjCvKb2nRGiM5EaVhQXYy+fOUZpEz1qcSZgw52D3\nKvV8jjGWajZnY107Up9/4Xmm07FScFMEWvOShauUQYhNIIWgwejQ/iLS63tKo9nl5rFjVE3FtFKT\nkFgfYFLqoLsuK0ehwRhnGtzEsH/9gHp/wtpKH2fh3nvuw8ic0lkMCiU5Z3IPhmAjJHE4a3F+QHMg\n9JPuKpqUdMwnwYhQGOhlOqczDm8sNULd1BRFyWw+5+KrlzDGElOgbtTaUZUE8y5CJNeKOPRMlyIM\nMTSMVlbZu3adonT82I9/hCMbW3z605/lpRfPgjWsjEbdmHutwyXhyCBx8qhwZG2dr371W6z01zmy\nvUYKV0kmZqrld8+gb4RZXg9Aea3581rHG8pmCUG5qHXS1XR/b8xobZ3j25ukFBQ3Txq0m6D6E+Pp\nOD9ASx0iR0YrTOopvYEOumS1qUBMwmEJk4omZ8praysgMN6fZ0xSuiJeinOOrK+wMvCE0FBLpCj6\nWmicq7+g65U4ZxgOh3oBYkghZ64xdEqPYiLWgRApvaVfeKxp4RLFbUkL3Rh1Vr/5wS2EsxSDbVt9\nba7ESlKYp2oa5nP1A42SCClSihbmogHwWBLOipqwo5mwQZ1QbOFJRGLmy/7r3/sdpgf7WFsQY53V\nB280CFnWtmg1VRZBP8V0yBO0Bd4XjJ2grIqMo3unv+8WgjbQJG3AaTW4czjPGSWINGDVSIOUg6CR\n7lwcrYqgurdIEsQu8Hi9npq2U9YYh6eEMOehM1us2H1WCqjngf66FjlfeeUC0RpWVkasr414+Zlv\ncWJ9A7sxouwVGOOopCGGgI+Bnjc4VJLh6iuv8Ce/+y8pjKfwqvPhrePK5Vf4vd/654xWdcHX7bdq\nhes902AOulAfKpgZEKsdjSc311kdrOHLPmIcVaWMmaauMM11SIlgyYYQon6srnVj8sTQEDAcO7bN\n9VnN+9//PmaTMbOdHb796h4jZ/DeUvgCU/aUmloKpraMJw3eeubjCdYU7LmCXqmFXpeL1MkYkk8k\nJ/iiRwBIM5xYfGkoVwaK1w+GhBjxvkeazNR71ijLK4WAtbkwLEsmNFbHhi55UNcRYzzOWZo68clP\nfAZjDHUdKIshkVrtI0NQaCxDNCbTllML+4ngLRQyYd1VHCmnrA97jMpAT4Zq90beTeJJiazNb2kl\nB5bnd7tT1L+RDrrsakO25aHn+e4X5iuvd7xhwTwlJek75/DGQTLMpnP6vYFaWUlUQRynjh0Y1URZ\nOMoASbeKK2trDIxQm6haFkbV6tK8JhkIFobDAfR7TCczmqZhpexrE5DRrEFIrK2tMp9PlcdsIGSz\nY3H6MOp5RUKz7pSSklVzS7IqyCWCLBTenHOQz7lXlpo1N0E1SbJOilveasFSgDS5nV7A5uIdbYVc\nX9v+fSR3xDYNdV0TZHZDEWYRdI1xHS7Xcpq3to6wP9nX3zXCfDZVkTASTfbQ9Lk1fSFips0trWog\nkncw0m5zXzuraAfv8iFtsL8hM3fW5oWgK8HSdqO2A8F0UI52jyqDYKm7L7/e+4KqChikc5jX4/C5\nJkkQG1b6UJgpTqAsoU6GjbU1qoMZRa/AGs/etV3+4s++wAff+S6Gg9MEEaz1GFfgvSFUMyRFkona\nsm2cTu5s4NtirIUTjm0MSEaIKaglYRKleXZSDQrnuZ6nClNKb29qfktNRd85RA50bpRa5ByVYLID\nklleCKTdymjoqUWlcAf9IZNG+OM/+SQmCU4cw5jdakUr8yvDFc7cewYvNS9ceIkX5+e5/+GHKHsD\n6hT47b94kj4L+bTCa7D0xmgtKgkPPnQvV5o5Fy7uMK6g9hac4WA85v7772c6mVKslXg08ZmHBhcj\nvghZuXMRFBe64dm2LiRc0cMGcIVjZThgfWOVV16+pN381jLs99VfV1JOJm4er5LvrUNwKdE3hp6J\nlNZiUtPVKgzCTUTxWxzL2Xmrtf9a+rVt4P9esvQ3LJiPhiP6wx5K6Uk0tTCbTHjogYe088oIprD0\nhiuEjJnOZzUmFxarpsIP1mhizWi0zdzEDJHkwR8bghWmNvKm205QlCWxEWyyhNmc4do6JKWXVWFO\nLZGIbpdiXFJftE5NMoyu2QZPqKOatsZc9LOqA5JIGkeC0KRIsh4jkVBHSp87WUOtnYMCxtFlAbQt\n8y1uay1FqVg4hcs8bp10JjuR0ApMRdHdRZMgZn1pk01g0eKSoG9tWzPZmYqEpdTkphthUDqm1XVW\nBgUR1VN2pkCCKtNphqFB0nvprOEUjtLuNVcU2FTCXOl1iDIDVC8kQlJcvPQa1FK7MC0F3eVjYdxt\nchFYZ1yHohiX74lVI4+0EEIDiPn1irEXjNYGHEzGeq554yMs6gGgYmHGgDc+a5Moc8Rbx9rKGo0d\nME9CEyKhFuL0Gl/47P/J3/n5/5LKeapkaGZjTAjccdddXPzO4/gQMQSgycXvLPoSA2E+0WdqACO4\nmDX8URMS2xbLiSSrvGObzSokw5XaTawdspYaiQtSsjWaIZKStvnbAowQkuTu3kATImItNsNwyUC0\nhoRHjBAMzJ3B277WklJDY+Bak7BFQU3E9kpeeOUVKAqu7+wghWHcNhNY5VWLCKM+lKVnvHfAdrLU\nvXV2wq5mx4UnBWFu4OvPPEdKaF+A0VCVMr4vTUWdakyKJGtVp8Xog1NWiGVzc43eao86JkKM6k87\nvcbJdWWhER29wuHNnGBrCopunKVowB0uVDrjidk0xYpQiDoEKT/WgE2ZA79YZDDSuUYptNwmfvqf\nMbpjtmTMuUtOEpIXiRTBhn9P1cT/P4/HPv8YCcWFQyailr7g2W89k7PvgAjEvK2uQ9AbYBxXr14F\nlO8cU+DSiy9QWYUUrIW6meOMqMO2jZw6fRqA2ASuX73G5OAAtrep65BpecrXNl4nR9lXnQdrFjzp\nfr+vpRUxVE3FrKoVw804rLeOqjVmMMsiWa7LrFqMWwXqtWssSswLR5vRs8jqUROEaG1nIaXZ8Q0G\nHTF12uNFUSxW/Ux3DCl322WVQGNs3lILp28/xXw+7d7v8sVXKcuSJoVu22etzWqGIRfOdKu/7PVp\nUht+cmDMz/nGDrhlDu2tnFNuHLDL2LZe02uPqeX37hYFs/heZVbnugORZoG3ol2P7es0+4+Zbq5B\nwlihrmoO9ie8uneA6/eIQQjzmh4JYyvGe7vsS4lfGSrrxHke/f4P8PHnn1ZOUTLYLH4lKTd1Narz\n3umExMW1SpuR3ziJ8y5NFpu0pd8l9Y5dEnyybTZtWu7Q4tB7m5kyUiCyWChFWs/b9gar3ol3Huc8\nVVXx0vmXaZqKKkJylrpqqKczKMrF50vM56n4enSrBFswj2OSHzAZH1ALYAsIlhMnTnBl91rWBcoO\nUWahEopAIxW0AZzFwtWdamwoCTjbYKIqcKo9mJpAJ6zu+pnjJNArPE1YZM0ssUiWx2TKQlltD5PJ\nC0cHl7RzX2yec4uak+5Gbx7Ar8dYkZh1q+Jizr/W8YYF89tOn+q2mSklQpO4cPESvZ7FuQEimvEG\nsaSoHYjz+Zx5E7j7zJ2c++YM72BcT5lJgF6fu++5B2OgV5TsXjzH2Zde4KMf/JuUgz5VE3n1yhUu\nXrxEjImqqjVjTJFYJ+ppzVOPf71jS4TU4NJCT11EEK/qjW97x9v40pe/QmEsMcRO+TBKwvkCVxa4\nfsnlV3eITcVsf6yNS0bbmJWCqM0lbQOPzx2fGIuxVgtJuXV6ZWMDax3OqZmvNtBo4B/0h3hjkZWU\nVelmrI+OUAXdPYhoAFa83SDOKyfdOGZGdZlF1LFeUuCViy9r0TQH6XZhqRoNAx12JwsNemv1GQmS\nn5m6HF2/us+yVaqIqDu6UWcib3Qge+81M72haLm8YC0mVvfx6nYkStlMJnVF5+Vj+T1iijRNpOwt\nhv1iUWk/W+mcAM6oWQYSMsQl+MIynU4xdQXGkao51imL6Otf+zJ3PfIoBSPEZeiw8bz/B3+EL3/2\n47QO9amD5lJ3T61fBI2YmSsmZapcy7YhB+SojBSy9nVI8ZCa362CQ7fAtzsgsd1CqcXPCCh8qDs5\nx7Sadzun9rD5/GKKtPsFEVF7OJGuzrNcNM8+y5nFsUy3M/T7JRerie5EBO646w6uXLnCyeNH2dnZ\nYT6f5+aw/H5K1syU2KX/kO5clI+vfHEjkejQxZPQ3kXFow3YJFjTEKP2TBgDErPgh9XQG/K9nMzG\nhHhYdG9xLfpME7kGRtvluUiI2uL6rY6OCCCiz54cyLXNmiAqffF6xxsWzDe3VkFsp1UQY6QoJTuq\nG9TU1BByC3lI6mRTV4F+z3Dq6FH63rHljxCcZ5YCJTUSEtV8zLAseN+73tWpDe7s7LCzs0O/36cs\ne1RVpatx1v8onGfj+CnmscqYoOCix2CJkg2Gy4J+6XGu4MSJYwzKvnb5iQ6lumloMhaeSkdpC4Ik\nKmuJTcgLQ6CpatUxXwpYbVcjNut2Z+U9YzTjjzGysbFBVTXZSXy+CLYYtalrVKBLyiF1Y5Gs+JYQ\nQlRZ09Cu8El43/u/n9OnT6uIV1Ew6BXMZlNWVlaQnNmNx2M9P6vNEK2AkpGFwJBiejrRWkOQVy5d\nJmCITUDiYiEoyxIn2WGetjVfs7iOPrjE1b0ZW1/akeTXYqwWnLugvwjO0sE3iy7aVpb2Voe+R8qv\nXbBfNIg2nemEJiHgnUOswZnEs09/nbsfeKtKOkj2F8Vy5MQdzFNBaaNmqcYsnZfuMAtLZyTR8vMX\n50O+P/n7GA8Zii/zkZezt1vtiszS1+1rrPGHCmzLAm23vj+qWlIUHlB976auIRmVxGUBU954Xoe/\nthSFI4Qa4x2nbrudCxdfYXv9CLu7lzl27Cjnz59vr7J7nkm0xpVyotXuYpfPL2/rIZuPO2sPsZlE\nwEn2gWWh2AqH72EHj4goYaNp5bMzlt7d+5t3lTe+hyZr5qbfLZ+39lvkK86dk5J0YZfvgse/cXrm\npadJDa4ApGA2Czgb1aTCRpSC6OhZhyQhiacm0bPgU8VgkCiNXoGnovRgosIFySVWNgecPH2UmAJX\n9q4xrQ/YPD7CezWCbeYTesURDZwSGK2vMBoNGQgU/RV+/Cd+ktXVI/zz/+Nf8Au/+PN86tOf4rlv\nfgOfVNfl4TP3Uu/sM1xdZXe+T2NqXYTy9mr9iDqAH+xf5f3vfZS3PPwWVlaGqmGeIlWjsqbTWdW1\nzLeLWh1DNxFTgnldsb8/xrtSmTO1ZHeZOc9969uIBK5du9a5zZRlH2tKxT2D6pA0MTKfN1R1KzOb\n+MaTX+eHP/wRhWmYs725xsc/8XF65SA7xwu/9I/+Mbu7u6oBbxwbayN+/dd/PcNYQ44ePQrAbH9C\nCg31bIqzUDeRaaP1gdJ7itKxsb7JpG44ceIo3jqu7lxh2CSqeUPhtH4hMeQEVDOSRG6q6ObAcoDS\n4mgS3c7qs6w1k8uTxiUUh9QUF5cS6n+caxxG6xYKHbURvoFYUnqv+CUesgdpVc0gzLHeIXiMgaap\n6DFja1jyhU/8Nj/5c7/EPBkkKruolhLjVolMwbZwymICTyYT1ntDbGqtBPWadE2SzIgwRFFs1RpD\nNIYmKe6tdEwtSnaF0qVALrloIpK7I1Nq02TlWFujsF8KWDZ1J7UAACAASURBVCN5b6KGCx2c0LGM\nIoPBgEG/xHX8/JaUoNeSpNAs13LTwtkGLLJkcr8smY1nDIoBl16+hIhw7dp1yoHjhZfOdp/ts4pi\nMi5zVSDEJl9PTijIBeHcXKb3IuWCr+DSkgy0azkvCcRmD07lh3cEhtTWrzSLD1VDU4cMe2Ts2zqF\nX217f9rdYMrzTM9K8vZErS7bJEPrQQYWDKz8p/oeylFPIplz/vrH9xTMjXZhfAV4WUR+zBizCfwe\ncCfZNk5E9vJr/ynwn6Dr4j8SkX93q/cse2Cz2p8kwQ490vTygirUdSSEhvX1I5k7HXDOYEyBLyIy\nTBRIHnaSsTODcxbnPMdPHkespakb6nrOYFhQlNqpaI1hPA9aPAk19XzOXWfuJFpDVTVsHz3Kf/5f\n/7f81m//Lv/pP/zHPPmtpylWNnjz2x7lqSe/zG133sEn/rff4E1rx7l8/Spv+cH3UJU5c0VXbo8w\nWl9hc33A2Re+w7mzLyAiTGdjZdOsrPDggw+ytX2S7aPH2d/fZ3d3lyZFmuwcpBotlqLfY3vQxxi9\nttKWmNLhvOehRx6mLFQ61nvHaDSidCUXXr7Ic889x1Nff5omt4/PZw1lWeBcjxAN65vbnTdnWcL+\nZMzJk6cp8u6jaRq+9uQTlGXJytoqGxsbbG1tdc9w9+pV7rzzTq5cuUI9n1Nm2KhpGlV6KxzJGGYp\nUNWJyc4OKSVe2d1ldXWVQaHu8dqUpOqWNx1ZjCjnkfqjblubOthAQovxp5syK329tgMq00mznRsZ\nBJI/yBhL3TT0yxIyPU2WMjbp4I4Gh6OpI26gXYjUM4oUmTaV2twlIUZDEw1S6IRVB/lcRHOGZl5R\n1yWlU9aE7k7arxfUtfbu+NJhmhygk9Hgn1QPfDk7lHT4PtzqaLf/KrkgajZszFIn8sJSrWkaTp86\nRT2fZ455a85tGPYKrRN4w+5e1d0nk40lbnqssvgcZy0HszkpLfSO5k37PFucucWy9e+NCCFUOKM9\nwSkdNrVevmbJuPbh42bW1Y0YefdvXgQSSgPOnhOHmupu3B2ocbglxnaXk2HOdmfS7nAOmVnc8Kxu\nyNqTpJteu3x8r5n5L6G+nqv5+38CfFpE/pkx5r/J3/8TY8xDwM8ADwG3AZ8xxtwncvNZWCzelERU\nqwODmp0mwZmSctjTrHQyx/sCa6yK4VsHUXClR4Kav7a8ZxCKXsnW9jbOl4S00AUpfMvGMLlIOCE0\ntRaL8vZr1sw4duIkMUYefcfb+cVf/AVCMPztj32U9c117j5zG89++yk2VtdYLQvibMYdW8eROuKH\nJSGpyLxYo408WL02q0L93lrWyoKUAoX3nDt3jue/8xLzumYymbC2ts7DjzzMD37wg6ysrHD+5fNc\nvrzDeDal7RA1RtUbbdK25rIsGAx79Ps9pQymmhhrto6OePfm23nXe99BURbKQBDDhQsXeOJr3+Ds\n2YuMhtolWviC0WhI6Qzvefe7sU4nkS8ca6sb6grkdLt66ZWL/N2/8zOEqmY8HlNVFUc3t/AYYj2n\nCRWz6Yy6qWliTWgC48kESYlGFgO9mY1599vexcXnn9HOO3Fa0T9ETTTYlDvmdE+rg8e0E1TTGGNM\nFvG6eWK2dZnFuMvZkNFqoGLY+vcGrS1E0VbwlAzGhnZo6YTxDjFCEypEPFWdML0eKU1xThgOHF/7\nypc489Z3M5/PGVdzXEoIKv+6zMcHhVhGGxuQmSsGWeDBS7MmqpvDgl0RI9Di5K+dtd2Eneeiqcn/\ngyUxNN2mHIIWuvNMEe9U3KuwRk1UhGy+nGhixDghekfhhSa1D+31C+C9Xk8TCtE+yZxaA7nRT9rO\nXXPo+UZyvaR7P5Z+v8DkFxAbme2yWFyWr295IbgVFTClVl1Tm8/0/W5Rm0gqZ4E/LJ0hImxubiJW\nPWNNEuazucpUGMk0y7T0WTmjb9P474H2+F2DuTHmNPAR4FeB/yL/+MeBD+avfxN4DA3ofxv4HRFp\ngLPGmO8A7wK+eOP7thSdlJQ5EKKAcZ2voCQVl+r3+tlEt6Ysy5w5GUJMFNiOCZPPltms5srlXXr9\nBlcoXOC9x3nDjc05KSkla2U0UjzXwqsXLzAPiaay3HnHHaxvbLGzs8PewR5PP/FFdq5f5ZlvPc1b\n3/Uo5x5/hoPJmNOjNfZthbEOlzRLcLZARHH0DsJLCecMNmc91lp6ZUGvLNgYjTDGcPaFF/j2c89x\ncHDAdDpldW2Nt7/rUTbWN5nOK+pag3XKXaOShLCTGA773HHHHZw4cULd2W0rPxspipIYE84WnDx1\nhEcf/T52Lo/54pce5+zzL7C+vs7u1V2OHj2KtZZBv890pjCLwi0JE1LONGpi3ZBCZNDrM+wP9N55\nh7dCXc+ZTCbU81kOzvo867qmiprJtdn7vJpSlo4mCN4VKs6f2QpCLmx2AVsnZeome/vENbAbm1va\nk1PYxbWBb4EnawBZGoN5nLWUzzbjttYiKZCiYNvML0JZWjbXVyFvfRF1i7G2RJLKJpTO8OKz3+Se\nh9/OxsYqI9boZfd3i8cYFbZS/ZXloNnWHTI2bg4zeRZNMXr5SRLG+NcJ469/tMF8GTvHtQXFxf1Z\n7AqE4VBFr2xKWlwkO0BhlCWTRIN94ajrmHdJhxkcbaBsC6XD4VAN2sUceqpGDgfTLh/Mgc0AJgom\n2TwGlDiwbAW3/Jzbz1blQrWBvBWOf/g8u9PJP/NUVYOxVi0mZdGF3S6IJhn1Ic203HZHZ4xhd3e3\nI1gU1mV4rM3cF4nM8lPSLNdkyO3mLH75+F4y8/8Z+GVgbelnx0Xk1fz1q8Dx/PUpDgful9EM/aYj\nWYNJyjdtGqHIxs2qGJkfbBsAfUlvOGQ2nmBIFGUfWw4oyELxkjJGpjf31VevYNlj7cgm/XJAUarq\nYWuM2m0j80RfXR/hjPI+VwY9XBMY9UrwPYoiaMOAFKwOelRVj17p2avHbN1zGiuWcTMlFRo8rfG5\nyt3qkqj2siE3wOStZ+dtIDlcieQBbPAG1tfWWFvTjdBTT30DEcPe3j5NXXPy5G2cOXMXR48epShL\n5vWU6XTC+RfOcun8BU7ddoqjRzdxvocRoQpTzfbiFCwUfsCliy9TVROOHl0jxsB9DzzIuXPnOH78\nKGfOvAmXDQ2qqmY2HzM9GDMej9k/2CPWAs7Q+oAaEUrnqKsJTVURs7Z6O7hCEkrnsTbl4KBOTn0D\nq0e3sWmmbecHY8Vs8+TwRYEvCkqv8JKxghjFQCVr8ko72PHZ8amd6JpFkzsGu4lwKJMy5FCOyZRE\nROGMJkQGfdMVMTWACBdfOZ+DeEJQPRktgOfAEBtsmrD7ygWOn7lffSmlQZzDiKPAk4URVUvHOWIK\neNtTbDllHP8WhTLJu0xf+A46MNYemug3ZorLi+Ohn9EuXmrMHTEY6zFEnNFzsyKk3CFtgLXVNQrv\naGIkkSECm8XSkrbWG9SxalJPls5CPQOE2GXGktUT85arXZXRQJ4hmvyMWFxet0srbJbayEJkQqbs\n2sPMkOX7t2yCfui+dqtXNyxuOuq6VpPq6RQjvW70dJmzZCG8PP4kSr4svU/qzdri9LnGLakr3C/O\nOWPx3Tksn8y/R2ZujPlR4LKIfM0Y86FbvUZExNwS7Hz9M/jSUy+BqNfjXbdtcWrrCHjlf0puLGlX\nOhEhmcT6Zk+tnpIQQkFR9ompUUusJS6ydz0m+wdU4zGDrb5CNNaCkw52SZIoCocJBSsrK1p8dJHU\nqLuQ91D0HDHVFKaAeoZBGHpLCg3DzVVcJTgctl9oM4eosI6xWuXXbXMr95tddsgZwo0ZRL5LVhd0\nLR7lB+myNO7Ro5uqvBjg/NmXeOmFF6mqiqv7V3HOcerUKU6fPs3V/i6z2ZjTt92hWXNPoagUwTlP\nHebcd98dvPu97+Ta/pTJZMbK2jGeeOJxtre3uO++N/Hss8/yyCMP8K1vPUndVKyv9RiteLa3RhgD\nsRFCSHjvaZqag+t7jK8ZLl8ZU1cTnLhczDMMCv38OkWS0YU7pYSNM7wxlL2C0nl6/ZKYdcj1ZnhE\nhHldIyHLoKaQNXqEwnsKo3xuWxjmjfJ8iyLjwI1gM+/dGquFwxQhBIqiICRNANQsWst+CUipoKkb\nYoq00rPkxqE//+ynOXb/25jVM1TvXJhNp2z0HamZYVJNsvDYZz7Fz/5nDzHHaou86ARXzF4bk1JS\nMbei0OavGPMamceCse15ZfwandrWe+oYKZzynTWnU0obLPRcTNKOX9rq52Je69beGMQGcO2i5cCq\nT2ZRlqQUiCmytbXFtZ2rGBHGkwk+NxcFCSrbkJPvJuhOW3XMQ05oMmQhBjGa4YtJqISCGjUkATFJ\npUCT6QK3EUhLRuLtzxDpMP02mErn52kOBccbYlWGMBb34RDUoi/KncQKq2DUULqqKnq9QhOKENA9\no2bXxtiFwFx+b2eLpf4BpSTninA+TQGjzxy0/X95V9R2+p3frTi3e3BT/LzV8d0y878B/Lgx5iOo\nXeGaMea3gFeNMSdE5JIx5iRwOb/+AnD70t+fzj+76XjzmROZeVGyNhh1BRbVO3YdDU5Et4B1Xakt\nnFdVs4O0h7GWXtnvONHt0Rv0CVVLmTP0+31On7mNk6dPcObMGYwx/K//y//O5uYm0+mUd77zHXzo\nYx8jpYpmb4/Lly/zzLee5ZlvP4eTw5zZ4XBICg290QpmqINVLJ0EpjEloAXJfDasDE23/ZO2oMTh\n4lLXWJA/L5k8SUXwS7oh+h4ZCrBK5VwZDRBR27jnn3+ep7/1FCEkxgdTtre3+Rvvew8PPHAfRc8x\nHo9pmsD+9RkvnX+Vnd09QhKOnriLg4MDVldGfOynPsbdd93OxYuv8P4PvJu/9UM/QL8o8F4NnltN\n9NCkzMQJbG4M2Vof8qZ7b++20KNs1HAwm3Ph/MucPX+W2XhMnSmKRbZOcVlSodfrEaPt6I2Kk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78WVBVc2p64bbTvVZP3KMnZ1dmrqmqmuaEDsp305RM6p1XkLFqwxW3WbyOLFWfVetMVhvNZh7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42Ey7l+elv31Pi2wVxEvhf4qaOXPgP8Z8D/AvxV4LuptnGq+ri+58eBH8USyT+p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\n9T3DUAH/UtBkeNGYd6gaR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       "text": [
        "<matplotlib.figure.Figure at 0x7f3211514710>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Inspecting the properties of `VideoCapture` Object"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "propId Reference:\n",
      "\n",
      "http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html#videocapture-get"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`VideoCapture.get(propId) -> retval`\n",
      "\n",
      "```\n",
      "propId\n",
      "    CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds or video capture timestamp.\n",
      "    CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.\n",
      "    CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.\n",
      "    CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.\n",
      "    CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.\n",
      "    CV_CAP_PROP_FPS Frame rate.\n",
      "    CV_CAP_PROP_FOURCC 4-character code of codec.\n",
      "    CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.\n",
      "    CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .\n",
      "    CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.\n",
      "    CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).\n",
      "    CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).\n",
      "    CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).\n",
      "    CV_CAP_PROP_HUE Hue of the image (only for cameras).\n",
      "    CV_CAP_PROP_GAIN Gain of the image (only for cameras).\n",
      "    CV_CAP_PROP_EXPOSURE Exposure (only for cameras).\n",
      "    CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.\n",
      "    CV_CAP_PROP_WHITE_BALANCE Currently not supported\n",
      "    CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "propId_dict = {\"POS_MSEC\": 0,\n",
      "\"POS_FRAMES\": 1,\n",
      "\"POS_AVI_RATIO\": 2,\n",
      "\"FRAME_WIDTH\": 3,\n",
      "\"FRAME_HEIGHT\": 4,\n",
      "\"FPS\": 5,\n",
      "\"FOURCC\": 6,\n",
      "\"FRAME_COUNT\": 7,\n",
      "\"FORMAT\": 8,\n",
      "\"MODE\": 9,\n",
      "\"BRIGHTNESS\": 10,\n",
      "\"CONTRAST\": 11,\n",
      "\"SATURATION\": 12,\n",
      "\"HUE\": 13,\n",
      "\"GAIN\": 14,\n",
      "\"EXPOSURE\": 15,\n",
      "\"CONVERT_RGB\": 16,\n",
      "\"WHITE_BALANCE\": 17,\n",
      "\"RECTIFICATION\": 18,\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print cap.get(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "131.0\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key,value in sorted(propId_dict.iteritems(), key=lambda item: item[1], reverse=False):\n",
      "    print \"%s. %s: %s\" % (value, key, cap.get(value))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0. POS_MSEC: 5240.0\n",
        "1. POS_FRAMES: 131.0\n",
        "2. POS_AVI_RATIO: 0.04\n",
        "3. FRAME_WIDTH: 1280.0\n",
        "4. FRAME_HEIGHT: 720.0\n",
        "5. FPS: 25.0\n",
        "6. FOURCC: 828601953.0\n",
        "7. FRAME_COUNT: 8618.0\n",
        "8. FORMAT: 0.0\n",
        "9. MODE: 0.0\n",
        "10. BRIGHTNESS: 0.0\n",
        "11. CONTRAST: 0.0\n",
        "12. SATURATION: 0.0\n",
        "13. HUE: 0.0\n",
        "14. GAIN: 0.0\n",
        "15. EXPOSURE: 0.0\n",
        "16. CONVERT_RGB: 0.0\n",
        "17. WHITE_BALANCE: 0.0\n",
        "18. RECTIFICATION: 0.0\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "FourCC"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "FourCC is a 4 digit adentifier for a codec."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_fourcc(cap):\n",
      "    int_value = cap.get(propId_dict[\"FOURCC\"])\n",
      "    format_hex = format(int(int_value), \"x\")\n",
      "    fourcc = \"\"\n",
      "    for index in range(len(format_hex))[::2]:\n",
      "        value = format_hex[index:index+2]\n",
      "        fourcc = chr(int(value, 16)) + fourcc\n",
      "    return fourcc\n",
      "get_fourcc(cap)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "'avc1'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Video Length & Current Position"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_video_length(cap, current_pos=False, in_ms=False):\n",
      "    \"\"\"\n",
      "    Measure the length of a VideoCapture Object\n",
      "    \n",
      "    Parameters:\n",
      "    ===========\n",
      "    cap: VideoCapture object\n",
      "    in_ms: boolean (default=False)\n",
      "      Returns the time in milliseconds if set to True.\n",
      "    current_pos: boolean (default=False)\n",
      "      Returns the time for the current position\n",
      "      \n",
      "    Return:\n",
      "    =======\n",
      "    if in_ms=False -> str\n",
      "      example for 1 second VideoCapture:\n",
      "      > get_video_length(cap, in_ms=False)\n",
      "      00:00:01.0\n",
      "    \n",
      "    if in_ms=True  -> int\n",
      "      example for 1 second VideoCapture\n",
      "      > get_video_length(cap, in_ms=True)\n",
      "      1000\n",
      "    \"\"\"\n",
      "    fps = cap.get(propId_dict[\"FPS\"])\n",
      "    if current_pos:\n",
      "        length_in_ms = cap.get(propId_dict[\"POS_MSEC\"])\n",
      "    else:\n",
      "        frame_count = cap.get(propId_dict[\"FRAME_COUNT\"])\n",
      "        length_in_ms = int(frame_count/fps * 1000)\n",
      "    if in_ms:\n",
      "        return length_in_ms\n",
      "    length = datetime.timedelta(milliseconds=length_in_ms)\n",
      "    return length\n",
      "\n",
      "print get_video_length(cap,current_pos=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0:00:05.240000\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def show_image(frame=0):\n",
      "    # Set Frame\n",
      "    cap.set(propId_dict[\"POS_FRAMES\"], frame)\n",
      "    \n",
      "    dpi=72.0\n",
      "    height = cap.get(propId_dict[\"FRAME_HEIGHT\"])\n",
      "    width = cap.get(propId_dict[\"FRAME_WIDTH\"])\n",
      "\n",
      "    plt.figure(figsize=(width/dpi,height/dpi))\n",
      "    ret, image_bgr = cap.read()\n",
      "    image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n",
      "    plt.imshow(image)\n",
      "\n",
      "    plt.grid()\n",
      "    plt.show()\n",
      "show_image(frame=130)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5mtgD5Tj0u2Upg8ydzJkC+tKzQXoQ5TVjBJxCDLjKTnik1PWl7t0HHdfrtc4S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yAAAg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edpWDdmChvTg3Y54GzGPAzVWE9w4JZNnV9T3quqG+fwgdl7QLmobQt6ppiB6rDFRdwdgKxiQY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\nuiHP81hSEARkWSwWrJer2GcHgwFt2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K3FtkbKEqap5PT7sSR9pfVz0Ja2bajqElNXtE1L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c76II5p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\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7NA8ePmD/sX2uXb7CP/3N3+TOvX/OyekMA0xPTqirir29PTG8Tqd4r9jtX6BpGmazGXfu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\nmM9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Tgk5ZCBXrfn5PoRQhAwDiHSSeI3JtFlKZpa8CZ88LZo4W4qik9NljLVBM22Uou/zG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WYfSGUkrFtV7M4zIMADBhF+yayRjCRLHnt78zgw3vuQtbTCdDodOa9p3MbOohd97DhKNJ/P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Q4KKBdx3KsgLIw/sWZDjoBB08J77da3XKuILWhUfmRrzOL34mWyt5T9MMZESdpEeFmibY\n9taRiMCeUZUTfPD+j/DZx19isVhgtWrw+OwMzL/CO2+/3TsrV+0JIuiy4/3sGqxgKqR2kxqaRWi/\nql2HLNlY+2O2XKCspJ0tSgtzeiqRfYUFCgsqLAoTCotWkvLJ7DHZm8K5Nqb6jEYjdF2Hr756jtGo\nBhEwnY7hPeHx48coqIh6Ss4z9DnyKMXWh6KpIcKnaVqcnUqkhZ4zLTCnzoT1eo35fB6vW5Yl1us1\nnj59GqMFqqqKHv0crBsa80KaWhdL9B014rWtsq67XrcoipjKFYENazcKCOa6mY787+8FCMjGq/jE\nNxk9WbLTzBUdwnkvESheQVuGNelvTaHUxGiNDOrzy/5ZT7fYBi1v1/lfNV7bVGPmcyL6nwD8AwBP\niOg2M39NRHcAPA0f+xJA7o69H17bGP/1v/rXeOfhHRAR9qZjPHpwGz96/xEAwm8++gLOObz78DYA\nqTdQFAV+GHrJ/+ZvPodnj/ce3Ynvl2WJn/zwHQCSM87+/yPuzXotSZI7v5+7R8TZ7pZ5c18rq6pr\nr26S4IhAc0BigAEESIAGBD+CoBc9ENCbXgUIA+gr6E0ajCjMg6BXijNocASIEkfi0ktVV1VX7nve\nm3c5e0S4ux7M3SPinHMzq5o9aE8c5D0ndg93c7O/mf3N8/47zXZtND/86F1QKm3/8D3J9f367hPq\nuuKTD97pnP/Dd2X7V98+BiSqQbY/BKX5+P3b8v3bR1hrW/fzmCzLWud7iHOOD8L9fHP/Kf1+n0/j\n9m+lXnfkVLj76AVFUfD5x+/iw/m8l8oMSim+ufcUpeBHn/4gPa/SOZ98cIeDg0NeHZxSVjAspATJ\nolyk8CqjNF4rdi+c50c/+pg7d67x8PFTHEtu3ziPczV37z/Fe8Xta/vUtubB49dY67h+cQRO8eTZ\nKcaUvHv9MkYbHjx7DUpz+8oltM54dnjC0sGdokArxZe/eoDS8OH7N0N/PqK2Ne/evBKe/yEKxcc/\nkP786u5jqqriB+9cQykVtsNH4fgvf/WQqqz4+Ae38N7zy28fAY6P37uFQvHLu4+pq4qP35fx8n/8\n+7/l1vWL6ftX3z5ivpzzgzvXybKMX91/xuHxnH/ye59Q9Hv83U+/orY1v/v5B2it+fuff41C8Tuf\nf0RZ1fyHv/sFAL/z6ftkWcbf/uwrUIo/+P3PMcbw9z/7GucdNy9f4HQy5mdffkuWZbx36yp4zzf3\nntHv9fj4gzh+ZHy9c/0iOM+v7j8jzzJ+EN73V988oipLrl/ewxjDV98+Rin48L2b4KW/FosFd25c\nkv3vPsbh+PDd6+F5HzObzfjkg9vQGs+fhOt/++A5Jsv43c8+TP3vnJX7DcdXVckHYT58fe8JWuk0\nf759+Jzh4DTNj6+/fYw2hv09CWv75u4TUCpt//KbBxwfHXPr+gW5/v2n1A52dkY453jx8iVFb8zH\nH+3y+vVrXh8dYb2jFxSBZWAsz4PRsCwraltTZJkYF7M5/UGf3d1d8jzn8PURVSDAs9Yy2tpivlik\nRfT+w4dYa7l25QpKKQ5fH1EUBT/48AOMMdy7/wCtNe+9e4fBYMDB4WuWyyXXrl5BK82zZ88wxnDz\n5nUeP3kqJYucpdfrU1UlT54+RRvD9avSn0+eyj55nnP//n0eP3qEB9575w5aa569eE5/UHD1kgRe\nPX3+HKUUVy/H78/wHq5cuogxhifPnjGbTNkabWGt4/Gz53jg6sULKODZi5egHFcvXcQDz16+AuR4\ngKcvXlGWJTtFD+fg5fEx2sOw1wfg5ckJeLiwvU1d1fzPf/7nuMzQyzTOOo6nM7QxXDq/R5bnvDo8\nRuE5F8oEHpyOcdZxri/nezUWormLAZ0/OBXm5Eu7I8Dz6nQKOC5sD1EoXpxOQBsu7+3g8bw8HeOd\n5/xogHNwMJ5QliV7A+GROFkKD8T+YAje83o2o/KWIs/Is4xpVYPJGAwlsmEym1NWFVfO76LwvHx9\nTJ5pblzeRynF05dHANy6to9C8+T5EVorbl+/BB4ePHmFNprb12T/e09eoSDl999/8grl4c71i7L9\n8ctmvkP6fueGgEP3n7zqbL//9EC2X7+I1pp7j18GI072ifvfuX4FlOL+k5egNHduClHfvUfPUcA7\nNy6A99x7/AKtMt65cRnnHPefHjBfLLl6SZj+Hz17BV5x48pFBoMBzw4OAXjvzk2803xz7yHL5y/4\n5IMPOH/hHPceP2JeV/SzjCxTTJcCqvVNjgeWtsZjyTPxQJV1RVlXVHXFu+++x/37D6hryzvvvINS\nip//4hcoBR9/9DF1XfPFF1/gvOOzTz8jz3O+/OUvUQp++PkP0Vrz85//HIDPP/8c7z0//elPsc7x\n8UcfoZTiiy++AKX47NNP0Sh+9otfyHx+7z2cc3x791uUh88++ZQsM/ziy1+A93zwgw9lvfrmK5Ty\nfPLxJ1R1yf0HDzg+Pub6tWvUtefBw0cYY7jzzm2yLOPbe/d4+OgpZVkzyPtMZguUVuwOR3gPr0/G\nVFXJ7miAB05ncwB2R0NAMZ7O8Aq2R5KzP57OAM/2aIDWhvF0jgd2hgMUcBq3DweAZhzOd357hFKK\n0+kclGd31MfjORnPUUpxfmcL8JxMZiit2d/bQSnNSSCC3NuS+Xt0Mma8WHDj0nnwcDyRlKm9nRHe\nKTleKXa35H5fn4yZLpecC1EFh0enAFw4v4v3Pn3fP7eTtmsNl85LSdhXr08AuHhePKDx+4VzOyil\nODg6bfH4KCbzEq1hP8ib17MF3sO5UQ+N4uXrMV4bdrZE/tx98JDaK2q7RAGVXaK9IzN9vIfaCmFb\nkffx3lFWEpmWZ4WAZ3UpRlMuQFlZLTEastzja8WLF4eUpePWtR1QmhcHTxnPThhtXQRXc3h8hNJw\nYXcb62pen06p64rdkYDVp1O53rmtAd57Xp/OyDLD/q6U1z08meK849y2kK6+Pp6itOb8royvo9Mp\noDi3Ld/Hi5JZZTFKDLhZWSYADKV48PARSil+9PlnADx8/ITa11y9GtaHZy9QXnH92hUODg64d/8R\n89Ji8hzrHLWHFwdHLBZL+r2CR08foVCJB+DxU5FPzfeXoODG9db31vZ/+OnXXLiwx/Vrl0DB4yfP\nAc3N69eoqppffvUtxmTcun6DaTbnl9/eRWUFRZbhgScvXlDZmp3dXXr9HqdzWZ+2t7cBWJYlKCk9\nbIyhrmq8FcJJoxSTwKsy6BditJ+OKcsluzuWLNN4RI69enXIq+cHTOaSh74VwvSniwUez7DfxznH\nZDbHe+j3cpTWzMslWhsGPcnCni+X5FnGoN/HOcUsOPS2RkOqquLgUNafXiFRBLPFAu88i4WkBUfd\nvleIPrMsK5TWjMLzLcoSozXbW1so75nMZnjv2R6NcA6OTk45Hp9y88Z1tNK8OjykKArefec2zsOL\nl4dkecaVSxfp9wc8f/GSqnbceUf078dPnqK05s7tWxhjePBI9Mt337mNc46Hjx7z6vA1sZ1Opnjv\nGfZ7eO+ZziTlczSU+TmZLYO8E3k4Cdu3hn3AM54tUMD2aBDko2zfHom+OZkvsX6aykhP5gu0UmwP\nZT6NZwucs/QLzcl4xpNnL1Fa8c4tsb8ePHoGkL7ff/hUvt+8BSjuP3wCKO7cuhm2PwbluXnzKgq4\n/+gJOHj3ppBW33vwDO097968Ct5x9/FT8PD+LdHn7j56ilKKd29dQSnP3YfP8R7eu3kRr+DbRy8g\nHK+U4u7D5+Dh1tV9cI77Tw6CLiDz595jCd6P3//6H77mxeFxks9vaupNCIJS6gJQe++PlVID4C+A\n/w74T4FD7/3/oJT6b4E9730kFfxfEN6A68C/Bd73KxdRSvn/5r/6UymbpFQKl4kI/KqnS9inG7bK\niLytevNiqG17e9wnhuJGxCwibnGfqqooioJ+UFrbeTmxtZFLQc+b0Jp4HudcYJYsUt5N9GB62+St\nRQ96PD4+dwxzHgwG5CHkv/1MMbRZKSmNlvKPTI73mj//X/93fvqzL3FOk/VGUkpFKSyaqiw5t7vH\njSsX+eFnn3LrxmXyzLJYzliWJ8wXE+p6SV0u8a4WnoHgKTAmR6s+2msybzBKSlkZY1A6IxKAOC8E\natvb25w7t4fWCqfKECnjEodA7K88z1P+dkTcokfYOWGCFqTRdfp6uVwmRFL6UYh48CT0U8JhNd/c\nf8pH791MxCPzecnJ6SnaaHZ39tnfP8/u7g67e9sUvR6VrUM4ok/l4sSLZplPZ4kszYTyfWWo3T0c\nDtN7z7KMqrLcv3+fFy9eJA+3cp4izxgOBqBjeUFpy+WCslxS9HsUwSsnikhOGZjb8zxnOGwmtvIy\nVmNI3GAwoCgKXIsUablcMh6P6ff7jAajFKoY2fVPT0/p9weMRltpXrQ9Fl4Fr2+YQ+3QsOPjY4BE\neAOgArPzwcEB3nu2tkQxjN5GgBcvRGhtbZ+jqi3L2nPw+pjj4zGZHjIYbrO9s8u3397jL/7yL8Pc\n7KVQtLIsO2G9qxEhFy5cYHd3V3LdT095+fIl4/GYLMv4Z//sn/F7v/d71OWSo6OjTr5w7PPhcMi5\n/fOpnyJiX5ZlOlcE2OSecnSmefT4MdcuX+5wEVjnUsWEOH5tVVNVNf3+kF5esJjPk3zL85w8b9UW\n1403OZLhODy2qhOKf3p8ys72jnBfeCHb0zFnHPDapfHSnmsqIOfL5ZLZyZhhr8/R4QG5MQyKHkLg\n1XBQOOeEgTrXgZvEYb2waDvn6Bc9XFULOZOzgVk3yLiqDilIQZmP4agqRFYkj3ITLhfzEjFSdkur\npkRS5HBoVzTw3mN03kQIeOEYWLqahbVCvGkMKi/wSjHa2WZ3Z4s8yyi0Au/IM0WvkJrQmVIYJX1m\nsoYlWQINIlu0T7mf0TNrdNezqmP/rYQKrrazvBjrnjfFvceveCeADGGgEG4i1h1M71gGQvCYew9e\nuAbivqenY5ZVTVaI0UkobbS9vc1od9Sk3XhNluccvn7NxYsX+fLLX/IXf/lv6WWifApxqXjMQKcK\nA7VqvBW1syyWCyazGX/2Z3/Gn/7pn/Lq1SsWi0VnzQQ6a3j0dMXWjiJY9fyv5vXH85oQaqIzKZV5\ncnJKXVkybcQgMEYiJPwq+Z3D2prZfMrLl884PDwMkQGSJlcUYiwWRcFiseDf/9Vf8Q9//zMuX9iX\ncqIhRz8+07Kcp7zlzr1Hr+jaMIi57A35Vzsusx2kGdP0YlUO8T5JypCrbSIkFQZ+Gg+iNngUBoUL\nJYnLskQbw9Fkxu6wj4nvJ3g6vWs8kM45Aeb29ih6WSjh5lv3eLaHTgfv2CqfxlovBF2uKAq++voe\nT58+Y3d3j3PbPXIUuq6JuoJSit3dbbK8wCnD0lkuXLzA57/7e0zLkn/1r/81jx4dMRhuY7Ie3sUa\n7u2IHU+7BGFTvUYT+ZG8t+S54j//z/457793naPjY2pbgx+SmZxH9+7z+vAVvTzH21OgRmnhBYhr\nmPfdmvKZkUoVscqNRKplWCvyVsrdOYmQVErGhdEhAivqlWDrMJaUYjyeMx6PRRdVih//4Y957/33\n05q3t7eHtZaXr15ivaW2VeP5dCKL7927x92795hMpnINazk5HjMcGf7Fv/gv2NvZZjafvMUbG0Kj\nz9jn4dNXAgYARgv/gkRvLnny+CkPHjzGe4XRBm9Flz49PUn58Uop0EJmG0snrkameEXqO+2b6B/l\nhZzSOZu84o18cWlsiQfXYJB0WkVT9k/W52YYdeR9K1pO5EtTXaaJlmt0mbIsU6nBdI+qqZCxqcVz\ntEtCN9HBjayMulSWZShD0qfjeQeDAbu7u+zt7TEYDLDWcnJygveec3u7DAb9pH/0er1G9wzXiikI\nZVly7949vv322859Nwz5duN42fSb9ispSx1CPgVetvV6vRR5Vi6XiTMl3pt1NXVdUfQH/NMf/ydo\no9JztGVQNx0pQ/mYcNMiFHYWpzwWlyKZlfdo1fS/itxeriEJbUcIhAsEhcV19Il43zFVLNq5MVLV\nE6u9rPfbpiiB//5//N/wfjODx9siBK4C/5Nq6Mb/lff+3yml/g74N0qp/5JQdjBc/Aul1L8BvgBq\n4L9eBQNi++D9myFPOLJGS4d4JUzrllBbWotH28Zw4taEig+8qcVwc+VDDrVT8gkKU/v3Jizd0H7R\n7RbR6HB2uVdfoZRJi0JkvyUMmMjIGYW3JN2Hc2gRWj4oTCAMmbG2qqyQUaGJ4WmxlFTMC9Lhfg1a\nZzx69JT5ZIGrFcoYCdvUGqMzjFbsnt/iww9/wEfvf8D++XNMl2OOnz3l6OiAJ08fcHzymqOjYxZT\nUT6MhuFQkM3RaJsLexe4dOEiO4MdMiMTQ5Qaub5yMe8U/LykKub0+j1UHjKnVpThVUWu1dtEZvy2\nEAZwWBwWrxxeOdAer2RhjNdwOKy3QQhoPnrvdriOSnOyqiyZV/SGAyrneHFwSO0de+f2GI5G5D1h\naPVKC5NxCI90OLJelurCLhYL6roElQdmUyldoEzG7tY2P+h9QO0sT548CSy7krfo8GgXlXUC0Uz4\nE5XYfxWE2sp0Pk1/+PRJY0qJYulDH8ehD1oAIjzKx/Epn1UFuCtIVveNCpGQWskCJP0rwjQqVrGW\nuwnn0p3j4v/Ox+sR5lWThyhKq8Faz3JZp0WmDZDFsL4IAm1tbXF8fMx8Pmdra4vd3V1u377FfD7j\nwYNHvH79msViISkCzlM7MR5MnuGqitpaFuWSZ8+ece7cHibT9FWfXOdkue7m5WlJmTFGwtTv3LrF\nsipRWpGbUOInkPngLArIjSbTeQhBFObw3rDozACLbcSNb4GRsaYyJBZdZeS9WrzUVnYupAqoNJuU\nE1mkVRPS7qJAVUpy8ePvLsgqZ9E6pBdEhcpoMtOPsw1U9JcA2qEicZoSxcr7EAbnRdlSKAlJFppj\nAhYg/ekJ4W8QmcoD3Trg0EYhk0YRS/M4H+VBGEN4lK/FuFFiiPsY5q2gCmOmrJdU1tLb7uN8MDqV\nJjdRpsacP5lXygSZrOWZVRYI9ABFqESiAmt36M84RoxSycDzuFBaqtsaIs7u701JrHSC9P2d6/uI\nnAz9pyQnPghB2U+ppDS5wCEhc62WFdWHfHQFOjN4DU4FIjyEdMt6Tx3mo84QrhlqjuYnXLxxidHO\niMXcoozwyWhvwvuTNdYFgIqg0Lqwxo+2t7h+/XqqEBJbFojAADHmTJbA9qj5iMLbpAKthrZrT1Cs\nNPga7wRYkrrmnrqqmM1mVJV4g02RQxYqtdi6mSMBJJzNZlhbY12XP8QHOSpda6gqKBcVs+kigdnx\nnUgZR4LMyMIcaFJAtBaQzSspBQZSsUBkagBwgkIqJexCyHgygMWAlWUgGBiA8jbJEBdlhmoUThmb\nNuhXMfRfurrX62Gt5fzWsJuK5D1Y4QkR0e/wWJT2KO1bBkh7MHd5uNP4RNLxhN1elOE290q8ZuLE\n8Qaje+AcGZB5h7Ei20gpVR5tBAhRWgBGpZoSX95ZrK1k3CNAkQ9STS4nciZcuRlXOgK1Ajx4LM7J\nOM16GVWt8S5LdopBoZwmUznOgnaFzIOqolyW4fwxRz+QW2oBA2RtNTSViAI/Q5Cl+DCXapn32stY\nC7SiwTjS4Tk8HoPzDUjnvcY7hY05XTrDWY/zkZ1c5q8K/eAcHB4dc3R0gq3F4aaNYolnL+/jrKd2\nHh/0CZRLYGRXz9ONHbRK9gbcunqVuIMKVTuU92xt7fD66EvG0xla50TTbLZcCCAVol8hikmFtx6y\nLKWoxbGhlE99H0VPnC+SjaHlfSkj8l4phFM96jLNmuK8bQR3ADrichYB4jTOfWO/aEMHPIj3gxIi\nvLqumc6m1C7OAxn/zrogD94QBq9AZU3f6pZ8jSCdA2rnJaLAlThXp3K1JKBpnSQRINM5RV6k9IFY\nYjA6VXd2dtje3k4OpPF4ynIRIjujYxZkXsbxsPYM3R9FJgS9ggiyt4EeH+SQ9K3zdZBH4ILcbhys\n8v7O720noCXqlG3wvTl3WIE1sjZFkN2LTi+EkTrJYq9UUFEESPZB91Nx7CgoaxcS+7rPqQK5cZtL\nQta7GlhPW1tP+1Erfzby823tbWUHfwb83obfXwP//Ixj/iXwL9965dA2GYXt39pozWqEgBiAwZBq\nDd6zrnNWi8jc2dvgjBHb2e9Nnp922/RMm7aroLCvRiZsbqJZLxZLlDJkWQEmwyMe5qhMTCZT7j98\nyC+++AWvDp5zdPiUxWJObUu8Ays2NkrJkrdcCv+A4jWP89ec33vG73zyKRfPn8fkJhkdsqAl7Z66\nqpiOJ4AnM1q8CVHgtp7nrPZd+7L99K2Dgx4fS+ep9HM8t60tUp6lyXuqqoqTkxOc9516rbHfe70e\nWRYUpghwEIlvmtw2FYCHmJNuMjFOamvJVDN1kwfYe2GTXxlDSRC3Hm2VpI4z14OWAeFBJ6CrMRTj\n2FvNYz7rPrpd7Df+v3pMVKC6uc8r1/RR4Nlk1NUhSmNra0tyAk8nK2SAS+q6Znt7m0uXxJvw7Nkz\nTk9P0zWroPS/ePGCwaDP3t4un376Ke+//z7eC8P55cuXGI9POTg4SEh8jH7o9XqUVYm2rXerVIoC\nSR4BfOeZ49jb1F/d31wA0Na3rdg3zZyJxwHe+cBor9KYjwZfu69jJ0e1MC7GrZMnQsdUlSHbvDR0\njvQb8uVXxhGeTp36s2c8a2NOTtc9X/t46f91joy2cRgNxzi2XF1T46icp4yRQEpR1xVeaYzKgnGn\nhZ2YwHfg1slDW1pccw9qrZc68/f7yrXv25p3352XyaGyst4455IRagKwpJUG3TxvzJF3zpFlYpxP\nphNq7dg/v8/27oiqnqBNiOBwQpjmEVCptsgYQwyeXBnKqmQwHLG/fyEQ3Goyk6XxC90IgHgf8e/o\n/Worre19Ig+FczbMJVHE4pxdLssA5tYp+kf4DUJUjWredSQUOhkouwAAIABJREFUreqSqiLxDFjr\nMCZ6ukBGd81sNqPf73P58mUGRUGeF4jXNoAciTgUlArKfSBd8EqiF1JOaVCKi7zAA7mRaLr5Ys7p\n8YnMVVyoac3Ged8MjgjiR3LELJWgTOBAGCxKKTKl8LVN48loE9UMUE2EjKOp8LI6xt7sKT67repk\nbe9pPG8zHpr7dj6OVd+5R2stykh0ktJKgDlbJX1ESFdNenZa62e7xGPnHhuTUiKMskJkjbPg5b04\n71guK5ZlieCZAlBY5xHrwqaztOdb9PrHZ++sn/HaKvIbtQF9Be21J+1j1mTPJr07Xt953zE8nPP0\nen3u3LlDnuXY2kv63MFLnjx5StHvo42ULA14w2+oNYBCmwFfq0K2pfUzwbdpiCaAOJ4pjiH5koCv\nuK39f7dFh0ccFaqzjbXfktqFGNY0d6GgUzM7bo82Js22unapilFsbU6o79Pa73p1XgGByFcn3rKO\nrdHSQaNNor2Ue4yReu1IIILelxcFRZGn1TBGUMUI2nidVSdh60SdZ4i22Or42nSOTv+o7jlWAQ7P\nur67fi/fr6loQ9Uu8FG4YJu7MB+jg9E3TsF0rDjKvXKpWpL3PoDEovdFwCk9Xhz4K+3svt3cvjfd\n22+qfX33MR+8e6OjZJ1tzK8/TPSCRkQued6/Z+sY2yuGethj432pDQr/dwEFNk3MjmGoVAcwWD0m\nfl8NrYwCLoW0Bc+FGL2FhARP53z11Tf80n8t3g5b4f2cuBgZ48mzWJqm6VLjwFqonOSrffPNt+j3\nFJcuXsSYACB4UfYIBng0uufTGf2iR17kQaE/+7k29eebWjyf6EyNwpDuPSh2X/7qIR+/fxOlGiOu\nqi3auKQI7uzssLOzxXgy4ejoiLwwiVU+NmebMLJFuZQIgaXkiyndDv0C76AsKybTCQ4hFbHOrhFF\npvFGa2l5Izj13ca5ahv9VqpKaK2D8ciakNw0/9L1zjD2N93f20CfqMhFI8N737oXn77H+rz7+/u8\n+84d/u7v/yGFFAOU5YKtrRGXL1/m0qVLian3V7/6FfP5PBEPxnlibc3OzjbXrl3jypUr7O3tcfv2\nbS5dusR8PuXVq1e8ePGCp0+f8urVq0RsJqFkZo08LM/zVN2grUjdf/CQq1cup+dvswyvvSOlN8q2\neK6N/etEyXHyQ/KSrJbcaoiY2u+ydR7p7lbXN++toxz5ZrBEZUohESyifm6eo+1zeOea8X3G86bz\nv2HOp1KFrf5JJd3acrxlJEZjJhLMOSvxNHEMOueoylK8bsE4IoZ/Oi0ec9eeH9GYUkmJ6wAwqU9a\nStdKn6z+/V3n9OqcuvfkFXcCz0Brr3DS1X5c7xevNDqTdAzrvQADOhc5aRvgfbFYpHFhbI3DMZ6O\ncbnncu8Su/vbnJ7OhCGbDLxUZdEqw1uJkNDep2gaMXY1W9vbLOZLjo9PsdahA7GbV13DMn7aRKLt\nMdDux7if9FUEBbr955xnuVwm4rD2+YTc0nfGqVKhxjwSqh1T3GSbzGNjcqytsFZIfQeDAXt7e+Ta\nBEbuCDA3RLSS7tboAM45nJaTRsZxCV9ujABnA7lilnH0+pjZfE6/MChlwtoSx2kLEAj9IuBhhtM6\nAQICaNTBURsMAtVEq2Uqp6pKXh1NOLc1apzlYQcBwLvEi6ttHaRojdYVQ0K1jO9V5d37xmiO8lgp\nHSIwwrV0KDnqxNCOYdG+doHg2JAVGX5isXWVgjG9tTgrgIA2ncVx7bkiGBzvX6mgP2kBIqx1KGqM\nzgTwwWC0kfzzpQlBDBLJ4eIr0r4zP52TdMW25zLphUqes63zJKPBiXyTcwmw19Yp2+9pE2FkvLZP\njx5TXiUV9sc//jHPnj5nMBhy+fJlvvn6a37yk38XUkvyprxh7C/FBp3vzXL+8dMXiV9AgBoPAXgj\nACAqRBrKPI2dGA08id2IOHlbf4lzw6kQedndvEFviZaXTucWHcek73G3dktEunHNTHMg7tzIj9ZR\nnZPJuqNpg97tfvzHGKzt88iVFUpCFjpr6Fn7RxshNk2TAoCSqKOqFm4no7oh+Kty/Ky2+i4icKd1\nA6C86RzpOmGQdNbp8PfJeBZ00kZfiXOtfR+r8m11XHX6J+hiqMaOiiH9TWrX2fctZZZ1cvboUO5c\nmWbcRN3MRX3NOTD6zPf2XW2H3xog8P3a2wf/6laPTh/JZVPisaBR5MQZIKE3XoPXBh9z4Qn1rX3X\n29MItJir92s8zXd4MSoszDFU13sRYl5LaoFTyAAwJmVCiLQUP6BzomApDItFSVVJeJoNeYGCnmu0\n6ZNlGrDgpHZzyndthYYbo8iVRXnH0ZHkZG+NhLSo19No7cMiJf2jlKD11krofZZ3GUfbguEf0xJs\n0+rTVYDFuzjJm9AgW9cQ8oXafBRGa5aLJSZTiVl+MS+ZTqfMZ+JBruuaytYoBVkm9YeNyVHKSPhd\nuDHrHJPZLOUJWucwStJS5OOCcScLk3vDpP2+iPDbjt2ElK7+/l3PvY44f7eFK+Vjt8Z4+x6WyyXb\n27t89tlnPHv+goODA8m/t5aiyNjf3+fOnTuJBffGjRvUdc3XX3+drhvL4Q2HgwSYjUYj7ty5w/7+\nPlmWsb29y2i0zdWr17h27QaPHz/iyRMhtnRWwqZFGfdpEej1eo0C5hy1l5Dmqm7KHK4qtRt6j7ML\ntGyeGzoqfmrFwIxGb2tOtf+ORkh8NyIuuuFn8f+NhutbZFbkN4jHrD2v/+6L0mqTxa/bn/H3TeH3\n6bgACrbPE4+JkRIRHGly/lej06LRlyRNGkcpJJcuEBGVj03PsaoQxmvGvlkDvkMpolWlJAJ+36Wt\nnjfmeSptQBssiiyXmtJOBc9NuN82l4/WOqQmSTTVfD6nKApu3LjBwwcvINS318QweTGw6kDyqI3B\nBO/6oN9ne7jF3fv3mC3mXNy/wKDoBYW/AbyVUomZP1b4SOkDrCvJq3n/kq7X1PqW1CKZo22vVvt8\n3nqUbvosgUcxZNe5wGnkyfMicBwVaC1eS6M0h4eHHB0dcfH8frqvsixZLpcCKGY9lLKddIfaLqlT\nqpDce5ablsdYochTtRNrLXVVU2tPnjXeZKIP2XuMbsZiNEzkmqE8qG/KQmiTIekysr/WiiJr8TLZ\nBmBte/3QXSfGG+d4mG9r+yQQc11GdMG91jwO/ZJ4JRQpB1pAmlg1QmN0D+cqsqxA08fVnuk0GBWt\n68hci6mKYZ6mKEP3hjkoZqizRjgatMVkGm08jjGeJUrleFXh/QKlwzsNpQlNthoN0DWE2uB2O2Yw\nGkkuGeISybFq+LT3P8vga8+f1WN8SG1pl9LNsozhaMjW1lZI19Od86O6zprVa8Vee3trQGBnA88P\n6xUm2pcwKGx00244W5T74cj1fdbkdxdwbu+zUdZHa3ClbQJENh3/mzD2v29bBYw23U8DCKzff5vL\nKY6TdrnVLli7PsbOal0ZDzFNNfwS9yIoGQ0wFHWFN3VlMOBlLDXPunqPSqkk85Ta7AaR41zXsRIQ\ngaQnhG0uXLt93dgMje7RXgfbEUQN+NekzirVRNX9uvbCbw0Q+ODdG53vq8bA6gOtLvxrx63sEwVn\nfCnOOTHITBfZ8+nf2YPT0yjQ8vemERYHqezxtmfY9NwbTrmx+Q2bZCKqlI9Z1zVa1Wjv8SouJNJi\nrWWBnaIiFCZByOlW8iNRmHsvS6RBYx28Pjphb+8Y62EbQ4HGqkCCoGLygKeyHmWg1++T9bPNysBb\n+uhN+8qba03gtE3+11rz4Xs3w4QRwihB8W3IdxqE3BwZIzu7O5y/sI/HMplMePbsGXhhpFVKhzyp\nHl6FMMMkAPMUlpdqs5pcyOWsS+W0iqzJl5Kx2V7Q3oyct1uzoL59/+9j3L9xPJ5x3rMQybZy3vZ+\nrIMQ8Y8wjo2UKLPWcnR0xGh7hz/+4z/mr//6rxmPx4yGI87v77K/v5+Uvqqq2Nra4rPPPmNvb4/T\n09OkMMZc3qqqEgnlzs5uUB4bEkoBC7a4fVtKAH7xxReS/+XFQ+K8E74R3RCgVmUZFE4R1lcuXeo8\nd0OcsxkQ8Gei3Jv7P9jGnf7f9E6EIKnp+wiIEe5FxcV75fizohnieYAmKoFmgYsyonsPIRwuGvRn\nnnn9Gs33cE6CUopqLbgu5ALXKdd11TMVQ2tRCmUMxnscigzxmMp40BRZBs6T5VkoEypGoVYKbWjV\nW2680OJVUJgYaaT05rXpDfOq6dPNLcq3eGz7/OvRAWecY8UIiDILpRLxWwIrWkqRgKnrgIuthdi2\nHE+oqopLly8JuKxAGx3YzA14LYST6b2oJB9j+kFZljx7+oydrW1G/UHH2I/GciR7iqWtojEcFaH2\n99jXUTlLEWGhTCiQrqEggNikyLo3KZDx/W9vC9v7eHwCRJDZsbOzy/lz5zBKc3pyzHw2JVbTc15K\nO3pnQzm0c8mbnOUZWZYznY9xWExumM0nzOc1nma91tqE0nZayENtzPddVc7Xo4HknYcUDMKU0BrJ\nla6FgycYbxKS6rEWltUSa2su7O0IUBLHRTh/Q3bVNWDb7Tdt3ERwSORYO7SZ1vhWSe6ZQABqEfmt\nlGGxrKhq6RvnlchvRYvrI10N53wnhUuMXS15/J3nbRwOWimKIkcrg/eWul4KIOFBaYfJAs8DPuhM\nNvUnEIh/u/2Z1hXdrJnSF57I4bNJN920TnQiBlrrRDN/1s8Rty8WC5TSnJ6ecnh4yGw2ZzQaprnU\n3EOjD7d6b+1eVtuNa5ehU5Od9PzWujRO157vDJ1lXfYGwCzcT0cXiYCIlr5sQJLNYPbb9KR2v71t\nHrT1hjZ5amzfRz/8ddrbAL3O72KZxy8dYxpa0QKhbbKFViNU1vdt6caQbIf2Gn+2kdTSUHy4x7ip\ndcb9vW15107IIRNJ+4peHk27TX3g48LpfbpPuY44Abt6QJena5NPOZVxNwSZHeRdIJtYtXVTn52h\nZ34fnf63HiGwbhyseICcwwfio/aCs+Zh9s2xznmcktw2Gya+TmRrsrvzwcsW9oududp57QUijgWH\nkrq5SHipTPZ4jzFvvfs8adCc8eybXpbirIDcDYozgiBtbW1htKGqBRSQ0JVI8tI9XqmMGGOshKM1\nLYqxDmYkgFHKY4JysXSe49mCl8djap3jVMEWmZDrtYuOK49XloHuUSwWjPJB8EKEfmyFwf06wi4u\nZkm0K7XSp+0FVR4sAgLeC4vqcDBiPB6zXFRMs3lIJVCJaGxra4t+vy8GgcpSxENVLdI1bS0EjLZ2\nmMIkNK+uaxbzEq0DiOCbdIyoNLQBkrMM7FU0fy1d5E3984bv362TkZC9DeeOys8aYn7GfWwCBNqI\ncQTwVDBU8BKu7DyMRiP+4A/+gOPjY8rFkrxnkqczVgaJVRauXr2aiAajrMjzgul0TlEM6PdH9PvD\nBAzJ/15yPZclzinOnbuI1oayrCTkOAgP8Q7mSf7Y2mJMkCMtD/2qErdZmaCz78rWja8jAQIblO+2\nMaRai5XsIx4vFcNJoyEorzgZVPjwTojKTPfcERRN332D+kcv0mprgNI3t00yoH0PClAxl9ZZIc/y\nwo1AJHPt6HyBGA9QwWA1Og9yTkHIDTeBS8QoTZ7FqhGisEsEkEKl+tfyf1VayqrEeciDcm9ykbMK\nJNpq5Tk2yvggK/zKvulvVt7pP7KtjhcBBJq1JkE+WggSvRFFyrZkk9KaunYsy5LZfM7W1pB+kVGV\nNWCxvsY7j1EZioY5PhpvWZahlWJnZ4fd3V2m02mapzqQ6cbc1CqE9UdApt/vB8DGdAz9qCsopZqx\nHHq1riWvH2B3dxetLWW5QOcZpgXupedrfaCVKx/qxWdZxtbWFovFjOfPn3NyckKWFfzws8/YvX2L\n7eGIC3u7PFQeXIU2hiLPqJZCcNXPM/q9jMWyZBmIpXRmsHhMbhhtj5gsxzjlUUaLnLMurGcyl6tq\nybKUcoICjDlcuF/dngbhHbdltAuVT5aVkB07Z1G6EApkH6OWPCipZJIqNqyMvyS3W+k3m8ZYTN/6\ndcdpfC+r55UonWZ7fNera4t8FIPeNpkZ4F3OcknwqivwBrzB4ZJzwa3NNRVwRRkX1ktkhHJNKpFS\nrTB2Ix6+qlrgqTCZxRhHhkcpS6z6AEF/8TqM38g1oTprY/sZfcsAA2Erj7uJURMjBNb1i/aYjlwY\nbT2srYO0bb72sbWtA5+SzHmFOERiPxFS4WL6FSvA91m6Xuc31UovbR0n5whpMy3v6Zl69Bkis/l5\nXS+PcyrLcwb9AbPZPDgTIkfCP94of5u+230P6+/vLOP9++qEze9vjvBZvY+U3rWqc9BEVKXIstRn\n3d/b83P1uqv2kg7yXnhMom0i/8VZkSoTBfJHfKxK5ESmxdM5h6JKcjGmFcW/V9eAlGpLq8KLiiNb\ndL9YOIPodFFgXZC98XlafdU87/r7ss6CjVxuTVUCmUPCbcTmQ9fkxdtszNX2WwMEvvrVIz5470Yn\nHA7aRn13gdlkNHaEW55LOJH38oI8nWPaA3fTQFzdZ7Wtnst5ElGeBMv6EN7fHsxpyITnkN9k4VDh\nE71yEqoXvRqE6gHi5QtqbVByJSQtoPsujEHluXjxIjs7O/T6fcrJIty5F0Z+1iedsKdGJNKFdAOE\nyVzRWkzkGWqkTJLRjrI2vHh1iqOH8wWOHFsvAxG3KH9oD9pT2KxTOqr9nqMi2P4N1YA3TS5WfJwY\nvtdmvm+/r9jXzS9f333MR+/eQCOegLquqZ1jMNpGGZ34APqjnrBpO4/JFKOtHbQxknvsFXUlaQLO\nyafIiyRA8qIAI3mZWhuWy4rpbMbpZMZg0KdfDDDedCcoLr1T+aFrULUFx+rnLPBgfdyu/BCNwA3C\nYtMiFBWkN82PtXsK51udv6v7SwtRKioo8KEPDOCcxeGZz2ZMJjOUgiLLmdVTvK+py4peryce31zy\noE9OTiQlBpLhHkP49/b2Uv6vtRZnBfiqa4uznszk1MbhbMl8tqTfH7JYLJNCCTp4KDxF0SfL5lTG\nJu4RpRQvXr7i8qWLa8+9CRholKb/OK37fsVgNrp5N1FKtQ38yGTtnMPrliuqPW8RQNVau0KI01SQ\naE9g74Nyp5s0JElati0a8rPHcbxm/LvzbK1njC1GQ8RQ2qRXao12DqMNOsvIgufVh2PyPGuh7ysK\nQQAElArlOp0YUiiFtT5xTuBTfYRGEYi3ENemlWdzzgmpX0sDT/NNdWVke57df3KwFiVwljyI62wX\nbAhlTz2YcN+dakRhHTXGUMTymnnGrFyGNCvHeHzK5cvn2Nvf4/GjJ+T5ABFrntrbMGcNDhWCxxSZ\nycmynJs3b/LJRx9zdHTEcDhM40+Yq0U1GQ6HlGXJfD5nOp1ycnKSogbip/2eumu7/F9VFePxmF6v\nR57nzRzO8hTZk2d5qFSDGATImizvQvhfFos587mUW8syw7Nnz7h79y6TyYTpdMbhy+dkBj7/5GOM\ngbou8b5HtaxwpaZX11gP1XzBeDxmsVyy9JalU5zMLbWzKKtZ+gVWS0SLR6NqyJ0msx5fWbxWLOcL\nsE1cowsRL9rJXNNonBLCSKXklyhqKltTVlIhaX//PErB0dFrHJ7BoI/RGSkix4FShsPxlPOjUbOg\nhHHitERatA3xBlQQo9LaqvW7Rm/yYtMFMVaNg7cZkLJ/ywjRosCn7UHaZXmB95q6imIhwwXuBBtC\n9YNmnx5TKYXJs5DDHqISVUitUBIxKZWFcrz2IVVJpxQXa0W3yns9VL2kKcvZPEPUdzthwS6S2UnO\nPMEIU7rbFyYTwyc+aZRdAqqTQreh+bsd1t3uw2jsyL2pEPVo8b6Wa1iFq7xEyikhUBZniVQsqJQV\nR1u4FYkQi0JY7lurEI0R5Pfq+3zy7CXXrl7ogIlaa2rnqMNzOG8DT5S8DxRp7YrvjRAxkSrQpGu1\nR9uKPFbNTxcvX+LGjRu8fPmSu9/cC2to+7iz1yzV0Z9XDerNhtsqsJHuWTt8YPtWqGY9C7bB6nXj\nvNvcVudR3NfQrHnNfa3vF/723fWt3WL0V5S93gsfhjG5kJ2jWnZHHG8xgimCWz6U2ZTyp8bkAlym\n9TjMk5BC4lWsQyfjzPpQ6pmcXBm8tdS2FlnuXarQdDKdgxL9sej3UjSWd2EtVqr5RO987Jtw38pF\ne0WFSjIKb60QNeOJuYsyJ2KVkk12pgcVdbAYldLYN/Jugn6GB43orni87sYGrYIAbdD7Te23Bgjo\nyPiq1vPP2h0lAj5LKLP3TV5je/EX5Uw3MxF5iUpppIx1U2Ozs/jE/1uTtM2wutqSEPMxHDWEfaVB\nE9vmPJTVUKD4mwhs1VH82ueKk29tAvrmGYzJuH79Ol/88i6nk0UgwdtM2ihKQoPCSRmjlsBTUZlv\nutT5QB7iFdbB6WSGMkeIj04x7OdEpgZRHjVFkaf3GL1DXS9O86xnIlhrwq2lLIRvMo5INZbjhPOi\nESX2/6Z/dcpNnc/nzOdzLl66gDGG+XJBVUtIcFI2lUYnZFrR7w3IMpPGpSineWLhttYync2YTKZp\nwRRm1dZEVT6FdW8CujZ936Twb1pc2n3VMc6hQ2zYFXAqAQbxbzi7XMkqgJAAHmGq2jheV3PWrbUh\nZFkU7zZwlWUGh8ZaAQfKpdRd3d3dpdfLEkN4LEW4rMp03jjOYrhxHcokvXr1SgjF6hrvVAofrqqg\n0AbZoZRiZ2eXqqpYLuZhu9xXVTXlDkHGg/Ldur+b+ug32dr9KuFk3QVgFZHe5GWIYGZbtsRqA5w1\nF9v7blDoGgU3LOzer03flLu9oUve5jlZu5eV+2ruo3u9NqigVOPxj8/snBJvc24C4NcCwqDpF0hK\nTjTybVBUiqKQPg/hpm8yYt7k2Vn9exW4W5cPnW8br7l6HqVEZkdgOh4rhGQ6rCs+9ZV3Tjg0dJvo\nzDObztBmn/Pnz/Hs6TM8QvqkCMzmwRgS40KDVinstygKer0eOzs7KRWgWRN0CKXPGAwG7OzsMJ/P\nmc1mLJfL9InzvP2cjV4gIfVVVbFYLDh37hzGGKbTKcfHx6IPBKMoypJ2E9LdxjlhreX4+Jjnz59z\n4cI+k8mEw8NDxuMxs9kMX5e8evGC+e3buLqW9Vcp0Jp6saSnjAAsWcZyuRAFVcN0PpMxZBTKaubL\nOWVt6edBvqAw3lOgMZkAMsvFAoLha4xJzonVsdV1AMh6XtVSvurDDz/kRz/6Id47/uqv/ornL58T\nRaBPcjDksnbSYdbB3dUKHO3LdtanM4yo1XncThtp/776rtvXjOBju8XIEgIvRMPIH8+l0sVjBEty\nvrRayofuzE15IjHkjaRhtQxG0VVbYe5RXHoAIT504YcY8t8AiEH+pDp1jQ7ZkWPBzm7r0DH9UyuN\nNs38iPPAWks/cOrEbW3PqDYG52qUUqGiTx3SHhsAwXtPXdUpdSpGxcZ3J+SUcY0A1fL4u4DS+lb1\nmW74uApRQJrMFBjdw5icRVW37jOMAbqOh3YffbcVt9mzOU9M2zDs7u4wmUxSWlBTQjnd6nduEWj+\nrvu2QZy23ZAiOlqz6busmWc5Os/aFn9v6xCp/xshkeRjjJ68dOkSOzs7SS8zEUwOUSmxTGG87wh4\nR76naA/FVM75fMHr14ecnp5S1yXWelxlAzF6sAND9AHeo7QnW5Zk83kTxeoAGm+7tY66tiilKauS\nXk9SsWI/Wy8cO6tAUuoDNKmsstISkOAVSsk9RCeislE3Tj1KxwZgMzjgAyjSFkSxWpNSKpWqVFqj\nWhE+7ff2fZ2H8FsEBP74D//JRmEUF944wOLkj88SB0r8Pw5WMfZVYDlFyJKsxRY2TaB27m86v3NY\nbcFalMkIxUHFowEBmU1mZyPMfWCm9OAs2FrIquSZQuhoZy2OxvV6SGL8OxonbeG4aSFaNcBkPwM4\nLl68yGDQx9qKbNBLxnxzvTbgEAGAxgMKCMGiIylyEULR4YjaCw2eUorjWYVTYxwZV69dFOZWLyFF\nzml86fCzGWW1ZLaY0Ct69IIhXhRFKkPSRbTiM23OMeq00NGbDAlZnOCT995JCo0LYFKeZwENlPDX\n6XTOdDJD60xABaUoy7olBJ3UFA7gggi4nDwXRFFpg8Iwn89TOavjo1PGpxOKvEdhwNZ1ykH2oZ/i\nIDlLoAtws3m8QMId1wy/s87loUmbwafwI98a3jJmgqdDqxWh2O3fVQMlGkpvQiO7x7UoY1veDelz\niQDxHvJch1Jd8TnbFQRspyxgzFFu95f3Ivy3t7cTn4BECLQIWlpGkzGGQX/EaFgymy4Zj6fUtZTN\nEeOvT683ZDgUws66LCnLkqtXLne8ZZ2+/x6Gbuyn9bb+fqXsTzdXrc0h8MbzBoM3PX80fOP9pnM2\ngJKnm1veNcT9+jWIivDZeZRaq42pKb9Oi1VP6kDX6ZGQd6+UyDYlhg4xz9hLWlrXoGzKZVor9xdz\ntmU9EsJK6QcphWU2yPX4f+qPVSNmZb9IsJlCeIkeyZWF3cO7Ny51nlsF62B12HwXhcC3jvPR1xKU\nEl8366V2UNUWvChFEkFjuXX7Kl99/QW1L+mZIjAk+2CoxlKGKo2ruM7PZpHlucn3L8uSxaJO62lM\nEQDSmhEjB6LSWJZlqvrRNIniODo6whjD/r4Q/E0mY4whAYNRUXWuqSKiWvcJYhyMRiOstdy/f59H\njx4ymYyZzSc4X9Pr5wyKHO0d9WQCixJT1gxqx7BXsHvuHH1neHbwinm94CSHq7dvMNzdwfklHkdp\na5Q2PHr0mNnRGJTH1ZZCKS4Nd7iSb1HnnicvX+BPJyhvJSItb0K/U+iv7xrQXWXRcOv2TX78T/+Q\ny5cvk2UZd+8/4OD163C8CfJSQHalFOd3txPZcMfw0t1c4GZcb57LmwCtNOreIiM6b9ZJ9E/M+5Vn\nbinPrrlHWXMrPBbrLNYaFguPoocJDrhYejHMtqZMclAhnbIoAAAgAElEQVS+a+dEWwpzIj5x7IsI\nTIln0QQlKpNShj4Dn1HXYFwR1jsAKU/oO7I79kZ3Te/Ii6C2m8AJEQ3sBuRIPdrpzyjf2npz2wMf\n/9dKY8MamAAVr9LaF/vVOcdyWUKIEiiKAqULvC9w3mKM2lxnvXWPm6II33/3VvLt6ZR+4JlOp2mO\nroIAZ62tbSB4tcX10YYQdK9cEx3rLYvFrJUuVOMxZ5ypudZ3GbvfpxljMHnjSGvrTu1Im3ZE9Zva\n6v1tWodW22qKqjFGxn+rY6uqwlrL1tYWf/RHf8Tv//7vhyiyOpTga/S62KfxI1xe/ZSGEtPFylL0\nutlsxk9+8hP+5m/+huXSkWU6bC/TGgBRR4njugzrp9hORrTS9IzOSeTBydEhf/P//L9orRgOh4xG\no0QYPRgMOlWliqKfwIw863XK4kpkcIbRsu4ZpVEVVCzx9XeLAhXR4pN+Ludu3pOIjUb/WH0/v4n2\nWwMEVgVdB6Fc4QdoWG+7KGRs0SiI3g5rxZvhTAx9a1iG20r/6iSKHgLvfRrgBNbIeO14HXk1Nd41\nOYbeS2hppo14DcM7a15YNzRok1coTqIsy8jydbS/Hf7VRtfkOSxXrl7k448/4N69RyznU/qDEUpH\nlDh+Ws1Hce0iNCA/hw+qdUxcyHzMI5aJO5lMQk5yyc7WFsNeL5zD4VxFZU+obU1lFxitObd3jv39\nfa5cubJxbFjbJaM7q60ulLH/wta0aEWPuPdQVTVVWdPr9dMCFz9VVVNWdVBaVVhwo+dBhSgJj60r\nTCbh486Jx2e+mLJclpTlEmsdy+WCgwPxHu3u7grrrXNk6myQ4/vO6VUlSM6x4gVcmSuokE0eQADr\nXUh5Cb+HtJFUtud7LnBy/XByH5VLJQzVCpQJZXS8CmDb6gLVPFtUbEXh0EkZEKbuLo/Iag5wnHN1\nXaf98rzgyqXLoY6yRXmNC5E5AmJ4vJU80trWDIdDnHNMp1NeH4lnUinFaDRK3BJgOTk5YepOWS6X\nvxHB/NZztJSctoKwKYf3u7YYAuhc4/mW8dMAAWHPoDQ34M+mexfAq7NFjv61FKZ4TFPFxPs3n6fd\nPykXV9EB19IngE4+ccKE47zDeoUOgIGELIpHQKJ+VFgbuqD2pv6P4zGGqJ8JCLSfOgAC8Z6hCf3f\n+Mx+HQyIv7+piSET3k+oRx19T7HfmvN3QcColF2/doWt4YDx6RKVtbweWgVDUjT8GL7bDvtv7k8l\nYEsbA47E2RKdBHEdb0BZQy9Ui4mepagPWOtSRMG5c+fQWsj4okzo9YpABqsT2Wi7n+N7i7XPjdFS\nSjDPOTh4xXh8ymI5b3QDLyRS5/fOsadztqYl2ztwfW+HW+cvMZ1MOa1fsHQl2U6P8/s7DHa3QBWM\nxyfYuWXQ71MYT+4sZrkgs4YdP+DW7hbv989zTMnx7ClqUaGD4impKs27lPFCZ9w06z/kWcb21jZ1\nXfPy5Uvu3LnD1auX+eUvh0QeEPk0My+CVe0xpZTCqUb+vskoa8ba5vG4Op/bY6w97tL61hpHq4ZS\n5I+w1mJrCSWuqgqnF3hkHM1nSxQ5vZ5O7PjWy1og0SzxfsT73ayhbZ2t6avMiEHgbCxzqfAYBFjU\neGdwzqBbQZiJpskroF7pp67e1wEXWZejm+YmNGVvY4WLdmTedDplNptJ6WTvOTo64vT0lLIqiRWZ\nYpvP5x3ujkiSHOdmVVW8Ojggy8R54lyNdXXI1+6m/Lbfa3TutZ/BOUdtpXa7rb0ALBgq55lOJkSw\ndrVt6pPuG2vt61f3a/SHuLaVZfnGcd2+xurf7SCT9TXhjPvcOC98klGroHA7ujSCNxvvpXUfq20V\nzGv/rZRKsi++64acVdJv07oWnTBGs3/xArfuvENVlkJA6eQ6RRg/i8UijZ8o0/t90ccb/U5TFIbz\n588znU5T+e9GtotDONppMnYca4+oQkqTh/Y7jmP78OA1r5wL6TWiZ4rM1601Xbht8jzHhIjUIi/S\nPQtg0CPLCga9giLP6Oea7e0Bu9vbeK3fqpsJcCXPYFo2giLoxEEPbkeCrc73swDg79N+a4DA198+\n4nc+/xBoBmXbW9cNQWsjv+sKUQzZttZTVpaT0xNOj8ctj6dKisQa2toCI6JSEJsxhjwrKPI8vfiy\nFJI4pUBpBy1PkgshiqqVA9L2hsRc0/b1V4VBzHvO8zwQaZA8mg0S1pAstZUprTWZyXn/vfc4d+4/\n8ODFAaaqMLlOgrzbWt/Vyve11kb9BW2L91JXNdPplGU5ZXx8zPZoJBNAebyvqewJzlsW5QyNYjFf\nSO73W0LRNzGtfpfWfsfee768+4hP37+NCwqms47BcJDyhaLgk/rbItREMYhnjOOzBdpYIZubjKfM\nlou0MEaQYbFYMJlO03vrhXSCNlq+CmasPsPbmvRx01/dsEG/Jjii0IlpJJFULuJEHWETrrEKXL2p\nv5v7bvrOaFFWffhkWc5yWYbtYd+06HavI+O7MQDjeJdUj64CFRfx9jO3vY5ReZTxWmHrOoQjhoUs\nLHB4Tx1KlcXwteVyidJyvn6/z2g0ot/vE0nQhoMBi5kQoz1++pSrly+/9d2d1c5UMNrPubK/GObq\njcdtPE/LoFc0oEC7DKQYaKTwOSBVLVm93tr/8qUD8nyX8dRtqyDm6vezzye/t+XWZg9JuM2OMiEy\nO3ARxJrT3iXjINYqj4ZTe23pXr8xXECMxrO8Mor19+82lJ+M4/X+kwPu3FivNLCpP87iEIg8N86F\nMoQbIlDaoIr3PkVZee9ZlktOT0/Zu77HzvY2J0czeec6yuAALnglee5actljGsBwOEyy8/nz58ym\nU3Z3d7l8+SIoOutefI7oHYrPFNf2SDIaPaBlaROIt7W1leRyv99nOByyWJQIg7xOIMGqk6K9zkaj\n6LPPPiPLMiaTUx48vM/9+/d4/PgJVDXLsuTChQvsDUb0nWKY5fS9Qs2W+MUSXVvyTLO9NSLPDdaW\nzBfHlNUCow3LxYzZdILyDoMn9x5T1RSVI/cVvdzjZwu09XjdnlskYMBvGGPNOJO0kOlsysOHDxkM\nBly7do3lUqoJ6CAPI7N+nBevT8fsb21tOF8zRmJaoOhva8PozLEZBmOSF/Eam/ZflTGNoeQSiNlZ\nA2minqyriWHsVWWprKWXNUaIoxVhkSLv1teWBohvxkhRFGQmY1GVYV+RU5I6JeuhrR1GZXgnfA/J\n0eBJjpi3tTYYssm73tZ7tBZ+g+jVvD3cTQZ45ER49eoVi6qkKIpU1rfX67Es53z11VccHR2RZRmj\n4RaffvpZ4h3I85zlcimkv85x7949Xrx8wXw+ZbmcIel/Qvwa+z+GhU+n06TbtfXy2MezRc2gHx0H\nml4xxJiC0jqUzsnzwZos+3UNoE1NQN94/jdHXW5aBzfNjX/M/bVtnjXQoQ0QbDAMV9um3zfZRNAQ\nT0bnx3K55MWLFwEU0B07LY7HKsjA+XzOyckJT548YXo6Zjgccn7vHFmWcXJyApCApPb1syxjsVhQ\nliXvvPMeRVGkVLGmAo1P8mZ1DKxzMsnMElB6HXhZVg6T7EufgOc6VWONVS2SaAoprAIymACaGqOo\nHYx6hn6vwNclH334Hj/64efs7WxTVQ5JYXtDi7KtHSWe5Dlh3sdd19MIz4oUiO/zu0Rc/9YAgYPD\nQ54+fZoEQhv5bwsLQ/S0t4WeSfncSilMJiEetq4olyWLec2yDKqU0kGR8yi12YtIzJukCQePrVdk\n7G5n9PoFeRFJjIRcKtY4jiQt3of7Dmy1Wqm0uOJVcoZuAjTi/3FiZSEcTCYB9EyP7eE2SinKqgxo\ntrSIIoLC25KLF8/xR3/0Y/7yJ/8nh8en9HOFc73OQhePS/fidWcbYeHpNsmR88rjfOwjEVZl7aid\nGNt1bUM/erJcMeiPyIucrLhAkRuGwyG7u7trOZvtvtgEmqwKvK7yrYJCrcL7kXuM9ZmF96DG4jA9\nw/bWFgaDqyU3fbFYhHNoSXeI3mslzy0XkdBz74XMo65tWPANRTFM9yXIpaeuxeNobSRv1GFxDMCE\navrfWtdRPNoTux0O3nkbWifjtd1WAZEIeEAw+lqh2cr5yP2Ir5vSW3Ef9YbFZTVKp/VlLQS/3aJH\nIM5BAoO2VBMRYkYVWGI3GXPt8bAaTdTum/gR40GifpxzTMYTJuMZu7t58FpKOLMOf2cIUZyQSBmG\n2zssqpL5bJr6J6LYWntq25DdhE5NiO5/rNZeBCLIuNpPobdIxEMemrEcmD/iAuoskf8jjpXa2TBg\nVECq32zQr2+Lq1cAoJD5KaG+qg19J0dZu1RiuE05PuTr1Q6M0TKfvcI5Fc7fyEOngucOJb+qWENF\nVtVMa7zqhlx67yHUBHeqSafxXlNbISnKMh2U+RBNIlaYxC2cAQbIuhVhHBWIWxXEnNt4bDA83Eop\nsxi2v+7JiZ/NpGCrwOBmz0EE7mR99CGEWdDB+F68jA3vUN4FOeHwtceQo2rDYlJSFHDx8jkePHqB\n0wpttGQDWZ9Sz1QwoJyHrdEWe9s7YB2uqhmfnnLv7l0ODw+5fv06W9vDlN7THuMxBxXAOkdla7x1\nkrPpXAhMUmA0i6rGKcNga4fB1g6l9dS1ZzjcDiGqw44SFWVJG5CO7zX+3uv1uH79Oru7uwwGA370\nox/x5MkT/u//66/5+//vb3l47zF2VjOqDHtVTjGxbPczetZSWc2W6rPIHEr1cEtJaTA+Z1T0cApO\nTk6pa8fAZmw5zY7PuKIKzleaUe04GI+ZHR4L6FEoVIxqCOMk5pbGSDAQgK/t347EjTEy4vj4mMlk\nsmL0RiVfxpN3ks+uwpjwWq0pmG15b20E2MJajcSdOMXaMSrViw/yQMn483GMxpgkEUL44H3X1Gid\nIbPVyv8KMCGaJxOQX2mFyhwoi9YWZSpqV1ESqkYUGTrXaCseRuGEER3ORY++lUjDKFdUkCHeO5zJ\n6Bd9MpVBXaKcgrCuehtqhGtAOZwvxZ3SmXeuG5HT6pvuvI6dF40U3ciCIAPjmmCt5eT4hB98+Al/\n8id/IiA6OXmeS9TbfJZSdipnU5TNaDTi8uXL/PznP+fnP/uC8ak4NXZ2SgaDAZE0WWvNZDJJUXG9\nXo/ZdMZyWSaSXaOFqFOZRleoK+gV2x2ZFQdmeL0YU1JkIcrUK6xTlLW8q6KXnw0qNSdN4/fXauEw\nmf9Rfm9ubf3jNwlKtM/nrYMwjlMkEGcDEN+3RRsogh/eR/0Jdna2Emg4GPbhpzWPHj1JpfFWHTAa\nJArWOk5eH3F8+JrZZMp8MkVbz3A45OjgECDJ9tiHUcZGz/9iOqOcL6GG7P8n7s16LEuSM7HP3P2c\nc++NLSP3quqlutnksCmMJMwDRwAhCQOJT5Qw/1HzLgkYPahBgm+ECAjD7uaIC7qbXeyqrKrIzMrM\nyIi42znubnowM3c/NyKrsskh6hSiMuIuZ/HFls/MPkOAV5b/rBw/wvovfl1bLjBzvLk61q29UQ9X\nsoHuBOkpH+w9IIQq57qu02sSCB45TdjtJsSYcHHxCj/+cUZ6D7CvAB1mJ9xxtI0S2mCOKvx3AkK/\nzTr51gCBe8sen/3ms4Iqe+cL6YuQNogz5l1Nf3P64DUSqkYfSdumaUqY1EmblADJOa+GoNYjOS7C\n3KbAaFEJQI7TbJFP0Yt95B2OeAlChnMSZWVVXs5L2iOpU0qQ+l0nlPsoKKOrCk+ib5ZZIIvON8Rg\nVncjUeXC0gQGMPBQiKyqMSPKb5wiKDA++PAJVqsVXr15W9BakC/XhtyW/I8MTTcD8failGiVjhHR\nbDEm+14GcsyY0gjihIcPz/Hf/pt/jaOlsA9MaY8+eIQuYBiEUAoA9jthhZdNIey93nfSPSFD24NI\n6p6Q7NTNwGogkEagMgPjlIrBnXLG7/3wuwBJxCMlySZZLBdgYmy2W8RSBiK1QFazWywrY1TW9cIE\nkAsInayN3jnElBCVtOdmvcbNzQ3iFBG8tLYqLYbIDH+Ga1LpbNTteRgkDkf5G2Xjm695KGYOBdqh\nk0gkxpWBSQRCRhaAB7mQloiJYi2YcnFabp3rDiACNkbNHrrrmD+zZS80z1iHvUpCW5uke1bPIJkN\nt88NoKS8pTRisVhgsVyiGwb0Qy9rVwG72mdeZIuh17u9dOvo+x7bzVpaSe52xfBqgUGA8dGHT0tq\nuY2djXV71D14OD5c/5nlNcpZ2OZf9y05L/WNxOXH9rOcvpkHc+ipnGF2L2JkJFjqnSlfkAw3oWJk\npIBCG7FidaKbpwBIylJkbwGRU02b1/m2qbX1aQ/cPqdZi47mRsidyq5xLpzuJQDFcRd6FK1vVYNf\n714hEfm+7VNSgCwl1R3abcI5NUDcXW0D21dkfRIBlSBcZKztAtlrDGfPpCK4kI7NojGiEz/+6NGt\n55d/DZginSMh65SIL5u3p2M7LxGSfuhaqsC3x5hIywtyRhd6OAbG7Q7TtMeHHzxGH/4OjjOCOqlZ\np8Lp+vXeI8coWTolGhMV1A7FSdntdlgul7PrtxEhopo6Td6VcQJQsuFC6BB8wnK5LJEnQDoX9H2v\nLamoGIoFDOA6njFF5CzyMkbJhJumCfv9DicnAiz80R/9ER7cf4CLz7/AL375S/zlX/4luqsbnB/f\nw/3uGKcYMIyEl/s9nOvgOwbg8PLFK0w0IsVRpqIf8ObyEuMkJHWJAZDHietw3i2xigHTdofdWloo\nOicEcBLMkBVsezTbqqKqR8w6AQn/BVhKHLb7HfZTLOSbHnUsRZYDp8erBmgD4AjZyXOwLAcQiT6c\nYhTSMDKdLmC7Qw8i0SvlPCxGt90XWTBFnW5Zqrp/dE07MMx6E1u+kvMZ87zUoLN2O1LiZkcqIyOm\naQSBsJ/2oEBS7+473acOnCzgJMV0KeVZxqLZojmLWdcFr/e6BzDpdxOYJ2Qe4YgbnVB5cAiAYydy\nt8gFk36q6+zHhAJD969JSNnn5qDlKDL85voay8UC3//+9/Hy5Ut8/sULLJcSXd/uhNQyxijA2n6P\ncZwQ7414+vgJ7t+/j5Sz2ElgkHdYLBdN2RRjt9timka9Dw/nOgwDoTT7LrYkq32q+xW+vD63M0Wu\ndf0CzpFmBpHKDVnr8vgWPFH92ujJqk3s4AK2FiF8YDmJLSN7yNabgS1VE9T5ILT2hgSgrBRKTwgr\n20Hzb/mGyffZPcu6M8eQVD8lI+ed3SvXNcjNOQ7NhVsW4tweOzxXcThJ7pFzwnazxtXbN+iCwx/8\nwR/g4YMH+PKLL1Hw/INxJACr5QqnJ6d4+OABgvOYxhHeeSyGoRDJtlwCLSAAVD6E5fIYIXikKIEJ\n62CW8G7Sd7sPe9ZiF1SHZTYGfRdgTritRm7WiHCD6ZigkaFQsBRik2Sw+qpByGRZAMT9bg9W+4JU\nNlmWkq0tKnIMwCxMcTDA73pWNZFEhVPdduUs33weO741QCD4IMPLVBYGAKSYEHMs6dDOHSz8ZrLj\nFDFNCSMzpikKC37o0PU9uv5I6pdyAlwAkUNKBCDDEwraa+24SiyS68JkZsS9tOjZbNYIzqEPksEw\nLAb0Q4faPgNwWZ6n6z1WiwX6IOR5tvhZFRTpBkhZWmCYodeS9qQp1vtIAh4kFYVETlJVZk6ag/cB\n3dBjhQ73798Dc9Y6n7opitOlh6kUnr1y9wIS501+qfWEhMTQ+yJwBvZxAtIeP37yr/Bv/7t/i8cP\nTzFNI9abK2FgBJA5FoEUJ6vXFIZvgqKhGpWQzcQgL+nlKR6k3pCYEIJOA9vdDl3XI2eA41SyTnKa\nsNlusFwucXRyjMwZm+1Oe81nDP0CIXQSGS1PZwIAADEsuM4QcAkkhEPjOGKz2eDt27fY7XZCWDVF\nDP0A75wyO9cxNGe3FVoCEqkxZ6ijCpsaIVGH/SAae+h4H2ZRzN6vUwdu/oMzoU7gyIZl1daPmK+5\n1jhv94y1/zkEBdoMmHI0zryALTWTplHDgD0/GNU6lBs0p9ScqrvG4fT0FI8fP8FiuYAPYvJKGxox\natsIv4ECOWdMUQxlKwWJDWBYlVlCihGZU3EeynzZGECuc3uuDpV20fDlu3oxdVzNhRQ5YABKzAm9\nXtPGA8wl6l6MG6ICCKhHVe0pjYawEk1aupw56oWt/2DdJa3Bs9KoNmJiRHtmbSRYeQMXok9pv1kN\n2kLopeOQsxJ6aYSyTVO0f9v1bo6Q6JcDk5NN5WrEWofadEHOWWv+AO+lJ7BBEzkDFDqRQ2Bt4yaR\nycw8Ixmq8VlCy8DtzXGfqX17rbHzWQ1P0jknmq8U1fqHe1xKO5oUUJXXOUUwKpdOzgr+MQseTgp+\nQLPdlDBXxtYMVvlXwBRCFzoE55GmiLeXr3F+7xSLoZPsIjWIDES1yJMMJIuR6H0pCTg+PsYHH3xQ\nopR9P5RnAuapxSinyUL2lrPoVZ1Z07nBB3gvKa9ZZbSld3ddB4setSBgSkI8NzMaNSPh7dtLrNdr\nHB8f4+rqCrvdVutIOxyfHOOjjz7Cz3/6M/zpn/4p/uvjJ7h3fIYzv8STk/tYRMbn4xrL5QrHpx6j\n89hud0I6xVoTu5kwThkpk0S34dGFAad+iQV5hClje32DaTciU4bvBs1YlPwXW+kMlDUL3TclI4dI\nQQ6Z/+Vqia7vRT85V2Us1JnXwEORG7JZCp0HWatRSGDEeWnBax11et+DQgYhIEcBIbxmhUm6cN2Y\nXSedekyuHmYgFNBLOWnEdsvgnJAcg7JkXDg9H0jAyEBKDLZcYLFaIfGE6/U1HDvENGG7k9bAlmnB\nDEi2p42hgGkWKWZmcFLYPCvPFAExTgAiQEkjqAxAWvYRacaospCL4Z7LNc2on+tpy/Yx51f3tNwg\nmCXIQ6bH1WxxzklwJiXstzvs1R6x0hwrjQ1hALQOmllKYo5WR0273qm67STg+Tjui5wUlveo8+BQ\nOV50H4rWaOZP9JWpfwEN5rKlyEV2AAWVGVJ6ISOQ4ZDxTodQ7WI+eN/ux2T9YbS06EctC6tdNbgs\neSqgTv0eZ2iA8cBBnv/RfAE6/7cP1nmtv8scutC0z2xtfhPuPL8c3/HbXcfM9kxqoeiei5xBCRj3\nO6zXaxAyfueHP8TQ9+iCR4pc5r0ttXLOI8WIoe/x8MFD3Du7B6R867rvChi1z+ecdijLAuBl5XPD\nwXfabLx3ncv8nFuf0vlsA1RoPns4dRJAMPtPwNCUxMk3uSr2l8PxybHyLyTJ8uAMIxEVD0LOWvnm\nDtflux15Opjw/1LZKd8aIPCr33yB3/neB2UxmQKwFKQq/LkoeECFipK++KFDCBJZJ9pjFyMo9KDQ\nI5IIJm+M/zBkUv415mPmDNJKTVEmvkwVCNiNO2x3G8RxB4eM3rGQBvqa0WBHKBiSl3SxLKylGQwf\nAlwI6PpOQI5Q2XFdCPDBYwgdBkXROjKIgjG4XjICugByVGrZ0aS6VDqqjM1mjevraxwdHQH0AjW1\nTBZuOXiucN/3yGq53tpwTCVCPwSP+48e4eHjR3j85AG22zWOd0sQxGlKWgNu3AqHxn1NOc+yBgp5\nEZCsRZwjwIlz50EgL6mjy6OF9CnVsotf/MMz/O6PvoPMCclFLBcdhmWPyNW5u76+xuXlJU7PzuAp\nYJqSrkNZP+M4wncdGJINkjghKfu2RI62WK832O8SCB32uw3ilHB2dooQesS4l3RnEgObMyE5WY0x\nSb/odAB6VW4NBYMIYCcIqTnph20L2+j9nWAAbguP1qE6nOfinOu1c1F89X4o+NJ72K59KPAPgYQ7\nBZj1NibfSuZbSvh9jlamWFpXSqO2hZyw3a3huh7ed7J3mkwiiYh0GJZHYOfgd9K+ph+W2Kgh2zpW\nKUWMux12ux2+uHiOD58+KePwL3l80/npawbOHGunRpsRvzGrIU7W2qZYqvWcmM+rc9LHu63DtqOt\nf7TaYtY+1JZhUZ6HCNbG1VpnAbLGRN7MHelWTrTPTOW8JGTfrKJAjTohirVyCcsJppKtFTPgvBMX\ni5t2kgxAs8EE+JHUaXOYrabSjPgic8EgCnINMIyhHzoH5BxA2kPdK2ksmVMf7jYeAXzy+Uv84DuP\n50auASE6sCklrQ9PAAkHhs1Z0myrzFHaaoJqGZftY7BGf7VeOkh2HMEjBAFQU5xw/fYKH370IR4+\nOsbFsz1CWBQnKFlNeWYEYuw5InQ9zu+dgiBG0mJY4fGjBzg5Xkmr0L6bGf2tEW+/e/LqBGaZL52r\nGCNiTqDgMCwXGPolOEsKc98FSWWmMLO3DjuV2PPGOKljlnFz8xafffYb/OAHP8D5+VkhKPz0009x\ndXWF/TghdAP+8dmXePJ0heXRfbzkhOV2i+WUsUmMNRjrCdjtEsbIgFuAUwTnCLfocH5yhtVwhgU7\nLCcgJMLrKaHbX+PlzR6/Wb/Gq7zDNiSQdwiDdunJ0uaq3Q+OcUsPGNCZdGyPj4+1jetQ2zIbMXO2\nTEXG9WaN+6erxnEzu0Plg5NgQEwJu3GPaX2jLWE9nPPwzoEwwZEAaQ6EKe5xfHyMUW2sLneYYp3v\ndx2OPXJiMCaAJEOKMms2VJPJ2O4WD+2FTmCKoC4huz36PmAYOjAl3ari1BLv5VoKmArZZm2lyzqe\nSdtLOohdQjnBIyM4wNEEohHkJpByjsBFFA+OzYGel/TckmeHY1FSpKEARgXbbH6tDpuZsVgs8Hu/\n93t4+sH3yromIgFx9Ty2bxaLBYZ+wPX1GqujE1zfbAAIaRrgCifHXbK3mSF9nQpgBEgsqD6LA9DV\nGSpODWGz2+JoudJAVkaMmkULc8jfkRl2x9/AN+vJ8jmIzQ7NYikgEGVY2Zeko4e6DkiyP9tSmLvs\nq3bfHN7vXUEMoEbKKTsp7STAO1fmzO75zmek939u+T4gAa9qd0umCePoaIHlcsDLl1/izetXojc5\ng3NGQJAyAZlAjJs1/t+//H+wub6qt9K6HAfBhKRuRvYAACAASURBVHfdi5Tkyvv7/R6ffPLJzPFt\nA1DzIAXja0SH3E/zgXHK6MPc3vz6o4Lws32Qua55cvCdx/n9hzi//xDTtAbSCI0gaGawAPDefcPN\nfsPR3suhnP+nHN8aIIBmIm1gK1vkfNG0Do4hZ/aefdQMTxEppGngWRBZ5zWdgyRrgA4JGEgNPDLw\nFZY5wFwjmz44eIfSCgWo9dD6SCAQ9us13nz1Fa7fvEXc7jXCpQhccAidpM17HxCCV9Q0g5NcJ4SA\nRd9juVri+GiFs9NznJydgvoOUdM+mfIMEPAkqYNXN2/B3GFUwV3HEBCB1gihf8bCmYfdUDarEch0\nXYez0zMhb9rvsNttwSmBSFLOLPWXSNLB5ktjJkHkMZ2tFyqRQjip+WZAalHTSoQzNFKmz/ni9Q3O\nz88RJyGUysnj5uYGoetAJOj4xcUFCB6PHj/Cw8ePMY6xEOa0tU12f5Uosv4AQpZ1dXWF7XaL1XLA\narkqj+K9ZHYAGiXnfGvtt8/fChy7xiEyftd+uevvNmJoANuhYr2LE+Dr0Fz7nszhvNXf4TPYnrvr\nu9QIxZo+R7e+/02Mv4ff67qukFfe3NzASI1ijLi+vkbolzg7uwdHYTa+NuY5Z3gnJQer1QrL5RK7\n3Rb7fWzAmoQYJ+w2a9ysbzCO4+y+58c3a513C3XCoTnRKsM7DSNVUHbdKu0EkXbOIRAhcWUOBmQN\nD8Nwaw7eJZuNnNIiS9ZXfreT6ELXdZpmvUcXlMGXcykHc27OjiwRKPsbEPdBHGeCK/vBPn84Jr+9\nROMZcCIjUSNDJicdtISGTV+w3BdRwyNjzOd1zTMbKEDleodGP5o9ZON8V432Xb/f+URcgdW6z+bE\nh1byYTp1vs+p7kWIoUzkxCDNCZkTxnGPrs+Am7DfS0eZ09MVLmgLQAxrBwcXBFxxjqAZ3Bj6DkdH\nS4TgwZyx223R910BJGzNtTW6h/KKoVwPmqFSMxE09d0HDH1AZkbwDsNigc57iUIfjKGVGRz+pCSZ\nPzHuS830s2fP8OGHH2K325WMsMvLS7y5fAPfBWRP+OzyK0z7PSYwLt6+Rtjs8Zr3eLm7xnM34qYD\nppRKqnHOCcPRgNVqiWFYwCdGGBOW7PD6bcIRBfj1iM+nG7yKW6RBms/llDC4oKVfTvgvjHPG2tAx\nF11DRBJ4CJ2058wCenXew5MT4j2NGzg0nQWKXdQaxFbaKUDSdrvFyxcvy16o3zMnWsuMqAGyAXEu\nSIFYyuU6Ne0cRfSJKSArc+iN7LPueQkYafaRBoOg/BUxJkxTwoocYhoFCHXNfmS9gv4u+8QI7yA2\na4lgc70f5+C9tDBmjZRbpmOcEqzjjjyTpgTbtQyELustlTE+XIu5tEZssrD0mYkqubWdzzmPX/7y\nl/jf/sN/wDAMGKfqZMYYsdlusR/3xbYRZ18ySte7PV69ejXT6QBmBJ8m96zUsz3eBxA/lIF32Rmy\nzipYYFmAdzHqt987JFhrn+Eum5dm7zlwsqgulxaqnM0nSOoAWqkV2UToDaHYngacsfomKCCaZbGh\ngEsE2afMrBla2pkJBmJL1op8pzrA7T/tHwc5ZXcftp/Zblz9Akg2W0oJp8fHuH/vHtbX13jz+hWm\n/V5KQJhEgGQpf3UEbDdr/PSv/hP++uc/03vQc1PNyi5z+Q5FzaDSrcORQ+gqbwxpgFDw5Vzbf5KN\nJFm18Hs8u5ACenpPPMDWE2fklMt8c65ltdKZTBz9Pnj0wWPaJy2x0FIoBZfMDpPlI2uoWGvvC1CY\nbWJrEM36fn88qBzfGiDwg+8+vWVgAijORTF0m/eKMQBzogTFtrTBMkGNMmFd5LJ4RFnMIg8gFL3T\nGGPW+k6QQgfXdfAO6AJJWwh1ZNr0VQdpb7O+vsb68hppNyKMqRp33qFzAV3o4J2XxU5BDE1iTCzs\nydN6jcv0Fm84o+sClkcnePqdD/HgyWOk4JByQnZWSaVHlgjdensD7xbYbqU/pzneZuT9E6zlW4cJ\nkLKxze+QASvCetIe0ZJurOULaii37eyMQb49yrpgETSWIcCS2yq/k0TVRbdmZKLGuAB8J9GTf/Pf\n/D7244jgBcV//fYGv/jVr5Az4+T4QbnW3/zN3yB0Hf77//F/wMOHj+Gcw263k3H0HrFxGFslZDWw\ny+US6/UaFxcXiDHiow+fYrVaIWWJKJ+cnGLoCMthIZEoV52Etoaq7YVdx0Kij9btoqzTWNeX1b3b\n3zHGcm/Vgc1I2nvb/mbmWQ/v1rBoQRCb+8N5ah1oEU639/XhuVuwo4yBpqrrhdToN9Dv/Z0hkyEA\nZv3Ju64r31+v1/DdW2kJqe3K2pT3Ms+oJT/Wn1Y6jTglxSEshh6rxX2AGEdXV4haQ3sIjtRixm/e\nhLeMFr6t3tuxL4q9BRQwH7fGHS2GemZJ+U8pwZkhinmnF+BuRL7sAaBEZ+07Rv5m/3711VftjZeU\nZYAlatfsJ3G67XoAICnHSOIwtHulHYvy7+xc3zjU+nmNK5pBxGJ8lNRTVpmVxYuy2ShyFZiN/7sv\n9c2AxV2O/13Ax8cfPZq9d5fRa8RNAGbyRj8kf6csBjBIjGAzSxhqfBFA0qIzxYQYR8S4x5gkm6Ib\nJrBnPHh4D48e38cn//AGQnBmYLkaPnBwHvCOMfQBXScR5MvL13DO4eHDJwUAMJnVOqB3Al/N87af\nTUmM9jBIVp21i/LkatS3cUIOgYcql4OmrAqnwe///u/j+voaFxcXuL6+xosXL5BSkojsFNENPbLz\neIMRm+0b7CljuGaEbcQOE96MW3yFPbZIYrewhQoY4Q2h82IXOBAoJix8hwd7h0XoEDKw6xz4qEN/\n3MH3vZQDTEIICpIMOhUHMjrFb7D0c0bvPBahw3IYgJwxbvfoXMCi67HT9rAF3GKRy6erhYAjWdeM\n6kRJ7RaZsdlscHm1RtxLD3pPkklSaoNJ5CkDSMjNfcqtWgVUXePzfWHGc+c0vtx1MEfUodaAO6op\nuJxRStj8ooMPPaYpYrtdI+cIaSeqe1fXaR07dQ4hJTStXrB1klJGogQjwZ2SECpiH5EGaJTTQ8hP\nq/OWmeF8ETeq52rU/TAKbw6jkX+WPUJaVqs8Ca3+Pz09xdXVFX7yf/8EU5wQU7VVDWCwIbbzSWvB\ngDAMxZYzue69x74JThgA/HVHqytu2w53f2e1WJYxzrnh4TkYE3utPUxvzUsYq65o09vb8xn45UiD\nfiFg2Q/IMWG1WGIx9JjGCSmLbspJQBAjJCbnZr65AXEMzO5fJGvNQGqHgK38RHWgBMJIHXQIQABX\nyizzbAPVw3T4+9j6Nn++jKXpdfk5u3cPD8/P8ejBfWkvnoUfxilI5nVPuuARvMPiILMLkEwlG+9v\nWi8yEAR0tbTEuVoi7eCUS6BD8g65a8mrNduP3s0vcDhObbn6e30HuDXm7Xqycw3DAE4RLy6+gKMI\n52Q+PQQNZSFH0+83wW68W+f/Nsds//wW5/nWAIGikAG0a5fU1KLi9NQFZMaX1WRKqpy2inFivHES\n55HVgCNX7XB2LKlmzYCX1FGrG8xpZnCWFBoFHMz+M1KZdjHlKWF7vcabF18h7/bomdDDw4faOaF3\nHXongIDU/1EVYl3AZp8x5YwRyvI9AuO0wWu8wOnyFP5kiUAOU04qGOxZ5H5FmAbsdhOGocOw6BCz\nqOBa5/UvdxQBTBkxbvHZp7/G9fUbOEcIjgASB7XzKkiJ4H1X6tosSmYKJ3jJ7nDejLpe0w9Jvg9B\n5VwfNJIlUZeUs9Z1CVrXdR5xYvggDnXfLXB1dY2Lmwt0XacEVm/wV3/1n+C7gH/37/6n4ixuNhtx\n/Jsofm2BIoZJCAH7/R6ff/45vvjiC2mzcn6Ovu+x2d4gpRGr5RKrZYflYinPFarjaoqqNWpbAWpj\nYlGEEknI1ZG1oxUu1gox51zq4AFI6UGq7fXMiGijwgA0xb6SPrYR8HbOZz/ubmV9l+BtDYV/jgA8\nvIaNQUtYNk2TtFHqhCeCyeP+/TWOj0J5VgNWcs4FSDDF0fkAD8I4jhrJC+i7DsdHR1itljg+OcLn\nnz/DdrtG153CEOtZNgYBQAZuybWvHyeQAaF3f641OoyotT3/LWMMpixEbuaciww2EOzQcGqvOwOC\nzPDR67x9+xbTNJUUZGMNHscRIrbMWVBrmBq+lIPWPM556cCRRS4Y0NIayrb+y/1yUl8oAyzJ+VJm\nlTWrqAFHyrzI56zjhpVLexAoV4faAcKLwB4ZubQrtS4igI2ZzvOtw9KEMRtbIpQ1Z2PvvdSRt6DD\nbEFwey4HgshAMi4QElLdYvjyvK+2J8mD6HxAikmMLSZ4dW5IjU7p6Cb3EGNGnEbEcYPsHfJWHLvL\nvMeXn1/g/oP7cJyRph0odeJTkZNWriRlKNLBQ8ZuShGvXr9GTqkAAlYyYY6Ird2W48M54VaJLHMq\nGXYHul0zgqQEodfoDQCWOvb2vMzzqKOdY5qEhHa1WmF1dITr9TVCCHj+/DmePXuGzUYI/pbDAouu\nR+c8ECdc8b4AZb0nLI4cYiQszu7hfhDiN+nEoBFzZ4a+9Iy/enmJ3RSx3U/YZKDPAkj2wwJhGTAN\nDj0xiBOuY0YojjBVgsh2fQMa1Uw4WizLMwfvEccRm5sbTPs9dptNsX9yzuic9OZGzsKZY2OrrWuZ\naibefrsTgBrSmksS6TXjQyN3Bn9YGra0ao4qkDBDBG7pA7W5pFyAsd8xAmUQJyRKGskleGcZPVIa\nmhJhTAE+dhi3DLgMmmS3jOsJExIIDh11IJL1nqGdWwjw6JByQkKSAFFjQzEYCADHjGm3R0eDghIO\n00gY9wDlDs47kAM4Mpij2J1TLsLG1mLbHroFzufD0DggTYaB8QMQEbbbrbyehAPHg0BhLssZkp3a\n2qF1rWiggQgUI5ahw+vnL7HZXmFzc43N9Q3SOCGQdAJjTnAsDm07f8VpnQF7Bjg2zorZ0uTACEre\nqjKYJQJd0JNZFkStYS82VJq3G57prsy3P09O5T3Bk2QTLZdLPLp/D/ubG/RMeHr+AG8vrxCj2NF+\n8Aihl9KjA5nxdQ4dEQBiLTEggOt8U6fjok656QELPrwLFG2vPb/Y+znGzjn0vpa6yXcVpAwBJ0OP\nAQCHgI8ePcLpYgnHrui91uY6BFkP77W1U7/pnhxXYKeADP72fNdnt2DC+1+jDdx801H8xYPQzKH9\nW/zHPOHZZ7+BpwxCKsSBrMGQzFEJJnPR9y1g8l8CEDhszfhNx7cGCPzDp1/iR9//cIaqtAsJmDs2\nrVFUEdRq2Cq+i5w1um+1qMkce60JCprmo+RJYC41MSklUBYBCjXeSx9uve+cJTVPjsb5UAR5u91i\nv9sD+wnaawacJfUQjjCpQOv7HkhcSI6YhWiJkyBHIpy0LjInXF9d4/nFBT48/rgGGXPFH+XzHpvd\nFilP6PsOR0dHKlgSQpA2Img2r5zk6zZDI0gPXm+A/VvtwpiEM4EcYbPdKjPzCGG9sVTbCmQYZ0NN\noKqHoapwln7lStYGnPUAHlQ4QzasGp/QNi2/+OQZfvSDj2QNMRBjxvHREZbLFbZb6QzgnMMHH3yA\nLy8u8LOf/QxdN+AP//AP8ejRo+KAHzrL5mj2fY/dboeLiwu8evVKHEldr8vlAkBCTiOYlVE7Z8B7\nLW9QVNaALZD2l5Z7z2QkJTJXUq/tSmqt4zYKNxfMphSPj49vOd6sCinldEtotKi69x7b7b7U2LYC\na7PZgEgYqy2SAKDcn0WIzTG0/Xyo4MAozwuoImlWHGlw5dA4OlT27Zqx99vfbY6ePH2Cbhjw4sUL\nAITvfvdjHB8fo+96kCONghpBTEXNV6sVjo+PsdneYLEYsBg6BK0pn6YJXQi4Xm+Qs5CkCWu61ajX\n59C7L/d1uOZtPu86yhyX79bTtfMmY6NOQBPtTzlX0lZmOI3GM1s7zKg95O0WWB2bDNeQ49m9eO9B\nzAUUKBFeVHBAuEyEJdp5qs1WykO5kroLdYDrvAHeB005b9O670LBqWacGCAA2wciZ1hTLwlNGVVm\nwLXPZfJI7ssrp4KIHG2p5Z1GzqkwLpvIsbGx9rR1PmluMEN7t0MIxgR4RmG/N54BizZVw0We7zef\nv8APvvOkPbvsGx0LZhzUmwqIQOXqckxjxDRKe1vnI7yPcBQQCphQDTJKEbvtFvvdDv3REn3XoQ+M\n5cKDGFgtl7h3doqXLy7hQgZ5GXMBU6di069WK9zc3GCZE87P78kcQOap7PNDEIsrGAuI3snU3NvB\n51v5Y63SbFFl7ZTQ2hfVmEsF6MopYrFcYL1e42//5m/w5fMLbG5usF6vsV6vwcxYDAOGrkcfOqSY\nkKdRAUfdIxnoGOi5R/IEdGIT+OBBHpI52HUC8quxcTocYRonOAYGWBs3B993GHPC1bRFHEcgJRB5\ndL2UQYx7KVEkZ8R7EgmOUyxr8nsffYSjoyOE4HHv7EwyKLzH0ZHwN4gtJHMRnFz3zdU1zpVDgAiA\nc3DeS3teEm3edT26sECJiMODc0DOAtC54JC0PSiTWGFCegytF7dwkK3X+S9WWhaUA2TZByz6DkPn\nERyVKK/36uR0HYZFD3IePTPuP3yK89NzUH+C/+rH/xrHq1MpGwTBK9grXrtDTixOvHfSTcP5ki3k\nNHCRkpDJMjO+973v4Pz+PUzTHjENcC5i6BZYDANC8JXc2TE4S8mRY2jXA8B4c4q+BwqwDpZnN7mQ\nueprR17tYCqtNy3S3Xcd3LGH8oOC3TzzUObBor1cnk3kn5uRa+ac8fOf/wwpjVivN3h7+VbAn5yx\nXC41K87DSHWhc2GymFB/h8qomf5uPnO13uD0+MQsoyLvravL4T4vBIBqK8pYyXgbMKoKsy4nNpmt\nn1OLg5Sk0jmHcb/HtfKs3L9/jqPVEVLM8KHD0A+i+0XRzJ7F9E0uEWB5wBKBhzHXMJB9kcQml22d\np2TZIhUELfaSu+0Q33L+7gIE+PBPnXezAWrOCKyLw263xVcvJ5VBwOnJMdKkGX7kix4vc21ZWDoP\nBrT+Ns4pASjEDK39V8ayXrPaCyzg2p321B1DwYznry/x6N7pe31ebIVKGp3vIkuE+DT2DJwzEiKQ\nlTvE1gkD1mVEHkWyTlp7gd/zOcq1Du6l+eO9z/TtZQgcgADta/b6IRpWyPQgDn42Qai4jWOHRISY\n5VU2xwjQRcKgTJqFY2QQ0l+5GpoVXayRsOrw1ntVdAzVkckJ2O1G7DY79AnqkOpUZRYCqgyAM3Ic\npYew99hvpxId4pSgDIeyWFhawIEZ25s1NusNVscrMWbQpFwrI/2i7zElIMOp0u+wn6zeFsVQ/+bD\nSHrah27eJTmXiYNCPudQWLiJ5N8QAqY4SqaE87WW6MC4l595bbC8RmWzUYv8lhZka+Sk4A4Y7AQQ\nsmKiV1+9wdGiA7K1CgkSqWBGSlScv5wzVkcrfPXqFf7sz/4M2+0Wf/Inf4LValXIdEgRdTMIVqsV\nNpsN/v7v/x6//vWvsV6v0XcdHj54gL6Xvr/jzmHc7ZHihDw5eCZgsdAMgZoyb5znzlen3rpYVOZ9\nVYpOiDXFIJxHHesapQJatEdJfQRKVkb7nTLHOgdCKFQdbAMblsvlrUitoKgOfT/g5OSkpBq2aWTn\n5+dYLpdyTt8jZUbUbhK2x1tDwdbIbHU2BsWhkjl83e5bItVr7Pd7+K7D5eUlxnFE1/V48uQJTk9P\n0XXdbF9Zf1nvgeVqhaOjIyxvliACcspIsJpPIdTqQihRw77vS6TGQAVWNK+yL9+V0kW4M5OnWDK3\nx2I2ZjD5xgUQEMcTSDFiMoCLJfLCOYGnEc4L4WJGRMQcmZeIbF1n7WFgANQJiSlisVoicUbMCcNy\nAb6+QuZc5LaD1D+Lfs9AqedtQI+iIwjkdOzYUj4tFVhSC1Ni6QqQGM6JuZW4gl05Z2TnQc4huA6c\nvcj9lPQ1hxCU4A/1upwFoJPU3ApI+EAl+k6kLO7gUstnYEkBGZTQVp5RORGa52QWADt0lWjLjBwz\nCKsxLOnRQ99joa1bKwBh53YqF+q+yw3Bl63F4Bmb9Q45Oo0siwwO3sGFTo3Qpo44RyyGDo8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8Gj60N9fpKoMqgvMsCOLnhsNmtJf2Wp1SV0TQQ3ghzw8tVrnJ6elsjOYfRRftpMj7vv+TZoIMqI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UFEqSwo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\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uzczEuGJeI1Mes4nhhJcvn/Ps5bM6/ou0jOzyFvHmHYIpPDWHaDtv53tNe8Q8ScZ1Ma3D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/lGIp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8OIQs5Gfj7X7jcfrjjwSMT+vT6VT5qPnimR0A2Q+TGRenXqmBN/Zlx+mhECQL2Co+JjnkLz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\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C/t4e0rB28CyEgAPLHRw4cidux5cRSYC3qD58nNm1+5zVd1hMbRByUIFaTNi936bdSWaL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S8EYahpjQgZGUhB4fp0JggpSp1HyQLc0GT0IvMTmOim343coiM8mUQVSODBA3oBGaI4KlPwQvUs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P0jgDrI88HkyyWmTd7bvv597q4GAKSDdOBhC5oLDGuw9jQCiams9BgZhsD/F+sHr5ObYYg2Qr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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3211501510>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_frames = cap.get(propId_dict[\"FRAME_COUNT\"])\n",
      "print total_frames\n",
      "interact(show_image, frame=[130,500])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5mtgD5Tj0u2Upg8ydzJkC+tKzQXoQ5TVjBJxCDLjKTnik1PWl7t0HHdfrtc4S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yAAAg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edpWDdmChvTg3Y54GzGPAzVWE9w4JZNnV9T3quqG+fwgdl7QLmobQt6ppiB6rDFRdwdgKxiQY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\nuiHP81hSEARkWSwWrJer2GcHgwFt2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K3FtkbKEqap5PT7sSR9pfVz0Ja2bajqElNXtE1L\n2zSYpo6CwE1Vx/U4TTNGwzGpE0ddeUHsIs1JlUYpI+W6tfJi8Q6VQFU1NNZgJUqBTjIBBJKUQSop\nAjpN2W5rNqs18/MLJsMRicpwiIbAK3fvyKi9xp8Ka0n0+2RIRYF1jNwPBzQJZK2V2cQF7rEE3lDQ\nWkuWFlRWs6wMZZZgPRsVpAxhYB4rrXFJglKpT9sDp50vWe5tUcBazXJrWG2XWLIYPKhrYcYmWYpS\nInysVi2TzRfPTb9WhsC+I+gIAjTBqQ77WG+w95zkaG2HSKEj5A2J86mjUqbFRRkC6yTiJ35R51Da\ngNIQHH2vKCYxPP8eor5dwP2qI7l7fTub9S6y70zhtQsysHs/rjnevpPc36c7RsjBlqV232kU9H7f\noQ5sg+Coh/SEflWDa4AYHDrkazlBZYU26PUHLAg1Wt6Vd8oE6IHOYZWFWSkl9BgFNlD2nYsTARpM\nAIj8NTsVyOwhAu9fKuhOSEmkPnPA3zF/rS8HC5y/RimrJKJ4KswKWtrVhjJcGtnPU2s1mlRLTdDu\n1oW0BucBkuBIi5NvAbQizTPMtkQryVRPlKdgWucjLz2whVDysuujnV5C9/y6e971qX7e83XgjHIq\nvsRICz/2bbD+Gfq8+wB67LIP/KCzPgXBOHmWMo8LBcxJdN86I9foDVTrnFRccFJ+EWVIQMANr3+B\n16kQDn9oiwccPAghzzHkTftovmck2F7EMxj8InooKQPhfgS2gnwXmDrSB23T4FojeWQmpJ+AShIB\nR6xB2dBPJPerbSvaRkQKsa3v0wqHlPwzbYXGU/+c82wQi5R0FCPcuUDj1lhTi24CBmNrHEMvkAOE\nfHBjKctKxqCPMAuKnWDRlNUaYza0raMYSIWQJNVcPIesKEQIr2kp8oI0KxiPJ+SDQn5vLVW5pUKx\n2a6pqjKOoaIo0IkYgVp5J7NtOTt9weXlJev1isorFU+nIg4YnLPTkxPqqsS0Em158eIFi8UcpxQ6\n0bx6/xWJfjvbRaq05uLsjNV6xXKx5Pz8krZtJVIyGnB0eEReiAFUbuXYOMt2s2K1kvrEaZJQDDzT\nYZBzcCD0+vF4TNuIXgNIbv9qtWC5XIrh05rIXCiKUE3BeqEuqQyRZRnWGJpWKKQCNlWkSpN42v/h\n0QFZnvHi9BRwNE2FsUaMPmuom4bVek1V15imIctyJpNJFL/SPSdUxq7y40n6pjiZltV6EynyR0c3\nmI4nzC/P0cqJDoUG7UTTQIA6R5KIEJU1FuMchwdH3H/1HVATHj96xKc//SkPP3+AUpZUF8xmMzar\nLevlGqU0q8Wai4ufkKYJ9195lf/89/+Aw9khprH8wX/+h6Rpzscf/5SqEnGx/+Tr32C9WvHgwUOU\nUxRZgUYzHU8pNyUHM6lbvVwsvcgfXufAO5Vxfpe52DiJoou9gMw91qKSnkq71lEEMmSUCQPJxnnN\nWgNKc365wLguqhMcHetTpdpWbIqwxgRwSQxziQwZazk/O8May9/9e3+Po5s3mS/mLJYLnj55wo9/\n/GPmcyl7uVytWa82LOYrNtutZ4BZUanxKSPa18VWrQATZbmlsTVt03rzRhEEiuWCevn0SmJuaZpK\nJLHIKAYZioI0zdBKU262CElKo1Pkb6u9c9KxIQIDIAIifo2O0TlCQM07F87P20oUjboSMZ2zKzJD\nHZgWgigvo/u6vc/DvKR8kOKKBad8elmwl+iAgKhmdcUc6uzWviP/soj/y2yquFAreT5Kh/4IDn0t\n8wIdtJcCqCJ2Zl1XHvgsKPKEupK+eDib8Td+67e5XC4ptzXbTcPZ+Snr1Qqtpa79fDH31+K1T5wR\nW8uXTg52V5YmjIdDiiKPYqYhVQZn0bJikyixpdI8lZJyWShnKWJ5aZKQ5TlNuUW1LZu6YnJ4QDEa\nsC43UsmnNaxWNWmbo13GaNCS5AnkCqskNU/XwblSEZjYbrecnZ5Rllsm4wn5uODF5QtO56c47ahN\nw2pTU2/WaJdAlkCeQuKYHc4EDAkBsESRFAk6VbSbmsEg5f4rd7h595jDowNJF00149kYhWI0GXG5\nnPP5o88ZT6fRxlitVuRpxmw24+bRDcrtlsvLSzabDduyxDYNYxQYI3bkMBUQEcdgPObmnTsMJuM4\nvnCOJE0ZDAqyPCHLRI9JJ6JDoNMEFJLmF/q0ViSuY0S11lL7vPwKScttm4q2FqZcYwzGz4MO6SeS\n37+laWrA0bQy15TlFlPXWCdz0WQ0IM8ymlrmoCzLKAYjmRuNYTKYYJoG0zS0dY2hJU8znwah0Kmm\ncbL2qQTIUzTSx1OdylzmwXDnnXBbiRZFMZD0CPElBIwMQCSq8z/C3B2YpkGHKwxTcUH8+LQ+CKik\nLHXwxgJr0wFOJVIq1MJ4Mubu/VfJDg8wypDnQ0ajCYnOaT0DcVuWOIK+Bh7YlvG09gEGrYQFsfCl\ndPMs5+bxLcaTQ5K0pazPuXl8i+lsxna74enTp7Q2wdJndFzdfn0aAl9586rDHFYFOofMzye+r/ec\nX+e8Cx8cP++0BSPd/7gDAgLlXYwh591t58/leg+za0gABLpJW6nQtm6iv87ZvgJ2hAXOtx3nHZqI\ncXT3Iv7tugh9n23QOedda3d+13Nyu/+vLjq6d2+63/edyRBpdt3/9PclfocKDml4BSBBoehKZ3QD\nDrqBtwNpyLGUF8aj56Q5G59zeFQq3g/iufv0Q6Xga7/xdnTkXI8NIY6DnzD3lHt3n1+IUogD7wJr\nxX+iFL1omdt5fsHoCznxzsVeRwAAhKGhJadeSS6u0po8y7BuTmtaUp37CIrtnWM3VaKjs3Wf9ftM\noAH2UyyuixRd2dzuK0Zy/P8R3LEQRfs8IBLupbrmOPExOmmfqL4Hobw+OGPj9VkrJWFkktbhxxKZ\nCrOBs+ASSTGx8nd8tipQtKwoqdsgNrkbBQw52PI7FSOPYRwYH1UJxm6SCMvgN957S4A2ayJoKFqK\nbZdugF+EfBTAtI2vIe2/cyLC09RVFCqM9xIXQU/ngRBcqI6Q+pxP002auAgIKA8oWSspLlprrJPS\nmUmSRfjbWuM1MRTG1DT1ltXiknK9Jc0L1HDIcDhmPBqRZQWDYUGSJCzXUipwu9n4udaSFzmTyTQ6\n9sYYmqbGNC0rX4Yu5LhrpZlOpqCQkmajYdQROD09jSycGK1Xkh9bDAfkuddP8IrLTdNQ+5KBQQF+\nOp0wHo89JXMbFXibWkCGi7MX5HnOcrkEYDQcxHSAoDQ/Hg2FUt82VNWW5XJBXUt5wcViQZIkFHlB\nUeTe2Qu54wlNI8+kadrYxj77YDwcMRwMGKQZSmuatmU0GpHnGYsiJ89Fv6Tx9+TenVucnZ9HKvrM\nl8zLUskT7EqKiTiiQ6J41jm2ZYn20enRaMRwNGYwHOB8iaYiy6jKFQHYU3gnTEkpM51oT3X16Si4\nWLJvMrnF4eFN/s7f/jv8P/+m4OHDn7Farjg/O+f5s+f87LOfMZvN+OFf/ZCyFlGvr3/969R1zf/y\nP/+vaK05vnWbo6Mb/PjHP+E73/lz3njjDb75X/5X/Ot//a/50z/9U1bLFZPJhDzLGQ6GjIZDhp7x\nYYYtTV175zZMTGHMEeeWCA0HAxnQHvxzBJFcP7crUYeWpcbFqE6Ial7O5/yf3/oWb775pqwPWjRA\nRMRxd40L97Gbs52P9gux9OT5Cf/kH/9jzi8v+O//x/+BN956G+csp6cnvP7GG1xcXLDZbNhsKkxr\nqOuWs7MLTk5OePDgAUsvillVFZv1GuUkYqZ1668xQVkbU81CelW/vyglkavMAyK3jm8yHAkokOiM\nNCmw1lEi5bxE18ZXpVA65u46R0+XomNdWNf6pINujbB765ELa0OYL3c2JbTY3n113sAP8yjsKu8H\nJ7a/BYBb7x8+9Plg58Ux0P1//eZtjx7rg97vXvqTK5sj+imEPuP7p73+WEp3Yn5hnsfhq8xAokUL\nKM9Tmm2DwjEZjxlNpiRJymQspe8ePnxIWQrQuYzOXu3Hu2Y4GsZ89BB5b8oqpsPEqKqPQAsrzIOT\nSpHkGWma+LSBpHtGDvIs47X79/js088wzlLVNTpNGAwHNG1DooRFV9eKRFv0ALbVilwX5EVGY31q\njU2i7WWNoykbzs/PWVzMha00GVKbmocPH7LcrDiYHLCttrDe0m42FOkAlXqGgbJkuaRTbKsS762R\nj0a+FLhiPr/k+bNnmESxWMxZrpY+ci26KjeOb3B4fMTlfM623Ebx29VqRV1WWGMYeHbFixcvmM/n\nNGVJDUwODjmYzTgYjTjyc+xgIMKxw9EIJ3XoYtQ/Mo90sFk6YdXWGkxr0NrgtGhxKQtpqFuvYLVe\nsalKSBOqREZfax1VU2OsoWqaWDa5aUxs72azwlojmktaxnFVVTjTRmDVtjlWWxIl/UMBpjE0dU21\nLRmPlFRuUJqqMbjWkFqHtRIlL0Y5KEvTVGQ6o8gz8izFZgkaAasbLYzepm2xrayx1sBkPGY8nYgt\n4l2UV+/d9fa8F4f2paz7LJswnna8FKV22K0QSa8kcbyHuUx7tqTi4PCQP/hbf4vRnWN+/OwxuIQ0\nHVBubLQh6lXr2X5rrGu8bZniFKzXJXXdcvPmTY5uHLOpnrNY1ty5e4/X33hTtDUuLjBGMR4fMBpP\nKauaqm5RusVeByT2tl8bILDvRAcH56+bH/bXaFFs1+7WV0G/nta+nwf4yzhb/Yjr1f19R7vCQugc\n6m6/oLTvF1e7307bu8fBWezSMaTO/e5vInq/B2z8h9u6Y/68597/vn+/X/ZsrpypB5b0oxbOdVGL\nPqq/c830dRQ8COCI1CCUpazWSB1mh3MS7RbbKEdpA6rdAU0COLXbZt/XjAVjuX37DqZuefLkKYOj\n/Mr1ddcOIX0lTER9GmzYt2kMaZaxLzZ63b3bfyb9Pt0dd/d7nCj2Nz7n0Nlg0ShfhtBFcTxhyQhX\nArzSvzNIvekkHl/OJ3locv4udUhy1oIYpO7AgggQGv8cfJ5ZeJhOqnKIlr/8jTHCXjCS5oBpY8Re\neeVrpX3ev2nEWLCtpDMQOoKcz7pWnPrYb/ROpDA8swC29anI1wGKfaDRNIEZY3AooZwhjAhjE1pT\n+2sO57o6pgOVWIALG41XrRwKn9ZgG5TT2HKJQWoV63TA4dERk3xIqlpsvaJpS0y9pDWGqgGlErLB\nAJVoisGAg6NDdCpU9KqqaGvJcQ5U8aIoyPKM2VDU+NNUexqp4ezsBeu1ROqNaeJ3BwcHDAYDHj58\nSNXUgI01kDebjZQ89IbqZrNhOBwym806ASzToDU0TcVqJaDBdrslUR3YcO/ePe+MC6Iecu/ruma1\nWnmV49pHQsUpLIqC6XQqhp3qFNuFKVBGMbwgUIqSOsNpJlG1PMs9DVGqZRjboJSwAi4vz9lu11xe\nXu6U/HKoKCpYZDkoTeP7ctivbho22y1KO3lOdcVoMubm8bEYaaozJmkM1hiJqnkneRdg9dEmnQjT\nzkOkSZJQ1TV/+qd/StPk/M6Hv8eHH37IV77yLsbW/IN/8D/x0Ucfkec5b731Vnx+i8WKNE159OgR\nbdvyk5/8hPV6zY2bx0ymU+7fvy9MicNDnHP8+Z//OQ8fPowlIzvhXil5qLWOANCO9s0181mcw7SK\nxYSSLI37ai3loVprRLkaibb3vw/R0OV6xQ9/9BGrjVB5sywRGrXrqryE/iBsgMQLznY54XJ/Qamc\n1WrFP/9n/4zX3nid//q/+29Zr9e8ePEC5xzFcEhWFBweZWidkOQFRTEkz3ISragqUV0/OTnl008+\n4fv//nt8/vEnPF09oSxrDA1pljAYZDG1oa5rrG1p2x4goBRai/M2niYcHE4ZFEOwOeVWyre5pIZU\naqJrlZDkCVmSC9UaAaKsF110gfUJYpjqLmAj3X53Lezi8V2b4rdKxQheAHKvsyD665rFoux16aZy\nrsiiuWbrO/c/b7uO6v/ynb/46/56EO2WuD52+wAx9SgC6bFiiWa7XfOd7/y7OA+qJMEBrbGMpwfk\neUZepBwcTpnORkL3rrbce+UWi8WCs7OzOK761QVA7LF8PNyxh+V7KX9aFEVMjUmSBJUmcdwKMdS3\nF7CJwmlFPhKxvXVd0ihHNhpQpbKmJaQ0zkLdkmxaXJaSk5O4RPKslSZRCYNCKo8sl2vOzs64uLgg\nSTLG4wnkitP5Gc9On5M4IIVNtaHeLtG2ZJbk2FRsDNotm3bNql3z0YMf843LE7KDIzbbBQ2NCCqa\nmratqVtL05QcHk2ZHR7y+Olz6rbh/Q++hFKKi8sLnj9/Is78MOfwcIozVkoA24Y7t2/R1DPefP1V\n8jQjzzJeuXmTyWjEIM8oEgmYWNd0Nqwfa6LT0NDaBudqnw7jdSB8ampglzWViPi1ZS3gaWuwjbAQ\n2rblfLEgGw556/33RUi4F8jQzgPMSpi9XWUaS1WVGNMKWK9hMExRRvmgDSKs3EKRpGQ6EYZR02KN\nI0lzbNWQ5wPybMBBMaGuKmxTU5cbjHLoxEgMpK5pVYP2Ee+x0wjDClTVYqywjGzbgHWkOmUyK5hk\nIljdny1USHvt2bfXibR24UzPx7UW1xopgKUUTQI0gAqMS+fnOhEbNcpxcPMO93/zawyOjvh4vuHi\nfMFyec5qWUZAIBtmbNqWJ8+fk6YpX/nKV7hx4wbbsuLx4ydUiwWTW8fceeMN1GjMsq6gyBkeHmBW\nK6qLcxqtaLXG6gSjE4zW2CQBb6u8bPu1AQL//ocP+NqXX/91nf7K1nd8g4CPsZbUSYfpi4lYa6Ox\ndP2xrl9UfhGnen8f6VA+8ql60fzQ3XZ6aSiJZ+P3nuje1QvoG0XgqdIvb7faGyi4n+/A7y/cu1Ht\n/YVSQIr+uTtUzvWOsdu2/c+iIBwh6tzdp7/8wcf85gdv9fa/6vxep/cQ74mnm4bfSkSs59wbS1s3\n2LaN19kXKFQxPN47poQyojCi88/NeoMlCPDcv3+fJ0+e7hi2/XvZP+bLylqGUm9ta8Xg3UFAd7UQ\n9jUjvqjPXmdsh+tQgHUmCh3J/ZWoeudgiFMa2Bca5wX7ArsgTMLdM1CESJBn+Dgj5wmMlNiXbIz+\ng2cPXOlj8lxCRYqQWiGGUnPl+vf/7l+7czKWfvCjT/ndb/yW30+uoRtjsXfKfGM9urzzt/QJi91x\naOIipfz5QgqMp+lrpXeU1EMfuu45hjy/fvt386P9c/LPsK4rjK3JBpamGbJZL3FuiXMKrUUkJ8sy\niuGM4bCQHM5Ek2Sapi5ZXay5vLyI7VRaMRjmDIoBWZZKZEk7mqbGuYTFYh77ZhBZOpwdiBChc7GU\nX5ZlND6yeXl5CYhje+PGjSjyp5SK+etpmtI0DRcXF8zn85hnJxGXATcOZ3EcFYWo94b871AlAMQY\nGo1GDIdDhsNBFM5ar9cCZkDUKVitVj4601LXlZSjy7r82TBu0lTEH+uqRltHVYtQ43q9QilYLBbc\nuHEjOsfHx8d88rPPefft1yQNIs8xrSFNhO3U9tTAm7alrCpmB5JHaqyJ9ySIXIa8d3wliSCE9fO2\n0M+UUmzWax49esTJyZoHnz9mcnSb9957g6/+5pejevbBwQHHx8dRyPHg4IB79+5x9+5d6rpmsRA6\n/LasGA6H/OEf/iFvvfUWb731JpfnlzFP2bUmipeVZUmWpRHozLIs6kDsMut250WF8qjuLtgc5sQw\nLnZqSPv5LAiBBWc6TVMODg6ikJfSHSAELkagu2NrEp1EMKCbj8WwdU4i5g8ePGCxWrJer8Vx0po8\nGfrxmZMkKdlgwO1bd7l1OGNYdCZdYxwPHjxGo3j24CEg83BtapSvJx+YUH1nrj83CPClOT9f8for\n90iTDIPMj3XdMBqNGU/GIvxpGpyVMo2NbXdqhQsIaTodGfrPJgQ4dvtV/1nt/61D6hTXzdG7dkPf\nVgiK3l2bXCe+1u8bPSZAvz/8sls///h6u0/xssP2f3fd+V+2Jof9rKfuBcHS58+f8+KFsKDSPCfN\nMpROGK82HBwcUpYN0+mUPM9lnzRhNBpx48YN7t69G+e08ArgqXMuzq/77Qw57H3gy/RtIWuxe+Pt\n6YtTdJailWJTlRhr0VkKTtGaliToDhhLvd3gEmHGJa1GJcqzEhJM3bB2sJwvmc/nlGXJ4dFNiiyn\n2pY8f/6czXrN0ewAZy2bbUWy3VI0DWYQql602LpiuVxSVxVPnz7lk7/4C+6/9x7PHj6kblvQmtnR\nIYc3DzlbLkiylFu3bvPq669Rt163YD6PfTOwA2azGW+//TaZTnzp1JzJaBzXrDxNSXVK5u0i59l1\naaLRJMJC8vMECtq2kTS2tvEio42wGjZbASr9eGxq0aep6ppmW1KXFW1Vk+mEwqf5VG3L0fGx6M60\nLZnTKNtVTdOJE6e7biNbRGtYrxPKcktr6jjHdfa49vZHwyAVgWKtFE1dk6YB1BwJU64oGGQ5bV2z\nWS15cVrj/L1z2lE39Y59I0y/lvVy44MmMsdqBzhLXiSM8oJUadreHPH46XNevX8/rgFhbujbs/4P\ngtn+0gG7t2nPBZYEUF8ZzQOgZBll3bIpKyyaNB8wVClOZcwOZ5y8eIGxz9Eu4+DwNrODQ6r6lO22\nQZGS6JyqahmNZgyHE+bzNRfnC0ajEUeHx5Tbiiwd4FwCLiFLB2iVEdiyL9t+zVUG/v/bwmSz/9n+\ns3VOXAMTI3/BCOhoxWG/PvX6SlT5Goftl93ErRF3JSSlWO9gBqEdh8M4E4sRqpDvHJzMkNtvfeG/\nncWxi5J8EYixs+Cy6wz9oleyn2rQXaGjc5J+MaDkunsdHMXgNPYBgbhAxjxJB1Fw76rDt3/tAb8W\nGiVSM560cyhbg2tbEYizHb2761+7YIDqX6s3iJSPpIeFM5RgefPNt/noox/t9Dv27uW+aFYfSAn7\nyeSpopjhPnsgvO9H1frfX6fOvH+M8IX0k9Ar6TndHbDSF1SUVA6Eau8d+HAChah8K9kBReL1B3rC\njF5TmNjWDgzonyc41v3SjxHYwdGv/NAXJAvbdfcnoHXOayn0741zYe7pDF95ToIcm9bEPmsDBdYv\nPn1RsPisEwiAn9Iqtrc/LsPz2H+FzXgF8v4zlXxP7cHP1qcqCLBjjAWdYustm80yMkCECj8gHxTk\n2YDxsJAcv7qkar3x4Uv9aK0ZDAYiKpWm6DSJxsF20RmX4/FY8v18ZKko5NijYiClAddrT0/ciJPU\nc/idcxwcHDAe/7/MvfmTZcl13/fJzHvv26uqu6t7uqdnpmcBZgBiOBAAghRFipRoMWxKpAlLomT7\nR8s/Ouyw/gottmX/ZIdDdoQjZIsOh6wgRURYFAEKpATQxDaYAWYBZsX0Wt1dy6u33SUz/cPJzHvf\nq1c9A1oMMCd6anm37pI3l3O+53u+Z4T3beUPpSQ96OHDh6n0XhRPyvOcS5cuhZrxLdNguVym8oDR\nkIoK5/7qAAAgAElEQVQRrmjQKfHPUpS1rkXJ2FnLarlKDvVoNCLPM7x37O7uoo0AYrFcXVT5V0pJ\nScP5XNI8ILEiRqMRly9fDqCGROq8a8vSRVGplRdtiCbUDs+yTHJKlRL2wmAQDEbbGlSqnfurxYLF\nbC7G+GLRRgSJefbrBn+kqILQQ69du8Zf+kuf5fBoyvu373P12lWuX7/O9evXOT095fLly+zt7SWA\n4+6BlIg8Pj7mxo0bXLhwgclkwiuvfpfxZMJLL73ErVu3sNZxeHjI9evXcc7x6svfEcO1LDk+Pg7v\n0qT5nWjLvgVKu2ueUoFCrgK9vzOvu2toBNBiR2ndEfHMMlarFVVVcenSJX7+538e5xxf+9rXMEZ3\nNAKEMt1dlxV6TVAw/h7ayOtoPObzn/88z954llVdsqxWeK1SaU98xnA44sKFi+wOBxQbUS2t4GQ6\n5eTkJM1BrcWwb4JuRaykIQwBecbYFzI+LHVTMl/NKZRN5TiXc8t0OmO0s8v+/iUGA3EIcZ6qrFk1\nq1RKLIIz3ttQTrhNz5JIcWtjn7GfVFhDfbAUugGNzT3K+6RG3n3Pa8fTggFtGtj6ftkFA7ap+T+q\nbdt7z2td5s35n7ffb2e4bP9eKUkDQStyndHLcyrbgmRVVTFfLJjNF5T1+0wm4lhcunSJ/f19dnbG\nxDJyEs0eMB6P2dnZSbZJVVVJ5DI3Gfi2vGd8txEMiHXjQaKl0Jan3AxKNM6ikFJ3i9WSxltMntEE\n8TaC0rxVYpOXSxHy1I2n6BeYzGCdYmU9VVUzP52xWq1k7c5zvHOcHB9LGlpVydodqif41RLV1Dgg\n6xWAo6pkH/AeZrMZ7777Lg+OjnjjjTekVN9oyN7Fi/QGfarjh/QGA6oHD3j48CE7OztMJpO033zi\nE58kK0wqDSv7joQ+nHNJ88h7j22kfLZFUgt90+CCNkpj2+ozdWD/CgA9TfPaOpl/Ueg07pPKI9o1\ndU21XIH3jHsDyaa0jqqusd6Llo0xqMhYC/PFGIPHow2sqmZNxDTLpFJPVa9w3mIMmE7qpbIW5wUg\nz42AqDs7O+A1TeM4nS7T2BkOhzRZBs4yHA6xrqHf76OzwITOXCqjODuaszhdUi5KVANFXpBFEUTv\nKQoolBaJKUPy5aLd1J3im3tFmlN0vZUPb/G4LLDprDM8OHzIYrlk0M+ZLZYsy5rBYEyv35fKNzue\nwWjE4fGcuhGWpdI9rMtYLmua2jEa7dDrDWlqT2YKdiYXglhwibVQ1w6tc6bTOVrnNLXH6AKjCwEF\nHtF+jBoCN84sll1kZq0pon5WapFqdl4zKAwKrAt05HYQxJ91tP/jZkSrDNltWx1F1tVvt7UPc3DT\nhrYWXe1E0r2PunU4137f9THj7/FJ4464qcff40G0XRXGSe5kjELgo6Gb0wQj/rxnljP/aCh5ex5P\nqkqghC4bX2iMHAst3AaQw6f3sbnhbdsAz3N+AD79E8+1NOkEJmz/+1geZ71F0cPOdVSkuwfRj66j\n6NavvxY9VzaNH1g3aCL1VWtxFI6OjnjusxfFcL57Nzk98e+cc5Kffp5jHu4tifuEuLrcZ3QyJcqu\ngjq/AF8ttR2c0N9jghS0FHofPo/UTd+qpEeDeLM9CkzaNv83j43jbxMYao24AAp5HVIBwnwP43oz\n6ifndhv/4ju3CMbURstc5zzp7xWgHN41vPj809JPwdkgXNvWMQIbNnxa4zQKS6JUAiGlssW60w7i\ngFZVvVbiKfQUbVoQa/0Sx0r8PuaRnwVImyA62FA3jSjK+xUoTb8YkmkPzQqnHFpnZKYHvgTrcZVi\ntdR4FIvlCk8mEJCXcjfj8RgQp7FcztBGKNXdmuvGGC5f3EuOVDJAlOd0dsIHN3/I6elpMjhHwwlX\nrl5lPJFSgTHPv2maFNE/ODhIERdjJOI1Go0YDAYcHR2l6C4QRNdWa2r93vtUPiumDwDh+1ZPokur\nNVqTZzmXL19OKttS8cKG6gs21fdeLBYiRNg04DzOWgZ5wXhnjMlzUQM3Umc7pibEqO7Hnn2a2VzK\nLC4WC6GeZjl4ycNMlHpI/VlWpZQw7I/QKpTt8y5F5rV34B1GSclDrRUmC5EORYhqt45MqmOPwXr4\nmZ/5Wb7wn/5nnN4/4p/85v/FxYuX6PUGDIc5L7zwcXq9AZ/4xCdwTspIVo3j5OSYwWDIM88+wwc/\nlEj2s889y+6uMCFef/11Hjx4wK1bt3juuefI85zvfueVNJej01mtSFHKftFD9foy3soyAZQRAFBa\nhcwkH9aLAMhpkwTjbGNFQTqI9XkU2pCYJtGxioyRp59+moODgzBu29x1rTWZWQcWo1L0mRb3FS01\nwHf2dsmznDzLmQxkDjG5RPPYE1gUOS2xPp06fJ0uSu7cuSdR3kt7zBdTFstTaq8DAFel8R1WF0B1\nABDAiQGvKs1b79wS/Y8AeleNwt25w+i9EVkmINnFi3tcu3qVsg57S7TbtMI3HutjvnIEA9z6TW+2\nDQu8BRHaKkCxnyOYmv60sy/EvsW37wRIc6QLIHUBgR+1bQYfolZL7OOPCi50985tYMCjbKA1tXjn\nUEGwuJcr8D28V9TOglPYxgdNiiX9/iwxhh4+vE+W6cTQiulTRVEwGAxSqkxyqFdBPybowURbKoKl\n3fvzuu3/roZAfNbdnTEPHjxIgF9dt2BW3VSBwetoXCPUb6+ELaehtKLZgc9oqoo6lP0sjKSnZc7g\nK8fJ4ZTp8anoNDWeelliqxplLTWw8h5MLuCDcyzLkqxXYDXcu3/A0XTK4uExrpdhspybxw9ovv86\nH9y5jTGGS5cuUfQK9i4OU1/kedDp0a2dGdX28wAOiFkoe3RtHbasYFVi65rVck59uuD0dBqAeVGT\nr0NJxaj7kFqwxQCyrKDnPQPtMUoDGU5r6AvbQsA8JyV2ywrtFdmqSe8tBhGsD6Ed5SVtTCsa7ySB\nMTMURiMR+YyyWmEM9AOIbpRmNTtlfnpKuViiLVzY2WMymVBXluWylPSLsDacnp4GMW5NPx/R2Jq+\n7sm48zVaWXTtsIsV5dEpblXTx5B5Te412utQqlxRNJB7RealrlqcPddDhYE49uIY7dp53cBP156K\n76mdi+tzW4BfQIvAsaotDx8ecfe9WwwvXuHk5JS6tuzs9GhqRVk2aGXIdM6FvUtMxpIaqTA4C/1i\nyIW9ywyGPbTOWCxWDAatbeO9ZzqdMp1OOT2dcnx8zAcffEB0Avf29j50/fmxagj8KMd1vyZjPB5D\ncPY9Mba4tZ0LNrQXS9HlzSO3OSpd5/g8J6d7710HML4kAYl9ctzb71tUKjoV3gVWgpOalz51gpLo\n9Jqzq8L1IhjScaAcwSEPboRnjRYZo6ebj6OUWhvzmwj92uTp/E3XCdzsx25k5rx+i+j/JgW0ew/n\nXWNzcp9tci4bIqLR4U7vZvMaCLMiGh8qsDacDVQqpbDBURChKKl7H6MB3TvYBro458iCQvhiscA2\nihs3bnDz5s3O87V9p2lrBm9GJeJCvpbzt/HOus+32adtf4kx7dxGxFxBFKELR9FG3rdESDxrjmnX\n4d1cgDd/TvfTqSoSF2W8DwKCXvLLvABdUkop3HtHmE+uaVuhyM4mEK8T71Mpn/o3/l0Sr3IhdcG1\nzxI3iLPvRLQE4vWsDbW7OwKh8ZmttUncLBqDEUSKBlt0bhKQFa7TvX43haBlRpx9593ryn3L+eum\nxDtHrz8gz42IUmHxTvQxqkqYA6vVgrouycoGbTIc0B8UFL0ejbVorULJP8tyuRDDRSvyvqjv53ku\ndHxPKt1WlSVVKVoDVd1wOp1ydHwswJXzFEXOpUsX2b90EZMZVmWV6hiXpVA8owMuAmd5oqjHfuj1\neqxWq1S6cDGbyhzWutU3yHP6/X6qCd8dK1FUsGka0SAwhgsXLpCZjF4hok9SUiljuSxTlKqxNXVd\nJccrMgCctcymp4zGYwb9Pl6ppC4MpMicjEmZB2VVpVrLVVnhnGM4GJIFAS/nfUhlinuWGOHz+Vzy\n/suKRagEobUhVxneNaInEcZeURTEKiiKSGknzU9xdiRqdHh4yMODe1y+co2XPv0SX/7S71LXJU89\n+RRvv/MuBwf3mITa2y+88AL/yd/8m8JYahxZnvH22++wmM8xWc5kMmHvwi4//xd/jvFoxOX9S0yn\nU373X/4uCmGXGK0YDYfMZjMiG0wpxWg0agEY11aegFjuT+N0mBuRbRceKkbuI8Mipo7UjV2b5901\n6t69e/zgBz8IRnnrxCVQsDPfup9t/vNhjVEKBoM+w9GQbS1DnWu8eWBeVty5e5/rT1zn8P59vjfo\nC9V7uRAHX7VshNbhlPca9Te8F+Dbe9nXikJKddE48kzTV4rTuaWsaprGsVpVlOWK4XDIZGeCMQXO\naeZ2gbMNzoW65K5Bh9Qsor2iEJDGre8Zcc9dX7td50nb47bt7+06rpJB346BNmVj8xwqvCedfupE\nCB8d2JcPVQDQQooUnHXsY9v2+23sgDMg9JotKcxF5aUqSIT/RfFX9o7GyudKZXgvLFilFMPRkKqs\nsLZJYqgRXBbmiEsirEVRpFKx3dJ1xhgGg4GIegb2RbQ94t6TUgz0Oitm8/t4XASLpfyqWQOqmqaR\nigdG09SOTGfUS9HOEbtAS/lihzCk8h69oo9BsVosWc4WZCh6JkNZ0WuxZU3m2zG3OD2lOp3R1A3L\n5YreoI/JMh4eHVEUItw6HI/xzvP6m2/y1r2b1F6CIXt7uwI8mnwNHKqbCutt2k+WyyVNVSU9kaZu\nWARR0MX0lKasUGUle2/TYGdLVssFuAaThb7Ck4V53OsVYjuEMeijPYTCeAkIFpmUK8WAN05KAoYg\ng20kVdAG2yq6EhFIxbWBpKbD+tBaRLAVHts0NLYSIVMjVZoyY8hMxmBvj9FgwHK+wFsoegWr5Yqm\nER2F4WBAL+9hTMZsdkKe5dBIoMxaJ3ucd9RlhWpqamC5nFOvKrCeTBtybTBojG8DK5lXEmBNqUbR\nxjdnkL81H/MRfqr3vmMXnHOOznmivfbee++zKMTmcU7G/OHhQx4+PESh6fV77O3t8txzzzGfzwMT\nZAXK0+v3UiriarWi3xfQ23vZe7WWgOJwOCDLDL1ej8GgL9U/RiOy7M9oysB3X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c76II5p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\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7NA8ePmD/sX2uXb7CP/3N3+TOvX/OyekMA0xPTqirir29PTG8Tqd4r9jtX6BpGmazGXfu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\nmM9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Tgk5ZCBXrfn5PoRQhAwDiHSSeI3JtFlKZpa8CZ88LZo4W4qik9NljLVBM22Uou/zG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WYfSGUkrFtV7M4zIMADBhF+yayRjCRLHnt78zgw3vuQtbTCdDodOa9p3MbOohd97DhKNJ/P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Q4KKBdx3KsgLIw/sWZDjoBB08J77da3XKuILWhUfmRrzOL34mWyt5T9MMZESdpEeFmibY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8Gj60N9fpKoMqgvMsCOLnhsNmtJf2Wp1SV0TQQ3ghzw8tVrnJ6elsjOYfRRftpMj7vv+TZoIMqI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UFEqSwo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\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uzczEuGJeI1Mes4nhhJcvn/Ps5bM6/ou0jOzyFvHmHYIpPDWHaDtv53tNe8Q8ScZ1Ma3D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/lGIp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8OIQs5Gfj7X7jcfrjjwSMT+vT6VT5qPnimR0A2Q+TGRenXqmBN/Zlx+mhECQL2Co+JjnkLz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\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C/t4e0rB28CyEgAPLHRw4cidux5cRSYC3qD58nNm1+5zVd1hMbRByUIFaTNi936bdSWaL\nwmAkkMUtSWLnKMMWmn3ISdZ6iCIcZ5TD3FwhbD14wMU8yhzkBDDp3IhLV05J3RlE+AfEJWe1WsGi\n+qcsgRJLXmh4RoJyeAYFjcwFwxi06WKdMC6Wfg6MWm/MljMeJiiVhS6MbNDnaGPNTg91W2Vw0LS9\nh0xIo5kgUmxCXKnNrnP1i182xnTCndxz163YKGS6cJ78ZudKfU1r9Kw9BQQSbdemo4+fadAgTjWD\nbzsv1Nr9Smi2VxFU4K/P1QIIyJkZ64neWmohQRRUzcjpPLbxKdrhKsy0mxpzNT4hePR4mrxvWmaV\nC032l5bpresyxt1AEKLyfgOIZkeD4MA9GFNlJOb021tqmi0GDB0/chDygrlnK2HK9q8hPjdcWqHq\nevfKtH4B8ABoMjB5cUGKnNfz3rh6fXPNwJ4yBsDruJH2F3og/E8FhPm/c8KjPuf81nXeZWu+mvFC\nacqal0xZxogXsG4KTsxqOS3bl5lJKu8JA7dca1K+SywBcU2dajCJCA/ec3TyPqWpwfjjzX4zjG4o\nH6nglSigNFB5VhcyPWdII8qR3lefDS3IZ+ta+8tZ+xJBrEo55mY6ZDwLzFAN+TWLvR/V+SSCX6Gj\nNjYEDbhezajEcoAq26LHe7FJdcs8Nmqva4QAcEbJqlDmY0NYz+LKwJbD2q4Fs1QWQGCjZ9ypsgMy\nNj6e8m4iUloWJEX4DdCCub1cP/X/svemUXYc153nLyIy8y1VhQJQWAkQBAlwB1ctpjZKlGRtblne\nJNkar+pu+7j7eObDtGe6fTztM90znjNnxnP6eBu3umd62ktLlmxL1GLtC2VJpGjuK7iAAIl9Bwq1\nvZcZcefDjcjM96qKBG0JfQGCAAAgAElEQVTJ9JxxkIWqevVeZmQsN+793/+9d+QKK/xhR6qcIiy7\n28uRXqv2L67XlfIarCa92kw5aJ05rT5J7YRIZ8gqF7uIM7TdXrkqA7GoubVZjSKKiFK2sw5ZVkSa\ntNL0+/0+RbdPnhcUeaehKxqDcU3Sv5SNORhdWiIQUmkuVLmsoltSRKiC0sqrqAy0a0i3kRlrraL0\n1mLwSl9K4ELwlBKQWFMuqB6Ikegx8yFa6kRAIGCDPm/qtxATDbWFr0ikWEXhIkqTTS1bYRF4MVQh\nKuSh8WRKiGMRGm+qtZZgHMbGDN0RVPGhhOjpssZRmQrBEloIhHWaoVZIFGSLszGmuIlQiAZW873t\n9SUePD6k+Cj14hR5/EwwOLp4H8GQ+FnnGqq+iMF7x2Klv3eKhE6rp9UnQ0hoqMNURK55TbUJPlD5\nsvGGR+ZAt9slc07BGgvWqxPGGP05lU2kmTpMNGyXbS6jmfi9GLyonyYYq2g2YDo5NrOYIFRVqJNe\nKsiSkWPJgFw0iV8Kv3Ctso1tnS2F2o98xX60M/0n0kTNFknjnOYjfh8xayxMr7FsuP4KNnQmOXbs\nFMV2g6siYBL3jVr9owJLhLpEpMQlZULznrbsFF1g2ndj8Vi8kXr/JiVO2R5p3xJBowbxrjxUXhQJ\nN83rbXmpSfGkBgVsJ9439TPNr1VApuiAcT2GG3oszq3jwP6TLJ2eg9xRVSVLS4tUg3kyhNzoYS0i\n4MFUoqEoxRoNh4mxeclrnrLNKxnTgwlY47E20wM3VAQXY99pmAfeeMRUGEIM+QkgpS5KhQcIoTF4\nh8NS04QQ8FISQokPi3XyPKXq+8iKiOylSom1CghUtVyRqil3tyxURKKCLUnlUaFgo4d7VQ+sgSzT\nJLHGGGTMS7miB9ZT96+dqK6lu4+1qBjWyn+7z1IbVSOKUWsMSQZjC2gCFMhJdagTQ0zqIWgd9HGD\nRXaTiMT3G7xpPsfIWk2LsTF0U4UTP/agqZxqO9YyvUMNe1CWgmttVEuTcaRlZRoTKUkO7KgnUPsU\nc/mYRsCIpIROUdVVqg+CCk9V2MfPvwaUftFm4jgaSatbFTBDHVKnQ6nstFClUpsyauzHLw350L4H\none9Llkpy5zk7dCOldaitDxutWdyRcPKABUi6Vwd1QMw4JyypxoWS/TAGh1fY0eTtKmBs8IYvgxN\nt05+SKTStwGHKLsbcKOJ3TamHft+Ea0G8C7GyI/75uXyYU3csy0vq0i9C0ghO8rmCXH5BWVS1pdY\nbiQnZ4Yy0F66/+lJ6zFaaYpGD6b4Pf0+6n2t+xNkuVwwCWxLe771LPG1lAOmnWA1uQBM2whtX7cN\nmhnVIwFwVrPDR2VPonEjVl8X61RBELNiQjcT9WD9iveNikuqsrEab8jEuZS4HwJgjTSJDk3KbxPB\nv5gvRs8hSIFFiSWSACQFYDzG6jksJIcfMRdBDBZJQy+MzGszVu21EVae91jRRy16WnM16qRLY5Uu\nYdugj2lYQfE0ruWc9su07h3nTXzMn+ZH1pZpx5dCAyToQdXqdzt0We2v1E+n6ZLxVY4Q4riGyOiy\nMQGvjq3WXrZYMiRbIrpImjleBditO7fKmTEOqCZdtgYkELxR50oKTRFYGf8c68uLNdUfVmYIvKh0\nXOn5ItiV9BRr4tkWldPxewjNnrnY9ooBAi+cOMUte/aod8CYWBc6b1G41evvrBZ+t3kGeQfvHAOT\naE26sA1R8bJERVMHoxJfe8ohCmAXCPhayS19RfASE3ONlmOppzshVxEdEytYY3E2V89s9AIloytE\nKn+ckShmdLNYZ7C5YTFkSLD4qs/i0hyLi0ssLc5TlUEVpFAQzIDAgMrHDNVRAY9ij1TmyEiuHk2S\n0atZyzWTqY2MC4f3gapqEEJjnApPYzDOYKzHWCEzHTp5TzclGVghZF5p/8Fg6NDJJ1QguRLvKyYm\n+lRlRafTxdlM65CLx5hSY56d08ykZg0hCHlRKT2708FlSjUKErACRVGAwOJChQ+WwZKvhY21ojT5\nVMheNMY4VIGyGqpCFzr1HIp4Hrj/W9xw02swiBpbw5JhNYj0JqW+SoinUG1cZlijYSKdokev1yez\nGTbVpYeYcK357jJV1DRpjcZ4S5QqiZXivdRATdsIska0pJo1hKC1xIfDYYzNitmmjea16OQ5mdOy\nmhrGoGBOMoaTp7iOkwYV5MZQZIY8c4gJVF6TqFWih1MyKFKzEuu+26z2dNlk/EkikxrWdidYKC5w\n8KnnOGph2yWXkBe5ZsgPgeC17xpuoyIn5YtIR2TAqEAWVdp9BNNcnmuOjhY4F2Jd+BCC0sZQIyiF\ngCSA0acc0UZLvwSRGjxUYKChVtc2VIyZqIJnWA3r5yQe8ppYrikjV49VMPT7jnv/+htcu/s6Tpw8\nymC4SJHKRpLmRWWPCwHrLFlm6BROQYFUNcSY2usQgjJZJO4nL6k6RlkDe6kiQ8qKTwjKlBkuUFVD\ngi8JXpk2auSHmNSvlbwvaMb6qqoQ36/BFs9AD3HrY/I1HQwbDZUsWqwC2PYCqpWjZt70S+Mbfe0N\nHj1Ycx+V0CjH9aMCdkkVQTFUMXZF31MiVArkiYXgyEOsRiFhtNxlaFEVQ6Nghdjt0ujPvqWIuhDI\nNUIWG5TyiIGqMgSj7KQlu4gxniwPFK7E4KJxbep7mODjudQimEbFU1AWjrHEnAdKffXBEEKumEKe\nUcbkt+mMOnzsFNu3bqy9OCaGIYX6nzRdjZHf3t/Jg58iaargyXJNHFkOKwxd7b9k9XUSZiIBfDms\ngVpnu8oYIAcy3acAUkVLQ8P1nDP4gSYgs9Y2xnbqPGqkGZRm1S5DuGqzjedRIhDhKOlkQqfIsGEy\nnpullqwN8eyLcs/ELC+az6zRF2ycKwUsdCViBBsCYnLETTA7WM/8/BKZaUqGqeIdakMiSKiNi4bD\n1PQ9tYBFJNKePXGXBMRUEbTxCijHyjhZss5DjGETgLwGh008/5JCrQBjo6D6lrLtyHBYLDZSkFvL\nxsCx02fYtL5hpKRErS5VP0jjkx4rosBt4yLUfYz9i+OQ0TB67MV4/SOrMVjbupwGC1rCigZjQFT2\niCYxaz1IKyFm85qpGaPLKxOsSGsPOmcSQjSsVw7vIN0p/mNamd3ra0WmZjJIkl5norHogsYc6u+2\njmWPJHZdXzUgkIAOFTAJaGuDn025zLjujYEUnmt0r9iYG2T8+dvAgI9sMJccTBFYMGgSPZflmqDZ\nxrjLMYMqzcpzxw5x2ZZtaSrSaUAhkXm0gtGnTyJa/lhCDLlVFhqUuurbVVRwCIUOcLC1TiNeEO/r\n/d4Y9gYxDs2wEIF9MZi6ooupjWQrjcFJq+KNqalhq62N5Bg0ifwKCMH4EcUsnRvj42DigIkJscpa\nTJAukIUENIRoJEZmYQyzMJHVimi574jjNDslCM6nsxtlAxgY2gqnSiEZeb3nXUD3YqorS4YLfcQO\nsHiEkjx0sOKorBBYBDOksgFHjkgBZi1Yg485l5I8M6IOj2BG66UEcch45ZGIfySQqGaDxXF84ewp\nLt04iXWBsljCMMT5JQjdOKWrG/0XxwZi1TW76qVXYBNgaBH2YiUCY+rnMG0wUDsXdwArgwurtFcM\nEIhZ7XSjRDTRxNfqUi91bI9SRoOpNGFbRD2bHCtxQzpLlhckeiI2GvLp97g6xKQEGhYrDokKrgmp\nhrfgK99suqgoEu9N/TmLSV6SSAdOSVKSQWaIdTeN3n9pcQlTQrA5ZSUceOEwH/vTj3Hw4EHm585h\nyHAuI5Q5wSwpKBCy2HsBqWKmdQU2nHU4U+AlhiqEliA0HudyrHVNYvYUNxxEvVwxc7kmEqwwxuNC\nB2dyDA5jcoINVLZExOArYX6uYrI3rfeyA71eTKCVZQXWqkIoMsSYIcNyiaLoEkrBDyfIs5zAfEwU\nmGGdUqCraqgC1TiMsThT4GzOYCj4KkRFx0ePuCLTKp4CJiVfI4B060PeGGFu7ih5MYUxmgwutw4f\na5UrAhqhWLG1MtXWS5zJ42EYD9cYSoBR761ED247TaoRpR+r0NDDMR2gtTFA+x7aN0WXfR2nRe0F\njwZ5fI9Nikw8fcQmindDHxea9QjpXKq0RJ5txZVFYK0WILHZGrDQfjtrY8xZW5JpDF6I3lAXQRX9\nfAzJSCBbVBLalGITw3wkGgdB9ED26dCCUW+JLl5SJnhTC88GdDHpNEsKj1CX1KlJfJLUpxVaBANT\nJub6trUB3fLQRGUgN2r4X5g7w4Y1mzg/e5ogFUVRYKIRWicaFIHSR/AokGcaj5+3cn8QIkgZ97oa\nBBKN8maO24n4UghBAhMyX0bE30MY6ntiYs2RTMyAjXLHWQdhTf26NwPEaPxfogSqvqL9zH2j3Kzk\n8ZH2v5IAAkOZohfbSjXQCa5+z8h82IU4l4bK6UGtoSDKaghGFTsJlgIXI0Q0eVy912I5DvXoNaU5\nJO6BEiEYUwMCFhS4QWWuFU3hoyFgHbBQhQr6wuR0l5kt06zbmGni7BbY1/awSb0uo0Idt4vOpZoq\npVfm3LAM7N9/hLPnzjM0zWpN6252fpHnDxysxzaJHwU8WsbXqk0V0EpCrZvnHWE4HCgjJGTRImmU\nrJqKaq0CyBGEtKaI3pbINIgANcbXctDEMJjgM82BU6f+l1pha+Tli6lio83HvewlJeMNdIynkwcy\nBzde+2q2bFyHEHCGCP6C9yVJXUtk4XYyMaCuXmSNrc/KqjKYrMOJM0OeOnicubklhguzJBmkAyUx\n10yFRMMSGsNFx8a0lrlBTKaAgERQJJSqVFsfe+gZiGdYDskyp4CAAME1DIyQq7FuwURIo3YcWGWj\n6Vglo0nXlRVlQJrQpCGoV5wxLAwX2F+cq/ubEgkaiVSvscXmWsk1a+A7hkdKjM9ORmnWCvFxFwEI\nSIyP85Gapysm1IDAeI6EZFRLXMdhLLHNSp4+m8Zzlf4sCw3B4kymqRZMZBmssoDTmLZvls5pkVg+\ns9ZVGxmaVmkWKhDfAAK1EZn03DTvESxILAjjoj7blrmB+tQ3DchPK1+F90Ed2mNJBtM5m55Hc241\nZ0MymhvzWNe2DTY+2+h5kVhOZwcX2JvvGxkvZy09m9dJvlca1AQICFBJrDZjBJFhBANSadFkJMaq\nLulZozHfgGvo59E8CRLPXzEhJvu1EayvO7AiIGCiSz2Fo7Xl6WizBKOhCInVoOuhGjHq2g7OlVrq\no5pICgBlPuXbEpw0gWOCUxaJj0aOGDKfnjuQ4j8NUIQOzmWaZDeP410IhBJfDcnp6NwLZGJ1ZYYy\njlUG0qdyA9VvzZDMJ0AAxCwiZoh3ggkOFwqsKBPcO4P3FVnm8H6ICZWWKHej5qsER5DR16xE56EJ\nI4BAkr0nqlnk3Bwzm9bgZgIZChWldfJiLKeLYQi05+tiP7/aZdNV2okhdQsuB9dqHTYhIhfZzMWi\nHN/LZoyRr33qU7VB0iT4y0kx/m1Pn2QWm2XkRTeipw6tvRs3aPS8mZjwz0Q4JRk6WjLKR5qxb3nW\nGg8uNAp/KkmVKP1JAYp9R6IHuSi62KITY45HD7+EyqY4j6rSwz04w5KUnFlYz5e+/G2++a2DDMOl\nGNfHFDaW+3O4iPpLZBvUGT+lyQ1QDkutTR6WIMbW59JBBIaDEu+FvMiwucGbeULwVGEYn1MIpa3B\nKAsYVyJSYr1gg8EFTWzmgdJYreEuAfwA8YuEUJJ3Ar5SSpDqBY4QjHq2q0UMAzAVeVZgQ0a1NEWW\nF1T+AiEK6iBLgHotjQzq5zMSyPMuEjJ8ZVpJxRQttC4goST4KsZIJ5O2ix6gqc52wFrN9aCZ3yuC\nKQCHMWX8e6T/Wy252A6rKGyuMVMx8ZY1FmccmkQNMJUK+1TnOK6p5O0wZjQ5ZXstpe8mZcQXSAd0\n7amydnT9+dB8zkbBPbbnx+9R3xtFVzENG6b2+xpGKKeJhmaC1Vj8Ffo+8hzxM3X1hiAjz1sfcK1M\nuSlcJtBQ0ZoxlBr8StJQwyw09ELSTVP/l8myeOQFcBI9SP7i5F2iP8NYnNrK71aFKbI4nDFUldYZ\nTlVJxp/bSlIqFFSyNs1FUgIaYW4EHQOvwFPaA20K8sj6ip/Jgx4WjdevAaXGn9UFq18YMhq6b0je\nj6R0EsNp4sBnLcVxxZq+RhXMRD210cuTSVROof6bMS3mQH2tmCbUqELjxDN00ZuNIyPHGsdELFoa\nGl4IRiyZZPV9RiiwLdDBYWoFxgq4eG9DDMlJhpNpe5gyLIbMODCBY+4Uh4sTDG6fY9P6aXJZ0t6n\nsY4VXZp5baAS7UqFkBGkwA/7HDl6kqeeO87Jox2qylKaF1+3Dbh58YpK82Grhq8VjK1ilYYSQ8bI\nBkPnuNMpMEY91eqr0PKU3ldYa2LeiEZ2GWNi0rGYzyMUVJWGsDQe0jDSH0MrhuhFml6baHRLne8i\nx+NEz9xNl1S84bW3sHlmDbkd4MshhGGdB0NC2bpg2yMcw2IQXDBUxjAgcPzC5ew/usjhwWX4yRvp\n9NZR5KP5HIz34L1mOPdenQ3JayNBnfqmeWqTPhMBOyslNlQ4AtZHv1xQUL4sS1KisCCiscxRzuqZ\nWirTZNhk6QYFSoJ4jJQ1ACXS5FxxyaBpycgQnSR+zFOePH/Bl8RsyCN/d4wCAkn+JfkTWh74xqwX\n3EWFDKiR42PYkRDZQCSGwOg1RmSjG1WeVzrHoN6erBbC0H6vhpFq2VxHBExfZAuOy+vaUZDmyTTv\nC6Y5yxPQqEbXSkyIFm09MT9tC5hkVA+hlkMJ0EmGph0x/i1WZbuMPnubvp1qpKt+pBI3nVMYokGu\n9zAhliQcD/lIybiFZVTni7VTmjKR7XmLGb5a4Rx1qE8E3xLIqvqO1PMfeRoa6oUCl2KDutwMaoe0\nAC+EOv9muoYapC8NCFgsNuh4uzgdemboPk0nYpCU02T5IjNRamfoGlCdLugzEXkR4mq2BzZTAEKU\nSSxB9YZgPSUDDZMgYKzQHXTpui6FLxQ0j/aVt0OVm8Fp/iMcTjrxZAgUkmnZcYSh0z6VbgErCv5L\nDSGB8xkuntudylM4R05gyRhdHjavc8zZtryRDCMZRvIRPcSaVGnKR+hstJ3oHucCZzhbnWDhhrOs\nW98FO9uAiC+xjy+mrbp2VxESCbBZdh1rVulPo8uMN7/CvX/n008isvKVXjGGgLRoflg1skeTnySP\ngQoWh0e8j6U4oqdOAikBFgihUm+RXqDxgA2HQ6qywpel0spjsqsQvb2Z01AFLTmX1UnfVFDrureJ\nBmUNwTiN+U0p52uksznwSELeoEZ9jJP05Bw+schH/virPH/wDKZzFWZiN9iuKo3xWayPoyChiWkR\nAS+IGCpv6U0oDTIPx8ikxCJ0ih74wHARBouAzRFTIKFHkMiItlo2MLMlEhZBPL1OIMs9whBTlYSq\nYnFhgTDMsbZPzlowPYKUWDvHMBymN9khyyoGiwUScrwvkKChAZYAWYkzQ7LecSZ6fRZmPQy7GO/I\nbIeqKllYXGDdTA+N1fMgFl9VlFWJVB7xQ/IsI7d9hoOSrFPERGeeKsyRWQ/W0plwVOUgwnpqJFe+\nrKnNxkBmOpojofR46WFMgZg5vMyRZx6XDzBkFMUERZHHOPAlXGRGdFwPqTId4wg6qemkQjMeGXqY\nRkAgKerjCsD4oZpSamksaBsNJr6P1ufbxnQgeaOWUxnNcuBQKgwaGqIee9uwKcb2aBLTwdrWtVSR\nMMaMhNjU4F77IByTRbU3u865EI1dY5rzOp4zttZPFHSpFbQkqBPI0NiNOvotBTQpIJlRtoN+JSZP\niBTWFYR1DVTG/rjxPy+XpSEm2BERzb4bE4SZ6I1sG+8I2KiMiygtWBOgojTeZChEOW+IgJERCFVj\nQCW7Ns2eicdCxIfyoAe/EUNmtKRrO+PziAIvlkwUDKh950YgxpKCa5SkYGuwKGslIM3G6XpxXpJX\nysa4UYulF5Wiut+RohpTfTZeJuJeNiWVVEg2pNfJCYAvhawM5OLYxjQdMhzCosIf8X4NE6DpkjRw\nuz4ZTqIvRUwDhGHIxCmdP/6tDS0ElGrdkYI1vaPML97PAy88xtYNN2Fi6b/mPIjrxihskUoEJqUj\niMOQ40yPI0eXeHrvCY6fLvHVBnzIGBpZpli0wR1luWWtdfDSyoqJ86PKoYAPeL9Er1hPORwSM5Wo\nApbGyjRJPacmJ6hKwVeWEAZkmaGsllgozzCR9al8hSt66u02GYtLQ4q8iy81nKKpIBQNhRqIUnDm\npQCBGkSyROU0KekxQWmocEY4fvRRDh08zobpNdhsiKAe1hS3ro/fADTtxZHGuHSOhdDjzLDD/QfX\nMsh2E7a/ntC9jEXJ8VVOXOxqGLgAMRmogRjyE8+mEDOeR6CvMQR9bZeZUGFCiZGKTlZijacczpG5\ninzKUvmlWFZXMG4DBEdVBTKX46VU2nDenIWNXAzYMKh/TnLTIpokuaoiSB7lgyXmY2qAUV3PSj0W\nqtjvFFqlTpYs6mG1TCbm4qintWFspdzwBhml86/a9BMaYqFryJoWICBh2fp30UgPYrDpoFnxX/3J\n1utg+Ro0KVwxfSjun9B6DjGrP4eCvxpWKOlKhvqQd/EFY1IJ4wY6Te8zRufGjvQ6AufxvDNGdYkm\nwWq0QWr9QZ9Rk89SH7J63pj6qhbLeAm8dNsEAsRtp6FA8TUXw2kTIKBlTUNkORrGS+DV+1EYKTHY\nnLfL9ZmRcRUwMbeLlcQGSIBH+jnJ/kZRkbhvI2FZ57AGBAwpjUiSUsFE2yPqKvV+iONpREMI9RqN\nRRN7ycohA3peOYmgfGvZiVQ1WNEMlRn7dNTVor5jJICxDF2F6WaIK8mMIyeHYRlBRYOVnCLv0hlM\nU9BRQEAnFYrAfHeezBqGiwusZQ2Fz5mUDkJOwFDKkEW7wEK1AN2MqYlJcpcRqgy8Zzi/hBlkTBWT\nTJa5svgMzMssVaeiM9mhNEPcvMEuGqZkvZ4NgOUCnsCCWWRyaoZQCp2lDh0ybNRV0nNnUsSx0/9G\n1oTRvTHCEIhtU7WD09lRnq+e4a/PPs/UdEGemdrB+71oqwIHq7y+WkDJ36Q3L9c58IoBAg8++ii3\n3nRTKwFa2rgmKtf6uo1xmkqH1aQ7mEyTEVpTJ8+wzsZEevo5Ew+oRNP1VaVx5sOqRfuNXt9cO1GW\nFcOhq40dBQAk9qmsUXjjIHhhMCiRSHlOsckNs6GF39ZKoaUKljs/8xWeO9BnZvNVLHEJA7MRLzka\nb6hIqlMNFFUqGwDEoEluvOlgZR5nHFdcfgU3Xr2DmbU5mzYZqiU4e0rY98x5DrxwjP3PHydIT4WZ\nOAZLAzpFRlVewHvP5o3rufmGnWzbPsO69VNMT8DiwgLPPnOARx/ez+FD5xkOO5TDnBAgdznr1q3j\n9je/lp2Xb+XhB57nW391H+JzQlDKqIgo0ELGW97yBm6+8VU8fN9TfO0LT1OV6iHo9/rccMN1XH/D\nZfT7XY2Dp6KqSubn5zhz8gSHDx3jyOHTHD86j7U55UBzLISg3qYNMzPccusedu+6FIPHVwPKQUzq\nNljk6NGjPLPvu5w+KSwuLuHIySgwFOTZBFjP3PAcU70ON99yDddeeyPXXHUD/YkuZVny9NN7+cZX\n7+XY0RMszA5wdBgOQnOgGmgy7SYFNx6e0QtclwOIayF5NfTXqNAgkd43KifqH6X1Sn3QGFJuCs2w\nHxXY9nXtqHhRAeqi8WqUap0EpSQBOmZA5VkNMNSvRaNa0j3iZ0ILEBiXYKPeqqh8JIMkoPlkYt9N\ni/IcdZ/RayXjyrT7JDVtvv4goskRgwJr6aBtU+3HW/38LY/++HOMNysCYjlZHWSj3U5KapnuNS6W\nnahHWyRgpQ0kJiNfmmRI0VSyIpiQxXW33DhMwENSHpzPYlSwxUYqXXtdtJkPNhnEJD+6ymP1N1gM\neT0HJlLyIfnJEyCw8nHSZgLocW3p0DAE0t90Nbu6z3EUCCawJOeZmpxh57Xb2bZrJ1jL0mLFyX0H\nObzvAG7e0qVLgUHIsZjIYlipT6PhEg4bn0P7keAIG19PSrerA2YMloBEoGSCPgudRWayGYIvY2Zn\nW3vpgJh1WVprNlJ6ifMtGn+/sFDxwoFjnD2zSFV28CGn8pmGSbQ9tMbgwxks61pP1ZRHvBhFwETF\nT7D4ULEkS+zYvJW3v/Xt7Nu3n0cf2Rvpws0YihkyDEtsWL+BD//8hzl+7BSf+PNPM1gswQQmJ6fZ\nvXsXu3ZdzszMDNYV+CA8+NBj7N37TCy9KlibjYRK6ty3lebVQwbquYtgU5X2mfGNB8XnWLTiTCfL\nGAy0fGWRE5V/ZRhqYlNZrrfTGNDOWYYBFivDUwdOs+Rey7rtezjZuYL5alrD60JkRtTGr4avmQjY\nBuM1B1GcqWREjQAC4kcMXJEBvSKjqi5Qlgtsv3Q31119KdPTkywOznPo0BGOHDnOwYMVSIGvBOc7\n1Ak1JVUbCSNy14Wcdvk9HX+loAepcKasjXjtbWA4eAqXX5kmC2JuAms1VC5VE0nPbyJDIETHTPt6\nNRQg6frJvId22N3qrcUQiJLK1VddgSGQrG6TQLAm34O0zrsGfE9n6HKdfRzIj7/ofY3mQtL8Csuf\nI53FgXjm1WVQR6/dbHNTg+oqJ+IDWNs8S2uX2CjXDFH3qA3f6DSI4SLpc2n0IYYutVhL7RKeIpHO\n0t6RrX6F1tpSpg6NsyCCbSlED0y8noIzI2Mbr3NmcJj12SUjpwMr/jz2qmi/TZAm7EV0dTQ/16NJ\nwxgIzcsxqVwNCESmb5JIIiYqKilXRK200CQ/bAMCoyFIegCszBAwogwBNWyXP9/oKKwGCKhXvm87\nrN20gd03XU1n4yS258lMRlZl+OGAcjjk5ImTHNp/lPNnZ6HskJkJfKVlDD0VGZY73nU7O3fs4Lm9\ne3n2K0/D0FOQI/7oBKkAACAASURBVBSo+zDQ6Uxx+e6rufLWa7lk8xbWTE1RDg1nT53hqceeZN+j\n+5k/t8AUU5pnAGFAxY7dl/CmH3wT5wan+e7n7mbu/CydMEniLFkCoQuve8vb2LX7Ro4fPM6jn3+Y\nwcJcPN+bcc0oyLDkWFzrzE95Vkx91dFxe7h6hu3FOibCND4ELdvuVJqYMCpHls3XxZ6zq7xnVfaB\nvxgZ+OL3+JuCGa8YIBAQjV1s0YqVYm3QWO4oHCPtxeSZKlk2R0Qp6oldoG9Uhc22qFLOWGzH1GEA\nZVlSFN1Y+9rjY40+TTKUBL1gsE1ilFjqLglECQZS0jlrMc6S5RmmVas6fa6mUhnLsAqID+x98jh7\nn/as3/pmguuxVBlsGODCAFPTgSEzgWAEL7HcVoz1ciJY4zGyyNTkIq/7gZ38xA9uYs+VXSYnYBI9\nLgfA+bn1PPCIcOfn93HXVzKNmRJHzwKyRH/Cc/ubN/PGN17OrbdcyubNhn6s0FaxhtNzN/L449v4\n6lce57OffIzZ2WlcllGW8+y5YS2//i9vYetG+PwXp/nWN75EYTdHBcBjs0CWGbpdx2/8m/dw2VbH\no4/2eeLxhzh08ASUlltv28q//bc/xpatOZ2OTmPyDIdYdu70ac8DD+zjo3/8de65+wEW5hxGMsRX\n9CdKfuqD7+WXfvmtbL00KfEqkMtKM8ufn4VPffISzp003Pnn3+HJZ56BMEVOzmI5Tyc/zQ+8egc/\n8/Pv4q3vuIVLL91A0aE+8MqlK3j0g6/iO3c9xB/9x29z4tiFGBLRUCCT4ZlCTJK9Vn/RCPPlgj0+\nN0loCYQXETQtSh6RgYKMXn/k+4hc0KSNtUMtnqLJU5/Gru2ZS15/YpxxmyaYRQPUMiqQNJmbGXlQ\nI6qqCS2PQvysFcEFIWtFPnjTCExvGzXRRNBiJN6xdU1jwvKxNhAyfeChbzwHqwIC8fAoqlArGi/W\n2l7acWqitpUOlhSP3+ZFNYpNEz4R5woibTGqJjKu9KbjztTrwdh2eYel+EaDkTzSG5sHEwZoxL3D\n1wEBJiomHqVdtoCWqEi3FcfVjpO6LJIxmBhfPpQxQMBo33sSS4vW1E/IxbLljhne8N43sfv1u+lu\n66mOV8Kpx3fy+FfXsPe/3MviyfNMV30WmSLHUWBJK0f3V6MAJiooNPerAen4qQRNpFEIcVyUHRDi\nHTLmCcyJ56xfpF848JUmSLXN2ohmkf5k9QwRCZiY8TxIhyA5p89d4LnDF1gadKhkSlUuUzYYYD1f\nOmeYVmiCiSpSNJTTGdQOPWo3EY8xAVyJxbN93RT/8td+jg/99Hv54l/eza/+6m9y/PgpMvo1uyXI\nkMqc5U1vfxX/4jfezcMP7+Wuez7Ns88eZ+dlO/jgB9/Hu/7R7Vy2czszM/06t+HDjxzhk3/xl3z8\nTz/JkcOnwMbkoklRGzHMUrZ8uShFJ5iGjqzgOwR6GATrHJ1cyLMKa4eEaoDUMdhEw0lGrl8zDmNs\nvPees2YLe4/nHClvYvKyH2fWdFmq1jJRljhTgpRNbK409bgJVbxWo+Sl5Lkqm9RDq7KwrL2PjgHO\nVchwyHQ/cMd7ruSd77iM667u0J+A0m/i1OlNPPzEYf7TR57h6b1HNOlZtZaA1cz4dr52fjSjGGIV\ngzaLCkwV6p+Xtfos0/2jcijlK4mU4sSGE5X1NoIQMZpdQxvi/VVEyUgfEgA5Hv+/cne0nxrQEmVL\nvL5l/DxqzonkLbQsjcnc5cZVOjtqttrYe5d93uhe0jxMAqZivNWVN+LP1i0Pv0vjm153oXWGG2UX\neBuaSn41n8tEyr2ar4XYOBem9tKbsa/loHKrf60/1ay/FZZHkmrJ4A/x/TFNRH0dxanjGZWM5rHr\npUgVzcOwPLSt4aK17l8DWvHYDS2YI5UraIYPoQkPMincbUxPGTkXDbVMFZrLJcZEinrQ9Dr6Jivp\nPEbPxzFAoCH/L29pDNsmYeRLLQME2msw3WHoIJglWCP88C+8nrf/7LtgvcHnaiqZmEpBAszPVzy/\n7whPPPAkD/zeXZw5eoisKBh4weUwsXGaH/2f3wPrYdvdG9h332OcP3EOmGCp6xnIkMkr1nLre17D\nq975GjbdtBF6JFoLlww2cfkLl/DQlx7kwS89zLGvzzFTKfTrJ5a48gM3sPlXLmPmxCWcOn+W7/7F\n3XQWLcEEKuM5tfYAb/jhO9j+L/bQvWSSSx7IeOGRRzj4zGI0/aUeC60N43FUmJYe4sjjjnAo92J0\n7ANQuoolv0TVX8L3KgYm4HwKE19dHl2s9311hsDLff/fzMh/Oe0VAwRuuP4Gmnqg8TiwUTmu+cJa\nm9vEGH6X5RiTRcU108+i6KwaY40Kmwa78k2ppSzLyJzD+ywCAk0WY4Ots5RnmZZNaRLB6TsUkLDY\nzJFluZZOtEYRpZgwKXMOH721KvBFk9iEipPnu3z2Sw9is51UrGOoeb4ICTFMtCwRvIQ6+3saC40H\nFryUWFfxvrdezc++fzNXbIT5ITx3AM4cXSI3cPmODpdcYnjTa2ZYu/WtPPnIQxw7dhbjc4ycxbol\n3vGea/j5D93CFTstmYXzC3DyHFQlTE/D5mnY9AMz7Lr0dXS7hj/5wwMsLs7jfcnMhml2bNJub7uk\nR6cnDOYtWKXMmxg3tWHDDLu3aFLF3VesZXpNj8NicKbLNVfv5ubrlcpcejhydBFHj04Xuj2YWgsz\nGxw7L7+KPXu28Qf/vscn/+RuFuZUXE71HW+6fQeX7dJkcufOBhbmVb3PCuhPwLZL4Z//yo9x/qzn\n6quu5bf/3X/k0UdOkllPXpVs3ryGX/ylD/CBn341ee44evgC+559gfNnFti2Ywu7r9rErT+wmWuu\neTtOpvm9f/cpZs/NYSSvjX2IhrMxKGtPmlOERpGq475qZdAs2+Pp15pe3vqLYdQg12uTTjo95EVP\n29oY9k35lxpwQC1MpfI251Wz6xpjXm3v0FwzIhBG0jtNVACbZqIlm3rfBgFi73WM6n5qyFBt9LfG\nAZoxDtKEEVHHqFJfsT1O7fE08SRX808/VOcpWKUlxT5dY6W/NzdpZMhGty32Jiq+IY35Ck1aPZek\nIqc5aG5kWs+UxIHqLY0yJOk9NVgU57fVv2Y82nOhLQUBpOuY0TtCs3Kay8mocpMy/LaV5uY+pg4L\naD7X+nssQTQkVRlQSKCyJdNbpnn7f/tuLnv15bAGeEaYO3qOYscEG96wiTdd+Rb6Sz2+/tGvUZ0R\nCoZYcqqWRz/dJ2ZiGXvydM/U/6RWp14YGkVOi9slEMHhGHCWRXuG2eoE66bX14ldKx9zXUvDpElb\n2ooy4VJ1vUocSwNh3/PHmRsEfLBKxU4K7LKC3eCYhEjN1jH0kX0Qvd9pQ9ULKsbwR9nig+aMGQzP\nsG3bVv7pP/4QP/Uz72RiLezYPUOv7wgs4qP/Vf8f0u32uP3N76CYgAsLs5y5cIxiQvixD76bX/5v\nfpKZzZOcOjXHPQ8+TFF0uPqaq3n1bZewY+dP0unl/NZv/Q5VOdRSZHXCM02Als7a1PGVSsu12R36\ngqTl2ABV1ivl3liyrKTfzwBP5UuarCVp38RnqynFDeOqMo6AZXHY4eT5gs7Mq6nsWrzJcOKiJ19D\nGdPebXyhgHURDIg8HxkNjNQEoEnXS3mRLCJ9jCyQM+QfvXsrP/9TV3HZNjh6EvY+N8B1LFfuytn8\nxm0UZYePfGSOfc8ep8rXKzNAAkZ886zS2pPR2Bo18pIu1n6N+ozLiivVihCS8FFpIa1zKSQ5ZWrv\nbx2akIRWHGvT/j06O3TkqrqfjeSg9b2WdLSZJGlXal+SvtSsHds+E2OlE+J7G/0urqNoBSqAuZLZ\nZeo9lc5ztYKSySowltwMIyOOJwkNq64mqLQM6ATWa8LTKLHq27r6HByR07VOoOVKVSdplJS6Jn16\n+ng4NvpBM/LtFupOiSafG9t/kv4GtZGmDFlp1gujuo7GtreUjPr+MNPZXsuokSFMg5Wu0dKxmts0\nYRO1/lX/XSIgrWuulf+4fnuox6h+ZB2D+j7SxK4HiSCKsqwSIFDLsPh9dLQMq5WCq+9l/AhYEmpI\nvb5Cu9f6SvwxE/Amw9qCPXfcCLt08bjDMJhfJO86rM0xE4bJ7RnXb9/Bda+5lB0zM/zF/3knJ547\nzpbBOrwM2b51K1wOONj0qi3QhdJ6FsRypjjH9NZp3vMrP8x177yWYnsHlqA8NWRxbomOy+ls6NG/\ndg2v3/5mrrl1D182X+PwXU/Qq3KKzLDx+mlYA1knZ+1l6+iFggKYzeZZmF7gdR96M6//0B10r1oD\nJcyeXGS4VJESrLZHIUH6NvL9Uktne0wdPrKXBeE6ruZQ9ixnzTmm1/TICOTB1IVblu+GsTlrLRaz\nisEu9evSXr5jmmrzLMuSaDcfWL03y5KfNozhl9NeOYZA9K6GCCU6tDxsSube0DGsUvIjFT+pzBoz\nmYSM1PvPEkuc5Fmd0Mt7T/AVVTkkVBXeWry3WOPrsl0ShOB1Q9tCM4Yn+n9ZliAxZhqD8Z5KwIeA\nzRwmeIwzWJdFz6iJWU5R4EGEvOv49v3P8sSzZ1i77joWw5SqJDKsvRJ1DDmC4HDWxsUUGgoWivZP\nr+vz3jvWce1GGAh84duH+csvPcjsc4HhYJEbb7yKX/hnt3DZFthz+QR33HEzH/voV7De4cw811+7\nnQ/95C6uvNySG3j06Bxf/PyT7H3iKPNzc2y/ZAs/9K7X8dbX9rj8kpwPfeg1PPpIh7u/cx9BIM8b\npTrLwVjN2JwMPUE9K9PTTcbyvNMYN4acbiev//bCoTk+8n/9BWePC1lhmVrT47rrtnHba/dw9ZVT\n7Llhgl/8Z+/k6QePcN+9++lkHTrZEtNrVRhWHr551xP85WfuZrBUUXRhYlJ45zvfze23X870esf7\nPngN5879BL/5Gx9jdvYsViquu+463vaDr6LoOI4fmud//c1/z31//TAXzpdsvWSGH3v/W/nZD7+X\n/nTB299zM3/+sW8ze+48hpjxlkZwWExTu7ZFkVQpEMsRtre07tj4S8pX0RJZIwdHQ2sT2zBoUlZX\nFRWhBgXSMZSo5MS1ldwDCjgwds+kclErRHGzpmprDZE7ZjhO3utRsdOmJqbnT4Zo3Ksy6voQ65pK\nHi3K3uhlG1IxLaS/PVarY+9m5JnGPeyrfWZlEV9fMSpm6V1NbGqjqIQRT8lIn2nYDsuMnNY9Vvzd\n2DHFIs6CJNOj/e5mNswKf4XxWNSVVPFmbRjaEMVKgEDL09RWWmrmgR6GZlkPMsqarm9wlPhcuPb1\n17H9jdthAo7dc5gH/uC7nD5wiunrL+H2X3kTa/dMs/tDN/Dgvc9y4swxNkbvZUovmJSFJjtA80wa\n/tAeE1N/JhkljVrp6p8FyMljL2c5PziBLxbZumVr3FupvJsCu1oiU0FpJ05D3KqKOqGGKzh19gyH\njp5hGDoIFpGq9g3JeMJGWcEwRo2ZENkOiUJdP1UwdTIpDRkZ4GWJyy6d4cO/8FP841/6cSbXFoDK\nd2NLjKmovFaS8XgsFdPrtnDzLbsIAQ4cOMXp80e55uor+elfeD8btk5y9NAsv/Vbv8PXvvlXTE6t\n4S133MG//le/zKYtk/zEB36YP/qTP+T5Fw7hbIFUEaY0WVMRRZocFavkPhobCv1ciIJLqwEEPAsU\npqA/YdiwYY3mvzHJAG15p2uAUGJNcZPsViocQxxHj89hu3uwk9dS2im8NMZvY9A1eyLFcwcTcyMZ\n25IBlsQKoK4ko8zEYDRhmZEOEha5dOtWPvgT17BrG5xYgv/wR49x7/2P4SYc/+QX3867X72ON9+2\njsce3s1zTz2DtxXB6JfzPspn0QoDPtLD24ZS/eyJITC2purnTABCEy/eXov1WSGQwNe0x1Iy3rYM\nNOMXiDt2VOKOApcmnXHNVeqfYvBNnQ9jOd2/mVdMu8D76hJeCat2RKGvGWkkOZh6Yms21/g5DtTn\nZPN46cnSs0eZI9GwiN+b3HcRvEc0DjuFJ0p6zlEZ79Pzt41bMzr3jcE8Kom1L6O/p3F6cd922gfN\noxlpQgrGT7PQTGxkKDasAGPS/h+xoMZuuYL8S1aTNB8wrb8ZMp1YkwDB1jmOSk27wnktrX9HDbP4\nsGOswBroXNbDlR6kuVJaC6PvSLT39k4wjYOp1TIBTAZimdgyVU/Ys199irvu+gY+K5lwPbZcupUr\nbrqGnbfvxKyx7PmJmzh++hx/9gcfxZ3QUOupiQlNIwMwAVIIpfE4Mhb6Q+74odew5/03kG1wcB4O\n/uUx7vrG1zh87AUm8x7XvXoPt73v9fSu6bH+tTPc9P49nLz/GZbOLNLDQC9o/3qQd3M6oQBKpD/g\n+rddye2/9h6KmQ5kMLwHvvTRrxGOnozBhqNhmMoMSud5myFg4smdAIGWExmhpGI2zDKbz7J543qc\neIoAS9n4fl2+p1+qmYiutvOJqHyIq2nsTG/rRi+npVxUK/7t5V3qlQMEHn78MW7cswcbDW+bZ3Wd\ncqVRKm0/d0X9UM45rMswxmlSQjTeE7I6E64VcM6A89hUni6WaHHOEKoUWxfjgIwOZlkN6soCpc/p\n9boYo3Xok7jGQBALIcOagDNS+5c0gZV6wZzVrMsud8wNlhDJOXb0PJ/682eYWvNqyC/FU2kJIBki\nUXpLaKnZEoVKKkUXByE3muRpzZoJLtmku/X0mSW++sWH+e43HyPjtcAUB758jnzySX75n1zD5nWG\nyV4PI1OEAJPT8/z4+27hlt1rCcDBWc/v/+4DfOOrD7I4rzSbe8Mizzy6nv7/eD2vvS7nqq1dbr/t\nSr7zra8wsBsYoV4RN2TogMSs/aZCfIlULcNNQJE9jXQqh80inj3n+eydf8WJ5w2YAh8y1k1b3vVD\nh/m1f/1Otm2d4urLN/MjP3objz9+gLAU6UAx45svhQfu3cuf/smnWBgU5MaQdQJf+eLDvPd9r+Hf\n/E8fJi8sH/ivbuFzd97LXV//LkEMu67YyMYNug32PvE8n/v0Fzh3WqDawPPPHWL21Le5/vI38Lp3\nbMFiKTIoh3N0cr23SPKcRg+oNAfMSnQjk8Ji6sM7DY00XoFkwI9A1cnLM2Z6tw7Q5veW37aFtNuo\nZCSPWP23Np0z3a592NTnoDQldEaUCTMiOMdgj5YHrNX/eP/6PSMH6mpKWkj6Zh0TP3KTsTbikaiF\ncBL0q6s2L92P1jXHFNtT/hAb3LZG0X6p648pEm3WycofafW7df0RtsrYd2iDU0TFO7EBlr+7DWu0\nzf3Rno3/Pvq50XaxYx3qWgGCUDmDWVuw+a07cb0c5j2Pf/Re7vmzLzE9XMOBR57i0pu2cPO1r2L6\n+jXccNv1fPXhwzAMYCpyyVonxMqU0/R7IPkbR+eskVDN8zZsigE5OeemznKQowyv7dBdaxDvsUGo\nqoAxGYVp5LePiRVz06GY6FKhifDMcILjx5/n/PyQRemSwIjGUzq27xGEOQxTYzOQ1Np0xqV3AxKi\n0exxxrF161Ze//rX8nM/90O8+c1voNdvD0xFxQLeLOLRmOtSShzC9ss2smFrn7Iccu/997CweJwr\ndt3BVVdvxHvPRz/+X/gP//fvMzt3AcTx2OOP8O53vI3Xve4qrrt+HTfetIfn9u/Di0fI455slWzF\nafktxqPB09/NyDyln9P8iQhehhjm8Cbn0ss2smZNjjNa8UZibpc6PGq8hKdkKtfdHAOmODew7D++\nlv4lu8FN42Ve2R3GYoMm0hVKEqtLpBn9PAhWFNBpK23t2PB2q+EpY7AMueXGS9mzs4sAD91/hM9/\nYh8nTs1T5YFPTT3IbTe+lS1rHXe87ho++2df4sQFj3GNwWNQWe5sh+CHSAz9UgCi8cxboVXirt0U\n2PDDZymyXXEXhAaMlEb2KDFGaoOrARpacxeBB1WV08++fq1ZsytJklFAwI0ACNFZk9h49d5p5Ulp\nnT0N2N3yRLfO0mZ+BGI+qfFerfRzimAd7/24BHyZevqyls62OvRk7OJNSEr7DxcDgv/tW/vcCy9y\nBtbEAxIgoOvv9PAgM8V2XmyUVD9efr22ARTSxePr+vRN6Ft7TlIGCyNtHYMWIDN23bFnQCJA/1JH\n/ku0dL92wEk2dtk0TnWui/FxiCFpuCYTwYEnDvH5T3yJrnOwWNHpdNm0awc/81//U6770C4m1uS8\n+0fexne/cA8L+08iTkZdLq1xdZll6y07efcvv49sg4V5eOyPH+FL/8fnOHX0DMZnzMtJznzrKNWz\ns7zt3/wwdoPj6tuu5ZEdj7Dv/DMUHZAszoDAQrHA6f4Z8jDk+ndcz23//RspNndgXpj/+lnu/l/u\n4vS9e9lWbQDyZaBV7BmaQaGRAYHEXGn0E31ddaB78nsYcpJq8zzTZgnIqGwPyzCCS+PA3uprclWm\n/xir93vfvnfXvRhN7fvSet0uk5OT+jU1Sb/fp9vtkuU5zjmyVqk/ayyZcxEwcEpHioZPyrJurQIB\nWvvXU1VDBoNFFhfnGQyXKMsB3pdYB1lmyDJDUWSx3JzBOYvLLM4poh3qmvYKJOSZo1PkTE70mOz3\n6Pc6dItc+9Xy2EKkagoxMWGGrxzf/MZ9LJU9it4GKjKCSQipUsca5oMqw8FI9BbE/0zA4/HioyIV\nGIgeqRMTBa+59Sp279rIVC9Q2CUsC3z3O4/xiTsPcec3T/LX350lVA6RwMyGCW66eR0G9bN++pOH\n+Ku7HmZxLsOxFvE98D0ee+Q5PvrHD7DvECxWUFVgTb6qsSORaln/LE2ptaZFrE4cedEsP18ZLswO\nCVUPX3Yh9Dl/Br74+bv5/OcfoqqETg9u/YHtrFs3jfcVznZI3khjDOINhgLHNF76iJ/kwHMn+cxn\nvsYTT2i97t5khze/+QY6nYKcLsFnDKPk233Vpdxxx1vYum0DeWHIbcbTzzzH7/7uH3LnHz3BZz9+\nNwf27wegLMtlz7Z8TFrKvJjR31dt4+976c+NCqm2gffyrjPelqPzbUrv31al+Yf2d9NaYMBYazMF\nTOu/7127+HXSvFMzUQc8Jrd0J3tM7poCB8PFIacOHGdNNcnabApZqDh04CDee2zHMHP1NJ01uZYO\n/b4dvklKBwYMWbCLnO6eYaE/x6ZdaymynG5e0Ms6TPYmmOj16Xf7TPT6TPYnmZqYotvp0O12yV1G\nkeV0Ol0uzA44cvgkwyUN8hQ8mnO9jF/DkS9V1yo1ROOXwhfx3KrBgfQ16gGenJzkZ372J/nf/vdf\n5z3veSOzs2e45zv3M4zC0DkNyROJs5GMWQs7d+5kYmKCYVnyzNNPAIHhcMjhw6e497uP8fGP/zmz\ns+dwWRfrOpw9dYpTp07V45fnWR1jL1ICJcIwfukzeympZIiWkfQEqvjlFUyvX2+PlX5WKAkMyDNY\nv36SLVu2YCJTMD1TmscmTKftmfOIDAmmwNu1vHBckN7VmO52qpADQUvUmuSZaRlAMWa/XWJvvL1Y\nKKizmcYd+wpnKq66Ss+3pQoefeJ5FuYN/f4m8mwtD97/DM/v131z2U5Ys7aPDwn0Mah3W8Mcy6qq\nPdfLw9G+t22Ugv49ueIqP38vVeF/aKPtb643jLYke9pf379Zu7gw6/HnGV1fTUWFi/n6+9cCgWEY\nUPkGVuhmXTp0Kcoe/TCJLFhe2HuYOz/2Wap9+p58q+GGW/ZQxWSs47IrJdLOrOOO99xB7wqVTeW+\nAV/7zJc5ceAoncoxGQomQw9/asgDX7iPE392FI6CKaGwHQxOIcDW8C8VS8xPzLPrbVfxxg+/halr\np2EAZ+47yZd/+06e++u9FEOLr3kAjUM4/ddcbvm8rKTZCMI5e5L57lmmNueYWNoYKzVDffxr9Sa1\nzTPydZFz9velvWIMgT3XX69U/hC0bqRNVC9Ft1K8U6DSclaxREtDeY4ergiDJkpzQprbpDWJLspU\nViRYQwhGE5/YLJZgc5RlQMSS5U4pkyaW53MGJIIORnA2xkgZg49h/ikzOETD2aJ1yck5+Pwp7vn2\nXqY3/ChLfoIBGVWmCKeVLCo0US0xcSGZlHQMQvSeaLIhVUfPnD3DA/u2cdmGgomu5UfevZMb9kxx\n91cNzx+c59CRk1w4c4hP/ek3EVdy/uxrcaZPKbPsvGKGy3foPFy4AF/+/EMszU3iZArxXYyUmNCB\n0OHrX3wO43tccbXjr755BCRfdWPUSfbiOCTFKDWlzeUY0wFT0K7r7X2GZQoT1iKSIRQgU5w9cYa7\nvvYUP/WB28gmc67YfSnbtmzh0POnQNbUHlNjwNmcqsqxbECoMB6yfIkjR+d56IGnuH7PZeQ5XHNz\nn4mpHueX4Nlnj3Ho4CxXTa9h645J/rtf+yUeuPc57r/nCPuePcD+/Ud48P7HeeKxfVQDx4W5BfI8\nx5cQgmCtvAgCOKbItIPWVmzjh0zr51W4PytRE1/8OumllyuqTPQuS6ymsLKy+//3tsFte6W78LLa\nKFX+ew0I6JUv9l2RSB1x/UBpKib7Bb0NfbAwXBhy4eg861lLUTrWM8Wx549QVRVZP2PdzmnsZCCc\nEnKbfc8dYul4T99LhngrPJc9R7l5kXxTT9MWiSb1kkzDyjyiHiSUOl76wGAwoCg0UW5ZeZ566gWO\nHDuNj7TJNCLt0VneCmDY6l8a6+RDaryn7afQr4pt2y6l3+tx1zfv5xMf/zOOHN7Pf/rPH6Eocryv\n8MFT+iHEAk8QyPKMq6++gsnJPk89fZinnnoW6HDffffxP/z6b3L8+FEeffQRPTudo/JLbN9xBddc\ncw2g582+ffvQtLfZCn1T9tjydVMLrhXGwbfmJTEkFpmY6LBr9zbWreuq8Q4RhEjuxSaLqYYtaCyw\nNRXOeIZ2glNzPU4s9HDrb2fgtlCFLiabU+C7xdJsK4HAGDW0ibWnvvfKzaIZxiUM6XYCG2b0vYMl\nOHZ4gK+mJNKi8AAAIABJREFUwTmsLTl7+iwnjgFXw8bNsHHLWp44kCKOIxdPQsxvEL/CSi7ll255\nfhWsQktdtUUl+m93TqSzbHz+/+Hs+b62VbnGL3PcTQIB2pdoy7Xlbab4252hFw8KxO+S+GGQ8mQs\n65ms8vPfw+atRzKwrbBeM7S4YU63miCLiRIKcex76HkO332Wy65dB1246eabuIev0gZOgYZVI5A7\nx+5bd9RDduChfZzcd4wNshZTQl8LAzJFj4XDS3z5D77AtY/dyIIXnt93COdzgpOm2IJAWOO5/I7L\necOv3E5vzwR0gafgM79/J4NvH2TLwhZ8gGGsZdRO95fAXNP6fbyNhkqqPPF4+r0hZ6bPUMw4rKnQ\nGiX5i8ro/6+2i5HDrxgg4GNSIYAsz7XknJGYGXnUyFNwIHG4DMY49ezHRDYu0xJamRHEqhczIxuh\nwBnR91uoKw4YEyJd0WFMVoMAztlIhTMY67S+sxjw+lkpXKwlqzyntBw1AYzBG4+P55i3BV/7zoMs\nMEGwM1TB4J3FhgEhCCKuRTXS+6rSkmLG0LjQGNsoEjCSMTgd+PSdh7l65+Xcuhm2TGVsuW4zr7sG\n5gdw4sw27rtnC/d8dy8PPXyIubkFbOYJdolLd1xBN8784VPw5P7HgC2I8ZqMSSqqMIvNAvOza/nU\nJx6n9OcJFGDW0gkOCQ0dSR91oAZ+qntr1HvVzrarpI4+RibwIVDJmdaKEJwzOOeQMETMAoQuXkoO\n7D9EWZVAzrp1HSb6PQ3daMcqG3CZkDuH+IxAzELse9glOH5kEe/BOpiYcnS7jlkTePThfXzyT+/h\nn296K5ObMq68biO7r93I+39WOH9cePShM9zz7W/zja9/kycfewFjOyAZzmn9qpftcZGISq0gtHQt\njqbnS83WgNPqm7oNxnx/W1LSVlMQlyc4WfE9I0Gl7cuv8gwvUtv54p67fb+LeP9LvufF/v7ic1W/\n52XjMmkMxsavDQL9HZ9lK5tqL00+a4csCMSyWlX00FgWy4rN/T5uUgP6BqHk6Ox5ZpiiwpGRM3tu\noQYdJ6enMEWmpQ1NRklFChkQGjMzhQ689I7VOWyHTdhoHAeEhU5g1l7gifxZbrlmJx13gSE5hbUQ\nAmUQfAgMxeNMoVmpCSwMh7g8x2ZdAo4TJ0/x8N79zPmUMK2dXhOagIaXais9WTKyDe19OTd3lt/7\nvd/mc5/7Cx586DscP/YMN95wYwPgGoNQIczTpAoTJvoFV125laKw3HXXtzly9DAQOHJ0L//Pf94L\nEfAV8ZSLi2zZvpNf/Ve/xpVXrgfgC1+8iyf3Pkoy2jVIdbzPL7/skrZQr6aO9cysKdi5bR1FvoCp\nSqwNem7poToSswxV1D08WuE6pxru4MjRtRT96zif3xDDCKConI5TAImf0/rnowCMjeugngmR0ZlZ\nhh9XVBWAw0pGnhdMTem8+wqGS0Os7VJ5T+kHBDtkYVE/OtmBmY1TBBY1PCUlT4vlB41xCL6WGeN9\n+fvf0toeDbn7+9S+n6yLf2grt9WOaCtNFpWmtU+qledqvPJBbXb8bTr5d9xMMDhvR/SlMpR4L2R+\nkr7rEEQow4DZuQHHTixwGevAwObNGymyPkM/wIaidVHITE5mcjo2o9eJfwvwwr4DzJ09z4xMUZoB\ns+4c/aqL4Mjpcebp83zrubtYsHNMD9aR4TBBsINmcHfsuYJbXn8bvWv6zdQswfn9J1mzAKVoSeJF\nNKxKIBYXboePLXdn2BhKpH9NNktFZSsWwyIH+48xs62HtQHvcgXuQ7UiqFAPxMuRPTJqiP+dO9FC\nc7+L6fUrBgg8vvdJbr3pZvUaWxsBAaXPW5eMfldn+jeGWM5Pvayp9Jq1yXvfyuDbsovambib7y2U\nPiZXyzKHSMTW4z/W2Jh8ScislqzQPtqRBeOshgBIgLKsgJiRwhoee+Lg/8veewZLcl13nr9zb2ZV\nPdfeGzSAJiwJQxgCJCESNKJIiRLNargzMdKMzG7IhD5Is7Ebu7H7RZohNbOh1WhHK412pGUoJFGG\nsnQiJVEiCYDwhvBAN0wDbdD+dT9XVZl579kP997MrHrvNboBDqHV8ka8rurKzJvXH/c/5/Ctx14k\ny3fgRPAKzrsQuVW1YRjqhdNaPEjdThHT+i5Ym/H440/xH35lnk98/x5uuHINayaFiSmYnIBLd3a4\n5GMX8fZbL+LBb83zW58+yYsHXsSrkufdmr0MQnKGo4NIjkTf7MwqqsNwpEqIx5DnvYCi8ELblzk0\nyweobvR+SgEZR4ikEsc4hqYbUSoIRVFibY7zLiqFPNYIExMTpJQ4ycAykU3EB5v6JaV7NCHNUakO\nrQq8HEBHfBmFgQ5RtSwuLvFHf/RZxAy4/T03s+eydcys6dHrCuu3C+/avol3vf8jfPyffYBP/9af\n8xd/+g0W50usyXCuIvlBtg+K2k/9AkuyIDVW/jbTFoWTVuqwFbXYrR/bMQDaLi3LH1qlgnOW13iw\npeg933He6dt7EK/WhZPVETZlO86rjtfH1i5X/tRnkrSH+Y1hUmXs3cu/N4qAGpsQI28JhpyczDrW\nzKylk+f1c3k3BwPGBc9jm4+mL8xs+H+z35u9md7V/my3d3xOx/+f+hSedfTtgBcGB5iYyVmzfgbl\nNFZBXYVKOINUNMSUQUhuQ1PT0wHGXVY4lEOHjrDUH5DRiX6bKymTVlKGDQimlPY97VWlY7839Tin\nPPvs4zz77ON4PxfqaeXHriqH9yUNOiAg07Zt3sHll1+OV3j00UdR3wcEa9bi/ABrcoILn7D38iv5\nsZ/4CX7sX32Mynm+9egB/tOv/yaLC6cIioCSRtBrZ95uuzdc2HkU6I+j04WdO7ewadNaXHEWE9uk\nmMAktRildpwViWkfTJZz9JRjvsiY2bqLRTI8jsq7EGROw9tSYOS2Utj7QKQ0+SOv2uQ2b6JxamK6\nwogQ7HZjuwx4dZAp6jxWbIiN0T7vjeC8x6YI/wmBSex7Hew/tCq1u459cI4zsiyfpZPthRHLoS7/\nq6P2x+vS+t56ZozLaf1y7nNaxj5XK9pStrbjHS5/LlpdxiPwvY4yyvek2fUtOnw+ymKadrUYiqbW\nldWw51fa89KcjXVt30ZSmTL8rNCCZd/TqJwqjrBpJIbAaINq9O85hKu2iJj6lGZ4+VNtLutCKea5\nB6uZaWn9u/KdKq3Am20ZpvXfUYXiaGvbW62dQjKVpOwtXUklDvUOLYZIFVFmLtLFSGqMaZ3JAkYs\nRgx5niM2vs3AoCioKk8Z0WpWMwwmoP2MUkmBClQ4sjxjUCwhrmpECAO7rrwIpgwsxd8mgIvhh37k\no9z5y5+jPFaRS04XC3WckNDZNLLLx0Jj9SFTUDp5PMpitsTx8hgnWGTX1Fo8gxD6JBpnV3YwSEf2\nynO+uj4wHUCtmdT21ddz7ozupPGWrR6kfOXyhikErDV10L+wU8NUhWitHmLQB5P8MCSycDEojpWQ\nYxkftIDGmHiPRL/HEODNaPIHiWlyvKujndcB4BhdBA2DavA+wP/LmKfXGDDqESOI8eAV76smRYL3\nVBK0cV7gL7/8ECfPbmGydy2DqZQX2tc8jyQ0QBqYSLBTwDKNxEB9gAKmNGaqnuHQc9+DT/HS4eNc\ne/Fudm2Z5tJrci69qMclF02ztWu4aCds2TrDghd+8zcf58grQfBPpXLgvOLKGayZxpgueMXYEmsr\n+kuz9KYsM+vWMXsmp/Ql4ixOR3OjiihagckMXiuKosCnnO+pawqiHcoiCrt+um6HqsFIN8B18OAt\nmCGlG1KWZb3UVSHLOjgXkAgN3VWSRrTyw3ggmhhpXLDSYomdwXlPqY7cKS+/dJTf+PXf5UtfuIMr\nrriSq6++lDe/eQ+XXLmdiy6ZpDMBb7p2gp/5+R/g1PGSv/7iHeA7eF9gbHL3iLnTtTmgXkuIDolj\nmZjNpjSbOQSckrRt4rhoQGek6y2FTTvTwHhdoyeTrki8l5cL5xi0bmxcz7pKJoH/DxVPK4jUa+hL\nCHh6gQ/Wt4/F4x6JoMgI3TkfgtO21r9WAtXsghSAS+uzdPxdbQE9/WoBF+HyRgOUXivBVw40hzIe\nsZQEL8KMrrUjzLI6T44hE0s1IlgmRFHKeP7qrHRSAKSRbTIPGCoqjnaO84J/mV17N0BegcmYLMPb\nvIkpB31wR/ME5JqKkGfBKl5UFfMLA154/jClRvrRCnib3rp6cYyGn2q3nNjv1dA6JqbDS4vF4cf3\ntXgCh5ZY6T4XX3Ix23duZ37e8fWvf62uMwiVQRngfZ+3ve12fvbnf4GPfPg9rJk03PfQfv79f/hP\n3H/vN2Obbf3elfuY5s7U71h9xlK7K4QCI57dO7dx2Zu2A0t4LfAowfPP1o9oRBTUPEico9JmzBcl\nT5yZYKl3Ka7azNAsYVCsOrxpgVU1CcDSchtwdcDCdog8aQcO9iHejkHCOKvHqFAxBOPxtsLmru6z\n81BUJYt6Bi8WNKPHAFs26BFjhYoQAE+9Q6oqnC8iGAlBlxuGVqOxI7TwXK4EmjIhRJurxLlJvEzK\nHJN4OMXHoG8pxlN6PrkDjYuA7TgM48Jf5BVYTlE1svnLBKURhjv02ZhoUBnn2lWooZirKs3Pr9S8\n2siZHtdwStdrJAbmPR9/JhNZGgFp+NUUQLGpPymzms+2u8pocOIoFsX1PhKoePUl8KpFxrtcfx/n\nPUYvjdymaUVI5Iml1b0kVoesIEEndw6FgBBQva0+tQPvjq+y5jlZcRxWp++64tf2T6O5MlhlkIV0\nlo8L/2k5Nes/rvk4BimDSX2/U9SF2C91sVrj7BNPbKRiwg2YqUIWmQCl0xDDRA1G2kraUK/FMp7d\nwDuL910qCiaYouO6WEoERyV9hh1HPjmBzScoZ8uQYcfqaFzntQaGcPArh5AO7Lh9O2atZfuHLmHz\n/Vdw9M8OoKWh69vuJi2euP5rOxOE38KJEzIEVQS3gJe7h3i6fJzJzUAv8BxWbUg3q55xtE/N10ua\nz/MvjS0y7cvV+axzUblVaqceB1neMrlAGeSNiyFwxRVURYkxIU1gyBoAYgENZMN5BSthYaoJQEKv\nGGvJsgwf8zynQy+gCQLb1maTTNTKtu+HQOgSqq+OMByu0F5s4feAELA2J8s65HmOyUBMJDKqIcCR\nBFWGydbxwMOP8+AjJ9i0+UacW0PmDU5dzKeeIj+3YLMtH8REbFNgwzpYn/d4qRCzxNVvuQRXFbz0\nzHHuOXySju1jv1ywdYdw9Vu287F3refmm6boZfDh903xd1/bxvHZ4xw/cZYhu+kCExOwYeNajpwJ\nabFs1kF9iI2Ah6x3kh/46Pu48pot/MNfv8Lddz2EmHUs9at68ZoMOj2PVkEJ4tRTuopet8P09HSz\n8AWMdCgLT5YHSGozxkED6X2IAh3AIhVQsmXrRrIYMXVxAQZLyaWizUgIIaWMxeOwmlj/OXoTa9my\nbQIbV3tRKHnVo5c71q7vctXVN/Dk0w/z5OMvsv/Zo3zuC19j+8Y97L5oE+//0LX85H/3Lqa3dNh1\n2Xp+8OO38ed/+Vmmsk0YMbiqQkyTTu+/VkkRb78LS/zHXc4XHfBPqST/ZHtexCepYON5FpUGHgFj\nyTsdin685iqWzs4zGPbp0sNYYWLK0jGWDhlDGYasNHHjWWfJhhmWjMqVeCosndUackE9TKQ6iDiO\nOeY5svgSvZ3K5p3r8FqRSwdnQ7YaosXVexcN0kHY9KL4KqYoMz2eeW4/LxyfxbMR1SEXfogMW98T\nA9cWotulHdTLjn13dDq9llABxFj5ylkCqzDg8iv3sGnzJA8/8hwvvvB05JQtXgfhzSbjtnd9H5/8\n1Ke46a1X0e3m/P2dT/CLv/RJ7rvzG1TGQB0A0QJdGmVNS5O1LPhYc86PljbNrlAGiBH2XrKTNTMd\nvOujvsR5B15xdIK3jQuCeJ7nmBrxF9REQ1nDC8ePMzfYyfTmy6jcOtT2o6BCYJhbbWly2TdWuFro\nN0nSUcTH7ytJDnG9OB9S54l2KUvH/ELzKpMb+oNFsqwHKlS+IsuTiBjQiRNTPbLcsNifpysxj0CM\nl6Axd7eJronjRaPQPhIUK/Ynyy/jtbtyfLe8ViXDay2vn09Y7Sz/r78GNnZ2LlcqRNGuEf3g1cf0\nOzfm40jkpqRz7XwqaaLuN79deFvECVmZj6CgNPfQUVxVURmP6RgWmKfIB6zZHQ1zAosLC4gaOrbT\nOufAS0AGewe+LNH5RuieWTeB7TmKoZLjoSgQ8VS2ZNjps/mqHbz3fd9L6XMe+ey9HD9yLKLAW41+\nBQ597SB/85tfRHqOj+gPs+mHttG9eIIbfvI2vvnsHIeeOsiGpU10sSGuHFJTjEbwHQv0HX8zFHTo\nIRhOZQsc1kO4HQV7d61F8FgPiolx0FqKtOhWHsjSt4/3Tmm/vx2lRiStVF+rzf+oEQJAEKJdUgua\nBn4nAAZvQpA/ESWzAYZvbJi0qqrqgZA6H3wQol3lqGgH+IkCu3O4sqCqqkYzropzWqccjA0DmkiT\noY5ALKuqwrmgBOhmMZp1SueUBQuUGGF2dom/+codZNlOhoUEN4EsRlaWRmfYVlA0746lpQQbcQ9X\n5dJLdvBzP72HqQnhDz59mLu/cZCzcwv0uh32Pf0K+57dx7EnC3b92x9n105Y2xO2bd9M5Q9z4MBB\nziy8ha3TsH0bXHHlXo69NI8rC6wWiGRU5QAnQ2648RJ+6ieu4oqLgf5G7rv3oeDf228Y0XVrYe3a\nKU5oQcdkVJUjywzDYZ+ZNVP1wj97pqA/KLBZRp6P9tt7j/MuBGM0ivqS0vfpTWRcedVe8ggbPnLk\nKAePHcbjqaLyIA4JVVkxrIYQo1AHJq9g04aN7L5kO2LAlfDycwOW+n3UZ3z4B7+P/+F/+jh33PEo\n/+6X/m9OHD+NoccrR45z4PB+ntp3NxvW9/jRn7kNMbDnkl2BsRLBZlm0CpyPvfG75bvlO1RGleff\n8bKaljvZCKn/TeRaI6xPGRYFpSpdOkzSpV8uUsbI99lkRqeb0/eLTMgEQx2yZe3m2o1m7swcuYS0\nQykC8be3LyEGgAcGZoB2Ky65Yjc2nw13ugjDjndbY1ELpUuR56FSTyXBqruwOOSZ/c8zKCuUYett\nbabmQvrQFqhhdBGkelsxKOr7l0DygMRqmaOCU0aiVB0Ew3XXXYu1wgMPPIBzwaNTTMg8k+c93v6O\n2/jkJz/JO2+9lqKs+Msv3ckv/tIv8+j9d429N6NBOIwrBNr3XUjfKzpYLt6xi02b1lNVBZn15JnF\nDauAWLPN+KgGeq5iQnDYGGz47ILn0LElutNb6U1sYK6fo+owNg/WybEgssHzL8UNCMGKk0VJtZXm\ntdXU5dl3iBae4JbonbKw0GduLtwXFPcTiMyTZYKrPL3JDlu3hrYsFHD48CuU5XpMnkfoUmQ6tW2R\n8rWQv3IWoDeyjPNB/5Ro6ht8KL9h5Z/SHH4HyutUBgBYLN2sO+Je6rwLxlaj+Ewp/YDSFuy5fA87\n37or3FTB88+/QElJLjbEUktFiIhpT1EWHN8/z+ZbtkAHdl+ym41bNrBwao7MdOLZ46msw/eEd37g\ne9j7s9fBaThwxz4OHHyRCewIaXvp4Rf5wu9+npNPHaGQJZ764n286/0fgS5suXULt330XfzFsT+l\n6Ff0NGvtpgYnlDiL9opTGmRfyta2KAucdbOs2zGD5o0IHABE0goIO05Hz7WWdXWB+w3cAuMZ4V5N\nJ/CGKQQefewJrr/mLfF/QYMd/NgkLr5gfKiqiswKXjzGpkwEJsBIk9+ba1L8uMozGAwYtNLCjUT4\n9W5M0G+AOMsjtsuyT2Ms1uaIRKIuPrRHFR2GuheM4xtfP8yjD8+xbutH8drDky8TgEcj8CbIYRiP\nBD+TiHoIGgdFrMGocuku4eYdwswkDD84wctPD9l35iheL8PKZob9SU6fOEgZ5fZKlf5gjqoqeO7Z\nOb71cMX3vStjYwc+/onrefrxr3L44FGMmQ6IDFlk0+ZpPvzD17N7T2Ddhm6RYXUCrTKOvjLglTnY\nuQY2b4J3vPMGXnnxpTDupiLvDplZl3PLO7bUfT5y5Dhz86dRhqg4sqxxXTC2xDNP4boxj3JBnp/m\n5luv4wd/6Bo6HaEqlUcf3sfp06fITAzuVac5DInAVJZQTmDFYsUwM7PETW+7nOuuvwyA/qDinjuO\nUww8AzfHrkum2HE5/PDu63j62dv4v37j9+j4bXQ6HYyfYH7+GLOzs3U7h8MC0tzXQZpasSu+W/5/\nXS4khsC3tbQDVdZy4esXiC+0NLb08TIKmxxFCARlQL/qU+Hp0qGD4impzs4z9+JZNl23hU63w/qt\nG3lKXkFsRt/0ufRNl5JlGZTK6edfIRs4fMzm/u3rS1AvCB0GDDFiOTJ9mGrbLPkOQJeoBp4sn0Ej\nakhR8kwR79EqIql8QG5WEpSZ+555ntkzZ+PbHNSZn5Mg/mrzl6zsqayGEEgCd1txmRQEGtzEcCwt\nztdItOCC18D2BcuamY285/bbKQrPvffeX79f/SLgecf3fID/7X/9X3jbzW+hKCt+/8++xKf+7ad4\n4emnaENiiYraZoST+8C4QqA9E+2ykoXQI1SsnZ7i8sv20O0qXgc458ijO5/YpgUSE5kHRBpY0wmp\nFn3OvqMbOSm7mdr4HpbcWkqXY6iwkofAu624AePpdRMjKbWyIBk8Am1X71tK7NYu8NFtwHQYDEp6\nZhLnurz4YhiJiQ5cfsVm8skXGVaLKJZLr9jEtt2hP4cPw7Fj8ywMcpZKoTfRRY0PyBQJCJwQk8iC\n0Qt22arK/eT5pef/wAUXz6jCqr0e/ikQ1jcIIfBGIwp1ZTTKaoGVAU4Vh9mU7/o2vPdClYqv4TUX\n6vL3HSi1fNNqW+UqNFOGa4YsuCXUeHqbOrz/X3wArgn3lIc99971cPD+lxJMf6ReI8HY2S8K7vz8\nXbz5Q3thO+x95xVcdsMlPHJoP2crD1WJ2gF+ynHF91zGFf/9TbAV3CuOM3PzGMnI4lmYyuwLr7D4\n1GH2zm5kSaY48PkXufj2l7no+y+CdbD5xy5l7+xbeOG/vMhwoUQ0G6EQWXQHFLRGKgqCi/EDcvKQ\npFc8+3qPsbjhGL1Nkxw+NseeLTPt0YvovQtVlK50TrXd/lZf76+vBBf70RhpsUUSXG9G23XuNrxx\nWQZ8ReUDAyRqMBqEfhETQsHjwsQgOFfWeiCJcEyJtCIRVIhW5spTDAsGVWPtSAoBawy5bYLdjedv\nrwO+xMqN2Po+YzKMWKzNyLsT5HmGMYYqDnYntcIYDp1Y5Jv3PsGadZfgZH2IJ6CCmhT3tMUsQK0E\nwCsm/mZ8hDGqgjpQQV24LsDJE8LpEtYZuO6a9Xzwo3sZfu4QR155iYlehy07Jnnve29nZn3o3Ymh\ncuTQKTpmDfNzJV/+8kGue/MlbNsI77luM8d/9na+8Ln7OXLoJFKVbN2+ju//gdv4gfftoCdw+GzB\nI488iRFL5QtOHZ/l0Uc8O99t6Fr4xL/4AIdfuosnn3oap5bJ6Zxbbr2R979/ByJQlPD4Uy9zdm6W\nyg9xxRCxzfKbWTPF9Tdex8KxaWzHU8kSey+9jv/2Ex/k2mu2IQInZhf5ypcfYH5hjtzOUDkb4ZBg\nM8OeS7fytluuZX6uJLPCzPQE7/3e67l4b4d1GyYBuOvr+7n/vqdRMoyxnD51lmIJOpPwr3/8+9i3\n/yXu+eYjZGKZ6WW86cq3cNt7bgiN9LD/2YNkZi1g8QRoVFzEgCZXwWj1XI4cOFfmwaAWW9lrVmWU\nnV8JvdTe+15G2ebRWAtpjTf3B1+0lg+SxNYkhVncEynSRzA8ybJ2MkaILqRIquxVStsP64Lfoedn\nq0ma4uXvPUe7JP1Fd5+RCVjhftX6HkWXRTiOLTnH/8ZePvL/dPdKT7TxSa+v6NjfajHAVwp+1KSG\nbX96OsaSq6HQkv78AifvP8TFH7qUfLLD1e+6gYP3HWF42rN17y72XHspxhqK+SUOPfM8S2fmmGRN\nVDOklpyvUCGtlrYpS9jPlhALwOE4WZ5kamvOIJ/FLw0wdFnsz6MUwbIiUJYhyJt3DjGBOHsUMR0W\n5pZ44eVDLJXJcaqgUQic7/pWRiG8SSGwUn/b0FUZ+QyI9iF5J28OCnWxboehg2eBPRddwc5d6zh2\n4hRPPP4QYbaDYL950zb+5//x5/me224izyxP7zvAH//RZ1F1XHPDjRRlQTEYcHZ2jjMnD+LrvnqC\nmN5ub1JenO/6DPd1jGHXjk1ctHszM92CsihDukF1ZDa4kYQsPdE2QAko4qHQDKOO2eOnOHZyF2t3\n3EDfd0Lavo4nkyaetboqCPhtN8OWq0Dt7hf3t6pihAC5jbB80vW4D1UAbxgM+0HZkGVUpeOJx07x\n4ukeezf0uOH6y7j+xlkeffRJpqfWcvv33sj6tVB6ePSJM/T7kFvBuQrxHaxkMV5ScDlLbDK1D3Zr\nBFtIgiBMxUFqK/HURMV7kwEpKB018Cd1/YGXS1mTanqg7blNo+UjZ9cw9M219md7ptOVZr82qpXW\nuSbpH6lpFXXcFl3hFU3tzdmtqUvtF6/0ddXSGJ205jFT0LdkqBIZPXdeU2kJ2VrPdoPyqeMGQPCv\nr79fwCtGjFhNGSU/qScBdaN+5fvHSVbL1bqOiaPNqgqn4qptFUYV4A1Vao9K833lsIwpNfl4H9Pd\nr0f+rynRBdZxPtRrdCxNCBbumvHYsHYTl+69jI5MU9ghGzav47K3XsytH78FcvAFPHvfizz/xPOs\nIweUqipbdSbh0lB6z1OPPs0zXz7AlT9+MWyF7//xj7F38iAP3v8QJ4+fJJ+eZs81u/men7gNuzNo\nYg89/Qpzp87SM110uAiD1n4blph+xVrWkVGwcLLkH/7jH/JjW38a3rcOtsE7//XtyJ2W4w/tC2g8\nmhjP4jX3AAAgAElEQVQrnk475xjtNagIjpzCDFnSIUf8S8ysz8goIAb/xYx59LcY7XZ8pvrb+OJd\nkXmN7ViR6V99RnUV14Qg8+uyBRSOM8to7JTEAkp9ky47yJaXNy6GwNVXBl9LwBgFoqBuwMTAFWlD\nex98/40YvKtwPhB1a22YyCi0J5+3LMvoZRZrQz3WWqwxdPKcThaYpgDh9C1XgeR2EKy+3jsEGzMb\nGFLWgzzr0J2YJMuCQsDFqGK5sYgNIuCBBx7n2LFZ1m24ltP9LMJ1AvE7Z4nWhBD8UOvTRzyBqUSC\nVk2U/fsO8o17HZs+dAkbJoUf+ee7uOLN/5KHHvT0OrBnj+H7boXJOMN33qMcPnwMX62nGij33vco\nX7t7yMc+eCXTPeETH93BO97+UZ5/DpbOwu49cO0VMJMFNvUbDzzM3Xc/EAIhGeH0qdN8/gtf4+3v\neC/rc+GtV87wK7/6IR586EMMC5hZB1dfBTtCtilmzw64594nmVuYA8kRo/QHg7rre9+U8Qv/5mfY\nOt1hUMHMBtiyCdavDdc98Fdf+Dvuuec+qionz0IcgXRAWwMf+tC7ueH6d1OV0Mlhy2bYvAMkjsHL\nL3h+57f/kMOHF8g6GZ1Oh3vvfZynn3oP1920mUuv2sgv//uf4+//7hlmT5Vs37Gea2/YwtXXbwTg\n0Etn+cpf30VmJ/FVCM6Sp6hyNG1JSoH4v1fbCi3FVOCy2oQqfV/tc0WZMz5Y3yOtVtQUaayG+HsT\n5bbNNCnJZ6VWYjWNH/1cpX/no0lfLcXPeI7v11POn4Fb+fdXa0NAB/gkGQDj66HVDqE1niuFSFre\njnO3v9YuvMrd51YItAMMXkhJa9DjW4EFV2pHuCuFJmqv4Q45eEjZO5bmF7nn77/Jm3/6FqYunuSa\nj1zP7vkdHN9/jM03bGf9jZvAwNyTJ3j2qadRF+Ou1FbnC1UIpM+VZsPg8czpHEuyyLq1PZxdxFcu\nBG3zjv5gienpaYxQKytNRLKlZVWWFS8fPMSxk3NRJIUgFJ/LKr5aWem+lfrrx65r/alaxnYlqz2B\nLvrwnI/Kine/+zayHA4dfJlTpw6P1PuOd7ydD37wvfUbtm3bwac+9e/oL/ZDXB9ryDAcPPAS//lX\n/3fuvPsbIYBerdQYDxa17BQYa/9435TpqSl2795Or5uh1SLiPR6HcxUuCri1cKKgxkdhLASZdSrM\nnp4l76zF5huofJw3GwR87zTI/ZFnqOMFRGFuuR9xEkwV5zz4GFS4dY569TEGQVBhdbtdEHD9ko7N\neeyxZ/jiV/fxk5/4ENdcNMFP//S7+ebdb2HL5mk+8uEuMxkcPNrn7776IGUFE70uRSExw0DIRlQH\nPh2RZFs8k44KSmMiEgBZfgWqLjCVy4LUtvaYtvdbIkLt+9uUKnyPKguCOJ/iOazCFI+8tb2O07fo\nV9xyJR1962rrq127LGuB9yHe1GrHSJ2poeVS0i7SEsbrlkTleTM/jPQ9oUvOPx7AGM0mBUgbRxqd\n73n4GssYU3JBVFuE9Z2dDVKWsVSdnKvl43emOhj73l4vK1fR3huj97x2HuS1jriMfYbvy2urV68J\nQSz7S31mCAz02z5yE1dfdQ3dvIuZBLsN2AmsCc8ce+4kX/rc37A4t8h6CUz7iNW55ifDfvWV8ld/\n8nl+6u0fY/2bdrPunZu4Zesmbnz0rSweG9Jbb+hem8ObgQ74V+CpO/Zxcv4ka2QSgwkKiyJctxis\nCiUlooYJneLY/ud55HN3cN1NH8Cs7TFxyRS7bt/D7BMvoEslShM/KO1oiWdI+C2YrwwZYPFWWKz6\nuG7F5HQHKwP2bJ4mOfmNnC0yzjek0yX9Nr6nVuLgVpq58dk6v0uj8eWWo8tpxMWRPowq416df33D\nFAIj6dOMQTJbp6ozJgj/IVtAdA1QxfuMTrdLlmfYGFiwfsbaSADChNlMa2EeiPUarEJZllRViIRf\nVQ7v2oMd8veK0EIHGDIbIIV53sFkFrFCpR4rGZnN6EYf98WlIV/+yxfpmWuR6gpsJvTVUXaoCX9i\nCkQkZCnQoBixAplXAp/iRxiYNB4k6OrA84efOcpgaTPve0+PTTMZ77gG3nZNYxcrK3jmqOPRxxf5\ntf94J2fnLNizZJNrOH0249f/j6OcPZvzkQ9sZ926Sa7aAm/ZGubEAUOFl44P+dyXD/Hnf3ycuZPT\n2MzibZ+cTfztlx5l144u//yH38rWzVNsWg/f/71NIimnMCzghf0ln/5/XuDvP9/HlcHdwvkhB58v\nOHYC1q8LAv273tkJbZdgcKkKOHtGefnAPJ/5g4f43Ofu5vipLhUZhfHMDo+yb/8xbrrlEnoTwuZt\nsGkb9YbS+P65kwMeuPcg/+U//x0PPnCMSvpYungPTzxxgE/+4mf4hX/zca64dgu79s7wk1fe3CxU\nhaUFOHzwDL/9a/dyzzcfAxUkP4FVQCwiNm7GhiFQAS9R4ZX2YMyEMcpLtTTVY8K1tn7ziYGTmOoq\nPuONG9nktXtLi5FIn4bEHMSMrG2kQNiIdQTt2pcKH+Ozpj4IGlERJlokQ/0hYrfVgE5ApA6E6INz\nVoz6m/baKKeQ9A8KlGOm8rZCIBzF0e6hhPDboYamntjXkKE0KDlMrLzu7jkPRgGBImszyqPtaX+G\n94H1sX4ZhRO3mlePl4l8W0BmRyGsdX/IkCKYMY1ubcRTDRh0QLyJlkCpAwGNRk5v1ljTYo+Ir9dm\nsOwHcpdJPEOU2rJmWkKAobX+1NKk4rMt219joc9pGBgbSU4KQ9iOghvut2gk0YKwzq2Bhzxf+eTn\nefe/fD8brt/Aup/awjq3BaZAT8CpL57m/l97mPJRz6Tr4OMaV80QsgjGD3EKUtLTJlmh0vD5Ll4X\nlGFsjYltdlS8wuH1T/Pk7OMMby5hy2ZsmWMkC0psK0xO9khB5iQNrjWRpgilh4XhkGcPHOCMb4vp\n345gXa/PH3xxYZ6F+SXWr1/PqZOnGQyWSCgBgGvffCXeK/fccSenj52KTwUxZvvG7czPLTA5NQEI\na6dzbrj6kvYtoHDd1Xt46ZlH+NrdfzvW5tfX9mlruXT7Oi7auh5TLuL8IvgK0WBHQkNEf9Nicp0x\nIA4jS3ifUVYdXly6jWL7D3HGX4UYh0jAhAT3NBcyM0gQ5GsrZluppwEZVu97JaIHPJUdxnFo8QEm\nPa9kaFCGeSXXAPM/vTDg936ng5an+fiH13DrjRlvu3EjOWH7P3NC+ePfOc0375hnMNxK7i24DniD\nsbaJcO2rEKdDtHazc2mnSTi3U19SYHgFfIw8nrqiEDLYxLNYTRyLFPtJIOzkqhb1U6B8Bbw2KZsT\nWtKQodFdwIw5+/gWU25rBEyrXhLHpyQMh2BBwbe2lIjg4xlmTXD1hKZfI+m5oEnbKoKYYATxNMK+\nCRdjDSnnT8wqoU22n6Z97baaGv0qorVAEuT45m7TVlD4QEetEVaOWaRhMiKyLwTpNpFGJ5o+KsQk\nXqBmTxCUFFTNr4hYC0bnlQScxnVpnObpWEUBpacBzN3ajz6lk6vXXyubEmArV9Ol5UVJWaZSb7yE\nOkTdWLIDX/cv8BsNjxb2yGi3om4C9Qld1OK3xv6fmpIU2gZDcn9uBmVl1cDKCMFmhkbqEd/ETsOP\n7BqXD1jKlnjhGwfYsndbyEx7OUxf3q3bhwfnPYtHljj08Cvc/St3cuSh59lQTuOkRBH2v3gEXgC2\nQvH4kPkyBCGcLJV1Zw0n73qBz//817n9X72bXTfvwu40ZG+CtXm3Bn7pEpy+b5ZHPvMwR/74UaaH\nHmSR4/YM80cWoQSdVwYvOWw5RYUP/KUOyKtd3PXn+5m86mou/2/ehPRg5pYZ8s90GSwV2BayTOij\nhLCynVo1YCjxFMH5kBfWPMczZ56kvPQobv0EYstorKlPtsh6a0itXg9WowTQGjm7fJ6Ws5TnpmnN\nO8Z+H+M42/QlFBPP8LTB2rt49DnfauhKbgXj5Q1TCDy9bx/XXxecV0QMYhrhxRhb58AUY8myPOa+\n7NLt9eh0o7+fJM1ITDtoQkAe5xzDYqm2/o9sYOfr4EkNY5+EH41ZD8JfQAUEpYI1eUAaWEPeyTFZ\nFgUlxVrBSjjW+/15nn/5COs27mVQlJSmChBSHyxitSyk1C5OMTwA1oPxwf+wESIZ0c4F2UtQJjh4\n6CR/9NlneebAeq64YjPbN00xOWkQA2VRcujlJZ58bJYnvnWM40dLVLuos1RVhlrhxGyf3/n0Xdz3\n4CW8853XsmP7WiZ7AaVRVI5Dx09z/z37ueebT3DqWAZ0qApPnk0jklENC/7sT+7nuedmuf6tl3Px\nxVuZnuwx1euCKidPz/PCgZN8429e5InHnmapX5J3uhF22OVb33qeX/7lR7jhpvVkHRDjscZjgGJQ\nMXe64PjxRR68fx8P3P8og/4A7yZjPmnLoG/5g9+/g8WFnN27N9RzGvw7Q/yJY0dPcsedf8uxI47n\n9h9lcWFIbqdxLsAfh0PHXXd9i2PHT3HrO67l1luvZcu2tRgreOc5c+Yszz52kru/+TAP3/c8S4sO\nI5MxgKvBa0opJKBRIZAmOKZTalCly11VlsWzEI1wvlClaLre/CVhCQH1VVzDyUJB80kKkBJFH1M1\ncMmasDfxD4yYxm1AAHGBRdBElmNX0+JMwqvQ+My2IEr1MZW07SOMz6gduraGAHli5GpUQtznI4db\ni0kdh/elszK9szUWKx/BK9dhU5919Iiub01MY6u5p6ojbMi3EjKE6KoIrZRmqVHKjL4jCavjmug2\nlLXmDQxJC9MwUNFqadopTUdqF0Le+XEGpbWGR17YfG/6HPZhowJsVEehR6b2Ck71GfLW94x269rW\nQUcTwE9x3P8XD7Kwr8+N772J3ZdvJ+tmmBIOPnaEu792H3OPv0I+MHQ0h1hvo6poem1af7Ta1f7e\nqFRDDyxKQcXpbIFD1TGKDZ7tu9ZhbIyAYBpotDUBblkrsGqkGyCerNPFLRacmVspXeAbW2ZnT/G7\nn/59br31Zr7wuS9x9syZkesPPXAPu3Zt5o6v/wNLRQp/H/r2pa98not+dSuXXnYxYnIQi4vpbYXA\nfJaDIadPnODLX/yrb3vbN0xPs3vHNqworhwiWiGaUBcZUdau/VZFJJxr6c9OsjBfUjrwopRaYgjW\nWxOZRKGxAqeUfSMR+VvKfhJqAB9QjGpAOuEZr6QUfs3O1yhIBmOEt4p3gu2t4ejxWf7ksw9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S8EYahpjQgZGUhB4fp0JggpSp1HyQLc0GT0IvMTmOim343coiM8mUQVSODBA3oBGaI4KlPwQvUs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P0jgDrI88HkyyWmTd7bvv597q4GAKSDdOBhC5oLDGuw9jQCiams9BgZhsD/F+sHr5ObYYg2Qr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\nOQevDMQAs5h5hipvwl1lFEtTI0BSwFLiafWNsvhqqLAq5cUIlfJ9y4Ji6Rx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       "text": [
        "<matplotlib.figure.Figure at 0x7f32114bed50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<function __main__.show_image>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Frame Length"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fps = cap.get(propId_dict[\"FPS\"])\n",
      "print \"Frame Length: %s milliseconds\" % int(1/cap.get(propId_dict[\"FPS\"])*1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Frame Length: 40 milliseconds\n"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/ARSSF/2. OCR.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:cf7c738a6344afc75be8e6c9bbef4fbd3a120a0084e353132c9f0c56fb08892d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "OCR for Digits"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are very good OCR libraries. But to get really high accuracy on this videos I'll train a specialized OCR model from scratch.\n",
      "\n",
      "**References:**\n",
      "\n",
      "http://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import datetime\n",
      "\n",
      "import cv2\n",
      "from cv2 import cv\n",
      "\n",
      "import skimage\n",
      "import skimage.color\n",
      "import skimage.feature\n",
      "import skimage.filter\n",
      "\n",
      "from sklearn.neighbors import KNeighborsClassifier\n",
      "from sklearn.svm import SVC\n",
      "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "\n",
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Open Video"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "propId_dict = {\"POS_MSEC\": 0,\n",
      "\"POS_FRAMES\": 1,\n",
      "\"POS_AVI_RATIO\": 2,\n",
      "\"FRAME_WIDTH\": 3,\n",
      "\"FRAME_HEIGHT\": 4,\n",
      "\"FPS\": 5,\n",
      "\"FOURCC\": 6,\n",
      "\"FRAME_COUNT\": 7,\n",
      "\"FORMAT\": 8,\n",
      "\"MODE\": 9,\n",
      "\"BRIGHTNESS\": 10,\n",
      "\"CONTRAST\": 11,\n",
      "\"SATURATION\": 12,\n",
      "\"HUE\": 13,\n",
      "\"GAIN\": 14,\n",
      "\"EXPOSURE\": 15,\n",
      "\"CONVERT_RGB\": 16,\n",
      "\"WHITE_BALANCE\": 17,\n",
      "\"RECTIFICATION\": 18,\n",
      "}\n",
      "\n",
      "cap = cv2.VideoCapture(\"video/2014 FencingWA Open 3 Sabre Final 1080.mp4\")\n",
      "if not cap.isOpened():\n",
      "    print \"Unable to Open Video\"\n",
      "else:\n",
      "    print \"FRAME_WIDTH: %s\" % cap.get(propId_dict[\"FRAME_WIDTH\"])\n",
      "    print \"FRAME_HEIGHT: %s\" % cap.get(propId_dict[\"FRAME_HEIGHT\"])\n",
      "    print \"FPS: %s\" % cap.get(propId_dict[\"FPS\"])\n",
      "    print \"FRAME_COUNT: %s\" % cap.get(propId_dict[\"FRAME_COUNT\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "FRAME_WIDTH: 1920.0\n",
        "FRAME_HEIGHT: 1080.0\n",
        "FPS: 25.0\n",
        "FRAME_COUNT: 8618.0\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Showing Frames"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def show_image(frame=0):\n",
      "    # Set Frame\n",
      "    cap.set(propId_dict[\"POS_FRAMES\"], frame)\n",
      "    \n",
      "    dpi=110.0\n",
      "    height = cap.get(propId_dict[\"FRAME_HEIGHT\"])\n",
      "    width = cap.get(propId_dict[\"FRAME_WIDTH\"])\n",
      "\n",
      "    plt.figure(figsize=(width/dpi,height/dpi))\n",
      "    ret, image_bgr = cap.read()\n",
      "    image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n",
      "    plt.imshow(image)\n",
      "\n",
      "    plt.grid()\n",
      "    plt.show()\n",
      "total_frames = cap.get(propId_dict[\"FRAME_COUNT\"])\n",
      "interact(show_image, frame=[130,500])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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c5/d2qJ1jpbCLAjyjPXV/PwaSvn6dH5anYuf98zr5hXPuH9z78ogq3/d9iDy3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zHRZh1d1/Po6hHvvfseP/jDH3B3d8dHP/2Ip798SlEUXCwvxPH1kfbU86//9Z/xwx/+DZ9//ozN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\nyHy1QjBzmqb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blv2xxt7c0XQWnRWcB0K9xaxK6gP7/ZbdYY8OaiS7g6x5q7M1X7z5nPl8\njvM9X3311aPguVKKpmuxI6I059zI2VZEhWcP2JC5MiYjzw14T9f3aBW5BiQD7LUEycf7rtY61H7q\n5HDJ+5pJbfNJUyG5Go8v/Rzel9+fZgaX78sBnsqyp2N8sizgsS3wqc+f9iEqxAD0veP29gN3d1Jr\nWlUVzjmOxyN1XSeo7dnZGb/61a9Yr9d89913WGv5xS9+wXK55De/+Q273W5yPeMSium5pwGLv+aa\nfsj2rO30THe8D9mWaKuoQKyrxalP9eZKAkhKIUz4yV6RGa0JxMXxdKfedexCfE3gJ8NngViPfdrh\n4Zo+Tfb43LiP70l0Xsef1SpDtd0owzwEVlIWVzxKsYl8hGQP5n4kV4s/s9mM3hpW65VkEz0Db1LI\n8p7a0xFRMk6ADASTMgdvbm7w3jOfR2fOTq4zZoxjOVW0rWN23WQiZfZozLwmN9nkGrQeSPHwIgMI\nJDj5uFRGiFurxPMU50+WD8gBlCHq1HsvMq3WDYouzguZoqA3A5ngiIwwyzJ04AQbl2xKFrkX89sP\nWvDRWY330xhD5gfUQhzj8/PzRHaYEm4jqDuQSri6rh8I73yEqZdBblALX5dSSdGlKArZewMrvxrB\n+SOK2FpHnocS1VlF23cpIO+dow0ovbZpaduGtoskgiJp12UNTVtTH0V+LvInFEWR5D0jEiQvDOfn\n50KIl+D/EhyP94UQ4BzD2E+J6dKzM0KFxADAUza+73pmgUesx9M3R3SeoYxGG4NR4ndcrP4rb998\nzt31DUWZ43zgOPB22E+8IKizLGO+WGC9Y7/ZcNjch+dA4fun1Uy6UFY3Q6NMHqR2NdV8meb0c+3f\nnXP/XIsRbBgtsD/wniU+8WOne/z/aW2MH733qU30Kad/8r3R+Z8LToyP8axj+rE+PHFNf9H3T64h\nvhahXkI8l4UgRUQu2IFk0Mu+6UMNad+3YsD3BX4U/YzR/d62uPbIbDYLkCyNFcHCtDEuVkuJDIc6\nI9v1OGtTtjDCy0DGeraYsVjOKco8OfxxQ5BMgUU5iwLqw57msAPbYXCSkaJD6RbPEU+HdTW7+h3K\nNHz77QOdm/N//z8//yl7/1P74ZoSqPXhcEjQyffv3wlUO9Yf8Zh9OBpYstmL41qUFUrlGFNwdnaB\nzgsOtZDiRIdjPp/z5s0bgQ7aQT89OicgxsirV5+xms+EeFIp2r7nm2/+kDbf+XzOcinZSpC14u7D\nHV0nyKPFYkG5yMkCVHnhI6GfEPw4OxgyeVHxxZdvefHiBXUdyHDqBqc0u3qP1ppqsWS+WrNYzOmC\n466UIB/q+sDD/QObhwdMMLhm6zMWiyUOhR3B5LbbLZvNJmiKt3gNswB5PB6Pyenu+56mbdhutzRN\nw93dndyDLE+BRSCwCQ/16Lvdjm+//Zbb21uMMaxWq1THGEl3oiE4m824vb2labpQ59jz5s2b1AfQ\ndJ2lKHL6zrFcLkf7mKLvBwNb+ETEQOnbDmf0pIQp3iNnHdoqlPUUShBnqKGeVRvwanDu4/fG8+4v\n3V8+TTr2mO9m+v7fRjz3tzrBz+3X0fju+z4hTxJ6I2R3/vSnP3F7e8t2u+X8/JzXr18nx0BrnZAf\nMcAW66Uf9/mxpvW/x6b1oM8+QO+lph6lAmN+THKMAkjy7cmx5L3hd6/8NHLENDiiGAJMp02c7IiC\nfL6dBplOHfqnsvfpfp5qDZ4cN8GRI3dSIsEbPhfH6+zsjKIQZ+/+4UhZhrFRw2ei3TPhVmCYu2OH\naVwCoJRAzA+HA6uVlE7GQJTWegIrjmzqce5HzpHxvY1jISfXGDXMgTHjfUQ3eDXossegbCRBjceP\n9fl5bkLm2CSWfTBB4z1m7qFPaNOe3gUH33vJtOe5SGD7gWRwHIyM4x45AOL1tm2boPljnialVAr0\nxT7PZjPu7u7YbDZcXFwQs9Gx1j6eA4QbYV8PARNrLZkZ+lXXR+63mxR0iBwM3nvZd8sSb3vW63W4\nHgmA5nkGDIiCGJjpuo6+E1Sr956u7cjMUCsvNek9Vmms1UIo6jxWCyIhzoPx/JF5PHBwKaX48OGG\nh4eHRI4XiXzjXB9QEVPS0dPnKQUHRvPKoNC947Dd0wB5lpPnGVmRoW2OyTOU1ny4vma5WLBaLvny\nyy85W67Zbx84HmtR8dLhIRJwfvJ/CEGx7W6LCmuXtUMZdvQf47oeecaijF8MxK1Wq0n/n2r/rpz7\nR9GVv8KJfbzhD5mKU7iZLBKPz/nnNufcpIie2nqWAAAgAElEQVQtRp6fc4AliyH1FvL3iaawtWlb\nOv3uU3+PX0tlA4kF1o1+H74XkyenwYrh4Yj9HOAj43oon2B/UTEg6uAKO7Yc1OKtyMtoLxE9HVQA\ntFZBGCAsHthQe9SkyGHM6vchc2eMAedxvcUrT983WC9Tu+trMmeEXwDQeNCiIGBmOV0Hy0WJVhLl\n7PsOi2Vzf8Nut00EVeOor9GaPDOJ/MJai7e9HNtZ8B1vPn/Ff/7P/5GmFuf+2IizcmwzOpfzn3af\nM1sVz0+e/43aTzX3P0zLywLrHdViLvV2rTjivm1Txks2DdDKoFWBsBopjMowKmO1XHN28QKlc5rO\nYVXGse7D/NWU1Yqrq6sUST/s7mmCCkaM7p9dvJKNVSmUtxyaluZQByb6LZ31XF29lLozI2R8BsNu\nt+Hh4QHX9yxn80Qo520gD+1FISMyyBtjWM6XzKsFfd/SZFKP1x4buqbleKg5HhpMXmCMZII+++xN\nyDZ5nDuw3W2xvaVpajabDWVR8tnrz5nNSra7raxzStM0co0PDw+pbrNpOmazGcvlirY7kBsdSJVk\nXarrhg8fPtAc9lTVnPPzy2RkYQZiIQgGmYcPH2748OFDymAsl2uKSuoIu+6WPC+xvch4dYEhX8j9\nWt6+fcvZ2Rn7/Z5Xr16x3++TURwzGkUhxHrRMBJyv7lkJIIx0RzrYMyGunFnMUolw7VrJaPX2YbC\n58xWZ2ChCPWu3vtQyjUAvMfOi9aa3jqySX3r8w5Tav5pxz42paTMLp3TDXuv7H+Pv/MXB7+fcPr+\n2hYNzGiYii50l/6OcyUGkmIWf7FY8NVXX9F1XTL8x9nLOJ7RMYp7ZRzjsX0wJvwb7/9/9TXxSb/2\n08d4LqGvFEYJHDsLaJdYG54+Y8Q+0V5y9DifpOoAlNNgHEaP7ZnBzIsOq9I+TZfHzvWnrsBzOtce\nzZunvvXEXDwNxsaW6pyfaE+VrkbnPp4jzofVaoW1fXLSoiMbHeZxVn4MDx/bf6d9j4iKqJzy8PDA\ncinyx+8/3PL61YtwnuEZiM59zIqXZZVI4cYKACkQgkiMjt832fgeiXMf57X3PtXjR6LX+OxFNQCt\nB5Kz6FSiBnZ46x069E9rjfFB8Mw5iuJIkVfkWYkxOdClEtCoST4eL2BQxMCPsuKDSkbkcxnfcznX\nlLB5HCQZs8RXVUVuMpq+SROuPTaUeYHrLV3TgnW0QUM+yuLVdc1ut6MsS85WwiIvfDRFyL53tG0g\nZe06kWwNyh9aK7z1k7Wtt5bedgHplZEFOcSiKMi0YX22SntqQiyE6xvP4TjnLy8vpyoBIWAZs9jj\njP0jdMtonibWfz/IfRql8V3P5vaO7tiwXq2Yz2fkRY6pCnSQwfXe0+wPHB62zGdzMqVTQAwlUo06\nBJsNggKR90bnDsFb5/qEVoiEhxG9MkbMjNeBuJ+fkhmO24/OuR9vPE9Flj/2+unfSYbHC5RQIIWB\nDEiRIPceRN/dTo+jlRJ1uXCMmE2OZkKiZPF+wjmvwvmjO/1UP58KMmjlRE5IkhCBFC+w1Y+c/RgA\neMpJl8XMEhn1h98JkdzTejU/ckoH2YaxUx6vVsYdlIrZhinbvveIA299qKMEbKg96q3oPOMCrDMy\ng/YMBB0Gazu8NqIHbB3YHmdbFFUgrtAorXHWgnN423NsGjRgtUYZI5u5rlPApG06jB5F3r0YCM45\nVKiRUcZRFRpVlpg8o7NzylL0qbuuS4tvfKj2hwP3D7dpMX358mUqg+h7kcN4cXHJt7t7LEdQFnd0\n7A8Oryo2m5rZsqCuuyQ50rZBT7z4KaX/U/vLW99FWZoM56QuKwvwcaUHCGvc6HKdS82hyijyiouz\nS15evoIsY1s3bHZHvMqwHtZn50kiZ3F+zt3dHXd394RiPhaLFS9fvkzcFpvNhsPhwGF/YL/b0Pei\npXv58hVa66Tpfnd3x8N2z/WdZKgX6zPW63XaiAX62bAPGYUI3by4uEjw+7Ztub+/59h2bPcHvv7m\nW5SSOtVf/v0/CLtzI0GBru7QRo7dHJpA0ik8KqvVGYvFHK0VeVGiVc3t3R3ek9hyz8/P+eyzz9Ba\nBwZg0c9WSmTy2r7n2LR8+813ErQwhotzWRsWi0VCCVVFAdbheyvog+aIR2pZy/mMvCo5Pz/n5uZG\nuFiUKB20IRO/qw8cdhvRvb8453jYc3l+ISRJzR1d09I1bSI10pnh+vo9ne3JipzZYp6gryAVWkoZ\nsqygVS2u92inwYZArvboPMJTLb0VLhc6h/cK6wY238GBJPw8vVePMyha6yBR9nyLGvFPZ6RJsm1p\nj2X43Sh9Wi3/Ucf+NJsaX/tL2lMogtPjjR2X+XyO9zplBscZLBCnbbPZsNlskuMVJaVubm64u7tL\nUlPROUvBZ++DU6BSVn8+n08yfrFs8LnSBunzNAP8lOGp1N8W9BgnNZRSydvXWpz7qiiFGDnOm3ES\nxAGh1hzlINjTwrUkzpoeJXFSsG10PV4NyZ5HSCce5XsetZjf/+hnvH/0HEzQAaPXYoZusBODlvkT\nzv3psxHJ1CRBMYVxR8e979vw+0hCL/BjxLkUHacxkdk4W3/a5/gZgPv7e169esX5+SXWilVcVRV1\n3UzmZ0SpROd6NptNMpinx89NNiJl08EhiokoDUExJO4jWSalmPE61utz5vN52LPCOmFiMEOn+xzv\ni3XQqg6rLQbJ2lsEro+XEjbvFc5JjXeE20dy5xhUjc91dMzxj8tw4xxJaFE1oJ3GjnB08KNjCAPj\nvpQLiMM+JOtsOld0yI02HA6HRHQXFVyMMShj2NY7sko4F/a7bcrMR5u4rMq0ZonD7FNJR1mVoBwm\n1wGpllMWcs+qqpLgdNfysN0ktF1ElER7ovcdmQpOroWMjOVyzmIxSzZ5/F/KfYfgwuCRqbR2xZ80\n193wulFCGNt3Df2xZu8sGZ5cLzlu9xz7jtlSVIS01vRdz2F34Hy9Js8yjPZkhXBEGC1l1s45fCBY\njPMpM1mS4VssShbVckLueLr2R54FIMlXfqr96Jx7eH7T/XMd/ae+N2TGn3auT4/1ZADhCSIZeLpW\n4lPtdFNM3x/r3o9IV54rB4i/f3xDjjN5FKlQkcXz+SDJc2MbN8PTMZNxcAGIIqRKznvwOvAEBNjW\nKJIeF72YXZAhcMTLEUm7GKgA73vohaHSOou1kUwoBHR8JFb0QupnQzbH58OlBqIUazuc6xGyjQE9\n0XdybOVhXs3wZTWN3OPpGoFLZVkmi6R1aA9902LLhv/23/5fvvrqK3JzDv4AKsdoiXzXdcex7vju\nTzcJHhYNMWcdfSv9y/Jnb+n/Uu2nrP0P045dS+8d1XwmATAFJs+wtgmbhgsOPlgrhsNqecasnDEv\n5xRZQdv0QUrS45UhL2YsZ0tMltP1iuOx5/7hT0my7fz8nLdffilZYmPYbEU//e7ujqZpUN6xXInc\nnWRkSuq65mFTczjc0LVHzs6WnJ29kWs4Htnv6rQ5931Pvaupj00wYHLOzpbheclZLs75bvttgn+C\nQNp/9atfBehaFuKWnuOxpW2PoyyDZrlYM59XOB91gkWlo+1a9vWB/eGA7R1ZVvDmzRdcXV2xXC5F\nWq94CBsxtK3UE3rvmc0W5MZQqaATb6HrmsCeL1mlCE8FyLOS2WyWjLZY1qAQmcG6aTnsZVyaY8es\nkrrOeTnj9as3ZFnGn45/JNKoFEWFcyLveX//HoB9faCqCs7PL5nNZiNyrCiPNDiZEsQWByntUwyG\npFIBkBb3Lufp2h6v4l4Q9wM/1DuPjFPnHEU+OAvRQFc8v4fJiaYwxPGeJ+vy4JDH7OPH9uZHe++/\ncYu1udPsy4CSi8be+PMw7LtJlaFpJlmrmImMdftAcuSybMhERqd+avP89dcjjvhf7tg/yvqO+iAZ\ntREEXxtyPUhFPXWuuJd/rBunme8hu/10yccQMAqz+yPjJLiajyef1HOvj85/Wms/2Iik+T3uX3xt\nfAxzIsk4dnrGTuaQuJn2eVybPx4zKeMa5nDMNI/rw62VNeXm5oa+77m8vOTzN69TbXmWZZhRXfy4\nn5GsLz4D8Wcc8CiyqQZ4lg0BLUJpZoRod12XglrW2onU8oD8GGrz5fqHO+W9IJmyAJ9vdEvfhzN4\nR55XFMWMPA+S0L5DKUOeZ9immzigj+ZLeJbHgQ7vJRtunawN0VmP8rDjuR/Z9ePx43HatsX3Htf1\n6fNaGwyKeVnRr9ccjjVOQV7kiaS2LMsUZHHOcjh0lKWcs+86dMgqxxI8r4ZAZVmUlMsi/b1cLqjr\nWdKKj5xi3gsfg3OO7X7HdrudZKLfvXvHzc0NL1++xGvFq1evmM/nMoftsJfEeRIDBXVd4/qBF2E6\n56fPUVprlBB1ShBQJL5nZUnhFbnOKDDoztEdDxzqAxdnZyhjONQ1RplQeiBlbUYXOHrZI6VGSIJO\nMasfUAZn5+c0bROuKSNT+WQuPOfrKqWSTOWn2o/Suf+f0U6d+ucc/dM1/VOGwacc/9Oo3A/dxoth\nhCY+t+edXu/HAgbj74x/5Hqf3yy99yFr7sXxDrJ+OD9w3HiLiseybUAvBE1HH0sChJAPZzEZmFCv\n75xwF8eytCEQMbo+16GVFvknbwPRoBjdIEGcztrECxCjpBDup3MId4AnyzTOyQZS13tmM6lZvb6+\nZrfbsZwvpM9YskwW5/nyjH/6p3/CFIOUUfz54osvEhlYURRBD1tRVlIL9jclRH5q/0u3OL8jlCvW\nS27rHcZkaN0N8D0nz4UxA2HQfr+n7Ttm6zXV6oysnIMqQJsU/Y/6qrPZjH/4h19SVTlKeZpGgly3\nt7fJyY4wz+VqSVHlkl3v9tzd3WGtZb1e8/r1ay4uz5jNBGZ3fX3Nu/cfRD8+bMzVYonXKsjrZSkT\n9bDdc/ewZ7t9wON5/fkXVFVF72F1fkGe5+y2B+paat2l/01g1JWNtsxLnO0lHGkt9/d39H3H4bDn\ncNhzcXFBkZdkWc5nn32W5JsiFL5tBUqd5xlnZ2vJyld5yMYIn4c20LZqQoIUIYtZllEWMzo3wCnj\n8Y0xHI9HPlzfMp9JZP/s7Izzs6Xcj14QRVHXGEgw/ffv36cM72KxCAR/LmlUjzVyx4FV7z2H/Q5r\nnUDxUwZ72MuSoaRkPTo2DYdDT7UY73dC2KhO6vVPDZe/LDM+lRyLmaPU3MCzHsyqRKJm8aM9cHTE\n0fFOHalxJvWvbZ+6pjj2Q3Z+yNKPYbynkHsgZTojsixm+4/HY1JjABK8s+s62naQhBo7HGlcQoD7\nf1abGN7RacsH5155ApN63AufspsC0e9HatM/df6U/eeJe/ivHAt6zrEfJ1HGbWxjRongGGjp+566\nrifHVErRNBIMjXNjPp9zbA4pczqeZ08lfMbOU6xdHyMMnBscq+iUxtrh29vbECTUyQkUpRRJdJw6\n+rEkMtpI8fUqcAUMJSmkv8XUlQCw9z7B8aM9GIMGZ2dnI8dvSmYnWdep7a/ic4im1b2gUEO5QizZ\nHK8dngEqf2oTD68NQblTpy3KDO73+3SccWAlOnve+6Q5P3Zql6slSgn3gTGiixWPa4xhNptJUqDv\nub+/Tyoyu90u2L0ej2O5XKSACKM9ICtyViFAopTCWZvKauq6pm0liBx5cbIs4927d2nu5XlO23cp\nyJlKIrxnu93KOuYdHz584O3btxRFwdniLBETxuBTDDAtFgu8JfHhTJE30HVTzXh9EtCCoe6/LCsK\nJSUjbV3TO8esKLg8u4BcCH132x04T1WWrJZLjEHsCe8ngc5TJE6WZVSzKgTMTAIrTROlT/uiSqmE\nsPjY/vQjdO6j0Bvh//i3GAxPtR/CKX4uin/q8AbAD0brSc+GfIW8Hz/3VI/Hxxwf+2/p93OvnUaC\nrSeVFzzVTvsy7uO4bvGpz51e1+kxlBLWfZRJEn3KW7ztwPXoEGV1CoGEeYd1Hc55rO1CRFUh9RMW\nvAsOugcfsh+pZsJLxt7Jw++7msjgKm8rXN8J1F95lNYkuAAe1zXYkNkAj9JCrpJk/sJ4RMKTSHgl\nC7CnrcXAqspQ7+x7tOsC5KfgbLWgWhTJsPLeYzLFdnOPMYZ/ublLi1Mk0Voul0mOrJwNRvNpjd2/\ntwDATzX3P0wzqGQItW0rWrLVnPb6fTCkMoZdRBBCbdOIuoVWKKPBGIrZnNX6nGPn2e0b6v0e6zwX\nFxe8efMZq9WCtrUsFgZrPdfvd9zd3YVsMAlyn2UZm82G/X7P9+8FTlxVFev1Gq0yLl5cslrOsd5z\n7Cz3dxvu7jY4p6gqIc+JmYTb21us19jOYz00bYcxDjCcXVyyWMxE3xm4v9tIdnx74MOHm6TJvF6v\n+eKLL4JBmqO1oW9bmkPPh+tbjsc61aUv5ysWswVKaclu6xzvFPVB6u7fv39PXR/D+ppRzReszs6x\ntqfIc/a7Hce2pbNjg1yyKpHoLhppWme0xwavRGZouztIPXUj2ablcslqeZbI1KoylPHULjm42+02\naQlHAr6XL1+y2QjcschFSguvEYUUTdt2k/UnwkgF4K6lZCsa7Azwx0Repj0q1Ln2EidFeYN3VnxE\nTyI+lTVJAqPKQ9f2lMUQNLXWfpRgXAyjqXyYzONhr9HKTPagaMhK1s191ClTahr09gxIrvGLyU5g\nuLb4/eeOO25P7a0TzppgvEVDfnyM08zlafBBINYDnH8s9xoDRyJTqNIaER0ECNnsP4Pl/d+iRURH\nnufIsjSQqmWngZiT73qmoooTGj015V44DTTJmIqyUDqHnnyFH4RY4OSck9cwjzLo8f9xImh832LS\nIc4N733iyNjv95PzSbazTI53zPrGY4qTapLjGM9x+pkx/Ps0w2/tNGD28PDAer1mXzcp6ztkk3WS\n1JvP58nJi858LB8avxYD1xGOLu8Ndepiquah/j1mt3f0vZR2ZqYgz0qKvBoFKyM8Ozj3vTj3sTlA\nqQ5Fh3ca5w227dEBEKu1Ji+KcM4W5zzO+oRAi2Mk1yxreN85yrKYBEBSLbkWab9xEDOOWSRjzbKB\nmFD4EwayPu2hb5sk1dy3Pd4Z6v1BSoBC7bzWBuWGeRKdflk/RFY77turxTzVp3svJSxKK7quEYLq\nrksqBk0j9zruy3d3d+I4hwKpzvaUIXkWuV/iWiXOdSn9QUqJfvOb39C2LZ9dfcbl5SVlIMl2biAg\nVEpRFbP0vIyJ9sZa90MpTpDPDGiE6NNprcm8RnvFcXfAHhqcd3z+y5+zKiooM7rlmuNmz83tLavZ\nDNoOlEE7D8oHvyWUbYRnz0mBv6AdGPgxTuOQIns38GRE4sSIkLm4uEjP+XPtR+fcx1rv8Bfjmu7h\nM9NM8fi1089MP//0YjpknE9rzUXXVR5G6HuL9j4Rvykn8m/KCfTbOxdgOiSj42M198850ZMNRwvE\nPT4QPrymdLwm0aKPm49W2WTDfmx0yJhKzT54ejwdHgUqwvKeRxJ4bwN80oPvUXRpnGKNv/KAs7JQ\nhmBC5A7w2FBtMPABDCSCsoh5K0vK05FqJ/BP5XBYHB2d9WQ+GD4olNfYUC/W24Zc50GwT2prI1TL\nOolYalOiMMlOFMPQQpItEmkL7XW6zwNENcpwDGSI8aFWzuP7LsyFDIsEI3zfobEYJQykNhjPWEs+\nFyP84nydSF+cc9R1TX3Y8Yffb9Aqm8A5nRMG7LOzs+QM5aVshvNFLoaCUsQ4SEARTe6xyHhJZBoQ\nErPRMxa1bXWWkUSCf2o/qjavZijnU3Zbaw1lTl0fSJKXypHlwlxrvYMMsiKjXJRcvHiFVwZlcg5H\ny7GxOK9ZrM7YbHe8+eI185miaT37uuP2fsfN9bWUrmD4j//pLft9z/3DHcpk3D1s+MMffs96VVEU\nGa/fvGI+l3m63eyFvK3t6buG7fZBWOYXK7Ky4PXr1+x2O5quY3V+RueEtLKuhUvj9evXvH79WuB+\nrTjZdd3IJu48v//6j1hrqeuaLz//Ihmpq9WK7XaDtYptqPPbbTbg4dXrN8lBzjJD2zU4B23XY13D\n9x+uubuToNtsNuPixRVN03Bze836/CUmn4G2NH3LdteSFzO6WtY3pQy9tawXSzH2DgeckoxDUeV0\nvadu6sSc/OryivV6zbt379hutzhlwXha22Csp+kaDs2e//HV/wgZEcnQey+s0EqpZESYPBMCP62p\n65rNdiMlEyMjTeo2c/K8om07LEpqe5XGRwk5L0FXr1TYhxTVvAINNr0X1m4CjDVm7oORrJxCe52c\nSI1KHDOnUOn4d+Q284oRZPsUkk+AK8uv8r8PAVsvxK1Kk59AmcOn0lqe1kMYleEJAkGktcaB8/HT\n51E44a+ZvPx0oHv89zix4FXkKvCTQ4wdrfF1K5VNnP7x2I2vJzpnQ+ZWuBsG50XhvR7G7XRs43Hx\nk+PEQfCIw3J6jUNA5gQx4QaEwqmVYUxGbgxaefnxHu0VyvkA43YnttyJzYRFj8c0EImK3ZZNejIe\nl9gf673wO8TXlA6BHUEORPNOPWNYexDG/Sdassu8mf49CtoASbp0sDE8gnkUIrq26ZnNDXiNdyr9\nKAxFXkkdsZbnpd4fBHbsh+dNTuPS/9ZNmfB1Ii+WOTRG9pwGhMavpfvgxSmrcsmwPtze8R++/Bnr\nxZL2eJBr5YSokMGxju9rhGMhOvWxdCmiwmIGOs9zcdijJJtXoHKyTOwgay3O6rR+Z1nGfLlGGWG4\nj2oxce4rZRLz/vgZyrOCPuvQvsUoT3s84Ls5utWU/hb6a7QK0H4PdX2kqoQEtfduuF8oMlPgsiPO\nh1p137NcLQPSS7hZXDcEPGSMxWYcl7nFQF0ckyhRmGtDM0rGyYX19Lbh2Dj+j//6f+GdorU9Dw8P\n7GuxGYqq5CEobtTHI8e2Zl8fWLcN+/2O3W4LSKlD1/cslkL+aq3l4eEeMWWDokDfcXa+JMsyHvZ3\nCZkhz7kBA045LBZcfF49eZGjcwnm0HW0bZtQg1/tdqxv16zX8tP1gsaL8+ew26frbdsjVV7hPVLO\n45lIBmIMyou8nVKisbFcLrGHI1nr0L1F9Y7DdovJc4wHrCNXhvVyyTvvcG2LC4iAIS8te4+okHu8\nkRIZHfbT04RcT09EDgMYJ4nMuL4xLEcURYVSPpVXPdd+dM59bJ/cBJ9w5J+qo4Jx1HG6mP65/TiN\nkJxGUMdBhr/02obAwmNH/7nPP/e5577z1HvPOe8ykSR4IOFr+REegLD4+ekxTsf0Y/2P+Q6FRyDx\nkvJR0fH1nojWiMEWYa51IcgRHOHUJ4fzLdYNMDbvRsEJ26IzhXJhE0GIlaQUPxIT5hMbTHnwfYfv\nW/nDiS6u9wrvJCJHgNGO4XPxPva9MGtbG4MyYnW6vhcjXfVY12G7jCRlEq/JisMuC+MAmc5zk+qy\ntDZpIY8ZHtG63vP1118nebIY+cyyjKurKy4uLgIkOSfPDFmu0Jmib21g1JqOgRrdU2MM2oiGNr2D\n7IfL8PyUtf9hWswAjglZQNHbgJZRQ2ans1J/HrP2XimObYND0zcdRW+4uHzN5dUFXsF3727RGrb7\njt/9/rsEBd/VB/7+H37J8diBgeu7Ow47cZqttVxdXXH1Ys1sFpmYFU0j0nA2rK3OtlTzBYvFijw3\n7Pd7vBsyUB9urnF4irJgdbbm1atXWGs5tg2HY81ht6EsS66vr8P15Xz55ZdkWcZvf/tbPv/8c+7u\n7hLK5sOHD2SZYbPZ8PLlSz77/PMEf+y9C9kFcGj29YGmkTIEZQ2myDm7vEiyPF4rytmcphFOACES\n3EldZznj9foNsyLnj3/4Pa1tQWsOxyPWKZz3bLYH7h926Xk9Pz/n8lLq4sdZcu99kuU8HA5475nP\n54mkz/cd4Kiqkuj8tK1FKU/bHrm/v+f+/h7nHLPZjKqqTtiFx/WqIdsLGD9inx8HA6NTBThrsYwy\niD4w7SMOP4T1ZJQdL/Mch5vsozAN1qe+RX+hl/PEYHtskuUANXKoJOMxJZuNWb2P1TSOfx/2ZXmO\nQpR0+A7jsSCEj2HMlfOoudH3R+f7VHs60O5RaiA9O7VJTu2S8V793HGdGsZrfG3/mu3U0BW9cGEZ\n11pjlDijMuVcYh+Pma0QDRn67IcMslISFBj72qfne9QfpVHmsZ043Oe/jZvguTbcL41T05KM6fwk\nZb4n99kPiA3vRa0kku+NzxEdKJSMpQtOydhRF9vLpzWiLMsE8T915mM/TwMV+IEDIJYC/Z//5df8\n9//+3yfM++NymXGJSszolmWZ6q3HpGNjyP7YuRenB5QuyLIiwf4PhwMvX75ku90GJZJlIhyNGd7x\n+uaVJsknhoiOArq2xbktzWbPsW55uKvZb3s2u46mifarkPnFNTGWxcRnNTrmRZlN1qOHh4e0P8bP\nRph4dO7jvXfOsdvtUuAl2oIxK227HowgP2M9e+TcKYqMsgxa987Q9RUqkzWyms8wuQSC93/a0TRH\n7u4cru9p2pr9YUc1k4CLdZb9wbFarcjyDLRPtq9zkohzeJTRwv/jBzszBoIVns72oAdlBpOJpnw1\nn9E9dAl2H1n0d7sNxijOz9f0fUtd78PcVuB8UC0wiXegLIXXJu5vkbyvbVusslSmkuDxCGVhHPiu\nx3hPqQyus1z/6R3nFxec5zm50uTK4HuLbzuU7aGXhKnzvdj0MSXvJeAjuUX9KMAoc29Ya6yPCMvg\ngnibEolt2wa+sOcVM+BH7NzD4032z9lopsbC0+8/NyDPfWdsfPy5/Y7//zl9/nM2dyAtmOOM8Wmb\nLtB/uzbvX9OeCrQ89ZnnDKz078TYir8rZ/FBfk5q36dR5gi/Hxubw7mC0emkpp5QGpB6GhZkj5Vs\nvQokWwxMoMorejuQII2v01qL7S3tsRn4BLzH+x6UwjsrEhnOYtsGTIC3eo/tLV1Ty6ZtLd4avB/V\niUbYnddo5TBa5GCc0/hCoFCff/YqRL3uojcAACAASURBVEQHWZmmadjvNtxcv0/SflqTauDKsmSx\nnJNlJmkll0VBkeXBoT+5j+aHc+x/aj9si5kPrTVuFHRKGZJgFMWoO4gTtN1umS1WVPMZy7MLiuqM\nyxfnaAO72nJzI/D2uq6ZzWa8fv2Kq6s13357TVHAZtNyd+cSW/d6vebq6orz8znbzUNwMttEfBfr\nNPM8B1dxdr6S+nQU9/ciiRc1d+fLBb/4+S+BYOg6CeDtdzXbzZ53373jxYtzDocDb9684Wefv0Up\nlTTlnXOJvX+z2dC2DUUhRmAkyBueCx1qBdvALTBHKZ1QMTc3NyyXS2CA9mVZxs2NaO9ut1uU8vz6\n17+WNSRAVuM92e127HY7jnWbMlGr1QqH59WrVzjnEpwxfhfEGIkGyXIuJQizKufi4oLD4cDd9U2C\nrUaJzpubG+7v79lut2SF1OjN53POzs4mNfcCywUdiMoGOSbwXof/p/PMB4fK9j3H3tGBoBCsBQK6\naQRfNolQZZphjmtzlB87fU/mbQyux+CxmxhCxgxr+7SP4yA0KTg6uYYnfh87HfL8PO3MjZ0ZqcMV\nJnY+sud6NWUXf7T3q2nN7vR6pn+Px+qU8V36/TR514+hjcd4XOM9wK4VSofgFj449yJVpfT0GsbX\nPj72YA8MNp8xBqf+8nFImXVC1l75kEV/5p6cTIFP25bj7/sQZJnas6fzIjrF6b1RP2N7LrsXSyyj\n43tqK7lAfBYRe1VVJWnNeJ9Ox32ofdfBhhk0672X0qHLy0vm87nUd1fV6BlzKaMe1+Gx4x6DkfEn\n1viPpeuMztPf3iu0KcmyImW2Ly4uEuR9fGxjTAr8RefSe09eVuR5gdIaowT5FMe4bVvu7x4w+o98\n/803XH/fUjdwPJQ4WwJHCNxScd2Ja08MYDgnqNuYlY4s9ZHHIN7vxWJBURR8+PABpXi0n8T7ETPS\nkTchR1NVM5bLZQo+x3sYj22tS/2J1x733cjPEudGnuesz5ZU+zLsCZ7tdsPDwxbvY0lBnYIVSon6\ngPM9Jqsw2VSZAiXPeF4YijIjywwm2JbO97Sdg9qhDSGx58iLwSZW2rNYzjhbnCWHPMpfVkFKMdrL\nzjkOhwNVVaW9fL/fSylBmP828AWIdF9PRS4P8ujRPRwO7PY7lv0FusjCox4RcLI3uUQqHhKhDASv\n1lq8fuyDjv2b4/GIDjxe1lpcL75KvEa5H326R8+1H51zf+rQTSPoz3/+zzluhKCNa1g+5fyeogX+\n1r3xdKE9DQQ8GWUdbwBPvB8Xw2hwfSwj8JxBM27Ki7NJLDNgynzwZL7/pK+P+j0551S+4vR9h0S9\nYs09DrztRb7QBfhnPLbzaCOwfxWiqy4Yl97F2nwr9ZfhfbwwnGJ78MMmlvoXSwicFXhdcP5972TP\n9h7vW2zfY9O1z0TyBEvXt+z3W7QB5zoiCMJ5UAhRFT6gEHoNBDUAB7Z1qCyja9oA258GKmQRLjE6\nx4VNLRrjcZPrlcIEeT2DpzAag2F5cc6bVy/lOnGBDKyn7xtub8WhiTA3hRjkcQNcr9dJXzTPc0yW\nExGdOlOPjJm/pP1Uc//DtDGkMf4Nzzzn3qOVkAZ1nQUrrO1XL16xWJ+Tz2ZoA8cGPnzYsVgseflS\ntNM/f7OmzKDrod63fP/uwM3NLWVZUpYVv/z1W5oGZjPoGthtjzQBck4wOF68eMnZRUnTSIbB9o7t\nZk/XtEFXd8VyqTk/P+fixRl9b7m/v0+G+sPDA5vNBuccv/jFL7i8vORf/uVfuLp6TbWYU+/27Pd7\n7u/v+eO333Bzd0tVVZxfXoSgoKw7VVXRdR273Y66rvnuu+/Y7XZJhmm9XtO2XZL4m81mFEXBw8MD\nDw8Pad2IMkJlWVAHeGPbBVZ+pWiajof7LUUhzOZv3nwRnm0xsntn2e/rYEjP2WzEwNrva8BTlfNE\nSvTy5cuQBbepjlN4OcQgvL295ZtvvkEpxdnZGf/4j/+IMoJsiKU8YyNdDLq9OMBFzrEW7gEddKJR\nTKqY09ob5tW4tvs5BJvUGBqcEibxumsp8kB8RQAOeSGUSxlBxHCyIXOuRnWH3g/OT1JZUTFrFltA\nW3mIe84pMuD0+Rk/M7Ef4kT5J/e9+L3J7ycBitN2atg954R/yrkPrz75/mlw/8fi1ENEcEy5AyZk\naXiM8lL3TQzwBPQYQk47dnpPbTgFyfkU436Yo6LgM50Hj8Yt/BvPhyGAA/7EqR1f11/Txs9NQmDg\nJ47v1J56LJEXn4kYMHQxoKHUIyccSGMTs5un/Y/zPjqN0Ske6tvFUR2jxcaBM7mOLBHAOeeEZPhQ\ns16vub6+ToGA2IZ6ecd8Xg2Eo4FvKDq9MaOvMIkQUPquJ869oNXkM4NMmxvI5BZzylkVkCI5JhD6\nFUUhtYtF/mygrqDi1Ys11fKK3/7zjub4jroGb0uMWoDfERWprPUIUWYMlNrgiwwke2MCwTgX2raV\nenN9wLZdIJn2k++s1+uJD7BcLhOq0zjIjQSkqqJgv9+znM+p65qyKKiKkroRVMCYW8GYDJTUoL9+\n9Yqb4AAfmyOrsyXOOo4jJ16HTLTOMpQxIlMdxs0i8rCnxdVaaynvUoq8LKnCNUd0Qzy2tVYIIo0k\n2bREV9BaUKTee5bLZdobRfBSodUQ1BqP53a75f3798znc7788kt+/etf0zRNkhGlt5jMiOxf31FY\n8M6RBUWuprf0rSAg0rOnY4kSIoseUbBh6jjv6PshIdu03US60HtJfNSH/VB+YUfzznl62ybnXhB7\nq6Qw8Fz70Tn3p+000/7chnW6mY0d5fGiOP2bR8eJ3x2fIy2yXqCKNkBZJZYkuoiTrf9kPRhX6kVn\n8PQ8k8/HvjtxBIesstSPRbi68AIMNYTjazuN9KYNxE+j+uM+jCGQp21s0GnvUMH5lt8tWgX5OLlC\nhkdZNh3RmPShz55xvf2gnTt8Rzkv2HnnwzFGDnjIqseSgSgfNPQxjPVoU0sBIu+ljjQFjSJXQBpi\niBwArkfpPI17vLlaC/le3zZoHQScvEu8Asp7+vYod8Y5MQZCYEhpj0aCJl3ThoyEkbFxXYBQO7Tx\nON+mEgPpq3RQ2TCWToyePjorlhTN9rYbFg96lPb0tsfTJkMny2QFkiihZrWcYzIJfLXHhuZ45HiU\njOZ3332TFl2JpM+4vLzi/Pw8GQkXL14K8Y1Rk8Xtp/Zv0+JaGYOXp1nK04DmarXi6sVLVqsVFy+u\n+OKLL6jmS9re8v0fv+fursbkFa/fvOb1qzWzSlEWa/IMvnvfcnf3QBdYdi8uLnj9eslMeJE4ePjT\nuwPHw5Hdwz3nZ6tRWUgICAFN03F7/YE8z7m5uWY2m7FannFxfknbHZnPZ+z3R25vb7m/v08Sbs45\nPv/8c1arFfv9FrRidbZmfX7G/d2Gr7/+mqZpePvzv+Pq8hwwKTtUzcVRPh6P3Nzds9/v+d3vfheM\nowVXr14TM015OcN6jckLvNLc3D1wfXuXZDDn82Vi7m17S9s7HArr4WGz5f7+QYIXXc3f/ervKfKc\nD9fX5PlAaOW91BI7B7vdgba9BwhERFF6Sj4f4bEAi7lIFl1fX3N/c4NSg5H95ZdfJpKqrrP4vplk\nSaPxFM8vhnPJfF6x3T4IykiJU+68l4z06Z4Z9pfzs/MnadhivXyac346D72C/5+9N1uS7cjO9D53\n30PMkcPJMwIF1Eywq0mqi12krGQtM4m65BVveKNXkOkF9A6S6TnUN90yk6llMtIosVtsdpWapEgW\nBKAwninnyBj25O66WO5774iMPOcAhbIGTe2wxImM3KNv3+5r/etf/2pC1QbnHaY3Tp1zoPevjzEq\n1F7H1hq4Hzzf9/muY+9u2869fSceWqq39oQc77hW96n7u52ycz7XVRRw3mNQ2FfOm/372xf1NVu/\n797PLSDiVnt9sGMXPNzdr/ve791PeyQfXMf0is5ZNUoccBG5ClF3PFoJeKMjJd77TteuB6h4L6w4\n5UIxNKVCMC8YwU60RmL981e1vtO0T7xqdwxtOcfsD7B0v2/vF23Mffbr7XNs56nvXmuSJNTetUDT\nbfBI7MY0TVrHfrc571qB4xZsc10ptg4s6T27XtDMWhueVUcjPzs749FbQx48eMDf/d3fbYE7EQCA\nQA0fZK3jHsXW4nZZlpGlMrdFp1jAoa72u1IGlDh7Sokw68HBAXXdpUyOx2OO7t1DDfLYuXv74lVt\n9nDIP/ndn/D0s5/x4Qef4ZtjhsmSqjzH0GAQ7aWmlLzsmK4mfWrRaZcaJTnr173SfOJrlE2N14rR\nZNKCs2maMR1NaEKAqp+uMMhyqrLE1lLRSTl5D5TzFJu1/K2qcT3HUAA3GUdGK0bjaVgjurF1dXWJ\n0h5rG8qyCNFzhQoAULQD+8cE395vrDQDtIC0UorNZtOCTH0dKRBgoP9+xBSD3XLccZ84FqJzb61o\nZfXPu1qtWqHfg4MDHj14yMnxPRaLBd5aDIr17IDnn3zOWCUMvCfRGmUMlW/I8lxADCUpLUoprHMi\nVO679UnejcAyCMPLOWE3Au2YThIjFQQW61YL5/HDJ63PE9dI6ALTdW0ZjSavnMO+cc79XZPh7kR6\n23F/s2Pf5VDfXiRvAwlioPxqCHg/J+auFl+yffd4G8C4fZ13ASCvW5zky+5lvitSsO/7fUbRvvPe\nTdfvztkHMJytUToJyvoBtFDdsbz3tPn3xEVVImLONaE0VQZER963zATXyESifNA9bu9BaDDWNWiv\nwJu2X+QcNjjvTYg2CU0+6hJI9L6kqRUub4KooIjD4IPBYRtQViYE1YScVUkz0OQorfCNCykErte/\niGVkRYhIaR2MmQheaNkXJ8689zTWtkq6PklQXtBriUI02KBurRrd2hyR3tvmQEWF7NYxEJT1+vq6\nfYKfffGspYY5B8dH9xgOh8xm8668X6769ifwH3Puv64W36uI/ie69YDCP17ELcM7LzlznrqW6P3i\nZsXZxYJnZxc4n3F87wmHR4ccHWiMkUj91VXB5aXn6uqSw8MDvvOde+SBueEcrEtZxFYbx9Viha1L\n3nrr2xzfy3AN4EEn4Gq4WjScnZ1TbNZMJiOGwzGHh4dMRmPSVIzBs9ML1sWmzfG/f/KQ2WzG1dUV\nWTrANjLWTl+e8/SL57x8cYa3jvl8zsnJCZPJhMOjOUVdcX11DVpRFGWbmnBxcUFdS2T+8PBwq7zY\nwcEBzllubm54+vRp24cnJyeBrq9aAadYe7hpalarGz7++GOca5jP50zHE/JUKIdCjS/QKqGqhf3j\nvedqsWhVoqMBO8hH5NkQlICNMTJjjOHy8pLPP7toQZzRaNiKKy2Xy60cUu8dRaB+RuMXZF6JUR4x\n9sWIbnN5XYjkqDj3deNIgGAx2h8/fnyrKkyMzMTVRaKRYTgCadrpjcQmIqi9dTjOub0lqpsH+3m+\nFr8rNxxP1F3BLb91d63anmf7f3szpPKrYJlbQQQloNCrI8C7f9v+PQnAtdfbZf/6zuKrmkQYvy5U\nVt06rzFSp34bINDtOhMBKvk3bEPw33m1ndEdr4tM7zq33gVmn9/vPEOwIdrruH0PLbD1mrt/U7u0\nfw3Red/HE+l0Fbrfd+/h1r3cYR/3qyVkWdYqmkcdocQk2BApjFR56Ojz0VHr/2zfEO1aE+3Mqqq4\ndyjlTB88eAB+GxyI55f86EE7F8X5EGg/p0mKUqbn2Jv2d7HHkpBOKHN0PpuJXhBhWvDBl/8KDv1u\ne/DOAbPZnCR5TuNSsnTEfH5AUZ0HO6lf8k7RND7Ytw15krZ15aPt5L1v52WHROjHkwmzyVTsuVLq\n2udpFlLICuqyovS+tfOquiZxch5ULUCJV1RWgkNZYpgORlBVlI38vQ5BIu99u66tN+u2PJ+zVmrA\nj4aU1YaqKnFOwl6urmi8Ix3kLDdBMFEptDGieZUYpgdS8SWmaVjnaKoa7x1ZUL6vQwlFR8dGsd5j\n0hRtLWhNkmVo51qdoNrZdjzH8ZAmwmIrigLfbAc2YppDpOp/8sknbZndQZ7z6MFDvvXwCZ8cHHH+\n+TPUumA00KwLieyTGkg0dVO3FVOsDWX3tEZjUFrAaa0MypjAgg3ruh3cotP3S0v2UzgiE6evy+C9\nD2WyR/+wnPt97avSnV51vDc95u4EdteC0G+71L9d4OCufXeP/zrhl/8QTeLofQDgNdvvABS7xuU+\n8GG3v81Onynnt48RlGJlX9Deg3USUfGyvXI+RJGcsC1cYD8gzroPM/5W3pmzQCITccyhQcJZggo2\nLYIdF94kEcSwrmuyVPJcjYvlLsTYlbQii3JNqL8s/epdYBFYiaR7dVsgCaWwjahNay11RX1kZITJ\n3TYenYSovfNgJcVALCSP19DUgjo2TRPEO1TIe+0cwj4bpn0WxrT3Hsdn7IPpNO8htxrbeMqy4unT\np1u0w744TpqlzKZTBsNUgPb/GO3/ldo2hbP7LqLGLuSuNk0XubW2kfr0m5Kq8UwP7/Ht7/xQ1Ne1\nYlPCzbIrsaOU4uTkHo/uZ8QpyjpY3Hgur4Qelw5ScY7rkvFYHPuqgrqynH1yznpd4JViNpsyGQ3J\nBxmLxYLJZMLFxQWb1boTpVSijj8ajVp6ZpIkrbN6cXlOWW44OjpiMpmwWUlu3XQ6FfpbKg7var2S\nspWjTmtiMJDqFB988AF1XbdGTVmWrFYriqLk8vKa6XTKfD6nKApms1lbTz5GB+q6DjS5uhXvsbZm\nNpsxzAdkiQ5RIxGkapqG9WrNzc0NVVVxfCKCl4PBgKqqWtBMDAaD1qJO/PTpU9IAEnjXMJ1OOTg4\nINVdNC+K8fWjeXFOiq2u665uMQSnWlFVNY0VQ0NqNms8qhMkU/25nB4ltmtCrQd6zn2/9R192HVA\nYoQlODk+Opu+nXfjPlp3nLh91Hat9q+f/X75Mq2NFPeOsw3+b4Mgb3zcvoOvdQDK7hLle7VzH5sR\n7/S1gYRfR9uNNCulyLKkBY207+YR2aZf4k9hCOMnUuP5cj5YPwqulMLZvm1B22Utk2DrWpSU9+o5\n9fucV++7nPvdoFM/AHEngOC3gzTb/aW3HuvudfSd+93Wp8nH92yfAxCF6nbr0/ff7RhdbcG80KIj\nGgNVcW7pR+LBb2H4cc7ebDbESPpysdpy7mOfxHKh/Rz7wUBqgkvt9W6ffnQ/iudprVEmgzQTTSOl\ntgaQav/39TStYTyStKyquQzK8QaTGKqqi9THvun0BQTwSNJsa+7OsqwFcTUSrR4MBqQ6ERE4op5S\niXEe6qZNnUUJ0NwUJU1ZgbPUzsNggG1qyuWKoihIUFDWYLb7XmsJGkVHWcTbLEmi0WHdsa7TEJCI\ntYCyTdNIACekV0j5QoXzsj5G8VfRpVFb9n8GrRMb00+hezdkPe1Kg8axX1VVWxkgOsaxL8UeT9og\nV3yPnBNdGwhl/yay5hpjKIsCW5QcjGf88Ic/pH78NqZqyBrH0+fP+LuPP8Iq8KmhcbVUoYnzmg5l\nC3USRMAl0KgAk6TB3s2YhZTZTlRS6tZHKn687vg5XntkZ/TnlLvXiW+wcx8fQj//4utsb4Jk33LO\ne5u/KRK+75ivAgjuQlo7A+BLn3L3BBI9pnPUNaDCd6/X/BeDSw6jtiItd59y22lW6m5wxHsfROiA\noHIbc+ljzrwCjArlYRqLMrSllvAB83Y+lHML+0dhOxW28aJoGSNb/X6N/R8nk6j4LJOO3o7iB2NO\n7slTlgVZIotdYgRgwIrQh1WRNisiIzaK8oXjOSspCj4xcp4Q6fcuXmO4SKVB9zxh57CBhaBNgjYK\n1/SQPmtxzooxojxee7TJsHXT5g9K91ZdxMALGmmbhgZIkwSTZQISBD0CY1I0Cts0KKVJMhOixSG6\nEICEfCC1ZmOKQV3XWGvbyMG/+9lf8ns/+X201m2u82A4IMsHjCZjRqNvHsj1TW1xvrTWYpRM72XI\nY3e+JjExPzqALUryiUdBkXiej7j34AlJknF+vqGoLbVtSNKEB/enjEeGyomjHrJTWCw8XzyVKHJR\nbXj322+hjMJaw9XNNet1zqIuOD8/Rysx1I6OjhiMRnINOIwR5/fq6poXL07JkoQHD+4zm01wSApJ\n03TqwWdnZ1xfX6O15uGj+1KDdzoVgbrxlNFoxGCQ8/z5S7744nN+8ffvi7BQVfCD732PLBUqYZ4P\nefbsGXXt0DqlKCrW6w3OebJswMnJA+raSu7/4SGnp6etcVsURRtt0VoHY1kADzHwUskNROoZr5Yb\nLi6uROG4kvf18PCQ+XzOcCwofJIkUkkgGDllWWISRVM12LohS1JSoyBJyJIhs9mMQRYNdNe+V9JP\nUkHDe0+aiyM/HA7bPP3INojXH43OsqywtsGHuukOSRswRm9H4r3QAz/++NOWji7GhpaUn1vOfbdm\nVnVNlu5Wton1vcMaoxQog/MapRweDVroux6PtZ2N7tGio9K7PqccfcfNt5x4374r/bYPaO4f73Ur\nnW9TF7bB/Ddp7Xaqyy/fvYatCHRrQ2zPjcqrrb/vnqN/PfvBjW21/DdvHpA1RseytAqSYNRq3d2T\n8rt9HbkRUrBWKy3sOpR8FyL5b9pcjPIHgMCp7o6i090HAPpjRLa5/fzaNEZ6Y87vYT7SO9cb2Je7\nTsyuo9//d9ce3Xfu6Oz6QMt31u0FeMQZ79IN+lV3jDH4UKs8RhPj+XaZDH3bLvaTOK6mrYfe3+fj\nTz9rK5nsOmIC64BWhtFoTJYNZPwkA7JUlM5NkpAYyY1PTI5JU9I0QacZKk3FLvq6vffXNOc8Vb3G\nuTqAE4rMpJS1wjYyRyoFaZa0JeOyLMNICRFyk3J4JHnlxiQyzyqNshZbVdQ3K1arNZkWBsU6zOmJ\nMiRpgqolAi3Ns/awWi2pVhtJAfWKapCTJimuavC1Rftgz/bmgJb14uzWmGh9Er+t1ZAkCYbgRGrV\njr3pdNqWi63riuVqQ1EULQi0CxZFtkWsKBDHXXw3I2gwnU5bmzE6tovFQtbxQXprPus7xdv+h1QQ\niIyE1Wol6W5OBPVsUXJxfsG9g0NO5oc8ePyQcZIxf+sh5uSACgmUFUXBYrmkaBrQBmOSUAIxVvaK\n/ppq5/XEGJzd1vhSSuyAYSqMXa0SUpNtvVf9yjbxPqJY9l3tG+fct5ViAqJhw+Tc+CAap0VJ1GnJ\ncXDaSSzWe1S/BFpo/QfaX7T2RYn7kQKtTevAitGCfO87h1NKFOxHdu9y/rsIskQnJKc+1mAPtTBD\nDnjMR4908niNAnqIUEea3o6cvLIpqcfrCQpGSnLbnG+2qGBCmZToNm1N+uAEis8pR1ESkXZIvXbp\nJtf+xMi4nEsMP430WxuT8eIwKq0kl5/tqTlOLKhgoPlGhPQgLBAuIgBowFpRIXXe7pzb90rZOXwT\nFj3fL8kUXx65b4nmR8G6eFUGb5sWKBEgoaEqNygc15U8Q9W7Zxei9EmSYhKFtzXehfwij9y1c6A9\nzioSPSCgGVInM0Tnvfega7x1+FDyx9tGHHzrwNeoRDpLtX0XHHIZAKKEW1chPSGOefC6R4v1HoIj\nj/eYVKMQ+r/ytlX7jwoLAL6psGkCQZ3UIzTl2Xzey6HVQh1zmmQwJM8GzCYzDqYHWDpmx3pV8PL0\nPBhegibPJnNZAJoKheLevQMGeUaS7wyY/5826z2NCz/Wk6cC4Un0oBGwJc5zLoIwMuZHoxHzgyM8\nhouLK5rzNePxMT5JQMHhvQnjUctspKrhtPQUqxJjUsbjIcvlkvF4wGioWJewWNywXgpl0DUVBwcH\nPHw4QytNXcNiXXF9cSn6FaFU0fXVgpP793j85ERSOABrodhYXrx8yfXVFXXTcHR0xDvf/rZEy6uS\n4WCEbSSncLVa8elnT9ls1rx8ecpsPuadd7/Dt975Fmdnp2T5iM1mxXqzgpsVH338KUXVUDeOsqqY\nT+eMRyNevHhBnkmN3CQ4ulVVUZYly+USEGO6rmvm8zmj0YjFYhEi/3VwZgw3yw1nL5+zKTbUdc1s\nOuPxozkvXr4ky3PyQd4yYqQiQcl6XbBarSnKDWOdkySK0TDn6GAulTS8zGGT0VRyKJWicY7lahWM\nFkcWBJPe+dY7FGW5FQmLebARDCjrktV6TdNUVE3BYDwA12BtIzn3aQJacgsd4LyibiwOzfMXp+La\neRcAkwyCYRbr3HvvqTvJFxFDiqJEACaK30WwVLW1yp1SGGVAg3INEiGXudn2HV60rG8xsqcUXgvz\noN1ma03ecW5VbwVU0T6IvxOMtu3mfef4aQ1a3a1bIzvsOtS3N472gQ/g7vamOxo7wqsgdIqsOTiU\ncmgvttK2GbKTE7XbelHj6ONulWxXu190YIY45godajAbo0nTLqLmnBNBPGVFVDbeRwTtlQ7undxD\n35X2wf7yqKBh1LuGWzXld563120/ymn1VvTvtv1020nojwPvo6NLj9EXzxXO43aWpDuAgP45Omfe\nh8oRYRVXvmX1iZ3SOT67AFWr9u7kGZT1fsEtbyUPOAYBRN2+C4bsHnc6nbJcLtsIvTEaSdmOz3/b\nbsWrdlSkqQlAj2axWACehw8fUq0/wQZHLU9yVGrweGaTOfdPnjCZCUtrOrvHaHzIfDbDZBl6EFKJ\nIrD1ZezfX0OztsLaFUpVNHaN9yVJrklSoWTbqmQ0GpNmhjRJODiec3N5zWw8xmnFeDggNwbvrNRy\nX4szjHesV0uqTYHHSyDFe6qypCpLMiPMR5wN/SptPB6T9qLqyit040kTReKhcjIjuhDK0z6kwoYc\n8ujTOOdCwEkCaQpP04i8aQSQPOC1lK7rMzjqum4r0LhYmrVusA5QWhinBJDNa0lbVYlokOiuBKzy\nSAnppiHRmqJNGzHUzrG6WdOUDXqUoLXYngqNlEf1GEMLdO2CYlmWhZK6BZWrsYVFozCJzFdFU/Ly\n6hynPYezOcPZkO/94/e4WFxTwwl62AAAIABJREFUuoZ0NCAd5rgrwKtWQyQGGhVKSm7jSJQiy3JQ\ntH0Um/eWRGkm00k7X4l9r9oKRSqIa2ttApMjR3zCu5lZ3zjnPhr3fSpNiw72JlLg9ucdRPhN2+19\nOsO3//d2cNBfdm4fa5emte97QSpVSx287VzuLqDdtUXFzLjf9qVHp1rk/oTiJc67rBMOUX8XS0ug\nBUf09f3W2W6ZMnLXPl6r7qDwO/vyDk2EFinwwWl1qLbs2+6iK4tHBFjAt4ietw6VIOr+t667W9C3\n6MoS98ETqZkxHyoiZd3kFp17+V3OrVDBkZZTOFtzcX5G42uSxHB5sWyfp3NiZMl1WwiTTmMrpIxf\nXNBFE8BZR1U5tEl719ItttY2WKsxCWFyNXgs1tWCuDYa5xP6ZmjUD/DOkWYK5XVYi730daC7qhA5\na/vJ2taRcNbglccqhW2s5OPisbVux63KMqQGsFCOGq+obYO1Q2wjIj4qlBa0jUcpyY36vX/6+2ER\n6PpdKcN0OiMxGVoLSOA9FEXZigB98vFTQfoD6uucQxsRCkpSzcm9e5gETMItO/72eKSjrbzG/v2m\ntqppqKqaqqopq4o8MbjgUEL/3VQhequCsdqh9GVdU1pFPhrxrXdF/O6mEtzpag3XiwrnPHmeMB0b\nyk3CdGaAEdZLzt7zlzdsChGGOTw6YjrNsY1lMslIMlit4Wax5nJxja8axsMhs9kBs5nsPz+YtI49\nHj777Izr68uA4A958PCQ+/dnJInm+fPn1GVJFSLhMZoe1WiV0tx/IMKPWZaTZRmLmwXPnj+lKDaA\nZj4/5P79RxwdHXJ9vWA0HFBuNngPq9W6ZQpE53s+n3N0dNRSRK+urlpFaYnkG3HMi4Jnz56HUkhw\nEACAQT5gMhlxdX3NbD5jOpuxXC7bUnrL5TpE1VM2mzVHBzOMtmRpSpKI0aa0pm4sV5dXeO9ZrjdU\nVqJus9kMnWjyLKOoKnSSoEPKQRZUkyMDYTQasdqsSFJh/JhUM5tPedQ8RHlHdI0VIqRUFAV11Uiu\npZf9b9biQOSDnNlsBsh645yn8T3nQnXCp1LqaY/jsTcS6rE+lNNTRrbRkdXUzfYxPq9gi4XWBsVb\n3PJNbIT+2vPqfdo13tGWMJPz3r6XL+WG7Akc7P0sKC6R1u5ciIT67XXwjdodjlLXG7ePFe/TaE0W\noldpoEpHuyyWEwOHVgYRrXVY61savNZir+z2W7QHO3C9f0W378/t/kV1jibQKl9H5/7L2YyqB6hw\na/+40r/RkXbtwd7PLpsgOvJKb++za1cOh8P2GEDLANptOuQC9st57h63nwLTj9h2ecCyhni/Tcfv\n7kOOJYCiOGuTUc5yecP9+w9j57X36z0oDEdHJ3zr7XfIhkO0Vrz11tvMH9xDfUNL8DrX4CkwSY2n\nZLVeMNAlBwdzJvO5pHcqhbWVlBa2DcVmw2a5JEkT1saQOIV3ktq1WlwL4JHnWFuz2Ugql/Iwm8/J\nEcadbxqSSMM3wgjAQ2phmGU4VbZBNKzFKBE4VXRjTwdQRmtE70MrsF25vSRNySPgGt4spQXccc6I\nyLj3LfAdGRnSL6G8oor9FO1xLX5KHGMaatswMMPAXuhhmgH4j9oPxKBg9A2db6P5WusQJGUL/NrH\nfIljNE1TTCbq+DYAXY0TAMNWno2qWD7fcHp1yXgk1RV0YhgMh7x7fMR4MOIXf/33ZIheSlVXNL4J\n6YpK/AMfdQyS4O7sAn3d/CHvnny21rJerwMTr3uXjNGMRkOMEW2Eu9o3zrnfFwXf1/ZR1F51zFed\nazt6//W210XV9zEI4s++nPTdwfGqxb9z1eOS00XLu7fH9z6/7lrD+SBEOXbh6de31rn2fUfuFduG\niSW+vF3/CLNA8uI71Hjfsfp9Ep1I6VuJDkiZo/19fwvYUardN/aH8+JUbjbrkMc6Yvu5hr7rPQ/Z\nP1QGUGIwCB3KBjDCbp03sgfk2qwADVqc9His7pqkckF3/4FqKQ++e2QB4Rc2g8V4HSIEtIZlO0G7\nrhSfiGnJndRNGVhwiqbpamc7Z5F0/5qmqYIRYdr9rfVo5Wiaup1onVLt+WTx8LhQYUEWBumLWMdb\ns63fABbbWOqqAOW4vrrBe79lKMk4kls8vnfE4eGMzkoL0TfYLnHxD6F5KMqyXQgl9SGjCt91Tops\nXpYlSZK2i6HzHm0M0+GEk/Gc0XRCmkLRwHJJW/ZwMskYDkRTxmhIAvV/uZS8uqqqGOgRs9mMPE8Y\n5ULlLrynqqGs4OLigouLC/LhgOPDQ+7dG2G05uK8YDzOsdZTbuDi/IqrqxvW5ZrxeMi9e/fI85yj\noxFKQV3L4vby+XPG43Gb1/ejH71HkhmuL8WJBalPu16vef78ueS51wXvvvsu7777berSUleiouz9\nNWVZcnNzw/n5OS9fvkRpqKqyTRu5d+8e3vu2jF4EGiONPuYOTiaipF8UBXVdcHR8DHQ136M68uXl\nJRcXktYQhf2yLGO9Xm/VXfZeahgTqLblpmC9Xgt4o0WwKOY8Wm9Js0xYBJWUO1qv1wE86FgHMZ81\nH+Tkg4yqLlkF1WIVSoCmJsEkUtpqMpkAisSk1E3FeDLh/Q8/wyERiVg6az4/oLYNmUna8j5l09VU\nFnqq3yp51HdKbs297bwb3cvosXfRbd8b4P41UdJfR9t17PbZFHfpAHTH2N7+13WtX1eLTreIWCXk\nqQh9xUj9Li3b+UaYaO3+8afvvL8+OfDLXJ8nVjO4TUH+ase8/d3rntM+R77/effHm9ug0u3tbp8n\nKpBH5+YuAefoiJdl1eqMROe9f23xnYs59vG4bYpF73riPm3uc0z90tsl76KzOh6PubiQEqVpmjKf\nH3J8fJ8f//jHPHnyhOG9AzlWmn6j12KlLSYtGYxr7qdjTPoWOl2CrrlZrbm4uqSsSqqqYLVaUZcb\nlpdXFKsVODDOkSnNaCjvjq8a6qbGOMjyhNrLS+KtQzeOUT5kUdY0dYMxCcMkpU5S6hCkMR5SpUiQ\nsshKhXKIPoBS9Meg2FWRxeXD32I1gflsznqzxntHYhRlJVozJuTqN3VNWVdBw8q3tPz+eIjPLoo3\n9uf6WO0gz3O890ynU+qmbFM3lRNBvZge0get4rsRP8eAaWzt9up2oDZem9Y6gBUynl0t47ypG5qg\nO9N42Kw3nJ2f4bxnMBxw7/4J9x884O2336ZallyfnofqYb5lzEaPKwa/jEm4M9jZH09xUoRemdfu\n3ezrTvyDde77n/dNxvs66U0n7Vc5/P3Pu8d7EwR/3/67g6rvSPYpI3HbfQ57dBT3Xeeb3uMuIvvl\nFrkOJIhOt1JgFEKrb0vkbd/b7Wvo1PAj/W7fPb/qu3j+bdS+M6/6BkW8jg4Q6vY1RvL6umB/dKa3\nqTzxOBIZiXlsus3Hn01njGYjmdhIqTYFo3waFkeFt2DZLs3XdzhjHUtaNoELjpcwVpwL+gAupFW0\nytLyN2sjumlommq7750LefJO1PG9lzI3oStcFNBKQq5r6Acf0hrkGhyNFUfcOouzonugtBF6nAen\nPNZqvLdY52kaARlsXbX91Tn34DDYxPBv/91f8pPf/T28QtTPvcMbhyEL0TYl3zkBQAb5AKU0rqml\npmpoidGkSSKGXIhIx/IqznuauhFasROwYLm44cO6Js0ywJOYhMFw0ArYjEZDQUgzvlQ039kIinhE\n6bxbQGMdb62/3qnXedF7cLYO/VJjbUWaaFCWNDN4pOSiUiKcFp3R0ajBaIn0psHQWm8ctYVVITn3\nhwcZaabwDgYp3GxESmJ1LQ6m1jAaDbl3b47JEspSAJr12lFXJavVTVsC5p13nvDkySFVA66ELFOU\nBZydnXN1lVDXZWtgJEnC22+/jVLSp5PJkKry3NyIyu1qdcP9k5PWSX306IQk0zhLK4rnvQ+5fyuU\ncmSZ3OPhwTF5NqBYi1J9UUjJPW8b1qsVk8mE4+Nj7j84QSkBCC4vL1ul6LhPFImKtMTDw0MeP37c\nll2SZlunf71ec3r6gpubm9YhjiKTw+GQqhKgYDgcdsrCVgCb5XIJNkQCTMJ4PJZ5QxtG43ELgEbB\nwVhiaL1cslgsWkM+alsYY9BO9A6yQRbGUpcG1liLayx13XfCDHjVzgXD4ZAfvH0f63Osc+R5RlmW\n1LahckVrlEiJKylhtd5sGI3ytv8EdHtzR/YuUH53uflVnLi79n/V2va69ioBJGCXCHfnGv4fqgV8\nWD4rUChSbUiSlOFwSJ6m4qQ7jzFC8Y9RbmuFli/pQb1jBsdeqUi6/5L31+801dkot649Ohy9z3v7\n0u9CLLeO9OWub881xM99x7g7/e2gwrZQ6jZQ22+7EfjdyH0LpDtHE9ISo6jeLYChd4L++SN43C9J\n1r+31pkPwsdRrAzg5dkF7242eOcZZLmUGPNy/Lfeeovf+0//M558911U+g+HPnd0OOEf//Z3yIdw\ncXVOY1M+f/qSi6szXpy94PJKWGcox+XlJRrHOBugvbBrjPckKBI0A5PCcMTi5gZfSbT6cDyVEnrU\nGOsZpzmlMjhXYYuadAQDlYC3WAu+qElHmtwk+MaiVef0D/Oc2lnyVEoNVlZYkNrotuJSBJS11pgk\nlpCtqUpZl4pixWTarTXebY/VvrBdfBsVyJwQGTwmQRuN0UaqH+RZYLgZNkXTitUmXhToY9AnjrGo\nddNnryglkfJ973YM8sTr2wKjVHSeE3ySdnZjWeGsxYXno5QiNQZnHRdn51RlRX2vJNGGQZ5DeDe8\nDqW/kXRu6yyJ78q37mv9900pueckSVowJMuSFgiJlSPiM7qrfSOde9hWnG8Rlj0T4W7bder2ITZv\n8vl119c+CE9P8M2/EnPuP7x917V7/H1AQ3/zffe3C4jctYjtv9f+dzGP3fV+4u+qddBlbHXOshJv\nMTj64qBGOg/eSZqUN9vnUj74hyH1wt9+fv17213wbt+X/HTR8d10h21g4NZ5EMrpvvNqbbbp+87j\ncKRZKkjnfM4vP/os0NzF7rDeBfpoN6bruibLovqooyzX1HVDmmqyTCZqArIq+fa2Xc3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YzrBACV2F3Wte5TnHNtarH2prvyoRhtJSU56HWFwI9WmkhHL8y3hz+G4H4TaKx+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KfUA3SvbtoHYyMgQDD0OACXswOwEao6ICFkVAY/pV9pD2tCisAKN916NrHZr9LgmZpqyw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\nYl0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J0PlASkgZZL+RMIY8L1AUM7C3IORgSMhHWZY9ThhOlI0UNPm6i1rtv0xJ94N0De/b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5QKnBwGV8M+MRwdXeNv/u7vMlkumJclJhWSwdlqxcPHT5hMJmit+bV332bv8BIvVku0TkEl\nKBDF1psZXv7u1EcVTeO8C1VNY43MbdOQ5wVWSf32PBNCRu2I5nBRAUmi0UaM80Dal4knOHEywnuQ\n4j3HK+qt8tdVZi+SUf438Vhvf7Z9jlKqJTJUYXAktSgy6uO2dQGLHUaoq15hUYF1W1JyqnMe3YuO\nbQM/NlYnsym3b9+iqmqXfiD1xD2u2TQN66rGh63L+apz71hh87IffDhpLyipVVXx/PlzFotFSGPy\n19hsNtR1zenpKUmSMRgNsRAUTA8ArFYrsvmcyWzKwaVDrly5Gp6nrirOJhMxUCLFUwDK9Fxb/Ri0\nZUgjIICuwRgb4u4T9zmd9/HRMRjPD0pn7OS1Cv9qnYTNaRs4MMZQWwmL9Yz5OvHlKbt61nZ7vskR\nP89F13vVZ1prqroC5TziSEWI+XzJz372c5dGIvn4Rd5jMBoync+i52kNFNM4QsPgdIlZ+pvuvYN+\nKBFY8dr3ekOHNDPSXzzILTJPPKSB5NCH/RqNRIs5fU4pqNv9QmuNVilKZeCiv4wR8mZvUIe2mvPz\n6KvGRJ5NgIa6lvv1en13zwRTtyCbl9GPHj/jzq3zde53XV9J57d9rqDcbFguF6zygmvXjkjTl2RZ\n5tZ6GqJw9vcvsakafHle6c+23U3TUDUlm82GyWSCwpWfq8UZkSSappaSqLuiQne1d1t9a5qa9WoV\n9ohtGeDbobXoQWVZCsiMyKU0TSmKIqRnaa25fv16YJXfbDbivHD968PzfQqx51ro96WCV11XKIUz\nzj1JpxKC4Qh0adNBuiU6/XjHof8epGjX56vg9u4eF6dO9Xo9Dg8PKcuS4+PjC/t4ewzaPa/7uQdQ\n/DoI1S12nBfeb732xvT5vdrvkXCenNsvKs4Jwm1btHNNuqBXDF6ILNkNwr1q1f7KG/ff5Ng2nF/f\ngu9e4+v89pttY1//iBWyVoHHCTxB4JM0oa0Vbbemog0e2vV6zXpT0liX02ihrp233ZeOMxIYK+zw\nir3xiIP9PSEDaRpIBen2Bh5KQopCqScl5QXtFuIWFpR+lWiwrQCJHsJa58WzyuXJXHy8SkHwnyul\nQ07jhW0J7W3zHYNA3HG/12nL6861eE7vOsdaC06gVU3N8fNjklQzHI+4cfMGf/BPZvzhP/1DhoMh\nv/Vbv+VbQzAKtpQ1oKOwPn/+nMdPj9FZgUp6TpD6m0so+mAwIOsVZGlKmsmmm2Yp169f4tbtq5hG\ncNG8yLl++4h/+/vfw1rx6H/66aeUZUPa0yzma16+fMmjh4/4/NEXIf/fM7X6kobT6Yy/+3f/R5f/\nL962TVVz6dIleg7hf/jwIe+9936oaZwkisViRpImvPPO90jTjPX6jCQRg1l0pYq6Lhn0+yHnzuJr\nnjf48EofIaPQ5Hku3gAL9aZkvZIc/iv7hyTa50spenmBsggRZaNBO68j1kUaNUHp8so1bId7xyFv\nMobbG5XaloEm/h6qqub09Iz1ah3WVwdsU+cNtfbobua71lfiQlhtU6PSxAEShidPnvLBJx8xWa1Z\nNmXYhH1/7u/vU9XCHF43woOhtA7Po5QSrpVXHh4QluiUumnQCa7En1cyElTiDXWpMmCNlXx8x9xL\nMHBciGOSSB5+KGkknhU/TrtAudhY8n20S6nw43Lxd931LyBMK0O0krrXSZIFPpZYaTs3P7ynIfJI\nWGNYrTc0xkpeZ1ajk4zVerUzBLO9YHtdn0rm2xh7l5RSDIbDiFfA0JgKz3ljrYXGAVwBFOganV7e\nai1rLs/z4KX3f2VZcnp6yrNnQta1XC6FD0NLLqtXTouiYG/vgLe/9y5N0/DixQuGQyFiWq/XAFL2\n7+QFKkk4unqNd955l15ecOXyZR59/kgi5OJx2FLQ4r5vdZMtLz/dvr0IDPLX2p5PIZLBe0G/Yk/Z\nnkdiVG0b/y0IkChReTsgkjNsAvD3FcefxdiP99eLnmH7ut4gMrVLi4gUfKVk/AF8taPM5hhjWC6X\nwWurnGEdgIJEnxsPf/+micZSiWdY1pzoXp05ger0bbym47kdrh9AmWj+mJpNaUKEQJJmDkhoDerx\neK9jeBpjHIj87R1e76zrmqY2Uj1Ig3JVrgRQOL9uv86h3PxaLhbCUdUY9vb3uH79eihpCDrIu9ls\n1om+EeOu/X69XlM1Ui5QjGUp0WnqkqZuGDuSyfPP+frz15hu6dRda1lkW8uNorQNHCQ+xW+1WgUZ\nXpYl+/v7rNdrlsslK0ci6+eWD21P0xSdwHg8pihkLjdOnoKUX9z1GOcBWcL7WAeJx89z4CglFaP8\ns8tcf62uCuCEl+cdnVq1AO7rHEqpTtWZIAe+Zoi+n1O7G/wV58oFAlD6Kjkcg7C+nRfJ/W7bXn38\nyhn3F3kw/HfBfHXAaGvOXny9oKhecFz03TnlasfmfO5almB4enNJI40MmZodd9j5e+16395zy8Ny\n7rfSI1pLvo1i3q3DrJzXO5wLRim0SlhuKo5fnjDoD9gf9tBaaobXxrqyJDIJrTIhtz7RikuHB8yW\nSyazBeLtaY3DNE0DKtk0FqMMWufARQqi2gmSbIc6dtIV3CSQevMJxpfNoR0n70WJEWGPrntEXP78\nAIpx54nLdKKlTIUSY8WHc8bHdojo6ypWHeVua0y7CPk2WLV7fntBr3VKr+hzeKhoTMX/9Y//Tz5/\n8Bk///nPggBNkjYPsMsfIGihdVp/0wiy3ev1uHv3DlWjpEiZTmgaw3q9Yb1aU5Y1s+kpy8VUoi2s\nhMTXblOVfHApDaXTPCiNf3D8OKR/aJ2QZwNQwsQ9Hu9xdHSNx0+fk2XWGVQt2eZoNAokbKenp2w2\nG0fKWGCMKPRN0zAej9lsNjx9Kqz5JycvmEwnjEYj/vJf+ivs7e3x5ZfPI0PIzzNYrtYMBsINUDmF\nAUBSzrXboEXhslbC4jzBXVVLVMyo6JMq2KzWEsXi2t8YqTgRG+DWGqxpXCmW7fH389JidqDrF80X\nAxIgAAAgAElEQVQz/3rbGLdWcibLsnRMvvbiqRsp9e38VNFHyv2/9ZrhNnpPcKrQNLWhqhpW6w1F\nr0e/J5URxuMxh4eHQbm5evXItV9kitaJlKMxLnlBGZGnr9jrtA4mOspAY2qRgY1lVdZkeSFBb0rC\nF8tKPNWJ92oo0EE+tMpp7I3ZVjx2GWMd+XWBYt+O18VGTCzT/GdemkpUR8XLly/JsiK0bbw3dmV2\nbNh+Giul2YJRZi24lAljxDPUGEuixaO+zlbizYrC8s+1T/kIKxkWXz7OE4j5eXCwvx9CMUUB32CN\n+72SkpM6AhwlBF2TJClZlktEhQNXiqJHkRfCEF5uWK2WvHjxguVyyXQ6Y7lc4MGXsiypqtop+ZL6\nI7XF+1y9ekSRFXzx6DEvX56QJCn9Xp/lcs3LFyc0tmG6mDGbz3lx/JK9vX0ODw6kLrlSDuyDNFGk\nztDyxlysXLbj7QE0P/aKkKEZKbS79JMYYIl/0+6PLmw44pSRhbkbMPLvY6Jh8Epyu84F93UAQCDb\n7PI0fJU88tF4X6WexmutLMuLFV27tVpcKeU0zUh0ymIxcfPWhRJvxcgq3XJoJG4O26TddwWw7TnC\nMhjvjTvyxhvzVVV1I93w3smLny802a3nupYykBqX3mllv9yGTbRWoY0SxWdIUk++eT6NMAbX6qYO\nwJKfh+faE0VJBl2pc8RKbrRvYWjMRoBu672aUmoPC7dvXuvIu1fp5tufe6Pcz9Nnz55RFEXIT7dG\nQBulFKcvJyxXpUsLqF0EjXIRg4bGGmorYOx4b8SVS5d48eJFAO1FD+kCdbva9OqjXZM+uiB+3iRJ\n6OUZFhNy7n3Kqp8LrcyqQgTSL37xC8bjMavVKtSbF54eSYn0+fmelDrP0yiCSFrmSUZ9mT9P/B0D\npmJgdx0J25GsxhjKSspJKqBRCm1dyoNqw9JBZLjILSfXQ03ANmLApwakadpyBeguOLo9Att55xpF\nlqZopB/zPA1tjY+vllNb99q6sdtN5ZVrl+gRr77sRW2wxkiUilsrblNub9Y576uv/atn3COhIrIx\naXlvJQfVOuHh36EVrky4hCUZuYKEVMpLFby8boLSDkpIP1Kt54NoIm+jqf5r/0fkOfS5Wcoalwsq\ng6WMxbs1NVaEtbhI5Npe09ph8AMhnzY8zy7AIzLwvNA2xjIY9il6ORImj8tll3q9uD4Qg1VhXfjR\ni8mMy5dWDPsFbf1nCfuUhWixVvJ5TVNTlWusalCJRaeAtjQRSY9v03w+p65q+oW0x0Q5aJ1nivrh\nvMKLjJryCqoNCmVjG5kHKgzOuT4K93Omhwrfu1qWym827SxRXhFJVEBqxFPt2IINLkfZefyVsMT7\nDd+6+dVY8aCGcJ0tECtG7ESAthtfLJTacfZggp+XQa2I+soyGA15483vsCk3fP7oIR998hGnJ6fc\nuC6hcYlDnqJsHtd2nNBtvzXWsFwtaYxhb3yAQaOTlDTNaBrD6ekpSkm4sqwpqZEKMJ1NgwdXOCCE\nUG+5WgqJngVrG1arEqVTegcj8rygMdKv165dJ8ty1usNaZpR14aVXTtQwpBmsnmneeb4L0SxmE7P\nyDPxrCotJYKyPHM1txPqqub+vfvcu3efR48eRV5XCTnVifx2OpuxXK3IciEM8mCScrWHTY2QuiYK\n21iSNKHo9dms1yyWcwb9gvl8hraW5XojaHxTM7q0hzENVhtoFFgTSB5lHNu54ARCqIm8fWx/1m56\nkSK1tUMpB7YIT0NDRurkg5O31qeZKH+CbF6Oo8S9CQZiywzr5akoSXXdOM+Wdjnzmht375KMh5wt\nppzOppRlyaYsOZtOwjP2shytM1AplgRlE7d+jcQdWtMBE6MnC8/sWWjnqzW1sdRG2IjzXo/GQp4V\n5FkeDJTahbbqInNRRoCW+WOsdX3Qrm+vHO0aA9/HIg918GRDN/eycZuKCt0chWtbJXLPK0JWIqri\n/cKfD6Ls53kBzjuYJEkANMJ+sVUFwSG+gYvFWENjavf8ssaqWvg6VKJdXW9CjrXniPAJVz7HXyLD\ndDD4xfg3EUN+n9VyRVWVWKsQqggBEtMkIc97ITpCu3DQRKehH42LMHj0xRPmsznT6YTVahkAPa3a\naJA0y8jzHv3eMFSrONjfYzQeceP6De7cu89iuWSxWNErhpSbmiy1HF29zsuXxzx89BCdanp5j+lk\nygcffMBv/Pk/j1aam7du8cXDh5imxgvPdl8zQtoY5oIw1BtP7IDfF6KdyXn0fSSaB7NtgKk8yNfu\nCbEX3696pXUACGU5tjK+A0brNn9Vmu1BFdVJLfD7oUS2aLf3WhKfPmFbLh9/zrn92AMXHV2mux/6\nwxum3nhp2+Ge1elXyiKglMXJF4XSIjNm03kXGEO1Vg7g1XJ38QBeBLVRQZIm9Ac9Af10Ql3WpEkS\nhk+7henhGYlwdG0zkFhXC9tHAah23OK2taHQraRWrs98WpC0zbi5ITLJOIPAGot1OXbWOl3Zs3gb\n47Re4/RUJ3OsdVxQvkOcURMPm90eH/+btu9a3KhxkiQGqux5/bYTiSP7lVs57ayPjEgTUhOkz1aL\nJcvFkhP7EtD0spzbd+7w7jvv8NHHn/DhLz/BNIU4fIyR9MOmDrLINJWLxDAMBj2yLKUS7j/SJJU9\nAYmyS5ye75eqTCEVUkvbburm51ugyHvs7x0wGo5ZrZeBh0gI9Uqsk4dJokPFoLIUY97UxnncLdZA\nUfQwjWE6mdEf9B23g/AM+OhY70xZLtes12txLIyGDIdDDg/3GQxGYU+2Lopjs9nQNG1Fh7BWnHML\nfHUngzGNiwAQsMKXIDRuPglfk8gHIVkWriyrFNoD4lga6+l6DJt6w4uTF463wgh4mziATEFT1xI1\nlCQYl+qx61AOUMjTTPagxpCmxSv1pe1a8V2Qq/3MEskeN+2NB8est1xxssXLyih9J14ASua57zMs\n2KZdk9Y2wU6Tvo3m1OtY9vwKGvdil0spHkEsPX2LJ6SzwSiVMk1E6KFy/WyDgArDFL52RFWxwRd5\nIWJPSscgDIPr0Swv7yJ0TyRoO4jW4tj+AuJ0LiPFGfbKI1lEhmz0HOHf7e+h0964/b1+weGlA9LP\nj0VRw6lvfsF6zEArGoSUZVE3zNYb9sqaNE1QWpimlRVETgS4D++vQTXUTUlja5Q2nZUSvFVIaH5V\nVehkiNIW05SRIhuFFrq/uO87z+a9TwTzQ+aNacLJinP6xDmhu310UXTCmAXvmD/XbTh11QjpIGLk\nyHNqvHEdDBxHsBcNb5gD2/Mr7gfPehsL2thT5x/Wz0Fv8HuwSRD6ktOzl6RZxmg05uDwgLIs6Rc9\nennB6emJhM854hDrcvV904X/3WCUBzmEDC1NE/I8xRgR0nmqaJSiqTbs7Um+V6ITrFLkjq11bi2D\nXkGSpFRVSZKkHF65xMnLE3768w/5/rvfoWkaTk6nLJcbFJAlOUkqHtKDg4MQZgc4JLsJfVK7sLa6\nEfZcYw04L3rtNgNjjJSUAxbLJWdnE2bzJX/uz/06SimeP3+OeKFwTLM5aCh6PXSqOT09Ra+FwFMj\nSl6R96jKmqoUJNwaS+3lA6B0QqITnh8/J3ObTl1bXrx8SX844OrNI5nLDpC0tnFGNWGGg43mRch/\nCO+tFeZ+P0f92kj8vA/nqw7ZXVAyoZWJyIYl5a5U0GQCSaj/bYfcLCXNMkGe3fxuPbuiiHrlLMxb\nrZiul3z66CHHL59zNp+xWCxCWOx8Pufw8JDBX/iLsq5UIk9kHHBkwSiwKsGiUecqwfh1Y0K5I4uS\nFBFd0BhDNhCvj5AEaPyGTPBCajeObSSDhLUayroKCjrGBoWjKIqgoG9vwt6gjUMbra1d1EcbNi+G\nlzNsAIVP9XFGh+2GqAOhVnILMsQePItSRZgzSima6Fxo9xBlLcqI+pskCUZJ/mmSiCFXOa+fr6dt\nbWvgW9ffSinP3Spy1O+dTk4aa1it15yennL84gXVRipk+PxcnSYMR0PSRAUgxSLXrMsa01TUtSjB\nm03JYrEIufNVbUAl9AfjVgn3m4Ib5TRNmUymQYlOk4xbt26jlGY+X3B4eIn9/QOm0ynr9Zr9/X2M\nMXz24IEw9lcNdVXz2aefcufOHW7fus3e/tjtfcrNn/N9250OFp20c7Q7a1vwVpRbF56NlEySMoB+\nrnQ9+2G/hOCRlr3JK75JADC3Cd3aPNqu0d22z6uvysmB1qtmIxkVIArVeiE7h/JqwkVKajd82xtC\nO7281gYHiNJadjIlfypJIRgxHnxQmAi88K9iH53soYogd61xlWHEqLlz+7qT9XVHN7PGtDCjQ/2V\nO1974CMGDjqPIZ9003n8XuyVTffnLG/jQqG1Wx8Y6ftQRcTpeminPSv3ZxsEifaGm3duWD/iYT+K\nx6MzPm0rLxjGOpS6DT+w8MWT59y+eUSrv+gQzWOdt1+IHUWw+HkQg6fBg6uUAFcWynLDsOjznTff\n4u6du8xnc375wUfYuqbRAogbD0A5fSbPRM4Mh320htGoz2o+wSD6i+zVZUcnDWsJ4ZCJ9TPfN/I7\neWcaATyzNKdXJORFxmQyCWuwrkspq1uVwXvu+VsAaFqnkdYyHyWkXtZyry/RfzpRIQrTe/nrWvSl\n+XzOcrlkMFiwWi25cQMuXbpEluVOhm4ijoduuL2sPYnkkfD7mpBqgRj2cRlXrYV0zIPWWiukknBy\nbprUTR30d7WCxjQuGlaexxgrZMjI2koyIUdcLpdbunAXXPFkltCu8lca91ufqyBv/XvZhzzw2TSN\nlCf2PDreuHf7toB8ftKAlJRuyy3G9/Z7g9f3w5+JnG3oYNyHcy6Une3xK2fcv+roOLnF0m6twT/L\n9TjvyXrV8We8zcX3fw0E5nVRmouOvb0xhwf75HnGMqodqZQzgPACXTwCZdNwMplxMBpSFHnwDivR\nYZ030KG/ypBlCb1+wWjUZ71ZC9q01VGxAPRIfgA7tpCokGtzwfNf1B+v20u7DPv4mh7E2b66X/Ao\n+U3t6pPGR0zaoV1u8LnIhK95vOrcV4EVPg+vLDc8e/aUK1caLjnjfrlaMTk7Yz6bOq+OExZOuXdW\nZgv+QFAwkyQhzTLSNEHr1ktflWvyXNHvJ86A1KAV/X7OfD4nzxL6/dwZxOKJG/RyTmxNnieMx0MJ\nVV9mrNdVhwyzLMtQXg5ioKOtHR4TLgUjIQpX9UKzruuwiZ7NZ4yGQ773ve+zWCxDzrRHWqu6JC8K\ndKIZDIfUTcN8PqesSoq0CHlyPhcuz/OghBhjqMoSYyzD0ZjZbMKjx0/QSlGVNVbB9Ts3g0Hd4oFd\nYBGtiGf39nrx97fut9aN2+4F0f1C1llXkY//9V6uuNoHTpH0oYJNY8jygltXr3B6chqd117Twrk2\noxRPnj3jj/74j2lsjeeZ9uPlS6TN5vMOcOWVJ1/z3H6NDaDIc9Kij1WKqqlJ85wkzZGyVh4QVEFf\n7XgdPbgRGRqeTM3UTdjc67omz/NgmPhzoJu2ExNp+T7Zabz48xU7ZV/Mw9CNHmjv5cNL8cBdAGnb\n64cxiuaeeMcFbPJkTdsy7yuPSBH3h9YalUoZutPTU7Ik3Upp0qxW6wBOCLGUhEUXeRHkizHGGehl\neP6qWgcgOSZ7CmOApa4qhsMho9GIo6Mj3nnnHcbjMSdnE8bjsfB09HosFgt+8pOfhHJbe+M9JtMJ\n5aakrisGgwEbl/tqjeXGjRs8+eKxgDHWKZpBV/ZoB+E5teqCs5LH1DWEd4bcKxU4YbycO9/trWEc\nV0ZQypL6z3XLWh2TW2ndkmpiBczTynNPdKVEnB+7qw2vPl5P//Frbdc1u8B/3DaFcmSnPtw4gCHW\nx6dc1IruOCkHnG02G5aLGW+9eYckTVgvVx2Qy7gUFBVV+/Fe6+396JvqBbuOjnHQARe9sdm2xb8W\nEWfC+7ZHvDy6KM/5ddq+K/XS36fdi17VD16nstYG49Wfk0RrIMssg8GA+/fvh+9SnTjgVVL2lDVu\nv/OoiXV8QCnz+YzhcMALD5ZGOfL+/l/vUPgy0j59c71ZU1Zr8jxnuZToos1mTd2IXn6enK5dX0mS\nkOe5EAxHpee8zhF73T2hnucXWK1WlJsN8/mc6fSM+XzOnTt3uHnzJkUhXm2vW8X7lm9TbIhuz1sv\nd7uknwRdS+tWn/ZrwKeq+N8ArNeix1RVReP0MA8MGiMlt6y1DIfDUHYxvtd235/bb+JxjMCXXee/\namZ7I1x0ALdGbOyVb6MdPH+Kt5li4z4+LgIeYvBBqXauC7jenDtn+/j/l3F/kWH3NRbeNzGWffk8\nf3y1B9h2/v22j13CPFakLJBnGYeX9tFJu2D9Rq0s+JJYXkU2jWEynTDdGzLo9cjSDKwLocERBimJ\nnNCJJkk1vV7OoN8ny2dUK5evdw5g7woGpVpkfJeXa9ezfpv9tmvsQkjSllA31pL6djvBEQtD/7s0\nTVtPUfS88Z9c46vVmq/qk1etBWslIK7alIwGfZpGcbi/T6o1vTxjVlfMp2LYK6eU+AgTv/eeu3qE\nnud53ka7KJ8fVrkSjIUjsEkdct9gTEWWa/r9FixKEo2xFY0p+e4bN0kzjTFCvDccDklTCTMjESby\nyWTCyckJeZ5HuatNR+BBS0jiFcJuNIQJhn3TNDQWrl27RlEUzOfT1oPgwYOmZbS2EAi4qk3V5qep\nJBgXHYDBSN5er9eDLGU0HLKYz1ksFvSGCYeHh+wd7MVRja8Y51Y5jMfeb1QXerXw81N5J6rM7WCw\nxmDAtmcxtKAzH6q64urREW+8KRUHil6PO3fusVqt+L3f+z1y51UICrcV895YG5Tvqq4pegVVXWOs\nod/vEzsLm6YJocAx+y/gKji48VFwwX55rg+NMdTWQC0RTCbyPoDUwlbKyry1XUMM2nUPBIImH1Yv\n4d/tM/tyQdt7gSeYA84pQ36stmVFfL4fn3i+e5AgLqcnR5wW1VCbhsZUCDjSdpxSCnxOY5KSOFIj\nnAFpjCVNCpI0J+/1XJnFgISIL9e2HrRzU9CN/7aydXhwyGazYTgcMuj1g2K72Ww4OzsjSRIODg5c\nebIrHB4eMhwO5ZLGR0ZINMhmI+Gni8WSzWrFar2SUH+Xs1pXFZuyxJhaCC2bhuFoTFEUXL92jWvX\nrjObTTFWcl/TLKfo9en1B1y5esTpyUngf1it15RVxbXr17h79y6XL10Oc+LKlSsoayk3GzbrFfVW\nCdXufIjHvI1yESI8LjzPgy+KpD2/4wk+v+90gCN7fpDOy402wke8oxarvd7Q3T+/roEq4LmAc6+1\neOmCtxcZxdtrLW7jer3usNUH1Po1D99lvh8PDg9FVzLG9ZPChyv7aIvQDlpwIm6/7Vz/q/Wfrzq2\nQZbYcGq5nmhTuiLPoAeYLtpDvs1DvPavd3hD08/hbZJSwSts4LnK0lTC0GczTl6+pF/0qOq2uker\nl7WGZr/f5/T0lOlkwqA/oN/vsyxnIZ3wa02UrcMDPp7Vfrlc0tiKNE0DyfRqtcLSBNm9i4jVt9/z\nvLQedTHoY4eCn6NpmjIajYLxPp/PmUwmLBYzzs7OmM1mfPnll9y4cYODg4PQ3/G6frVOsVsHjYFE\nH0nZ+D0yAim2ryuVWAS4IOh33ejpXXvm9toJEXpp2jk3fqbQ9nZD3Xq+8/t/dEfpeyOcSPKR2UqB\ntMG4D1exwjcWz+ltWXYRUIt1uedEsvAVpXL98Stp3G8PSnxc5NmQxX6xYNoWqBfc2f3PhPD/cK43\nfHa0I+7g7dfbeWJfdbzqN/Hk9NffBhC2FYEkSej3hXDImCWpFTQ2INe2DXEXh62ETL48nbA3GpPn\nBb0slZIgeIBDQrVBlN5e0aNfFAyKHpv1nMpY6Fae6Bhfdgdb6/ZCvkioflX/vOq7XQIrVgTiRWej\nc0RpbcfPMyhbayXvLtp8AjIctym6Zrx0X8eA3zZe/b1e+bwWMp0yGvT5zR//mPWmIktzfvnLX/Lw\n8885unKFt37w66RJQr9XUG82IXxQ/LIuD8+YgEoGX4T1iGojnzmPgMVE42YxpkanQpzY1KXMn6Ym\n5NmRYGioyg29XoFCPEx5ljKfLrEmIS9S5/2WDdLXm27RZLlnmqbMZrOwQV605nw/ejI8rOHtt98m\nSRLKsgqENz7qwRrrjIOaqqnb+aMVNOJJ3pQbyqqSEliJdrlxcv8sy1ApaJuDMdy/fz8Ib2MMVb2h\nampM3Zwby3MbEV15En4DO+dQDCTpMHpeQqqw7mXNtzwc3rCLQ4Vj5N3X2v3N3/xN6UeluH79Jr//\n+79PudmQp21tYGu950dCIj1/TtlUpHlO1dRkjuG8YatskrXBI+FZrZVy3jnjwsJDCNf29G/BTZ/z\nJv1taMwaqxS1NRRpgukJB0RjFY22NI0mSbzC7lFzFewhb7y38kIHY2l7DP08tU4Ox++3Qc2YeG3n\nvEWM++1z4pJv3VBrItCpwUt56UNR7pXbM7VSoBOSNAXvwVViaKa6rfuuXX8rcCH2rvutlcjfHe0G\nHNu4A0hQFIHockVV1dSpT2lwpHb9PpcuHQaSqTT1dcW90hSeRvohScmygtE4IUsL8qJPr+fC9Kua\nqpLIHyHWa8hyIbjq9QouXbkSDPYkzUjSDKU0jWuzJ1sc7+1zdO0677z7Paq6YjQakmWpcBBoje0P\nePHlc2azibTVOtJGLpbx2/v5rmN7zYe9xLar2c9x2Wu6xn28h4AHx/w6iZRm90FM4BnmnrUonwbg\nvPoeCD9H+Bc9zkVyyYM9r9q/4sOvGz8P/XqUkOcuKBa32cvaqqpoHBCcpil1ueFV/jkx5s+33a/7\nkQOZpL8JHtqm8TnxhBnqQbnt591ua9xXrzMv/O/CdTt94IyrxqBVEtZdCNlHUZWNhEtHv4fua2ni\n7n76NoCAWKe56FrBULSmA15ob6g7mZg4MuU//ZN/SZIkzCdTEiRNTBwubYKvcqq8z+ueTM54+eIF\nN2/cZG9/zHK1kpQ8ZV0q2XnAKIDiFxyejE7C3te8fPmCstw4uaBbkrx6g05ax0kA4v09XWUZf814\nDWwbu/4zL/eHwyG9Xo88zxkMBuzv71PXFWW5YTabs1qtePnyJVVVhYoDMcDgrxfLkG1i0Hj8/H7o\nS8RWVSWpet77jt/nQKls634RWV90bR9RGgPnFx0B3DA2gOj+Ht0w/lcf1itG24fb74SfoCYExMkm\n6M+WPx1pWw48iyOl/J4dz6fYGdWRB7blnpMoPAfQW9tyxe04fiWNe4jCs+PFFB3SEWnn811KU0dw\nqFcbgIGkTTkDny5gYGkRnW3h7I+LhPYr7/saBp6/xi4gwU+Mc30mJwlrvva5mn4iOqPViRIlEtCb\ndizWa85mczJHaJQkCQmtR9IDH8L4mZOlKXkixpjK3MLdepaOsezu7/OgtxVfdhjh2/3r359bFFtj\n8jpjELfP0lWOgODVBWFtPT4+DoaQClpud9wCMo4NuexKGFja31ww9tvf7Zrnu55HvPCWcr1mvVyg\ndcrB3pi9vQMeP3pEohS3bt7k7p07jp2zZrmWHHdR+F2uIC1/hLJtHlMrYKzb/OoOSZhvn1IuU9gK\naIC16Ih91DQ1WqdY2/Dpw6d8vz9AKSFIM00tyqRwc4Xn7Pf7wWsqbZHxmM1mVFXF/v4+IDWqrbWS\nT027brzwD0ZjlvPDH/4whK555c/P3cY0mEpywHXaolVZljnSO+EgMNYEsjWlFZ4OKE1TEqXRFlKt\nKArxQLdM+7s98h2lHG9odsPjunPFuNzX7hxX4S+SH450tAUFLIbWi+2fPcw/1QIBvg89s/7z588x\nxjIa7fHZZ5+dm78AtW1QGGpjAkm1hMRn4lWxlrIWBuOYCRkI4dDxJhjARSwYQvRJV9jEQXJdw9wb\nySIzkLxk1SqyXQVKci+1sm7hyuHXQay0xOs1lsX+z3umfcRH3E/bCsv58XVy17aKlT/imsPdOW5D\nhAoYx2zu26iEI8CYVum3YvBbZYNC7H/ry5/6xvhznAiI8nO9IeOWfbTX1FVFokTd6OU9JrM5k8mM\nZ8+e86Mf/JDDw0OuXr3KYDBwaXctuCNEWl7eeeNJ4XGVXCfoJEWXFa2g1aRNQ5Y3GNOnPxiy3pTU\ntSEvBkLm1++xWm9C+H8/E6+9X39ersk+KuSh/cHQfS+lL01jUUZIt0xds5jPKIpYrep64rqv/TP5\nPS2ap7t0S2dcx0Zc+7oL38U6gd+7JI6KIHOaaO4GmRPmQ3dOy29ASWJ38IxdxOa//bzbz/GqIzZa\nfbviNC3/vN4QiM/Zvo61EnLcCcPvKOO77t/O4/gcn04U5kV4vraajLUO3FXWm5ZoneBD1LeNsV3G\nfXzs9FjvBDPayLRwKaNIVIJynCTKCldQudnw/MsvuXp0iaIQ4s3zc1MMrl192v3d7u8uOnzO/bZ+\n/lU6sNn6WsS1ItUarRPGwyGjwYBHDz5HKcXlg0Ns04gzSyvSRPv6SSglfC2JVpTlBoshzzNW6yWD\nosfh4R79vnCUeJC7uw7cc1pcautW29wzSYnZExYLARV7vR7WGNabVUgPtNpgTKtbKZKgU0pFhFYv\n9rI/Jr3zOfrQGv9x5GLTNCyXy3Cer1BSFAWr1Yr5fNrhMtoe313G/fbvlFsTvpRdqqNqKpUAg3FE\n5Hq9wbooqZ0pANYDY47/Z4f+vr23xmtBcX4ubtsWeN1UvvzKee3lH1iXFtGgEl8NIrpmsB/9504H\ni3QQP04+OnGXUe/nN0CCpvHAvJXUABXs04uPX0nj/pWo7r/W49WbkYp+8brte120qDM5X3HtXQZ+\n/D5eLP46WZbT7/ddPuj2teKLyz8GmC3XfP74CbWBLLtBmiQScuMU6ZCCrjTDfp9Br8+g32PQ77No\nvvqZZZ0oUdBfsbnFz/ltHF0F4fXP8QpGmoiX+PT0NCxQrTXGzVkR2C2I4c/v3C96lNcFdmPKwhAA\nACAASURBVL7uYRuDqRpq1aAtrBcLyuWSTGvJuaxr6roSUh4MiVZi2EMXOb1gbLxAjwW//N5fobtp\nbz9H8Abqtk58mrbezbZv5LyiKPjhD3/IP/tn/4z5fM5wOMTaJhiA+/v73Lt3j7Isg/HpFUJ/PR/u\nnefCA3D58mVu377NbDZjsZiHDUieq80NL6uSlMwx2kqbDZKKIJ7BqrO5eadP3itIlMZUNcrC2dmk\n46HdJkVzPdvtb4+PnBuCAJMAko9oTNPhfUjcOdp6cMMprv7ScT+7cds534IuI79PI6Zya6UMjygw\nkWEbzRNf38T7XKumIcky6qZhU5USwVFVzF1+vT9PDLB+J9ywsU0IgVO03ruLVpEH3rwBL+navv9F\nObOu3JQQx7k5SWsoteMg1/ERHkmSSHSGpZMr3V0PrcFfOs6T7fWktQ5GW3zEa8q4sPeLQhPj19JX\nu8Y3Aordf6V/IiOhvRBeEQ5gndI0VS1RPY1jQBbUR/rL+o5yMsS0fBSr1SqU5iuKguvXr1NtxHv1\n9ttvs7+/T9M0EkLNboOnzbFV4hlRrec4IyfLa6TiQUKSyPyS3PsGpWt6/SGrpaEoeqRpSpb3UCqh\naUq01uSF8C/EymrhCEF9nmusiGmtybOcLEtZLRacnZ3hgbZdc+HrHNtGc1Dc8fIi9vq0I4qrVhDP\nhxDV4cf4nCHXfi4h/q/ek+Ln+iZ6Wus6+eq9/6vmfcfgtd39abVakXwL+qS1DuR2xlB7TzprMzbA\ngTAfXtVXcV9uyw5/bpeXomsYb/eg7wetdag9roC6LFnM57x4ccLRtasopR0zKc6YaMGmf5PHq4CC\nuq7FyRD1gUZ4ArXSDPsDrl85Yr0usWXN3t4ehUpIrZPziUalSQBoEwUmUaiUIJMHA6lrX9d1MH53\ntavT5gvmbTx+vnxfnufkeRb255bArp23WmtslJ+2rTttAyJKqcBy7/ejNE3D3HxV25RSjEYjsiw5\nt+9ug0lxG+I5F3vxQ3pYdK73UBvbddZaa6NSfO5+54zv9tjeT3eGrm/9Pujibs1+lY3R1bnsue8u\nXLtuz2sBwxY4DKcokXNeDmw7LHbqybHsd/xond8EXOLiZ/qVNe53ftYxljwvqXMTfAOB1CJxTkp3\nNsuvfbXoXN/Oi9v3bRmt20YWgC/2J3mSjSBopg4LMGzo/jwLWPm9MZbFYs2Tp1+yNx6TKLC5Js+0\n8+Y6BwmCTO+Px8wXC/b21ixP521PxIatGzdruojT6xj3r3O8SqBtI90XhcBsX88b9TgDDuDZs2cc\nHx8zHo9dTVpIXC5UVVXoLJXwxddYgN82iOWfJygUTsnfrNdsNmtyV3LFGMdOi+TniwB0lR5sF+n0\n/eU3QncngMD+/ap+9BuPfw1dAfed+zfDRhGPkQjnFkD40Y9+xOnpKe+99x7z+Rxo2JQl146OePPN\nN7l+/Tp5npOmKb/85S9De+PNcDyWXNvlcsnNmzfJ85znz5+zWCxCe9vNQzEajaUsmJUQc2NqTNMa\nmx7x9uFvWEJoa6/XI0tS1osl1aYMxneSpK3R5Ncn8Tx1Ii3kc0pb2v6MN1vfvzIOHTZ2Ly9Nu9ns\nUkhelc7kFWUUTi7IGPlQP6U1VS2stzpp8z47AIJq51C8QY1HI/bGe/R6Uov46OpVdCJ8BL1ej4OD\nA77zne/wf/yv/1trxNuu7HhVmB50cwDb/rNOFrWebyFr8t4DKeNkcWGZymIRUMGH04U+VorEGV3x\nevHP2Lnndht8/6s2DSj+vguY7QYm/Rw8ryi0CpjWGpVofGkq01h0npKmjqXXQG0l1QFj2yo00baV\nuLW6WCxZLZdhr9BJSlb00FrR2JokTQPTr7StYb0WVvqmkTX9/Plz3nrrbebTGbdv33acF/NAgmcj\nw6WTKhXJcaXofCevE9a6Bl2TZBqVGHSSSrSETtFJilJJAGVb3UszHI7Istwp2MJI7cEQi5USoE3D\nwcEB+/v7znNtSLSUzTo7OWU2mzmwsX9OnqlgOMVj3zWmlFMCfcWT7fli3Rj5MNZdyv5FhnKSJGAM\niU46ymuHmMr9R/o6amEc3eL+4ki2P8vh56jyN+H887ZjrjvRMO35u6/r5WNsSKT6z6LqxosgWq/4\nFJF2fWrlwvb9b5Qvb2td5E/XINlu/a7nusjg9YZE5/uk69TxcnIbJDo9PaWuy04kxOv3wzc/fM59\nvJ6/Sv+rqppEp2RpG2GaocmMJVMJuU5ZT2ZUlaGfZGRobFXTSzOSNAGt+f+4e7Ney5LrTOxbEXs4\n451yZo6VVSS7WKZgstWU1YJaFhrwk57af0S/oeE/oH9hQwb8ID21CfhBcAsQBDVNNks1soqsrKyc\nM+9whj1ELD+sWLFj73POzZtVRbnaAWTee8/ZQwwrVqz1rYkzKUXIBLRBuYdltNR5uACIyeFS6/jr\n2jY+rXemLvRVVWMyGWE+nwMQ926b9ekifaf3XrypejSRJD8kSWx7nmyV0ozOtfceTczOX8dEwim/\nPa+lyr1rW4BDCCJFn64eb7KmH0agY9C+ALJ/Xicn9/nc7uu7vdeFEqW6zrfS9HnJT12XWAkk2dOc\n8H/9bKvFflv/BsO86Bi+c8o9g5NSTEB0cIip8gEpHRd2KncHUXctoIp1jImMCotmPAzXxQzTOvlh\ns3FXQxSg7tUDRpQSGlNwq4Eq9AZdzXR9ducammA8stADRGrXQbZtcbtrWCX+sNkYkiiKQvb2jlZi\nH7jrB3Ei6rNHVTX46uFDjLJrGBdTSNkpA/KQJH1BDhwVBfbnMyxWazw7WYnrCMtoTRAWPVl4ZNHt\nj4IQZ20GQ6E+sOIAoAi2DF2ftqknYWK2wijKiIbgB9DPUq30ETGecG3TNGAi7O3t4/Kly1icrlA3\nDX7zm89weHiIIqCly9MFjo6OMJnP8PL4FagQAVDGmfY8WfltigeCuxdRzMbbjX27K1L/p7wTLO6i\n5B081yhzg9m0REZSJs6Ik2YEaZRmsIXRbkUXoW5rnftaWIbeKg0FllTo0nGCIHXQQ6iDupSpXVas\nZw1+9rN/g/F4hIcPH8aQgJs3b+LqVXH1G41GePvtt5FlGT755JNYdibPc1y9ehXXr1+PFvbrN25E\nGpDyMyFvgilgrEVmM8xnezDWSvI377FarkVRaR3aECtfFAWm0ynyPJe69mfrWPbMklh3q9UazA4g\nyaDtWerTZtYit0VMONO6cNjCwXPbKYyanIX0+FSlkOBC/D+RlSyqlCLvvgMLErrTdWJIP0CKoAt/\nVAAwcBDEjcEi7NssgzeEIs/hCThbriAx4GOsV6tIpwC6A5AohuAQEe7duYvlYoksl8P/8OgIo8kY\nx8evcHwi7oLTyQTL5QLkO4u97gOw7pPNfRTFcu5o13sPb4LSHr63xobSQjI3bdvAGAn58sbAxhAU\nmQt9Tw80UMBBdWJO1LawlwQo8DEusbcGCa9O90vcZUHBIUbM8YFEeQGLN0R6L3MngLWtZBtu2yrQ\nE6EJpTxb50K9eoOsKOW5hjCdzwAkLpFh6ldLqS3NjkHBepxVdQCVHIqR1IsWHm8AdmDX9YeZ8fjx\nY/zqV7/CD37wA/zJn/xJHA8DkqgIWwRNJcEQIsCUhKAFZdSHvqrF1hiK/5q2RZ5nmEwmmEwmaF0b\nE+RmWQlrCxgyUhXESviKltUsSimd2DQNXr58ibquMZvNMBqVsEUe9+JoVODkWMFB6ZP87pPV1HVX\nOYMBJGsqowdRKrDrmcC9zxLyi3TUCZUiUCodZpkNF3Xu4cydVUv7FOUA7Vr4K7NZv/QwA+w5Wt06\nwOB8wVOBP+VJUcWnzfs7YNnBOal9rUqBSeQYPWDb1sn5MZCj0mz5Cex4bj/TS1Rm4lDSzlobQFkg\nlhJBl7uAiGR/kJTG8kHe0wfqm33gCxxekk5jzMS95dzVdZa8lsEF2xHyzKJuNJN6F1qSAjgvX75E\nWeYho7+Pc5fKEJSAGlE0Dp91f3/ztqGkJUsiY+94maUMeW5FniEBrksPZARkJMo8tR6TsgR5j6aq\nMClH4jpthSZMYeAJcIbQgoGMsajXcdJVKa2roDDvIJF0rrSkdnc0yZic6yro7O3tRdd3ay1msxms\ntSKbYMC3B/NDRoxnzJoXp/ODIyC49CORqUXn0P5oSVtmhJBBCbx1odpT0zQxSV8aYpaCxAr2CYig\nayZygnNOgF+WM3E4T7q/U8AhluLWcTMjJn/nbg4B5Q3JXKf0AsSzIm3bwGAdi45HXNt38YD+51r6\n3ETQVXQSE0IctTZ9or4MWhfqZEOSWu+1Akm4IuhdPIw/AeBZc+boiC/Au/CdVO6D4EbUGw6AJMlX\nF8nWs3CoEk4U6pwKaoekjEsidskhamxSj1O/159GNhLp9aE3O5R7zyoUdxtAaiW7INxIv6KczNIH\nD8BxF0GxS6k/D6nSfx5CNFCFkj0sMUYhtoqsuP4wU9gsnbtqh7tRsJ6IcjgalSgKgGwF7w0IGWBs\nWAeJzbYG2JtNcbZcYzw6QbuoAJYZtDa81+RoHJCxnuUixAjoYAFuw8FPMdZaBIA+6sbc30IyDhM2\nX9RkdrZ0HofWxMgEiNC2HqtVBWszXL16DT/60XvY39+HoRyLsxV+8Ytf4Oz0DM+XS7BzaPcblOUY\nN27cwJOXT5EDABmQkbKBUPohjtbdNN66z7QQ6FbWQ2uqMgf2nlpcFBCI7svK/BmWGcY7ZBnh7u3v\n4cmTJ7DGA76C1rcXpcWI4mkp3LuJKGuSlA4I8TEWVpRIPZA7N6UUndW4bhEaPNpWdvYnnz/E99+5\n2yVsBIDgcs/hgHdOktpNp1P823/7P+Dk5ASffPIJsizDaDSSeDaWw6YsS9y/fx/Xrl3Dl19+iWfP\nnmE6neLo6Ajz+TzGux0dHcF7j7IskBcZVqsFyGTRgjjOCzjnkWU5cpuhdjXaukHTOtR1QLuZoytc\nta5QrdfRKiJTIfQ1Ho+xXJ5JJAo5nJ4ucHqyRLOuAJYkM9ZalGWB2WyKoijCPpWY6Q5w4064NoBh\nOSyaxqEoTC/BSjh74IliHhFP4TAK+Q8YFA9tQ5nwBY/gyhnoL5SdowCmjsdTkM3QeEZhMzgGnr14\nAQtNKMjB0yEowugO2to56Zc1uHx0CbdufA/vf/gBAGA8mqBpWrimxWKxwHq9xrNHT3B8/ArWAoRg\nbSILCZNmOBb+PFS29SdTF8/YEXOXmwIg+DBHhnzIQ8HwhgG2MMLhggIf1lQ9IVSXghc7fxijZ1kv\nE/mX7FlVyGTLd7yeiCEasNngA7L3xHU2pYE2CGPr9To6YqfWJiIbMymrW3njmvBsWVfH4nUBEIw1\naGpJkJhlFpZMOD867xKrfxPBQP8ZcOvQNAIUth4wRYksz4WzOArJJ21Q0mR/fvjxR/iLv/gL3L9/\nH4+/ehSyKrsekKu8wxgj51N8v2qFIS44KE5yLnQWK89eBE8DGAsYS2iaClWVI8sDfbN4XpgQmyyf\nyVpZk6EoRqJUs/RntVrh5OQEbdtiNptiNptgPp2hLAscXTrCk8eP4T0kFIASA0NUmBIixBCUomT/\nph4nvnen9M/1nhHPRBXQIWChaKYOBAOJZujozhDBcQfk+TYAg4SQR0S7EvhF3F+Aax2888htNlDu\nu7Fta2JIMQkW0CmSckE/LEB4ug9eTyYChYCPeABZA2NzrFcVTMawuVR90NblngC812S+u2UECU/T\nfsl1hqSkWl5kGBeFeEN54Yk+KDaudSEHheatkXmThHtJslBBp6RCiLBU1G0DgkXjPMgzyHQAl/KD\nfm4NFm/6oGS13sG5BlXViCwZjolhPhxJHtvCw+Px06dSrWIyit4gAGB6Cn66tJ2il87NUEk/z7Ko\nMfd9C7Wu9WCPsAe4BTwjowxlaZGFazMGCmZkQHBtN0BhO1DKe7QBeHImzLVnwGp5NYfleoXaCYBu\nwv5u6wbL5Ur4pu88U4ZAC9BVz+qAy6BZxKRnjP39fUynU5ydnUlJ4KLAfD6PbuyLxSKA+eH5hN47\nCEkYkHMwJou0SUQhgWMHxIMBSrJZGwDWqs4j60YsgC4CyD6fz2PeIn1Xl4WfwjvlHNLzg8jIvtwS\ngpsq1nrGCobUnU3dEa1GQPU2GSrqkq8iVe4dOzh2cUjpPakulOoMIkPl0DKl8rGCYMMRDD9QXuzg\n2QVl2/dyzQAcgafNB+pgjXjNGYJ3Eg2jHrbsgnygd6R0BkQ5mAFwYvQ5r333lPstnd41EHWf2faM\nXc9On9df/M13DS2Ou1qaxOKiLe3D76NRPDQ9yGi9XQ/DGqXb7wtBz/COERBJfOm161exf1BivXoG\nw4RRNoGlElJr2AEwyDKLMQrszaY4mO9huXoRrZFlWaIcj2HzAq1jGMO9ZPr9+JOvPx9xQ58jXKRt\n1xp476OQ6ZzD9es38PY772A8HmO1XOHg4AB/+qd/ips3b+LJkyc4fvESlghHB4f4/ve/jyvXr+Cz\nB5/j+Ow4MvFtLXUHe1M62LVHhnRNQQF3jjGdTTBfz0RAZj2cO1pXhD49xLYd2Nv6mh5+qWUf8PC+\nBVEOTYCnoSrDd+hPY4QJiotuIsCG0AARUGocHR3FeHt9b9uKYjiZTHDr1i3cuHEDJycnWAelm5mD\nhXmEPLiwTybjkEm2ioJgnpVgJpTlOAIYklG/gPdnqOsadVUDwRVe19GSPDvLMtR1jdx0eQTk0JEx\nfvXVQ3z60ee4d+cO5vMjPHv2DE+fPkWWEd5++23cuXMHQHC3vgBpnEc/sgphteP2FpjZtz4mdul0\nYw8KB3pHIYHGPGM6kWzRHgzKLBx7LFYrTEyOxWIh5cDaNs5LasVn7ashPHz4EA8fPoQxBmdnZ/jo\no4+Ql4XgsZnFpUuX8OGHH+LVq1eYhWQhRNSNAwKKbo43SIAU/5Ns/CL5QO0fDEioAThYgkUwU2sv\naSI9CrDnQLhLXepSa3OXkK/zGFCQJ+UH+l0Kfm1L0AcYeDa9dXDcn9NtXl/qrq+eEmoBAosFy0DB\nRRMENwV4LQyLUuxD0j3nNbfFnnh6eAa3wkc09IeIkBkL37aovAOMJE1sXRvU0y6UZd3UuHLlSi/O\nfmg5SudhmwA2dNVuWxc8CET4yWya9M0iy7pzuihKEAQUKkLFBmvthqdUWZQwbOB8i6wUet7b2wv9\nXuH5s+dYL1e4fu0qRmUZ51vHkmV9EUsFz3SdtlmetGluiF08N/0plKIKNMexx+8UxUenCGTUuSPL\n/Z23WErfw6a0lSqFX7+JgWBXG+ZhYTgowCHkTEG5dYDx4LaFq+vY99Ozs7g/hwkp37wNXJ2NKjAm\nKFaAannMDOfV/X143spvOn9t20bXZBOUU12DvtUzmeukHy7kzunWq1PSdA4B4OrVq/jqq6/AzFiv\n1sGzp8He3hwmCmV65lxMjvqmbffZ5YGw1haEUT6CVeUeHkUrhisAwbAV+K1zgPPwwaDiSICQ09UZ\nWmK0BDj2qNHAZFZWJWSlb6oa68pFvretf7tlL0QlNpWFdO41f8disRCvPmtRlmWsAFQUBdZ1F0MP\ndMp9twfEeONciywrgmV/u3zZhZxthqSqvFcURcwlpPdoE9ojtG0X3hUNF9SFrA5Dp3a1Tsbf5Y3y\n7TSl++E5QlEeUBmZcVEal/UV2cNTJz/EPDMIFXwIIKvj0zVM5B50+Rai/E8exhsg0X02aGxQjeo8\nAE3bd065B16v+P6+FOJtfUgP43+J9/4+mqGkTiip+84O4lCrCBhgj9G4QFFYMDtYA7jGSYIxk4dy\nVHItAbFG+aWjI7w8WcQsnWVRYDadwhoD5zprrLaLzOs2JfPrtpTJbHuecx5VcKm6ffs2rl67hsls\nirqucXa2ADxhMpngpz/9qdRXPj2ThD3OY763h9Y53L9/Hx98/EEn3GM7A941nlSY2QZA7YqZSxXk\nVOjy3sfsvUBQRnoWkt3g1BDw0n9pNmYVJIfKz64xpO3tezd6f2timfF4DJuLwGythclyseYGRXg8\nHuPu3bvIsgwvXrxAURSw1uLq1at4+vQpXrx40Ttc8zyPydDyPEdRFKiqCpPJBFevXAER48mTx3DO\no2kbmMbGg6IoimBBZEwma5ydnYmi0jTR/W42m2E6lrJtTfic8kKsrG0LY01UoNfrNa5cuYKf/exn\n+MlPfoarV6/i7/7u7/DXf/2/SgKoaIXt3PDT9f06bRN82bQcyks2702FgtF41EP4vfeSDX1S4q17\n9+Cdwye/+U0PvIo2GRZPB/aCfBdFgT2zh9l0isl0iktXLqMsc8AaXL16FX/7v/8fWC2X2B8fdTS3\nBdB9k9abg2j9oEBnkgQoywPNGZJkk0AUpHQNovWfgsVmIIirgKHKvc63vnvofZM+V/e43BOUzgEo\nMFy//t7rWzHUgt9ZU9BT/MV1skskp4CMCimqqJTlCG3rsTxbACyuwZT024aSkCdnC8CEhIleM4d3\nyvskL3H37l0cHx/HZ2usfjofEfjdwT96NEYKzGhOhI4nFYUkZxyPx6Es1B7axqNeVzGPQmpd13nN\n8xzWWPFGaT2OLh3i6OgoZKmu0TZNsBaKN0UP8El45i6lMj2Ldn3X5+k4V0GNcgsURO2EWaCj05R+\n0gzrzB2gqvSrnh+p5VhpsxOa36y9yTkugjB3fClUdQCr5RQ9ugF38ebOOVTrdc+l9ps03R/yLgRL\noHi2qKVTlWPvvOj8yXkZBh/7qqC1gC0hlETQuo13R4+UwfqrESazOYjW6Kzr6PMgADdv3sTx8TGW\n1RmOLh0FYFyVyW8KfLy+acz9NnliU9GSuXShIo141Mk3zrdwjQMFKyYAeFgorbPzaFjouQGjams0\n7NDAo2HJ6+GsB4f1US8s7zxahHw/b0ArEYBMhlBVFdbrNZbLJbIsw6VLR7hy5TLatkXbiheiel8R\nkVjxXRuVP+HL/bhs1eN3GROHsq1U/9mUt/U6m2UbAKQ2ay2WyxWqqsZoNIrvVGAiVe7Tdw86hCH7\nfp08eF67qK6wcS5+izjVeY9S2hPjg/Q1S/hmT3aAgKpGyBZkLkJvYX2x+0zU9p1T7l+n1JzPDLY/\n55v0JRWQdm2o9LqthLVlDBft43kgx/Adw/fqpmvaFq1rkedFOBAljq1zdxcmSCQOPpZI3Mx9hfF4\nD649w2rNKHMCPME1DVbrBaZmPwjFgpZaa1HkGQ7nU1w+mONJvULjPcrcYn86QRZcgtQtWPqJDSYq\nAFvKDSAuMNR3wel9z7st9hxQ6F0MJV07TZST5zkuX76Mu3fvYjyZoG6byNgyk4OdxN1ILPcVEAjL\nxQLHpycgS7h69SpW9QoPHjyIieG20UWKukYmrvPRE+42Uc/tY+1i/brkK3JIyt/ymcT8dDtIY9wz\nm10IzBqORZl8Sq8qOO7q5y7AxhiD2rU4ODiAyQoBUBYLPHvxFR4+fIjHjx/HeXj58iV+/OMf48aN\nG2jbFqenp/jlL3+JZ8+eYblcRku8xuLfvn07jrUcjbBYLLBYnGG9FjT98uXLODtbwjmPw0MRfqzN\nUFeVuNYtFrHUHof1t8ZgPJlI9lmboaqqODYXyrY575HnmViC4TGZTPDuD+7jxo0b+Kd/+ie8++67\n+NM//VP8wz/8fU8BvIjlfghC7lixcNCqd4u0mOE/KEZA/3WU/K1rNp1OgcAPrbVYrRao6grzazfw\nr//wD+Gdw6MkSaEJ212tWuOyxNnJKe7cuoWjgwO0zsFkkrX3+cuXOHlVoaprfPj+P+M//9//GeNQ\ngzcd77Y5eN0c6bhNiKUvyxJlUcCIv3kUhmwCwqT7zTvfSz6UZeKSrAng9Dotp6jv1SRxaT91r3Sh\nNH1FsC8IuFD6z0P9ThnJOg6UsfTMUquQMQYZ8qBMBDfVyBfE9VIBhC5ZWrevjWdUQRG21sYyjsSb\niuJ4PEZR1Hh5fCpjIkTlXse7f3CAO3fv4NmzZ6FPXWhYWqt994IiMi+x1Isbftu4Hl/RZxhjMJ/P\n8fLZczAzmrqB84wm8HVdozRZIiDWNo1JRXDhrOs61JJ20CwvTdtiuVhGUEXXUt1bXyfMpn3ePPM9\nqKfs6bO2yAasIEcIM2CN5Q45AAbXy9mUuBVTfEhvDruY3A6YUvAx9ooUODh3qFvHvl0vkHe3IdTM\nEsFGDxrfO7tsllgT2xZ5UaAoCtR1fW728Is3AQGLouyUoZDnBLAhcWSL3BBaL/IQhXwyaYwx0NlP\nyIuV3oBRRL5D3folPCWV5/RvjfcmImTWStnOBGwHKAEihBYnkwmuXLkCW1D0MGtbH6/fddh8G0aV\nbe18vi3u9nW9Ql2v0bZ1tNwbdsi8uO1778GOEdMZGoILgJBnoFWF2Ir3lshY+n6Jp/ath/NyPDML\nT+nB3gkvSfu8TZ5XkLeqKpyengq/CQmZJ5MxVqsVqqpCWZbY29uDMQbHx8ei9LuuXKrI1v3N5L16\nupogd3lold5UZlCrvax/GhZ5/niAoUzaAYqaPDgmzyMJoUmNO0N5j86hqQu35HYfY9LT5/bfkfKs\nuIdIx/kN+yIv6GhDZei0KwHQ6C4XeSMFUVJvZYaT8LVz+9YBtUR2w1NvW/vOKfdAX1BKCXADARVR\nDYn4GQWjFNHf9lx95vD3PniwydTOs5amf2/b8MPnbwMC0kdtu3ZoGRgKMj1hNGzI9WqFtgkKvmWF\nnIEka6XMnYNjhjEWnhs0zQrWOoAaGCtxz955EBv4ltFUNYrROJzqgq5mRJiOS1w92ke9PMWr0zPk\nGZBlRoiYBHXXWpB9C5G4BZr+zoBzBHYhppS7GKM4T96HmFRJ9rcpKcghIXXVdYN036boo7g8ORwe\nXcIPf/hDFEWB5WqFqq5Esc8yuNahZolhlcyjFcCEtq4B6sqC3Lx5E4vFAqeLs5i4RF1YlRmm66+H\nQtsGgYsMfGCuWpxL1nWT9vTe1AU8MlpK36N06aTsChmxKDMDnkG2Ez620eCQ7vrxjjB0yAAAIABJ\nREFU9NuVdu1PSu8iBIog/tnvHuH+W7fjmJRu//7v/x5kc5ydneHJkyc4XYgnyM2bN5FlGU5OTvD+\n++/j09/8Bm/fv4/RaISPPvoIZ2dnaNs2uLYLgv7gwQOsVisURYGDg4NgheJQ6/UUVb2C9w6TySSu\nX9vW+N3nn2O5XMq/1Rqeu2SGeZZhMhalXsG/qq2SMjdd0q8hL2KW5H9EhFevXuEf//Efe3OW0sQu\nHjRco96B0VMWQpKdKPwguokZ1izPm4c9UYgBTnhSlmXYm+/Fa1R5McZgPJ6IQL1eY7Va6avkWYpX\nhSRcX3z+W/wv//E/YjQe4+GjR1gul1gsFqga8YKgTITVkc0wnU571gGd0+0JgNK36lmb8EzvJRGV\n5vTQ+UOa7V8m17PUizcILM50rvOqSFsTYvITPqb00bPMo982Fbh+G9KMIYMyl+RtxhhxnU++94N7\nKSRy7LWQPLZa16iDsiq5KgDAonLBjZ8gSfjCedRVWlF+13T9546WiBhNU6F1JYgEyNXwAY9gLXMO\njXc4ODqAzQ0WqzPh3QaA80FV7iuW6RonExiTSug6DucvTdpEJGCO8sXVqkJe2FiqSvfiUA7Qcoci\nnAsXVkXXe8k1o++SeencYZ3rSlPuUo42aUDyVmjSJVlvwFpR0BW4FT6vwHW67h01pHOngq5W6ZN5\nYKSJDmNfqDNmdMCfno8SYy6JD204W/pH7lDuvIgr/Mb66uehH1HiI8k/I3Hh3d6V90rOEZ8I9Z59\n9CB8UwVVwQsFPBQEg8lifhgw4FupIOQ9wxFi37wPxT+5O4uji3RQxDs+SpiMRyAjiQHFKWi3op3K\nhN57cUVnt2GAkqoOfVnz8PAQWSnuwP2cQ0BUlC4AKOtFw3mNe2jLfV3Mff966V/H44kohoi2bRvC\n8Qy8D3uUHTIWt30fPE841Iv3RGiEKaE1YSwm5FZhib527MSAxcDdu7ewXizx8PFLuKA8jkZlVAjT\n87U3xoE8PgT16rrulPa2xePHj/HRxx/GcyPLMvzxH/8x9vb2sFwuUVUV4CV2W/E1len6oatpiFVn\nodczqW1bCTsM/H+oi6gca62FJxP5+ZC2hH+ZGLKk96qnU+SxJCX9jAWc54F8rvM0pJOLK9myFwhg\nExzguoSlgCby6wBxYzsPNzNw8mN2ohyHPukz0j2zk54DDUwnE7AvgbZFRhRyhsl+UF6p/7pndvH1\n+g55D4vLvSN4Fjofysny91CXfD0v+04q98D5h+Gug2DzGd92r7552yXUnTem84TA172rbR1W67VY\nMFuHvNQsxAiHuyK20pxvwO0K67rBOGOYrEFWOBhDcHUb4iw92AN13cJkHkXRkZG1FtMRsD8b42w+\nwXq1RE4EtA1yADkJ+v66+djVhgDGRZ7RCUlR1YjMZgiuNE2Dqm4xm80i+lrXdbxW3IJT61aYN+ek\nfneWR9dek4trsXlhcXoqNc7tDvf8XW1IFzp2Za5DEKubh84NNpkJcEiUJHJJh0AOASQRZvr9GB4S\naX9SZT/9fmiF7PVmhyeMHubz+RwwYll+/vx5vH61WuHw8DAK66cnJ/jNb34DZsbp6Wk8vKqqwngs\nZakODw/x7Nkz/PznP8f169fBzCj/7M+Q3chCLdYWx8cL1HUdn8tBCdS4NM+Ml6+OsVwuMZ1OcXh4\niOl4AqJOAEmFAQUpOuG4U4hXqxWePn2KP/6jP8K///e38cUXX+D09BSnp6fY29vbSQ/bDp6hgP86\npTH9XRMJYePwDdcM3jUajXDjezcA5hgvSCRuqXVdYb1aoanrJIGVB3kPuCCgMFAET4iHD76UcoVh\nP80mE0wwCcJvcJPnkAwtCnv99qa8UYWydHxqXSAiyShMHYqul6owMOQZca/4DmAYKjPGmJDpGL19\nq/s4BYFSRaoHDpBFlsTdst/kgenvRNRLkOS9l8zRAKq6FsWENJO2JNSzRuL6u3eociyKq7FAVXXA\nTRqTKnxIwlFOTk7AAeT0TmOhuz42rcf9t+8jz6UEokW2cx37PGnzu8hbTL8c1PAaVbTH43F8Xts6\nrFYrEAiHh4eBp7dg7s99UeaoqlBnPiT67K19UPLW1boH6qW5OIbA2a6m/e3XUd+2518DDG3QKCDn\ngSh1Sj8dLQXM33bvADoLfcy6TqTlwqOD6JsI6ue1bTKQMQat1/CU8MbhuYSuxjdMBgoKCZFYP1OQ\n5Zs2BX6ZnVi8Ww/KdS9kUg6SGeT6ngXdORjyMLhQbo0DmO5aNHUVnk0dEIu+C6+OWQC+xGCUvGc4\nf6lSoDSZZRlaV289g4mMuAjz5v5LgYCvKZZutN20LIkuq2qF2q2RcwZCHoAqDwcPJuEtxBBUyRg0\nluEAeCOKfOSB6ANBxTgHLPCDH7yDjAy+9+gRPvv0N3j6co3U0+KiLQWSRf6Q8D2NoZefUr2mCff8\n+te/xv1gmGiapuOtYc9Jvqx+TLvyW+89inwUwwY1sW/K84RmfG8fa3iiMQYcPEROT0+3KJSEPC9Q\nFNQDKdP+aNMwQiL6ttjB1vY6oDA9U6Wv5+fhSsf6+ndLlY7ReAxLLLIJQxLNBo/YbTw7PR+3vY/h\nQvUKOSvSEs76kxIjtuj2/w1a7lXwGU7Sm6Ku4WmRGHcpEul70987ZKd/kH5dRfv/i6YbXDe8lJwz\nklgKOsbg7QAPAw8physZW2/fvI6r164gzyycqwCWmDBuBUlq21DzO7jMKXKeWYv5fI79vX0cny5g\njMF6vQawLflHX5H8eut8/r0pPXUKKgD0PTx0rorg0pfGZ1ehLqhs1M4tl1mSvaxdFZ9jrQ317g0O\nDg6wXK9wfPxSBFkjLrtKRbuYzuuaMghlHGnJm1006p0mitn9TP0Zpie2dO6G+/I8wXzIjLeBMm/f\n+17vPPBekrxNZlOMJjO8fPkSH374YczpoDFsKrSVZYn5fI7lchkVY7XWaLkXVdpVoQCAvb29AARY\nzPemmE4nWC6X0W1/Mp5gPt+LYyzLUSg/JAdI0zRYxxjHTcFqGxApfwto9OGHH+J/Wyzw53/+P2G5\nXOLnP/85nj9/jkuXLu1Yoc0mbsz9et/fpP50r//xHX1QaT6b42yx2KCD5WqFX7//PpYhH0E6Bz3a\nCeBalucoyxItd4lRNcu+5gc3nmFIrMxSykcVkYG18YJN+mwAG4SUBHRReuUeWBbusyZYUC3Ss0nG\n0wn9AKSubkIL3guP1Oere15qkUnnsZdQKXzu2aNNvWSSErAChvTDe7Ypk1kmlSDAIfkPOosUi1YP\nqPDIPhkDwbsGGQymkylevTrBuqmQkQW06gozvJfzpK7rmPVZBUrHXSKmzBL+5//wHyTTvzGwZCVU\nwDVh/i5+DkQ6S9xGBcDp70WNGc9CEs26ruF8gzzLsb+/3wnOEG2LQ2UGzy2slao5ZKxU7WjWSU4M\ncZlfLhd4/uwZumzsXf/apsGbttRibpL5GIKlKX8xAAyH+O0giAIAeaVLAFlIyOUZ3puo3AMdgMXA\n5p5N6En3HxkTrefpWuxK8LW79XNPpO8SebBvyRLa7wAWCu80xgh8YUS5N8agdbvz07xpM8Z0Xhlt\ng6ZuYE0GZvFqJBI69AbIDPWw9fSsVoto6uGThkcJV988V4cyapdHIIRXkEvoAwAzJpNJ7zmvA5kU\ngM1MBmO6dfdO+t25N389UGcYc5+MbuNafbeC5t57NK6FD/mfWsiYu31CIGvQslSFYe68aIhIkuiF\nZ83nc/zBT97DslpitVrhYL6HGzdu4OmTJ3jxah1BkSFt99rgHGKI10aX56C/R4uiQKpo69i++uqr\naISYzWY4OzuLfNNmnTKoY1F5ugl8Je1jalCQkJQR1KqsoI6W6SUitIwN4CvVmaw10UsgPfPSSixD\nr4JvW0U6T8bcdb325yKg3jbw9NzGAJlQZYW19gz1Ucddt27h22mLnrA0ME7zsJ//jVrutw06JTht\nb36IbDz1XKbPPJjgr/sWJbbECqQ/FfXvEgehB4kq89/1zCHjJrJgPdWD4PXV0xd4dbKQ8kSRSEQQ\nQ8gITb4FuIVjh1GZ4e17N3H71hXszQvAreB9C0Ni/SFjYAlwbYt1s4ApJrAhnhPh0BnlBQ729nB8\ncgpPFvW6Epd7NmEedO3EmiAuhxZdmYzNg23XWg0F4fPWoJsvCv9C+TwglHOy2J8f4HD/CK5lECyK\nwmBVrZN39S1sJhqOgnKNwPRyE8u0qVLpEVx6QeKaR7HYVqxjvO3gVCFcfw9VyeIwJK41+XzLHLH6\npWE7qJC62BOZ8KAum/bmdpOYw6ap43XMQ+beR5v7Xgabezi9jlniuyaTCVarFfJyJJns8xwnJye9\n9Xz56pWsF4ngxywWfo25JxJvivFYYt729/dx+fIV1FWFFy9eoSgyXL5yGd57rFar6H4sibPkeePJ\nWMCa5TK63fmQoVvKzaR0pvWViySRmYeFgXNCa2enK3z66Wd49uyvQWTw8OFD5Lmg5fIsK1Uber5l\nKUhpwp4xEJc1gvEmiYcO9CHiOhDKY/Vi71QI5Y4GmDmUEe2j8ESE2WyKvf09nD5eIiMDCwiCDaCu\nanzwwQcYlUUEXjaUey8luAhqsQq5HwJdZoEwdCsbEIymdldQjDta0edueuekUyaeA0QmKKIeFBQb\nOA+TBVon3Rdh4hhgbM9mno6LiMAGUk8Z4rJM/U0w6E5f8d9UnvqgWF95l5KTqovLizfb6841hDJA\nEezovuj1Q8Eozx6jcY5bd27gv/zilzjcn4X4IFHw5RoXY0LB4Q2BT1qo5dHi+/d/gJ/+9N/A2gxF\nPgaHkpiSrV5K83XCZBgPzlcliD2IQ5lFqVcYxxH5tDGYzqY4OzsBeyk9OJ9PcXB4EMo3Sp6BVPDq\n+IeuTwfKyF5igD2ePX+G1WqFLChtun5VVcG1bYoSDQS7rq/aX+1ramTo1mTT4EDcAb3D9dPP9X2u\nlRAI9pTwe7kvt5lC7b174k8Oi5qsjwb7bAMyv25Lz3NmSWTonQEhh/cGRD4CUyLQIOaWEH81tTrm\nqNoGjQ/goFc++PUB9dRVu3UNMjOGZBSX/cQIpX0hcpInH8Io1NjSBoXIwwSvNOc4XqO8msn0zsth\nyJUo94nXnPdoY4ksAZtNkMe28RQAoJCAjkJlBQJgTYbl2RKPH34JQ6JsHh7ui2ePLUOiuRZS+ebr\nKfjx/SlYEeQHGS8B1NWZl0R6jLWvYSnH9cvXsWpWqBbHgA3x3iAUNgOsheEQQgQgsyYosgYUqvW0\nbYuTkxN8/vlvQeQxm05RZhnauoVrHQwBWVbAGJuUEuxy0mhrnYQuqcJNkLCNoiii15SOswN3Oi+U\nPM9x/fp17O3t4fnz52jaFvPZDJcvX0bTtNBymrp+Ck6WZYnMFqjqCtVa8vuo56CCRyKTZEHGysL6\nZcjzAlmm7vriKp4CiSlIndJc6oGkIEGaf+ZNFfA3aSnd79L50ld33nEXNHKw2QCDd/bFs4CknsCk\n5fm8SPBRuVc+07tz+KDeFWwIzjNq14ZtJfsgjv1r7LPvnHKvtWo9GYRI0CDUicAntbTV4iHKoB7I\nUvt113MDw2VI3DYLuiqO0kEITeI2thNqP0a68wjotCwOpZMYDCYf/wn8zjDcoa1SF1F8pK0xUlva\nJAc4icIndnUGWSNuRurGaQmAk8NZCvmCTRDKQ4+a1RqfffYZFqslmAowPOBFiJXkjASxk0mxqKOj\nPdx/6w7u3f0evFuA3RIOFYhrcdkjRllMZB2qBsevFhhNCxDnoe49C5KVGRwe7MF7jy8fPUa7WCFz\n4sri2cTK3Vr/WBVtrWupm2HbWjD5HrF7OJk38oCRdd6FuAoTUhcaA2aP1kscDnuJ17lz7y72Dw7A\nBCxXayyWZ2h9g7LIJeEcRCFSoUjqXtZwEIsOtK4qhFlOxzN4b9A0DJMTPFG0kDlmMElSKx/pWPpo\niWDYRz6RMtAhHZ5L986LlUWCJUGeYu1xVZiUxhQcUKUm1vWOSYIQP2PSZEwOWo891RQ5CP0K3BiT\nRTRY5luEzE8/f4S37t2CjzGggRYCPVy6dAXT6RzPXryKbrVxbJTBk5N6q6rIkkUxLtEywdcqmBsY\nk4NZkiJ1Fv0KIMnWra7/x8fHqOsajx49wqWjS9jbO8BkMoFzDvPZBIeHBzg5ETc2JgRrnekdhipI\n3bx5E59++ilGoxEAkrI8Hji8dAXWnOD7b/8Qf/bv/hwff/wx/uZv/gZ5mcMxgSmDNxm8ayL4BKBn\naXZMIO9R5iWadQOaEdpaeaAAexJnLX/DCM0KnakynMEWFo4EeOsytIc5Dvo9h0Ljl65cwfd/+ENQ\nIXHIRVHgyaPH2JvPQBlgM4OmbVAUWeCNDp48HBysQ3C3V11aNXgCtf3D1SIFRWVPODCMNyEpkgJz\nHA9mzx6Gsp5F3EPKQA6r3pDcAO+ldrCAKLoHA+bJGj9I8C78btKcJi3EKEogn1jSgVCDevMMGVpE\n9TOPzpojJ1ECYlMGiza8U5+rwlawFHMnkMmFnaeFWFkZTbsS7wcjJyuCkm8MpFyd4wDiGbTed3We\nWQvZedy9dwdMjOX6DKNiLHXk2cAzgYNA7hxCxnWblF70QaEGDvaP8F//6/u4dvWaAKoOqF0Nz208\n2zPKIOQi82+9hGZwsCb2SwOFbqJzfx6CJSAFXJJ8CRawhcG6XvX46VBJTvP4dLyt83rwzse8HNPx\nBE3VwLkWeZ6BrIVzark34pkCC5MF2hUEJL7PgxGXJgK3mtVBsf8AzofxSIxu8DQwMic6NxTpTJIf\nWmKABait1rUoPeqLbyTHBrHkHdf9SQGEbdkBxCgyi6ZNBFgWTxUOiJPfIPskd4Qxkn+B1UNnc59Y\n7Q8MwOJqrN4szC68VXgLOEgCIScEw8Kx0CR7AhupHe+cQeHV3Z3jnG5rhBAGpAxDlaugDiOcda5t\ngUKU47qusFqtMSrzADwk8xr2q/fiyi+5fzjSBTMjz1PPOwKRJHRj52WPpWsZPF00pKrxBo2TfEYx\nwSUbFOMSxho49nCQcErYwD/JxHOe1eBCBnkxxro6wS9++StkNgvW3wZ37tzGn/2P/074Y0sQ9xrf\n0eyWWdzIjcQODx4+xa3vXRHaMQWYcjDEkwpsYNQjj3I564zFum7QsMdkOsHlo6s4PDzE8quHoMxi\nNpthvV5LkkvvMBmVsBAlib1Bno8wGUsencVqGdYlg1s3ICZcv3YNWQ5MJyUWrkZbr5BBQsfAwGg0\n2aBPbc57SZjrPaqqBsLcN00jme/bFoeXjmQ2qIuLT70OG+fx9PkLrNcS/lk3kkV/NpsF63jT8+SS\nsrPiXUjGYDQZx885kd/ECGdBNsdkPul4FRt4dAlfyUKMTFkeQrcywFg4DlZv7s7hIQAgXjNiWIhA\nkYZKBAWXwOEcpQDabwHWXoe12RBzbjzY+KAHcTBMqL7W5aIyRvJ7Od/C+6zL+zM0qsb3+z5IPtQZ\nukLCUf6N4H3gwVq9Y1sjADYAktq86/fHwcE7JF5U+ng1HoiHVdf314MR3znlPm1DJPjbQIW/afsm\n6JSio9qI+tnEFQnH4Jrhe3f1gQ2BYEV5hBD5y+OnePb8GI4JxuTo0CADkAN5RlWvMCoz3Lp5G+++\n+w7eunsL7Feoa6CtGK4FuLGAUwGzAdDC5xkOL89hCwgTMJ37kCCCOS4fXUKel7hz+44kTIrC1ubc\nxPG+4RRfxHKvTcdOlFhn6ho2E2vppUtXkOclTs4WYusMSHte5MhCghI5/IO46z2aZg2QR1FmG8oR\ngJiQpKpCTF2USTtAw3t3YYPCLgTzdePv5mn79x5SDxaEkHCs9/TeM7a1oWKrv+/qb+qeNrQupO/J\n8zwecJp1Vg+blrt4RL1HwyqYGa4WdHm9Xsfna73rtBZ527aoqipa7Y2R7NqHR0fYm+8HlzpgsViE\n0ICuXJ0hyAFBirAS6maNUTlB08ih3SWhMciyHD/4wb/C3Vv3QF5c7O7du4e//Mu/xG8ffI4vvvgi\n1m32G65enfVI6SedP3VNjlen1hryIaFjtw8dBhZ2TeQlRL7hWvry+BX+5m//Fr/94nf46quv8PLl\nyyioxIOH+vSwAbAFpJvDNWbLOaV8gEhcovU5jroY4TTUKj0rNkGWYG2wFshy+EA7RVFIhmlrwa5T\n3LxnsJGSmIgu0Rq/hyA9qXeBB4MgVaaTDZMKAMMV3Kb0g7GNx8u7u2zpEjaVAtCbIJ8oABLbKQJ4\nAIzDc20mEmDTtKjWNbIsgM8sMeej0Qjjsoh5AgCgaRtU9QqTyQj7+2OslqugYKeDMEmxzSD/awbw\ntoExBmfLBd59912cnJyI15IxgANa30blXvd5FxtKwSpmeuNN50otVi7UuFdB1LMXwZNJlLEwTxoa\nkYbppEmQdP7TCgSikAk4qfNMJHPz+PFjnC0WKEejYNw2MQSKorYbwFRSDycfrd4G0Sge2zaPDmUH\nKkt0+0RBj+5a7XcXQiBrBJKY4NHIhvMqCekICvougCN0rBNMQTv34bfVmkY8Qois0Bi36iQT3wmE\nRIfMotybLm9Fy6K0bJM7trcgsOtcB+HeWKAoxUvLM5BlBBtKgUguG4oyUARGiHrgkCRjDOE90DwR\ngE8SJqYC0LZzU+i6P3YLiskHlV+xIWRlgTaUfvPgCGDG/eF9BCGYBez9wb96D69eneGX/8+vMJkd\n4NHjr/D+R5/hD//oj0Ux5VbKhyoK+rXawIuAFAWUfFASb25g8gyNdzB5huneHOWshDMOzjgcXbuC\n+cE+6rrGy5cvUS1XyMYlwB65dzAmx3gyRZmJLDCZTIIXgEdZCl9q2xZPnj3Gw68e4GC+h/F4jGYO\nPHjwAMA/JH3rrwUzo2lbrCoBFrxTI59c3zQNlss1xpMJTCaJO9N8BwURjo+Po3eg0ser0xMcn51i\nfCJlO8vMRoU6Va7LsozvSveexmvrd6PJGOVIyvMqsKAlgwHgbHES+Z56E6RjJWOiC79+ruMYgqi/\nz5bKhr0kkOdcn/bpW+VJQU5iI4AdsQdR4v6I/r6gLR1NvXI4eGZ6KCgenqTzywwmjrxWx/e69p1W\n7v//0DYsNfya7wcHa1eCJk3msjvWdEjET589i7E5aR+Y9d0eeZbh9u2b+Ml//x5u3riK3BKsmYBH\nhLbNAZ4ig7h5OceoqhVOTk+BhkFBWbJMfUEiMKKyzHGUF9Ftzjl3kVwQ/2ItLTGUZRmmkwkWiwXG\nAPKg0Mm820SgAcASy1eH5GF5IUxRBUWNjwKA8XiMK1euxLwDzjkgScAV/wE9Rp0m30qVlVR4HV7/\nL9W2YvUDRp+ObxtzVXf3t+9+Dw59oTSlb61Z74N1PMbQRWvO9r2QZRkmpZS703wKAHBwcAnvvvsu\nZtMpXNtgXS3jwailvowxGI/HuHTpElzrY1+zLMN4PMJ4PBb3yqDck6GeYlCWJZgZDx8+7B3A3nuc\nnp7i+fPnePDbLwHXHfLGGBxePkBZlr1DemPuoyLuATv4bCCUM20q90gUybg+waKlIRmqECF4TBkj\nVRUePHiAv/qrv8JyvYqPnc1mkljPbHrMMHeIdkevHYQpis35ITfq+puOQ1yf1Vuk3zYUIurqzpu8\nQOs9RuNRTAaohyhRsNiJ1tRD1nW/9cKnertgs//nWYOHn23Ls7lt37yJEKX91mSfSGgUQAAmW1ib\nR0uCxlCWZRnnQABJxouXz2HMBFdv3sQH//wpJqMkPInFiqOJ3DyxWBuoA3G897Bk8c477+APfvwH\n+OKLL+JcilurjS7telZIluwCZRYSNw7GwIEPb1MwlW9qvP0wl8FoNIp7LQUh0zkfKvtpWVONSfbO\n4+nTpzDGoMgLmXdre0ntIuBp+8/bpXBuE5pFaezGJ7TKyKgDqz2hxxdTkAJAtP6LspHHUmipkWEI\ntHbAgexbF3ihWMi2hJBcsA1p+bzQGk0E1r3DhH8MC/FuAQtoorSjz/Q+qWpw4d51feTg6aR0pDJU\nllkYywBaeN+CiIMrdBOAHS9efckZWBRFdMdX5V4VznTsPoBUQ+OP5NZp473p57BZfIZa+cqy7CVn\n3BaDnJ5Pjx49wpMnT/H02TNYK0ppnudYryssFgtcunSEpu0DEG/SxGq/vRljsFqtegnFnj9/jrqu\ncfnyZRRFET3u9vb2Op5iLabTqdDIqETGjNxIicKyLEG+U6ZUKQbEw+v58+eo2zXKssRytcJkMkGW\njbFer/Hxxx+fS9vOc8yur/ObZTlIgT1jgLZF47oYeO2zyiOnp6exb5rjSUttnpyciOfmYI30WpVT\niqLoAYG67rpH67qOSvl4PI6AZlEU2NvbQ13X0ZtgPp/HmvZt24qHc5KJP5VF0/b7APW+rfZtAg/K\nAyN/jKE++g+D36U531/HHm+GkRh7ZtiBvK+yE/v+PefJvdq+k8p9Sqhvcl0HLA8EsXPu/zrtTYjl\nvEW4CLKkyswwkc62powDCWq9ODuDZx6U5NHD2mJUZLhz5z7e++9+iOtXL6HICK5d4vjkOer1Gdb1\nGXxdo1mvsV4uMR6PUI4KlOMSR/M9lHaG5UkNX/uI8Bkjwh0RYI2Br1tQK/+MIzjrI7L1ddZgW6m2\nXU2/HwppXZx/cBFnYDbdi3FWs9kMIELTEMhq2HPYWABaz1hWNeq6QlHkojTUFVr2KEOcVxYUfmst\n7t+/D+89nj2T2Ex1ddR1YuYYcz9UclPFMBUU9PBNwZ9dc6BCzq75ToVjSbYVLApbmNLwGUPBZJdS\nc947owAZBBpDkikWANqW0TQeJqMeaKJjHwpAKcI73Z/h4OAA0+kUx8fHWK/XePbsOQ4PD7FarbBe\nr4M138Zss9qcczh+dRzi1MTyY0KtYGtfdQITATbrwBbnHKqqQhMOVT1odbzWWly+fBmjfAxDGW7h\ndhQc19UyHvLq2s3JfFKS2dp7D58BHMJAvPeo26anLA+V+5ACS9YLcqCQ1zzo4wo5AAAgAElEQVSt\nEhLBweqjK6cgdGYtxuMJ2ACT2TTcAbimDfk0qEvaqOvquec9cFEBW+mMWdzvOjqS8n0ZmSgAb6Pp\nVBjS5pmRBSWlKIqYdMsnNGisRWaCVY5C0hxjOoDDSOImsYxrvHoALIa0ntD0tv70Lh2Mg/V+QOJp\n/RDE6Ls6pyFjw59xnyX7WedYfzIYrW/ROhe8d5LyXZlBOSpBJErHj35wBx//8ycyB1kAQ8JjFSs1\nDHiSLPxpnfGiKPDee++hqqoEGAAyysBs4Y2BtZ0XinMM51ZoqzoKs8aYmFdmyCO3KcXGSPJLH9y7\ntVSUWu9TPpxmJB/yzFS41r+JCGdnZ3j16hgunEvG5qFPUqLLGKk1LV794gItAn0H6KvdUsOhzmv6\nfmvE3k/JnhYFI+uV69MxxZrnvk+XAgyIVwJIQlWUT/XAgQjydbLEkKYprH3aHHXebJ49XofuD8+p\nWMYvynOJp0G4Ljc2ZiAnWFibA+TRtjWaxqGYUij9WgB0foJDPYu78cnqeO9jEjvnGpDmdiCP1tUo\nywxiwSOx4vvOEh/BRRP6BgEk9AxpXafQyvP7oIkqsc7JutgBDVqbS1iMPgN9eeA8mVrntm1bLBYL\nfPLJb/D40SMYyjpPLmOi56ECot+0dfJE179f//rX+N3vftfReLA2z+fzDXmBmbtcSATkZSGhJZ6R\nlQWsup8bwMLGsMfIjyHhXdaU8M6g8h5gC5NZkGWwR6+y07BlGMg60HxSCE7qio/3lTIFc9Tolnq9\n6d7V+dDEiiktOOfiM3adfaq8D3O6eO9DHoVD8dCayBl4FpLgHh8fxzxRo9EI89kc5SAOv+ean+Qh\n2H6+UVzftH0dQ1Qq26Vtm6ysstjrdMCLvRj9EB0fPGEkrhRgl7juQ34OLPdi3e/PVfe3gwOLW35C\nJ+m403FetH3nlPtUMEkFfqBvEbzIc9IDQRgS7XyuCNGv719PCUpcJPQwHLaUmSjiu62f2+ZAW+ou\nuG2c+p44ruTddV2jDQlJtMaqxEEjWCb38dOf/hR7exMsFif47NEDfPngt3j44AssFxVOFx6LCsi9\nEEuWAXlOuHTJ4PKlEvfv/hDT8gDzYh7dfVrHPaaYWxKlpbCwLsRu0yYiqZs31vCE2vhM75+ct/01\n6M0lbyJkfWuPuLAZkthS7wByHqOiBDzj4YMHaJsa+0eHmM3nqGqD1tdxTsXzQd4/D65cq9UCmtRM\naEliAoUREq5cuSJgy2KB9Xod4lk1ZtvHuLXUapK6Qw3padhSK37s5zn3DZlhisp6DrVWIbFcXt1J\nYzy8muO6KhTDPSoCrQiSalVJD6N0XJ9+/hBvvXUrrpn3HsaqVa4JbmBZUC66Q7FpGqybGm3bxtJH\nWlNWE9pUyxXKskRZlrh27Vo88E5OFliu1zg9O0VdrQBCRKy1D23boqlbjMcTjMfj6Jrfo1sSF26Y\nDCZLhWFCNinBnqKgNhROalfBcLAGGaB1NSiz4kYa6FizjicL1wMK0nlvOQjyidXQgdFPFpN4gTDk\ncCbNvNwmwqzG+ar1leFByPOwrtov72HU5VV5HXdAlVoagcQVjbo5lGt2W6eVJ1BMKijuj5LnwiUu\nz7uVWnV5d22L45MTNF4Un4ODA6n24R2sJTg2ITeK8pJUCNkmxCT5DJAkfxucYdqXtG3ua78x7u53\nG8fd/yzMSIfnRst76jZpjLjQqsVB+xOVbpI8LuQAY0LJJiA+lEn4VNVUKNsCb719H7PZ/wUYSIiL\nsYAL8r6xcAk/i6W+wnuzLMPd+2/hP/2f/wm3bt6SvgRXfFsYWFeC4SL/UyWf266clAq/EhPKUTEG\nAB4o40QE9i4mnKqqqucNN/S00soa6qmzXq+3gibee9S1xJauqzVOzs4wn07hWZVrsa6u6gaegJY9\nWufhPcOTeOlYsqIIc6gPbaVGtzWdsB+Vv7aN15LtC4GiiPYBpCEtpcBr97cAU0IvQOs40LPZGG+k\n5R5dbheYNkGu7vNUQO/21jBfRJ9PbnNFJbJdIIwXGYH17EoMO+v1uvMccXKuyDh295flw94YVfA+\nPNwD4NE6J/W0ySMzAHyoxT4qwNCqOhImUjctFP2SfSk5fiS8QEBs33SZ89MyhTIdHa/XPZGCUMQS\n6ieO/hLp3mI7oJgCU0P5lUgAvadPn6BtWpRlDucdqqqNOSJiv1QGGzx/qMD0m4l17tM+RMAUwPPn\nz3tytL5PeZU+00U6764bjteBRfny/WcBgCdJgMfUArDi/+Glj12ikNcoUoEvoseB9O8ORu+mRAE/\nUfDFk1MMS3KUCtgguYE4PlPuHxqoBBzcpexpeepIU8n58urVCV69OglrNQR7P4YChGLhr2GNJFZU\nS/90Oo1hj4szqTag3gNS9x7x3cOZ0VCQ/nHYPxt3hZymPFHPN+9DiF9Cm33FOV33r+ldwKqHyEI5\n5yQ5Iwt9qVt+LJ9I/TERJKWEvlvHJ3uYumTMIY+G9wPeAwY8YsWdiyr433nl/nXXKrHsFoy+IWrT\na+dPqizadqUqEt1FA6tD6x2sO6/Zfe9yuQqu4wY2ceEFxE3o+PgE/+UXv0S1PsWzpw9x8uoUbe1g\nrSjyRQEUOWCcigKAc4znLxwePV7iow9+iT/6yTu4enAZdV1jPB4jK/KQCEkINjMk7knLJbI8CCfm\n23EjH25m5r4L7xBcSQ8nEfg92tYhsxmyrACYMB5N0DQeL18cS2xdngEg2EyZK2CzDMVoDGaGsdQl\nqCEIeqxJ9QhgQ1gsFiKgRebWlf9SJvgm8TQpPQzdVF93vSo8w3ncNbd6f8/1NXGVHiouKVo6ZLQX\nHZMq9k3TYDLex+1bt/HJZ58C6OJgJcbW4fDwEDdv3oQxBp9++imqqorPq7mOrm/7+/u4du0arl+/\njhs3bgBg3PzeTRwe7ePhwy/x+PFj8aoIh1uWZcgzF4ECYwwKIxmyFWFn78UYtwVMkgzineKWAjeR\nd5Eo/9uarlM6vwZ9ftK4Frl3ki/BS+Ikm6xnPJSwnReqZaZ1LQxvxuwP12fY01QM0oN2i1ea9OcN\nWXE6BuM7C4++a9tBNwRu9TkyN4AP3gXRlbuVJGHeS6Zo17YgA3hjQST1kkWRlHjvPj2LtTMVutLf\nU5B1W5++zTZ8tir5qRCfNrW+tG1Qhg1JwjUjSd+aRJhmEKpqJVnm0eLevbdweHSAxekSJiMQi6XL\nsdgvjAfYMQDbO7vG4zGm0z3JQJ9Y84EE2IUNilTgHwjzqyVakzN/WPJx15wyM6qqElA1Kd2UWuYV\nLNDcGKrgp/Qkigglc9sJze/96F1RvIHgPiwhCnnu4MMzvfewxSgkW5V4/yzkHaAA5FHwIErXqGka\n/Pa3v4WrGwFLNrzQkvEn8pPSY1pmjWB7/JkIXZy4ycR6nzQdf5ZlUZlxvgsNe1P5Ku3jrjbcK6oA\ndrx19/1iZc4iENu6NUABvOG878m0q48EwIQzkikkXJa+2yzExzoNCXLwaOB9A+caeJ8J3wihd2r1\nY2h43yZ/0n4rLbIP66R5NrL+Wa/WXJ1PTVqrJVHTeYxj4t1WzPTMun37NvK8wKuXx9if72O1WuOD\nD/8Zp6cnANDfc53++bVbKl+oTNa5zm/ytTg2z3AmTDFRSEqcgg0clT6HTW+Srsk5DTadP5uCUa+R\n9zfnsgPF5f7t9/R5z45QNv3JHb2mvCoFb887S9Jr0/Opa9v3g3o7gRmNa/DixYvIDzWHlHgcGPGy\nJIq803PqSSkTMfRS7fdxZ/c3xnKRNqTzTgfQue2U/TdV+ON56hPlHv5cy33I+xfDAPUZ0i/x9JG1\n7s5rvU7mWJ9z/loP23dOuf8227er2L95S4XPb1uYu+j7l6sl6qYGzKj3OQC0TYvjV2s8f/YYTbMC\n2MFAlHlx+xIXJ+c8XKBXYyA+RwbIDWDZ4fMvHmBeTpHnucSU+SQEILzLBFS4rmvAMix1Qsewz7/v\nOdFx+ZgQr0FmsxiXOd/bw97BPo5PT/DVV1+hKHPsHexhXkzgfSc4i/v1GqenJ8FVqopC4lDwqSqJ\nWVMrjLZ43QUF/xTs6R/wtPPeIUAk5Zu+ySymeKzZ8k+v6uJcJT6zL4w3bQWQx/1718KB0Pd8YRbL\n2f7eVbz33nv47Re/Q1VVPVCkKArcu3cPR0dHcR4++uijqLyp3pxlWawNO5/Pcf36ddy9extv3buD\n0ahA00h2/E8//RRffvklTk9PAUhStQgkBDABQESuJaay7SmbbSPZsq0lNI1DXbe9MaW/M+8OlWBm\nOB7Wze7HFquVanhoxt8HFqqh2y+ji/Eb50W/Vvg3aD5RAFI+qHG630QoVMEohqQkbr7pPoj/AuLO\n+jm6fA9AoOY4J6ki182D9wxjFGzouj/c57uE0a2KWNKGJND/Xix9u44zXf/0vpQ/RMCBuuvzcgyi\nFYrcS9ktiAs9QkhHmoDSWEKWk5SjLDIUeYGr167ii/WXkvQqVAVRW6qsjYug1cHBAZpQH346neKL\nL77AW2/d7wmqRCRZkb38rvvNh2zU6qHBCQC1C5Ac8kK9R5VEtX4Ow5zSeVPFejQagZmjRX82m0bF\nkQioawENJpMJ9uZ7MGQwHY9lPZgBeGSmS+DnINnK49oFhUaVez/YrwBirHlVVRIDm4+T9eWgeAY+\noqB6cg71knEhDc9T5V7c++VM68r4bfAl6j93CPhuhr5tb92eGsaZh78H10tlo06ZUZAvzmESLkMA\nlqsVMlsgFS1EKRnj7KyBKc9X8Cn+3/EPtX7GPAvByqpz6LkFyEtSSO9htGQlyfdpzh6VQXbxjV5f\nknsU0Bp6YWxrQ2X+PBkhXcd3330X9++/jWpd4eb3bmO9WuNv/9bg/X9+H5PJ5Nx5u0hTq/2uPg/7\ntxOwU7pPrkvHuKtFoJNSJQ89UODNG4FhdpYgHvY85THMHc8ZKuoCxtHG87at3662DYTr87rBSJK+\nRDoLVWnSZHtKMySHZ4zbp+ARkbZtYFwP1PmWW+q5Jl5/FPfxt/E+AcNDQkkRKjaNttS/3lAfdNAz\nLhqBvI8W+6G8hC06wkXad065Twf/bTzrTYRU0k3KjFRZSUuaSKIp+VPLLqmsShBrj1zqk/sRnrFp\nqd4Qxs4Z9jZEKnSr95n+rQKGNVLqSojHSZgIA1K+zIEA5HkpyjuFchZaAgoePmQCj9YF9qG8BWDz\nDN5O8PDZCxxcvgZQDu+MuGInruYqkPs1Iwtl0QgQfxX4UEbv9SjkJgOUeU3ne5fQF+fLI4QpGLAL\n5cNGFpTlaNsGrXdYrpYwlnB4eICiLJGXGU5OTuC9x9nZMs5vmuRHs7GqG3mIagZg0NQOZ6fL6KLu\nnIPPOhfwIUqcoq3pWqdzoT/1ul0x9+l1Kpy9DnQSe116IOx89E5AYXgQ7VJwogLWu1/oS+PX33nn\nHfz4xz/Gr9//VYzXPdjbx607t3Hn5q2YhOfOzVswDHz88cexfJ1ml82yDLPZDLPZDOPxGNeuXYWx\nGVoHOG+wf3AZP/zhCNeu3sSjR0/w6NFXWK9XcI5Dhn0BbFUwIyKpigQjnxPD+zYoEhwtxP117AAO\n7y2A9gK8LkHvB+uW5iDYJtz0rLbEvSRfHA4l9SjRA0e/U2VoSGvBsNU7jNL7UoaUAjp66MYSa1+j\npUq7F3N7sBJvXhPHEUq5kTHgphOOnXMwIGQhLMJmVhIjUueFpeO3oU6wgDd12OO0kyfL2LH18+1n\nnH6/odrgIkhIqnANBTkVDPRzYwwMByiNpOysfBd4/hbrCjNQ1y2WC8mSf+v2DXz5xReSEZ/F8kWE\nGJMstCIK52g0QgHGYuFw+fJlfPDBB5jNZrh6tXPPTfuWJsz0LCE2bVX3aGwXjx/GpioIq2BfKrQO\nPRqGMazKK8fjMWazCYwRAFu9dooix3Q6weLsDM+ePJXKHNZgsQbYe/i6BcOJcq8ha7BwTsKdiEMf\nnCRmsrnEv3PWJYC01ko+C9Mlxkq9vtK+GmM27HC6/nq9ujNTvIckEzMJOKN8dPgO8aow0DgLYofc\nEhDK4wF6ZuiZl6yL7/9tDO+wF+oNJt5HTGDnYBgJjXoglDSU0sIuJlosigzrdR15jXpxyRgkfKvB\nbu8kmTMDMsGqRugls7LWgkmqYzAAxy2YMgmJgpQZtYmnVJblEruOrjwYIPspDRlNw8EItkfjSrsa\njpZmXU/3zZAP9QwBQ1dfjwhOMCO6STvHWC5XePH8OQ72DnFycgqbJGKT/ukadJbqi8jtvT2r5fd2\nXmdxXmlrSUBmESo/ggJk1uOo3H2i+Wu8AlTMWvRErt35pvNaN/43aU3TRLmkbXnr2bCrbZMDz1OU\nt+sL3c/0luH5IaCjxq4PDFbcyW1MbuuZAYRzdPCxGCi0b2EBk+uHMj57L/QSwl57pffIBOLtaGVo\nXJPfd8vGF2nyLA40lva7y43Qv6F/r05buue7cyhcF9gDcTgfBmv3Opl92L5zyv2wad1XHwjJQTao\nMRRjajQuVGppkzD7oOx1tbZFYWeIa6Ymv2CEGr0sS9dVfA/vsZLJ0FDIDMuNlNchgjceHh4uxFZ7\n45EpE/EttP63xrRJvGtfsdpYtMFGV4bfK2cDbGxCIFUI5bO2bXH16lUU5W9xumzheQ2YPByeFF1J\niEw4uBHL8XRMj6MrGwfGaKILE7ByBDptYFyFFy8XsJxjPJZa8MZ27kfwUu4DDbBv92CsuLlTOCiN\nDCr0v69gpKgsxzpyylQZBhaGCeQzwNngg+U35lmepYhjhrIcY3m2hvdAluUoyxKOHVZ1hfF8isO9\n/eCy3ODZs6c4OzvD3t4c0+kkom1q7VfBsyynMDSCa1niWEHwbFA7j3XTIitHWFbrYD3S+vAx/UpY\n24vH1Qxbd18XU6mHkB7omkBQkWv9J0nRABNyEjhmqaJspHiZZ4JngvOyVgZiKWJPotxGIZ9AMNE6\nOqRTofMOhPnks4d4695NIAGBQqFzgDxevnqO6XSKf/2HP8FoXODBgweYzWa4cukQ0/kMbVtFUG06\nHeFHP/ohrlw5wpMnT2LsmIAwFqenp/h/qXuzJluS5Dzs84jIzLPUXnXXXmd60DPoATCiAQYDCJKS\naJD0IpIPAmQy/TmafoCkFwEGgJBRkEgTiGUAqNEzGPRM9/R693ur6my5RITrwSMiI/OcU/f2YEhr\nRlv1rTonT57IWDzcP3f/XGuNw8NDTKcH8J7gnA8edgfPCtP5IV57YwIQ4aOPP4KHCiHHalDuzHuH\nxeIqKHEGWogC4Hxfk5m8ENbFTw2mlQN5GW46eG72Mo3Bg6goxqazcSYSAzd2Rogp5WCUmtE6rEdO\n/xKT6PNEadel/RhC+n2UDywS2pMXEi0KhEQ0VBzkXplCwuNnzAdpW2FxmYxmZljqny8q1vm6Jg8o\nbYT4UEuazcHBAQAID4hS0AowJM8nZaoIRkukklKUQuqsbQO7ug15htsey/Qvtg3I+MxjRaxXtLcN\nTHl9qOjmyoLIum3v/SBdgEUWERFc59A2NbrGITJ3SzRFyJTPyYDS3Ck0TYeua7HZrPH226/hL/7c\nQupxT+FJp3PYeoe6rcEsBtamqVGWJequRTGpcHV1hb/4y7/A7/wPv5POuMiVAUjp0Nl8gsm0BLPI\nZV9JTnMsHTVIEWKGc232t0o59ik8O5NFu0CwXfMTFTBh6i7gvU0RWLPZFPfu3cXZ2RnefONNfPzR\nx1gul5hOp9AArHWA99CBzf7s7Ax146DZ4+DoJJGXzedzsPN4/MUDEBUg44FC9o0hjVIJm/16sYb3\nQFHopHBzNj8CyiDtxTj/SbaHMNu8XJd41CgRl9a2g8sArbHhmRTOmwBbYvkZG3KxTwyBAFQvE1Pm\nvZLzI5Y39Wlue4Ixr2yo0ClEEz6cRRwILpUGjo4OJLdeMVarlTyvZ3hYMLngxHmJQcUSGp+ZHcGo\nKIKcjIB0NJZDn5mFWDHojAQFYyQsV2R1b1TlAPjA4FI+8aEETUbK14WykspoFDBQUCAWY0fqxAPw\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mEdXcHrP0XQx4ciCv+vx4pfGdb7+LH334Cb54cBWESv7hmGOU8Pods5IftPF/4WWv5IsV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L2zQP4vsEHq+yhNsv07B1bytWqQIF/jprb7XBkkyDb/dfd0XfvMOfe7EXtzBJLAWu0MaBKVyBHb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55iRnV1DosekGOdqvVwGJ2mfC06qDftexaryztXAFqqQnRdBRgYpyICWEAjMGnUlniIsvKo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\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nZkcKyfom5UCua2tCNVW6vcXss+ePn5xzvzbEGroKbaO89i7bn6tN44Voh73QKOBX7Sgb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GHzVsvJCi0hlNboARsBmUMADPhlAYYjZ/kuD/K36olcQFipPZW58j373s+i1WshqvOFYkdgRsuz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\n9A//0V+Yi2lX3DAz6noNoZDQwzFOfMIXX3yBD2XBd++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DMCYKXq/gPg0x8bz4THj/WeJP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GMpgeA1npu6EQdiQgTNwgu5JiAy9mbcZ2o9+cssREqMcDLXJOlE30B2HZlQZTGxnTESwoZT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\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WgfsQKqR4HKR4eZQsyxDfGvxxm9cQ95jn03iUXj5VW7ITTT4dk8rq9Xq9Ds3eGENZFQyH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\noSuaGN59k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vNBnSnCSFQx9lpPyhyNe9i8nTRmXJ9rM+zG9MnEORLO9SO68+DOHOyfHGXS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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f99167d1ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "<function __main__.show_image>"
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "OpenCV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ret, image_bgr = cap.read()\n",
      "if not ret:\n",
      "    print \"Unable to read frame\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv2.imshow('Saber Fencing',image_bgr)\n",
      "cv2.waitKey(0)\n",
      "cv2.destroyAllWindows()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Normal Window"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv2.namedWindow(\"Saber Fencing\", cv2.WINDOW_NORMAL)\n",
      "cv2.imshow(\"Saber Fencing\",image_bgr)\n",
      "cv2.waitKey(0)\n",
      "cv2.destroyAllWindows()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Full Screen"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv2.namedWindow(\"Saber Fencing\", cv2.WND_PROP_FULLSCREEN)\n",
      "cv2.setWindowProperty(\"Saber Fencing\", cv2.WND_PROP_FULLSCREEN, cv2.cv.CV_WINDOW_FULLSCREEN)\n",
      "cv2.imshow(\"Saber Fencing\",image_bgr)\n",
      "k = cv2.waitKey(0)\n",
      "cv2.destroyAllWindows()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Building Work Interface"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roi_list = []"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def on_mouse(event, x, y, botton, *args):\n",
      "    if event == 1 & botton == 1: #mouse down\n",
      "        roi_list.append([x,y,botton])\n",
      "    return None\n",
      "\n",
      "def show_frame(param):\n",
      "    frame = cv2.getTrackbarPos(\"frame\", \"Saber Fencing\")\n",
      "    cap.set(propId_dict[\"POS_FRAMES\"], frame)\n",
      "    ret, image_bgr = cap.read()\n",
      "    \n",
      "    color_schema = cv2.getTrackbarPos(\"BGR/HSV\", \"Saber Fencing\")\n",
      "    color_schema = {0:\"bgr\", 1:\"hsv\"}[color_schema]\n",
      "    \n",
      "    imge_or_threshold = cv2.getTrackbarPos(\"Image/Threshold\", \"Saber Fencing\")\n",
      "    imge_or_threshold = {0:\"image\",1:\"threshold\"}[imge_or_threshold]\n",
      "    \n",
      "    \n",
      "    max_b = cv2.getTrackbarPos(\"Max B\", \"BGR Histogram\")\n",
      "    min_b = cv2.getTrackbarPos(\"Min B\", \"BGR Histogram\")\n",
      "    max_g = cv2.getTrackbarPos(\"Max G\", \"BGR Histogram\")\n",
      "    min_g = cv2.getTrackbarPos(\"Min G\", \"BGR Histogram\")\n",
      "    max_r = cv2.getTrackbarPos(\"Max R\", \"BGR Histogram\")\n",
      "    min_r = cv2.getTrackbarPos(\"Min R\", \"BGR Histogram\")\n",
      "    \n",
      "    max_h = cv2.getTrackbarPos(\"Max H\", \"HSV Histogram\")\n",
      "    min_h = cv2.getTrackbarPos(\"Min H\", \"HSV Histogram\")\n",
      "    max_s = cv2.getTrackbarPos(\"Max S\", \"HSV Histogram\")\n",
      "    min_s = cv2.getTrackbarPos(\"Min S\", \"HSV Histogram\")\n",
      "    max_v = cv2.getTrackbarPos(\"Max V\", \"HSV Histogram\")\n",
      "    min_v = cv2.getTrackbarPos(\"Min V\", \"HSV Histogram\")\n",
      "    \n",
      "    if color_schema == \"bgr\":\n",
      "        display_image = image_bgr\n",
      "        if imge_or_threshold == \"threshold\":\n",
      "            display_image = cv2.inRange(display_image,\n",
      "                                        np.array([min_b, min_g, min_r]),\n",
      "                                        np.array([max_b, max_g, max_r]))\n",
      "    elif color_schema == \"hsv\":\n",
      "        display_image = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\n",
      "        if imge_or_threshold == \"threshold\":\n",
      "            display_image = cv2.inRange(display_image,\n",
      "                                        np.array([min_h, min_s, min_v]),\n",
      "                                        np.array([max_h, max_s, max_v]))\n",
      "    \n",
      "    cv2.imshow(\"Saber Fencing\",display_image)\n",
      "    \n",
      "    show_hist(image_bgr, limits=np.array([[max_b, min_b], [max_g, min_g], [max_r, min_r]]))\n",
      "    show_hist(image_bgr, color_schema=\"hsv\", limits=np.array([[max_h, min_h], [max_s, min_s], [max_v, min_v]]))\n",
      "    return image_bgr\n",
      "\n",
      "def fig2data (fig):\n",
      "    fig.canvas.draw ()\n",
      " \n",
      "    w,h = fig.canvas.get_width_height()\n",
      "    buf = numpy.ndarray(shape = (h, w,4), dtype=numpy.uint8, buffer=fig.canvas.buffer_rgba())\n",
      "    return buf\n",
      "\n",
      "def show_hist(image_bgr, color_schema=\"bgr\", limits=np.array([[0,255], [0,255], [0,255]]), color=('b','g','r')):\n",
      "    fig = plt.figure(figsize=(5,3))\n",
      "    if color_schema == \"bgr\":\n",
      "        image_hist = image_bgr\n",
      "    elif color_schema == \"hsv\":\n",
      "        image_hist = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\n",
      "        \n",
      "    for i,col in enumerate(color):\n",
      "        histr = cv2.calcHist([image_hist],[i],None,[limits[i][0]-limits[i][1]],[limits[i][1],limits[i][0]])\n",
      "        plt.plot(range(limits[i][1], limits[i][0]), histr, color = col, hold=True)\n",
      "    plt.xlim([np.min(limits, axis=0)[1], np.max(limits, axis=0)[0]])\n",
      "    data = fig2data(fig)\n",
      "    data = cv2.cvtColor(data, cv2.COLOR_RGBA2BGR)\n",
      "    cv2.imshow(color_schema.upper() + \" Histogram\",data)\n",
      "    plt.close()\n",
      "\n",
      "cv2.namedWindow(\"Saber Fencing\", cv2.WINDOW_NORMAL)\n",
      "cv2.createTrackbar(\"frame\", \"Saber Fencing\", 130, int(total_frames), show_frame)\n",
      "cv2.createTrackbar(\"BGR/HSV\", \"Saber Fencing\", 0, 1, show_frame)\n",
      "cv2.createTrackbar(\"Image/Threshold\", \"Saber Fencing\", 0, 1, show_frame)\n",
      "\n",
      "cv2.namedWindow(\"BGR Histogram\",  cv2.WINDOW_AUTOSIZE)\n",
      "cv2.namedWindow(\"HSV Histogram\",  cv2.WINDOW_AUTOSIZE)\n",
      "\n",
      "cv2.createTrackbar(\"Max B\", \"BGR Histogram\", 255, 255, show_frame)\n",
      "cv2.createTrackbar(\"Min B\", \"BGR Histogram\", 0, 255, show_frame)\n",
      "cv2.createTrackbar(\"Max G\", \"BGR Histogram\", 255, 255, show_frame)\n",
      "cv2.createTrackbar(\"Min G\", \"BGR Histogram\", 0, 255, show_frame)\n",
      "cv2.createTrackbar(\"Max R\", \"BGR Histogram\", 255, 255, show_frame)\n",
      "cv2.createTrackbar(\"Min R\", \"BGR Histogram\", 0, 255, show_frame)\n",
      "\n",
      "cv2.createTrackbar(\"Max H\", \"HSV Histogram\", 180, 180, show_frame)\n",
      "cv2.createTrackbar(\"Min H\", \"HSV Histogram\", 0, 180, show_frame)\n",
      "cv2.createTrackbar(\"Max S\", \"HSV Histogram\", 255, 255, show_frame)\n",
      "cv2.createTrackbar(\"Min S\", \"HSV Histogram\", 0, 255, show_frame)\n",
      "cv2.createTrackbar(\"Max V\", \"HSV Histogram\", 255, 255, show_frame)\n",
      "cv2.createTrackbar(\"Min V\", \"HSV Histogram\", 0, 255, show_frame)\n",
      "\n",
      "image_bgr = show_frame(130)\n",
      "\n",
      "cv2.moveWindow(\"Saber Fencing\", 10, 25)\n",
      "cv2.moveWindow(\"BGR Histogram\", 900, 400)\n",
      "cv2.moveWindow(\"HSV Histogram\", 900, 25)\n",
      "\n",
      "cv2.setMouseCallback(\"Saber Fencing\", on_mouse)\n",
      "\n",
      "while True:\n",
      "    k=cv2.waitKey(0)\n",
      "    if k==27:\n",
      "        image_bgr = show_frame(None)\n",
      "        roi_list = np.array(roi_list)\n",
      "        image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n",
      "        image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\n",
      "        break\n",
      "    else:\n",
      "        print k\n",
      "    \n",
      "cv2.destroyAllWindows()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cv2.destroyAllWindows()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "OCR"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Training Set"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I have built a training set using Gimp (a photo editing program).\n",
      "\n",
      "![](image/ocr_digits.png)\n",
      "\n",
      "I generated the original row using a text tool then replicated it several times. Then I resized each row to a different level and resized them back to their original size."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Pre-Processing the Image"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Change the image from BGR to gray scale."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "training_image = cv2.imread(\"image/ocr_digits.png\")\n",
      "print \"Origianl Shape: %s: \" % str(training_image.shape)\n",
      "training_image = cv2.cvtColor(training_image, cv2.COLOR_BGR2GRAY)\n",
      "print \"Final Shape: %s: \" % str(training_image.shape)\n",
      "plt.imshow(training_image, cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Origianl Shape: (336, 375, 3): \n",
        "Final Shape: (336, 375): \n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C09nZmT7//PMavSdMKCwspNdee80mwjM4OJgSEhI47bKyMrNSE1QdZDt37qy1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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f995ce1b210>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Threshold the Image"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Fixed Threshold"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fixed_thresh = (training_image < 30) * 255\n",
      "plt.imshow(fixed_thresh, cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F+eh0OrkOH/UVOSptcYZhiNXV1SToQLfbTR0OVeHJaJJmXc5bECpqQahhlIoQ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\nLgNzddpX3nLz6RuGoVOcSL2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       "text": [
        "<matplotlib.figure.Figure at 0x7f995ce1b650>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Adaptive Threshold"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "adaptive_thresh = cv2.adaptiveThreshold(training_image,255,1,1,11,2)\n",
      "plt.imshow(adaptive_thresh, cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LOxQvH97X4dChQ1MJB5rNJjMzx3HMlUpl6rFWq6XVZh4+kj+o5EOSjCOKIq7V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\nJBjOQCAQMOT/A0UcbrD0I68QAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f98d4013090>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Finding Digits"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Find Contours"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contours,hierarchy = cv2.findContours(adaptive_thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)\n",
      "\n",
      "plt.imshow(adaptive_thresh, cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fFnId49RB1MmfGFiN8CTWotEEOvL7NGq+y/eJz3afZ73kNca55gxnIBA4fQiG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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9954383e50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Filtering out small contours"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boxes = []\n",
      "image_gbr = cv2.cvtColor(training_image, cv2.COLOR_GRAY2RGB)\n",
      "for cnt in contours:\n",
      "    area = cv2.contourArea(cnt)\n",
      "    if area > 100:\n",
      "        [x,y,w,h] = cv2.boundingRect(cnt)\n",
      "        if h > 29:\n",
      "            boxes.append([x,y,w,h])\n",
      "            cv2.rectangle(image_gbr, pt1=(x,y), pt2=(x+w,y+h), color=[255,0,0], thickness=2)\n",
      "print len(boxes)\n",
      "plt.imshow(image_gbr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "60\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f98d49490d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BQUEENyjZSpOTk8PKlSvlNru6uuIKoNBAq1KpZHbSzMxMTp48yaZNm1i0aBH5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MAAAY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\nRMQrFR0R8UpFR0S8+j9fzm+WAu/8FgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f98d48cf810>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Checking boxes - Using Nearest Interpolation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x,y,w,h = boxes[0]\n",
      "plt.imshow(training_image[y:y+h,x:x+w], cmap=plt.cm.Greys, interpolation=\"nearest\")\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98d4049b90>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Labeling Digits"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "row = 0\n",
      "for index in range(row*10,row*10+10):\n",
      "    x,y,w,h = boxes[index]\n",
      "    plt.imshow(training_image[y:y+h,x:x+w], cmap=plt.cm.Greys, interpolation=\"nearest\")\n",
      "    plt.colorbar()\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f99560c4c10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98d474d0d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98dc079250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98d48da410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98dc02fa10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98d4016890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f98c0c3fe10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f9955e9d950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f9956191b90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f995cdfaad0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = []\n",
      "results.append([9,8,7,6,5,4,3,2,0,1])\n",
      "results.append([9,8,7,6,5,4,3,2,1,0])\n",
      "results.append([9,8,6,5,3,2,0,7,4,1])\n",
      "results.append([9,8,7,6,5,4,3,2,1,0])\n",
      "results.append([9,8,7,6,5,4,3,2,1,0])\n",
      "results.append([9,8,7,6,5,4,3,2,1,0])\n",
      "results = np.ravel(results)\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "array([9, 8, 7, 6, 5, 4, 3, 2, 0, 1, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 9, 8, 6,\n",
        "       5, 3, 2, 0, 7, 4, 1, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 9, 8, 7, 6, 5, 4,\n",
        "       3, 2, 1, 0, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Resizing RoI / Normalize"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roi = training_image[y:y+h,x:x+w]\n",
      "roi = roi / 255.\n",
      "roi = cv2.resize(roi,(10,14))\n",
      "plt.imshow(roi, cmap=plt.cm.Greys, interpolation=\"nearest\")\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZsoQlS5a0Kpolu+GQiFgkaZyk3x/MazYSB6tZirRgnTlzJjNnzhw6vvLK38lo1Da7YZIh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\nB6tZTThYzWri/wBekLpBASRXsQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f99561a9190>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = []\n",
      "for index in range(60):\n",
      "    x,y,w,h = boxes[index]\n",
      "    roi = training_image[y:y+h,x:x+w]\n",
      "    roi = roi / 255.\n",
      "    roi = cv2.resize(roi,(10,14))\n",
      "    features.append(np.ravel(roi))\n",
      "features = np.array(features)\n",
      "features.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "(60, 140)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Training Classifier"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "k-NN"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Training"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "knn_cls = KNeighborsClassifier()\n",
      "param_grid = [{\"n_neighbors\":range(1,20)}]\n",
      "knn_gs = GridSearchCV(knn_cls, param_grid=param_grid)\n",
      "knn_gs.fit(features, results)\n",
      "knn_cls = knn_gs.best_estimator_\n",
      "knn_cls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           n_neighbors=1, p=2, weights='uniform')"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Testing on training data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "knn_cls.fit(features, results)\n",
      "%timeit knn_cls.predict(features)\n",
      "knn_cls.score(features, results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 1.48 ms per loop\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "SVM"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svc_cls = SVC()\n",
      "param_grid = [{\"C\":[1,10,100,1000],\n",
      "               \"gamma\":[1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8, 1e-9, 1e-10]}]\n",
      "svc_gs = GridSearchCV(svc_cls, param_grid=param_grid)\n",
      "svc_gs.fit(features, results)\n",
      "svc_cls = svc_gs.best_estimator_\n",
      "svc_cls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.1,\n",
        "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "svc_cls.fit(features, results)\n",
      "%timeit svc_cls.predict(features)\n",
      "svc_cls.score(features, results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 662 \u00b5s per loop\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "RandomForestClassifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rf_cls = AdaBoostClassifier()\n",
      "param_grid = [{\"n_estimators\":range(1,100)}]\n",
      "rf_gs = GridSearchCV(rf_cls, param_grid=param_grid, n_jobs=-1)\n",
      "rf_gs.fit(features, results)\n",
      "rf_cls = ada_gs.best_estimator_\n",
      "rf_cls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "AdaBoostClassifier(algorithm='SAMME.R',\n",
        "          base_estimator=DecisionTreeClassifier(compute_importances=None, criterion='gini',\n",
        "            max_depth=1, max_features=None, min_density=None,\n",
        "            min_samples_leaf=1, min_samples_split=2, random_state=None,\n",
        "            splitter='best'),\n",
        "          learning_rate=1.0, n_estimators=192, random_state=None)"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rf_cls.fit(features, results)\n",
      "%timeit rf_cls.predict(features)\n",
      "rf_cls.score(features, results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 24.5 ms per loop\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 177,
       "text": [
        "0.80000000000000004"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Running Classifer on Video"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Retrieving RoI for both players' score"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roi_list = roi_list[:,:2]\n",
      "roi_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "array([[ 702,  928],\n",
        "       [ 811,  968],\n",
        "       [1106,  928],\n",
        "       [1218,  968]])"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "left_player_score_roi = roi_list[:2]\n",
      "right_player_score_roi = roi_list[2:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_roi(image, roi):\n",
      "    return image[roi[0,1]:roi[1,1],roi[0,0]:roi[1,0]]\n",
      "plt.imshow(get_roi(image_rgb, left_player_score_roi))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f98c0d1f510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ReXbGdL6gnC+Yj67QlbJ8s2RmZkDjPRJ1EdqcKLsBSftt51Pb77Db36mkfagBHhfvctDx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\nAie2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9955f62fd0>"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(get_roi(image_rgb, right_player_score_roi))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "<matplotlib.image.AxesImage at 0x7f99166adc90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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glqx9zeQQdluFQiFTWK4tr15lvHie8PLTlFdXGTfXzthZNAQmpXRdnQIzQXUK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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9955da9fd0>"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Pre-processing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roi = get_roi(image_hsv, left_player_score_roi)\n",
      "plt.imshow(roi[:,:,2], cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pCcI5Xv4M+JDmpiy/N/8bEbkOeCdwXX6fT0puNoeilNogA/yuql4P3Ar8toi8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\n2BqGYdQBE1vDMIw68P8DzO28JGyOU+4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f98dc067550>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roi = get_roi(image_hsv, right_player_score_roi)\n",
      "plt.imshow(roi[:,:,2], cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eHeWSMIOJ6/j29bQJr+kOMdO77g2Fr9Ud5tKylVPPXw81qe/Ttt+oF5M5vD0q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\nD4jYCoIg9AERW0EQhD7w3wFkP+RpUa95xAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f995ce2b450>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roi = get_roi(image_hsv, left_player_score_roi)\n",
      "roi = ((roi[:,:,2] > 230) * 255).astype(np.uint8)\n",
      "plt.imshow(roi, cmap=plt.cm.Greys)\n",
      "plt.colorbar()\n",
      "plt.show()\n",
      "contours,hierarchy = cv2.findContours(roi,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NRXz+7pe0zGwQoUD7S3dvmyO+3oKpgMysAGhIXA17dS2w1MxuA4YAI8zsl6TW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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9916120b50>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "boxes = []\n",
      "roi = get_roi(image_hsv, left_player_score_roi)\n",
      "roi = ((roi[:,:,2] < 230) * 255).astype(np.uint8)\n",
      "roi_gbr = cv2.cvtColor(roi, cv2.COLOR_GRAY2RGB)\n",
      "for cnt in contours:\n",
      "    area = cv2.contourArea(cnt)\n",
      "    if area > 100:\n",
      "        [x,y,w,h] = cv2.boundingRect(cnt)\n",
      "        if h > 29:\n",
      "            boxes.append([x,y,w,h])\n",
      "            cv2.rectangle(roi_gbr, pt1=(x,y), pt2=(x+w,y+h), color=[255,0,0], thickness=2)\n",
      "print len(boxes)\n",
      "plt.imshow(roi_gbr)\n",
      "plt.colorbar()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x7f98d4790488>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tLdDZGbRz59IdWbTl5QU/u8l2881w1VVxLYtLikUs/TKzEuDbwFaCd+2/ChwF\n/h5YS2xXmkRXXinZisykogIaG6G0FA4cgL17g0/elWzntmxZkFwbG4N21VXhJtvFrUb4U+D/uPsv\nxG7iKgJ+H3jB3b9uZl8CHoi1BVOyFZlJZWWQaDdvDr4ODATLu2Ruk8n2llvgIx8JkuyyZUELQ4Jz\ntrGlrR90988CuPsY0GdmHwfuiJ32OLALJVuRJJqeIGprYdMm6O6GaSUIZQYlJbBlC6xdC+Xlia3h\nXYzE52zrgW4z+w5wHbAb+HdAhbtPVn/vAioS7UDJVmQ+q1YFI9ycHNi6Nd3RRFthIVx9dfDOIOxE\nC7PP2bbugrZdc70yh2AxwG+7+xtm9k0uGcG6u5tZwp+QWqq25TAzj8KWHyKLNjgY3DF1/jwMR/gW\npSjIyYEVK4K2cmXcLzMz3H1R2dnMnM/HmXP+7OL+zKwS+Gd3r489/gDwINAA3OnunWZWBbzk7tck\nEp9GtiLzKSwMWkXC7yAlLAnO2caSaauZbXT3I8BdwIFY+yzwcOzr04mGlvIFcCm5JW+JxZDu/hVD\nNPpXDCH0Pxpnm9nngb81s33AtcAfA18D7jazI8CHYo8TomR7BfSvGKLRv2IIof8LcbYZuPs+d7/Z\n3a9z95939z5373H3u9x9o7vfs5jqhppGEJHMsVRv1xURWVIifLtuSlcjpOTCIpKRkrIa4RNxpp0f\nLH71w0KlbGQb9h9ERETTCCIiYVCyFREJQYTnbFO69MvMPmpmh8zsaKxiTkqZ2WNm1mVm+6cdKzWz\nF8zsiJk9HyujlsoY6szsJTM7YGbvmNn9YcZhZvlm9pqZ7TWzg2b2J2H2f0ks2Wa2x8yeTUcMZnbC\nzN6OxfB62DGYWYmZPWVmTbG/i/eF3P+m2J99svWZ2f1p+Ht4MPb/Yb+Zfc/M8lIWwyKWfqVaypKt\nmWUDfw58FNgCfNrMNqeqv5jvxPqb7gGCEmkbgR+RYMWeBRgFvujuW4Fbgd+K/blDicPdhwluL2wk\nWJh9Z+zWw7B/DgBfAA4S1AYlDTE4sMPdr3f3W9IQw2TJvs0EfxeHwuzf3Q/H/uzXAzcCg8APwozB\nzNYBvwHc4O7bgWzgUymLYSzOlg7unpIG3Ab8cNrjB4AHUtXftH7WAfunPT5EULkHoBI4lOoYLonn\naYJb/0KPAygE3iAohhxq/0At8CJwJ/BsOv4uCLZsKrvkWCgxAMXA8RmOp+XfI3AP8HLYMQClwGFg\nFcG05bMJ5dzlAAAE1ElEQVQEexkmPQbAucnja0FdmZT/3Ke3VE4j1ACt0x63xY6FLWkl0hYq9lv9\neuC1MOMwsywz2xvr5yV3PxBm/zH/FfhdYGLasbBjcOBFM3vTzH4j5BimSvaZ2Vtm9pdmVhRi/5f6\nFPBE7PvQYnD3HuAbwEngFNDr7i+kLIbxOFsapDLZRm6drQe//kKJy8yWA98HvuDu58OMw90nPJhG\nqAV+xszuDLN/M/s54LS77yHYvXumGMP4u7jdg7fQHyOYzvlgiDFMluz77+5+AzDADCX7Utj/FDNb\nBvxL4H9c+lwI/xbWE9SFXQdUA8vN7DMpiyHC0wipTLbtQN20x3UEo9uwdcXKpxErkXY61R2aWS5B\nov2uu09WCQo9DnfvA/6BYL4uzP7fD3zczFoIRlMfMrPvhhwD7t4R+9pNMFd5S4gxtAFt7v5G7PFT\nBMm3M+x/BwS/bHbHfg4Q7t/DTcA/ufsZD3Y/+J8EU4yp+Tlcocn2TeBqM1sX+836SeCZFPY3m2cI\nSqPBIkukxcPMDHgUOOju3ww7DjMrn/xk18wKCObH9oTVP4C7f9nd6zyoDfop4P+6+78JMwYzKzSz\nFbHviwjmLPeHFYO7dwKtZrYxdmiyZN+zYfR/iU/z3hQChPt/4hBwq5kVxP5v3EXwoWlqfg6Lq/qV\nWqmcECb4jXoYaAYeTPUENME/qFPACMF88a8STNC/CBwBngdKUhzDBwjmKfcSJLk9BCskQokD2A68\nFev/beB3Y8dD/TlMi+cO4JmwYyCYM90ba+9M/vsLOYbrCD6g3EcwoitOw7/HIuBdYMW0Y2HH8HsE\nv2j2E+zjlZuKGACnzuNrafiALGW1EUREwmRmTlWc+awjg2ojiIiELsJ3kCnZikjmSNOyrnikfKcG\nEZHQJLgaIYzb3DVnKyIZwcycgjjz2dDlc7ZmVujug2aWA/wE+PfAx4F33f3rFtR3WeXuCd1arJGt\niGSORSz9cvfB2LfLCGo4nCVIto/Hjj8O/KtEQ1OyFZHMsYibGlJ9m7s+IBMRIbjNHWg0s2LguZlu\nc7dFbPelZCsiV4BdsTY/d+8zs4tuc3f3zsXeVqwPyEQkIwSjznjz2cUfkJlZOTDm7r2x29yfAx4C\nPgKccfeHzewBgjvdEvqATCNbEckgCd/VUAU8bmZZBJ9lfdfdf2Rme4AnzexzwAnglxLtQCNbEckI\nwch2cP4TASjU7boiIomL7v26SrYikkGG0h3ArJRsRSSDaGQrIhKCdG2dOz8lWxHJIBrZioiEQCNb\nEZEQaGQrIhICrUYQEQmBphFEREKgaQQRkRBoZCsiEgKNbEVEQqCRrYhICDSyFREJgZZ+iYiEQCNb\nEZEQRHfOVluZi0gGGY2zXc7MPmpmh8zsqJl9KdmRaWQrIhkksZGtmWUDfw7cBbQDb5jZM+7elKzI\nlGxFJIMkPGd7C9Ds7icAzOzvgHsBJVsRkcslPGdbA7ROe9wGvG/R4UyjZCsiGSThpV8p32ZcyVZE\nMsjORF/YDtRNe1xHMLpNGnNPeUIXEYk0M8sBDgMfBk4BrwOf1gdkIiJJ5O5jZvbbwHNANvBoMhMt\naGQrIhIK3dQgIhICJVsRkRAo2YqIhEDJVkQkBEq2IiIhULIVEQmBkq2ISAiUbEVEQvD/AduvxSEs\n0mTSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9955ff2110>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Prediction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_boxes(roi):\n",
      "    boxes = []\n",
      "    \n",
      "    contours,hierarchy = cv2.findContours(roi.astype(np.uint8),cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)\n",
      "    \n",
      "    for cnt in contours:\n",
      "        area = cv2.contourArea(cnt)\n",
      "        if area > 100:\n",
      "            [x,y,w,h] = cv2.boundingRect(cnt)\n",
      "            if h > 29 and w < 50:\n",
      "                boxes.append([x,y,w,h])\n",
      "    return boxes\n",
      "\n",
      "\n",
      "def read_score(roi, image_hsv_loc=None):\n",
      "    if not image_hsv_loc is None:\n",
      "        image_hsv_final = image_hsv_loc\n",
      "    else:\n",
      "        image_hsv_final = image_hsv\n",
      "    roi = get_roi(image_hsv_final, roi)\n",
      "    roi = ((roi[:,:,2] < 230) * 255).astype(np.uint8)\n",
      "    \n",
      "    boxes = get_boxes(roi)\n",
      "    digits = []\n",
      "    roi = roi / 255.\n",
      "    for box in boxes:\n",
      "        x, y, w, h = box\n",
      "        digit_roi = cv2.resize(roi[y:y+h,x:x+w],(10,14))\n",
      "        digits.append(str(knn_cls.predict(np.ravel(digit_roi))[0]))\n",
      "    if len(boxes) > 1:\n",
      "        if boxes[0][0] > boxes[1][0]:\n",
      "            digits = digits[::-1]\n",
      "    return int(\"\".join(digits))\n",
      "        \n",
      "print \"Left Score: %s\" % read_score(left_player_score_roi)\n",
      "print \"Left Score: %s\" % read_score(right_player_score_roi)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Left Score: 14\n",
        "Left Score: 7\n"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Complete Video"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_image(frame=0):\n",
      "    # Set Frame\n",
      "    cap.set(propId_dict[\"POS_FRAMES\"], frame)\n",
      "    ret, image_bgr = cap.read()\n",
      "    image_hsv = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2HSV)\n",
      "    return image_hsv\n",
      "\n",
      "total_frames = cap.get(propId_dict[\"FRAME_COUNT\"])\n",
      "\n",
      "left_score_list = []\n",
      "right_score_list = []\n",
      "for index in range(130, int(total_frames)):\n",
      "    image_hsv = get_image(index)\n",
      "    left_score_list.append(read_score(left_player_score_roi, image_hsv))\n",
      "    right_score_list.append(read_score(right_player_score_roi, image_hsv))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "invalid literal for int() with base 10: ''",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-140-c79e3598ce1e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mimage_hsv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_image\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mleft_score_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mread_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_player_score_roi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_hsv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mright_score_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mread_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright_player_score_roi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage_hsv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m<ipython-input-123-8a9a5b49eb8b>\u001b[0m in \u001b[0;36mread_score\u001b[1;34m(roi, image_hsv_loc)\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mboxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mboxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m             \u001b[0mdigits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdigits\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdigits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Left Score: %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mread_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_player_score_roi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: ''"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(16,4))\n",
      "plt.plot(np.arange(130, len(left_score_list)+130), left_score_list, label=\"Left\", color=\"r\", linewidth=2, alpha=0.4)\n",
      "plt.plot(np.arange(130, len(right_score_list)+130), right_score_list, label=\"Right\", color=\"g\", linewidth=2, alpha=0.4)\n",
      "plt.ylabel(\"Points\")\n",
      "plt.xlabel(\"Frame\")\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/C0uWwM8WUpMkSVJG9tRK0h64UitJkjRc/A1LM7LvoCzmsXf21A4f3xflMIty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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f98c09f2ad0>"
       ]
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(16,4))\n",
      "fps = cap.get(propId_dict[\"FPS\"])\n",
      "plt.plot(np.arange(130, len(left_score_list)+130)/fps, left_score_list, label=\"Left\", color=\"r\", linewidth=2, alpha=0.4)\n",
      "plt.plot(np.arange(130, len(right_score_list)+130)/fps, right_score_list, label=\"Right\", color=\"g\", linewidth=2, alpha=0.4)\n",
      "plt.ylabel(\"Points\")\n",
      "plt.xlabel(\"Seconds\")\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VjKn0yL4ppfSpxqA+md3axpokSZK6R6Mdqm8q5wckSa0w5R7ZHbYNpZQWta0oe2QlSVK3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\nIitJkiRJ6ipOZCVJkiRJXeX/A2brUfJEiAOuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f98c0a22990>"
       ]
      }
     ],
     "prompt_number": 157
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "game_data = pd.DataFrame(np.arange(130, len(right_score_list)+130), columns=[\"frame\"])\n",
      "game_data[\"left_score\"] = left_score_list[:-1]\n",
      "game_data[\"right_score\"] = right_score_list\n",
      "game_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>frame</th>\n",
        "      <th>left_score</th>\n",
        "      <th>right_score</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0   </th>\n",
        "      <td>  130</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1   </th>\n",
        "      <td>  131</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2   </th>\n",
        "      <td>  132</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3   </th>\n",
        "      <td>  133</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4   </th>\n",
        "      <td>  134</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5   </th>\n",
        "      <td>  135</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6   </th>\n",
        "      <td>  136</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7   </th>\n",
        "      <td>  137</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8   </th>\n",
        "      <td>  138</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9   </th>\n",
        "      <td>  139</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10  </th>\n",
        "      <td>  140</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11  </th>\n",
        "      <td>  141</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12  </th>\n",
        "      <td>  142</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13  </th>\n",
        "      <td>  143</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14  </th>\n",
        "      <td>  144</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15  </th>\n",
        "      <td>  145</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16  </th>\n",
        "      <td>  146</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17  </th>\n",
        "      <td>  147</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18  </th>\n",
        "      <td>  148</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19  </th>\n",
        "      <td>  149</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20  </th>\n",
        "      <td>  150</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21  </th>\n",
        "      <td>  151</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22  </th>\n",
        "      <td>  152</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23  </th>\n",
        "      <td>  153</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24  </th>\n",
        "      <td>  154</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25  </th>\n",
        "      <td>  155</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26  </th>\n",
        "      <td>  156</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27  </th>\n",
        "      <td>  157</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28  </th>\n",
        "      <td>  158</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29  </th>\n",
        "      <td>  159</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8316</th>\n",
        "      <td> 8446</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8317</th>\n",
        "      <td> 8447</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8318</th>\n",
        "      <td> 8448</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8319</th>\n",
        "      <td> 8449</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8320</th>\n",
        "      <td> 8450</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8321</th>\n",
        "      <td> 8451</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8322</th>\n",
        "      <td> 8452</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8323</th>\n",
        "      <td> 8453</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8324</th>\n",
        "      <td> 8454</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8325</th>\n",
        "      <td> 8455</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8326</th>\n",
        "      <td> 8456</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8327</th>\n",
        "      <td> 8457</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8328</th>\n",
        "      <td> 8458</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8329</th>\n",
        "      <td> 8459</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8330</th>\n",
        "      <td> 8460</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8331</th>\n",
        "      <td> 8461</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8332</th>\n",
        "      <td> 8462</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8333</th>\n",
        "      <td> 8463</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8334</th>\n",
        "      <td> 8464</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8335</th>\n",
        "      <td> 8465</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8336</th>\n",
        "      <td> 8466</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8337</th>\n",
        "      <td> 8467</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8338</th>\n",
        "      <td> 8468</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8339</th>\n",
        "      <td> 8469</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8340</th>\n",
        "      <td> 8470</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8341</th>\n",
        "      <td> 8471</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8342</th>\n",
        "      <td> 8472</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8343</th>\n",
        "      <td> 8473</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8344</th>\n",
        "      <td> 8474</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8345</th>\n",
        "      <td> 8475</td>\n",
        "      <td> 15</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8346 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 165,
       "text": [
        "      frame  left_score  right_score\n",
        "0       130           0            0\n",
        "1       131           0            0\n",
        "2       132           0            0\n",
        "3       133           0            0\n",
        "4       134           0            0\n",
        "5       135           0            0\n",
        "6       136           0            0\n",
        "7       137           0            0\n",
        "8       138           0            0\n",
        "9       139           0            0\n",
        "10      140           0            0\n",
        "11      141           0            0\n",
        "12      142           0            0\n",
        "13      143           0            0\n",
        "14      144           0            0\n",
        "15      145           0            0\n",
        "16      146           0            0\n",
        "17      147           0            0\n",
        "18      148           0            0\n",
        "19      149           0            0\n",
        "20      150           0            0\n",
        "21      151           0            0\n",
        "22      152           0            0\n",
        "23      153           0            0\n",
        "24      154           0            0\n",
        "25      155           0            0\n",
        "26      156           0            0\n",
        "27      157           0            0\n",
        "28      158           0            0\n",
        "29      159           0            0\n",
        "...     ...         ...          ...\n",
        "8316   8446          15            7\n",
        "8317   8447          15            7\n",
        "8318   8448          15            7\n",
        "8319   8449          15            7\n",
        "8320   8450          15            7\n",
        "8321   8451          15            7\n",
        "8322   8452          15            7\n",
        "8323   8453          15            7\n",
        "8324   8454          15            7\n",
        "8325   8455          15            7\n",
        "8326   8456          15            7\n",
        "8327   8457          15            7\n",
        "8328   8458          15            7\n",
        "8329   8459          15            7\n",
        "8330   8460          15            7\n",
        "8331   8461          15            7\n",
        "8332   8462          15            7\n",
        "8333   8463          15            7\n",
        "8334   8464          15            7\n",
        "8335   8465          15            7\n",
        "8336   8466          15            7\n",
        "8337   8467          15            7\n",
        "8338   8468          15            7\n",
        "8339   8469          15            7\n",
        "8340   8470          15            7\n",
        "8341   8471          15            7\n",
        "8342   8472          15            7\n",
        "8343   8473          15            7\n",
        "8344   8474          15            7\n",
        "8345   8475          15            7\n",
        "\n",
        "[8346 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "game_data.to_csv(\"game_data.csv\", index=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/.ipynb_checkpoints/4 - Numpy Basics-checkpoint.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial Brief"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy is a powerful set of tools to perform mathematical operations of on lists of numbers. It works faster than normal python lists operations and can manupilate high dimentional arrays too.\n",
    "\n",
    "Finding Help:\n",
    "\n",
    " - http://wiki.scipy.org/Tentative_NumPy_Tutorial\n",
    " - http://docs.scipy.org/doc/numpy/reference/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.\n",
    ">\n",
    ">  *http://www.scipy.org/*\n",
    "\n",
    "So NumPy is a part of a bigger ecosystem of libraries that build on the optimized performance of NumPy NDArray.\n",
    "\n",
    "It contain these core packages:\n",
    "\n",
    "<table>\n",
    "<tr>\n",
    "    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n",
    "    <td style=\"background:Lavender;\"><h4>NumPy</h4> Base N-dimensional array package </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>IPython</h4> Enhanced Interactive Console </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>SymPy</h4> Symbolic mathematics </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>Pandas</h4> Data structures & analysis </td>\n",
    "</tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importig the library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import numpy library as np\n",
    "This helps in writing code and it's almost a standard in scientific work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will generate an ndarray with np.arange method.\n",
    "\n",
    "####np.arange([start,] stop[, step,], dtype=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,  6. ,\n",
       "        6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 7])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  5.,  7.,  9.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10, 2, dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining ndrray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = np.arange(1,10,2)\n",
    "ds.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\x01',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x03',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x05',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x07',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\t',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=ds.data\n",
    "list(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memory Usage\n",
    "ds.size * ds.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why to use numpy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compare the time it takes to create two lists and do some basic operations on them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture timeit_results\n",
    "# Regular Python\n",
    "%timeit python_list_1 = range(1,1000)\n",
    "python_list_1 = range(1,1000)\n",
    "python_list_2 = range(1,1000)\n",
    "\n",
    "#Numpy\n",
    "%timeit numpy_list_1 = np.arange(1,1000)\n",
    "numpy_list_1 = np.arange(1,1000)\n",
    "numpy_list_2 = np.arange(1,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000 loops, best of 3: 15 us per loop\n",
      "The slowest run took 5.51 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 2.25 us per loop\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print timeit_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to calculate time in seconds\n",
    "def return_time(timeit_result):\n",
    "    temp_time = float(timeit_result.split(\" \")[5])\n",
    "    temp_unit = timeit_result.split(\" \")[6]\n",
    "    if temp_unit == \"ms\":\n",
    "        temp_time = temp_time * 1e-3\n",
    "    elif temp_unit == \"us\":\n",
    "        temp_time = temp_time * 1e-6\n",
    "    elif temp_unit == \"ns\":\n",
    "        temp_time = temp_time * 1e-9\n",
    "    return temp_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: times",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-1c0052a89b77>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpython_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturn_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnumpy_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturn_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Python/NumPy: %.1f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mpython_time\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnumpy_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-640f956687ba>\u001b[0m in \u001b[0;36mreturn_time\u001b[1;34m(timeit_result)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Function to calculate time in seconds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreturn_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mtemp_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mtemp_unit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimeit_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtemp_unit\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"ms\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: times"
     ]
    }
   ],
   "source": [
    "python_time = return_time(timeit_results.stdout.split(\"\\n\")[0])\n",
    "numpy_time = return_time(timeit_results.stdout.split(\"\\n\")[1])\n",
    "\n",
    "print \"Python/NumPy: %.1f\" % (python_time/numpy_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture timeit_python\n",
    "%%timeit\n",
    "# Regular Python\n",
    "[(x + y) for x, y in zip(python_list_1, python_list_2)]\n",
    "[(x - y) for x, y in zip(python_list_1, python_list_2)]\n",
    "[(x * y) for x, y in zip(python_list_1, python_list_2)]\n",
    "[(x / y) for x, y in zip(python_list_1, python_list_2)];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print timeit_python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture timeit_numpy\n",
    "%%timeit\n",
    "#Numpy\n",
    "numpy_list_1 + numpy_list_2\n",
    "numpy_list_1 - numpy_list_2\n",
    "numpy_list_1 * numpy_list_2\n",
    "numpy_list_1 / numpy_list_2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print timeit_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "python_time = return_time(timeit_python.stdout)\n",
    "numpy_time = return_time(timeit_numpy.stdout)\n",
    "\n",
    "print \"Python/NumPy: %.1f\" % (python_time/numpy_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Common Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "object : array_like\n",
    "    An array, any object exposing the array interface, an\n",
    "    object whose __array__ method returns an array, or any\n",
    "    (nested) sequence.\n",
    "dtype : data-type, optional\n",
    "    The desired data-type for the array.  If not given, then\n",
    "    the type will be determined as the minimum type required\n",
    "    to hold the objects in the sequence.  This argument can only\n",
    "    be used to 'upcast' the array.  For downcasting, use the\n",
    "    .astype(t) method.\n",
    "copy : bool, optional\n",
    "    If true (default), then the object is copied.  Otherwise, a copy\n",
    "    will only be made if __array__ returns a copy, if obj is a\n",
    "    nested sequence, or if a copy is needed to satisfy any of the other\n",
    "    requirements (`dtype`, `order`, etc.).\n",
    "order : {'C', 'F', 'A'}, optional\n",
    "    Specify the order of the array.  If order is 'C' (default), then the\n",
    "    array will be in C-contiguous order (last-index varies the\n",
    "    fastest).  If order is 'F', then the returned array\n",
    "    will be in Fortran-contiguous order (first-index varies the\n",
    "    fastest).  If order is 'A', then the returned array may\n",
    "    be in any order (either C-, Fortran-contiguous, or even\n",
    "    discontiguous).\n",
    "subok : bool, optional\n",
    "    If True, then sub-classes will be passed-through, otherwise\n",
    "    the returned array will be forced to be a base-class array (default).\n",
    "ndmin : int, optional\n",
    "    Specifies the minimum number of dimensions that the resulting\n",
    "    array should have.  Ones will be pre-pended to the shape as\n",
    "    needed to meet this requirement.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi Dimentional Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.array([[1,2],[3,4],[5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zeros(shape, dtype=float, order='C') and ones(shape, dtype=float, order='C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "shape : int or sequence of ints\n",
    "    Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n",
    "dtype : data-type, optional\n",
    "    The desired data-type for the array, e.g., `numpy.int8`.  Default is\n",
    "    `numpy.float64`.\n",
    "order : {'C', 'F'}, optional\n",
    "    Whether to store multidimensional data in C- or Fortran-contiguous\n",
    "    (row- or column-wise) order in memory.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.zeros((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.zeros((3,4), dtype=np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.linspace(start, stop, num=50, endpoint=True, retstep=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "start : scalar\n",
    "    The starting value of the sequence.\n",
    "stop : scalar\n",
    "    The end value of the sequence, unless `endpoint` is set to False.\n",
    "    In that case, the sequence consists of all but the last of ``num + 1``\n",
    "    evenly spaced samples, so that `stop` is excluded.  Note that the step\n",
    "    size changes when `endpoint` is False.\n",
    "num : int, optional\n",
    "    Number of samples to generate. Default is 50.\n",
    "endpoint : bool, optional\n",
    "    If True, `stop` is the last sample. Otherwise, it is not included.\n",
    "    Default is True.\n",
    "retstep : bool, optional\n",
    "    If True, return (`samples`, `step`), where `step` is the spacing\n",
    "    between samples.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linspace(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linspace(0,2,num=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linspace(0,2,num=4,endpoint=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random_sample(size=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "size : int or tuple of ints, optional\n",
    "    Defines the shape of the returned array of random floats. If None\n",
    "    (the default), returns a single float.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.random((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.random_sample((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set = np.random.random((2,3))\n",
    "data_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.max(a, axis=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "a : array_like\n",
    "    Input data.\n",
    "axis : int, optional\n",
    "    Axis along which to operate.  By default, flattened input is used.\n",
    "out : ndarray, optional\n",
    "    Alternative output array in which to place the result.  Must\n",
    "    be of the same shape and buffer length as the expected output.\n",
    "    See `doc.ufuncs` (Section \"Output arguments\") for more details.\n",
    "keepdims : bool, optional\n",
    "    If this is set to True, the axes which are reduced are left\n",
    "    in the result as dimensions with size one. With this option,\n",
    "    the result will broadcast correctly against the original `arr`.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(data_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(data_set, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(data_set, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.min(a, axis=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.min(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.mean(a, axis=None, dtype=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.mean(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.median(a, axis=None, out=None, overwrite_input=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.median(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.std(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.sum(a, axis=None, dtype=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.sum(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.reshape(a, newshape, order='C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.reshape(data_set, (3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.reshape(data_set, (6,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.reshape(data_set, (6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.ravel(a, order='C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.ravel(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set = np.random.random((5,10))\n",
    "data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[1,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Slicing a range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stepping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4,::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Matrix A \n",
    "A = np.array([[1,2],[3,4]])\n",
    "# Matrix B\n",
    "B = np.array([[3,4],[5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A+B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A-B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiplication (Element by Element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A*B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiplication (Matrix Multiplication)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13, 16],\n",
       "       [29, 36]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A/B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.square(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.power(A,3) #cube of matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2. ,  1. ],\n",
       "       [ 1.5, -0.5]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.inv(A)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}


================================================
FILE: notebooks/IPython-Tutorial/1 - Notebooks & Cells.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The basics of IPython Notebook. It is a web-based interactive development environment. We will cover the basics of managing notebooks and cell types.\n",
      "\n",
      "Finding Help:\n",
      "\n",
      "- http://ipython.org/ipython-doc/stable/index.html\n",
      "- http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Index.ipynb\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Notebooks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notebooks are documents representing all input and output of operations. This includes code, text input and  numerical, text and rich media output. These files have **ipynb** extensions."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Cells"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Cells can contain code or documentation. We will explore working with cells."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Markdown Cells"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a markdown cell. In can contain:\n",
      "\n",
      "- Markdown language\n",
      "- HTML\n",
      "- LATEX\n",
      "\n",
      "```markdown\n",
      "**Markdown** *Text*\n",
      "<strong>HTML formula</strong>\n",
      "$LATEX%\n",
      "```\n",
      "**Markdown** *Text*\n",
      "\n",
      "<strong>HTML</strong>\n",
      "\n",
      "$LATEX formula$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Code Cells"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "May contain python code and ipython magic."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "3+5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "8"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1+2\n",
      "3+5"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "8"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 1\n",
      "y = 2\n",
      "\n",
      "x + y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = x + y\n",
      "z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "3"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "IPython Magic"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "ls\n",
      "echo \"** Creating New File **\"\n",
      "touch test.txt\n",
      "ls\n",
      "echo \"** Deleting New File **\"\n",
      "rm test.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Raw NBConvert"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These cells are like markdown but they do not render their input."
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "This is a Raw NBConvert cell.\n",
      "\n",
      "It doesn't render its contents like\n",
      "**Markdown**\n",
      "<strong>HTML</strong>\n",
      "$LATEX$\n",
      "\n",
      "The problem with them is they may render come tags in IPython NBViwer."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Heading Cells"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Heading cells are great for creating bookmarks in an IPython Notebook. You can hover over headings to find their link. Usually it is capitalized slug of the title text. This helps in linking to a specific part of a notebook."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Head 1"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Head 2"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Head 3"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Head 4"
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "Head 5"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Head 6"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Linking to a heading"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Linking to a heading is like linking to an HTML Bookmark. You can do that like this:\n",
      "\n",
      "###Relative links:\n",
      "```HTML\n",
      "<a href=\"#Head-1\">Link to Head 1</a>\n",
      "```\n",
      "\n",
      "<a href=\"#Head-1\">Link to Head 1</a>\n",
      "\n",
      "###Absolute links:\n",
      "```HTML\n",
      "<a href=\"http://54.186.15.239:8080/notebooks/IPython%20Tutorial/1%20-%20Notebooks%20%26%20Cells.ipynb#Head-1\">Link to Head 1</a>\n",
      "```\n",
      "<a href=\"http://54.186.15.239:8080/notebooks/IPython%20Tutorial/1%20-%20Notebooks%20%26%20Cells.ipynb#Head-1\">Link to Head 1</a>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/2 - Markdown & LATEX.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "This covers the basics of using markdown cells. You can think of markdown cells as documention for your code with extended features.\n",
      "####Finding Help:\n",
      "\n",
      "- http://ipython.org/ipython-doc/2/interactive/tutorial.html\n",
      "- http://nbviewer.ipython.org/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "#Markdown Cells\n",
      "\n",
      "This cell can contain markdown language and render it.\n",
      "\n",
      "A good reference for markdown is in this link:\n",
      "https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {
      "slideshow": {
       "slide_type": "-"
      }
     },
     "source": [
      "###Most common markdown codes:\n",
      "\n",
      "#Head 1\n",
      "##Head 2\n",
      "###Head 3\n",
      "####Head 4\n",
      "\n",
      "Alt Heading 1\n",
      "============\n",
      "\n",
      "Alt Heading 2\n",
      "------------"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "#Head 1\n",
      "##Head 2\n",
      "###Head 3\n",
      "####Head 4\n",
      "\n",
      "Alt Heading 1\n",
      "============\n",
      "\n",
      "Alt Heading 2\n",
      "------------\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Font Styles:\n",
      " - *Italic*\n",
      " - _Italic_\n",
      " - **Bold**\n",
      " - __Bold__\n",
      " - ~~Throught~~"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "- *Italic*\n",
      "- _Italic_\n",
      "- **Bold**\n",
      "- __Bold__\n",
      "- ~~Throught~~\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Lists:\n",
      "\n",
      "- Item 1\n",
      "- Item 2\n",
      "\n",
      "####Numbered List:\n",
      "\n",
      "1. Item 1\n",
      "2. Item 2\n",
      " 1. Sub Item 1\n",
      " 2. Sub Item 2\n",
      "3. Item 3"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "- Item 1\n",
      "- Item 2\n",
      "\n",
      "1. Item 1\n",
      "2. Item 2\n",
      " 1. Sub Item 1\n",
      " 2. Sub Item 2\n",
      "3. Item 3\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Links:\n",
      "\n",
      "https://www.google.com\n",
      "\n",
      "[Google](https://www.google.com)\n",
      "\n",
      "[Google with tooltip](https://www.google.com \"Google's Homepage\")"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "https://www.google.com\n",
      "\n",
      "[Google](https://www.google.com)\n",
      "\n",
      "[Google with tooltip](https://www.google.com \"Google's Homepage\")\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Images:\n",
      "\n",
      "![Alt Text](http://www.google.com/logos/2012/turing-doodle-static.jpg \"Alan Turing's 100th Birthday\")"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "![Alt Text](http://www.google.com/logos/2012/turing-doodle-static.jpg \"Alan Turing's 100th Birthday\")\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Code:\n",
      "\n",
      "```python\n",
      "s = \"Python syntax highlighting\"\n",
      "print s\n",
      "```\n",
      "\n",
      "```javascript\n",
      "var s = \"JavaScript syntax highlighting\";\n",
      "alert(s);\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<pre>\n",
      "```python\n",
      "s = \"Python syntax highlighting\"\n",
      "print s\n",
      "```\n",
      "\n",
      "```javascript\n",
      "var s = \"JavaScript syntax highlighting\";\n",
      "alert(s);\n",
      "```\n",
      "</pre>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Tables:\n",
      "\n",
      "| Website   | URL           | Rank |\n",
      "| ---------:|:-------------:| ----:|\n",
      "| Google    | google.com    | 1    |\n",
      "| Facebook  | facebook.com  | 2    |\n",
      "| Youtube   | youtube.com   | 3    |"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "| Website   | URL           | Rank |\n",
      "| ---------:|:-------------:| ----:|\n",
      "| Google    | google.com    | 1    |\n",
      "| Facebook  | facebook.com  | 2    |\n",
      "| Youtube   | youtube.com   | 3    |\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Quotes\n",
      "> ##Quote from some one else\n",
      "> *with multiple lines*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "> ##Quote from some one else\n",
      "> *with multiple lines*\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####HTML\n",
      "<hr>\n",
      "<p>Almost any <strong>HTML</strong> code will render<br />Just <i>fine</i>.</p>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```html\n",
      "<hr>\n",
      "<p>Almost any <strong>HTML</strong> code will render<br />Just <i>fine</i>.</p>\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Horizontal Lines\n",
      "---\n",
      "***\n",
      "___"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "---\n",
      "***\n",
      "___\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Unicode\n",
      "\n",
      "If you can think of any symbol, it is available in unicode.\n",
      "\n",
      "- \u00a9\n",
      "- \u00ae\n",
      "- \u2122"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "\u00a9\n",
      "\u00ae\n",
      "\u2122\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Markdown cell - Latex Script:\n",
      "\n",
      "Latex is a languange to represent mathematical formula using text.\n",
      "\n",
      "For Latex documentation please check the wiki:\n",
      "\n",
      "http://en.wikibooks.org/wiki/LaTeX/Mathematics"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Inline and Block Latex\n",
      "\n",
      "Inline Formula: $x=y$\n",
      "\n",
      "Block Formula: $$x=y$$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "$x=y$\n",
      "$$x=y$$\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Greek Letters\n",
      " - Alpha: $\\alpha A$\n",
      " - Beta: $\\beta B$\n",
      " - Gamma: $\\gamma \\Gamma$\n",
      " - Pi: $\\pi \\Pi \\varpi$\n",
      " - Phi: $\\phi \\varphi \\Phi$\n",
      " - Epsilon: $\\epsilon \\varepsilon$\n",
      " - Theta: $\\theta \\Theta \\vartheta$\n",
      " - Rho: $\\rho \\varrho$\n",
      " - sigma: $\\sigma \\Sigma \\varsigma$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "Alpha: $\\alpha A$\n",
      "Beta: $\\beta B$\n",
      "Gamma: $\\gamma \\Gamma$\n",
      "Pi: $\\pi \\Pi \\varpi$\n",
      "Phi: $\\phi \\varphi \\Phi$\n",
      "Epsilon: $\\epsilon \\varepsilon$\n",
      "Theta: $\\theta \\Theta \\vartheta$\n",
      "Rho: $\\rho \\varrho$\n",
      "sigma: $\\sigma \\Sigma \\varsigma$\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Basic operations\n",
      "\n",
      " - $y = x + 5$\n",
      " - $y = x - 5$\n",
      " - $y = 5x$\n",
      " - $y = x \\times 5$\n",
      " - $y = \\frac{x}{5}$\n",
      " - $y = x \\div 5$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "$y = x + 5$\n",
      "$y = x - 5$\n",
      "$y = 5x$\n",
      "$y = x \\times 5$\n",
      "$y = \\frac{x}{5}$\n",
      "$y = x \\div 5$\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Power and Index\n",
      "\n",
      " - $y_n = x_n - x_{n-1}$\n",
      " - $y = x^2 - 2x^{x-1}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "$y_n = x_n - x_{n-1}$\n",
      "$y = x^2 - 2x^{x-1}$\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Fraction Type\n",
      "\n",
      " - $^1/_2$\n",
      " - $\\frac {1}{2}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "$^1/_2$\n",
      "$\\frac {1}{2}$\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Roots\n",
      "\n",
      " - $\\sqrt{\\frac{1}{3}}$\n",
      " - $\\sqrt[3]{2x}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "$\\sqrt{\\frac{1}{3}}$\n",
      "$\\sqrt[3]{2x}$\n",
      "```"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Sums and Integrals\n",
      "\n",
      " - Sum : $\\sum\\limits_{i=1}^{10} t_i$\n",
      " - Int : $\\int\\limits_a^b$\n",
      " - $\\int_0^\\infty \\sin(x^2)$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```markdown\n",
      "$\\sum\\limits_{i=1}^{10} t_i$\n",
      "$\\int\\limits_a^b$\n",
      "$\\int_0^\\infty \\sin(x^2)$\n",
      "```"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/3 - Basic Python.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Coding basic python in IPython Notebook interactive development environment.\n",
      "####Finding Help:\n",
      "\n",
      "- http://ipython.org/ipython-doc/2/interactive/tutorial.html\n",
      "- http://nbviewer.ipython.org/"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Basic Help with Coding"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import io"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "io?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "io??"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Reference"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# IPython Quick Refence\n",
      "%quickref"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Python Refence\n",
      "help()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to Python 2.7!  This is the online help utility.\n",
        "\n",
        "If this is your first time using Python, you should definitely check out\n",
        "the tutorial on the Internet at http://docs.python.org/2.7/tutorial/.\n",
        "\n",
        "Enter the name of any module, keyword, or topic to get help on writing\n",
        "Python programs and using Python modules.  To quit this help utility and\n",
        "return to the interpreter, just type \"quit\".\n",
        "\n",
        "To get a list of available modules, keywords, or topics, type \"modules\",\n",
        "\"keywords\", or \"topics\".  Each module also comes with a one-line summary\n",
        "of what it does; to list the modules whose summaries contain a given word\n",
        "such as \"spam\", type \"modules spam\".\n",
        "\n"
       ]
      },
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "help> \n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "You are now leaving help and returning to the Python interpreter.\n",
        "If you want to ask for help on a particular object directly from the\n",
        "interpreter, you can type \"help(object)\".  Executing \"help('string')\"\n",
        "has the same effect as typing a particular string at the help> prompt.\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pdoc io.FileIO"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pdef io.OpenWrapper"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "No definition header found for io.OpenWrapper\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%psource io"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pfile io"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Tab completion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Browse an object with tab\n",
      "sys"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<module 'sys' (built-in)>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Shift-tab to get more information about a method\n",
      "open()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "More Magic"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Good Reference:\n",
      "\n",
      "http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Cell%20Magics.ipynb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Listing all magic\n",
      "%lsmagic"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "json": [
        "{\"cell\": {\"prun\": \"ExecutionMagics\", \"file\": \"Other\", \"!\": \"OSMagics\", \"capture\": \"ExecutionMagics\", \"timeit\": \"ExecutionMagics\", \"script\": \"ScriptMagics\", \"pypy\": \"Other\", \"system\": \"OSMagics\", \"perl\": \"Other\", \"HTML\": \"Other\", \"bash\": \"Other\", \"python\": \"Other\", \"SVG\": \"Other\", \"javascript\": \"DisplayMagics\", \"writefile\": \"OSMagics\", \"ruby\": \"Other\", \"python3\": \"Other\", \"python2\": \"Other\", \"latex\": \"DisplayMagics\", \"sx\": \"OSMagics\", \"svg\": \"DisplayMagics\", \"html\": \"DisplayMagics\", \"sh\": \"Other\", \"time\": \"ExecutionMagics\", \"debug\": \"ExecutionMagics\"}, \"line\": {\"psource\": \"NamespaceMagics\", \"logstart\": \"LoggingMagics\", \"popd\": \"OSMagics\", \"loadpy\": \"CodeMagics\", \"install_ext\": \"ExtensionMagics\", \"colors\": \"BasicMagics\", \"who_ls\": \"NamespaceMagics\", \"lf\": \"Other\", \"install_profiles\": \"DeprecatedMagics\", \"ll\": \"Other\", \"pprint\": \"BasicMagics\", \"lk\": \"Other\", \"ls\": \"Other\", \"save\": \"CodeMagics\", \"tb\": \"ExecutionMagics\", \"lx\": \"Other\", \"pylab\": \"PylabMagics\", \"killbgscripts\": \"ScriptMagics\", \"quickref\": \"BasicMagics\", \"magic\": \"BasicMagics\", \"dhist\": \"OSMagics\", \"edit\": \"KernelMagics\", \"logstop\": \"LoggingMagics\", \"gui\": \"BasicMagics\", \"alias_magic\": \"BasicMagics\", \"debug\": \"ExecutionMagics\", \"page\": \"BasicMagics\", \"logstate\": \"LoggingMagics\", \"ed\": \"Other\", \"pushd\": \"OSMagics\", \"timeit\": \"ExecutionMagics\", \"rehashx\": \"OSMagics\", \"hist\": \"Other\", \"qtconsole\": \"KernelMagics\", \"rm\": \"Other\", \"dirs\": \"OSMagics\", \"run\": \"ExecutionMagics\", \"reset_selective\": \"NamespaceMagics\", \"rep\": \"Other\", \"pinfo2\": \"NamespaceMagics\", \"matplotlib\": \"PylabMagics\", \"unload_ext\": \"ExtensionMagics\", \"doctest_mode\": \"KernelMagics\", \"logoff\": \"LoggingMagics\", \"reload_ext\": \"ExtensionMagics\", \"pdb\": \"ExecutionMagics\", \"load\": \"CodeMagics\", \"lsmagic\": \"BasicMagics\", \"autosave\": \"KernelMagics\", \"cd\": \"OSMagics\", \"pastebin\": \"CodeMagics\", \"prun\": \"ExecutionMagics\", \"cp\": \"Other\", \"autocall\": \"AutoMagics\", \"bookmark\": \"OSMagics\", \"connect_info\": \"KernelMagics\", \"mkdir\": \"Other\", \"system\": \"OSMagics\", \"whos\": \"NamespaceMagics\", \"rmdir\": \"Other\", \"automagic\": \"AutoMagics\", \"store\": \"StoreMagics\", \"more\": \"KernelMagics\", \"pdef\": \"NamespaceMagics\", \"precision\": \"BasicMagics\", \"pinfo\": \"NamespaceMagics\", \"pwd\": \"OSMagics\", \"psearch\": \"NamespaceMagics\", \"reset\": \"NamespaceMagics\", \"recall\": \"HistoryMagics\", \"xdel\": \"NamespaceMagics\", \"xmode\": \"BasicMagics\", \"cat\": \"Other\", \"mv\": \"Other\", \"rerun\": \"HistoryMagics\", \"logon\": \"LoggingMagics\", \"history\": \"HistoryMagics\", \"pycat\": \"OSMagics\", \"unalias\": \"OSMagics\", \"install_default_config\": \"DeprecatedMagics\", \"env\": \"OSMagics\", \"load_ext\": \"ExtensionMagics\", \"config\": \"ConfigMagics\", \"profile\": \"BasicMagics\", \"pfile\": \"NamespaceMagics\", \"less\": \"KernelMagics\", \"who\": \"NamespaceMagics\", \"notebook\": \"BasicMagics\", \"man\": \"KernelMagics\", \"sx\": \"OSMagics\", \"macro\": \"ExecutionMagics\", \"clear\": \"KernelMagics\", \"alias\": \"OSMagics\", \"time\": \"ExecutionMagics\", \"sc\": \"OSMagics\", \"ldir\": \"Other\", \"pdoc\": \"NamespaceMagics\"}}"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Available line magics:\n",
        "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
        "\n",
        "Available cell magics:\n",
        "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
        "\n",
        "Automagic is ON, % prefix IS NOT needed for line magics."
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Measuring Performance\n",
      "%timeit range(1000)\n",
      "%timeit xrange(1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100000 loops, best of 3: 13.9 \u00b5s per loop\n",
        "10000000 loops, best of 3: 198 ns per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "# Measuring Block Performance\n",
      "\n",
      "for x in range(1000):\n",
      "    y=x+1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10000 loops, best of 3: 65.1 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "# Measuring Block Performance\n",
      "\n",
      "for x in xrange(1000):\n",
      "    y=x+1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 488 \u00b5s per loop\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reset (Delete) all variables\n",
      "%reset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "name": "stdout",
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Once deleted, variables cannot be recovered. Proceed (y/[n])? n\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Nothing done.\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%bash\n",
      "echo $PATH"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/usr/local/sbin:/usr/local/bin:/usr/bin:/usr/sbin:/sbin:/bin\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Output and Input"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You have access to all code inside you code cell and the out put from them in two lists: In and Out."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "3+5"
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     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "8"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "In[18]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "u'3+5'"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Out[18]"
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     "language": "python",
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      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
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        "8"
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     "prompt_number": 22
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      "_ + 3"
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      {
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        "11"
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      "_ - 4"
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   "metadata": {}
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 ]
}


================================================
FILE: notebooks/IPython-Tutorial/4 - Numpy Basics.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial Brief"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy is a powerful set of tools to perform mathematical operations of on lists of numbers. It works faster than normal python lists operations and can manupilate high dimentional arrays too.\n",
    "\n",
    "Finding Help:\n",
    "\n",
    " - http://wiki.scipy.org/Tentative_NumPy_Tutorial\n",
    " - http://docs.scipy.org/doc/numpy/reference/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.\n",
    ">\n",
    ">  *http://www.scipy.org/*\n",
    "\n",
    "So NumPy is a part of a bigger ecosystem of libraries that build on the optimized performance of NumPy NDArray.\n",
    "\n",
    "It contain these core packages:\n",
    "\n",
    "<table>\n",
    "<tr>\n",
    "    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n",
    "    <td style=\"background:Lavender;\"><h4>NumPy</h4> Base N-dimensional array package </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n",
    "</tr>\n",
    "<tr>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>IPython</h4> Enhanced Interactive Console </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>SymPy</h4> Symbolic mathematics </td>\n",
    "    <td><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n",
    "    <td><h4>Pandas</h4> Data structures & analysis </td>\n",
    "</tr>\n",
    "</table>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importig the library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import numpy library as np\n",
    "This helps in writing code and it's almost a standard in scientific work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will generate an ndarray with np.arange method.\n",
    "\n",
    "####np.arange([start,] stop[, step,], dtype=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5,  5. ,  5.5,  6. ,\n",
       "        6.5,  7. ,  7.5,  8. ,  8.5,  9. ,  9.5])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 7])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  5.,  7.,  9.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10, 2, dtype=np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examining ndrray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = np.arange(1,10,2)\n",
    "ds.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['\\x01',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x03',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x05',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x07',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\t',\n",
       " '\\x00',\n",
       " '\\x00',\n",
       " '\\x00']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=ds.data\n",
    "list(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Memory Usage\n",
    "ds.size * ds.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why to use numpy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will compare the time it takes to create two lists and do some basic operations on them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate a list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture timeit_results\n",
    "# Regular Python\n",
    "%timeit python_list_1 = range(1,1000)\n",
    "python_list_1 = range(1,1000)\n",
    "python_list_2 = range(1,1000)\n",
    "\n",
    "#Numpy\n",
    "%timeit numpy_list_1 = np.arange(1,1000)\n",
    "numpy_list_1 = np.arange(1,1000)\n",
    "numpy_list_2 = np.arange(1,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000 loops, best of 3: 15 us per loop\n",
      "The slowest run took 5.51 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 2.25 us per loop\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print timeit_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to calculate time in seconds\n",
    "def return_time(timeit_result):\n",
    "    temp_time = float(timeit_result.split(\" \")[5])\n",
    "    temp_unit = timeit_result.split(\" \")[6]\n",
    "    if temp_unit == \"ms\":\n",
    "        temp_time = temp_time * 1e-3\n",
    "    elif temp_unit == \"us\":\n",
    "        temp_time = temp_time * 1e-6\n",
    "    elif temp_unit == \"ns\":\n",
    "        temp_time = temp_time * 1e-9\n",
    "    return temp_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: times",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-1c0052a89b77>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpython_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturn_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnumpy_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturn_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_results\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\n\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Python/NumPy: %.1f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mpython_time\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnumpy_time\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-640f956687ba>\u001b[0m in \u001b[0;36mreturn_time\u001b[1;34m(timeit_result)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Function to calculate time in seconds\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreturn_time\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mtemp_time\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtimeit_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[0mtemp_unit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtimeit_result\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" \"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtemp_unit\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"ms\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: times"
     ]
    }
   ],
   "source": [
    "python_time = return_time(timeit_results.stdout.split(\"\\n\")[0])\n",
    "numpy_time = return_time(timeit_results.stdout.split(\"\\n\")[1])\n",
    "\n",
    "print \"Python/NumPy: %.1f\" % (python_time/numpy_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture timeit_python\n",
    "%%timeit\n",
    "# Regular Python\n",
    "[(x + y) for x, y in zip(python_list_1, python_list_2)]\n",
    "[(x - y) for x, y in zip(python_list_1, python_list_2)]\n",
    "[(x * y) for x, y in zip(python_list_1, python_list_2)]\n",
    "[(x / y) for x, y in zip(python_list_1, python_list_2)];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print timeit_python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%capture timeit_numpy\n",
    "%%timeit\n",
    "#Numpy\n",
    "numpy_list_1 + numpy_list_2\n",
    "numpy_list_1 - numpy_list_2\n",
    "numpy_list_1 * numpy_list_2\n",
    "numpy_list_1 / numpy_list_2;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "print timeit_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "python_time = return_time(timeit_python.stdout)\n",
    "numpy_time = return_time(timeit_numpy.stdout)\n",
    "\n",
    "print \"Python/NumPy: %.1f\" % (python_time/numpy_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Common Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "object : array_like\n",
    "    An array, any object exposing the array interface, an\n",
    "    object whose __array__ method returns an array, or any\n",
    "    (nested) sequence.\n",
    "dtype : data-type, optional\n",
    "    The desired data-type for the array.  If not given, then\n",
    "    the type will be determined as the minimum type required\n",
    "    to hold the objects in the sequence.  This argument can only\n",
    "    be used to 'upcast' the array.  For downcasting, use the\n",
    "    .astype(t) method.\n",
    "copy : bool, optional\n",
    "    If true (default), then the object is copied.  Otherwise, a copy\n",
    "    will only be made if __array__ returns a copy, if obj is a\n",
    "    nested sequence, or if a copy is needed to satisfy any of the other\n",
    "    requirements (`dtype`, `order`, etc.).\n",
    "order : {'C', 'F', 'A'}, optional\n",
    "    Specify the order of the array.  If order is 'C' (default), then the\n",
    "    array will be in C-contiguous order (last-index varies the\n",
    "    fastest).  If order is 'F', then the returned array\n",
    "    will be in Fortran-contiguous order (first-index varies the\n",
    "    fastest).  If order is 'A', then the returned array may\n",
    "    be in any order (either C-, Fortran-contiguous, or even\n",
    "    discontiguous).\n",
    "subok : bool, optional\n",
    "    If True, then sub-classes will be passed-through, otherwise\n",
    "    the returned array will be forced to be a base-class array (default).\n",
    "ndmin : int, optional\n",
    "    Specifies the minimum number of dimensions that the resulting\n",
    "    array should have.  Ones will be pre-pended to the shape as\n",
    "    needed to meet this requirement.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multi Dimentional Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.array([[1,2],[3,4],[5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### zeros(shape, dtype=float, order='C') and ones(shape, dtype=float, order='C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "shape : int or sequence of ints\n",
    "    Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n",
    "dtype : data-type, optional\n",
    "    The desired data-type for the array, e.g., `numpy.int8`.  Default is\n",
    "    `numpy.float64`.\n",
    "order : {'C', 'F'}, optional\n",
    "    Whether to store multidimensional data in C- or Fortran-contiguous\n",
    "    (row- or column-wise) order in memory.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.zeros((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.zeros((3,4), dtype=np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.linspace(start, stop, num=50, endpoint=True, retstep=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "start : scalar\n",
    "    The starting value of the sequence.\n",
    "stop : scalar\n",
    "    The end value of the sequence, unless `endpoint` is set to False.\n",
    "    In that case, the sequence consists of all but the last of ``num + 1``\n",
    "    evenly spaced samples, so that `stop` is excluded.  Note that the step\n",
    "    size changes when `endpoint` is False.\n",
    "num : int, optional\n",
    "    Number of samples to generate. Default is 50.\n",
    "endpoint : bool, optional\n",
    "    If True, `stop` is the last sample. Otherwise, it is not included.\n",
    "    Default is True.\n",
    "retstep : bool, optional\n",
    "    If True, return (`samples`, `step`), where `step` is the spacing\n",
    "    between samples.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linspace(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linspace(0,2,num=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.linspace(0,2,num=4,endpoint=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### random_sample(size=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "size : int or tuple of ints, optional\n",
    "    Defines the shape of the returned array of random floats. If None\n",
    "    (the default), returns a single float.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.random((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.random_sample((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set = np.random.random((2,3))\n",
    "data_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.max(a, axis=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Parameters\n",
    "----------\n",
    "a : array_like\n",
    "    Input data.\n",
    "axis : int, optional\n",
    "    Axis along which to operate.  By default, flattened input is used.\n",
    "out : ndarray, optional\n",
    "    Alternative output array in which to place the result.  Must\n",
    "    be of the same shape and buffer length as the expected output.\n",
    "    See `doc.ufuncs` (Section \"Output arguments\") for more details.\n",
    "keepdims : bool, optional\n",
    "    If this is set to True, the axes which are reduced are left\n",
    "    in the result as dimensions with size one. With this option,\n",
    "    the result will broadcast correctly against the original `arr`.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(data_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(data_set, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.max(data_set, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.min(a, axis=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.min(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.mean(a, axis=None, dtype=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.mean(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.median(a, axis=None, out=None, overwrite_input=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.median(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.std(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.sum(a, axis=None, dtype=None, out=None, keepdims=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.sum(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.reshape(a, newshape, order='C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.reshape(data_set, (3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.reshape(data_set, (6,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.reshape(data_set, (6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.ravel(a, order='C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.ravel(data_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set = np.random.random((5,10))\n",
    "data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[1,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Slicing a range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stepping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_set[2:4,::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# Matrix A \n",
    "A = np.array([[1,2],[3,4]])\n",
    "# Matrix B\n",
    "B = np.array([[3,4],[5,6]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A+B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subtraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A-B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiplication (Element by Element)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A*B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiplication (Matrix Multiplication)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13, 16],\n",
       "       [29, 36]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "A/B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Square"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.square(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.power(A,3) #cube of matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2. ,  1. ],\n",
       "       [ 1.5, -0.5]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.inv(A)"
   ]
  }
 ],
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================================================
FILE: notebooks/IPython-Tutorial/5 - Plotting Charts.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This cover the basics of plotting charts and controlling the style and content. We will be using matplotlib library.\n",
      "\n",
      "Finding Help:\n",
      "\n",
      " - http://matplotlib.org/\n",
      " - http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb\n",
      " - http://wiki.scipy.org/Cookbook/Matplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>NumPy</h4> Base N-dimensional array package </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n",
      "    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td style=\"background:Lavender;\"><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n",
      "</tr>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>IPython</h4> Enhanced Interactive Console </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SymPy</h4> Symbolic mathematics </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>Pandas</h4> Data structures & analysis </td>\n",
      "</tr>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "What can we do with Matplotlib?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://matplotlib.org/_images/major_minor_demo1.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scattering"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://matplotlib.org/_images/date_demo_rrule.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Bars / Histogram"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://matplotlib.org/_images/histogram_path_demo1.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Pie"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://matplotlib.org/_images/pie_demo_features.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "3D"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://matplotlib.org/_images/surface3d_demo.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Images / Contour"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://matplotlib.org/_images/mri_demo.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Importing the library"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Most of the functionality we need to plot charts is in matlotlib.pyplot. Always import it as **plt**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [0,1,2]\n",
      "y = [0,1,4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x, y)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4T4kdJ7EsK+K1YRgxxw0MDIS/9vl88vl8drw9kLDNTWlmRvq///vufxUV0pkz0rvvEnbk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\nOwA4EHEHAAci7gDgQP8P0lq/n0Xbj9cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10cef68a10>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Using NumPy to increase the the number of data points"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_highres = np.linspace(0, 2, 20)\n",
      "y_highres = x_highres ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x, y)\n",
      "axes.plot(x_highres, y_highres)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7OFp8FJ8Hfo7QnqFcgiHSAuNOWhEz6ABw7c41LEpfhPiT8ZjVfxbiw+LRtlXb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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce545a90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Controlling Line Style"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x_highres, y_highres, \"r--\")\n",
      "#axes.plot(x_highres, y_highres, color=\"red\", linestyle='dashed')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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j1QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce4f5c50>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x_highres, y_highres, color=\"red\", linestyle='dashed',  linewidth=3)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lKiqK8ePHk56eTnx8fFOZ/Px8SktLKSkpYefOncyZM4dCdx8Tfe3YMWNR53N/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce53ce50>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x_highres, y_highres,color=\"red\", linestyle='dashed',  linewidth=3, marker='o',\n",
      "         markerfacecolor='blue', markersize=5)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9zdGjTJ70FFs/XYc5ygXAICHhp7z77vKmdps2wdixcFHxNxHxHRoK2VO8+CJ8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce535390>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Line Styles & Markers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "================    ===============================\n",
      "character           description\n",
      "================    ===============================\n",
      "``'-'``             solid line style\n",
      "``'--'``            dashed line style\n",
      "``'-.'``            dash-dot line style\n",
      "``':'``             dotted line style\n",
      "``'.'``             point marker\n",
      "``','``             pixel marker\n",
      "``'o'``             circle marker\n",
      "``'v'``             triangle_down marker\n",
      "``'^'``             triangle_up marker\n",
      "``'<'``             triangle_left marker\n",
      "``'>'``             triangle_right marker\n",
      "``'1'``             tri_down marker\n",
      "``'2'``             tri_up marker\n",
      "``'3'``             tri_left marker\n",
      "``'4'``             tri_right marker\n",
      "``'s'``             square marker\n",
      "``'p'``             pentagon marker\n",
      "``'*'``             star marker\n",
      "``'h'``             hexagon1 marker\n",
      "``'H'``             hexagon2 marker\n",
      "``'+'``             plus marker\n",
      "``'x'``             x marker\n",
      "``'D'``             diamond marker\n",
      "``'d'``             thin_diamond marker\n",
      "``'|'``             vline marker\n",
      "``'_'``             hline marker\n",
      "================    ===============================\n",
      "```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Colors"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "==========  ========\n",
      "character   color\n",
      "==========  ========\n",
      "'b'         blue\n",
      "'g'         green\n",
      "'r'         red\n",
      "'c'         cyan\n",
      "'m'         magenta\n",
      "'y'         yellow\n",
      "'k'         black\n",
      "'w'         white\n",
      "==========  ========\n",
      "\n",
      "In addition, you can specify colors in many weird and\n",
      "wonderful ways, including full names (``'green'``), hex\n",
      "strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or\n",
      "grayscale intensities as a string (``'0.8'``).  Of these, the\n",
      "string specifications can be used in place of a ``fmt`` group,\n",
      "but the tuple forms can be used only as ``kwargs``.\n",
      "```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Title and Grid"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x_highres, y_highres,color=\"red\", linestyle='dashed',  linewidth=3, marker='o',\n",
      "         markerfacecolor='blue', markersize=5)\n",
      "\n",
      "axes.set_title('$y=x^2$') ## Notice you can you LaTeX Code\n",
      "                          ## for more about LaTeX check the Tutorial about Markdown and LaTeX\n",
      "axes.grid()\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZKVoXRkKKeu4SmI4ehXnzjAdmXDiuPvwQhg+3Ni4Rk6nnLqEjLw+uuw5+//uG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\nFxEJQiruIiJB6P8D8y+aSvZCangAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce566510>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Axis Labels"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x_highres, y_highres,color=\"red\", linestyle='dashed',  linewidth=3, marker='o',\n",
      "         markerfacecolor='blue', markersize=5)\n",
      "\n",
      "axes.set_title('$y=x^2$')\n",
      "axes.grid()\n",
      "axes.set_xlabel('x')\n",
      "axes.set_ylabel('y')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uXbqwevVqM8MRaTm3G9avN9Y5cjjgwAEIC7M6KhFTaeZzAHK5XHW3odK47Ox8\nsn6zGQ4cYOb/vctEjhsHUlIgO7vuPOXSu5RP77LlHYOIL8rOzid9+mdUHlsOwF6ewcFjpFxzhRa9\nk4CgOwaRb2h0naOICWw+8Afo1s3K0ERaxCfnMYj4jJj+KgoSMFQYAtC5x9sEY9/l48c9Xpo5M6HZ\n6xwpl96lfNqDxhgkcO3ZA3PmQL9+sG5d3csTJybhcOSTmfkoALNmJWidIwkoGmOQwHPkCCxYYGyW\nc+66eucduPVWa+MS8TKNMYg0R2YmXHst/P739UWhUydjfoKIACoMASmg+3H/53/gyy/r26NHG6/N\nmtWqtwvoXJpA+bQHFQYJLIsWGTOXv/Md+MtfYONG6NPH6qhEbEVjDOJXsrPzycraDriZOXMoEyc2\nMmhcVGQMOF96abvHJ9KeWvvdqcIgfiM7O5/09MNUVqYCENrt92S8eLWeKJKApcFnaTZ/7cfNWrHx\nbFFwAA4qj80gM2ObqZ/pr7m0ivJpDyoM4vv+9S+4917YtavhsWPH2j8eER+nriTxfVVVcO21ZJd+\nSTqPUclDAISGvEhGVoS6kiRgaYxBAtuLL8L06eTEDSHz0oHQ9TLNWJaAp8Igzeaza94fPw6ffgo3\n3NDw2JkzsHcvxMe3a0g+m0ubUj69S4PP4r9OnYLnnjPmG9xxB5w+3fCcoKB2Lwoi/kp3DGJfNTXw\nhz/AY4/BZ5/Vv75qFTzwgGVhifgK7eAm/mfSJPjznz1fi4iAHj2siUckQJjelbRp0yb69etHdHQ0\ny5Yta3Dc5XLRvXt34uLiiIuLY/HixWaHFPDs+Kx4dnY+iYmPkJj4CNnZ+caLU6bUn9CjB6xYAcXF\ncNdd1gTZCDvm0pcpn/Zg6h1DTU0Nc+bMYevWrYSHhzN48GDGjRtHTEyMx3kJCQnk5uaaGYrYWP2M\n5SUA7N27Focjn5Q7x8Ptt8Ntt8GDD2oHNZF2Yuodw549e+jbty9RUVF07NiRyZMn88YbbzQ4T+MH\n7cs2T31UVcELL5A1Y5nnjOXKVDIzt4PDAZs3wy9+YduiYJtc+gnl0x5MLQzl5eVERkbWtSMiIigv\nL/c4x+FwsHPnTmJjY0lOTuaA1sX3f19+CUuXQlQUzJgBX/276XMdjnYLS0QMphYGRzP+ox40aBCl\npaXs27ePuXPnMn78eDNDEizux/3DHyAyEh59FCoqAJjJJ4Re8muas8ey3ahP3LuUT3swdYwhPDyc\n0tLSunY8fKiTAAAKkUlEQVRpaSkREREe53Q7r4tg1KhR3H///VRWVhIaGupx3tSpU4mKigIgJCQE\np9NZd9t57mJSu3ntoqIi6z4/JgbXiRNGGyA8nG+PGcO8kGO896Gxx/L3vhdCjx5RnGN1vtRW21fa\nLpeLNWvWANR9X7aGqfMYzpw5w3XXXcfbb7/NVVddxc0338z69es9Bp8rKioICwvD4XCwZ88eJk2a\nRElJiWeQmsfgX4YNMxa++9GPYPJk+Na3rI5IxC/Zch5DUFAQq1atIikpiZqaGqZPn05MTAwZGRkA\npKWlkZOTw/PPP09QUBDBwcFs2LDBzJDEbKdPw/r18Otfw6uvGjulfVNODoSGavxAxKY08zkAuby8\nHk12dj5Zz2+FsjJmHtnFxC/PzlKeM8dYysKPeTuXgU759C5b3jGI/8vOzid9+mdUHvsVAHt5BgeP\nkcJxeOUVWL4cOnWyOEoRaQndMUibJCY+wpYtSzDmIAC4Gfmt77H5sbGQlmZ0GYmIJbS6qpirpgbe\nfhtqay9+7pDbYMECFQURH6XCEIDOPd7WLCUlsHAh9O5tLE/xjd+dOTOB0NC1eMxBSBvuvWBtrkW5\nlItSPu1BYwzSuC1b4Fe/Mu4Szr8VfeEFGF7/xT9xYhIORz6ZmcYcBO2aJuL7NMYQwLKz88nK2g4Y\nf/lPnHjeF/rvfgezZ3v+Qo8eMHOmsZyFiNietvaUFqlf0TQVgNDQtWRk9Kr/a//f/4ZevYyF7pKS\nYPp0GDdOk9FEfIgGn6XZXK++StbCPza+ouk53bsbE9U++wzeegtSUlQUGqE+ce9SPu1BYwyB5De/\ngT/9CXbtAgZd/Pw77jA9JBGxH90xBJJt22DXLoZydkVTnsEXVzS1E83S9S7l0x40xuBPzpyBd9+F\nLl1g8OCGx//0J2O7zA4dYMgQcqIGknmwA1x6qZ4mEvFDGnwOIB5PE6V+l4mXAa+9Brm5xqqld95p\nLFT3TUePwuuv47r8coaqm8grtLaPdymf3qW1kgJEg/2Rt6zAwUJjbaJz3noLTp6Ezp09fzkkBKZO\nbTBJTUTkfLpjsDu3Gz75BK67DmhibSJuYjMfGs1evWD8eFi0CL79bUtCFhF70B2Dv6ithf37Yft2\n42fHDjhyBA4dMr70G9O5C8z7KUyYYIwtXKJnCkSk9fQN0s6ys/NJTHyExMRHyM7Ob3jC974HTif8\n13/Bn/9sFAUwigSNrE10+WpmrX0EnnwS4uObVRT0rLj3KJfepXzag+4Y2lF2dj7paeVUfnl2fGDv\nWhyOfM+ngWJjoaDA8xevuMKYiYzWJhIR82mMwWwffGD85b9/P4lbv2RL1Tt4jA+MfJTNm89be2j9\nenjwQUhIqP+JiVH3kIi0mMYYTNbkgnNuN3z+OZw6Bdde2/AX9+6FJ54422jGbONJk2DyZO2HLCKW\nMfXP0E2bNtGvXz+io6NZtmxZo+fMmzeP6OhoYmNjKSwsNDOcVjv3iOiWLUvYsmUJ6VP/Qc7I7xvj\nASEhEBUFP/lJ4788cGDdP5s127hDB9OLgvpxvUe59C7l0x5Mu2Ooqalhzpw5bN26lfDwcAYPHsy4\nceOIiYmpOycvL4+DBw9SXFzM7t27mT17NgXf7F/3ggsuLw3w1VdQXu75ExRU92WflbX97LwB4wu7\n8us0MrdmkXLuEVGAjz5q/MMHDjR2Mxs4kIkDB+LYX0LmamvHB4qKijSJyEuUS+9SPu3BtMKwZ88e\n+vbtS1RUFACTJ0/mjTfe8CgMubm5pKYayz7Hx8dz9OhRKioq6NmzZ4P3y87Ob/iF3pjaWvj7340J\nXidPkv3Wu6Q/043KY00M+B48CNHRDd/nqquavgv4pssvh8hIY/vLDh08j3Xr5rF/QcqAAaTcPaZ5\n72uSo0ePWvr5/kS59C7l0x5M60oqLy8nMjKyrh0REUF5eflFzykrK2v0/dLv+ZicPk4YMAD69DG2\nmmxMdbXR1x8bC7fcQtail6g8NpMml5e+6qrG3+eLL4wvehp5RLTTb5l17y3GDOOyMmMZim3bGhYF\nEREfZNodg6OZ/eTfHDFv6vcqz8wj89N1pLCv/sXG/kL/1reMPvrmjsQHBxsTxzp3hvBwz58zZ6BD\nhyYeEX2gee9vQyUlJVaH4DeUS+9SPm3CbZJdu3a5k5KS6tpLly51P/nkkx7npKWludevX1/Xvu66\n69xffPFFg/eCPsaf6vrRj370o59m//Tp06dV39+m3THcdNNNFBcXU1JSwlVXXcXLL7/M+vXrPc4Z\nN24cq1atYvLkyRQUFBASEtLo+ILbfdCsMEVE5BtMKwxBQUGsWrWKpKQkampqmD59OjExMWRkZACQ\nlpZGcnIyeXl59O3bly5durB69WqzwhERkWbyiZnPIiLSfmy1zoK/TIizg4vl0uVy0b17d+Li4oiL\ni2Px4sUWROkbpk2bRs+ePRl43mTFb9J12XwXy6euzZYpLS1l2LBhXH/99QwYMIBnn3220fNadI22\namTCBGfOnHH36dPH/Y9//MN9+vRpd2xsrPvAgQMe57z55pvuUaNGud1ut7ugoMAdHx9vRai215xc\nbtu2zT127FiLIvQtO3bscH/44YfuAQMGNHpc12XLXCyfujZb5vDhw+7CwkK32+12Hzt2zH3ttde2\n+bvTNncM50+I69ixY92EuPM1NSFOPDUnl4DvLkzYzm677TYuv/zyJo/rumyZi+UTdG22xJVXXonT\n6QSga9euxMTEcOjQIY9zWnqN2qYweHtCXCBrTi4dDgc7d+4kNjaW5ORkDhw40N5h+g1dl96la7P1\nSkpKKCwsJD4+3uP1ll6jtlld1dsT4gJZc3IyaNAgSktLCQ4O5q233mL8+PF88skn7RCdf9J16T26\nNlvn+PHjpKSksHLlSrp27drgeEuuUdvcMYSHh1NaWlrXLi0tJSIi4oLnlJWVER4e3m4x+orm5LJb\nt24EBwcDMGrUKKqrq6msrGzXOP2Frkvv0rXZctXV1dx5551MmTKF8ePHNzje0mvUNoXh/Alxp0+f\n5uWXX2bcuHEe54wbN45169YBXHBCXKBrTi4rKirq/oLYs2cPbreb0NBQK8L1ebouvUvXZsu43W6m\nT59O//79mT9/fqPntPQatU1XkibEeU9zcpmTk8Pzzz9PUFAQwcHBbNiwweKo7evuu+9m+/btHDly\nhMjISBYtWkR1dTWg67I1LpZPXZst89577/HHP/6RG264gbi4OACWLl3K559/DrTuGtUENxER8WCb\nriQREbEHFQYREfGgwiAiIh5UGERExIMKg4iIeFBhEBERDyoMIiLiQYVBREQ8qDCItML7779PbGws\np06d4sSJEwwYMECrgIrf0MxnkVb6+c9/TlVVFSdPniQyMpKf/vSnVock4hUqDCKtVF1dzU033UTn\nzp3ZtWuXltoWv6GuJJFWOnLkCCdOnOD48eOcPHnS6nBEvEZ3DCKtNG7cOO655x4+/fRTDh8+zHPP\nPWd1SCJeYZtlt0V8ybp16+jUqROTJ0+mtraW//iP/8DlcjF06FCrQxNpM90xiIiIB40xiIiIBxUG\nERHxoMIgIiIeVBhERMSDCoOIiHhQYRAREQ8qDCIi4kGFQUREPPx/ThY9bIqTiZ0AAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce712e10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Controlling the Figure Size"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(12,8))\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "axes.plot(x_highres, y_highres,color=\"red\", linestyle='dashed',  linewidth=3, marker='o',\n",
      "         markerfacecolor='blue', markersize=5)\n",
      "\n",
      "axes.set_title('$y=x^2$')\n",
      "axes.grid()\n",
      "axes.set_xlabel('x')\n",
      "axes.set_ylabel('y')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/gVNOgaqqYH344fD3v0ML//Fb6aGhc6eZaikB5gIVZfanGt2LL8JJJ9UO1Hvv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\nGm8KAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce4e4490>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Visualizing Numbers as images"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "noise = np.random.random((128,128))\n",
      "noise"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([[ 0.31743783,  0.89968839,  0.64593546, ...,  0.92315307,\n",
        "         0.40529038,  0.57868866],\n",
        "       [ 0.03509869,  0.41812159,  0.09614452, ...,  0.35230104,\n",
        "         0.4358769 ,  0.42696774],\n",
        "       [ 0.87243532,  0.16665336,  0.30484216, ...,  0.57570625,\n",
        "         0.41494208,  0.60731167],\n",
        "       ..., \n",
        "       [ 0.8604435 ,  0.91615867,  0.44869023, ...,  0.15850609,\n",
        "         0.85534071,  0.87021376],\n",
        "       [ 0.01936096,  0.56630654,  0.49670692, ...,  0.63496359,\n",
        "         0.15847743,  0.53732171],\n",
        "       [ 0.67941664,  0.33243944,  0.41832329, ...,  0.7415799 ,\n",
        "         0.56672038,  0.93639001]])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(noise)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y1yC1ZqDw+ujMK2c4LpMnMgOUhswE1gTpcxfw8qn7mVWnEl/pY131ecpUl/i1\n8zpar6xAYi4k4vLDvBXvHpiJptBZV0FC0SL3zr6K6cAckb/D0+H/4N207QwVpyNMEhLwypAWB7mx\nZoxCn53ELhe3TH6AO/cUg3dmo7jiJ/vpKX6b8Shv1dzBo/uWaQgeJPbYCU6bbmL/d57Al6pFE/Mg\n7I5RqFrkq8WvUZXWTmBaxEuRb3Jc8TDWbXasd4U4q9yModPJnW+/z+tnV/Lc1C08tv4QVStnOfq9\n1SS2LvKN+FdJrrHhXqemKXkvp4Prue21RykQDDG8aTWmNDd3+C8TECtwi7SYo2LGfAYWZCaUGR5q\nE5ppHWtkrkOP4JUpYvIxnL8QIit3UyrqwOicRxQOUbA0QrJojqqGdtQdLrr+VE3edROsV19EVBD+\nzEx+IUVwZV09j/NTBltVeKeH2D+3hapYOjesuIR/pZafJf8If5KSXeUH6G3RszywTCgYgixABZu1\nJ9kX9y5vnKrg1IlKKKxAViLDWG1gz3Araw89Qqr4CnE1NlayH5dWS+geO+JkGdLJODq2JjBes4st\nyW0IpRr+5PwPkrLm+eWG7yLwx+BqGFYtsuSWsL91O1fnNhBcK4apSegeB1cEt66HPp+HFKeVF5L+\nwnvyuzih38mdq96iuqCDJbGedM8stW90caUpAxFxZDNLgXKJtoI68kcGuenDD1EP+ojoRHg+CmFV\nq2iP282pomz8fhPWLiEcbMNeEEZZrCXAJMaEBYrXeVmT2cqmxdOIqsKcSNnAJctahM0ONry/n30V\nPVTd+Vf+1XYP7R+u5a/bH2DMkMltt+2nIneI29PfQKsZIM3VQ8P748xeyEU8OUejfoS7Cw+j7VzE\nvwQfRHYyLCrGq5SiEEPizbB34BOyzDP4b4hDk+Mk8YNhTOZJXsh+nEhEROiiiLOuDRx1rmXBaaLK\n1sHd8/tpurSCg8breV13L70bymlMOo9uYpkfCZ8nkikmulHAVVkVf576LmOBNJJKgtTfe4zp1iTa\n/l5K6kdOckZGORJsYCEvCfsNcmQGET5/hO0NZyjZYCaQocE8mEXLGw0U5/ex7+4PCEbt9MokRLfl\nsLwni3FjFrOBOOaXpOQqrPxk8Slkfhd2UxbFmwdQGYbRZr5KTCtCke7H+NcFTLZ5gqlwwLuSswfS\nSJ6a5UXdd5F82MnArAjzXVq6PTX0TFYzuKREEJUTPxYgUe9EpjWwbBTRaRdzrK2Yi1OraFVmIzCB\n7Lp0UovcJwpKAAAgAElEQVRnSPG30vZBAS8evZlIfhqh5CQ8yyrUKS7yt/djG0jg5Ac72Fh5lgcN\nfwbdHEbHPGXNVk4FymhKX0/O+R7ST2n5ZPdddCoaCRySku6Y5rl7n6RuqRXh21F0dy9/Zia/kCJw\n5sQx2p2LbcRDpN+LJVlPbmE861ZPYc1N5qQ7A7l8kWRlH8ltatRnlxCUupAbl8jzdbAx4Sh3Jv6T\n+YEJFqacTBKHSOlGmSek0jZIWqAF8sBfIEeFDbfMj3RLkMiCj+B5H025jcxUV5PhvUQ4GKJdV8WO\nxGPclvI2cyNJ2Cbjybxpil5lOm9PXM+My0CVuYOZJQcLDhvJl8fJjHURUTqRaHzkasfJy59mrHye\n3EozuYWTaJK9pF6wkPaeheIZM3Upk+SpZkiIuFH6PGTOTnND6yHCDglTSenYR8ATzGN45TomFVmY\nOscxjg2RHh6ByRQmBRl4vQJkYidxfifaGQdqj49eVR59ykL6TOWY8uYJ1+SQXjpJRuFx2k6u41zb\nds41rMGfBvnJl0lMsLFTeZig2IY4vIjC6kMTcFE2fZVEt4MC+SCC0RDzIzoc3jjmpQmQIcHVEM/4\n1gqEYg8F3n4M61wIE8NYXo9H5/SwfeunRNPE2F16BsxlKCwBim0DVLuuUhQcwDGmZShYxPD2QiYq\nsxDK/WznGLu9hxBrwzjLNfQuFjMtyEFbAIWiUbbmnmRyKguRXEbx5ARp8zNo5y0s1Bvo3FLKuDIZ\njzFCdv0yjVsHaEuqwDeuIDwqxhtQM9+RzGJIgzUrRjC9BJchC6vQxGyckpkiMan+I2TOtNHnrWZJ\nkk+mfooc4wI+fQvCmAu5bglFnpiQRkuvs5j+GR0DZjFJskmy08bp9CgZGjainHTjjMTojC/Hnh9F\nlWBHshhE2OxF4JESlCpxx4TMhZUMBxNZtiiQuiHuQQVplRGKhsboHU9lcCgTZcAE5iSmRjNIShqn\nQXOQwFUn7nfzKfW2sK/6Q8zBTKIeEYZeH0G1ghFdPlZPPB6fiOlQMgPWPJynQJsxTcHNfQTel9J5\nporw1uhnZvILKYJ63xUeGfkt73t28U7KXm6/5098af1HZEnNpE7O89zhp3hvspoPpsvYMnGW1d4O\n5P1WjJJl7hP8mCL5IuK6KLeu7SZzIsBv2nVEWtz4NBaCqz3w7f/5ZlahiT+H/oOuYBkJ6Ra8rWoW\nfpPMfIUJR1ocE4P5JKZZKH6wmxJpF4KhGP8auZ4T/k08Ovk71CvtZD4aoW7kIl86d5RXRLfxUdF6\n9o6dYEfoDMV5y1wJFPO8eSdJuyJsv/EwF46u5d222wnulrLDeoSK+T62pzRRWjWGIE3A0HwRC28n\no+/3Eg2JOJ6wlT9qHiIsAbdTyYwii5zedm44+GtKaiZJ/UGQv17+NocPr8Q9I0AYjnH52TCD8Q0c\n0N2C2y5A61/g7vgDpBTP0/GNeuQ9cSR/+zT2SS9CaQBpcpDxmJKfvbuW60sv80BmJ6966zgWt47b\n9/VRXTXPI33/5HL/ap4ceB6fRkZwpYQpcwosxINZQntZNT8u+RFhtQRJaYiH8v5IimOa/bGHMeQs\n8eXb/4EzScuMMI2NIyfZe/UgDEKnoJJfrPge9RNX+EXX9/ht5yM0CVdgT9Nj92oJzoiwuXWMqLIp\nlPTyFe0CZEBa+wwN/2iDEQE3Jhwis2ISpdHOjWcP0LTcyD86H2ApnEhkU4w1Ky9SVtrDotJIYp6V\n7z3yW7qaK3nxp0/iThIS3AGxPjWRZSnRPVHyavq4+cVzTPwjjsdO7kPqzCXbZkN7sgV5Lizu1BP+\ndJHgPxdo2/ofDJduw/eumjr/RZ5Y/2eaEzfyqOlbhFLcpMrHuct8mDj5KfruL6JVWE6PJw/X/hhj\n/4rg8ThIELupSIywev0A23cs8eujuXQsmkhTzZIamEayGKS4epZ7tp6nsGcW0VUZv158FOtAEM9A\nFynOE9xoO0LW0SnmLuv56+xX8StVfNv6G/BGECiD6HZHKa508nDvP9h28SOuXIXp+FJ+nvVNXPIE\nhHYBtwX3A63/NpNfSBEonIskzbSiceYRldlYsEroHsqgz5uKYFwG7RrmFen4sozE2RykuCZQ62Eh\nosRqEaMYERHfAfLyMCnGAPLmEC6XiomCQtQlevz1foyX54l1h9A55khNU2MyLNC1XE3z9AqqRVdZ\nabkMk6CxzpN+vB2pbI6u2hJaPbW0T9UypzdRkTVLQ34PJt0iuc5hqkP9LLoSyP5ohPjxOQwx8IeS\nafNsIH9iHMWVIURN04iWVUwm1HLRu5Z30ybQiGYQLTlIm14mNWZhU/QsYbGET/y7OOraTnOoEWF+\nGI3YTslYM9nT3bisCdj1MYxrXGQ6LVQtdtETrEBqDVE714Y67AYpsOwn3j5N2mwLfrGS1nW3kroY\nI7f5GJLoErGEGWJtYpblKhb6V9EgWMTQeoZs4SJF5QaMaW6iihAWh4xeWzLN7nqSSudIqp5FMhND\nOCkkNiBAmhRAZ7IzEZfDbEI6p22bSZmeZaoxF5kkRHQggtzjRZ9kJythFGWGn15/BW6vmlTvDELX\nPGa7g2LZJeTjPpwtesYc+YRk5/ApFSyIjVhDJhwyHSmF0yQtzpFknyfO6wYtLJfHMVuUxqw/n3Fv\nFR32ehb9yRCD6qVelINeZuZ1yBRyVpQ5SfAvED9lYz6xhElVFnSHEVkDyDb7MWUacKclMnY6iXZ5\nCgWiMHF+AWctm8Evx3mxlEKni6z0HuR+C/75JcyCXHSqCsZUjXR782g3q1hRZSE71cGiOZXA7DT6\nUAumCgnahkz6P6jF6k+gImcAnSxCu3srpvQF4tY4UAhiiIfDdA9UYhq3sSrSgqHQRWn9FBXmfsQR\nAZnV44hnxeR3LxAxhhi+Uc9ct5VIX4S2WAEL5JM+Y4beKF/SHqAg2EfMHSRXOEyiehhdFPqjEQbE\nuVyIJtPvyyVxaQ2w/99m8gspgiU7dFnB4h7HyxXeerWGD6TrEMSCINKDNAft1+ykfWUcweNyPMch\nbgX0+7L4u/02irsv452cxPC8EVt9Nl5tOvYEA1efkbKUN0eSbIGGsxfJ+/sAd4f/hKBBTFx6hL8H\nHuBk8jb2BT/gwfm/gAxs8xH6fxZi9tYSDj+1jRFRLtETYjyVcpSlTrZKjuMvljOZnUI+fRjnRrD1\njDAyANmzQMgI0Wq6PlEwc2qR+30fsEEr50+nXqA1u46+tSUILw4T93Enz2r+yb71l6mp6+Jw/B6e\nO/UE5rF0cMYQPRIkOW+U2y/9Fu90HC8X/oi4XC8lCT1sufUElSta+eWbj6PrdPG0/2kyTFOQCaTH\ncM9F6GzzcqFnIy2f1rBldInimAADFmK2AYKvJIIkgZhgFVhH4JiQvboOdmzsRRYM03oxnd+8tYWr\nzloCSWLqyy+z/uaTvO25DftEI+FzSorT+vi6/I98EreTd2W38sGrN6Oc8ZF75wAFA70Ynp/DUOpB\nslWMY42SqxlV/JrvkDRj5Ym25/jQkszjcxv5ke4Cu5cv8tThZ+iW1BBKf5NYioBQTMoF53p6veVs\nNX1CkspKJFsEUcABs2WJNG+q4zXRV2idbMAnVIEF6IWi8BCNXc0cvFTHRHo26c9byFw9x0M1v+P1\njvuZbMoEsw+hxoc0FsIiSeZTdmJLMCAqUJIUdwEpQj7lARZHkom+ruaR3ZNsePJjVM//E23XCAfv\neIF2WQ0DnUUEujuQmj9hb8EJKjLD/FL3OO7WOTb911ME7zOjKArQFLcPbYGTH9/wLF6/iucOPIkc\nDyXxXcxti0ec5efNF+7GbM+hcN8YEmkANR4ki2FEXki8dwadT8zmF4O01ldz8fvfJPiTv5Le18SS\nGIYCBfxs4gfc7nqLn/Y/hXd2CXMI0r8Ouk2wYhhqxJ2EXUM8F7iPtshdfGjZ8ZmZFD399NNP/38T\n///pmWeeobz2JnbXHcG3Lhn7plykq+JRrRBRumKMhqIBNphaEUSFTAzlslrbRNaaRc413MDZ4i3M\nlGfiq01itqoM2XotSqWP4FELWrkV9Y0KrGNSOv8hZ+RUBtOTmaSrrITUGj4K38R8v5aywXOUe44j\nkczSv72MltLVnJvbTHtgM/2LjWSpJti86jiJtXNMu7WcficH36SQ0qRpuuzVnLBuZVabiihRS+my\nncm4Qo407KGhoIUbUo7idBnokNQyvHoFmZo5bpl4l3BAyIC2ipAvCe9UPOkL04iGl1jodlGc1826\nPVfYGDxN+dVO5q4o8AQ01CePUloxQFL1LClSC7qAg5SrVrJF44Q3CeiMZXC6uQCDx4HOb+cjSwGn\nnSuZcjeiSQkRd5MPTTRIo6uP9eXdZFQ7sVTm49XFYVlMJlouRF4V4FjbTi6Mr0ZYHaOqwMwuxWWq\n3adQjYwwmFDGbGo2YZEUWUqAWEoMpdhHUaCXuUMCXBNByjYNIU8N0a8poUW9hiv2lcRZXGi7LJib\nvbidYaYlpVxYLqPPkYoANT65iozqOUrrBhBXBxGmxkgOWjEE7CR4FpntTCVgkVOc04+oLIS7QglC\nAUGznHPSTYz4M4g1L0CvF2ai7Cw6Qk1NJ1dzKrHVJyGslIFWgEbuomuwmpnuNPZ6PqI+1oRlTo19\nSovXYUQrdlNYMMwO43HW2C5TMDqB1OVleC6BpPI5jOvdBEVyvNkmemvWEe+ws+fk+9SmNVO5c4ha\n+wCmLitx0QDiBQd9V+IxFcXY0jjIxNEERgZSCeWp6NVV0aauwxH04WoxUyvtYYW4i9GP41H5Ftm+\n/gzqBQfRAwGunFnJwaG9NC/ng99HaekYg8oEjjVnknOxjVTbPL3VO/GvS6RsayeaAiejsnx6y4qw\nrE9Dv9pPXJYXf6ICoQgULR7EfSESA8vYpNlYe17m30X+C9kRKKNSku/SkZelosIgwR7yooossE5w\nhbrJDspb+vnjoYdpe7eehBfkKG9O4d3lexgV5lGi78MWrKDJu4sU3XskTR6lhHM4QvG4HBsZPZXI\n+WeSUIpqKVIGKDRYCYT0/O7sIzQuf8IPPI9gFUTpUCRyefsGOnQ76B8uJdwjQ3k1wNbvH2HfjW/R\noazl7KV6PvprBtvyO7g+r5NJcw5HLXvJaBxHl5/E0tgcQakExRYH6xQj3O3t4fGlBzjovg7KE9jq\nbeKx8z/nT6avczZvHx+fyWGuKZPi7F7SI5e5wdKLal88pofjMf7SwczxBJ70fQ2txsuDkn8gEUSx\nRIy4hSqICrjV/w7T2lRe23gHFw5m0tGkQZ4NW3Qhuimn31+Af1CIZW0Sl763hVtDH7DJehZfg4KT\ntVbaEzbQ11PC6JFclg0qVqcp+Fv3/SzZ4/nKA39hZaiZgk8nGGsK0XkkHkGyB0lKiEBKjDFJLm+7\n7+BOwevsWTrA1LyDPms28Z45bNWJHHlgFwufJBI9KMbQvMj6uQ8on3ufk5I9/EnwHH6FF5luiDNB\nEwvBCV4sfpnEtT5OGDdQMDnE+o7zVKT0MBgq5pGjv6VdU8vS196DhCBLQR0JrzkxNS9h2L6EOmLF\n12ImYlGCPJFAohD/ShUGU5g4nZApaTaCQAylbwnPogz1sofdCUdIsg4z8G4cCxnxuDZlULxpgNXr\nz7Fu7AI1rk6keUHet2+mo1mPbUZGe7Ce7G0TGGIa4n1esoY7uXfqz4i2CHE8YiDyaAT/WS+77/4Q\nvbySk4obqRE0s9d7mAuePE47S3nNdgfCVCWiTWGiR0M4XgnxUOwim2tGafWoEUghYBQiOhvA+OwU\nTYqH2S/5MtH+MarXWjD/IgHb+QiRp3oRCT1IE7QIG6UkbLOyqfEYE9YcXmx6jIzqMSrLW8nxLREf\ntmPPNCJ+L0LcUw4alb1UJ0yyNFTGZ/1/+MUcH87V8/iHP8W7VYJ/lYRtbSdpmG4lST2HMWpDLA+z\ntfI4Sdo50nLHmFtMwv+amlznFF+p3I8yw4s7WY1S5cQpUNEsepBQn5/GZ85gmmpEKNjCzRmHWRXf\nw5mFTXQu1rJAAuoiSL0BDBqQm6QcS8piwmwiMDvFOuVV7qk8zWCriufMt6O9W4lNnkooqYretDT+\nmGxAfXaGH37wbVLOuAlG4nh14j5cUT83LP2YaU0Wj8t+TmugEFLTQSP/n8ed90OsH8Q9PqqX32Cl\n8VPCty0Ty9BSbnXQXV3AAek6NHs8KMo8XBe4zJLDxK9sj7FSc5nrrIcZ0ubiCqmILAix98bTOV7H\npD+LWJ2E9zIeZES7mvWJJ8kXXmV/+iaKUia458O3yF0aY0mvZ//lWzjvW43mKzZuXtPCRuM5ugYL\n+cuhrzLck0eWzEzJoWEsvnReaX2IQJ0X370hRqbq8T6vJTIvJCaWsmwwcMS/lkmPiJpIDyurLJye\nuIH4IQ/f6fo9tv4wk8MqrgSLOBb8PRYx+GtV5N81SrpimOxAFzqWyfTNkj08x8hkKZ/mXY+57wS6\npvNkVABaoA/EqijqEwG67Hm82rUL+YgOvV3AOs6RHzjNflculsoq2FHIiXU7sOiyaL1cT1AgoWpt\nK6bOTvz7Wwhp1uIwrOWVK6up9Am56/pDlJpc7JdnU9XXw3WWo0wVpTFflcBq1WXqQ338bPL3KM77\n0VudGApckCshI91Cn76I3+/+Ie6MOIKzMmJSH6KkIGoVpMeP890dR5BWCzig3cNYZTVEk6BKhq7Y\nTlbiCDaRkBndRgLmJkQXW6kfOch8VQqnDKsoUg9TTAur9p1HVuXGtX+EzOkJKt+epnhaTIVmmKYV\nG3izbh0TSeWkS+bJFEwSSxKQtNpMvmGAEkcX/jcWuTiQxqnsL2OzpCPNCPKl5ffYPn2UG32v8ffP\nyOQXsyMIuGhZrkPrs5EZHadqvosNY+fBCDE9xAygT10iO/I/mLmvILurM+/3351T79xhd9qdc5Q6\nKWcJSYAIEhlhYwwY29jAjGEcxpGxwQlncCAaEwRIQlkop1arW51zznnnnPe58Km3zjmvfTM1p/yu\ny7XquVr1+9Sq+j//Z5zZYAp9k8WIp2LkzgxTu3QFc908YrWIa+4GeuPrWCisA4cN12gzkuUQGTjI\nTbGQmubm4kIi1mgCueZhVFk+htNKEJikLBkymZ81ER7yUyJuoSa/mYotnQy21TI8nIl2VIdYq6Mw\nJUhCupAJfRbFwkXyHQNUz02y5M/kYOAWEmKj7LF+TGPWg3SZd5GUEUSXOIJrCQyCMQT6CEmJS1Qk\ndrAh9wRVSRexbikmWlJGitvGkiODoQ4denMCKSUu8pbHmZ80cmTwToRyWB+6wnQwE5tfS451lFBv\nGFGri/RyC7lrBUzJSnFIMrgz4SQNui7G8zqpCXVQY2ljMZhCV6ycvoli5qUpiO0RcgpG2WQ6z42b\n1Vz4bAtIpKRr57Fd0dDvKeH9yX3IG/wk5LixXUoh1ixGp7ATCYpxO3T0uMqYQ8a2B7qoqB+nQxRA\nORgg/egiikUPAZ+cOdVKhg0VLBUmY6ycpjjjIpm5FtJMIVYGBzAPTGA7r2LEmko4LMHVJmDyAuiD\noMj0U7Q4SFQvwm9VsNRhZPaAAb85H32yhJqhm+Q7x8n2uVGpBcgy5AhjUSbHzExdz0ZIjGCGEsew\nCkuLEXWtlbLibpyJYixBKeVpLQQSExGLImR6Z1ix3Mn0inQmszPIkyeTPTfFPYZzjE+lMTaSjm1M\njm6ll82Ky3h1Gn658gVCJgn6iA1hXoy4XEDILGWVVsotyZcYTsnlwsIGhIkKaquGCRZJSc6Yp0rV\nxlxFBv0pZYg+UuLtklOV3MOCwYF9bgteTwLSRKiu7SRp6wJdHQJoEbB8NZUspY09DUPcqH6UtsJb\nMSdMUqbtJUm0TFwNddomypd7KBroY+mEhpEbGXSXZuHOyEReA93t+aTNdpFsGoOlf5zJfwkEzyf9\nFx2fq0ad6yZNOIvZNPX3g+S/QxBRw+nR7fyp+SmC81LEuSEy7p0kd6id+TcCKBMgsVrMhYVtHJft\nouCRASJ3xjm29A2Sjl1n13sv0JKwl+v6LWyUfsKdWW8jeUZF22Ahz77+LPE8E4FEA5NTIYpE13i2\n/gDO+nReWvvvrNx0jactJ3i//wlCPQqeNv6WtPRpAkIxJzft5JzqLp6/+Aql7c3cMf0zNAk+GrL9\nFO86we2buhBPgW0AWg5BtsOCWO6h4barJDxjpWSpm7hQxzHzBmakOUi1YRRHW8n/61sUP6dAUJvD\noeP3cHOqAZdcS1d6OX9J/hyd/moijjhJnl4ygr3cH/8BWp2czHwBv2x/js7FKqiFEl0/33b+F8Ks\nMIsbDLw7dh83Ruq5W/Yptf4eXjv+FDc2r8K0bY7xtDwwy0AqZCRi5qcjj+HxJhDyR4icUuLvVxMe\nl6JLslP5xTbc41o6f1dDOKIjostmsqqI/G2zfJ4/0eWv5seSb2ERqkAc4OGUQ9yz4hIH9+3B77KQ\n9/KHjGzZxqkHnyRp9qcY2+d5bWwHEaGCr2t/hjBhDDt//7cnLbbIU4pXmclOo29LIfqQgx8Lf83k\nvlLG1pRz7LVbiQwvUBN+k/TOJkyL75FhDBFNSOIlj5GmpFWcv3ILYtUq4o+6eUhyhOcVz7P8mBjP\nkpux9zz0ZkDkXogaQaAOUWLqQS81MWHKIJIkpkLez1nTLv4sfQyhG8om+nlO9EtIACRQpu9iU845\nZPcEiQZELOhM2EQGfhl+moWONGzvJ/GY6g02pl5iPiUJuSRA/sQYDrWW6cI04o/FGdu9koKZLlYM\nzxP+8ylUfh+6elCKlokvq3i3+ima4/Vo2mB7+Tke/OI7uK6q0F+184U9f2GN+SoBuZh0ZnmCP2Fq\nsiD4NMZvRvcz4DGxsfddirMWyLofzgbz+Gb//ax/eA7+8+1/mMl/CQSzi3qsnV484woC8QLCUzCJ\nkRr9ABKrg/lWiMw7yE0dw2eSQWYMQ5WFBKMX2UCMoVgJ587V0dFQiasiAXFyCARifLYUnPZsbKP5\nLNZnEkhJYqZVhUKnQF2dgE1rZHEgjbTUEJmaKVQKN5WRfhpUg1z2ZXJ9ai3mvCkqTMPUNHajGA6x\nJuU6molFlg4o8cnvokO/iusNqwmYwywtKokvLmBdFKCcnyFnfA6xSUWKSoTC6UHbFUE6BknCZXIL\npczPl2CZTEItimAUTLE4D7HZGKLMFMb6lGDXoArbMGdOENfFUaW68MukLLSYWDyXxJmFW8lWZhM0\ngUEaJtwfwhVSQDKQCbpkB9X2DubmFIza1PTIM+lfX0aS/E2U8gBhv4zIoASd3Ik8GEZcHicpcw6j\nYwLN8UmUSQoUK8HkcqG1BegsqcCVpcDvjeFf8BH3z0NYSywoxdEfYS4xTlhtYmCqhGZfPW6FGo3B\nRaL6bdZHrxC0iLFOetG1DCI2pSOe6WWyNxvXqAxxmgS5NMycIx1NegDFAy6seSp8eh3TqVkMSQvo\n7C7HGFikeF0vCdIlcm0ttJSvIJySSG4simIoyFxrmOw1NjJMDip6TrPgCrOoKEeSp0evi2MM+DHF\nFggUZhKxqBB3Cyn3j3Fv70eIV4U5kbqL5YFklgNJ2Ir16KIz9LhVzKfKyS6eoSdWRrOxlsOCO1ia\nVhNos6DSzZKoXMK6mIR1UY+tTw6aAOkFLkwyCzG1hJLMPrS6RQY7NCzLlIhSzTindczfNJGmnUOj\nCREsTyUekpJ+dImJhBzO195CfNrF0rSWHlc9Q7Z6sEUQWiLIlrwMD2XAgB1uDyDRBFFEfWiDThKD\nFsbFefSYy3BvNSEcVWPvyEQ6v0DtQCsjS1Gawmr6F3X/NJP/Eghebv8SkfYuBLICBNJixN4JVmY1\n8pPkP2CwOGh5DYpWfcZd+xqZLzYyYs7mmmwdMZ2ajG8L+fAvW/nJq9/FlD1N+q5JPCIVANmmEWbr\nizgR2Ubh6mFSpJOcOLaKEBtJxkDiBg/1NTPcIjpLubCXzmg58p4g6vdDBM4psTSa6LuvjMwVU9xl\nP0ThwigqkRdnb4xwd5joygC+tQrObd9ER3Ex09E0sg5cwPWdEdIHPCSdESJ8MQnVrTLK6qbQno4g\nfw1iMhGugJa3Dz+C5byJ7275ITrvTa5ehJGHtzH040cZflGM6qiFZ753CPHqsxwS3oVJMk9FtIu+\n09X0vlXNAdtXEaeGia8D4bQb0Yd2vLdnkLnZAUX/921GwX02wNThIO7Hw/AtCbgFf38SToB5fJZb\nLp/nRvp6ZHUBSjZ3s3LpPKU3juKrSmbkx9vYdO4GxddH+NG+FzjGFrp+nEG41U3EOwBkgl1F6K1J\nho9GOJL/JCOu1XiXlJAJ5ANWSByyctfYUazOOHOeAPWus6gn2viv7m9zZHkP39nyfdxhDS82f4+G\n9Vf4/LdfY1qcyWC4jGPTdzJ4vpTwKxKEBVEknw/x6OHvsur0Sczf6SO2QUsqSdx8PY83Oyrx7brJ\n7Xu6qf7WO0T9nVypfQ51mYhKYReSuI82KumTlKBSe7jza5NsO9FE+M/dvKHcz4+LvoXlsAnvjBqe\nCiOwDyOczeLpkov8dOPz/LToWY6qbuM3A0/jv2HH99EoPqOD+VtSudS4jd6TRcQ6LOwuOM1zj/8e\nU4aV6G0iRgxZXJ5dyXu/q2chIYOsFyXYmzKZe9HMi4XfYWvdJa491MBMRhob1Y1c1m/gW+YfEz8x\nTLTJho8siEYh4KejuYxBazmBSTsS0RJXwitRiELc5T1OunMeoS3K68WbeXfVA9TGW8i/scy5738J\nYU8Rt73UyypbJ0LPJO+99c8/H/5LIHjoucPIQ6PIHC2IXEnYDCDMDtBc0YCzuY6bTjVb1INkl/ey\nmJyEV5pAvbsNz7SaN/u/xPKglN3O31M0sYTsuoTzg1uwGfSkbJ2jdPYaRRds2LKLcdUZKXu4h9Sx\nWTLe85Eic5Esd+DdqKA7u5yk00tkjM2hKPSRPdjG7dd+Qu5GBwqVl4FtBcyXpJCqmWfJqaFng5ms\n2dxkBw4AACAASURBVEVeGHsZRcSJUBvGhwrrqjiN//YFMoJW8pTLFJcu4rOLOXDsQWyTSQg2iild\n7CD/5R52txwl6E8gzztCgtdFzA8G8QJa4xDVJhvxpTDdsnpm7PkMzJYhTokSzRCzS3yK2rQOxm7L\nYliSyPCQAv+cEaTZpObbya8ZIEHmYiSSx6m0nWhyxqjIP4VRFsdh0/PB0gNkjA6youMjNCkuPrlj\nD8FkIRtV59jWdY4sSz/ue5IxJkJB33mmyeH10g30a8uRW1yUOI+REx0jN8mPOFlLRKdnZjKPC9JN\nTFaUIEsJULC7j2LrEBXOPqorO7AbdFwTr2IxqkHgs1GdM4o5a46IX8zYWCof9VQQmBUzPSZmbb6P\nLP8yyqYwyiEfaoUP62gSURsIrSCaB02Nh5E1q5iSFmEfSiMQ0JC80MNX5ddYMz+FbiJAaHs+dlsx\n7mNR3N0a4mUrmRwykzQ6R7WoC73IzUHRgxSPDbGLk6xZbkI0EsNnSiCQJCdshIjYQiQ6QVBn5J38\nBxCZwlTFO2iJryHLbGXX/kMsB5JpfrmAqnATVYoWTnsrmI0kci25nniSlGVJEinKOaJSGd5YPqFY\nEgrxNLagH7s1yvn8VcQ2xdCYHCTo3Nz8fBXebg/rz/yerrQV2O5NY2/TMUTjcCpeT7ZvgZ1LzdhX\nC/HWx8kochCclvHOkYeRGUOoNri53L6Rqc5sxAIfufYx7tQdZd2aJmR5ISblK7gpqUOHEn7wjzP5\nL4HgC9/6CDVuErudqAf89NQU0ppbzSU20juVx7RBh85wjnxjmOF4AVG7mC1zF+lorebVM0/QMHiQ\nR7U/oNgSxduUz+UjW/GaNRjLezEPnCflyBlOr36BqXWbWXf7KGsuXCb7h8PoLH5UegF/1TxAi3wl\nuz46TJJrHsELMdJCvez4eATNUiqCcA6X69fhEyhZJWphSZDCdeEa9v3hE249/gbziykEXDL0UgeX\nijdyNf8prDjwMkKG9ATxDvjrJw/Sq6lE8riQp4/8jE1vHyVdNIMkWUBCLIxEDEYDyEVWhK5+6jK6\nCEaVfDP6Q66OryfaI8OUP4svQcQO8QnScpc5cd8WTi6XMnU6iZCvEnFOFrm5nVRmtKFedjAWyeZt\n/efYWHyZezwXMUoF2Hs0vD97H5Uj53m06xncezP55JHHyIxNs33qODs/uoDUE+Hwt3aT7Fim6uB5\nzmTv4lfFzxINQ/ZiI2vjB9lk6KbOrECVG8SdouQ/pD+nWfoA4dVSiur6WJt6idvPn2THuQuEt0no\nrS7hY+GdzMtSKFIMonfJSLf5CKskWJQK3v5dPfH2CESDyOYc6BfcaA5byDs3wI4VFxECYTGEvTIi\n/UqO3XsLp+u2M9laylRrDh32Or4w/Tpf0f0FyUgAq07L0q31TFlXYfuejECiGuu+IoLHwHh2ji2q\nixiVTn4h/yYrFa2sVZ6nYqGN0u4uJHlx4qkCfCoxnqgQj0jIHxVP8zf1Y9wjOURNuJVpUQ7VZaN8\n9c4zvPeXTZz/TS2P73mf8tRxZtVfZkRVwBHdrSwmpDMTz2Cf6GOyhNPEFWak0gQMwT4CghhatYim\n1RUs7UrhYf+bqNUBrj9Si+h3w9zyys9x/eBF2JnHfY6DKOasDIZUrBF18Q3Z7xnenMXkvWkoZEGG\nrpXy/t8extJgRHOPHXuznuBLAgbIxJA8yf76t6la0w9bYCK9mlbDPVSG2v4pBP+ShiLV95+hmXrk\n50KkH5rnSP5tHMu5lZF4PhmKWfaXvEfYruaz926npGWIvBsDHD3ewJErKxkbEBBZKcb2dDnLG3MJ\nZqoo8/WxQ3CWNe7rLDTDx70rqS1c5N7YefIOtJM4MEdygx/RbiGBW+UkCLwYRl1cUW+huXY18roA\nral1fFD0JGmzbnI+GubyyXKcl6Ts6rxCdW8/FUN9DF+U8k5HA2cmbmVuqJDy8AB5I1OUnRvA1pTM\npfYtDMbK6NVVECyUUrO2jftKP2Bz6CIpoWU+9Nbwma8Ys95BotEHeXDGvp53zuzFlLVATuUk2cNT\n1HpaqajuxDx8CdGrzeS0LCKYVnBgcB1XBuuwKqpI2iKm9JFR9swcZfunpxCdnELY5aTSN0KufgJr\nvYbzHdX0HUuBASHmhDm2779G7k47mRkWcg+2kfZ2D1nJiyzXZvC+6SGudqyg8eM8RorzENVFCR9b\nJO3CAPdpLhNem8Mbdz7NlEVAuHkUz2Y9kk0SHAsGih2DPJrwDmWBAZTyAF1FZVwOrubSG/UIBqLU\nF7cx0ZXHgcP3M5qSTTxXSEiWRCQpGxJTUK2D4DoZaouPNNky4lUgzAKhDC4ItvHy8n9wMbKZcX8u\nunQ7GrMDt1KLQ6GnW1/JSVsepzsyaOqqZHI8G3dmFtWbJvj8xr8SjsuY02Rh2reMZVUSLZ7VSG0T\nGGdPcWohnw8X1jJXmYNXpSP6VwcdZyt5w/MsTc487L1WMkzLZOTYqVa1k540w4g+F23Uy22aS9Sn\n95MitmMWLyAXiLk5uJ484Tj3F79HRCyhd7mcoWMl2OeNOBQGqpR9fLnhTbRrAyDwo3u9kaVWAe35\nm+jrzaXtVC6Tu+oQlUjJPXOW5MEBqkRTmDZ6GPlaESfGV9J0OJuKwBg1wX4qA70YKqzM16aQcfE8\nFVffopDTrOAK9aFRdIIAAhkkuL1kzY0zdEBF18XL/+c0FF0Za0BoFqFb8qDoCzNsz2cunMaiO5Uy\nfR+33X2Wcws7uXlxAwmKJQzCMZZ99cxFtUQEPqb1mXgLKwmab5IQbeRW82cY++1M9mQRDSbjr0tB\n5xgj68o0iqE5ZFonruo4dnMqc4YclFdiiMdhdl02oQIxjmAzU/ECrufcyuar7RiOWvEE5fgSFCQV\nWEnVLRORhhkYKecjzyYyZjQYZENIZDEMbisZnWOkKOfQJnlxCfTE84WUGfupT2piS8JFwiYFXSW1\ndAvycTgF+AwywhliAkUynMcVOA5J8TdIEGVGyDgzRxJWBFlhuodEtB3T4dY40SpiiPt8iBNFCEpy\nyCzpZWvJWVYN3SB7ZJKm6TRi4ij1zs8YCxdyLmUDU7NGZAMuMk0LZGXN49xahNDsQO4KEe4WYruZ\nwOxTJhbrTQQlcpYwMqcqRR4UoJiZRTyoACuw2czcmnzO1W1lssfFQv8UkdvAmGpBveRE77eTHpxD\nK3IRVQqYX1AxOytDd2gBU4GNnH3jdCwnMtKdTyhXhCbBj1oTxZ0qZSGuxiMyMm814RYqCakhIAI0\nYlgtZWkoheHOAibHswhIZKypvEhexjB6XEwFsnk/+ACZ9qMkzzQSuuwmKXWajHtibElrYV/sY2Zy\nsuhU1+HYoEccj2AKzROSRTkvq2I0bmYumotE7UUaFRI+O8TAsolLNTuwDi4T/2SY+SwleQlCblNe\nJJQgpzG8Fk2Gh7wNTiRBIfGgiDLjFAsL05wdj6IZdGBOHcWfIkfgEYBPgMemxjOhZufaz9h5y1XE\nrhg910woDs0zL8pgPDeDgAMScgKYvBa0g7O4LQ7soQBpsiWGzWau7NjAzdZk7OehPmOGwrLr7Kg/\ngyHFinNaTUTWh6Sol/T5KTK8PiZmDcRNYnIcNjSORZJsUhZP1v7TTP5LIHC/E6H42QWua1ZzPm0H\nK5U32Oq9wOG+vQQVSuLlQrbnn6H6lk7cLRMsW9x8vvpjCkxeXpF+Feu8Adu/BVA+68ZUM4/cGqDb\nXsErqmfJ2tzND2s+5NQ7D/GTmw/xxP5fky5qouWjGDdsK7kq+SLCuAatKUq9vpFaYQs1n3Yw2amB\n4UnkIjeaQjGS2VQQ6kAl5bqznFfG72VIVI06L5n7P/cWd2ScIu3MHFc7q/jl6ENs2NzLr2u/Dh0g\nPBFBGvOgW+kk+U4bf5vbz9+CD7Lh3sPcmXaRjAUnbm0Ck2VpFPb08x+xPsod83jnDLw6+xVUox6e\n6nwVV2ATJwoepSH1l9Qln+DLmmNkSeGV2EpKhwd4ZOx9AisldP1HFR9de4BIqxdBxyv0dJp4/0AD\ny+4SDIVKvvDgGyTW2TkYvJf5/jTiAQExdQTFOjd15W3kZY+zR/AJkV1xPFUSLhwt5uqLpQRyM5jb\na+ZvG5Qos/0UaIawSLL4yPdV4gdKEEylIN4XIFoa5YZ2JbEFAYUdA8gG+igYmmHt1Hn0hUI0BMlN\nmmZH8QX+0PkU7RdK2DvwJ9xuLe8rnqPc0cfnhv5GtMPC+ASElBBbo0DwZCqlWd38Sv4Mv3I/w9nI\nVnQxB6tiTXwh/C6nbLt4ZfxZ7qrt4u4NB5h5R0rUKiNzUU7aVTtpZ+ZRV/uQrghRrBugWtFG6n3z\ntG0t4az1e5TLu7lX3UlGtgvl4DwzBHGV28l7phfB3/TM/yWHvnftqBtd3FEQYEXZKNmVcxwK382/\nSx/nzryPKTN2MhPNZCmYyDb/CUZPmvjpc0/yyH1HeSD1I8b9hcyqMyEfFrMTaTeWkfDeJKs/6CBp\nwkqHIBv3j1TUF3TwlQf/jLVPiu3TGN6JGTpjBbwRfJI5VxX2pXScBimRFXHeKE1nqqKCr0Z+T9lE\nL08fsvJ68j189qW7eOS9V4jfHOb3wW2sSFjiubKzHD9TxZ+PbmdieeM/zeS/BAJ5JEoZfXgz9Dir\nDeSNtqPyOrBqdIjdbj47nEOu0EH6Ng/O8TBRRwx1bQBNWhzhrIHogIBg8xLiRTthoYRL0o0sClMx\nBGzkLY9TOD7B8Ow4fnsyXcJ6RhMNuAonmLIXMUwJpdEZ0uQzBHojDLWl4zyvI+hQcY/wOMYKN3Np\nmVRfG8K2aOR6fB3zKXLk2VGk4zJingSKRFOYIgtcGa+l159LeskycbGN2VE3Na5ZVJ4YzaP5uNQC\nTFELulQ7KfEFAqIUlpbLCLotiHVhXIlqEksXKVk3w7C/hu7xOmz5WgwKC+oFD0b3Ekn+CXqdecj0\nt6Cp9KA0xSjz9JDb0o36/Djt5dtpLV6D2h1HLvQyp8/F1ykns7mXTLUdU36MdQlXUOii3BQ1IHcG\nUbiDdLiK6XKWE3CoWHClUyAdRmLz458Qkyh0sb6wlVC9i9CKZJYLk8mWTrDDeY6RAj1dd5oZHkpE\nNKpgQ/Qi+qUJLjdm4AsF8ObK6LCnMrWgIVvsJwsnSixonfPoxocRz92OcD6Z8uUetMYgsYoMKtxN\nSC6O4pkDnweUJrDZUmlvvYUK6TC7U09jUi8gkwQpsIxRZm0jvbkfzXwJRONY7WbGPbUsSkUYhEtk\nDbWSanAi10Cd/CZulYKMlnaCITu2dDVBtxDd9BQxiRKbtgxzUhtyvASI4nEI8PWLiCzHEMdClNhH\nyR2doMNiYkGoRrNCSlgtQqu30ZdUxKjGzGLcRHRZjHrBgyIQpDhhlMTeISSjHoRKH4jiMBhjISuZ\n5so6omoXqkwrksIxgoI0AkE9FlEC48tyVAOzJA9aiKeBxODHujCH0SJG2D7KYGIhi9uSyExZIDW2\ngCwaQh9yYPQ7yPcP0x3LJFKTwkyWgQ7PFrRp3UQnLxBQqgnWGKgfGeJkxz/O5L8EguTaOA3SFgoK\nJzCErEy9GcBql/Lwj2a4PpfLK9+rp/h+ARu/4iXrxEX0SzMMrylkKCGXUL8EXPNADwKsLIpT+CTp\nftK1C3xn/kWUJ0ZZ/oOf22Tvsy6xnR91fYe5tXvZ/m+fotFkkESAh7sPsLblKj889BhvdN+BMJjM\nA/UH+fmeF2ldvYK21Apuj33KUmMqL/q/Q8raGR578k0+/IWea28kIb8aZE6dyi/6nyAvb5IfPPRb\n3jxXzjff38FPtn1G9oogv567mxzpImUpM2wxnSU3MMAvf/U8zZfWUGgeJ3vzKLHVQhQrBWhEUg52\nPsiFwc3cse9jtiguoGr1UHTsHHfdvMnZ0a/znvv7FD44Sc6madbELpI11c5Ql4BPZzbTHdjK95J+\njHnLBOcfWUPKX6d4sv1VRB4hCbNx8rp9KNLkPN7wKjJlmFS3lR9OPMXvLj/CQF4+I/JyzuojcM5L\n/PcuPv/Uezz+04M4pUZGpAVcEm8k3zrGA2MfM7/KSPu6ct78WT7+fg0Pj3/A7PUA//nOTqYfXcn0\nM5VcaNhM3/U8xL9dYk38Bl/gMLohD4FPXTiiC0gEE6TIfawt6mXtvd9j4kKQm5+BJApaHaTWwLwo\nh/d/9gUcZafZvfM0ZIJS6mfFRDf5fV30fBpmMhFYDYeu3caJwU3EUmTUKhoxd36bhGonso2wJ/0I\n672n6XnTx9XhOj7ZuQft/ADbTn2flsiX+TjlIYwvWklM7kJCHG9LAsN9Zrx+GxrxCPuzjlCimebF\nvp3MWgvIM2vZmNTMM7Gf8QfvlznluJ9YTEy8RYTwLQFPlb3KN55+ib43fFxvKcTZAIRjcCDMnDKZ\nxp1rGduRT6xGzL7UDwiJZISsiVz6sISbv7uN3YHP2KGy0FALa6Uj3HrpVwjmRUjOCvjTni9wedVa\nvjz7JzZOXkFp9yEMA3mw8vARop3XWfzPTQys3YlnfCM0B+CImNy7A+x6Ypbdb1/4PwsCWXMAQ+cM\nqpUWYgUCmjJ3M2dN4o4jVynVL1LxQAHOuJxzv4K7u8XolhO4+kE1o5WJ1KxqJC3eQ26okSrZMlrE\nuKr02FP1nHBtp9BioHS5EUOXH93UBHv7P8FiNJC1cYS80VHMpwcIlMc5s34rMY0aQ1eIuR4tvrgM\n4SU3WbJRZLVhZmvTsKcauH30KJnaSarHBmiummbwGQsKYYDodABnbB6/fgHFCg/hjhTs3lqC0TYi\nUgcufREeuYK4T4w67CJLOs0dqUewVCSjqbERq46TKFumK17FB1RhKLFzh/owPrOCExObuHStlGWN\nlLlnVBR57ZQLT9La00CLbzWJBRaEditFwcuEGpX4xWoUiQGy9dOs8bXRlpzP8ae+zo6Wz6ixNyPM\nUzCam8Np+VZSxEusz2skkBBE7R1n59JnKGxKThl3EKsMk/3kBItrMzkovg/PGS2yhTAbChtZKezA\nYLEzlJDLVGIm2dFz+EMizurqGE9Iw1ZfxeCMA+/vrSysVWOqtrPptrOs0TRR5B1H7nMSkEHhZi+O\ndBmHGx/g2rwXwaeQ72ykvPwM4mVQCEAjhbhRgneNmkCiHCKAH+xuPR8P7WM8YCTj3us0ZAxhMr/I\n+EQMa1eU/FwxWpWQU6F7sctauTtyisa2NVy0rma6N4RtLk7OhdNUxrrYLltizhylI0eBfsFNYWyZ\npDvCJG3pp0j4Gudn67g5U85Z1wQLESk1906Rt1HAsnYFXQtVjE4W0eutwmPTwYSAlK4Bins/I1C2\nyKeld5C5vYm8NFClQrpklprq65TJ+sl/dYz3Sx+kJa+WRv1aMsXT3JVwEFXqCKqEXrTbwVVQz8GJ\natImfWyXnWLSrebMjUpYa2NX/BhZF4eILwexbNDSF6rg+vA6ZoqFLJeIEJUbsYcTiVxWsOxP5uad\n1WhCPjYebKHXUAFc+4eZ/G9BMD09zSOPPMLS0hICgYAnnniCr33ta9hsNu677z4mJyfJzs7mwIED\n6HT/ezeTrNlLrCmI9xsigluNXCi/l+mZHG4/1kn+aif134Yr78bofinCZomCJFESXYcKCYVE7Hrx\nHJu5ztqJZpCAJ6REmeXkUt56DrKXbUhYH2tB/LYI8TEve6aPEByREPCJCN/0s/ZlNx+++BSXd95C\navkcVfVThI+nE78WYaFRQbp6GpPWxeurP4ejWsuDVw+QZx1H0e4na800GftmUF7wIbK7UWl7kSZa\nCaaBUG1AGTMj8CcQDvqJJaQSF/qIzAoJCSXEE0RsNZ0nphXi3SbBmypHGgrT4VvB28HH+EnFf7A6\n9xq/UX6Zsy1bGWtUEt2tR/VCAj+d/ibr+z7lW8dK6OyqY/DWUtI9/Uh0chI6I2gWvQgbIiSkuagI\n99Ga38Cxrz3DqtctpLW0Mpmroy29hE/Ed5MtmCBBb8etFpAp6WW/913UHiUd0jJCq0Ws2DXAqC+P\nxslN2D9Mprqnm8/f8S7VmR2EkDJqz6FdXkqO430iIT+Hk/+TWVMtcbEc58dDeD/0I30pROGt89y/\n6SSrIs0oHH6EsRjBTCkFt4forUjgmONu7JeMRN+L8+UKOXeuu4q0N4zAGscfV+BLVCJYHyPskWCf\n0ROIi7F5E3h/+D4GzPk8fn+IFekd3Bb7LZdPxhlVwfaSGMvJa/m+9/v4FCY2Om9ybOg2fjv4FRTW\nJfL8l9na/DPWpA6yskTE6WIxwpwoGocbs9BN3p4INYl9RGJ9iNteoKtxB8cubmBYJOXb959BWu3j\nbDiVm3319DZWIQ6DZsmH74aS1Kl+tnp+wQRb+WPaE3xtU4D8vFHkNhFmwxT7VnxC3bE2cl+bou1z\nK7iSu4q2WDVqkZtH1W9QZhokscBJ653lXKtaxcGXH8Ew66JYPkhLwMhvJm7hi66r3OG/QcINCzaX\nHM9ePdccq/jt2FcJ10uQN/goNPYj6wqhPu3EVaLiytdXs+6dJoo+HuFvTz8EvPY/B4FEIuGVV16h\nuroaj8dDTU0N27dv580332T79u08//zzvPzyy7z00ku89NJL/1t9+s4FfmP9Lqv0N6iSdBOokUA8\nAuI48/Z0zv3xFgqWG7l/8yk6yzcxmJXBHlEzBboJMiaspA4s/X0oxTJYB3UcvLCNpSkDD/IXyugj\nLAgxWZfH+JPFtFxchUFj517XByQn2YndDbdHL1B/dATFhA+fWMGOVWfx14s4v3wH6x2tpNlsjE0W\n0OSsZ/BwEbtTT7F/z/sUJw8QjIpJc8yh1np55NkBEiMe9H8Jsvr6WaLhEQQdXfQPafDaBnAuzTA4\nF6VPv5Orqr0IIqAz26lY3YZ3RknbzXrMqhl+Xv7v1OpbSIpauCfyMYoCF298/SEogAz5GFx0ITq5\nwOOh37O6qpG2oiq02X5aa3aw4WIzuzouoW7s4zN1CWdT9yNIE/F80ovUbWnCoZVz4HQFjT2FaB5e\nwDYk4s/vbqbW2cs9ay9SlTPJmLoERGDHQC9lrD3fxL2HD/NWx34Qx8EQY6wwm6umVZy9WkvHb9IY\nvbkHZaIPo8xDTvoVjAo7BR0dpF8a5sNPnqCvrYafC77B7tST7C/+K7osB7FHhEx1JGJrEbNv3bvo\n0mZZej2AqjjOgQfuYlXHTTS9Hv46tZ+rjjWo8pz0NhfztU9/zU00iAxNJNySgkMj571Xd3M8YQuq\nbDGLFvDkxuiucpNW6ea2qoN4OjQ8f/2ntBWtQLvZzv6pv5HW286la3UsldUx+1SA3sZMAic9XLuv\nGn2FhVUzzeguWIl2wrb5k6jdVj5cs4XBkl38camGknc6KGg9TcrMRVYtG8mWg0+cxbuah1GbIG8C\nFn1aJudzef3QFjRXihgWpFCZM0mhdRyd0YHthQT8LSLivwij/LwXfZGDZKcdYUGc2ReSELVaybl6\ngy0NemaKi/jN0afJKOnjpzveR7RWSZ++FMPjyyRarKTYHGy9cRLlxSlULiFaVwRttgNhKIZzvY4u\nfyWf/eFWmtQbSHzShnhl6J9m+r8FgclkwmQyAZCQkEBJSQmzs7McOXKES5cuAfC5z32OTZs2/UMI\nUtOWeMfwBPp4gFp7B3mxMVQyP9PSTKa8WYzP5lFu6qWsHI4U19OfU8wOw3VqFwZIbLLhcGrpzSwh\nJJIxOJPL+dMbEbUF2Bi/SsQE/dmFeLYns7w6kYFoIfqwkwVRGkG5ArdZA/E4qZMLZF2ZJqQQIckL\nMJFqZk6fSrBDSWTKyXLIwLglk9hojAlBMvM2AfqRcapFHvSLFpS6CCt2uRC0+nC8E8E808t29QAT\nylS6FFm4tbDokHH1QiFj5lL680vwGZUYlBaMogWcQR03rKvJ973FbsEBFpbNDIiLESSAWhJCWKtD\nKAojGwkzMZnJ0LyZYslNMhkmWTvDkiSBGQzsHm2iarqPSRe0xCv5VLqHjZJGvsqvSDbbsDlltBzP\npmMwlbo7LPhHooy9L+b24knWVdzE4jUwa1eSGxlG77GhdTkpnRygYbaZwWg+LqMGVZIXv1bBoiAZ\nX1cEPlpkkgzESVIqxH6MMQvJTgtmlYPsnACyGQlLS6lcL1aTpp/jXtsB5pVJjBjNzPUmIXd42Ljh\nIpnF3fTWSujNWsMx4WaCBhH61CCHbHcxps6mKK2bxbCOS+0rQDWMomQW8woXAomGmx+sQOGMkpbm\nZd6RiVutw5M6QLy0g+zCUabCWXzQdj8m3Ry16TcpEw0hi3nx+qtYXJeMd4eE0b4sorNRFhITGTAV\nIL0RQd3oJNQNaYJZ9ho+oTlrNU1pZVxozSM4aGfFxBK5jilUfg+GtETmk1dyLWU18eU4zlAFcYca\n47UJXP1gnzDg80uRhYMYi2woM/24CpVEjwoRXY1h2G1DWhBiypdNVCJCmelC8p4D7XUfibvnWEjN\nZniqhOKCPu7ffZ3joV1cn2kge8U4Qu8gOTcWMY3MkzUhpcw4S0HaMkuaRKIKMcmJy/h7pfzu8mMo\nNwQwl8xQrWv/n4Xg/7kmJiZob2+noaGBxcVFUlJSAEhJSWFxcfEf1iS12xF6Yxhn7FTe6Cflwp/o\nuVnBwdm7Ga/LIuWJGUaTzfxK/GW6hypx92s4Xb8doSfK7rbPuFm4kgP797JgT2e2P4PJWBoIgvw8\nnkzSJhtJX3eyV3CUW5bPkVEzy6w6lWvqOkYGiui6thLujJJWMs2z7b9F3BXhD//1VbLEIzwoeIss\n9TJBmQoWXBQIW3m25A1SfDdp+mkInWCZRJUTaYWfQIOOaXEmFq8DpqYplUbJr1RyZd9t3Mjbiaur\nHOc1L2+fNbN7azMvPvk8o9J8fAkKikwDWONGXLvVGD4aYfDXUj7kfq7KbiOeJMKWa8BRqkewFMM/\nqmQ5P4XWh1Zx18FXWDnexYbFi4x2iul4K44k34l0LWTkQob+72MMVcoAqSMW5I4gNpsCQgokhhPj\nzgAAIABJREFUCjF67BTTSwMtaMcWuLqk4Lh0FcGaZB4u/guZDhfitgi2LB193yzkto9PkuxZxpw5\nQ2xRwP3nD5LXLCMbDWfZyjgrWCQRW1suvl9pOJPsR3FXgImpbJRSL8W7eshTDSEZD3H8+npevXk/\n2vvC1BYPkXHGgkSmwfZsDUOtJVx9Rke/4SEkKSYmqrJRrXChS3DgwwX4ISMZVZmaNZrjCDVCFrbt\nZcXVRh5t+T3vxr7C9fTtbAmcJzFs4aDjPobTiog8LGJP4zFu/+MRDorvZjwrg8KvdGEvC9Mvq2DJ\nnIqkVky+cRqF08drrV9i3ptG7BZ4LPN17k48iHBSTPzDAHR14ilxMPHddWTON1M63MvZlWvpyKsj\nUzrKUl8Sryu/RfHkFZ76xbNkb3fj/ZKZX35WTzhNjHudgiQZ6Ps8yO1CpIgwM4tbmMDL0m9wa9MJ\nvnLsD8y1hXHEdFz1riGYmswXn36NukAr0pko/dcqOTm9h3WPXMBoshP2SugQrOFnSc/yZPbrpJd8\nypmKLbgdau76+Ci0emFhis2OM9zec472F9b+/wOBx+Nh7969/PrXv0atVv+/zgQCAQKB4B/WnbzR\nRNbk52nyu/htoJJH5NNUGtq4NFoOAhvFOdNMpGbTEy8j4JYicYawyXV0eAvwD7roLGigvaoS04iV\nEucg5u1TCAxhlJ02vGY1sw1ZtA/W4p/UsjxjICYDbZITE/M4q8doT6tmTJtNbvUUSqWXwWARxtgi\nCUIf8ZQ4SKLUjzVhcmTiMWuQGE1IlAtMBEoYJAuVoRVRWMbN6/XE+meoVHcQj4ZxBaOkymwUptqZ\nEckIKwVojRIKCudY7W1Cle5lSWbAcG0SwfICOfgJe3y0FTTQvNBAW6QGXbKDdM0ct3uOIhsLEr8p\nYLYmFUdFCo3D21iM5GOeB83oAGVj7SyvKuCzqiw8Lg2zcxlsjl2iPLULR7KWkF7CeGY6noZ8FEo9\n6apBDPkegvfpGGoxY51MZKAmD7lJykSnALF6AYPOiighii7BSVr9PMq4n3GzGadVi9OkwbvGjzYP\nJK5ENEYFNTN9OAeTudC0ipzbreQ0jJJsG0bu8VBhGiIxNMnJ7iJOtFXQ2p3H3SsuUCAboGuikmWj\niXYqWDJIUOdEmJ3PwjlZDKWQ5pqg7MRNhFOptFVlo1gtI3mlm0rLEClTSwg9UsQBN7OObPIqx0ir\n/YhtjvP4BxRcMG5DneKiNnmGXOcwifZlMhemEVl8FEx10O8pYfnGBrxtKuSWKLFLi8QEw8S6C1lO\nSmWquIaLJeOo0rzMSVNJDlhZEb2KNidAj3YdqTMOshy9uFJkCHMi1Pa3MinMZnhTKcttGSy255Ba\nvIQoLwHBJZibTubYtV2sD19m5WwrBcYBtmy6wKqp6ywfS+CStYQ0exaDGYW41X5sAhOznlzUkzEq\ncnrJHR6Hi3GyJyaoc93AeG6QBWWAE30VDIymIAhOIZQ4Caql9MgqmA4ZyZm+yLI0gnjldaYsh7nq\n6mP+g7n/eQjC4TB79+5l//793HnnncDfXwELCwuYTCbm5+dJTk7+h7UPfiWPVT9u5c+Zj/NXw36K\n677N9oZPuGvqFSRBISW+KIdDdzOkKEBTZkcci6KVOBjyJPPexD5ilgy0Ahl7DZ+wa+VnzKxNQXgl\nRsZPFrgkX8cH7OO44Db+ZDUT+0jIZtd5ftjwPdLqPyHw7xK+Z3uRN91f5I/bHkdzpwOD1oJMHGKG\nHCKiRXQLdh4YPMjNq/X8fPK7lN3ewb8/OsafXXdxfOluzLZvYRpZ4sKh7RS7e9lc+BHTI356B/ys\nbTpGntLCQHEJwjvC7HvgErVHh1G+Fkb+eAiJ0Qu/siC+4UTLEPNPbqL7lX0sXc5Hvhgkd/MQO2Kf\nsb/5Qww+O1GHiNPKLZwv2kjTzvs4MpOOaA4e9/+R7+a280bdBg4V38XkjwuoaO7lO5oXUd7jom9n\nPjaBkbH8XCxJdajiEXKNc/hT1LSv2Ezv90uZ/SSVssf6kGTE+O1v7sNYZqXyuVbu6jrKpqYrTK1J\npaOwjEFZIYOZRQyWFJMUnUcXsOIb3kzaSIAvdP6KydYEmn0J7Eg6xXOF7zH1DgT7Y+RZIlxbLuJH\nh3YwNVOEMOyk6P1mMk5P8NucH9CqXkfotISiNd2sfaWZGz8M4TwDzEPS7Dzrjx8nnr6DI7eVo9m2\nQEbWBMUHh1h9o5kts5d5Z2E/L0p/yXN3/IxH7/oF6rMBhiyFVN3dRiRJRD4jyO/wM7Aqjzv+dhjV\n+VlGfxZi3CvES5BQKIY4HGW+0UoyTWzwd6Le+ADziSWcN22gy1yI1Wgkf80Y/+Y8x8yMme+1PIX0\nzDD65hiiVYtUJsdY814zk+pJJp7IpKuwitcKb2HNimsksciSQ8v8tVR+cf05LHElOYIWGr53jspN\nnRS9NsrNy1kctKQx9qCEw9/djSFuIWiRETxiRHfOgnJDGOn1CNG34I6yQ2zMOEnHByGuTubzl/A9\nJIenuTXyPEUeJ5Gwjhl7Fu2LJjLsCtxVAtL+ayW3qKe5P76AJmLh0D8eR/DfgyAej/PYY49RWlrK\nM88887/29+zZw9tvv80LL7zA22+//b+A+P+uAU8p28KX2Oi8RHw6xoBNxZJiPdU7u8gutaFWx0kR\nzJMXGqL7lAZPuxcJV8nGi/5zqcToQfLDEHmRFpTxJXxiH6OThZxxPojaFuKBwY9Z6En7v5h7q2hN\nzDJt8/rcXba7u9Te5e6aSipKjCChoaEbbSz8hEAgoaGBpkmwAAkVl6pKVSh327Wraru722f7c5c5\n6LVmzUFnzVoz/5rMff4eXtfB8z7vezPak8vp8VJ6HSH+HFxDtTWHoj1uVs7dRrcY4G5VHTPCbNwt\nJrozGhA2J9kovEpz6A46S4Diiik27L7EcpGJV/q/hivDRHVBD1dDm9BrPOxYc5405SJdaU3ceT/F\nwEyCjOQwao8f8Z0kJtEyK0Vd5HXPE3dKaLu2iuuSGuRjfRidnaTTztI1FR2KfJKlYQpLeqi6+A+S\nETtvGh5i7Yo7bM67hE2TweJ0DjsUl7EnrJy+tRvbpJKFRZAf7yZvVEW4PI45dwnTkoPxtDzeF99P\nzfm7ZN4dRakNEStSIIoncTst9PSuxlmhJ/lNMcsr08lXznL/viPkyGfImJmlxDuGkhCWUReCMGiL\nAngSJs4tFFI2McbeyUvUOSZwLUi5MakBQYIv3/MOqTIDf/E/TW7oFFmxQRxCC4sU4gw2YGgWUbZl\nkrIxJzmRZUrXjbCYncOkOB+TY5LGP5yiTDpIZG8a5AjIjw1RsXGebmcYbiUIalVMuIt5Y/EJxkwl\nrFh5G5XIS12onVFLCW90P8k+4WniZjE2iYXMvgXWXmklHEgQcifwtM8Rd7nJyoMMhxvB8BzrNnXR\ntGaO6YUyemJPsTGjha2FfZSZf4FInCDph4/OHyDWLSEeihCVJQjp5WiaxRQ0CrldYWY43sCwu4qF\nqQz6P6hlMZaD121mNpSLKsfHA3uPMafM5nTPHnwlJiJr9OjcLoxvuNBPu6lOS/LV2uOwQk66Pob0\nzDKxiyk+1RVHnxlGvttHUCpG4IoRGgniiQgYXbuSgdoNLJ5eR1HmFdbtWCZtRQJ/jpDIjSls13Vc\nW3qcUqmTh4zHWL3chnHSh9oV+lim/x+J4MaNG7z++uvU1tbS0NAAwAsvvMB3v/tdHnroIf7yl7/8\nn9eH/1O65qqxhzWsCt6g3nab7449zNn0DRQ9v4y8Jkw8EMQYsVMl6GH2hAr/q14EiVsUHvBQ8UI6\nwY88OF+0YRXEiKbi2ONuWqUWTmqf5gHPWR5vfxPpSJSJMR3Drr2cs9fxsn8rKyY9bA3MsW/uDPsm\nTvH7wqc5GdrDyLlKems0LK0wk5OaYUWinYhOja46yK4HT3HSu5/XTz/Fpo0XaCq4xUnxQdSGEC82\nfY9Imoijhv1c6tYwejbKXnmAskgCTXeALMcCNfEBjAInXpmO3u5aTob2EY+WUqHWsjs8h+u2hrlh\nOZn/7qKgaoKyly6wGM7n3ce+S3i1mnWW64zMljI1kc8/Cf9AxCfjTlcTgdkE08EURvttVgzPIPyx\nmoxiAZLrMaYzczmb2kn+lR4qDw+hbHTjiUsQVSUJTapYuJiDYE8cxZYAkZQSbdTHI/e9Q/HcKMKx\nJCmvEH9SjW7Qh2XBSYV8hOlYIf4BPdZzLrZcvoacM/REzXzbs5P8Zjc/3/4+f8z9Ji84vsWnhUuo\ndJOEFOk4lAUI9cXk75xh7femyPgogWpSTPm2ARZyzdiEOvT/NU7u81co+cJVsveALBlHkkogLE2i\nORdF9qablEyCfSmdI8FDDBWWEDkI4sw4q2U3uPH+Jm7fXEtu9TzKXD+T4gKMPR7yfz9H0B3AFkow\nHBYjy9CweVWQXKcXo2OEnbsv8fCXOvlJ93OMhrezqT7MKm5SNvPviEJxXF4jwycr6ThRjtMrJFQT\nRXPIQ97aMBX1Qm6pTHSN1TKeKMc+nkbIrkCii6Exe0k4hchLohzcc4o5Uw63BRtI7VQQ/oKZ1FeX\nEb4WJFAmI31VgKd3XcJTqsbm1xM55yXxlwBV0T4k66WEhXLmJWbEgjCeiRDTYTVdX9jJiHEPgpFM\nLFVDFP6TAYww79HCjRFSx62MSA9QJbzKI8Efox/yI2wREVxWA8v/+0Swfv16ksn/uT7p/Pnz/7fn\n73wo4HuO3TxScIe9zQMc3NdBbl6Y88X3MNwzwea/H8O0coo920LUGsUMFim4OFfMYI+fLS+MMVuW\nzvyvmwid70Q7PEZzMUiK55CU/APnqJWvvv6fNO28hfq+aVzTRjQmAdlPpIjECzj5nSYKpsfQBBYZ\nHytgoSIbxR4fGcXzFAtHyJ+ZQTQg5HDg00xKcikWDlCV1cvPt/4bHZm13EiuY6fqLCtDbWT7Z7hu\nX8clx07GBrQg8ULuTfLyW/lm23+gyvAh2+YnrgSxIMSjvE51oo3BkIXpNgvnjz1HQ/4gv1z9W65U\n7GA+K53Av9ZiTsTZV3SSXO0YY/JckpoE0Ykof3prCxJ/AuPXZqm4O0/5EYgcgMk1Mi7OFhPpFxCZ\nkVGiGuEhwbsoDqXoyWzGfS6O6vI4efoJynJGKb53hKOWQ3QsNXLIfYL88CSvij5DXCbA0ODA+54B\nLol46P43WVncirQzgVLrJ6Npimtda3Esv8A9HCY92cne+A2Mo1Gir0UJrQHBKgH6ezXkqhWYM+aJ\nGQdo/MkghiIvgkSSd0cfwnbGwNxNAY41Zng0Tq8kl98l7kF7SUGOP862R8aoNcyQ2WEjo9LGmt+2\n0XylnfK+EVIHBSzoMrnw+93YiqwEtijZIrpKVcE73MpYTY+qmvFAKR6pCXeFhbyKfrRZ87Sdq0Q/\n7Kak9TC1FaO88OzbVBbNkzbm4vPRvzKsKMEmsjDgr6DQNoOwM05qOIWsOoCi2AuvxakPtPOzue9T\n1jmC3B2nuqEfuTiKQhOno2QFh2ufoLawg71ZJ8icW0D0borjaQcZspai/YITeUGAWTK5QRmDSQvS\naA150ghNebeYnMvn5rvryCocRvucncUPi/DOq0m+4EcgkCBcqaZm+E1MwW6m2zIQr0hx8CsfIJ0K\n8swPnyXeqESUHafScYGt1r+hXneKIusCae+4+Mfyfk669v/3YhZP/O8Twf/bTLTrmGANFkuS0sY4\nEpMOhdFAS2or8yN9NJ08hcHtQKd2Ui6AzLJsPtRsZMHlR3u2F1NWCs1OE7JlOTKZkLS1UioKPIQV\nl/iw+wDvXXsA8VY3tXluYkoVUpMc4z1ShBeEBN5JMKXKRqauIn4thd5tJ5CnIF0+R4OgHW3Ex4Iv\ng/OBtUzLssiOjFOUNoGxzMW1+Bq67XVsXLyF3uZmVFtM+/wKejvqEIYTZDfNsFBUhk0/wfrIdQQZ\ncdwbNbhUuXjCOkSzLirjt9GWFhG2ZHOp/17Wp7/MwaxzzKpKcamsaOuVWIU2zPo2Uj4R1+zrsUkt\nBBIyLg2uJlO1xKYNnaRHXcwcr8ZgXUCWLsHdn4bDrmJcWogpbmPX3Dm6dHX0VNZhum0jxzkD3hS5\n4WlqNJ14Fo1EhlXkL86gCoWZV2fiy1JiKVTimrUQ65bTvrUBZcJHqXscYTyIzDNPn6yanuwaKtzX\nyYh1YTZLSKo19Ia1RGIiSqSj5DYEMOjk+Ebk+AVKlGuSWJJussfmmfaVMO7NwdM2RTLuxPiAFnEW\nOJoycC/JCQyAQSZHrpVhdLmRmoLoSpdoGmhhS+Aa/ho1t0VruHZqE7PKHFxBIzvVF7Bm2TisfYI7\nomaMCQcOk4W3mx5i9+p/0FDYxpBjO5plD77pj6g0LWF6YBTPtJGRwXIsVjthiZiW4VqW5+VYBqcw\nD3tJOSRk7pkjYBFia88hd2GZe50fIgrGCI8KSQuOIzfbScsIojIGGbaUUmQdpiBjnMaBblKTYi7o\ntzOTmY3QEoVIFN9dAXeTlZwtXAXZqygwhbEr9UxMFXHp6A52fOYUVY09LN4tYMqWhn1GiFSTQm9I\nYpYZ0CwnMPfbsOb2s/HQJUY+yuDC0V1Ik2rStSGaciSsNM5Sse4ukYiakbZyrhs2cNq4E4vd8bFM\nfiIigHJAza0cNaGalUwfKWB2IpeldWnkLc6QpRMS74T+MSiqAOq1sG0VgnEXglMfUrI0hrXNjkHn\nJrpOwnxzGr5pEbm/6cDSbUEQKaRksJVmcTfHXUVMZhnwomdLqpX9iROcaDrIayVfZvulo5RfbuX1\nwc+iecTF2q/fwJaVTmu0joX3Yhjmu1lpb2HOWMQvU99kfL4Ex6iVVz58miOLD0Az2G0WQrcUNN57\nm/qH2rmg2cLARCVf9/8addRDm6SGq+GttEyvQvBGL+mOIbZ8cx6N0YewPkmgG+x/TqG0OClJG2V1\nSxsqmZ++dWVc79nIldPbcKab8MTUxJKZ5MkcPLp4jP7FXL7m/jab3ztMWsckrq3g257BkewDbLVd\nYvNH1zg/uYdu9yqeqvwjBWVj3K5ajafdzK5XznNf4kMqEsMccR0iEFHxuOJ1crOnkJZEiI1JcUpM\nfPDB/fSO1vH1g78iMBtm/gc2fCUBNE9r0LVAwpHHyapvMFWdi7XaQb51hqcMf2YVd4gMSfnDH++n\nJbkB+85KnvS9yd7Zc6zNa2PmUQW3/xJmigKC7KBs6zAN+VfQvh8hNGvgvHIvt5MGaqMDLF5Q0fJa\nBivvt1LycA59OVUsCww8/O3X6ZbXcSFtGxfkWzi/uIPJSD7W4CI7Mk8xV5HJef126lLdHJg+Tbtq\nNYFKFapc8FRb6FFVc3lxOx1dTZTsHSAVD3Pz95VEeiJcDVipv2+Bxn9aYI2vlRpbH2/sfIjFyUaq\nh3+FoG8Z+1IK73UHjgoBI5+qRxGG7x/9KRdvbeWXWd/j25ZfsH3leR6rfQ1LeIG3Lz6Gp02EqqsP\nSdUBeGIdmPUsGhOcmd1HaFgJs7Dy3F0eH3ud4JKa3rpyjj20E0vHFOv+fAaxewJpapkvLr6CYt5A\nIppAzBj3c4OKdCGZq9VcaljLCf8jWJxv0j9Vw6+VXyfcJKa55ib3vHWSL30MkZ/MW4NiHao6F7as\nElrGxeROzZM/OY1TbSGulCPYI0PglREPi2lrqGAyvQrN7BxqhYfeXWVUakfJHZuFTIhkS5FqY+gV\nEaQWB2tMXSy7NTAcoDeaTkm1jVTOInODeTASJjfeSnFuMb6VJsJ2DR6tAo01QCiiou3KKsYDBiad\nOkw5C1QXjZEXn0XgUFAknEDWuYTrmhDZHQhGLYwWrkChj9O44Q6mcid2pYX+6SpG50oprhtGoQrQ\nc6GKu+ZVdIlqYdFMxbiSg7fGaTa14VO9T2OsC/FYCv/5OK5ojIQwilbpJX9xmpnoNJmWOXxjWqSu\nJI2Vfawt68RicKKtMKC9389IWzpDI0q890sJZCu5FV1DzvAk286cwDvnxxnxkZGYpMI6ikdsxqa3\nciTjPnJEM6hjLqYmtIQdMooKBilTjRKwKEEeRhXXY+dz9AYqyOtfxbxLiSeVRWnBJA1b7hAxKrlt\n20SfaQ1+BaTNDmCc7SZD0MeQOouZQCndpkamZfkEFGZEEwmy+hbQ1nqJ1ZmJ9ZejUmhYYb+NvDCJ\ne1U55R3tiH123gln4U3EiMZkyDRxDIVBxNEoqYkIWrkXvdxHoW8chSyEXWMiFZYSsyuQZwQxG2ys\nUN5BspSPsC0fhWsCvWsG6aKNyXAF5zQ7MClcOMVaEEEBE2QtziNN+BAHfPS6rbRN5+FZzMW1HKKx\nrwtDwENWwzLK9Did5hoUeTais36628zMjaShNlrI0odJjSSRJ4OoNT463VUEEyL8dSrk0TCbu66i\n9zgZLKinyODmkdQFYvMwN5VFT6wRYTBF7v4JipLjlC8OQwCEBXFaa1dQYJtmg6+VoDRBTAN56mGk\nUgULAjMxwkRxUC9KkSaw8IF9B0OhYrJNiwz5q2lJrSFfP0JxyQAVjR9Xb/IJiUC1NkX2D+M4O8w4\n3tXy1cjvKdGO8L2xF4itEOH9FwUGjQpTSsl7kgdoHSql5tXfYdCHOP3sVqITCnKvzEIpSLNiZAWX\nSJmBf01y78l+drw2zvPDh3hneS1feu4c1dYovz66i8kbRkZisNVwhA3lPfwk+gwdojoKVw+x1JXL\nC3/eSXRoAG2il4ee6Wb36h6MMwGyF25RJ+mh5bqA7vfAFIHZjFUc5gXSmv08sPktro1u4cyl/cQG\npYgVMV56+AukRkR4f2MkvEkOu8VgKkYx46HgtIeVlrvsTLuITBImIREw956M270a1jyXRnqhi7qh\nfixWBxVP9vDHZ77MUE8ljz/3Nxq2trEkNZJR6uSbuw7z5+9Xc+7yShTZcpIiGDxfxdClPBavCwjE\nx0im1IQmnOimF9mfcYp3ix7mO996nh3Ks9Qt38G1EEAZWYKVEfzbVMxsSod5B67LEGyGCYuFX72x\nh4RZR+jgWnaseYnPV/2ZD8oe4PzyDhYH0yg9f5kd7/wWtX8Bh0TO8bVP07vhHkxPzWNNDzMTjSBY\nTJDyCViSm+ktaubi/i+R6VzkK+PPck26lpcq/wmV7NfUiAewOzKQiqLE42KyNnvZsHeK3J/Oo//h\nDPn/soDCDOLTcSJNcuxFrTRO9pDZs8SZ0q14C1WUiIaYvROCZzPwhRawJROEEw7G9Fp+XvkNSv2T\nrEjdYpflAhtzbiLqiyMIpYg3CHlHvI6BxScYv13NjLuQ8wt7aLB08IOG5xGviHBy0w6sLJDlmeTM\n91YxMWjhAbrxVyg5kb8bU8rF49G/cuSH+/jjmUeRNgrYmrrGt8f+g7vr6zj89D/zuXfeZNvJl/EM\nwfnlHfxc+jPEj8eo+VE7ltOLcBGIgyCYQpRMoJEkyFT/9/cYpIOoEOIZMQzSZXwkUAESPyTGYfkV\nAZ2JHNw/fhJ/VhqRsBRhLIlYESex5+OZ/EREsKfhLLdGmjErl0nf2E/PmWI6lspZbLaQrE5xxryL\n3PbbqC6PM52pZ1meg25bGrLcKKNZpRzz5NKjb2T32GlqxtpwziQQCMCcBd2DtZxZ3oKh0MlTVZdZ\nFRvFmYiwdu01yuTDFClSdE5kcuuNKvr0WdjSMxBOJSlQjvPw/tfpXm1hyaODoSXi4xOk1GEC+Qrm\nKy0I90GOOUpBix21z4syGWdZaKDPUElJZIS6pT68FUoCMoicdxGLq0geysYc8KI56uF0VzHuuIAb\nzVUEkkHULT1oxiPIomLWlXVgrovRpVhPj2AHer2PuB4CailzKPBFfdxS1JNSJ6lgAKfUxIiihFl5\nJbKghYMdl2BcwrErzQQH5/GGUkSLMxBYK5DOanFIM7mh20S3pZIi2Sizfbk4+i2kb3RRWTKEYjpE\n79UK3o8+SCIriOQrXhrK+lipaCchn6NHsZJzBesJdEdwX14kf08H+/URtg9cwUuKjicOEryewndN\nzECXFnH8LnsSt/DmZvG25BFSbhECcwqDwIMltkzKIse1qGDqHxEMF7s4kPYW2o4RZt0CAtkixlUV\n/Hb5X/H41QwEy1hbdwvQcmJ+N5OOEsiAPNMEq313yZ2eRd/nY21pK/2Scs7p93JLX0J8bz43WhU4\nJjYztm41mblBdofPUx0bJHdxgsV2Eb+7uh1BowUKtKQcYrooJCKuQl6hQH3ASSQgY0lj5ELpJspk\nQ6z0tzHYaeJ4Sx1Fo/PUSacJCjPol5jwSPTktM5Sd7GHU6LdOCqzkVwFl95McK+MItE4jxz9EJUm\nwOC2PARz40QkDjT7lii3TPLQa+8yESrit5p/YVfyNNbAErs959CXugl8U4nSFyYSUnJauJsFURr5\njGCWjVKhH6Ort5G7vnX02hqI5KXhU7gJz0hJDcaoVvSyLXiFdstKoOV/ZPKTEUHdac7f3kHuqinq\n197h6tVV9LqrCBVboRiuiTZQ2LpM2m9msTUqSG3UYXg4B2FJHDdWemX5nFLryRqao2ykg5kRCQlE\nkCfkon0dP/F/mxdLfshnV11E4Y0xoRTSvLKVIt04xQl47Xo+r15rJLjTQEymYjxYQl1pO4/tfQWZ\n8AGuLqwg+SMnod5Z4psEODPzuG1ageBAEs1mP5aXBgi3SVEqk4xHDbS4m/mXxT/wuaXXmNicw7xE\njvvNOZKFamQ/LqHgyBKm92y47Bs4V1TLrS3lzM4K0b9sI2/JTqE8xNbqdlZsjPBt+UNcSm4mYpaj\n0zoxs4BL6Ses8XFWsIloQkO9sJNlgYGLbMWusJAt9vBg51mE2OjoX0bsm8CrlxKtKkRQUo/oloFF\nfZR3LfcjUCZYG7rKza4t9N5uYN+nPqQmskTkf8no6q7m6MSD+B5WYHzcxnMzP+He2HFEayN8kEjS\nabsXz/k4Yx9BSVYHm8p7UN6NcM66lRef+gZTiXR8V4UoJ6/T5P0HB0IXmM1byTHzg4Tf1OSPAAAg\nAElEQVQjStxmPaJADI3Ng1gWY9knpu2Mgnr3II8o+rEFBfRoKvBnixg2ljBsK0Rmi6KcDxNsSiNU\nkcFbR5/kzPwuWJniy8aXedz5JqKZOMlhIUU9E8zJcrmcsYNhcynaJ6GfcnrDAtiWZE1JC093/o3y\nxACxEQm/vLOPl27tI7KxmmRxNjK7kERUSlilJHflGNmfHsQWTMOflHFasY2kB7649Bc6Tqznoz9X\n8Sv531jbMMNvA19heLkEvcCN4Yqbkt+NY33Mi7I+ReqtBNEsEbYfGCi/PkblH0e48I1NXNyzlqxL\nHjxEUT7uovZ2Fw/++hjf3/AzTuXtJk8zzo7kOXYtnMdbp8K2xoCl042/R8fb04/QE6xgn+c9dieC\nrDZO8P5EPX8cOYQoKwdDugitOITMFyM0KabK08vK6Q5+XHXgY5n8REQgKg+RbxrGZ1XSIW9g5z3X\n2JN9hcN9nyUrMsdTe96gsymHo8/9lGhOFqW5fZSn9aPFg4QYsukE1mvLNFnuEl6tYeK+BnqmC5g/\noadbuZHkfhXvLjWzdHKeJ+67gzeczsV/34lztIX6+fPsruzHul3K4awmejKLkGUG6Ou38OI39zCR\nSCMqdlJeFKFuq4hkhZxOby1vvPJphOvj6BqX6bpnEMFa0KkcGEYi2J8JMTMWYciu5Ej4EHPluWx6\n9ANKtRNY20YZyq3g7P/aRvONEVYnL7KsLWOUfC7xHAeUpzhgfBd9AqR+G48q/45B7eAj70FKU8Ns\nEZ9j6mETU2uymFRm4p0xEMuUIZImUQqC7HzwCuXlgxTMzaBILvO97afwKjQsiNfgn8okPAy9DqjR\nzXJP4i3EN6LoDk9gqZ9m9t4ahu6U8+fuf8YYdlIQneBHA89wcXkzLYlVvHHyfiYnMnhs2/s0lHfy\nrcwX8X8qymLTTioCfXjfT/FK9/3MJxRsnv5P7k7tp1O3m8d0A+wsu43oQQVzaRnEbSLODe/AMWmE\n9/pxW2Fkt5mYUMMR7sG1dQgeWuTiYSM375QzvWAGpx/GFthYfJNPZV9ALg9zKbYO2wYztCfhRBTy\nYrABojOwFNPzQcHDzGbm8Zmpv6JSBCETnPfqca9SQZsL3eQyC7t0tC3cx62X68nPHeanXzrM6z17\ncF5L43F1K/OmMg7veIKm0rvsDn7EW4efYHo+l41PXkeSEeF583doV6cDSkBBZFbG/H9mo7SEeVD8\nHo2eLqTVcTbWXEaUtUyQecr6xin7+wCdgTUcy7qPeW868jkvtYVtyCblBH6Rjj9TD1+B+/I/oMLc\nQ6heRutEI9Uf9GKbt9L12ToaxnpRXY2AHVIxN6kbd0hFhsCUoHn3MP9c9CHZp71YFvwoPAESFiGh\n3XKGE+X8TP59qgX/P5sRpMwp5NoQybAAlSNEVqYNQ8BL44VOiibG2Dx4jcncxxl7eAe56ilM8nHi\niIh5pcgXItTY+1kvaUFkTLKcb0BYrGM+Xsqx5RXI1Bqay7qJ9CfpTOTTmO4mkCxicLYCi22CCFBS\n60a1cY7TUzZ0oWnKNRNIbDPYPlQTMcZQlUbI3hEnc6MYt0XG9Gkjtz4qxGQNkL9GjKQ+igYf6czi\nn9EzfdWMPyAiIBMzbCtiuriKtVtvoxAsobljZzyzgFPVe7lv5gMabO14I+P4Q3r8qXWIlWYyLELm\njJm4pVpWOO4gCCVxxq2YxUsYdMsomkJYioNIrsqR2n0Mq0qJCaVUeQexGO0Y6oNMR/NRhi2k5YMy\nT4InS4fp8BJp810kFctEDTH04WUMA07ST/SgMAqJl+oYb81nvruc2xlreFj7Foc8H+AQG7ghWsOd\nQCNxh5AD9nOU5o6glx/hbOVObubsIveiBOWym1FRHoK5aRr6TxMoNBFeVcmuxAC70kaZ1GcjEvnJ\n9XQScesYdpdA0EswHCS0JMWbMODK3k5eg4LktuuEWvT4utXEbX5UqkmsxkHWcp4Hxt+gLX8l06Zs\n0mvmqQoKkdwNIp30cFdYTHQqyVLCRJe1jKRGxcahG1Rb+sksnaXLVE2/vgDBB6PEQjD0hVpGlyvo\nv1RIxT+Psmr3DO0/bMfdLmXH3kvMp00yHC1lvfc6m29dpOd4BQJHmOw9U8zm5HBUfQ/RgjDGFRMs\nLmbS7ZcxM5ZNgW2KlfLbZOcvQK2YWn0nhvg49hwb4qQA21AaPckSWuIlqEYhzxPCnyzF5dPgvSxj\n5n4z7V+uB1eMNN8kLaYmeqdy8F4TElJpmUrmIZ1JoekMYpY6qAvMk3erD515EUEFaOsTZDUGKLw6\nTJrdhjQiQpoRR74jzC3Pej4MHmSltPVjmfxERBDxKpi0l3Bg5gQPz77De/6HWIhlcqj2CCsCbWgu\n+DBvdFBeMoBcHMaNnuPcg39Yx9TrxTxY/A51/9aBci6KdtrHhqMteHp0nAvew/pQK19d+hOLe6Us\nNOjpzlnLSKoa4bNhrA436W4YKcuhXVzG8mt2ihdO8TXHR+QtzpHIDnPp/o2MHihCmSkgaJCSkIhI\neeZg5CyNrgV2MUsSITEkBFARTKuna205+qSVAl2Mhs0DaBoSkJ5kQZDO8kYDfWNVdJxfwfwVGY3u\nQp6uOcamhSME41ep0CwRzZBwvHkfwyXFPHnsDbYErlFWNc7xqn38XfMkB1TH2bB8gdoT/2A4WcHh\njCfY6L7FU21/5y+Rz/Fq/Glk4igiZ5zU8ST7Gk/y+Qf+SqRriPwpOdv2TBJoLuVl9xdpCHbwtbxe\nTl9ZxYnLe7l37jirjNf4w/qvI9T/99ApVSYmppWgeiqC3hlAnEygkEWwLi7j8GZy3rebyZoCags7\nebjwAsIrA4xcDlK6uY3az6QoPTGIvC1C7p9mWRs4TdjVSbpcTLk+Bfu9jJXk8OuZDbT5c2B/I2mG\nDlZe7qQwIWV91gi/tonxZ+k5+MV+moeHCPwblD49RMZBF6X6EXwbVBiKnXSeyOKbr32GmEOE1hBh\nj6QVWUDNX25/jrq6Lj6T/SfOXN/F+2f3w8R1VOk2MsijVjDFj8RHmVEVcsywjzxpG02WacK7peT6\nJ/jZG9/HcH0BZdTFzonXyMkvZYYmepK1hOMKcrYvUFY5w4X/XIWj38DUEzkU1E8jUAuIasVElTKs\n5x2opvxwTzp3RKt4pf8+jK1dPNb6VaomQKG2ctR3Hy3LpTjCdm4qcvi+5TlC78QJHkngFkpIeROc\nnF9BWWKRBqY57tiPYzGN+zYeo1l1E71gAb0chGq4s7iaV9qfQLnQhxY7ZrmK9EIvWdY5BpxlxLxi\nhgxFH8vkJ7NH0CXEl9Jicrhp8HbRE61DqEwirwgwvZxNu62Bu6PNhC+oMFW7SNctYOjzErvtxDAb\nwFjjxF5lZnqwEHurBfqD+Bal7BLcoChrnGCdFKfAinPQhGoyTJWpn4KcSZqVnSjVKZTGKGqhD1OJ\nD5XOT4VpEKFYTteOFSxpc/ANyQgOCZiXmejQr2YsnEP2Dg+o5Ux1FxL2yhH5fJh9vciHreDW4KzJ\nZ7S+GlvciLc1gQcPARTM0kA8kGCr8yJtNiWDPi2iSAixJYl3Yw4+S4JYmRNdyk1a+wKimz6U7iVK\nl5aQ+jYxHC/H7/4ARudZHsxnOT2NpEBIQK1kwZzOaEsxEyMFNJrasKhsRDIkuH1qLn24ihGEBDeH\nUWy2ISuNY0x6sOdkcHLNQ4TcRipCo0hK4gTzFBg3OlDoA3jDalT5AfJS0zhjFhajOZwW76XXXgfd\nMG4qQG4OMW4vQmkP82TwHSzGaYQNIJJPopoIEcwUcye0gsWzGQQSQbKrR6kKOalxB2i3lzMrLCDU\no8QUtlNobaPK241lyYV+CSJI0OaIkRXGWaMbRGYVca54N0ZjGHE4wWxnEpl4idwVU3QW5NMq3IK1\n0UZl0TDZk5OYR2OMq0oJpCk4qdnNjelmxu5m05SbxJibZL61iMweB5pYL3PLtbTYN7C7fhBjhpjb\nvg1oYkkq6odIuQ04XCpmPbn4gybSrzsQJHsRFifJzpulMH+M8REtMasUSSqBb0rAmAKEZaDNgv7p\nLGb60hAdkhMsTGdcU0iatJ31qnYCqXqmBLnYZCYC8zKSLWEWxjS4juWi8fkwZjgp6x5BngywvM2I\npCmFQJjCpHAiVSXxezU4w5nks0AyFKR/BhyVSRIaEaPaSjQpJ9nSAayiJcxCJ4WmMdxmHROKvI9F\n8hMRgfgCCMoBUQqlKcgTHGbSkMfp8m1c967nrncNnlYdqQ8FFH5znBUV7Ww8fIuMySWSeUIcFj2z\niUxebXmKM+9vhdg8W6WX+YH+Vww2VvLyo08z9JwByVtefmT9E5sa2kntFyBTRBH4UuSqp0kVxLn7\n9CoiqIhJZdzwbuL55h8R+MMCpp91sxMhEWM2v6/6MsEdcupebGO0t4ZLJ6tJDQvIHm1l19RpEn4j\nJBP0ryzn2Jq9XHy2keAxH7ks4sLKP9jG57ae4TvrfsUz4t2MiI2gDTFf1sCZnJ+iyXif3ek/Z+/L\np0gcSeGZDrEQBMkw+G0iUn4JnsseRgbgqP5zxFZkszn9AkmrkD/VfIa+zjIsdxf5vPz3rN7SwvIz\nBo5d3cXXf/y/SHxDQfan7WzU/Yx6eQ8PCN7kjGof39L8kq8bfsV3jC/xiuYpWlXNaORuNEInCykL\nRpGdlf47XDy7i7bxVQysrkVkT8BFqDjUw+o112n97XqcF6zEBFIyc8C8H7x3nLieCXD32S203r+F\nq3e3kW6e46Gf/B3RxXZif5zmrSM7eNX3IIFYDjWpWzwiepaVpSNQDeExCPiUxB+sQ1Mcp6RtkfaK\nOl767ZewKO1I5p30/sVNbmIK6wshAtYcaKqm4r4LbKoYx/C8i/zJCb5zYJSPVh/kV6J/YWFRjX52\nls/9603M1iA/ePVROvuyyQuIaB3Lobe7ia17LxIIKHj/lc8QzpFT/+U75Kqm0C+7+eiFgyRPifnx\nK8+RtnSOru9WIpLHURJg3WNBJisL+K/nq7DfgRYBKB6L0/hZIR/MrOX8fCMPhDuRmsOkrZ2lqNlD\neUTBzxc+xUn/Qeoz71Bzq5+bw0V4r4qIt9mpfGaB9c8Nc99PT2CO2el7pgx7pQWfSMu9WccxFXh4\nvu8HXHVv4meq7xONurkwDMK9Q2xousbVG5swLst5THKERncnyrEYWdnzaLNdXPJv/ngm/7+C//8a\nQ2c33xD+glVVtwmVi+kRlXM7uZKrY5sZpxBRTYRN8cusFN+hxNBPdmiY0OwCnriXzEaI56fwi6Ts\nqD6H5dASd9OKMahiGHGTvnqRPM0U/m2wbFBxUbWdYFDH6rNXmIzWcFW0m8SsAFFJjCLrPFnpM4gy\nU7hEBpwxE5srLrF623mGW3OYmMtgPO4kFdMhXMynStfPFtktzsR3I9LKqdmVoGi6Dd+1FzGmFGCA\nTXtaMVhmaHIv4RnwoW59n0r9JILmJPt93YyM5HDh1r1EfCb2rj+LLt3LWekOmvydyIMhTtQeQpIe\nYl/aabZmtCBNe5HQXuiv2c1SSw45/R7WHmulT2jgtEOLLZjEvDOFIdOHvCnGYl4G85p8nJIC1rZe\nYov8Apl7p/FWaOgXVqCRePknyR8Jzyl4de6zTKzMJ0OwyJq+28RGg7w6somlpBqvKMpq00fs2/AR\nUn2CsFaGW6dhMT2difESNomv0pzTRpZyjunyfG6vW8FkPIO5JSvTd0rwLRkp3TmAptxLR85KhlO5\nSB1OrhVuxK0qgmEVS3Y9l4M1JLK1xPaH6EpUMdKRT3lfGznCOdzrVcjdDra89C7dDesZy6ihZMtV\nGpVLZOpclBSPsvmhK6yYv0JG+yDX/NsYKvTxQPkZ8hbaqX/h72yZFGAujjHZq+CmtZjI1gSh2mLO\njHyHAmmcptY/sTSoZtxTQvnd02RMuKkWzWE2LyMXhIiMR5mWZ3F2/WYiajVLr1ioXdfByg0tqNUB\ntGkBxEUJ1HKoKoJJajnzh9Vc6y4hvBwj++gM+R19GIOLlNWNoV8fI6ZWEEdMk6+DLOMYZV8qhM4Y\n2i4X/W2FdPn1mFc0UsQ4otOzpJ2cJRMl/fpSRnbX0iWtJTIt53fqr5Bw2xiJQeX1Ceo8xynq6ies\nsHKtdwuBfAP7VSfRK9zkCGe4P3aM9o9h8hMRgXZkgM8rJxAUCVnMtHBTt5IL7u30na1HpEhQtHGY\ng+KjPGZ+m0RGErcvRU9CgFOvRNogQZQpwBq280DD+2zOu8JLxZ9Dpogi9iTRKT0UBcYRbEoxtqWI\nM6J9DJ8vRPn+JNftm/kP07OIXHFK5kf5Rea/saHyCrMGK8moEK3Py/aGTvbmt/Bd76OcXK5AtDxF\n6lIGzpO13Hv/KR7d+g+WpFZcWSaa7ksg6uyCu524hFtIKFbRvPsmZWuG0c3HiJz2Ujn5DrEMPY4S\nPesFc+ToFPzo6FdQxTz868FfMSQv4iPndgweByaBl2N1T6Fd42dzTT9NoT7q7YO8sfFh2nybCPWp\n0PSNUiIYY8ZmwD6QjuCeUjR7jcTL5SxkZ9POCuZFBeizYUvPRT41/UeM+UKGM+rpFlRTFRzmC6lX\neXHie/zV/hnqiu6wLXWB+24f5+r5Yv7z0pM4YqC2LLPvJ2e4f0Mnlnk3bo2W0d15/Lnri9y8vYkn\nhG/xYPl7CC0JrpSt5+8VjzJkL2NhIhPxHSgcmWDP8x8RaZBwPHSAWb8IVzJErLoCYb4ZWTCIM6Dh\no+Q2guUTSA8tcsz1IDNL6Xyh4/tUSXqZ/GIZsjMutv78XcYeKcNzsIl9B8Kss7jRBaMUWMfYu+cE\nOd+5CX9b5vLGryCs07MibQz1pQGaXzxM87o4GStU/PDqfi66qjC+6CWQzOfyme+yqftZPnfnJX4w\n82UGvFY+Lf09TbMTZHcq0KaFkZoi6Ge7uJS+ij/vfoHBpWb4lQCxP8q66stEkRCMK0kUCTDWpajf\nJ+Ltvzbw/K8/g4wJqmU9pH9ooyo5Rt5SF5pHokhzUmiSPtIjS6xwddCov0vdl3pRXgliDTr4Scf9\ntPRsgn9vZk6ko/KbUxQPzlCYCnLiB/fx1/u/QmpZQkos4mXZF0EqAK+AvBvfo/rSYYrMlxgp2si3\nOn/JvCCXHXUXEAhB5Q+wM3KJZz6GyU9EBM70Sn7QvZfAcibiThVVD3fzQN17bF53BVVfiLzfzhCt\nFnNk3X5yDNPIDD4cX7UwQTHvmOvZ1HeDQ/1HkRnjRJIpps9GUHiixAwpBiVVvCn6NAeMJ2gwfsjv\nrZn0RFbxXw/8iCVJBilLnKq0TtYLbpB2ZgHhcgJ5TZg1puvoyt3MRnN4Vvdjuh8uRbVNQrZxhGCL\ngdnDWo7f3cmc3US9vIVazSjZR2cQhmHrSujMS9HvTzB9OMDMkIn+uodRlEP5T++gGR1H+G8uPix6\ngLnMBqo+34W6wEurZiXd55T0vOmmuSNCngtCHTARquOHcz9GNetGOBRi5YYOHtW9xcvOB+i15vHc\n1h9SNXGT3/pPQqqT4EguVzo2MRYpZDaVSbWpj988/jXyNL3IhGrkvWFyL05yT+jvjK8o57kD38OV\naWJX6CM2Zl+mydWGwb3Mmswhfvn433hTdIiz4p28PVCFfaaVz5hfI1Ch4HreOqY68wm8ruLv4Se5\nk9dE2mfnmJHk0P9OHRktN9nY9iLlaWCslbKkMdLTWc7sYQsmwzz1vxinbzgL/13YNHeYqDDBleKd\nqNJDZAnbKdvUT1Qa5+zyFq6zGgVmYiUyQg+KkNkHaH7nV4yklTPi+zzCowKapHfZVXIW7/QC4wo1\niYYko9YSXnjvewgG3NgsMLZqhPr9o+xYPcl6+TxzmVXc7d7AYm8uU0PQPxdkv/EjDjSrydjiY3hm\nI3/44EH2hk6z03eGsw1PcKTkHhZ8BTArgCiIT3sRTNk5zae4EtzB9KwMy+ogU/szcUoUiLGziWtU\niic5YnyINxIyYo4h1rS0seO5u+yQvMeKjHaC+6W0FDeTlAsYtWvo7jBSvmqBF9cdJlmoZdaTw+nG\nr7FO20pz8DDWbB8muQPvjJ5IuxRE4f9uig0rSc+EvDw5/ZtWcE27keVWHaQAA3TPNfC+7RDJTRJg\n+H9k8pNpOtKrmBzLZmhpFX5dHvmBMdam+imKtiGaEyG5puK2vpmb6mZK5ztIi88RWWNkKVhE20Ax\npW19qLoixDeKSOkTmPpmiQ6J6NOauZUq4Fa8lC1NEqy1TtKkdmZMeQxvr0ep9dOsu02RbpRM5wyK\nd4IIRxJI74TIy+7HQj+/4QccjR0ipRWgU7tQpDmR+sTEmz1MRvNZDsvZlfw5qxItzARyCeqKoQgw\nW5DEo4yPZzB118I1DlG0fpn8nZMEBgMsvG3g1OYd+HeW8fTWl5GaI1yZ34TtyjyKt/sYx4Rfp8Qg\ndrCYKOAj235iw0kkd/1kWB005HcgViRwyFS0yAooyelk54Z53GlBhuQiPli8j5vDawgOSineN0zJ\np4bx6XT0uVeiO7uEsDWAzmhDaC1k0p2H3BIhQzdPtbiXqoV+5PYE2hgU54xjlEZJJCw4eoQshWeI\n1smwmazcnlpDckZE1Vwfg8FyRlTFrFNcAU8Mzekl0lzD5Oo6KNIEESct3B6+l6GlfNwDGsyr4piK\nbWQ6ZoiqZOQX9rMcMSNW5RGUulnypJOUCxFnx1mozSMliGEcioFYT2y7lZzDXWgHJllyVuGVpBOd\nVFKiGEdmjuIQZTGSlkekQoY7TcOpqztJ+sSkFEL0ppMUFjpYXTaCSepkUu5HIhDjFxkwxOYJBFKY\nTH7EJhWuokruJHbyLp8iNznPDs4TMqaD1kTZ7ATYJ8AM2VOjJNt9dCmquCraAKF5HGoVtzqbWE5p\nKW6aIm/Kji4VY6gsiyV0iLxe1POFyMbi5BYnsCQXub1Uh33RjDIQZWoKuqdFVKxdpNy0hHR2hohL\nR69qB6YyGWHdKXLyllgtaWXakMeiQo9rCuTxCEZZCGVBhOC2QpZ25bLkNxE7LsC+IKNNk8GAN4u5\ncBbLfsPHMvmJiKBJ3M/P6n7H4c0+jux8gMUcC/0zeUh/08H4aA3XzV9hIZGP+66KOye0ZHn62PCN\nJcqW75DzyxM0xWdIZQnwZ8hRFMX5wsartCUz+a/u1fQFjCQFrVx4rJzF+7PIlE9xv2QURFAcH6cx\n2s0/BDvpE1axWXoDwUQc4Qt+PGIB44AzL0nKKIYhNwGnnzGZnPy1djb9dJgpaS7BZRmCVwQMzhXy\n0j1fZ0ReBjPQmLpNqbyX1u0P0S5tYPlqHmn+ITZm3+KEexVvxw/h6K4lQx1CtjmC36uh51gjTben\n2MUVzvFZuq21bH/8CmWV07zh/jQuuYF4WMu75Y9xuuAAk74MrEO9rH79JbTb3Vz71ipapGvpEdYQ\nCQrJuzrA+C+tXJPUM635DxITYhLdUSQT/wdz7xkkV3Uuaj+dc/d0z0xPzjlpgiYo5wRICAkhTMbG\n8TjhgG2MjcHGBh8HjjkGR6KxQSBAOec8miBNzj2xZ6anp3PO94e/71R9de0f373nlu/6udd6a1et\nquepevd693pvo8laJO1rKhp8Jp79zUu8s+pRrqzYwIqM69REepHOxLnRn8PLkRUMI0cruczj6Se4\np+AGubopehZquH2gke3Co9x533FeHvoGs+JM7pv8mLSpAYa75XTd1czZPd/m9J9HCXwSwXqhBF+z\nAeE3RJg7wPl0nKUP3CTzKTmDoc2Md5XgP6jl1txSzP3ZeK5okJn9tNxxlhxPG6LXBilcEqFqj5Q3\nDTu4YfoKW7hBfe4lsh62MitP552MB+iyVTCqLIAcCcayWexfTiZ4SE30TTm1gz3c0XaayZoc7Jk6\n8jFxX/6HbN19liSxGYFDzZ+sj3Pp/GaiJhE2TwoBmwKyQZnu5+Gxd9k2duLv9UN6YA3EO8x4WyGa\nBagkYEpi7HY9bz1bRO22dh78ZSttL1cx0i/n7juPU6WcQI2V07fW8OrYC8i3yBAtk+I6ISL8sQxR\nrZplt4/wXX7P5Qub+GnfMj6n3I9O6EAUjGHZmErr4w1UKfuoEfRx87GlXC2s5OLLBWSZetkcP4yv\nuoCPd++gIrmP+q42LjsauDmq57vjm8h5KMj2Bw+wzPN/WR3BKemdEIJwphJ91SKT3QV4LmxCetnI\npK2c1uJlRMIqpGo/hgxIF7ooGp0iI2jGl72INZjJe6LVeGYURMJi1L4AA5o02nIakGk8rMloxymo\npe9WOnsKD1CeOUJEJyJ7YIqyy4P0ryzAm6lEVh1CZE+gnIpinTRwbj6TyUQyckGQUtN1xHMOhpOq\nUGX7aDSdJyjdQLdrKVcsy1EsuLk0UchMSS6SJRpqZe1UmYdZzM4kvDyNW44MFlKMXBMtx1mVTtH9\nTvIkPWgKwyxoUpmZzWWhNx1LoIKZojWMWFcwEy6jZHYGv0RJbF4E0wESAQ9TmlR06VoaRd2k2HuJ\nDiUTq46hFbiZWcxhOFDGVslJlCEz7XEDiQkNSSfU9KuXMpOUSeZKGzq5l+wkB9KokGF1GSqlj/p4\nJ6m3bNiHU7iiWMLZ0hLG5fkEBCkoJBAukDKpzKJvpoQhSxYVlqvIchxM5ubhrVETVktYSDMiF80h\n3RjEk5rDyNRa3OICAql+kGRTIPOyXHcSiXaIiNyEeM6IdzoV3VIfOepFcI0RSZMQVQrxatUInXGW\nBE2kWedoN+chN47R4mjlRPVGYup0FoazcVjHKfP14ZQIODe8BsNUkKXeIcTXLAitURI6HcOCGm6y\nAsOYg/xrU3gyFEypc2gfbSZVYKe4ehS1P0pQowG3BH9YzqIsheSInd3FH1Ot6UGkjZGdmELmtNM7\nkI7WFaBBNI2jKMFkgYqVqW0kiSVgDuENKbBJUynVTrMh0IVYHcGUkUkgSY85S0jyFj2BwiwE01rM\nmzOJ54upvdBKdBg63Q2EzSHyE2McNt7HrYx6pnqPY7BNUqU4TCSWxIm8LexxHKePXH8AACAASURB\nVKTecov2uVzcrhjRbBXZAi8bwzc55yjn/M01ODN0qD0eqlf0EVZb0YwtUqWfoawgwtzH/xz3f4kI\nviZ7BQYg0zlJWsDM8McVWD5YjWDWSTQoIexSIF/vJ6nRxqaaQbZOtbHk8DBRlZj+7xVysHsPHx1/\niPglAXgFCLwJIlohvjVSti4/ykNrDvDuC+VMvKui5MExVm68gbdGRuxGCNuzCfJfGCTpcTfGzVbE\n2aDphrmruexf2IhXX4Aux81d3R+iVjv4U2kdydNBar5zmtvxEibjd/KG/2EEMQf+98xIPhVC80gB\nhTMmVnbeoHZJF03LbvBc3s9oFzYwmFTAPfkf8/X7Xscn0DIjzuGacjm9E7UEXXKup2zmVvkqAq0q\nYlNi3nv9YRJiAUGfAgJmiJoQxzPJ1dn5ytRvCI3K+FH4B+ROnmDlqef5ZGgv4jkBWzTnKbaeJdct\nRHdTQFWPkP/4xvPYH7qPxrscbJ7rZdOZK5zW3sFTD/6Sz+T8ke8If0b6G4v0DtTyi6bvMN9koKBp\nGLs4GbcgidOiJM51xpn6YQZ1t8/zWOxHnKz4NE/HX8S3RoW8PsDfcvaSKWsheccCw79rZPGHOcTX\nZsLeBKhFLBEd4QfdvyDNMIv/CQHPv3c/V88Z+MyLl9G1iGj/XBMhkQyROMb1lJUkBsVUfzRIbEjC\nhdTvohSc5MGbnWSvmiHrTjOXX1jF0AUtgcRN+gUBugSL/FB+gEclnYxeixMzJihpEbLf8RidsmaY\nAGkoTPmKIWziZF7+8Lto853seex9cndMotvqoibeSSIRpV3QSEPoNl/z/I60yQUSFgHuJSq6bLn8\n6vtrKOxZoGjciuKLUbK/LOIJxz7E3g+ABFOqLDr0tZSeHqXxD93URAboy6nkJ9Yf0ZdZSfrOaXLE\nU2yOXqZN2UzYI+Ory98m4A3z/YufJ2CzYgWCO7RE16cw/hMZcvMgG0Mv0Op6gP2+p6lxDFI22sP1\ntzK5PFVGuKGCtIJ+ljmFXGhN58bJZjo3tlCxdoiHvvgWTQOtFL1rIpYmwhJM4hcXtgEX/yGT/5r7\nCLqvUzHfQ5nPQZY0imR1nIishsXxJAoHx9nU8zcGrlVx++UGspcuUpVmIrnCRVQmpiQ+Q3HOBAV3\njlB1bQDlUJCTxq1MFWWjXO7H4LKQ/toQuXoTnk/rGagvR+VzkfmHYSyeLAa/UkWBZ57q3w2gtrqZ\nUmbSWt+CrUTGo6uHEBftIylFyUpNB4rhACr7HwjnJpjcWo/NV4ggoCBFa0WrWCRKAI9Gi/Okhivp\nqxCVxQgliQkpZWzOOU7G7DQX2tbjLEnGWZVMd6AWRziZJm8nK1Nv4rwviZuyZq5pl5PIh5SBRbbZ\nTxBYVHDCtw11s4uCFSaqEtdYemWM2sweenfUEBZpSehkKPxBKqePYp2fZv4eMYsbNtK5JoO4U8Mt\nj4wBbT2K4TANxQPk6edpbWomMibk8wf/QLPkIiqpFWe6ipncDKzCFNLkFu7THeZscDPn3JUEnAYU\nIR+a3U68Fekc7f8s3dmrsKdooXOKxJib8YZ00hUW1jpu4u9L5XZoGY0lnRSV9SM9b6NAOYlpXTbS\nhRC5PVPsUHRgzA5jmqllwZ2PeSibstp+6je0MZeciStbjyrLS0guQ1oVoz3UxI+tz3L7QjmTZOGv\nUaDJEFA8ANFkGQXlSfRcXsPv+xuwFkvJ0EyS3H+SYttlHnW8QM6Gbsxb0hAVh1E7LDQO7WN6vJRD\n0p3cLTzClugpFIlusmNzLIn0o/bP4nHPo0qEkEsEKKNBcl0LbHZ2Ysz3It8axkscy5+DZK2KEi3X\n0C2qJeSVsmSiD3QC2rfXoTw9waLFh0spIpoqQK31UnF7kBXnrjOjLWBEV4IkK4zoviD6egFZt4WU\ndsDdA+cot06ROm1Cqo/QUOokXDHPoGKCG/5ljM3n0mXNRSaNUbGqB59FxsuffB5nuZoNVRfoTm1k\nMpzJmY/r8MsjxHdJGV4sZ+DVEgq0k/+UyX+JCLInjrMyfpDymJQ0eTqBzQoijWKGequpPtbPp02v\n8kHHo7SNrkR5fxDtRg/BUjlicYw85wzlxkGWVHfy8Nw+0metTOfmYK/TIW/xIH1rkfhP5sn4zQSu\nf8thOFpM7HSIZX+dY2BdOceee4DHX/srOW93gDXKVHMZ+/fuJK9okoejH6AXdqKNedAWBZF1RFh2\n4He0V9Zz5ItbCdoySLH5qUwfIU0/i0tqYOaSBP/rAm7dUc/QskLCcRllsSG+pfkVuf5JblxfhT+m\nYT4rnauh1YQicvbED9BouEHkjhgK6WfoFpYhSgmRmzfGE4N/xjqUwvVQM9lrbWx6aoi7/nCapmud\nsBSG1pWjS3chtwSJjQgocF9g3jnAQPk2ZratYEBejd1lxG/RImqPkX9rgkr5MPoyF4eW30X19ADP\n7PsZbh/YUhW4fqz/+95dC1Ad6mOP5yPG3aV8NJ8CowLSRbMsva8dx6yBfce+RUKcQCNcRHSpG8G8\nm+jcMoxKB3fMnmFuPJuzyg2sKLnIhpwj6Dv7cSQb6Xt0A/KZMEU3xrm7tIOi7ADfmtrNedN6IueV\nZDw4S+76aQrkJqwpacTKhYSQolrlZnC4lNunlpK4EkDgCiJ/NkpyXoS802rC+QYKNyXT6l/FvukC\nBKsUNAkvU/luJ1nWHvYoRhA2GjDtNSKTxInPBVnm2EfIvJOP53bTGL6NOujDELdRFh1EHIJxr5Sb\nbiXxYgGp5UEUiyEKAxY+5Z+DRgnRxxXY/hZm4T8C6LQiAtXJHBNsRrvg5dHL72Iuz6TzrhpSbnpx\njSQIacIokz2kieYpvjlG3b93k1poY6SxHP/n5cjq4mQRo+A4FCTkGG+dZ3H0NH0SEBZKyG5UUFwc\noVQ8SptzKcPm3YgFY+RmjlNTP8TirWR+a3mYnQ+d554vHiI0p6XzajlX9lfhqpcQ3ZvE9VfWMvB6\nNT/+9o/+KZP/EhEYXirhhO1nDK+0URGbo2JumEZ3NyO5hZjzBfySVZg3ppF+zxSHbyznxht50JRC\nee04myuP06Oq5Kanmc3285SHh9lSdxRBUZjWkWVcma5jjnvIRkNNoIfqkUGEggidT22kLbCC7r8u\n5XyOGdk3AtSd7Ean91MomWByNp+fDj5PWXEfNRndrIi0UaCYgjrIF0yx85NjtCzcYnFBT9CzwLgu\nk57mT5MZt/K5+rdQD7kR/zhCrFSIosJPSo2NLpGchFxA/qUp1l29yvg9xbQvbeDtxEN80L6K4Psz\nDHtr8RKl2vUeqxTnSF49glWigO5JShxt7PQcJyfX/PfLZ+ehWtTLj3KfR1+2iKUgmdaOrRzpWkHk\n9ykYxmOsfeQscn2MqEhCfmCGzCkLvlUyppTZ3O0+Tq5gClF+jNHGaoZrC6j2DbP++mUyZYukui1o\njgQRFUQR6CMkpsUYLYvcITrDbGYGg3dVUH38CsuPHscoXkCTH4GJQ+QXWFBv8bLs7Em+fH4eZVSC\nU2ikSDVGzug82T+9QKrAAUYgGzSqWZZffAVPdIzb9z1C17I6/hT4LCvjrayIt3G8eCtd1lpGLpfT\n0t/GfX0f4VknZSYzlWOX1nNTVMnzdT/EbQ4y+g0IlEbRvuhHme9APTiHRRlmYkUDXY/sJG/8Csan\nBuiveYC59CriX3KS3jrLNw88g9Wv5anYZ1mWOEW9op+SVBjVNvGu6lHuzTtJdcUBrPUG/HEJGred\nblE9+689QuPiebYY96M7Hcc0KGNSUMK4zciAKYk1yR1szruEXGphfEcWCqWMCXcqg7Jy7NJUTunu\nImWLhfvvepdQrgR5VMhnA28TLRFz6At3UPWnbnKco8irYCCpnJcHHqFPXodJk4/tlAt5ZytV2y3o\nUkX0fNxCvnqcb7/0J+o0g+TfnMOQ48K6LAmlzMbwbCUn/rCTsmu3WS/4mMnklH/K5L9EBDn1Mq7Y\ndhOPd5LWdhaBJU6SwE1m1SzmzFS66goxVAdoyL9O20fFnLzSAh4NDYoBpI0uRhRF+NwqxvX5FOeN\nUZ3XTUgvxTKegT8RwaFLoS5mZ4ljgQrnEG6FjoHqKuxXDMzsV3Jzaz7C6qUEaiWIYiKCowocAgOT\n9kJUHg85qhkiM1I88xomRXn43UowQ/LELEnmUazCCDMpWqzCVCozp9mTsR9vj4D56zrS7/EQN4jp\nIRe3TUSNpAuj20rArkQTdKMVODBPJmO5rmf8QDX6RQGl8R6y6UVfOIpkmQ+S4pAUROmYJe1GNwon\nIAKmQJ+wUy/tYFZs5KqziQVdFYmkQsw3klHGxmlpbEVb68eRrCfTZ0VminJxbCUJA2xRnyUskdGR\nupQrjSsYWlGC6K8Kaqx9tGy8QdAhp7ezFpEuTk3WLaYteTAYQZQZJ021QLO+lVWho2x07ke6LAVS\nkwiMeYlqxHQXLUEw56LFegGToRmHOgV3dTbY7ISOxjA3JGNZn0ZcFsY75yO35yrVOTIm120BoxjP\nsA65OIxW6MMqTGVOlIHAKSBlZobS4SsoGyMsZqXSez2fC/5CjhTXILJOIxu5Rf5aM5nr5hBZ3agj\nE4waC5lqbqDtU7to+bMF0dlhpp3JjC2txHW/jjvjn7Dz4H/wsW8DV6JrUObmos62kZXhwCIo5Jrn\nPjKToDxmxqxOQ5wUZcWSNuJmDeMdRaQ6bMxnTeIMBLCYMlDHA3g9ag467kAZibM8NoS/OAOf1kj+\n3BzOcDoWXTqeWT0uuYvmgus0VLXTJm7E4s+izteLT6pgNi+NcV0pYbUGXbMLp7qaI+d2YXIUIrDH\n0Y+eJH/oMit3e1EbUmj7yEB5rYl7HztJpENBoE1Do6odUX4MW4sR040mbrc1kssA6eXTnM+q/adM\n/ktEUPNmP/vlEQrN3bSYD3I2/WG6C9cQCsowppvY+NwVKs92kvOUBefEtxj1VkHHKOPZUf62cw8Z\nWjur9Zfo2F7HhCOPh6Lvs95/EVlFiKTxOUo7uzD4IsjHwZ6dRCIW5+7hY3hv2jnblmDE5GKhKIcr\n97QgEBux/zaZqvpevvDZV6kODlA6NEryWTtDPeW8HPkGo8nFkAt4TMjsJqq+ZCNcnE74vA5MQCEc\nc9fwQaCJ+8u6MRgD/OXPTaT4Q/yw+jlaNy7nhwXPoiz2kTM9TuMr5xi9UsibsS+yVnqKB4J/ZR93\ncsFZx/qLf4JMJSzPZXzewIkfwXI9VIghMQPTqiyOK7bQcaOavv8s5v6yg2zccZWXT34JxZyQ6qOj\nuKI6OjYv5aRjO5N9Rdj/nIRq2MvYE0WENTLmyMXu0OMbU9LasZrKUC/b7z+AWZjN0cA9rBae53vK\nF/m983FuORr4pfYbbJi9xDeP/ifZUhPK+2WcXLGaa+mrGBsrx+PTgQWWNN+g7s7rePKTCarUnPns\nOuZSFfT8So+gxIBilxb/Hy3oDg6y0XKY/DIfBVnjtEz3cO+ZA3yUsYcL+k3sin3MxuTzHFu9BdOC\nlK+//ykeffs8DTfMlO2eYJ4Z+j5IQ1kuIetXKu5PHGf19auMHYzRZS7jUs3nmK0vxx/VIrsrl7yi\nLIrf+IixG/28v/4LCDVSUmphe88NipxTmB4vx7J6HWHHBfABITh+cQvdB8oJOpSU141SUGGmQdrJ\nT08/wzHlnTxZ8jK5a02UlA6zx/8BFZYKXp74Ny5k3s1MbhWhd2dJeWeC3fkfUSfq4pWFL7HcdZ0v\nh/5I+swc7n4NHUnL6ZA1ool72DF0hMfa3+Tt7kf5reArNFffwF2ow5tQIcyKIqoL0niyjfXuD1hx\nIEZ6ioRdsSNovS6yri7w59uf5dTYFr6mfQWDy8YrgSe5JlhBcLOcC6K7GFTVIq/4b25w8r87Bm8W\nETGKWTDJGRzTEMxykbwwisIBRWvHabpnhBLzMCmtcxgsNrRWB5WL10jY5IxHt2EYnCav5zRuYRGE\ndPTMlZGtmKWhrJNs6xx5shkciiQWNMmYkzMIx6WkuuwkSiQkbw5QOj1JsjtAp7CRsD6d6rweVqiv\nsc5+EWsgg3ZvExVp/UTCItLdc8QVQlAAuhlk6QsUVDqhJsai8yZJYRsdWXV4F3TkY0Of4UWpjaHL\niUBcxmxFOgJZnBzbDJZZA7NjaUxeL0JhFnG3+jpNpd3k5Vio7Jhh0RpCbQtBlgSWqBmnjpP9n0Ln\nakcRszIYaMAyk438UojgtQRdrWnUZRYgzRLjl6Ygt8wTuepFa/BTVjoKIRkycQzM4B7S0D9RjVum\nIbRSQVHqOBVBGx3JS7nsXo1wKILa6yMnMY1W5CKuEqBoDqKV2tFOTiGQubGl6lEUpOHL03LLXcLV\niQocBQVopvzknxlBlDaPu8SJ0eclpE7nsn81Q4YMFrbH0awRkpyXQO3zk7Vgo7AozIJeSOyyDJEw\nhjbTxawyk2FhKRmieQq8g3g6Y4z5NViaE0w7yvBbi1FMzbNcdoxy4RiaaIA0q5kqvQmZyI9tTIVp\nWMu43IhzwoDklgpDJEGVy4leNInSJeCTMzESHglSvwBPSgaT2Q0MVDWRVO7HP3cLQ9RGjfw2wx1F\ndM7UUy4YQZwa4ULGWuqkt6n2dBERibiRtJzJvGwi6WJ2Wo8hksTJzVkg7PCRGJkkZd5KUXiWkvRR\ncsJOdnQdRR9xM64uwBCyYzDbKe3sIxQRIlFCXnyCnKQZ8ksmmcrMQVvoQpfkZEf0MCZxAab0bOoL\nJ1lVMYU+X48gS4OsykfC68d5KohA60FWFaQjuhTNpBdV2EeLrJUWbStuBzgXxHhd/7gFIfyLRPDO\nzL34BEqu+ysZjm3lUfMZPrPwO9J7QYmQxDYhujU+xNkJpD93Ypwa5hGO4MPIH1iD+Mw4mp8eZ51Y\njIBifhl+El2yj59U/pys0BwJt4DptAyGKouZE2Vgx4CvVsVMRQb596fx6aNHqb/Vw/eD63CpQnzl\n2//JUvMt0s/b+EnyA+zPuJcv3/MbNojP8C3Tr5BMR2EGyI9BdhSBLoE9RU/hfWOMCorZL9rJ+ozz\nPFazH7k+gkepIfFVOeck63mRR3n4wD5+vO/HPDv1bfbbdnJdsJHt2lP8UPUCoR0iph8uZP3TF0g+\nukCO0s+wsQbKwSTeyHygkeL2p4mPXeePqi9jGIvx3KvP4bE6+YgsPnRt4YAlG19QSb3zHOO3BDSk\nj7GncppY/CjRfDF44FJwNT/ofIFYs4TSx/t41PI3mmZv8f21P+O46Q4OHNnDA7H3eVH/NOfEqzmk\n3Ynr00aqr/Wy68ev4S5N5ffPP0a9oYcqdzddz6YxPSEg54UZ1gauct/hN5hz2jApw1StgXh+Lftm\nH2a+vICi7/eTl26igEmauEilvoeUbUHOxBqY//dCuu4LcfFpE+bZ9L9/C5FBaruZVb89xKZKIclf\nhRd6nuPDnnXs2vccmzUfU71VgtaWQPRijFtfrubM8jUc1GbTtWAkdGqa+JyCuLWOjJlFGhb6EWRG\nmdMLEb4hJeaSEBPA2frtvNzyTSJqCU2udrwxDdm6Se7IOYgg+Q46pY3c2XSEgo3jvGX7HK0ZTTy1\n9mf4Z2TEJ4TMh7KYGijGb1KiNTopW9eH/u0bVP/sQxqMcXLrRTgeSiLJP8sPHS/yl4XHeFL4H/xM\n/n0esr3NY++/RmRCgjYNlPcEiX06wa6KT9i2eAJPthzMoOl+j3PStbwle4jaSidVYSk92woYLy/A\nKkhB99dh8t5YoOqb58h83MpbVz9PZFTGD+IvUCEZgACMnIXbFw38VfjsP2XyXyICb6YWFjwsKxti\n7a6boMrgvL2M+Ek9sTYx0V+ESF3vRVcbYVRbSyQzDcuWKvI2zPMF4ZssVMUY/spuKoWdGKxhfMeV\nJLQivOtlXBau5JJlPfMJI+ELUDd0BYU8zrWmlXiytciTA1y3L2G0TYVm5CIFN61oMnu5LS+ki88i\nmovx2Ojb5KVOEJDGsZxzkewOkpkCwkzwCNUcu3gHbWcrWBDEWSCNeUqoFXaiUYYw5eUzn2JEqBUi\n6rezeHSKSPscadMWdmUeozB3GkxyBFoR++o/hS2ux3VWzd7cv5D7yCKXq1dzq6yazIIplkpvsiTe\ni9OTx/uuJfT7jWSm2LmwvQVd0M2z4+9xPPg4XY4SsnZOI3YIOHzxs8Qmz1O0/wSX7asZiZWxVX8S\nTboXUgS4xpOYbs1nvjadYIWI5WmXycieJinbidwV5o3QZ+i+lUdvez4+eTpBgZSRrc3kiObZuf84\nhvA0msAsuw0Rqitn6E5fhXQxQW6Wg0FJHefE65gpiqLJSmDsuEBCM4JTUcmkvJBIRE5T/U3S8SFb\nAsIpIbGwGCdJTKly8Paq8QypubG5CUW+jdzqAQxGL+qogMqaAZzFSpqyZpFOG/moexsZs+MsHT/B\nzY+LOGvahK8hQV2thabQGUyzw5y46kGUO0moScaJlJ2ckW4kuESKIu4hKhLgU6qIyGRs6ztBdWsH\nRxYasQrFmJI06EX97PzuDK5GDbfcJaQdPIvEEODEno30JVUh1MfIMJgpiI2itPhJTSywx/MJSs0k\nxloX/uYirufn0XktlYRQTOldflQuN1+af40scw+Do0lcrN6Gb7WBMuM4sroQsWQh1XmDJCW5uape\niSdLxbI97VhSjEy6izgwfR/TIyUsWXqL8qIRjLoFEmILQn+c3lg1Xao7sJXrMKZZiSSg217FDfNK\nQstc+GqDeGJp8NN/zOT/lghisRiNjY1kZ2dz+PBh7HY7999/P5OTk//V+zApKel/DswQw6idluIh\nPveZm7yZ/RhHTDsY6qnC1SqGQQuFWCissWES1hDNkjH5mXoqa0+wZ/5NTlRt4c3VD7EoiSAbmUQ+\nGUImCeLbLOeiajU/m38G2UCI/KPDNJw5hTIpyCxZkBBSJJvj/FQ9zu5itot+S43gBiFEXC2/k9eX\nfZUfjf6ELwT/wOTqDKZFMmYOqsmQy5HtBGVWBKc0ieN/2c7HV9bg9wwSiwqBDCYrDSy26OhrKWMm\nKZNU/yKhDj/h14aJeq1ItSHuqTzBrsyTRMJiDmp28sOGF5gaz0VwMU7jpi6y7nJwvPwuTEn5FMcG\nuDN+gp3hwzxjepEzc/cTGJ4mkSLi0L1b2Js4xo/6/obrzDLGFpdSdN8wwkiUSxOfQj8qY8PZNk4o\nN3Nev5ns7GkkhVHiWULcV5Jwv5bE2HeKqFuhp0Z3k+XGs+QVT3Jg5l6eHf45gYM+BOecxDXpRFqS\nGPzmGkomj/KpX3+IazbIgkhI9femmL3Hik1XiXQyga4IHKpmbvAk1+sTZKZ1c/eBH6C3p3LEtpxF\nTTpehQZfiwFpjph4uoCoT0RCISAoluNI6En0CYi2iri6ugVlgYeCjXMofCEE1hhlzYNI83w05Swy\nerqYt19/jFzrDQTym1w6Vsm1zmaqfz3K2k1DPORo5dKHI7SeiSJpmsKxJYn3/Xs5ptxKSuk8IlUA\nW1yF3yRFNeBjT9t+ctt7eXLia7T5M0A4z8NP3WLX10fYr9zLeHc2zYdeIZJh4NjazzKfKMEgs5Nv\nHCNXPEZiPoHW52Hd5GWQJoiuSOLklnrOyuppfUpKWKWi6Oda7gsf5/s9P2bqA+icKmH/9+7HuqmA\nNYazSEVhPHENspT9FCtNHI9tw6xJI3FnlNFAAYv2dE6NZdPfW8NL431U5/WzENVjj8VwJ8noEC/j\nYGw3RdmDJGdPMouO22PNvDb/LTJWjJOzZBSfyPh/RgS/+c1vqKysxOPxAPDSSy+xefNmvvOd7/Dz\nn/+cl156iZdeeul/DpyQQZaB4z0rWPx3L0v3mnhY8T6/i3wJV3IJFBlZMnCJnd/+mLc7v8i4uoy8\nxARZs2bEp6I0iTpQG70MNxQyaijinu2nKRkZo/jNafqazJSt62HTufOU9gxzq3odAg18peP3GMcX\n0Ka6+F3So1z+eh36zHIEmgTd5KC7HeJ7Z77F8pROEqUQ14iY9RdxmGV4jEaSm+Qsy7xJjbyHnYJP\nyM3v5C+f1DAxowbmOTabwfztB6i0O5BMCTn03m56RvMJbNAh9E4hDIDAD96AkrFHC1B7XPyo6zkO\nDN7Nx+M7+Zv8fs5F1jMsL6REP8K9c4cwXuuh+1KUjdn7yKyd5i+WrZimyuh9X8AK+mEOsmomqaq+\nTWbxNIbuaapdB/Dnp/OddS9T1jrIVyy/ZnRFHn1La7DFDH//I00K6VcXyPZM87fF5XR701HH7Ez6\nSwm5ptE2xlH9OoHDFyctYGXn+aPUaLqZ+FY2i3EVc+g5UVtJWKlla/AMFfZBlFY/S2dO8rmwjeHQ\nKhKlcrK+pWe2J4vIr5Ko2trP1vuPo8uzMZyah1+hYmwkj3BEgj5ip0w4iPHuReaWZtGnruZ6bCX5\nyyapG+2iqHuSjstNXOtbTmnBJKmZA6yXvIBpaTV/2vFrRo5J0Yxc5K7hyxSEAvzh0hMk1A6++u+3\nKSv1MqtNxv9OkEiXG4dOxTlxNfPsZkBXATJgCnAB0QTojJBZhLB9FM33+tnz4DuMG0q5UHkfowuV\nOF4rZaPuMhv1ZzmtWMeVolVYq9JQtnrx/VFOfDpI3BvA7KgmoZOw27KPQFTN6f94jN5ADkNm0ORD\n2VofK6rbCKkG2BI5RZu9mZOO7dQl9aKKOnC8AcO9Sv5GPfUV0/xq+TcRihOo0v3UqHowjeXxxut7\niCig6KVh4rU6iiJ9FL1/mET7CB+Qynh6LuESEdZTHiLvT5P5kI/Rf8Ly/7IIZmZmOHbsGM888wy/\n/vWvATh06BAXL/69hPGxxx5j3bp1/1AEOZmzGFOtBPqDtB5LY0nzFOklVmSJMAaNh7ziKVYMt7O2\n4xzD6mrkaTEE9gT+iIqEVUiBa4LsITMWaSpTVblkL50nJWRj8q00bAINymVeVvivsSTey8GcXYgU\ncb49/hqZYTOuiJa6gjF8xekU1Agx6Ay47EYKFgdZ5jyNqlKMu0qF0xZnhx6/dwAAIABJREFUZj6J\ndvUWJlIrQKPAmaJDrAuwavEKGWldDEsTJMhiBuhypjM+XYzRe5b8YJD56Qz8zhTycrwExQo6Bbmk\nyIMIdFKcRVrEpigFw+OUjXZTZM1hwpNFj6uKYE+cUsFt6ic7mb0c58KlRso+DXkVMyivS4mblURO\nL2BOiGgP5KLZaKFhXTt+j5qYX4AxaYr25BIOpu/iu/kvUanu4Q3to1yX1IE0hlJix48Ad4+MqfE8\nbopqOe8vhhk/iLSgj5C/eY7C5S46TBq0fS7q27tRlbu5uHklgXQ5AYWMrskiBN0Rqr1WRLM+TOn5\nJM/PsmN+H4dcKYxJm3GuXoIrkQXnxBT0jrO26BwiYngFcnwiFYF5OYlAAmEshlQQpkB/i7RIH66I\nClkC/HkK3CEN7mk1Jn8hfc5qrOoUFCmD6MS9SPOzWbizBI1/CLWin1TxPN6FLE5ObSandIiG4lYC\neXpMaDAuzLC0oxXQEFe7GUs2okh2UqPtwa1RM1ZRDDkyFDoVwZxC7LcKmfgwk9xlbtLy3Vgam3Hd\nzCXr2jyr86+wt+ZD2iYaMEmLiGaLCEqljHQVIjdNYggPoDbKySsKkJMRwz0XRHzSRkgZwGGQoK0W\nkLYxzDJjJ56gGrk7hCugxxQoxq40EAsLUJsdqAd0hKJx5DI3JRUjiBQJBFlx7AY93ZRybaaZpCIv\nRQ0OCvQWNHYPsfNmPIdCuMQCZLV2amQdzF2Zw9odpjKz/5/y/L8sgm984xv84he/wO12/9czi8VC\nWloaAGlpaVgsln8Ye/czn7DLc4jJ92z0TCXo0WzihH4dM5IsGqS3+Ib2ZUoUPWilYR6p+ID6khHe\nu/0gzrw0atf1o+r0Ib4QZYvoLPmLU5xZtp7X0+5l0RNhoasM66EqnAYDgs0g8P4/L22GqYJsWkuW\nsnSsja2LJ5GGvEgtIRrO9KBZcKPbFGd+UzIzpXrG37Iy3W8nVCgHoxo6hMxIc+kU1ZHxUT9FB3r5\n/NxBsmjiD2zGRxoCVIiQkpo7xYavniR4wofqrV5G7Eaekt3Dtq+bWd5gJu/cDLc7G/i15SmyQ+38\nm+Rprm+8l9tNLcy8kyDWM4s/FKHTtZk3JV9BZYREjowJZTZGdw/LBv8TCyKeTNzHPe4hVgeu8Gbv\n5+ixb0O29W5cvVLiv1ug/4ECxPduouejfLw34+R9aRiHIca4V8Ah13KuJdYy+XgSAqWExO/9oFLC\nWgNrIme4Z99Rfn5Th3daDbIEw8py3jjzBTKXTVNaeJuCv5zEcyLEn6MrkDTvIOezETZ/cpqW967i\nncnjRvdq+jOWICqKonreTcbpKbKeHEckBJEYhHIxM4t5SF0e5kmhLdZI1geHyLl+lSf2jpLZACle\nK8FMKSN35xOOCVAJnbgMSvrnlnBauIaqhI1v8k0Gd6XRf3cuJ1X3M5Eox9ZkwHM1hZe/u5zCz0so\nvSvOXeVX+ZJ9H8TEBJck8G6NobwpJtGt5cTdm7lVcTeCqIS0eARzIszN+BYmJ6rIFs8jNUaIboNN\n6pM8bH2PivJBEi0CIlflqG6G2PTwOVx6LebaQsrCt9k4+QqVjVKkd+Xw8dxu7FdDLH/vFRpbTKR8\nWUs0TYhALaRh8RadlkZ+Y/4WvRkVyCq9GFwOCnxzrH64lyW7x2mxX6c3vpSnhL8iZhAhlkRIzrGg\nyPGQne+gpb+dHe+cIFEqYF6fymuuh1gUa/iC5g0y52/gf38f79g3cchbj/LtG/+9Ijhy5AhGo5H6\n+nouXLjwD9cIBAIEAsE/nBs98i6v2kNI5ixUlfswix7l1mwD7qgWrz/BlCnCoqScRO0y6pUdVDvb\nWaKpIknuQSKI4FTosDYamBzXMnJKzrzTyfRcEv1OI4FRPcJTGq6lFOGR25ib0yHNFnFl1Qo0FU7C\nOWKs8ymEJhWUDfSTKrUhVsSZLs3lYuU6kmLzqDvnSA76SUsOI80WQUIMXZCebqVcP4zaGMCXb2DW\nXo3NuIR4TSmYjQgdApL6wujSvPhz1dikMrxmDUORCjqzW1BlTBDMnCEzskCvv5Yb0uXcV3Kb1YYO\nRhKbCJuUxEUgSJchFAnJDLlYGp2gPa8Ba1YKDes6qE85Tq3/KuZpI12TWvwxHXN+GYVdE2gcASKN\nQsYW5FgHlagnZskqnGe5rg2nfgi9Psp8pQzVbg2RaD4udSrhkmR0ihCFO8bw2AyY3DlYNLlMqAsp\nzxtApfCiDzgICJTUWnvweuXMJzJoDp5D55tESjYjjgxujDWBS45PbETV46NS0Unfxlr8RSq06U5G\n+ks5LbmTxtkO0mwL3E6UMpumo2nbDVT1ATITs0gnFhG2L1Bav4BMZKTDUcGkOJ05pR5bg4K0sjnE\nhNBkhFm6YZGlqlFaBlsxl9yDpaCWMUklXrGKJaJuhL4Z/P0C5iYLCJ/UsCntEjl3ztMVrGXBkIpf\noEAuDRNRiui0GunWGtDlKgiHkkiYRaT5nFTHx5G1WlGoPZRkDJOicDNflY6gMoG22IF+1kaZqQvf\nFTcysZs7lx9GmDaLdbyKGUGEWE8WXZE65NoZtm6do3jJAgY5tA0WMOc3UJs9jULoJzcwzZwtDfN0\nBhJXDFkgSkBtIK6VkVvgxttvxnjORLe0idnUXJaoOqgwWqk2jlJg6iV4aRrLWCqDyVpM5jKkSXLK\nGz2U6E0csUjwyuwUJS4zIbT994rg2rVrHDp0iGPHjhEMBnG73TzyyCOkpaUxPz9Peno6c3NzGI3/\n+Nxy9xer+XnrD/iM8E88If8j33Eks9iWTtwnpHMxm6GL2xGsKkWxrZJvn3iaXeP7+FLub1A6EqjO\n+xi4s4TWL9fxyQ9KGPxIzIpThymIXWXUt5mAI4X4fIQPhUs4INDji6Wh26zm9ZxHWZV5iU2c4Z34\nE/S5a3nh4jNkZ5mxbDdwVLOZ//R9lS++/EseOXaDisdjiCvgwylgAOiF5YU3eaL0r/jvF3GzqonX\nnvke3fnVBL8jgtNSRIfcpJ/woO4Lc23HWjrNOQiiqwkZs4lV5NGdZMYk9+HMTcVr1eETqNA3QGGL\nkOB7GUxNVBLbC6L7h1FKxNxhPcuK+XaeLvoJ1zNb+PITr7HRfhaJ2UfoqBPvvileFX2dg/4Wnux8\nlaWxTnx7pbydt5QewQaWnLzMg7NX8X9ejG11EmZdDqaUPPLq8wkm7Dj8U1ztWIcsHGLX104yfrKQ\n6ecqOfzQDjo31fOk4RdsXzyM8aiDbLeZKnkfb4sf4qD0Tu6t1bApbmE7H/DxdIIfvHQ3fw1Wc4y7\n+PL1F2hZPMavG5/lNi1YXUaOlN7L1Sc28bPj36fx2hVeXbyfYJGcR5/7kPRiGxJCjDJNOAgMQt9w\nEc9f+SImTyoxYYjyn/loKJvCgIOqwhGW/Vs/ys4QonNxOr1NHBbuQpe7SLW2m+0cQb3GhbNRzdFf\n1dP/0hIsz6aSuLucF11P0X+rmsT7EgQySKgDBPbdIByZwHn3WhLRdGK3RGwdP8WT0X9n/q8xIucT\n5GyEC5Xb+XHLsxSVDrEy6wJ5u0bQdo3yyU8KKNbP8PTTP+HQHXv55eLLiH/rJfGHBP6kDJo22En/\ndhZp84voD81z/kIFx7yNPPDCbdaUDfId+895d/4R2k0txOQSfAkVbdPLsWfpWLq3jaKZbn74xnV+\nveLHHNn4ABUMsIorlDCC3znD5YEYJ7sruSjagi+YTkuFHXZBuElBVZaRV0J+CJt4R7GXzn+SHoie\ne+655/7/imDjxo1885vf5Mknn6SpqYm5uTk+/PBDpqamGB4eZtWqVbz66qvk5+ezadOm/0/s888/\nT23OLuokPaTnzOPM16PuDVA8M4qy0UPmuimWrOjHsFaEvE7C+vmLpLkcnFbvYMaWR+7YNG3iZj4Q\nfwqDwkZ5wwQsM+BaXk5wbRH50nkah07SuKmDkh1O7AWVOAyZ+J0qymdGuGPxFJGzAfS3x1me205A\nrWVf16c4s7iVgaQq1t++SMNsL2ead9KT30iFbJB1+ousy72IoCzBDf0KrrjXcGW4jltXVDh1EmhK\nAYsUpSXInf7j5KvGGV+bh0OaxMJ0LhFZGnKBmG2xU9xlO05dpIe8zEmUdR5QCBk0VzJrzETaHITl\nCRJhHwunA0jsASp0M5wRbKbT0Uh8UIJgQUihfBqdxY9yIoJIJUTvdyFOjhDWC9B1z6G2uimssBHN\nz6dP24x20YKvM8qxKw3EZxKsEfcwfSOF1tPlWLpzSV+wszv2CZFROVdHVrOs7AZ3Fx1CmhZkRFjC\nxYENzGvSyGieoT1/Kde1yxBKBcxlZNFVUoc3TcOSpB4ieVJMBYXclX+MVZWtJDd5kAliTAwW4Yup\niaVLWJNxgZLqQRwtSRjWeclodJA2tkj+ATMoEihrZOQK40SmYWE0SHnWMJs39+KvzGQxkc2yi22U\nTJiQ54foM1Tznn4vwfEEhR29SHPDBIOw+H4ErcXBqsI+9AIPep2DxYVUZtryKOofp+RmG8k32/Cp\n1Jhzy4gmi0gYDcRDGWSPjrC67x3k0gAD2c3clCxjIpqDzD6NZM5FhsVDSfIIxgILpVdHKT41TsaA\nFaUnxoC1mBFlGVQrWTN4lU2T56h1tbIicZkWZRcT/jLeEzzI2d7VmO15GLbF8RYl0yWo5droSkZO\nllFX0ommxMXJ9q2MdefiHBegdrhpLuqlbdVKblfWEw+IUA0HWXrjFnM9GXzke4Sehk14GkvY5LpF\nvaKX0Y1ljJUWEU0TobnpQvmRm5OdG2m7dJ5/hPx/Sx3B/5sCfO9732Pv3r28/vrr/3V8+I+G9UQ6\nL+58mitJyziVs57docNsSpzi8J47CJUJqQz1MioJMhiLo2/3szibx37vp9A7nVR7e+kbrOaMaBtP\n725j5Yp+3k/sxSGuRi+XU/bKYRrPvEHNJhE8WMJU1z3MtooJHNWAXoy2yk/LmbOUmkG3JUFf0ioO\nvbeb/rxKYmo5MpUIynUc1d3DoiyF543PUZd3G4U2wDOJn/Ki/3swIICOGXCdRhLwo7AZSESlKNUu\nhOEoSqOXusJ2rCkGLE3FeCaVyBw+NrRe5JGJdxBUx7hVW8v+2l1cPLWBD8/s5Y5PH2bNhnN0xZcw\nfVzCW8dWIcwJUrt+AcEcBEJq/tb5EA6FgZY1rSRJnUhTE2zou0S1b4ADX9jOtL0OwbfnqKw1s/nJ\ncX5g+ylvjN5J8v4R5N2TnEsU09I0xZLtA5w9t4Tx60mQEsGQZiPNPIs1mIo+1c7G8FkemniXfUn/\ng7n3+o+DOheu1/TepNHMaKSRRr0XS3KRey9gDDa9phAgIR3ykuSkkpCck5DASV6SQAokQCCm2wb3\n3m3ZlmT1rhm1kTSaoun9u8h3efh9F98F5w/YF/tirZv97Gft4Xh6E4OZWlqMVzHWz+BVa0kIJVyy\nL6PNtgSf0MDGmjM80/wi4oUUQ95SpHExJlWE+03voZxPcq5nI0G7lkxxhnixGJksyiYuMyPOxaXI\nw9gVQP6Gk/xva4i1ihHvBcPoHJu172FqFFF9r5ifiho4M7gEwccipKo4wTIR14pq+HPZozx48Y/c\nve8d9i15kCvqOvr/WkJurYP68l7yW6coqe/nd998msgRNT+1PYs81sf1GQgXaBjMXoNwWQXCaBrB\nsTTF7nZ2BX7D8cKv8Pf6/yA5JcbmuAF9PWzsvcKTliEWKixMrzdRenac3E/mWSWBfYur+O6bT2DQ\nwurt57i14DBL7NcIhqNIJ5KYDsKZ2s28WPkNMroY6qgXr8TNNYWN7vxavBkj8u4IUztzuGkrJ5BU\nMtuVzUdXN6LbusCtj11BbEmRlsXpulaHujPKnv4DzKSWcKzye0TXKrEUz3D71MuY5mb5Qex3SONx\nbk+9j/qqG+HLfmbCmk9l+P+3CNatW8e6desAyMrK4vjx4/+fZx5Z+zqqzhBuWzbddbUkb5VSMTdI\nXbQX2Ucz+E97cNY0cKF5HdNdEoq8vWx+8H0ScQO//ehpnC15lN3Wg613Es2JAHM+GC7Vk74jF0/o\nHkYyDWRff5sa4SSSziTVnl4eznqDmF7Gt6L/TRBIyNIYs0JkN/u587tvY7+xjI/+fCeRlTISj6VZ\nl3+c7nAdf770GKs1F3i49g0E6iQiUYz0dQmZM2LwaWk0DHJH9RkiPSGiA0E0d44ysSmXOasRXY6H\nLQ98Qo+rAcdCMZPZufQG7CgPz9B30sy57HWMpsoQC1M0XuziloFP2OI5RUgiJPZkmNGZOr5z8zdY\nb5ngCetL7B/cDYuAm38/A1rBtTqH+apsWhw3GLqexd8DG2kND/Po7EX2KD6gpaSNuq1diCqC/Cj8\nIX2LLTzz/m8oLejk9w/+GhwqvHNS3rxZhpYIv5B8j9Gecp6b/iEtRy7xDdPvCRYbGBaX8qdjX6ei\nspef236M4mCMiWE7f7d+jquly/ljzeOY9S5+tPgTBs5V8lPHT1lWcoEhSQnxjAQEaQQkke+PoW4P\nIIokmaq2cfDuHVAix/SghztV79E4286VlUtxNWiIewO0d5t45bdF6B/08tDq1zAbp5gbEDHypzR5\n9kv8Ys1PuK6p57dlz+L8QEm2Ypqnm06wWtSF+KUUk3fkMVZTyEY+wJuTxd+3PcR8SoO7G8YrGxFZ\nUxjy59GpvGiMAexbvcj8ZRQZgrSqzjBwrgZJCLLGIVpu4vyjFaiWx9FK/Ug2JkEKnIcK+wRf3bQP\n7dIoeXEvl8fqOT69hLtX76cmbwSRGponTvKtf/gJrMxH/JiMJWU3MYT8LLoMDNeX0v3bauZ7I3z0\nUjnJZQFqdvaRnZxkbiiXb7/wXTplFlLGLvK2qkjcKuTF2m8xcUlL4tgkhEwICgUoFgUoQyB8Gxwz\nxXx0+93o13jJz56AN5KfFkP+bCYL85omEA2k8I8YGGkvYQYNbq2Y+kQHWrcHx7CUKaWZIUsFQ1MS\nqt1itglfIpibx5tND6Fb6aN15QXsvePIO2MEHEpiXhGFq1zIMpDWl+Gar0bXJSJvbJpy5SBblhzh\nUrSFa5O7kEkFKApThFUBRKoRqgt6yL48ieCEm/G1OfS1VlC2OADOOK+330mO2UyqRIhYl0AlWyTX\n70Y478Mp0KJWyyjMchDCx2IghMgWIV2kRjOxiD02gEzkRaLyIkksMq2TcypQhaZXyuBEDoFCFUmD\nBHQg88XReoNI2lMkbQKojXPFVcTbg/fzhaV/oSqrB0U0gi9moDPZQDKZwpSawFFlZrolD+Nfpgj2\np+krKUFXLGJMOoU5a4Zc5SQpjZjYuAFbb4wb4zkc9G3n0ZopmpaNYmx3096Vx19672GZcJQ7Tf/k\nP90/Yv/ELlqzztIsvoZ3nYnhRBnHrm1HH/OxK7Af2zEH1oEpjq/bTG9uFZ9IbuGe0F/Z7HqPgehT\njKQqKPH0oyBCo76TobANd5eS+KAQ/6CO8ZkcusKVtDUsQyoTYi+YZERcii7pY6YsF1+WFrEozoLX\nxOAf8rl7xznWaDvwGtNMj5qJOmZR+RfJlU1yxb+cKZGN9IUxsmUTFG0LYBYkEDghPSNGaE9TXdDF\nmMTOv1Y+jkNYitISQlEcodg2RL5pHEvWNNm2BeyMk42CKiZRBY+R1eVFkIlSpAiitkuYXqPDZ5Ey\nl8hBpQwhsYTxlxhJGkQsLW8jmVARuWbkWriR8bw8diy7hNgwgm8KrJE+dgcGuGnbgr/RTqP0Jqag\nm9lELkmdCJfeiLczg98lR7Qzjqomgc4/h2s2i76FFsIuP1KZh5plQ+iKxXSpyplU6kgro+QnxqhY\n9BCQZhFMqYleToA2RGaDgFC1joVqC7Hz6v9dIni38E6eWPFXwu0q3O/nIGIaSVmKC1+uQXd7OWOt\nJmYDDbAgBIGRxFQecy9Jka93U3v/NarLBtkkOkHJUgceUTZ05FFnWODboj+RmzVLpl7AB4V7OGNd\nxx7zB5TmD+JaZUR7dIQn3vgWhcvBujyDUJCk/2wlbx+4n64belLRDo5Ha5n12PhK259pOXeCqQs9\nlDb6EcVjyMQxrKppvlT7DzQzi7zgfJxOyR5+7VpPUppEZfdQ6PoDSw+cxXx6iqkpIY5UgvWJ66xO\n5nBMsoRXYq2Ixu1YlwnY+NTHdHSv5dLZNZxesQanxcrYdCmLN4Uw5mI6XEkqIOLQOzs5q1vP9Hw+\nYluSnxl/zNaJ19l97Xf0zOXRHWlhuG8JaVI0Pu1HWaXnXeNu9BIfClGYUJ6KiYiFG6+X4y8xUvLt\nfjpya3DrvsaOikOEWmXEr66GuAwMIrgA6UEhi49o6dxYx0em3VzuWk3UL+fIpR04LhfwuY4XKZf3\nsHrVUQS1SS561tL+YRa5n0RYe/8/ubvlAJbBeTISEZvrz/DaiZ387cBOHPfpObdjHe9f30W7r4XF\naybuWXyfJ71/Yt/2W3i59ks8kNlLfbiLflUpm7IdPFL1AZaMh8gNIa/vXUU8muaxh0/S5VrDz859\nmzWjH/P9ye/gWYzgEJbxyidPsGH9NZ567AUqZUMYnW5G91iZllUSkynJkripaOzFrhqjUOWgUD6O\nhRm0LKImiIoQCcQ0JjvYNHQC0WCCQsM4cmWS8qHLvBW7n3cUe3j0vd/QMBDn4oPbiISi5D9/jE7v\nJs6Kn2But5bcuycRxYV4b0DPh5BdB0U/FvDubCtt+1ppKO0lYNPz1/wv0HW8nplXbWzfsZdNt7bx\nwWwtPW9ZGL4wh64mScXzczgP2shcNbG1/TC1HZe51qHiVPFOjjz1JLda3mSn6BD739vDBU8Nk3N+\n1ohP8g3TYaIqJdfCy3GLyj+Vyc9EBFMWC0dbNpPpTHNL+wFCTS6kBV58OQamcywMSKsxzMS427OX\n6xhJSz1ErHIShSqCVi0BjYbFjJrxOQnRqSDLJJeJqHPwxI1E9CrSDUI6ww2Mu+0Yl86hzvVzbPoO\nssWDLFu3H7syiWZawg2vneGMFocmF29WmkxmmKnBemKnKjk1NkphOIupqgwRgwBh7wq6JPXEzEpc\ni7nIJQk2tLQzqdLiPgjzglJiO4oQGKWk/WIGF6vpmzPiDECZfYKiCjcSJHijhfi0pWiaJ6hc3sbs\nlBXBZBW64TkU4QjDsVICehGlhfNIgmkEsxlMLhc5I9P4hBkigjSx9nEGBvR85L4Dh3sl7pgdbUUn\nOokfdasWrTRM9oyXoblSnLFCEg0i0vYk2mUBMlo5szETeY5pzNI5EuUSfEUmkr25BGV5OOvzsM+N\nccvCIWazcxlWl3AmthHnnJ3kqBhBQQaBLY1YloHFJIs3/UQUPqTFIRSzCdRtSSobRijSg9ibYTKR\ny8SCAVEwRm3FILm584iyBExZKphT2tEoA5T6h1k63sbbc/dwaXo5xcOdVOtSCFYnsdldtNzaw1Sp\nlQl1AQmrlkBGw5XC23Cqq4j7RHimNEwGcvFXyBhXNHBjZjn21AJplZCx/hzapysY2trIZGkJdZku\nzFEXNaFuFkRZzEnNZIQAGSxiF1HkdGYaiAdkCObBZJ9BvinKxawN5BbM0yRtRyUMExYquDpZi+tm\nGm8DRBJqxhzbiOgKKCh1UFEfpLh+lJwxN5E4zDhgvKCSbnkzOKQUTY9z01SPyJdAOx6g3DWEqXCB\n8kQ3tv4uCrtOEZorQmDwIdCZiISN2MKT5EeGqecmdYle1DMwK67k+GQUYVEcUUGEtGiGpDCHdIOB\noFKM42iU2eWljBc3ULhymqtv/M9MfiYiUGYH+b3uy9xyZB+/UH2HyYdtOB+wM6YqY9BdQMfAUr4U\n+Rtf57d8n01czisg/Q0lC0uq6fCtYNZvI2iQYTwWp+CDK9xX/wf6Ddt4LvxDpvR5sARC+1XkuSaI\n7hIxqrDz4ct3s7LhLLt+dwLl8yHmXlfzSnwjZ1e0kvVTH9nnQoSvRElfFOCetfAXyxOIy2PE70oh\nHBcgPSIiklYQr5byyo2v0Oq6xA/3PIfM2UvbH+Dylx9h/Ku3oJwK4hyx83+XPU1bdjPJSdh+50EU\nDxwmm2wq5rV0n29AblBhkR1F651E3DfK+oVDNOkcXGMF8bUiHv76US72r8X5SRN72vay3HWcZ5Nf\nZM4bZ/PAq4wl1/JHniXpyaEg6uFn972JWTvDXuPd6LpnuffkB/zk/LN87LkD5Y/9rFxznq9+/3Uu\nn1nNc6/eyt2RfTym/zPDj9kZ0ZSSGhThLs6io7KWlv4rrBk+z8+nf8j+tl3EFHKSN0VkbgqoW9XB\nHZ9/j4bxUQIfSTj3YjZDe4RYnxulXutmWQyMhzJInCBYDR2z1fzwvW9juc/Ljl93snrgJvLOFBpP\nCKU1iG3dKAbxPLRByillKpDLSx/cTnNhHvdVX0FaFSNlhU5NDTfkDdRVzeNc0PCH8S+RnzfJHavf\n47R7Ce+O7CR9SxZJi5boKdW/F7nchP3HW/ld730kNZVU6Od5tPgVls21UXRigt8UPMMbpV9Akh2m\nXteBUhlmRpjLX5NfYmHGDBNiWm65hP7BBdqjTaxOXaQ8M0ShbpzloovckNVwYdLCjt+9QdpUzCe2\nH7N7z1F+8MB3kUujKFNR9OIQExIICuC4cyNnT/2EH954jjtj7/Gfiu8g8qb4jz/+Gu0KH+5f6Zn/\nhYu5X/moi49hX21H+OwKOp02Tv6pjj09z3FH+E8U3h5Ca4S6Ebg6GET4KyfdsnzU9pXkzR1hTaqN\ng7f/jLbptfQ8G0b9VBnmb+fxhYf+zrtf+Z+Z/ExEkIpLcCyWcCK9g4hGywpBG/nzHk61lzMwX0PI\nqyZoUxIsV7KteoAW7TiNuV7mjSK2SA7jbC/gypU1FOsjBJ/Mw52nR5KjYY/ufXxyHQuFesZkRYTG\nFHxyZQ2hcQmT18WQCWM4H+Nq8Qo6Hqqg4GMvD88dwDog58p0GXszjSRVOtIWEYE6FYJqOcKCBEtc\nN9niOcm5U2vp6Gxkm+EITbXX6asrg0CSePAa2TgR6UeQZKJoMj5nDGMYAAAgAElEQVT2RD5gZegS\njEFUIqY7u54IalSeBKI+IQ6XnnfbS8i55uR70r+yoryDfJuPR12vkpIIafFeZzbXRtauWSSz8+gH\nx3kw6wDzZRpky/WM90gIHHOyRDPA6hwnpelxguMqRt8pZ0JQjMtiw58KsGzwVYb/vo7+8zb+lWlA\nP+Thy9d/QyQh4iXdV3FdMjOqKcbXpUGb9lEyNU6+dxqZL8aW8/tIXA5yRrIKy5yXHeILNETbqZnr\nxiJ0E1RZiS3UkxXWskF4Fr/Qwh/5AfK5MQokw2w3DlLodXC3+32M6SBVugkKkxOEUxqKK4eQRBao\n/8cpKnqvIgykWXb1IJPCGNc8zWQ0FopOzaBuDjFTYcZwYp7mzqsYNX5KQw7kQyFM5gVaanpYLJMT\neVyJ1XOKHKcPvQCqCgcZa85nkio8xnLo9yH0dWFf0Y/PmcUfj9yCuDHG04IXEXiTSM0R5grNdM/V\nM329gIhBicoSpMIzSK5zhuu+5VxSrub5/Gdw3ciiv8PO5HwO4mV+Fhv7CEU1hG5EuTZczhtdX2D7\n8CfYXQ7eku5iNi1F/eUOGsamsZz9E9OlWbxfdyfyogg2iZP4XSLSxaDL8pHcLEIs01DgDpIoniGQ\nP8zc0BIS3VLybDGKytMc1ewm7pZRt3AYlydCOjiHJypnSFpNvNyIQhXi7iUHmalTcy27gIrgGDUv\nXMSzXfGpTH4mIshEhATmDZxM3sI15Wpy3d+juquHmW4b8x4TMoJ4zBp6bVWsrusmTzvFgkCNIilk\ng/EYHw/czsWX18CPMgTur+UIeTSHOvm++7/JqBIMG61cKF/PVWcLHz+1DvcRJWSSSGVeNIcjXN/Z\nyLHWNTw69jvWjVwg50Ya1eQeDus2/jtaUeYj3CgjU5pBqQnQLG7ja5k/kL4kwBXO4a7H3qF4xwh/\nsDxOSGWmTD+EjAi5wRmEsiS67AXuNr2FUAlExewN7OEd/x7yZNPofD4kV4KMXVQyJqrlG5KjfFl9\nHGGzCGGdgNvPfIgkmUDujHGlchrdSjfhfVGS4gC7Cs+zsK6I059bgegjOYYzl1iaPcMWoxPzmAvX\n9QZG/1rCUHMFB57Ywy7V91nhepfZN4sZEFcxrWjmPtFRvif4Fc+nn+VP4afgMoglCZS9AfJUk1Q6\nh9HMBUi7YePsJ0gXJ5jISKnROvma5RVyoh5kfTHSMRBoilHH7WTLIywVtHNYuZN/Gu4nlT5Pw+IJ\nqgfc1Eed3C95D1ESlO402kiMlExMZWkPBZ0Rlr/xHuWLHoTaDMtHPyYdHcJd8huyRApKTk8jFsYY\nsNmwnJyh+K1BJHoxdsYo9VxCli1C0y6j6fF8JHviNH/vE8ouD5FTLmPOmk/X0ipms0rBkAVv9CFr\n68Icm6B9eiP/deE7PCN7ge/nPw/zGcYWC3lfdzsTjgJSx2So1gTJr3SyqvsS+Z3TvO+6m8uFK+jW\n1JM5HSPzWgwalJjXeog9tJTU0CLyy07au8q5KV+N4ZybpEPM3yq/RGyrlC985S8s29uObt8FfnPL\nf3Juzy3cY/4n5fIBJvKs+EVqtKlFZOuSaJfEMAxLEIjAq3HQG3Oi8/so2BnDtEPHJ3P3MTiq46FI\nB/OCEDrFJBlJDvMyK9NVKynPHeL+/B8wabUyv/5rNP7sEi2vHOGQ7dFPZfIzEUGVYAC7dZhxRTEh\nj5o39t9O43Aed6x9j12Sf+DcB1OROl6NfxmR7RWixgxv791Gp2kJvjvtRFZpKPtVD8G+BPPfUBFk\nlstKAz/K+QEEpwh4x8k8nEOiXEGmOAeq9RDIQK0BNkJV23Fi+3sxrnKR2aiBiSCNjf08fdtr6Nwp\nkkENb0w8xITUytLqS4SLVHx3y68wxId4uP9nBAtTnBSspef/ViPChOnZeZzuFjzPVbMsqx13XMPe\nrlWMLVSBuoDRi6XMOkxsueUMuaJh+hJSptXlYFrN8aAed7gCAVYEKQ04vNSbb3Jn6VE8Y1nMv5aH\n85qKm1k62vaswWGqpu3FXMqnB/jd8uM4zU20exqx7J0gNplC+uUIEmuceEJMR2oNDtRMkaCi7CwP\n3neAStsME5IK/PM5MJWBbh+lY708nNrHUlMHc2UGMlcyCOJw8IHbGSwr4hHfUVJKOX+0PMFG1xk2\ndp1iYQTkSQdf2/gCA2tq+Fi+k8BWLesspxjLKFDNVSA6q6I73Mirux/G74mg+I8x7tzWRlPxBJuO\nnCIZTqH8DqhGpWQ6YiRCIFEFab7nErnGJMpLIS4fNPLKiXqaq0qx/UhM/9kGpJkYy9ecZNpVyNWr\nG8hPj2LPGiTr8QwTFTW8enAr4x1V+JrzGDmWgvNXocgC61qg9BgMCmFESmCZBtfSLIR7A0hOu9ns\nPo7BFkB9f5CKsUGW/ekaS6KdzM1roduBVG5CZVERrVcSXqWCchH6Aj9rwxfQ2mbJ+WkDl8956Twy\nw7ui9Zyq2MpwbRl1ti7qUr0M1Vfy0lNP072sEYEijlCcwjlop+PDZqKFCiSbEjx49J8saW/nH6Yv\nkKwUcUfxu2xccRztiz5aTZf+XVuSgyJLREGzimbHBKuH3mK+qohAxoox5adozEHxqJORvAocNaV4\nIo9wrXADPrX5U5n8TESwOKJCUhxDbgkTK5cyP28gOCRjddNpLIsDDI/CUT7PaU0rU3daiZnjHHxx\nDWOUUVicwpg7hbZkko7zVXjOaylM9CJXCujOaUDqiaGaGiOVLyMuVKIsyiBb9KO46SVH5gYZaD3z\n6CYzzKwwIrLqyCSmMOcvsGfZcbQDIULjGkakRXTFa7BE5wgpNYyVlnBbwwlWyw9xXn0nnb5GpONx\nlNUiYnc1EH8lh/g7SZymfHwI+GBgJWOyUvTFBoJTNoTTKqy5szTnXqeioJSZtIGFZB2z4Vyux92k\nXUXEdLnMC5SMKm2YJGGGhssJfKQnJZURKtVw1byM7vhShrtkWEUByuwyXBo142ETXe01zCZNxL8p\nwhifIeuqhxmPCad8C+Qs0tzUz87tXYiqZLSpm/Be0cP5GPTFMGemuFVziEL9FC6NkaRejCg3w2Kz\niWitHuvwHEMUsN/YimZonPoeCAlAU7DA1sLDaHPCXBEtIZ2VwFDqwEUJIqkSQVpEWKhhurCE7itS\n5k7a0eTL0ajbqG/vRa0JsLhWiU9gxTFiJGZxkC0MUm25ii5bRCRfwsSQkZ7BXAz1i0TL1Bya2oZS\nE8d+5yjT5610HivD5h+kJD6G0hRhsthMh6WaUXktibAJnf8MjYv9OCrvIpZtZy4mx5cRkjGJcGTb\nuaBpRSOYQukPIRuSIVfF0C/x0nDhBps/OcRMcQG98kqCIjF6pYuKrEViBiV+g4ZYiYbCsnHKpUMY\nTXMEGvSk3AGE+0YZt+yizbQM7LA86wo2zzTOrCJmt5RiSnpRjjuIBhT03szhzL46vCUGkInZdPEw\n4sEMV1YuxyPRU8cNsuwL2AvG8A9qcU+YMardaPJ96IuVGHvF6IMzZHkiBNsnyJ5NYnH5UPdGUGcF\nME9PMJEpwrmiikpZz6cy+ZmI4OXXHmHmvnLEzQnyqx188dK/2N55COtHU8jdUC0FS/IY26aG6A2V\nckqxgQVyaJzo5lsfvUswNEuPK8142XfI3G3lSe9VCh3t9PVqMAsilOcH+OOhYo73qil9aIhSXSfF\nl0+x8ko/gsU0jo0rOb19A/MfKrAK5rn9c5epZ4ji407my7LwbNewW7SXsng9/3I/SLlrmP/yfp+c\nLePE9mgYuLGUWaeNBz//FoLyFN2KWpaFD6KdG+eSbw893Isj7CEv2sbK/tP0rLqf4ZV3wxxk+cNs\neXCQ1KCKI38M0uS5xM7EYeInlEw4Kvl43dfoLVjLb26U4+3LRpCEqk3QUirl+AEb41QR36bk9KKN\nccdGmlNXMIum+FDzAAPxclwpM6vaj3DfH17mbc9jHDbthp1JWOVHKFTgD+noU1fhviCFdzywQgMN\nRuiUoJZEsE25kBQnEezMcPfi+1zfV85bJ1vpdOczJZlnwLtIRwJK7wNLEcjOQGnnMF9Y/Xf2HVnK\nx39eQoh55MkhkuEgjcoJnrv2Q/7m2MzL8e0ceOdu5i+u4Jms52niOvp3Qhx27+LPs4/z5YoXWa3a\nT/z9dhYLs+h5wI74HhO7IiFKPuhC9EIC5YbNSFYo0Kl92H1d1Awfp2JkDmuHh/G9MYRhAds+f5L5\nynG8phyWac9gbRjnxaOrmDqv4kIcnFJIquFM30ZGxKWUb+lCuSXEeG8ls/1WPEN6rBeGqZ5Q8nr+\nwxwu3c14rZn6+k4eVryNZNJF4Goc16ZGJEtUpKUp5qUGoiIZ60uHuP9WBy9O1rAvugy0IAAEvbBR\ndYoS7SicSjMzmMvrZY9webaQ4HQPTAihTw11cwh3plC1+hkqK+At7f1Eg0omZwsRfZIh56Kb3Q0f\nUrasH69VzYejrZx+z0yr+BrVukkO5D6BIqPhqdgL1A9e5enh73LkoTtpv3MtD82/xaf9P/xMRFA4\nM4FIosAeG6dyrgflwgTjs0KSPWAMglYJUYuchSYdckGMcq8DUX6I8vQIqyOXGZMJmCnJRbUUkvlp\nqgdmWSLuxyqFeWUlY8bVCCMqiiQOVk5dpiF2BXv1VQoyiwgMIC8XIa1WM3m+gul4Er1Nxoy3GKtn\nHpNsGpXZz9yUmfH5IhyLRUinU4wOF+Mu1hOslTN6tAKPO5v4XRLypHM0H+1gYTzDlLKIrmA+02oF\ndRsdtKRusHLoCuW5egbKZcyGzJz2rkU1uUCeZxKxspGgpYBZSQNm+wTW2gXyVs+zaChmcKqcpEGK\n2JxCr4LsjJhEWE9QbYRCGbqQm+LMKLXxXsz+Gfpa6tHPeyg8f5Om+QvYGqbR9KRQ+0Q0iTup8Qxz\n4WQps/V5DGwpJ9vqYf2Sc7hXlyDSCDgh2kw0W0lz5DpiYRLUUBR2MD1tYLSvmqhYzMbm89RIHcgX\nxczV5LOYL0P1wSTSbg+NHR2cuVaH63IpiBfwmhNcW7McWUEfFoULfWQBBlLIjFEUBSGEkhSpxQyx\n3iROk5mr9ct5UGVGHhDhHNbQLypBbGnBlvGyeaCX7IFRfH1SVI1RYikl6YwIi3GemnoHqVwDQZka\nuTCOPBJjcSKIIDxIEe2kMxJcvlpyx/vIcviwGLwsSECQBLNrjhLhGK6IEZegGEe3CZM4RGtJHyUK\nB1I1SMoSiMpCCCYXyB4co0nczkRQx2BdGV6bkZTKwEn/JgwxDxqDn3hUxbi/ilSRBF2Ol3BGzUzI\nwjHNRvSuGZJX4tRcHKTOOUFLpApvOsUNuYKERAkaJWQk4BOQnhCxENDQ5rcR8ptwe6to7O6gdHGY\nJaJ21OFFTp9dy9B5KfIRJ9NxLVF5CaKaKFZtAGksjtoYpKjYQY3oIvGeMGFh5lOZ/ExE8H+Mf+CT\n4u2s3HeVpX+5xHPTG/j7wnruSxykKR3CEoVDla18/OhDfK3vFR4feZNkvRBxURJVIopwWQWSdS1I\nBTqkC2GErhT6UqjfCn9Wr+dZ2c9pyr3M5sXzbP7lYar8A2juiyO1/vvG9rJxmrRdDG6rYyRVxRl9\nFieDUhY12XxL+jxbI4d4p+1+jk7uIGxVMe8y0dneQGl9P+bqKRwLhcRnZZyKb2TH0GHuen4fL0We\n5FX7k0QdI5QWXuGRp4+yJtxNwWtxMtbDeLTt/KLwB+wNbOf+P/8YWXQR7I3cUG9gUHsf2+86QNnq\nfmzyWSLpQYbsZYSiOnCJYB7wCKFIDnlykAlYrz7NL4w/QBaPEZhR4b1NR1O3mLKfnYPVSqb/u4HA\nz01kveXh8Z4/ox0c5BfdO5i6qxbNBju33H6IpdvbuC5r4aZvCf83/XUcGTu1om7Evv93WasWkCtA\nWETTslF+9MuPyT/nQnxWyie2FoZl2RQuHqRyepKGg3E0/UoQ1IAkzZy9lve+UMzEsiusE5xmTqBB\nfMHLqns7uWNrO7kfTxM7BfMDEKgE7gcug39EzYnYUs6k1yOLr+KR8/t59Jd/Q5ZK4FQWoR6CUL4E\nf40WQbWC3Eeho9GGMy+fpu3teE5KOfZHI6p5F7dyikPmZ+jS7GHr6HPskn/CytYwwgyIRmHn4sc8\nHHuNHx39Oudcy0jG+1m67Ry//PwHWFILCIIptq07hThngbfeqkQxNUBu5QzHl97KH776XSTlUYTB\nNIkhJcvlV/ii+hXODTXy9uHb0P0sjXnrBNNH7AxQzosrv07sX14C/+3n6fhfuEd+lC8t/JES/Xb+\no/BZAuV2WC6E0yZSh4QEb+hwx9QIBqOkk1Ey8gy7Cz7kazUvoWoMcSndyqHXb8Pc0c4Tyd/zIds4\nmNzET+b2cXv4Gsp4GNfybDzPlJD7lz74QRcHHv8x8Mr/yORnIoKu22rxa7T4G9R4HjLg9dYSHDGS\nPnmOaX0hHduXImqSsmfkQ0ZOSnl1spXGDXOUROaQn4oTCupwGewEhrKZ6bPx+tTnOOdfT8YPl80r\n8ORmUaYbZYv6DCVbJtEMRRFegytZyziet52pDi3zizIK505Qlt6H/mSAQf9yzk/s4ZhnC75SA5Vn\nhqid74ciATdml3B4djvZB5wsHTqG2B4hU69k0/BJxCPwsuIrnA5vIpw0YNopRF2t47xvJzK3kULz\n+2gCIcQ3ZmktO496xwzqEgVav5+nxC9zZmAtZwZbGU2WoBMEWXmpjTpxP5frluJvMIBESPaBKWTX\n51mj+icFzkuYndCQvkwwvIBUCmmljiFjFZG4gPVlxxmUlPPBkfsp1jjYsO4k2TPXcEq1LNxWhX1p\nlN3+f1Av7yY35sR32MfkQJRwuo6QWUVGJIA5SHpFzFbk4MgrIDqrRpDIIPp7gEmrhbndxXiLDARm\nVJxIr8aZ7UK81IWbHGT+DNsrjmArdHK1oxn3kAWzZAF9SAI7yqFqnFmFiddnH0GsCWN6fJiCiln+\nI/pL5kZy+NfI51hSOEpTwTHEzkHyRrrpno0ypN3OhHw5jUNtCHJTuCPZtJmWstBi4pxzDQPdFQzm\nVuFbLWA6sAJVeoGbNgulCh/LI6+S91En1tAi2mxotHfz5Orfsy55ivzoNPfUHqR6rgdmvOTUBxmy\nlXI2exOTqgIUSj8RqYpUshSvScbNrb2MeYuIvq9hc+VJmiw3SIRkFGgd1KQHkHtCaLUhLkU3MBuy\ncZ/9XxQKHSjmQpydKebjxToOcQeuTB3KsXEW67Oo3NWNSCJgeKaCI9P1jDkDDLrj2FVtbLcexpiX\nBnsumwInMEi9pPSgMXlpuuMqUvkg0akA5RsmUKy1MKyq5XVJOYpUmNzSGYqUY0yttNNBJc4Z2acy\n+ZmI4MS2DcSEMiZqnWSX5hMMVyNuz0Y5pGahJIejz9zLbQvHuffaXn54eRdnfZu4855R0vEeDG4/\ni34tExkbi1N6Znry+Ifj82TmBZAACkBUlcIumKDONkBmPUQ0WjL/HeeKsJnfV3+DaHcQ/fAA98b/\nkybOIpNIkQojtLOBS8lWxlzl/PLGf7DLsx+lN8xroc9x0r2OkuOdbGh/H91zYUR1Gu54/xNOTW7g\npZKvsRDRIZqOYdsQRtWo5dDH24nPGFmjuUi2Z4GUS0hj3VUsraP4W/VYF+Z4eOxFxEMBTnXW4pzK\nxTSzwF3n9qHT+VFVB1gs1kAOaE+5iTljrIq8zTZZgloTLCRFDAWkiNQp4lIZI5Fy4sVShLebmQ9V\ncGr/Fn7Q9Evu3/oqva9GcKRXkbirhDrbJb469VfQgTcowbp3AnObEGnDvdAAaaOQsFvKoltFf04J\n/cWlRGJy0keShP+UZPLrVvofaUQlDiOaTnNBtJZhU4Cspm7cGQNm/yz3rt9LhXyAzn804h/ORicP\no6xRk9lQRcDaz2AsyYnp7WR0sPTJi2wJneSezj/wE8ePuTp/C//V+n3W5Z8lMSOl3SvitFLPKeVW\nFgTr+PHE9zCNz/Bu7AGmDHkMG4o5dWEr3ecbGHioGFlNhEy4grA5xY3lTXwl/Ap3jf6L8atCkuMG\ngjoorBnhiboXUXjTxD0SNumusinYBteg31LGacMqjsp3cFmwkg2CY5iEc8hkJYQsOi7tWMnMa2Zy\n/j7PtvrD3F31DsJCARJjCvlilOKIk5aSPiZ99cyN2bm/cC8bkidJ9UN68j72C5q5pNjCNdkOkouX\nsCvH2bCrG9GoiIW3TZycr+RQUA7+NJvzr/NYy37KVs2RXiUlfU6Eb0SLUBNHWbFIS80V5lMeHMe1\nFG+YoebJbv7JQ9yknmzmuD1+kHXBCxxr3Mjl8rvw/+x/WQS182oLaZOQinNXEZ92ICCKNCmlMEtI\nsEpKUKKjLbcJcWuMEkscjcvBTcc6BII8ah4cxFttYFhYSqYyjTHhwt+VRSyugAZAAWmvkHdO3UNn\nsIFMFPKMTpY9foFixzi/u/hNLmxewsyjGlpvhMhNmWhvbkKp1XAH+xkxlTEvsvBm5gHGO+084nsd\nPEHwOeD2NIrbtLTm3kQ9kELf6SPPNMmm3Ye4njDj7BCQ87eP0Rcl6JeXcS3Rwvdn/xO5LUo6P03o\nhBv93jE2cJUswxiiwgQs+MkkJvAeMdERaObFim/SWH6DJarrGM97UO6LczRrE+996Q6WHXiDJQvd\nkA3hpRZcrUXkXxsld3SeL+te5qa1nlf0T6AqDfDDNT/BpbPwvO8ZNurfoXRKiPSojIxYDFNwavda\nri+po6XiEEtVSc7u9JMpS+LO0nLmZg3HOurw9izHk1UJ9SlyFQtYzWksomFK9/oRVyTpDZZy1LqF\n0QUbbz2/nOWS6/xa/gxLx9vwyfRQAu2qJfxw7jlGR4tJOcS0mxuJVkq53/A2YaGKYxObGXVW8fHF\n3ehWL3DX+n+iOjlB96iVnvXLuF5aSltrIatujtHa/zOWGHrwW9VkS73EkDNPDuFqJbJQgJr9h1iy\ncB1DWkubYS3vte3mnzPbueA0ExzIJZ2jRVMAVaMXaH3zbToXV9IpXQd3ayFXAV4RVck+NgycwNGX\nx/nBWsb8VlTmEI9n/Rmf08CpX2+ltr+H3Yrv0zcl5Wnt5zDeoqbJOMKO/qOcq1jNW0330nZhGbJ/\nJKAlQygBc9dgYViPUFHMhvvPU72+j2HUePJy6DKaqZ0Z4fc53+DNlds5UtUKUyLc/hgXrsvptFUy\nt2c9rlUW4tVSVtvPEU/JOODZzZRFQ+YLbjY591PzjQ4WWWAeD4tMMlfqJr0sTaB7Fl9/L0tqXRz9\nFCY/m4GiBREurIxfzmbkn1J8kgTxggzeHXb8+Rq8vUq6ZBUEhBp2GQ9QJnNytdfKsAGCa1QsyLKY\nmC1EpEmizfdhzcwSymhwWgowxL0UTDhhEAanykEA6RYBleW95CemyY0PMVJYiL81m2iwjOlwPj2l\nqxDr/TRylJxsJ85UJePaXHpk+YSQgS8EkWlmzVmMVS9hqbOdwr4JCEGeYpLNucfwFzUybcvDNtxN\nscvN4roVzGbV4BQXMKXJY0pihrEhajrPsiV6GuwpOiX1TIryyFjThL0q3E41482FlBgGKZydwD42\ngXI8ytHbtzObV4jEqUQ9BaIySFZKiZXpGJ0ow+eOoLYGkdjinDOvotLSw+qcU5yPreK6rwlLzjya\nSAh9OIx8KkDgQoYhk55rygKs8VLS2UooFkN2mkRIzI1gNW8Hb0Gw2Ih+UUaVuA2tyM24uRZ5MAHj\nAuIiiMiyEFWZ8Q1UcOWyhA2157mtZT9T4TyG4qWoawIorSEc3YX4Z7WowoF/D0slI+Rnhon41Wh7\nljO5mM+4sJANlcfJ0s4R+DDKTFpDb24JN7PW0m5p5QHJ97hN9jGhmJLFMh02xQSSaAJ/WE+lcIAc\nyRRZlwZQ941gz1UzpCzHL0pzKVPNJQoRWAqgNIuMTMjyeRWK4ev0e4vokZTimGjEIy2AkJiHhW/y\nlcDLWGLTCEmgTISxJiZoFRxkJFLBAeetyCRxiupGuZqs4ZqpnHRODaEsC82Gdhy2PG6WVbPQaSB3\ndpbUopA5bxZXLhcQjutZmuWgoqCP4poucgxmBrQ1nJUVslpynQf1/2SiJJ9xeRPxMQHqzgwzp8A7\nVURHYhdO8omlhQTHIkgzKa6El5KKKiisciC8rCbTFybmy5D0R9H7hwivD9LdUMXMvA7B5CLWe/+X\nJc92Vn7Em3Of50qykuHMdlzJNKgW+ev6u4nHpEz+XABRMx5FAbcJj2C3TWDfM4KyPoTfoMQ9aMR9\n04KgKklRepTHVH8hHFTzwuJTrAue4Zvzv4M4YARWg9IYIvuImyOL2/hJyePMO4yE5pV09TYhnIni\nOSyhKnOQPK6wbksH5koV7ddkSOfDGDZMQ3YhONycu7Qcd7SF73p/TaFoAhogTzfD1qOn6DRXc+n/\nNFN7Kptb/dfZXPcCsRI1GT28duqL/OWjL0FZAdKKcsyTaiYkS3hJ9RTdlWVgywGrglyTkyc9f2H9\n+ZNY3TOIc2Mkfwx3St5le+QQpu3j6OMgyQVFu4vsb4X4OP0410W3gh/SSlBsCjDutvPzd59lOm5l\nMa7l1fwvY22epGTVILnHuxk6mkb+7lnyTjp5b34lDvNKJizFNKb7MPb5UQZyoXEFwjI5xnAva/7+\nBok+DT8K/5D4rRrYJoYRiC9K8G7VoK724f84m9AqOZNbzbw28gWuhVsoq+jnFt8B6kR9nMxZx/6y\nW7jHspfS2T72OdehHE3wzehvmVtvof2JOvomaxm88hDbPRPYNJPkM87QeAPxUyrSJimxTVIGjCUE\ncpQ0GtqRzSYQDQgJX1Ex1WbgjZlSjieeQOWvY8EvJhm9DvcVILitAJFMgiAQJdknZ8pQw4lffo3N\nY2e4a/QlXgg9w/GOYkgIyGRBJh9oUSJPK9itOcKm2feZn5wikKtgyzc/YjxZzY8Wfslm47t8UXaZ\nN9zNjKZL6W8tocgwwrflL/Dmhi/iKrESaZLQ09nEXw99jWa/u9sAACAASURBVAZlHy+UPMVbR5vY\nd6max9edwbLUz+XaFSTTYpDC2vJTqEq9zEbMiPOmsbcHaJvLpr+tjtAJAelLXo4KjQgNArwlErZL\nLvLt2ItIli4wf68V6cVCzJcU3HblGuJsNc83PoWnWk5BIEXQbvhUJj8TETRcPkIspmA6N4z3ASUW\nRtHmOcgxKpnstZEasGFYmKVA7GSsyUjQugqfXYfPoOWT0E6G0uVk6xfIUsxTLenBvnIErz8bc940\nqfY4o2PZiMrFqKujVK/oJTcwi/SjBFJDHN8GDVbBJNnxBSSCJKl0jNCJMCqDj/GWeoQaOR6xjLn6\nGFmJWVItYjApYNGMy19K9KKag9ZbWSgzQnWGrLkxrKevE8sLkCiQ0WdZSVlWkGbvdQJ+DdftzWiL\nfKx2nWVYWo4vlM/5wHLiuTKmKs2EHElwTkJah3JhhspENxWaITJWmCzMZbzRRunlccp7Rv4d6DQA\nFpix2rhkbiIpU5DDPNfnm1G5gtwS3s/weCmHjt1GY14H+bY2rpctYbrcyvLKC6S6pByQ3MOCFBZ0\nUuIFenS2EMq8ftJDQj7u3YW8QMIDFScQquOYxCM0WvvoppkhSpDUC9FWhoiggESa7PI5cqNDpBcD\nFEY6kBLHND2LeXqWaFIO+gwltQN0aGuIGeQElBoiBgX6FYsYbV4slmmUlSHilSBOplmck5K9OUEs\no2PklAWJws+mnOPki52EM0o68uqZUOVjHp2nceAmrT1XGUsXkao1YjYlGXdAb3c2gYgU5AIa88Yo\nKhmkY6iB2KCcxpvdxIxRBtU57PSEWR6+QcvgIZzeDJPCSqaytRzVbkSWl+Ce4n00GTowS+aJrE1j\nM8xjzz/DRYUIdywH73gBgdFcfB4zQVM2h/JvZe3MGVYHLnM8eCsTOjtz/w9z7xUkV3m2a1+dc093\nT/f0hJ6ck2ZGGmmUs1AkCYHIBgM2OMHngG0csQ3YnwPYOGJjjDA5CiEhUM6aoMk5T0/s6emc8z5w\n1a6/6rMPfv97l//ncFXd6+y63qp31bqfbBNyYRjNLXHyh60ss7Xwsn0d1mQVGuknZCrGKCsdwSXW\n8brsDixMsTF4jskxAwF/As02KYKYEc9xA/HmGPIxH+b6EJpCL9qCURT2IFND+Xh1udiFCortU9QG\nulifGMEtz8JrcKDRyknGhditmf+Syf+ICPJ+eZobq67Qf30F3V8rJYd5LL4F8obtfDqzk3btRqq9\nJ9gVfY4Tu75G9137EOqTJAJSLk1txaRboHJrFw3iDioEQ4TukLKY1JElmqJ3Np/TA08gPaim6D47\nX1H/ms2dF9CnfFgKptl84ATrBJdYnmxHFQgROR1luj3FhfU7OPbMV/jUn48/kI58jZP1kvNs9/3i\nHyd24Qp4Q4P3goCX1tzPq+vugqIUtXNvct3FfsZCTvw6Oy8fvIWR3FqePvkEE9ZCntA+zerlF3lg\nwx/5268f4tLx5Ty7dADL7nnK1/XClB/n4RAIy0ESAGUYtkPyVhgqLOXj1E5uafmQ7I8W/9GGUwXk\nQtfKTfyx6Um+73iGB8Y/4NvvPYNgJsW9I69xumMzxy/tZv9N77Kn7DhPlPyY+dwM8uRWRmSlHEq7\nj+gWCdo9Lg6UvkWV+QR+aRodH63kW58+w5dNv+U3BV+BeIqwScLSYxrm5DLMOMiXWymRj2DLyCSY\nUqKReTE5Osi59jFNWieWvDAPHnuJhq4ufmT5Aa5t6ay4v5W5uRzGLlXwQdN+VpTkcPujr5GZsGEV\n5hOXiTEJ7TQWH8KYYyewW8q5sys4+tQ66rdP8NUf/IyK42MEupW0FqziRGA7gdM67hs4RJP1Gpfv\nbOL8rjVsTJyi+PxZfv0dFb5UIxSs4gblT7l17j2eeOlpHJfTeVzxc3oTcr7z1o34DH5kWj8rZ1/C\n4erkmOARes2F/EryJe5Rv8lT2idYNBhwFmVTULaIxu5GNdOKOcdDrmmC9567jcsnNxBaq4TKFLOd\nFgx2L3unTqCKhYlkyLAuz6Oyto/9P3yTgj+M4HtSQEyXB+pqGNKSnjXFmpsuc0G2ma9If8OPF7/H\n3RN/IfXKBA6VltR3cxBdNcKPBBBToM1WcNPD05TsHKZVLKHro0a+cfbnxF6fJt3Xy+PJ19keu0Ii\nFCSCh3peYJp8RiJlvN9c9S+Z/LfKS/+/zJNPPsmTt8TJEYRQymMoQzH6LtVz4XQTPZfSaY1UMbyu\njvI1Q6xY1UU4lEDeMkbelWZE1iAzmmrCE2riZ6X4vGm4SCeskaGOBagb6SMUUNBe3MiKbX00lHZj\nk2QwK8xBpE3gDsYIn5mh1j1IRirEWeE22ufLEJ0dRx/xUxH3ka72kFbkotQwhhY/o/OVBEIa1ipa\n2Dx8geVT7SxVmghJBaxqPoLJP8n8mlJyRUl2L7Wz1tJLbnyCkQ4Zp0OraVdvIDtzjqKSURIiMeK0\nGA6hiTytk9vlRygWj2Eu9xB2FeBcLCSQY6ZLWcc5ayNLEj2ZxUuMtZVz2r6Nq2tXYc8ykj00z7Xk\nSj7M34/oaopAuwpz5Tx12edJ72rFK5Ai2K5kZ8kJqpLdODuCGOeGqSkYY2a6mAtXtxFOqZFH42wt\nP0OGdInmT9ZhW8pC3+CEPAGzWMgcspE7PIM0lEQdC5MrnGWVoI06YQ8l0XEqZoeoONyPZtKDq8HM\naKqW81eauNJTxNi8Dr3Phto9T++MiUWBkYxyP36bhsSklK1pZzHrF7EpzPRN13LhylYkihhkwICq\nBvekgZKj41SWTKLZG6Ht6io+HtrLtdoG5iNZuI8bqRvoZtfCcfqaKhlcXs68L59edwPDghoqKxf4\nbOMHlDpPkro2gp4Q5VXTqLZ6iShipHdNUbrahewGBSplkDyxh1K8yBIi+mcr8NvScMiz0GndGF3T\n2I8H6b5cysfjdzCmLyZUJKPD0ciMIQ/9+iU0VR4EOUlSkyImLxdxQbKB+YwsoqUiFtNNLCozMA45\nqbw2yinhHqYkJezO+wRyBBzhBhIiMSsKWtg0ex5Dh5OPuq5nLFpLdfY0caUCW0UGWetnKV0zwm7L\nSbTBEGcnttFnq2VemsNa2SVuVR2haqsVQ0MAbTzCRNzA+5PlaGMBKuQ22o+tZOziq//3ykv/345x\np45AWwrlQhjLhI2ByzW8PbSNUHKc1G4p4s/HoCiNpLiS4m93k33oKLJQiIFNe5l8fD3+zkzsr2Zh\n3VlE71YvW6SfkheZYWf3SfwWDSf27WG1sIO6QBd/Tj7E1bQ1CG5Kov5rJ5rftKLYFye4PZejhTdg\nDUrYL2hhQ/8AqxYG6PxGLf2ry0hEkwzYqzk0+SDXCU/yXNZjKFR+5hRZWJO5CGZyuP7YC7g2ZfPO\nd79G0xvv8FnvIWTeEN1uC094b+MalYjHIyQrhPijamrXdpGRbyOgP0DxqJedn5zGv0tB8eNVeLwx\nzjpzeM9ShCAQJfWGm1vVH3Lfjnf4lfybHM2+AdPOOfa6j7Lyv68hDiWR1Uf5pHkXg80VPP3sN6gV\nHKfntSjsmGbrEycxttmJnUux/PC7+I0yzOuy0Iq96HUuvFYtIncC/UYXwmiK1vdXoy71svfLhzkz\nvI2jrXvI7JqjbGkIo9VFWmU3FVWjiHPjSAVRxP448V4hrr8o6CpbxvAz2zn3Yg3NLxShZJrKVDef\n932A3W7iuY67qfqShy17z3H8vevxjKeDSoRMEUOmjTA6VcqbZ+6EtBTBQgUDggos0mnuN/wdt1pP\nO8s5Ft7DFd86NDEninAQ33CMqFWMT6ghFRKQ8Iq5NN7ErN9CfD2sVV3hu9qfcPGlBN0nhVx353vI\nr8vjw5pdCI84uPHMWcIrchi9rYSqbCg12FjTcRrVuIzjlzZwdOsWLu/Zyi8dX6fE3czoX2UcX1jB\nkbzHqTYP0Fh/iWCTAkl9lPSsRVRpPuJyET3NVZwY34kiLYRMGqIrUcdYpJCMmJ1C7TTxKi3JSTHJ\nWJJguZyQIYv+C8uo23CNBzb+CXPLIvM9mVxI7icWkbHnXAeWbVZqHmkDUQJTwE7BtQmc58wMLdYw\nmV4MG2Bbfidf8X7A1A05uMUZyIUiRs9n8/rPynnMf5V92hEMA0v/ksn/iAi+2vsLFNf5iHgUJGal\nVCoG+EbRCK/Y9jHq1BN/ZoiO6zNw7nuQxM0OjKZBdrz2No3KKeTZb5OuCpLjn+fV3ruxpgrY7L5E\nbtEsh5fvI2SU8GXl80wdLuZPFzYwFChGV+Vk9J4SEhuzmPm1Ak+ulTLjIje53kMdXKIg24ZZBQI1\n5LtnSJxP8OqFBs5MWHAiJbZaiL9JikMuZtQPXimIl4nR1qURK1IgVcQ5vWkTM9osth1+j9yZYb68\n92MWaoZwVhZSYp2g8udDiEjgS9OQVzWDQrSE6xUf5wyb+DD/M2RcN8PDhS9gPr6AVBEh/B0J+atn\nkaUiiFwJMmYXuX/uEDsTn6BXushLn2BH1jF6lfUIoykUU3HSBDGM0RT9rgpODN/NyZl9ZDhHyYn8\nnYpQN+YZGzcIj1LaOMlrxtvpyKpDJ/ORnbRRdN8IOePTbP/xKSZnTXQHs5jYamLIUkzB9DSdsuW8\nIryHMtkAKxVXKZsdx+9U87f0exElE+y8eAZXQEdX/WruEh2mJtjN5YnbGa4oQXaPkVmFmCNvNCLO\njlJj6UTX5UE366Vy6yC5hVbkN/opzxlidaiNuEyMoipI4ttiHNnpTAgK2bL5JDdVvYc0L0xfdxGH\nnDfRUWPhF3d+me5UPbbDWdwy+D6m6RGG3bBCO4LIEkdSVoB0WzauYjtuYz7nfNuwpetJu+tGdk9/\nwurvt/J+2Y0MppWB28G4U04wOQr9IuKJTEY+n4e4ejcfXr+OjquNhPskpL/aT2nbMToaqomaqln4\nNJeyvH427j1DV3gFDkcmt8+8wcrcZgaTxcgdIRq6eliSpfOjR79N6wsWHH0eXq3fR61inC9c+g1p\neS6cGDg5X4B1Uc36PR8g1Bj5c89DjH+gZ+qUDARLZKccbI8JEMb4x4V4FaAET6OKJYWONLEPd8DI\nr1fdS8Dr4ktDp1jvtsJMHHzD/5LJ/4gI+vpL0O9xE5aoESRENOmbySqbp3GiAcVUAK91AndPNdbs\nVUiKouQbKqgatWExzpFjmqNa2sOyYCsX5ptYEJjJDoygj8zi2NuAOO4nu3OAc5+s4dynG5AZghhZ\ngGtxHDV59N+1iqB1nKWJQdYOX8FsWyJUVopNECag8hFAjbPPgGdIQnLMQ064F9QuWpcV41kKMRE0\nsOjUEpGosG0tR5wWp8o/xEJeJp3mWvL628jwjbEmux9B4TjuCgNpC360U35mw7lIchJs3HIOQcBD\nNB7GP67Af1XLhm2jrKs8S8HQCOGEkMHifMJxLSNteejcDpYnOygZGkciTtCnrUEgTrJz8Th+k5aJ\n4mImpwrI8hWhi0wTDmbRPLWW7NgceSYxyeoScoULaKaW0MYnSYgClNVWsVSnxzOqQ+P0U2PpId3q\nINYiJ24NExfZmXswDetqC5lH7MwILRzXXseYx4jPFYRADKFGxlhdKekiB7qAB5U6ibg2jeX6BZbF\n5/kwcxm2qjzqdvQx2VxCx6e1VD/QiawmxOxoNsKlBCGnDDV+miRXKBKMow24SE34iETjpErAGVPQ\n36LnHtFFNmnbsE7mYutVIXa4GS3W4ctdTfyyCl2bm0yvlVpbG/kTU8hkYtrNFuyPFcH+PIZc+UwH\n8rHG8hlXleAr0dL0cR/G1mN0JCp4V3c9JD0IM+YQpk8inI+Qak8SmpFhK8mlN3cP47ZSsEUxLs1T\n5WinQd2GN17M1GgeElGSung3rmgGIl+ClfPN7Bs9TLz/BlImEZW2Qa5lLmeyIA+DeR7xnI2u9GpS\nShVNhmbS/C58bSquDefS5s2ksqINea6aFncj02MKUqNziFNu5EgYEJWgTqgxBMcw5alwplmYUuXR\nIl5JTWyAsExOW90K1DMLNKqu4osX0BFQ4tD//6yY5LuD38D8dhxBhoiAQcWJqi10GGq4Ze4dbopF\nuSYv4uKnBVz5kYTEF5LMr8njzYNfQa9yodX48cWDCMKtOKQQBsZbITNg5UDhO3zcW8Nz765lfiYN\nidxB1oMOajRdbPzTMTo3r+TKY+vof6+CgdcKORNchqIwBrcYKSidZll6B/0dtdjGM9h+x5tst73B\n/O/eZehoMU+1HyA4oyQUSsN2uQyxPI13Gw6wJXGGh4dfZCHPhDU3G9m9YiYra+G1XjJGvWQmF0kV\nCVj6rzT+Nn0v1lABX4o+T7V4juTmFLdOnmDT4UFkxT7UWzwYHghz9bKF3/+qEZu3HLHQwsHq0+yr\nOMF75/bzB8EjUAF7lo5y74sv011TT1dDNS+8eS8jbTk8Ev0dpiQInLCn9Ag71n1E685S7OM7iF89\nRmtvMb8avJHyQj971n/EqeBW1INB7rz0KpPxQr63/WlGz4dJTizgQ4Q7kkZ8UYzW5KK0qB/bO3He\n/bAUPhdl+eZ5bmv8O06BgSOKXXT0LCOlELBUr8dp0WAMLFDkG+XOoTd4r+8AffPLmPVlcyWtCfs+\nIyJvCpszh43N53n6wvcJflZOc3kVJ/+UQ+boEjcUxnC6QlyeCLJLF8Un1/M31wMcX9iMfVFL4sIs\nM9NjrHFfoyxh4+jy/XTp63kw/CyDS1qeGt9BrStEQRhOtO5m3p9NzmoromicrublSLyg1CcQXbGB\n3A5ZGYgblSh2pBF51Yj4VSkll6cpdY+hcQdAK4KHZJi8YpY7Ilikr7EqPsWzm/8LcVkcjdKHLBUm\nFYeIDWytaZxf3MziRjO2gxnUTPXxwz8/TTwUZ7Iil+env05HUSN//nKK7Vc/Ye/Xj2AYK2YptZI/\nuVZhrpGQdd802ckwiWgcNW6kfgUfXr4fSYuHho5DpIXrOZn9CBfObWCu08Ld+18mu36GOmELQznl\n/Nr0NMI0IRhETO5Ih3e/80+Z/I+IYNKfh+d8gFLjCHkZwyzYd9FuLCbP04pcFGEpzUxCIEFf5KTW\n001O7zShCQlLKSOTtmJwbca1MYW1rAhRehz5TJKwWceMqJYFTR6KIhFimQjUIlLLBSjFYQpOziGd\n6cB9/GOmfZlMZ2Uwdi0Xp9UEXhNRpYKCshFGLhcz1F1FnmSclESAvSbGYjCfRWUBMr8dlXseXeEC\n0VIpc4os7BITYlWMwIiamd4CZHUhFkoyGfIVYRD5scj8BAJqlpzp2KdUkPLSYalBkJagNq+fQt8c\nFs8i51vXcUWwBm2Vn05jOu19NTgc5YjVBdSrhhALg4yYLfQmqgguqChNH8aXrUZd7cOinETvmyUY\nEnF+xS7mlhdSkdtHeq4dqSVOPl6iESEnx+qZn9KQL1gkW+LHKEmQG58mEZQy4S3GE0sjX2hFWB4j\nUOOhVDKDZXwe+UKEhEJMKKnE7tLjnTbQLZChzBohhxkUvhCmpSWaEi3kC6bJnBxGFF5ibfl5shMO\nVnR1ckWwHlFtksrYECV9PQTnvfhEepJZGSRNAhJ5IiThGNK5JC5ZDnZ9MR+JBQQVLrYZOslPLSJ0\nxkgOLKC199BIiIWwkjGbBXe2A3+eCL3GhyWxhDYZxS230Jq+DaFmllTSzWC4ilhMymrZRRS6ML2Z\n9QgloDZHWD/XQkIhxrOxlIVsE7NzamqCA1TLrQSHw7QsZuBJuFE2TWFYmcKbMnJpaReRbgX+sJrG\nohaURV7CEjkhkZKEUkRb+SpcOTq6U3U45EZiBiH+dkiem0e8QYOtshifJ51QQIl/nQrhfAqjyI1C\nECUcUzK0WIFo1sN23Sd4MrW0Fq0iXREhIxxAbrOh8s5TbpoikG5CcGKRNI8Hc/YCIYOMBEKaxlsJ\nWsWcjtRT6pynZHKWJYeGf7X98D8igh9InkHYOc3dyb9xk+gwi+IY/UINP0/tRoCImNCM5vMKcn86\nwf2f/o3r3vmYpasCPnbu5TnVT7i85QAtj+wjniem1DREcTKJP1nL06kfklk5xa0HjnH4+BpapvS4\njRDU6tAcFLH1ahtrf95Hz/3lNO9dzqtPHMDZWwgXRWjSvRTUTqCb8OD9NI03Tt6GpPomEvdLyWqY\nozJngPynz2F4vZXuO5NMH9hMVClhSWDgsnYlZ57fwfnXt9L4/Stocj1cUzeRyBaSU23F8V4G/j9q\neNDxFKVF3by/6iHGzUUUKqaQFUaIpUs5fPoWXj97EMWPfESkfjyCAGgziWfl8mb/btRLtWh/IMEQ\nXST6ZC7WnRZOfm4TcZmApqnLrBO+j8Ni4ncHv4dmVZAtpZ/ikmi5wAZ2chxXIMXPR29kZWKU/175\nN1qyVjFEOXcKXsepMPKTgu9S09/LUxe+w+XPL6fzjip2dHWwvLUXyXwMj1bHyEI1TlkaoqIIkxol\nkI4GH01LbexvPoJ8PkxsUcTceyEi8Ti3fH4EnSqJYioMRSCpi3Fg+gP2fniIS8eT2CpykPykEddN\nFl7ddRv7Oz6kZmKKtH25nJdv4knfTdwveJHfyH6AaiRGsCvF2vkXyLdLSCfFWe5jgifprtqCt9HK\nk8GfsGXyOCF7AF/2TlIbVzNdMENUvIgrQ485ski5dAhJbhLhngTYQL0Y5P6l4+zRX6N/VznHmhv5\n+/fWsC36EZ8zHOOp2d28O7yGAA7MhR5qs+KMpufzw+B3cI6byXTZ+GLac2ToFxgUVmCXGYnrJbx/\n4+2It8QILqlIKAVci62iZ1rF3weyUd5ThGBHAUtHssh1WalN9FJUMY38nhSi15LQnoAFyGqxsdf2\nCZeWreOPu5sQZCYplIxzi+YImSvmGVlpoeuElvgPZ9n06AkOPnKeRUUGgikB6z5qxn06yqvzJewb\nOMpnouf4dt/X+Vd/G/xHRPCA6EUUq9zEC1S8Y/os48d1xJfsRHaZEVo0pFIqVqxpY6fkEgtjcv7Q\nuofAnBd/MM4m7x/RdcswKWBxvwF1mYcc1yKikQS3XzmEsdRB7cZe1BNRlg20YN+eTUHuBNQkEY7H\n0TncTJwtpXmgkYzpK9xoOEXRagnF5XMUCCfZFn0PfXSYK7nbmcqsgJiYlQNdHGw5QjRnCe/Xs6hb\nNYXO1U3L6430xarx5+pI6kVU3dGDS5yOe0BOydJxfJ50pv66moBSi/i+GN5gBsKYgrWnLhAIpvM7\n1xdJOYUk7eCZcLIp9TuKXo0wEy3kWHQTudEhGp1/Z36dgcXlmbhCafiGEiScbWiuXCH32W76GsqY\n1RaiLwQJboLnvAQVCmaqs3Gn9CRiQsIiGcFkmKm4hZQ/nT/YLBT3j1Ij6aLljJaeUDHOPSIG80r4\nU+6DzKzIwidT4um8SOhkBMc8aHImuCntLYbWVTBtzMXTnU5vpw5pZoylYDaz1kLUeV5EWyPYLUKi\nMQHm6hgSp5hwXMX5rioSQyO0NRYSLbuZ7o+TmCbd7Lvaz9WYgUuT2wgUK0grCTKTlU8JE2xPnWVH\n6AzpwRBzhWaGlFlcbbHgSMqo2eUhNCEhdbydzTnj7GrooT7QjbIkwtJKI1lTUxzs+SX5Ai+mXj/t\nI+kEjWKWihRkeGw8fuW/iSfF/FL+dTbWnEYsj3H53EY6z9cSXTCh0KbQGX3EDPmkQhaWT55A1hVl\n9tlSlhUMstPo5T3BrVgr8zmj3UJjoJWqxWHmC3MZ+WopWU0LZKXPY+mdZ9aRw/HJ6zDpIix7fBqz\nexztH1L4++RkSB00LfWTWz2NZ5Wa3NPDbJMfRlc9ycqKJTKnFjAW2Mk3jJN9oQ3d0FUCFVH60yyc\n+LCC1Iyfx7e+Sm6Vl4BGTXbURlCs5u3y25hQ69mxcQCXIp8Xkw8xMlf3L5n8t0Xgdrt58MEH6evr\nQyAQ8NJLL1FaWsrBgweZmpr637sPdTrd/8h+TvQChg3w0oa7eaf4NlxTPShHrEjuzkPYaCIZTdKQ\nGuKA/30eH/sCf+/fAEyxVnyRByW/p2EmQGFAzvCqItzL0lDNBdC0eLnr9ZcRrwZZJqT3T7Js+Bxd\nvnUo5ElS2QliaSKksQSD56poCzewyfU2G9acZO0mJaJSDU5PGpuSfdToP8FfZ8JZmU8kIqdoZIKb\n248wcFcpPQcryZTZUF7tp/uFaqyBImzr8mm8uZm6g9c42bKb0GUJm+yvY18qoXthF+KHkug/t4BD\nbMHTncvWb5yiY3AlT+X9lCVvBtKlINvD3+O65Jusfl9Ct2IP1xSbaRS189noU7TsuonmXfu4/K4O\nd2cEkbgDw+BVSoeu8e7+g4ysKoVMDXqnHf3pYRZ0ZQztq8CV0hNIqBgSlJIMu/Cr7PQ60xmcupkf\n9/yIbf6PePHMfo7rGsl8KMZUoYWehs8jTItjCs4z25HGwnkxCwIFotVL7NYdJitnhrbcRsa+U8HC\nmRyiDVKGdVV8HNuHqs6N5joXrEpCOIVACok+GVGZinDrHPG+Kc6UNdCycjV2c4hNc5fIONuBZCLI\nZIeZye8XkdotBwns9J/kUdlzZHsWcNn1WOss9GZUcjl7HR6TGvX94/gvx5G1XGRLxmnuy2mBpAi3\nMp1Ajons96e5/sN2isfjGBVJ9E4RAw0FOG+8juWLM9zc/BeeVX6VP+V9Hu26JbKSc1z800Z6miuI\nJcIgUyFQC1HmGclDzpbwJRbH47zzi2x21HRz58pWuletoLOsno/Vu5H7I+yfOEqgUM347nxKQqNU\njg7S2N9Jx2Ajl9XrqLjRwcEvdVH4rX5Mf5kkEEqhEKfIaYPIfUqc16nJ0k6xTjFH/rIJSpsSyI1e\n1OkuSs39FLVcxvTuFRafrGFAVc4Hr9awxTLAI589TlfFMoZjJawLteCVyXi58V60m9zcbniFk45d\nvDy2D3FHnH9VUfRvi+DRRx9lz549vPPOO8TjcQKBAE899RQ7duzg8ccf52c/+xk//elP+elPf/o/\nsk8kfoUsIibjSD+fWXiahTYXk1joOL+apfY49A1ywV5a7wAAIABJREFUdns+/p1fpUu+FsRmSCyQ\nVpakeg8sFdRwLqOJumgPutf9HGq7jxF/CYktIJSA6AOIFINoU4S8omnqF7tRd0aQtMdJeODmle9R\nntOM/0QvM8kyfhG9A+85BZF3F7nNcobN3+/jK+l/YVX6EB8ZdiGP+LBvTcM0t8iKv0Zp3b6chFDM\nF2QvIE9EEcpFXAqu4+TCbuZiFnTSYeICSJiBdbBs6Czr/+t9+mq2czq2g2JHDzWBDp6Y+Q6pNQqE\nTSnOHo5zaPwOPt5Yi3GZnJuK36FK3IY8ZWRmeQU9sVq8QS3qEj+Ze7NRy4uxMUPwsovgy9foCLlY\npp7iazsOcUZ7M+++8hlCOjlxYYJYpwNVxE7+A3GKOy5S+dZvqacNDDJQLSMaWIe9Vcv6yVZucb3H\n2Ip8rFlGXEI7nwqLaBbfjT1aSXJew/qpizza9Xt8Kh3TN1to3thIr7iW4Ykq0rOdFIf7qXjjCqLz\nAT4V7cNYFOSWrZ9wSbyc9zw7cR2VIOgPULurA6FDyDMn91FQ6uWXt/+AycZiJlQljApK0IrcxCQC\nTqi38Jb2DvaMH2Wlp4+BG4uxO6QU/fkMmnQjsV810tK1lr6frwRNLiXyRW4Uvo9DX8qhb30D+Sk7\n8mtuFlZYyFgT5NboOcxZbpq/sJxln3TwzNV+ajM7UaqCfMvzMz5K280rlttAqUSlDHBv6BA79Wnk\n3j3BudGVHP5gI5+UrGFmm502XyOCsRRiSxyxNI5AlaJB2IXCFebo23v5y5mHOWx3suQy4pwwYGqw\nUx/vJnVLnFiWAcNbPtSTEcQ6EE5FMb7iY2JgL0fZgBklldZhXC/PMFknIFEmJH+9gJWJKLZTk0yF\nsxAuq+Ry6Uq+qtiE023Am9RwSraTlFxATAdamZOIQEa5ZgCD2Um5bpRv/Aue/y0ReDweLly4wMsv\nv/yPl4jFpKWl8eGHH3Lu3DkAPvOZz7B58+Z/KoJ2eT3WuQxuc42zZaEdWUqPX5NHesCP2BtBNtaD\nsEKO1ZmHP5KOQKVGmJOGZJkaaYGAidoSPireS9EnVvLa51kaMdGqWIa1VEdkTomgW4Fl+zy56VbE\nVg96t4G+tmpMriVUlV7KSoeoymqjYxGupjWyEMtnoCebybdiNNwzxuaNXWSK5sgTjKMJLpIyh/Gv\nUGD5sw3jSTcjFaVEjVLqa7rJX5rGhIsRdxn9c7XgBUVUzlwqh5hZSdbaWVacvMq2I0fxTJVjs5Tj\nyk7HkrSy2/o+6pwkghtkXJy/k3blNiybsqmptlGm7iEhhGEqGXEVMj1lIiEVkFvpY/UWJ0WpKHG7\nEnFXgKTPTedULtKCJCvrYugkSbyzaeS459Al55kZ9pFMB/kyDXqXl+zAFVAGcRRkIK4zY/BoUCd8\nNEx1ccfIm3RRxdWiKhYkWkbKa7iquwF/YQGahI/6+R50Q350Kj/inBjjOfkoowEE4yksgRk22C+x\nYvg4qSs+JhdNmPcE2PHgMZZmMxBIszBPTWOJWDE3uXCk9DS7KnhA9BG3lrxPW/pK0hIeZFYnKo+b\nXiycFmzmVeldlAcHqfL2oCqJ4PKJ8J+XktiiI21XDq5+P9auFL7iOhLSCRRzEfybMzm3+yCpwBSS\n4BzhVTWsrRqhNPY6OqWTjvwa1AIfWusC3gU9Yb2YquA5xrTpiOvuIqTS4BOqMUw5EUfjJFcUodLn\nU38phj9LzXiBGtk5B2XWOSz1Ngo0U0jcMbSORSSji1zuXIlzWEdnLB9vKg2UckI+FYu9ZsRiL6LC\nKC51DgilkEqRMW4j1zlNKplJuLKKcDDAQpeTS2dqEHq1lKwZpyjmJt+QQNhqJyPlJOMWGYO5tbyZ\nWItlegZT0k5bWSMCQZiShS40KhtTWQWIvEnM83MUhrr/JdP/lggmJiYwmUzcf//9dHV1sWLFCp57\n7jlsNhtm8z8qk81mMzab7Z/m7818jmcvbsF1Iwx/bQdnPt3EtDuf0j0z5GeNYTk4QNHiIuYTQf7b\nej9W82bkX8zDmyyk+U0prd2Z9G1uwDFrIivtDF/c/TsKFs/zqxNrmF8oR+DP54b3X2XVmRbekDTw\nV/k63lXfTfnqKeoebWPr2QvUDXdRdqOAjLxRNid+zjve/fxK9TAjp8wc6dFzIfcBesSbmZsTknXd\nIMLH4giCSZSOEBtcl5iszKP9iw0stpvZcfoszAJKwAZOm5Ezkd0UZ8yzY93HrJ3opVIU5OHZv7OY\nbmHmQRODkzVU/6oNuSiERC2CLdVk1hfyYNMrMCHg/V/vxbcURMA0CykT8WwHmnsCLCvu4v7Jv2O+\nME7iuJ+c/WtRf7eGzmeX0RcV8Z5ajKssHdHaELdMvM0G+3l+/8BdXBU1MTmgZrEb2pJxrsueo35d\nBHW5iw3xU1Tr+lh7uRnJ2Rglw8OkRCH+UPJZWj63hXh1GiUF/dRndjEaLOLR4K/hKkTGZDjsejxu\nHbEWCXWbe7k7+RZptUv4hHFu/uBlogINC2ITDq8O6XSA2ze/Sn1hMy++vY1OXwPh3GISY92EvgmB\nr8lhRZTa33/EXIuW37GX0dp1sF7AcHEJZwpX0fxyBqNthbRKNhNPGQh3G/is7vds33OM9q2rUcmi\naM96IEcAERHpe0QYtoqY8QkgASTA3L/Iuv5WXhm8h3fSboUcyNB0szr2HGN6iK2DxWIjbco6Pnx+\nP+3jjeCEBlkH3y59BrXKT2IOhtrB70pStSNCkceF5pyfmUEYtsXYuu9Nyho7efbF1VwTr4E713DW\nvYXJZ/PZt/Q8JYvtfGR9gNHACvDHuXnhHb4g/y2rPtuOfrOf8s5JZq+aeMl9L3UtQ3z+h4fQuxeI\nhSEzH2pXuGja0EYsqqKnbznXDx/hBs+HvJF1CzMRJU3Pv8piVinH7v0c0WYFwpNhjraXAP98W/m/\nJYJ4PE57ezu//e1vWblyJY899tj/OPkFAgECgeCf5gfnPkWlnmZuNsx0KI3yHAfZqRiW7Ak0ZW7E\n6DB3TFI71Ye5cQmdLETBZhdKl4oLbdcjSOi4buAkueEZolop8yVZ+HL0ZMc8GPzjqFMOJO1zzI9H\nSCwXoFJF0M1M47CpOBm+DqkpSVQuxd2oQWP0UzfaT4HUhkBnIGhX4rHGSc3aEOnsRCSFjM5UcezC\n9ZgkLrQbfcizQ9iEZs6nNqGV+3GYMxGIU9zpfQ2SsCSWck1QiIQIm1LHoVjBiR23ojN6SVSKmFtj\nIa4AiUGEXZPJQlohklo5tZE5moydzDkysOVlYZ1VEO/MgiIN4kIV8TQxTq2Bfm8lUrmHEs0sUq+S\n4GwRrqgelTpAZvYQeZpO1kc8GCcHsVqF+M06ovEMYlfkmMZE1OuWyNW70acHqTF1s+Q1ER5XsjCe\nxvykgAlVIf05yzGWpdjc2IuoJoRYF0NEgulwDu3zTbAI6pCPIvUYmbp5wgk5mYF+BJ9O4VurJ75Z\nxTLHNBMUcO7YNhYCekp3DVCo6KDE3kyTS0s4YeSaoQ7hkgrxmIBFl5mFSBZrJq7AIlhryxDkiliT\ncRGtxYUjlo7DasHnyka9S0J+/SJZsm6U4hSTsnpsmbkYtUskcsXkGGfYojnFUsBAIJpOrmkebczN\nWfsmAoNyas9cIzM+Q5FxHHyQJrehWC9CgAwWBUiyYyizgsiXB4kqYowOpCFY0rHMo0M7qUYolZCR\n3k117gQlgTjymBSHRYcvEUWkCaGNT6GfCSFxF4IoBLNJNFMeLFcmkBSmCFTpSVaJ8MxrmOjMZ7ni\nGkIL+KIG5kYLEfXJ8AQ1yLfLCWKmI7qC2lgbkkSE3soGOlYux55jIjYkIdUPKXGKhCVFdEyKd07H\nzCULs4YCxu3TGAYPkx6wY5VK/iXT/5YILBYLFouFlStXAnDgwAGeeeYZMjMzWVhYIDMzk/n5eTIy\nMv5p/iu3ZGO6YwsFpmnqkr3k9vwBsTdJf7yEHqpppgGD3seyqj7kO6RkmaOsVzbj82n5+NEnuLPl\nLZ5o/ipKXZBJYwG/036RgdpiqvZeI18yRU6ii0+e0PPe2c3UPaLgxlQXK793kjPnbuL3gR+S2i9i\n9EAB47IiSkITWNKeBwNgApkfzF4Xe2b/QrF8lEPbfkK7+DoGX9qIYq8P9UEPGepFknNiRk5UEg4r\neXvZHTwa+TW/CX0F0uCaqZ4npD/E7FWzdqSdV4ru5bnHHqPQMozFZCVN4SZ90InOKKAtvZxjuutQ\naL2sC42QvbiAz5KG+gcRZH8wE+8xwhoF8V1SvBIBV0LpdOXV8LD6BZ6s7ibyWjq2P1pIRoTUbB7g\noaI/Uu/pJvvUIs+c3MHPx24kGCslKlGSOilgXWSUZ3LeQq4MEY+KKZBOcmlhA3/+4AuMntFjHvuU\nd2+9nvMH7ua7+qfYYfwEn1TOWTbyKnexMJgJx4AoGMvt7Nl1GFO1jaW4EcVvBrn8ghBRQz6GTVqW\nCXoYP2nhjaf2YbgjwtqfXST15DyhMw4eWPUBpeoU4/bNSAG1TsCstIAhYR3Xq0+haJJgeDyNrKJx\ndktPIBInWJo0IqIQY4GG9fefYlvRGbYmzvGLS4/z7OhXic2K2cgFrhd9TI2umy/mPMfLRx/gZMt1\nbD9wHK3Zyx+HHqZ3qIRvdvayo+wjdplPwQhEEiJcD8nwD2kQ/0GA2bFEg6AH4e4k+vo+Dj1ZQXtr\nLoOxhxDO5KCc0/C1LzxF/Wobyt4AXtQM3VaESuqhKLDA2FMhul4T4w2mAwZ4XsyG5Gm+lfwBU18q\nZP4zOWzhMvozEd768Z2IjCLk26D17Ab+eOhhRMYExeuH2f7wMawU8tXhX/GDjh9yw+IH/G3NfRyr\n3klKIiA8qyJ1VUj73XUEtklpfX4VU0fN9M3nkJhRkxgp5MEb2rn/hhM8XfFfvFT7xv85EWRmZpKb\nm8vw8DBlZWWcPHmS6upqqqurefnll/nmN7/Jyy+/zE033fRP88dmVyP/61XGVGUMKO+h1DCMvDRM\nr6CG8fEiZj0Woq0aBpuraTGsQ6QVUyMbxik08hFKrrKe5yuC0Af2qQw6pfUYE3Z2zlzErjNxVreL\n4UI1MUIsKzxJ7eI1Yql5vHNBwi1CCtZNsElwkVzBDOlOJ8orQSSCKMr7vYw51nDJ/jn2zp+iXOVD\nWQ3V4UF2RY+jGPMTPyKkfWMdg6lKAn4VZdZRdk0eZ5XvHOK4A8UuqMgf5D7l30i3OtEfdpNxnY3C\nLSOotT58KQ1zI7mIBtwULElJhedYIbzGNeEK+hWVqPUBEkIRjZprrN16CUPYQdguJv6RCIkqhbg2\nhfj6BIVZk7TqV1Bf0MG3y36Kv0FO1Bzn8lsFdM4Xoh2XcTmtBteWStAbwSsAHcgsMXRbA6TqEnhD\nWi4c3czpmW14SjWMBmt5xfZteoQbcU+nEb8aRR5dQmmRIBMkmPPnERSr0N2+xPYrp1nvuUT9sWuo\nOvwERSraNVmce+QBtqhGyZ+aQpYXp7h8ls8p32ZCVYrNlMuZmgP0hNbStGWUoEnJet8ZKvv7EHUn\nKWy5isvqYKLBjKJAxN2Gd8iML1AeHeLjC5WcvpLBYlxFVqmfhvROAkIVf3I8QveyGjQ5LiorB6jV\ndjIiKcA+lUHzc02MGYpRrfbSEOmkpqufiktjZMmmED4qxmnREDVqyLXPEfZoOPbhTSxJMnnkpt+y\n0XYG6UdRNHf4KCud4d67lmhOb+L08ZXIamQo9/q4YN1CpFvBbuuHSCWLOM7NI18ZxtAYxbYpDYtM\nxX2drSyJnbjrr7Fp9CJZLS5Ojt3I1curqWvspKa+B+Pnnyenr4PmizA57EbkHmKXoYO1gi6qI91c\nzlhLc90apKkIorkkKksAhSuI620dkTkpqeVgnZDgOyRnUSojdr2KuDALc18/xafeZn4iyou9DzBe\n/H9hLfrzzz/PXXfdRTQapbi4mJdeeolEIsFtt93Giy+++L8/H/6z+WSukbWHjjEsb6Qz7xZyvzOG\nYnuQ6fFivFM6EnYh4ydL+eD9m5HIY9QpOilRTbKkD6Awhmlb0UDniioSV6XEe6UkTCJqfL3sCJ3h\n/aybOFJ4M4r8EKWFw5Tr7ZjmZ+iQynEGQT9uY/lUO9fbjmPNyCLikpJqERCvFaK938F4vAmfu5gb\n26zkLvmRmWLU+Lr5Yuh3KHuCOFv0+HNVTOQWkpbwssZ2hcetPyPiCTMr1pC9NYzZssCB9NdhREjq\nImRXTbNeeQZ7LAOrPZ/htlKivbOUhCQsi9pYFkpxkQ1cYwV+lZqSxDCrfZeoruunqnYAz9fjhN5O\nIVNJkXgkiNaL6NPVcEa1gY3ll7mRwyzeoefk/HJ+8bVbsY7Xg7gIyS0RlJuiiP1JBHiIlUhINAlx\n3KUjIRVgXczj7HvbafasQf9jOz6DmY+mvkhEpkA75sT7SQrfZJL07AiphAS7K5vQlyVk3jfDDYHD\n3HzmQ+JHxaTiAEla762j/XO3suvCLynsniBwnZyiSit1ZcO8YbyVPwhXMVa2iZhKxtzWU5RlDdAQ\nuYzFMI4nqKHo4lVikSEGf7Sf7FVxbne9jdbrIyhSYvuggtbDWoINEopyQuSGp7nmXMmvnV/D1DBP\nSeYAW5OfkBlboFdfyeXuDZx4YTfiL4Qp2DdG+cAIO/tPsrv3E3zrFSx9S8e0Kh97JANhT4q5czm8\n/datlK0Y5Jkff4uMQw74SIhwS5LMVV6qDo6jkeVytcuAYcMiRfcN0/K1JoYOlVNCBwWJCYKJAKn7\nRciKJUjXmciq1rDz1RYk6stM35+F+kyQxKKKjr5VnPDtIlszzbbl7ez77GFm/uSj/cUUTsk8Jn0r\n+w1vsF3aQ2pJgN2UQVlpH+qol6hWikHtJHtgjsRfBEjMSfhMFNcHMeYvi+HBOILrEwjzVGR/OMSG\ni8/QuvAY7/Z9AcOmhf/zIqirq6O1tfV/PD958p9fRvw/Z3tdP6cc32N52Mqdmsfpk5Xhd6u4/eJ7\nIILpNVlcntrA1b61VOzuo275NfxiOR6ZkizZFOVtpyj7y3nGVm1kcnMjM2fzIQTcBGSmECri7M97\ni6ZkM5en1/PGwo04G5NUF03wXOJbrBzuQPTHBGl3uunR1fLXhoeZyM+jUDCBW5KGRuJHFghDnw/G\n+7gSzuLrov9GXJpEviFMvb6dH/t+TFiiQLfayfxnTDSn6ukLlnNw7AhZ44scuX0nLp0Oo9hF9sIC\n+376KS957mfIWYR/fgF9zjTLvidkRLuDF47ezkC0EokwxjbDeWoWWklc9uLboOXczRuIiwYJZQax\n37MJW+Ey5j/OY1Fsxq4yMmipJmf/NL4cFeOxPDzr60GkhgEbVRc/ot55kZpdINpqpGtTNV6Vmm86\nfkH8mghpR5Q1iavcan4HaWcYV2YaMw9lc16+iSF/IfaeGOPDEFkEdyEkN0DcIcPzRyOnyzczW5PF\n1Fgx/g4hXHHA2zMsb/85CVsv45liEiv0qMsSSB6OkZU7w9rEJQLtUgbaK2nPXMHogBnZsRFOj1Rh\nnj/Anoy3KdbPcOKtWhxvx9iT+Ji2phW8ft3dVKrO8r20T3klWMfY5VJ+2/dfmDbZOHjz39FovYji\ncVoda7D1ZRL4RI1DYkD6gyAJmxj3n4x0ra8lY8882bVzLJjNdMjqyMCGKWLnndYD9FiXIX0gTEHu\nFKqpKOKsFMmDICmKYbfqeOeV9bT7m0h+M0pR4yirxVewl2cT2yBFk4CsRUiNQkRj/l/E3debI2aV\nqPtXOUslqUqqnHOO3V1dndud3M6pjRMGw4zHwADGAxiMTebM4LHHzDBgksERZ3eO7hyqunLOSZUk\nlaqUc9oX++JcnPHV3s/x+h9+78X3fM9atJvyOfVhHatnFSimHVSVzpPVb2Usu4zj37uV8b8XILnh\nIc0xhWiXi/6Ha9CYreysH8exNcTVRimHNQ9yWvEQYYkSbcLJRtpZykrnbQ5x7eRWZNfg28H/Jn3N\nDqeFvBfazdHCjXB9gaRzmfihagQhCfKkgA1tndQ89nvSy5f43v/tEPyfjNysYNx8D/vFr3DI/C5H\n9fuZCeZTPDVFitxJXYsAl1hOl6qQ6qZOWu7oIC4Hl0eNeD6MftVGxpVJ1pvK8GSuofX4KZRMEy+D\nSIoYgSdBib+TSscFPui/kwHbJnKNFsoz+3hY8T6CywlC01Kcu1MY0ZXwsWA3MbeM0skZCsxzZLOA\nzurENxgn2e3HnZAxmVqEqEGAttZDs6OLLMsirIQRFiQI10hYiqcyYM2j+EQ2Jq+ajoebcDSaScHN\nlk9uUHd1GNegEcdqKlrzHOnlK6QXJpiw6VkayEUb8JMnm6egah6t3c3slJqZ7ArmVmopMPjQNs7g\najYyKq3hYu8tKL1BciUWRivLGW8qRIsHz5KWWEoK+jwX6dIJ2tZPsmPiMM0NIKrKILu6heOx2/nA\nfojQkgLj6Do16hFqEv2kd8yzVJeJaEcLJaZ04rEwnmYVk558ZDigOoLxllVivRKCoyrm9+ThrNJy\nI7CV6EKYnMIuCiILpE7MMztvwO8yY/TJUNWAZWsOQbGcqtgQC/NanDfj2EozmQtmw98lENIj0Zuo\naR2nrNiF7lKU4JyMgVgNV/O28Yn8DrIzFynPc6DyeggOh1m0pqHWOyn64jJF4hmUvgArQ1ksd2Sy\nOJBLvE6ItClA4iMR4ZsqvNUaPCUaDHIZoYgc34CGgtR5Ulmld6iEztUKdjxxnRLxKInzEdxpKoI7\nNQiiSQR9CcZHC1gvUlN+9zA1oS7Kenu5rN7OekMmsnUgrsE5m45Dns26NpeVJSPObhhYNSPVeCny\n+LBk5HE09zZmj+chdgVIsTiIzQq4ad9InmGF9F0xlDVmKDJxLZnPqsBMVKSmMdDN/skVLGnZWPVm\nQj2Q0ztPdcEwdeIh0ldsDAsqOCrNgPlFNH4beWUCShcs6BNx4mYvkfJlzFH7Z5r8XELwxtqj+NdV\nJLclERyMkZs3j3XdxB9FX6Vkfpp/fPt3mPt7EM9l0jBzlh0LfSxmm3H2GbD8Rz5TowX0yHbh+zgN\n0xU3T0r/wIaydmI68EQ1JJdETB4FydUk694ElcohvpX3Ci1VXQjLEsT6YN2m4XRiL6eXW3F9YicU\nTydQ3cwTt/+RQyVvkTawTH9fNYl4Fa3yRb4t+ANywni8Wj7++G7+euMBcFnZU3SZf/J9SP3cUZyj\nHZxayMVT3IQpFkaDDzdazmzezTnzbgZ/V41mJkrjE+sUh9fx/FuITSmn2JA3BGEIKuQslGZwaevD\n9GyrxmrJxX/YyNM1g+xv6iE4dor0mIielo20Ba7xzdVX+FB9B0OUs5czeBxSFq+KKC2b5dB3T1B9\neZTCdpCcARbW2fyNdqwFWVzK3YpjQzquZAp/7nuCoeF8DjlfZmqimtenv8nGgx083PYe/V8uYfDO\nbIo4jlntpcV4gz5dM4umPDLSV0h1WOk9WU+lYpKnnn+VIU0Rl/zfwfuyHs1KmO10EYmpuBHYTIO8\nlx3Cc+zifTIdJzn87lamE83gqYESHckNIiz78lmr7ufh6ncYn63gT5anWGkyk5sxzZnm3ZzybWXu\nlJdC+0d8KXYWS6SW970P8g3R79hn/ZTc96epsm7n1Xu+iUVSQOKIhqRThDTHRdXEOC1zfSgWg5hX\n18hfW0Szz0eoUYhscYRUr4+90aPUzg6x+nqE5QcqsLeVUv3aEOXTs+y7t49w/RhF2gVUR+ZJvOZE\nvMUFWZlghRFPCS/GH0BPglqxjTs3dyD0Onn/WDl92iweqxhkwltKz+WN+ANKsjYuoLhPhrsgl08t\nt6LQBRj7Ugk332xl6DdVBJJhaAqgfirMgj2HN059mbJbhykuHKLJdwSBMc6pf9yGI57Koc4PoTMA\nE0HIrKJQNcLTh18lb+Um/kiI41PNnDv1JFJPGHj3fzT5uYQgLbbKgi2HQXsNH7ruRZXuQaKJYW0w\nYUysoRnxo5hNEPfpmFqvZMiaIC1ixehxIsuMkOrzUy6ahrRp0rOsVJUPktpkw6tVEO3zwKkRVEtO\nUqRJJEoICJQsOHKITkm5nmhFl7FGIj2MbTJBDAeK3FSykxaqRUNsHukgb3qRmxMNXBDswVWTjzcc\nZnbJTP5QL7rkGFnucuYyzAyX57KqSUflDlFmcRBYcDJS0YS9poBtYxNolkexhLQIDUrQ6VhtmkdS\nkU602UhkwoTSDcIs8FYLcV2NsTQqp8fYxHB5E5PmMryhFFgQIUxo0KeIcKnUaGNuDrqOkWpbYmYh\nhZXzRlZn05hPphEYEBCdk+MvN7JcUEx8QM8czYT8IPJG0SWDzC/lEhmVkRAIEefEMA/akIriXC3Y\nzWpeFsr0ICVzkzQ6B+kQbmFCXIpKKiNzfYVd4xdIKMR4NmlxpuoIzSUIL7jwC30szKSiyoVmlY2O\nTZks2/PonpeQ4vWSFbJSIJ0nR2TFKstirTSLyFwAQl5QacgQLZDv6sE2lOS6r4K73efIk8gIb5Kg\nTi5R+UkPSbcZFHIqSybJMM9i0q2TyFtmQ18XWbnLCOVx3Pk6gulyMjYtERhW47hoxlRno3TrKHJx\ngFhAjCQYIySS41LrUPhCyHrCiFZEyF1xMk8uYpA58FbKGPNX0HF+L8vhTHSZHiyqCpJuAaJeGWWj\nDvKWJ9kVvIIrZsG0bscjF1C83Ya9oIC+QDb1xSPku2dQXdezaM/g5MVNzKRmk2J0Em8SEggoaY81\nEx8IMTEhRJCdjrNhNyuSLLx5GmJ+CTqdkwLtPK4lE9OWCsSXHCinJsjP9qOtC7G2MYjQGYdloEcI\nHhlUq9EWyKlJjJGTPc9cvZS0xCrG8/1Ey9M+0+TnEoLHpK8zvlzG6asH6I428+Ajb5DfOo35rkXS\nFCuIOmPgyyac3Mrb4c2Mrg7ys/nnKDdNkPPDeXZ9eokn3/sz7INwmxRrppHlFBNKWQDBwCyCP56h\n4h4LWx4T8IlVyMXpan42+Dyiq3E4laTuR13k5zisAAAgAElEQVRU7rlO6s9PUh6eZfW5cnZp+3jG\n8p/ojnpYO2/gHfdXOJx7J4EDKhxTKvoHf8DOk6+wu/NvHLj3Ndoe6ePlyqfQ2oUI2qHQAZqYmEv/\nVIA4x8T+P72FsXOEEZuQ7AYBmVtVvLnjECdKDzDuryJTbCOjUUbXlhre3XsXI1f8WM6oiFzaTLQ8\nj9hOBUmxCIkwCmfALdRx9pldJBHwzO9e5MjgZr659AzBU0XEJHomEwoSCQjGS3AHqxmf3oJoOoHQ\nliCZAYpyP6n6FTzXU7D/PIv4QTEZG1Z4QvJnTLk2fnLnc6hqvNyT9x5b/rud9F85CEh1dCo3M5DS\nxH3B93nB+jyBp5SMP1XMSLIcbzSONzmPs0fO5NiX+Oeaj/hOy2/4w4HHOao/SNebrWybvMpPhC+Q\nzSIJoZA/l/8DH9x2gNAHV8EGZEGl+zx3nP4pRy88zsfiTTQIe0i7xUHzC9cRnZsk94Xj1NeIKK4S\nwNYYs2U5nK3YRf7oIi+d+T6h28RMb8nnzScfZyRZQYFqjmSHiPWONEp3jLD57st4BHJGoiU0+gaZ\nE+RyVL6fHceuUXV4FOx5sJRN8kUxonsFyF7QMv16PR//5B4uPbcNycYY6xdMxC7LEIXjPL3+IrdW\ndVAoeoukTYRyJUAiC372pT/x2/yv85LzIbamXaWk/Aob1de4dH0Tr//4SUz/KGbzc5fo9jUxPZbL\nX1+8m8S1BQLRQQQZpbgKtyJ/1Ifqyz78C1p0Yid1hmEsUj8zmnKmT2hw+3MwPldP84E5Nqu6KPJb\nEOriIFOCSA+pAmgG6kGSLkWn1LHxxXOY3r6Ef99Guj7D5OcSAnXAh6A1iagyimRjEGW2nxzRAgeU\nJ8kuXUF+Z4itHe08P/IiyIUIidOevwGy4uw0nUfX4uFT0XYa3X2Yzi0QjMYYEVYxLr8H55U17vec\nYm6mkj+Ja5hdLia6HMBlnWNDYJx9sXH0cjuSXDezd9cw7S5lLVnIUu84Q+fXKUhLErtPjvuECqN7\nlYcX3mJJnsapW7aznt6Io2CO8UZIKBTkHzuFdzqFFxe+jUgqRFCTID/hoHDOxqdTe0hqNyE8sIxe\nN4xRM0NTSjcyQvh6L1LomkW8N0ZewsK+sxfwVjXj/G4axQwSkK/Rr22h2jDIdu15Fuf0/Hvfk/Sd\nbKEibxJF4yfoakOoklJkggCR9TjekwG0AjfNty8Sb9WwnmXEWpZJMKqksmwQfZUDX8r/PoseX46T\nXBPiC6k5aTiAMjXAvLmITbbrbLzehUCeYOgrFcRFSaqcg2wausnuyKekZjsRGCGRFLL77EW0gzY6\nbs1B1eajyT6OwO7iT1frSBVP8mX9a1i60ogsK/i74hDqDT4km8P0jzXiH84EVxOFBXNsP/Q6rWtn\naBpwoF24itc/TXGJFXV1mFtU5/GHV0mueejy7eeasJLGok6UciclHw6SK7CSWrdGYELOep8eX0CC\nWBVgV8lFFP4YB7acYypUyMilWspLppCY4hzVH6R/ppaOro3MDpVTFJ2hrGSeYv00F6Z3Yl3I5pah\ndiRS8NdriU1LSVlYpLLrI7LSVknfIyG4oOCXwz+EqQUyfLPsUw2TXbqOqsSPUB/Dn5TRJWkkkRVg\n5HElM9VVrE1XU7fcwYGXD2OvTmNVbWJf+UWKIt1gt4EyDeTXWNdpCKTKMIS9GKZX0L86gV5WReCg\nitl8HfG1PAoyL1MlnERAnIjFz+hJWDUI0DzoZ1/8NFVdXRwZq6OiXMXWjSOsBDdwSdJCjiL8mSY/\nlxAEQkqSGwUY2hwUbx7DHFki07VAoWgavdmL9O4ILfIuavxDSAUBpkMF/Mz8PUQZcb4s/hvTJUUc\ny9iP8S/rGI7NszYVpC9i4kjaITYHP+Ex9W/5i/UgJzxfITorQeKbRaG4TqvoHP8s/hS/WMmsLpeu\nOx5hbHUbnul0LDcVXD4sx/8vUnQHDAS7ReRbJvmn2f+mq3oDl++4BeGGYkI1bfQgQ3Jtkfy3jjI6\nvI0/Kr6Fb7cBXb2bF73PUGyZ4vv2X7G4KYOS746QNXeYmoEVShITFNpmUN0II0qJEd+doPjGFKWn\nZxh7oBRnm5z94k9ZtZmYHC6iOe8a36h4iR+d/gGvn38IjihRbAkTfFyGsgAydTFE8mXC8xGmJzyY\nokvsenCURImOOUEx/ZVNrBvTaG25gjHLTm+ykfVkCoJkGFE0QSgm433tPSTEIkgIMA47qX1vlO7H\n6uj4ahOSZJC28Us86f0z+cp5IkY5QZMSSTjG3sufUjo9SeTpR8lKW+Zrk2/ynx9t5tc9e/hV9AR3\nS89jn4RTwQP8VvU80QIZ2ofc2J/NRnRDjjJeQF3JBP9w35/Js80h18soHehB6BYQ3SFB0JKgTXQd\nuzDJnF7Eh6qddKlu57FsARsdFyl4Z4iUqgiur+uQvB9FdTyAfN1DhjHCri0XKcxeILhfwY/nfsz5\nj2/hwTs/RKRM8pHsIJ3jm7C9n0u7aAtmjZ2f5z5LZsYcz7p+wKIjn4PnLiM3R0luFRO9qUA15GSL\n9TVab+2jbLeOvwz9M/9u/z6xgR6ql66Q1RhAlxVBpAsTlydJRJN0CxtZMGaxdo8OR5WJ6PlszFfO\n0vybdi7cu4/VLTncV3OO7RmXEU7FkSSjSBRRhrVlWJMmipPzSKdXsb4aRL3PgeQfFETL9mBdzKBU\ns0yVe4wlqQm7Jcr6ySTrDydR3+bhzrOfYO4c4/sLX8RSq2O/tIdZVxtHlU+zO3HqM01+LiF43fgY\n/qsq9iVP8kj8r8iuzzGzrOJy7hcpqLDx5Ya/cjm4nU9m7mDnyhtk3+yj9fBr2LYVc+aRnRROL/GV\nK3+jODqDe1s6n+Y+ypSujN2bzrM10EnNUpI9+Z0QT6fj1UZSg/M8evdpwhMyvn3sGaqjFrQOD3Nv\nepB4b3LgtqtkPjiLvGUzZ0YrmHu+lpGBfEoNFtiSJKVpnbL8YTaZrtHGVT5lFz5lFrsLZWxI3mSX\n4rsMttSw2JZBxuwieZ55vtPyElerNnFJ0sbfB27j+utl3Ddymkytk78NHUKfXOdB+9vIiv1E7pHg\nvekldHSFgpJxtF4L0q4CvLdbWazKwddi+t/n0QeGCfgmWDZqmRvOZ/Z0KQ8dfIuK1A5ejVcTHQ2g\n+lkvxdlhdhlvMNLSzWJjJpn6RWwxE/O+AqxpOSQPKijYOUlGxSJjV7Kw95ngagqkAztheL2Wi7/f\ny63+EzQquvHuUPKR7U4udtyCKuDibuVHTD5QwPX+Rm68X0ZrwkcsXYi4KQO2tPL36RKuTnkIBkFq\ntXHA92vmwk2MspuYWESmeZlHm99gV+WnFPZYuNaby8fnq6nfHyRtk5R+XT3pCisP299BVutF/RsN\nXwgd5y5pB8WiCfzZMs4+ey+LA7m4f5FGY3MfxT+dYm/4MoYlJ6YeB9c1m3mj9lFuDtYTHExy7WAT\nI/ZMpt6T4xX7kT3hwaRykB5Z4pMPNxCe3ISlqZj80CJLlwW400CUE8e8eZnKg3O0TgQxFaXSr6gj\nWi6kXtrDXFjH4tWDvLJYzdSFT/lC4i3EFUIShXLsknTEdhflb3+Iu0PKtZUD3Kgu4me/+inF3bNU\nvzfMp2U7OVm8H9PdK1SphmgSdaPM8ZLilPDmmYcR3oxyV9EblBS5CQum6L6swXrWCLfYiW8Q49Op\nUSsgN03AmS4v/nUf57ZvwrSnEJk/nVTfPEIX4IDoqpT+a02fafJzCcG1tQYYdqFSraLXeJg/Y2Bs\nJJfJokJU3iTxIhHrIQOTgWKqYloyIwGy/eN4i1O5HttPocfKFssNLBm5dKRt4obnHtxmDaXVxxHG\nlKyktyDUy8nyLlCTKaRcMMEDrV3cEFRx5FQuukUP2Z0ujN3TZCQm2NfqIJhnZnTTLm5caGD4eCFZ\neXaK6yZQZAfIUC6zNXCJqsgQhsQaHn8KwaCKLK2MIv0Y1cygSbfRXboRZySF+XAOqSo7RenTTHvy\nsS8rmJzPYz4jl1hIx9R6MWqvj0FPPdoUD6EGMT63FPmIE++aGrdbTmLQiy8jzHKdGH3ISnnmKCse\nG55c6DVUMTpRzupwOoWVFvZJz9ObJ2BmLYX1STVKm4OtpmGogaQhQUZkBQICjPF17NpMPJWpKLLD\n6LROVBolGpEAjdNLXJukR1HLciAdsT1Ohs+KNsdLf3kV/cZGxsbKqPIOopnzcyOnlRl/NuI31kja\nQvRX5bDcVgRVRdhWUwjLfZAHVcoFbrEeplcVYl1QRbp2jZzCZVp23qRcP4Z+woO/V8xYXxqKXT5W\n01V0GmtIC+dRNj9Nvm4G/R430gkHkTUfCmGY2bR8ru3fQ/9SDYunMmGDgIztKxgCfjK6nUivxFiN\np9KT14BVn0ZUDIuKDCLRCOaBBRLlYhw7NWRpLWS75rh5rpRFeTqaVglqa5DIRJKYL4rY56V86yCb\nqropC7tZF5i4OdYGKQJ2ZF3idNWtjNrKGWyXYuiboyyYTTIRpDm7F5nFj2ZqGd1RK6FhJQK9l/Vm\nDVOFBey/cpLGqUEmTJWMxItYlIjxikP4idPkGUYT9NNu3URAqGLrhhuoyt2ohX6K5gaRdAkRNIRw\nuLV4hgRo5xNoRVDkn6PW3U28LEFwp4bqoBXjYIL+iw0sBbMgANKh2Gea/FxCwPvLYOvhlLOEQdcr\nBAFzcoG7Zj6idbAbdaOXPcGzVGYPY9o4R7hWx0T2VhZyW4ikqEhWCIjJxbzbfYi3ex5ivj+PqELM\nEe+9XFTtRR3345lTk+Jw8Zj6NbZnXiZtcY1t3h7yclewnfXh7Q3S3CrGpE2SejjCEfE9HE+9m9VR\nBVq9i4ee+JDbas6RPmIn7eY6X136K4uPpNN9WzMTM5VIR+JE1mWsL8OkHZb2iHGK9bxddIh1k4GI\nT0qjs4+v9f0BudRP4ICUG7e0MaWo4r6F97DKMvm32u/idWsQfhAnu2WEkl0u3j77VSzebJxFevxD\n6zi+NcFO3x+pN1zj7UcOsbh1J+8aG7HXZsOTQBz0q0EefGiQy/e1cmLuO8jCl9glfJnrmq2cmdnD\nt1z/wW75RdJLrRwV38mb7seZnSzGHjMhuttH8aEV6hklclnCs28+x967L/C9L/2CSWExfxM+Qo+g\nhQrpOD/d/gJnZ27h1T98HXetjpzEPE9pPyCw4uFXHbcxMdIEOngw8S4HdGegHtT1a6S63OgqplAK\nj1CeN40mEqC7to61eAr3DBxme3SKbPEa9g9irM5q2fcNNwsZDbzs+DYHlk/ytfn/4r+WHuJMYi91\npd34ZQqGfdWsRjNIahQUOy1U9E3x6sQ/kpwQ8kz836lKG+TJ7N/w0a776MzeQFq+kzZpD1/YO0tH\n5iZeU+RiEtrJlS8xvKUabZmG+i0DNKwMkxeOoevyIZ+ysPfice6aOIHsqo3xSA1ns/dzsOwkD5a+\nz7S2nJVmNTuHf4N8eo5fBTawpXaZV+TfRHgszuIFM6/Z7+dmcSOB7Sa2rR3mse//nnrdApn1Xr6W\n9zumllSMfhxiwNvIy9zHd7eG2dQ4iXBDjNV9KVw0tqE3rBESyNilHyKj0IKrKou+pBZemSLY7SSw\nkqD1Cxdpe3CEZJUAUSCBfDZO+8pmnhP8nEllCWq1jy86/sbzn0HycwmBsdaNc03NerWZSEUmckEQ\nQ4WHaFjBfGEultQSosIYcW+Q+KqIhF+EvTgbuVxM27UOkkY4UnErvkUV1SlDVBcMsRjLptvWRCAY\nItM1i8SYhrY0hKbYh1NlZNhdR758jg11PXzYk01PfwEF9SbsUjF0O+lUlDBfmU9BoIv62CUqbVdA\nssTZjkL0yRglWeugTceb0OLzaFB4I8RlIsQ5oMyCjEwHgegcPUNVzLjyKK+dwWCbRfXpIG5VHgt1\n5UQqZGjUHnS3rOMdESJyzrKa2sBabgnlNQOUKuZwHVGgs7qoAAxGO+70bBRIkWYmMGxKspymY7wv\nB6PMyY4Nn2K9ZOLM8q1k1FgwioQsT9bQqU1ysmqEMWUpwYQcuS1MrmARtcFLTCIjXiRnIK2aFbWZ\nVtUA1dFuMpbG6Zwqp3uykszhdMwV+XS2NLGkyyFrbBljZI3F3GwmBsqY6izFVGAlpcRNcLeWeZmB\nzlOVqJbXaRB8RI36PNm5EwyHGpnJLoT8Fha8WSx9kI0iEkecm+CGZTNFoWlulZ4mvdZKSpaPj8eb\nmF4ppjDmp1AzgzvTiDLoIyoQMxsq4GZiAwlljDSJnUrxMApDmNHiGqa0pbQHNuDuiyC3+5mvMxGv\nk+HXKKAiiSbVS6FijnrPCEWKOWRKESvCXESCGNGElIRHiX49zBZ/O03+flSeKCKFCGGhCEOGE12m\ni/maDGILUloWu2k09VEmnGBP/BwZyUk26i4yqRBz1FVPU2yZWnUvkuUY5oUseppasOeV4s0oRKRQ\nIE0kCZcoiGaEKI5NoViT4sox4p52kJyaIm3ShciYRLQjhlQXQjXuRpAZx21IIRELIoyuM6zcTsAk\npyl3GslMmNUZiBnMxIqqCIok6IIuGiR9qM0ewhIRMmkAdaaL3MHBzzT5+dw+fD6Mt7cNQ8UquQ2j\nmA7YUce8XEvu4IRYy5o8Ded1H8ExG3fN/JXymSls9Qry44sc+u+POL57Pz//yvf4Zv7v+FbiFdgO\np9b284ORX5LedZFben6E6GetiL9UzoI4navzmzl76QD3pn1MQ04vnSt1/NG6H/FkK0KJEmaHCeVl\nEtOL2CY/yd3O35D4U5DLolzejuyh9MEwjz87SNioRJkMIErEQQKYwVAN2nrILZslwx/l4uv1GGeF\nPP7LP5Jh7aL3owBnduznxtav84/q/6I14yoLj+fg/9jDnc//hGuHHuTaV79ChnyJyrEu0uaPsj4S\nwAMkn6rE/6O99KLHLTIgUgkwDLpYfSeLsoYOvvjQn3l3/RGODN/Jkzv+k2hIQvKwgO7NDfw/h76D\nTBym1DWCtteN3BEhXb3GbaaT7N51kf9I+TqnBHt5+Oo7tFw8z9yZKBMWJ8KwiRMnajk/dC/8REhj\nywBfn/svpmQlvJD+Y2xhMxJ7lMq0QTK2zfNx60EsOgWBy0laAh9xMPkeRn+AAVsZL01/i760TVAL\n8RMiYu+IED8RR1CTJHREjix+nNgmCWwWkCgWc/ONQ5yZ3McX1G/RYuykbeMV1Ek/NgwE7HLiAREu\nQwolkjHu173HlbwdjDdW8XHR3VzX1HLr/C+oiPUxfUcF47XN3BC3spZpJNVgpXJljMr5ScSWGJXy\nEZ5K2LjADs6HdhO4Isfcv8pu6SUabN0kzkaJValJ3paOd4+RpaoshqK16K95+ekbL6AvcCGrDPPw\njbeITomQGQIczipFGkgQkktYT1GTkvBjUtp47PG/YEizs3DhByy2FHP5O3cRFHcj9AyTfd6KQKVD\n+HAdO9sdNLzxCipBkOXlLETRGKYZK23/egXPVh2WunyGnWIGl+RcC9aTWqLhwPcuIDXbsA/EORK6\nnfPub5IMCKhSDfC13N8gL/KwL3GM8Z3l2G16/D/w/N9fVfZ/Mm3j55i8IEOqDqJq87Ee17O0lIr3\nbAzT4hKtXEU97EZe7MfQFEG7wcA2RQ/+QAZv1T8M5iSPOt8ipbOX8WtrxKQw73cTWQkhyEpB/EwV\nRd41jG93sLIvG3uGGVPLCv6YjCuyTSyoNyLdXUx9wQT6uUVc7aP4M4oJ7hBiFRZzVvkldgVOYVYq\nCRY1ENm9gjrtBiPhOi4sbWf5SgzhXIQ/1DxCRmQjHJ0h8mkeQWE+xhtzlCivkRm3YFWVczL7iywU\nNWHMs5PVtUxGdJWlLdloy5KU3u5C3tSPTnOMGgaRpyUYO7Sb2Y0GoqIYjSU22ha6mcwuZE3kJq2j\ni/GblSwuFCAqi6MU+9lVcp5a0RBRg4SpuSJC6zJ0ASdZ0kX050bQdC9x3LCFSVUZ+3tP4agwcH3n\nRjxKNYXJadZLdEwLSolluWgcd/P9sVMkXEMkFBlE5BKQCTmv3UVIKWev8QRdohym1lNo6vyUutgo\nPUtGsgJaNn1NRbN/lFa3k9Rh8C6vsnf+I8Q5Aa7I99KgGGB78gqnOm6hZ6Ye5sXMlBTyTtX91Diu\nkHOhl7y0EfbcKaYxtZ8y/xS6gIcpeSGXdNtIM65wa/IIMxcK6Q1vIFioI6SQsfngZYpFM5S5h6jd\nOEAgKee4czeyaQEP6j4gsSIkapEyNFxD/2QTyXmILogIJyVMZpWwpMhiV/5FWg3t5FXOIFNHSJRC\nTegyd3RGKC/pwpzhoDY2isIURHOvk56cWjrSNxJOKom6ZQhCEEs4eEQwyMbEHNJIlFPBLQwsFRH8\nMExEGee+8d+RF12ltHaeIVsNI4v1PDjzPn6PmEvTm4hPS7g22wbVMgR6MY2r/SRccEJ/F6QkUeFn\npmo7q55dlKXbMSzNc/jkRlwXd2CLpTIU28ta0AgRCNsV6K54ccl1dGVtYtGUjQ8lJySPAuf/R5Of\nSwg2dl7gnTNFiEpVCCKw5tVjG1bh+b2LzIFF9ijepjTfh7FexsDjFXjbMtiw3Esncl7e/i/cJjnG\nM7OvcuUKnPtITzgJY0IlQUkcQVsawmc3kvrd6+R+OEOkREbaDjvFZeMkxXFuKhrx1JaTmdCzXXyO\n3EtXmTtswZ1bQbhZRK+4lTP6ZlotK5gMLvS35aIr9KOTeJlfzOXSSCviaz0IXQHe23knzFnhk4uw\n3og6UswjyWfZuuEEUjKZNh7gQuMzpJUs0qhqJ/e6BcOMG6k5iDQ9QdYBGfL8edKSJzEGHdjlJjoe\neJB+6pEmwlSO/JX6G+eINyVI0drZcqWH3H4r5/17kIWCRF2wP/8ESlOEtwz3MzldBMEIqV4rRc4Z\ntGc7CJ9Y48jXf05PuoSWm51MyAt4c8u9ZCZs5MtnsZabCJbISN3joKJrii8cu4DAliSKGLdeSbt0\nI7/W/YAs+SJfE70MsVrmPHUU9/Swca4LYV+c4GYjxufzKUosUrgoIHpMifxakINL78F6iO5EKy2K\ndr6mexnrkJbBWCmxFCWzDXm8WfwFdg1E2f/7Xip+1U7j7eOUhibRr3uIrciYNxRxXHWQNvF1dkY/\nZeZ8MV22DfTs2UBTTTt7Nhxj59B1mlcGUG4IcDm+jc7JnTStdnN/ySfIB4LYu838uPMFjk3fTsCv\nJD4rAitIGyNkVi9xoPos96R+QLQMYgYxiSUpZTfakfVdp3JDjPQsIbpQgKhOQGxHknZNI7/jqzgS\nWXg8evDB/ugRfiR/lpyolbBdwRnPZt627Cb8Fwc7hNd4Xvx7KrUOdJViLg/v5sLsTppDfQQXYpzr\nbWE2UAJCFfImCaVZFv5t+XsIvEm+WfoKhnw79wjeZbHibibkFdxj/iH6uSl+/frdTPVvgHglRIXo\n/U6IQ+rsGpnH7TjUZro2txKoUCAyxLkuu/MzTX4+j4VmPbRVY9OlkBgO0HbqMOqLVo7NHcTVWMnw\no3toF2VhCVcSMydJlyyRmrZGgXiKH9t/zOj1Cr558TfYVsBZAPEIuNOyCNZmUFRyk9smT3NWt5sP\nax6iSddJYkzE6N9raKm4ya77LqAXOxkWVzElKKa9oBb7PwjY6Bviwfc+5pb8Kyw1ZnGjZyMRm5Q7\n7/iEdMUKFkEu2Zev8sjH3ZTmrWGqisGNVFAF4ekVuHIZ4WUNqkAXPlIYYAvjRSWkPrRMWcd5ap/9\nEENiGpHUh/YPAwyGzRz1bCHvAQGlhUkuvt1I31gNE7eWEIxoCZ+Lc2L1diyBAhyXFzBqxykqllK9\nZYgXXD8hbHGS+OcJkuUeQpUGJvRluMJxNsd/TeBiHie/dTvaikrUP7eSWeGhQTyFSuIjf2SQ2575\nT4IP5CDap6N+bYjQipKPpu9heWyAqsVJHA0prDalYk6xUW8Z4vuL/4pyzk/RX+fQ9OwgoNnFO1uy\n6N0yhcI1j3dOz+Iv66jzfEJDeIV+w8PYtjViKphhRlxM+LqaszdrsK8/yNq9cioLRpj9IJfArIrF\noVz8lbloXzYhaw4iEsSxSU1c97ZxpX0Xw2lVTMYLWV3OQrYQZl5XgCLHT1qjlcLJLop+e5rzVds5\nmncvj428DjPAGFxvaeXpul+jbXGRUuZic901ijpmeOP0o8wH88AOzYYOdjZ/SqFjiqRQQEIqZLyw\nmPZ7NiPY4kLrXiKePYNFoeEN16OoBte46/dvUX+giydu/QufuO+n09oKPhjS1fOr+p+hcgUQ/ypO\nRncvX+HPHGMzslQFhnIh6gSIPopzv+d9iqNTXDDspF9ZiENohBQxmLXsbDzF/opTZL8zxayniMRu\nkFZEUAt8SDsjCM8nUKYFacic4ocPvIcn7QKJSykInQIEg8ASmFbtZIsW8ZUpuX/r26Rr7eRGFqFa\nwKOfQfLzuYasCdJYZccdD6O86WXL6Svori1xPb6L9fpCegt3M+UvoH+xAiMeKkSTlGnG2Uw7B8LH\nmVov4p32hzBpLCj1HhzmbAI5WuJFIhJxKdHrCoZ0DVxrbUVj8CKb8iM86iO5GiHQpibHuAgyeHf+\nEJOecgzNXszz41QPjhKoljKZlc9h3278C0ruHXsTUUqS9pxN+CcWMF+eIv9RARU5PnJ7bxJMVzC/\nPw+DfBx1yMnN0XSGRHWMCeoJGeS0qG5S3XGJuqEbyNqU+JRS5B+tkFiQMKssQ9MWRRDz09dewrlz\nLaBNg5AYPojQLytnILcWTegUdeoVAvkSsszTZBYu0n0kjfNvZaJ7TIGs2MTseAHRhQibM08xuLqf\nc/0NKLcWk7rLRqV8GG3UhUgUJ2NkhW2nF1nJacCdXYrEmsCxqsO+bsZlTSG5JMBfJcWuVqFMqkgP\nrbM/dBrnRAqzJ3JxBosIqwu5ITYxaSqjctMEHIuz8OtUosJCVvI2cKPgdlZqtlFSNYp6wU/ltTGS\nHhezeamUVSyQV+LD1afELhKjiEQI6EYGwYIAACAASURBVEzMpLRRoRpFHXHTJWnkhqeZ690VWNRF\nrAVyWXLnIXGHSAtayFFZyFizYOgaxv2Wh8X7dbhSCvB51CiDfvKSs3Qlmvh79BClDNEkameH4Sj5\nqWI+EW8jNQZ5olXKQkOkr8+gWPWCANCDIz2VroYGpGtRTNZVpKsmEmtiBkWV5DinSVwQUGKaxNAQ\nYFjewFR+GaqwD5XSz5IpF/ukmbXeVJ6LXqc19RwOswFDWhh1aozVZDrL7hyUCj+FaVO8n3UvneZq\nJNFl9Ao/iRw1WY3LpOctsyDIYjmaTol2EkXAzkoX+K4HiLb7WNiXQaE2h3RjhCJ9P1rxMr5YFhG/\nkVy7hZSgk1iRGHFmGFNghSrVCLWKQbwq0Wea/FxC0Bzv4pcpPyBmESOai5Fhn2NWmYEsEMHWa+Ty\nczsJxtdJCgaIZBuxVZu5wE4SChEpWS4iFWLEzTF2TL5LcaiHE3d8lXFDC6FLai7O72DWUUD4URHa\nA+tcSWkje3SEW8O/wbpYwb/cfJn9DccpUE8R/KOKLPsK/3zXbynKn8RSnc6ssYAhbzGruNDOzKB+\n2cHSA2Wcf2oXNlT4I2KOjEvYoB7i6R0vMZ1TxEuip9m5/Sxbqy7w8X8+RI+7njTROht93RxcPo2p\nxILi6yIWKnOZd6oQX5qk1bPCtqLziMxKQuhQ4gCnF07HIOqGtRloSkNwSy7FJWKa5BL0lwQkxiF2\nt4D+zE28JngcS2k/2cV+rH/MInV1mdbbBUQMIBBBUKvCNphFMFdBisTNna7j5ClAVgd5Y9NYX47y\nl9ATzOXmsf2u87SKbiA+E0X1th3d1SDzT+WxWmeixjtGp6aFlyJPMxbKIhldJ/J6EM9snKWnzZTT\nyUP8N/07Wjn7yC9wSYqJRJRMtZezY/YCT7peRL9xCUFjANVgkvmJYgZ21yMqF7O98AquY6m89Nfv\n8cyXXmTDwQ46UlqxuNXsGXqJAectnLr5bWgBbbqDvV2/p3z5CqIPQow6cvlD4n4OTYxyh/Yo+Zvn\n8N6i4n7JOygCPj6ZvZdNly5xb8cfSISWGfPkEXBMsDH3Jk9XH+NEZytvn9pHgX6e8uxpZMsJVA1h\nNI0ehm7Uc+bjg2gW7BSmTnDgqSM0tfSROe5DHEqgvGQlv3GG6j09VCWGKe8YoeRv43xivZ83RV9G\nlwLVRS7+4cEzJN1xkn/yc6x+H69/8RsUGcbQpLgIyMQUxC2keldICkWE5VYm0ouZEj9F+KCK4ukZ\nvjr1J4ZOi3hjsAjbnJNAZIHXxu/lk7V7EJx2Uzl5ki2+P9NluJ2F6gN82/gybZIruOo19A9W895z\n9zPy8BDDB4voG1AB/9+t4vA5hUBcGqaAKSQLISRjYWLlMuKVKpLXlEiloM9zYpZbkErnyJNA5nQE\n/bQDo8qBsC5GuWSEQ8K/U1PTj8TkQayIk+G2UuMeIbEYxDsF1UMrmPO9uAp0pEun2Npwk2lZBM98\nHY6ICb9USXl8kiLFDFsWrqKKerEbjAjESSTCKBsE3aTKraiz/OhDTurPD3IxuoPJlmoay/tRlATo\nrq/HZjShEbmYTebjFN9Nx4adeFa0bLN00kI3JaoJNFI/MYGUM5YGupdKUPv6qciaYdu+FeK4WP0o\nTEHqApU7FliYSiclvEZT2WmiG9Nwt5VTUTRBWcyJpj3KaiKNgZQKbqhbmKAAkzZKMGuNlMJ1is0W\nzG1x9KYoWqETv00NfiGZrCBdd3HuSjFNc1Ga0xdIznqI9yyTTM6T4gvQ3HadXNcEDmccd56ScLqC\n8HgMl8PAonsPi6ICspqWMQvnSMr9RBUJgtlKnPI8ZDgx0UvS28ryygZIEaHxuyi92UNz4AIbi25g\nqHURqxHTs9LIcLQWf6WGuCaG63qY+Rk9g+o6BuS1yEUBRpPluA0JyrdKkCwDUZAWh5DlBwl4tNAp\no7hnDIE3gYN06nWjlBqn6bZWshrQkqUepVWjwmXQ02Zqp1Y/yuggrC+LiCHHF09lLpRPUivGnLKG\nP1XFZEYR3jQNi8pMtAIvJcopdCkBuvsrmVstomDwNXIyF3HsNrEkzmZOm0eiEEoKx9HhJHXeRrWt\nG69PS9Cgw1dq4GrLXrI3zmKwriHPjuMpSWGiqRDV5ATaiXlac514jOnM6AoJKhXEVQKsa7k45w34\nLXKkky6MljHKEzGaciCRtYpAMYogvRCbuJDeolIwr1AoKkeaA2mOVXo9DSzIs/EElFxfamWyq5yS\nxmlSN6xjyEl+tsn///j/v7O8I40gcrQ3HGjXHARuVeMoSSW6kEZ6po8dv/gUQ9oq2oSLrdZOytun\niL+eQJgbQ/qjCPvWTrNj4So3v1HL5ZY23B9nUzo+wwvJn5JkgUkvVHwcJ3ciSeIxAaKCGLL7AjQv\nX+ZOazc/6/4RJ6N38PxDP2V/ymlU5/wIOpKofUEij8uQNQfYzRy6QjfSb4sx2sao//0IsXwlli8U\n8aXav1FWOMpr2kfQSl38E6/w4eQh/jD9FKGNCuq8/dx58zj16z2E7xUSGpGQOC/l2tI23lm6FcFK\nPzv2XsP06Hn0H1qI/3yV+uemSBzM5tgP86jyjvOT29/Ev13FUEsVcnGI1NU1lKYA0+JC/iJ7gi6h\nCVjGgQJ9mpGqJ/upTQ6AIoJIGMQsWMAmzEHqjnFQdxz1zDi/e7uZPXEF9c3LLAViTC+4qOXP6EVC\nas+FiC1FmfYk8Ow3E3wkB80vJli5ouIv+U9RUznDTw69gCbDQyItiS9dzrihmA9V9xPu9OMgie+q\nEMbEsFuASb/IAx3/we78y6hu8UMmRJMS3qn5Aq9XPEo0M0miy8GZHxcTrTWR+LqQrqoG7Hodllgu\nsTIh4z+6m5W1QrCCMteLMC3Jta0PEj2bzaYf/ZJbvZNsY56cxgjr2wz88b/uxXZdyTeEv6Xl4fPk\n/myFqpJxDJtA9K8QW1aRpIx2VyWDY3dwzxN/54sPfUJcJOGKaBNToiKSEkgVr7Gt+Tp5piV+4PwF\ns1cKUHwYx79HQ98Xazgmv5VPY7vZqblAPnPMUoAqkUdDTMIOyTm2GHr4Rf0Pea/1Pr5i+B3buUTW\nHifmCjc5xmmKXzpDy/GjbLhbwEjDHp5L28VSdjqyHB/BaS2h63KSH3rxD/mYiSWo/YKFf3tuBTFC\nRBEhAreYa8nNvHDwhwhVeSTl27j9RC+Z5y7wC+cPOSk9QNImIDwlJZxQUL04waGJI7Tensarz/7P\nJj+fD0VH3ESdfuTxAMl75FxI7KNvqJEd3jNkrS1T2TVOr6qR9tAuGlaniLoknGrdzaw8n/hxKVq8\nGL+8hklmo7X9Gs6bTrwiHYcP7ka71YZsaZmTXUWEFo0kDy8j0MVJBtOQlQlQ7Ihglqxw3/yHjPor\n+V/EvVdwnNeZrvt0zgENoLuRc05EJAFmilmiKFE5S96WZjuN4zgHSQ6SPZ6xLUfZlpUjKZpKTGAm\nQRIgEpFzRqOBBjrnuC98aledGvtmas7xd73eu/95aq31f/Utr0HFluYLWNSZXGrbxspiCrHMBLv2\nfozS0kfwbJCkYAhjThh5pZ9gtZi2rA34I1IaLvSQvThLiWOMWzOPk5a+CmsCjBMLGG5M0DOZz3nB\nAeKda8QH7XSUFiFslFAmc1FTskKGcBWtz0vQJeDsmXKmBgupXThNWd44a41JDNlquPjvWynbN4Av\nQ01fooFRbyndM404CwSovufEoVcyekOAq0iPnWRGb1QwOVWAfTqFrVkXqUjqxX7OwY2hYuYjG/El\nRUkoPmF1dxFLO8yUdI4iFCp4J3svS8XJOCtD+GsykLgV3JH+GnV1c4Tz3iEzx4Y5zYogO4bToOHy\nte30+yvR7vaQqZ+nujTKtEaALDvMLsdJtjtPsn7vAE5ZNr9q3UN0g5TEtjhdqhq0Tge7u0+TNT1C\ndJMTb60ZV1U+ToMGd1zHrasn8NtUdNvqWQzmQQRCN8XE3SqiKUoCS6loUsUklQgQ14uQ5woIWSV4\n1tIJ+lXotSIcQ+kc/8/baYusoA4ts9IkYCozF+9JNSGxgJApiS5xE2GriFDnCp41KWu56xAGhGhn\nLBiCC1R6b3Bw6D2GlZWc37iZ0HoJjiQdg7ZKrLOZ9BibCEuVNCx3IfEGeP2Oz7BptYMdwTZiEhG+\nZTVJpz0IlAI66+rwZKuokvTh2l7MTfkhan0XqJgb4Imilzgr285l+2Yi7XHix50wuYw1VcHxPQ8S\nS27jtpfOcy2+kwlVBds2naMgf4KnBH9hYKSIK9fXE0pWkbt1ialjqdjHpBBTwIoXopNIfMuIrAlu\n+Dfzt9vU/1r/HBF84CK8IEHSEsV3QMXV8W309lfyjcj32eS8ivF8gB5BE2f8e7k1eoq0dAsf3Xc7\nF2zbcL6YSuaeWcqf6uPxv75B/cWzuCeucWXdFt7Z+S2ysx00xjs5/+MddI8UE2kdRRiKoBBlof3f\noNvp5189v6XR28m3V3/GCcMetE1rdAsa+Hnvt4gsSzDOLZO5d56UqTkSX1xAoBaguEdPqECCP03G\nJ9I9eOZUfP/CTyi/MUR8IkHWo8c5UHkCf78SZ4+UxTE4J93H70JfIDA/hWTtJsGKdIy3Rqg126iO\nrZJkCyILSogpdAxfbGIwWs6TgRcprp1gtqSei0e2cvQn97FPryDn1ilOBG5n2l2IcD6GutxF0m0C\n/B9oWLwgY0WVQo+gHl+bntglIVyN0rjnOgdK3+LZ47u4utJMsLiWSI4FV5KexV01LBZXsuklF36L\nhqOlD9GfX048OYbAIsQ8v8aBnNPUpt+gNP1t4johUYUIl1LDbDSDwx/czfBsBY8UvUyNapGKChXZ\n+WAsW+aON45wt+tdBF+EY5a7+MU3/42gVIxh3woCbZyy2AAP3XybJm8Hglsi2CpTmM/J4M34w0y5\nC9g5fwHniIGTk7dhTaSBEgKfSAn1J5DXCZGnxlHpQbBDjfsxM5JuD942HdKggiSdBH2ehNHFAj56\n7nbc8Tix9AD6X8QR7hfgHRVDMAAFcoYFlUyOZRE90kl0IkS0uZaESwZXrRQG+2lOXOCWyMfkV4/z\n/I7vMNJcilrmxb5qRDQiZDBYTUIi5sGbh7EJU3n1wOeJjL1GS1cbsQSIFmOoev14CtVc/Owm/Mly\n8gIzXNvdwlzNJnb+fpkmVwcPGl8lKJFyZn4f8gELku4lAtE1lmu1nHjsMZTteuqe7eNY7DZOZ9yB\nuDjA3uLj3LXyHuFzB/jLf+xm9WkDRY9ZcZ8To7ctEtMnIwivIFQPIBUv4/GpOT28D3j17zL5TxHB\n1M4c2pI3UDk6TPnxUdZt6sZVpeTdisdY8JXwKfXroE8QT0qwrDAQFQl4fOk1snstvGp9nEZLF4/P\nvExnUiOndt5Cyq4eUrJi7Ey5QlloiiZvFwZbF2Z/Me1l+0nJifNI5m/QbfXgFygYG9dy/PpDDOzL\nIeRN4fcnv8DaSiqxNBE4QNCTQFIYQUyYAHEuzzTT8cHDdE40EcuV414xsCo3srpBj69KgWoggKAm\ngVeo5nXPI1wWN+O7DZbz0pBUBNkY7Kfec56PpIWM3lzP+dd2MSqs5P2CVUTCKJFNYrrMjcgFPgq7\noCHuIHKth2HLBuImITen6pg8U4RtxIQixUdK/jJGsxVjYonkoT444+Oa6j5m06uJy4QIdsYQ3hbh\n2I0qZi5vo2XLHOXFZ/kgPZchfQHfV32PBXMGUbGI3TXnqUgf4RuKn/Jh+228ff1+9mw6zqHqo6wb\n7GZqOp83HA/jqVKRUzrJ/PlUxj7O4WZXDklSC5XXB3DrDHxf80NMw0v8uPObpNZaGa0tRVnox2H5\n24vM23ov8+Abb0BZAoEZlnencKq/FvU7fQh3i4kWq1ldM9I/Xssvjn6FRByie0C56MF3TQdu0Osc\n7N9/lu2Kc8ReX+Pce00c7vlfSFJiaGUeNuS2k1cxiWe3HF3XJE9NfZWz2kP0ZGwjVTmDJC2K/1Au\nkYAa0gXsKGllS1YrY+okRlaLGJLE8CeEyA9JuBbag8+Zxf1H36HcM8rnxl7kRloDVwqayc+ZxqBy\n0qWpZ82bwm9nP4t3VYNrTMdMN5y/AYulUawFOn59/h4UYyEWpyuJDcoQjcZI2mrDlGflnVvv5qRg\nJ6k6K10T9dAGB5pbqWpq592hgwzZygi8oOaKaSeO7xtYUOUhE/j4a/9dXDpVg8wxwOSkiUjUSjqj\ntMiH2d3USjRVhHV9JjKpC6N9nKxMB5Op2Xh6VP+QyX+KCC6lbeaEficrV42E2hUEd0iQNQaZzCim\n3xbihmOEuEnIuvRu7G4x/ZYMMoc8lM6M0aDvYoOyg4ZQF+9r7uVD/W1sK9ZTYhgiBwsVS8PUT/US\nsPYSTgwRrinCuFFGS3432iwHLoGSHtk+rmuqSdGuoYp78Y7pkInDNJR0MjeQTWRIzGK3mZF4KV5J\nBm3+nRztuh+VIkiG0oK9PZlleRrdVbVQIMCcbiMcFrMymsqHsVtpy9hMduEcksoQ2lonFWuT7Jrv\nxurtw+dKw7+gYlqchy0rFUlGFLEpRrJ6jXS/hchSBiv4YRoCMRUUQ0CiJGGDqCWCJrhCwWIXhYkF\n8iUrZK2cR+RdIzGfjUECKECgiyJICeE646NnPJn6h22YD3gZ0M0wKirhKAcJ+2Ukex2M5JVgVFox\nz0xQ3tPNumsVVBiHyc+aQhPyMBrP5ER0H26Jio2p57GvqFm+ZMDoXiBbv0i0V8xscR5XclvYOdNK\nbts0E9UmJjJTKFYu4FcqSaQKyBXOsGv5DIq0ILb0ZA5X3YlzzUDZ4BhhrYq16lTiVh+KETdt7c1E\nc8Xoi20YlKukjqyh1qySKZyhaV0HmbpprJeTuD7WwIcdd6Dd5Ca/cpJaQQ8qjYOb5gyCqhBpiUk0\nYg8SmZA80RwKqYclYxJye4DM4Co7I6e4TfohpzbtxuEtZqLLRyAqQZwrx6swMR+MMHS1Bo0zRHZg\nnlWLjkuuSuSCFUyCedThXKbdebSu7CQyKwF/nGmriVZ5AwvhVMIeMQtxIyJgLZSOx5ZMYEzJnabD\n1Ij7uGmoxiXIgYUEol4vuVdu0PDYdeo29nP2wt0Ir6Yg6w6wkJTHeGEJtaYeKqMDdF9oovtiLtle\nL2K/g6JYF7qAj7hPginThVFrQ1A4QNwgICaWYAll0OfNRmPw/EMm/yki+GXPF1lt1TAwU8XRwN24\nVrXoww72p31CWCPjJ9ZvsVHZxk+i3+atdyr5+bl9yIQl1OXP8ZW7/xNdrZ3R3AIc41oiHgm2WCpK\nsvGioWh6Gk4As5CsWuXh5leI7sihXVmHTB5EjwPtIRlbmme5NXGZkrVpSAFMkKiB3yx/gcOdh/jr\nH/ZzRrKBmE6OIyOV6LiA6spOqu8doDW0l7kbOfzpt58ld8cUhU+MYj2cxOyxNGbvLiG3cZwvT/8G\n/6qc9+MHEJ60E3priR1PHqVs7yjTDXmoBR6q9AMYQk407iBcAEtPOn/13slvDJ8HKVhy0xFmxGls\nuk62aITTF6qRXligZvR1mtMtNKaHkafYEdwToUL5B3yyd0AOsf44sUsJeqZ8DEkUtGruRKDNJV20\nSCghxRozE7YpWHVI+dB0gKvWChbfj1Pjm+KHW75N68Ienv7Tj/jKul+g2B8ADRiy7FSJ+8nPn8G4\nZQV/Z4Jpew6Hp+7FV6CjeftlZq3ZfO3Cz/G/NUv6+DCPftmOMC0OO8FWkszglhJyZbP4FXLGpYVI\nVT6qsiWc6zXw5y8VsyfUSkPsIm9Iv8xIajVBgZyisj6aje1UJ/pJ719iVFFIe9kmYt9NMD6xjsS4\nkKraXipz+vh4cD8rvfvx/2KI+FgSwmg5K04jisVVmvxX0VkXuHk0QcHoEp8RnaC8ZQbtlgCO5hQW\nvMkEX1siMhnDo8nknvoPuK/4XY6s3cUJxUGqy3pZccbp/xngykIpLMCRU4JHkkR0QAKrUYj5mdi+\nCdsj5TjOZ2M+6+LzWYfRbHBzsmgfvcZGxiRlrBvt54HLhzmQeZKEUIB8OcDooIyOHhWKXRoGBFW4\nxDpUVV5y9k3i7tUx+/N8dhRcYpeplZ8GDbhz5Hw5dAbt8jiz8yLaFh/jxYFHULoC1Nm6+cwHv2Um\npYDflXwWx0AS0pEQD5a8/Q9Gl/6TRKBaXMaTIWFJm8Z0WE3ldBf5HwzjN8lZNZpYNSZh8aUxNVHA\naG8u/RP5CLZUkWV2U7A8gLw7yOJ8FqLVGIpgkOKFCYoKRmFdHIs0nbfVDzIqBW/Iy46hQfQSJwlR\nJdEiKayDcFxPyGdANz6LITTCak4+3nw9gVwFfqGPmHMBe4ECR2ohPqGJXN0sd6cepb7mBuaiJXrK\nGhj1lDGZyGfNkMQC6WhTVlDlORE7p5H0zpE8coO0oJT6LWnkW2bR3/BB8RBBXZjZ+hQ8qFmYyCR1\nyUKBtZthaz1LCjOi+gipacuoMgLoVQ6MsiXkogB2SwphgRqTIET92iSVZgtp6ULsFcm4srUIQ2D2\nW0jzWnHqNMwUZRCKZbIcKmbQs5H4rJlyoRx7MIWQV0lkTEZ0TcDInmJSUyWoyybJEizRVN2BdSoD\n+0IyaoEXgTCOJtOFW6NmzFpGmX6S2uZB2mz1THiq6F5qJDYlRjobIJ4uRHmbh/k2EYnLCqQlAZLK\nnOiaHawWG7hW0IQ8GEC3toq2axh1VxCjOEyq003qzAwF9WNklMlRi7zEJSKCl1Wkq5bZRSsljklE\ngQTnO3fQ4W0gipjwapiGlXfZJrpOgcFGd6CJyVAqheZhUnWr6OqmEVpG0MVttETa8VuDyCetCCJu\nhDVRhLkx0CcISmXIlSG2FPSxLMphQGwiyWQn1zSNeouXGEIiBSKUtgBFeYtMD5cxvVyGsThKhnqG\nFbsSTcJJaeMElqx8pvz11CZ1UZPRQSzqBJ+TbcLzhO0xxgZVTDmMtMc3gCzxt0amuACVdoydRdeJ\nzxZiO6ulWDFKPEdAQi0gVzxHc7ST9aMXyB7tpjz4CSsGM+JmKcq4mvwlO2OCGYTHR5gU1KOSliDQ\nCfA6EnSdTsI9mI52ESay8v8hk/8UEdzn/jWXnn6AAU89thEJd775FkVvD/HrvO/j2p9CzZNdjC0V\ncqz9dnwrUwhSfQjviRP1RHD/e4Ikt4MiVQi92UWSysGB5RPUbehkMcfI+1n38pcd/0KkF8ydk2T+\n4TtslV1gh3QR1xNGFmsyWesw0vNBDet6k7AVptP9o0PMlJazLDGzGFhDHOkg9eFkRGXpLHycTDPt\n/CT/2wjWhZmTZKAtdCNMiSGr9BEQyxm3lnN/Sxd7W97jjz8sw/JhjNGIixJBhG2RVnLEi6TF4ljf\ndjA+6qP3J/nMJFpwv57M/d0voJoe4E9bH2Rw60Ye3/ZnWszXyAwuMyQvpk3eROuR/XSdu5WQSExN\npY0GtZiMPSK898sYEhcyLi4mmJCTuWRhZ/dFZtbncvxTuzj/xnY6LzYSmVfCFSFL0hziTiFhiwRu\nChA6Y7jK9ORsT3DPN6+zMdiPJBzjQPVH7LadRtXmY3C4jNRqC9OJ9bw98ig5iiWaGrt4b+hR3pm9\nH79NRaIDbBi569b3ePjpP/Gnb9ZgPyYk9aVVlAc0mL+ywKpZzzl2UMwE6ZZhan79NvKRBLLMAFVi\nH0+I5sm8V4PnzmpolxK7ISb+RxWpTiebIh1IwlFmErn0v1nLReVOQMCG1Ve5d/H7bDIFSMpI43Cf\nnYygknu+OMEGYyelgTlEZ2IIe+MoYgGuW/MRh2PMrs/jo58kE0tqpVbSS0wqICexzANf+5jhUDkL\nkSY8CjlWhYGyzX2YmceoskJWnJpSFcdPFWA9m0XpPZdJUc7Q1p1BQfIsT/3wOOeuHWD+7RLufvxd\nmm49wc9+eAv6a0k8vecka9f8vP9CEu/dtY0Pdz0IBf/PoJAMMZ8K/Jln491wagZ7m4fwN6VIjCHO\nnN9Lk7OHH93yXdxnXSx3hKlI/AnlpjJOH7qNtJJKqmK9FP70GoI/X6PV/BNYn0TiAHBzGV68Bs5q\nvOoCXhE/CHzp7zIpevrpp5/+/4l/AJ555hlkHGLS3cCqPY0AakSOBHNBM702I/rUebZvu062coF0\n+SLehA5xipK7G89SJJml19nAjcpmurc30NnUgGBdnO1lZwlrJHzQeyeXbdsY1xfToO+kMeMGy5Zk\nbi5W0ecvoa+misk9FXS3N7I8nkZyrQ3H1gy6SnYwPZzL8usavDYNFOjQ7xIjypTiChrAJMCVq0Vg\njmHQrGGXGEhNXWFLxnkKVJPIJEF2LF2kaaCL6+4WHFn5bNkxSnHqGqZuL6keJ8rKKLMtOfgr0yiN\nO6mbG6ZutZe4UESbejPtgUIsyxIiXi2qeIwqUz9Do+V88MmdLMizkKcFOLjwCeXiMQYba3BF5KRe\nGSfcBvIOH+ljK4RHlJwd2I3Dn0SVaJBRSTkLmdncrvmEO4UfsFF6FcV8gMlrhRTljdKy5Qq7FGfY\nNXeOpr4ezCEbgtwYV27kcuqDQtRdPlSTXiJzXpLaxzG19WJOthBZp0WaiJCSWMW6qMel0xJdL6fJ\nc4lbuj/EqLWTlhtjdrGBy8sN9K8oqJjq57b5Kyx25tB5tRrBZQtGnR3DvTJk5QlSMoOYVAl0k1EM\nNxyoFv0sirMIe4SszOmY3ZLF9J48zkduYUGbiWhzBFmDl1i9HPPmOMokOHN1F9NLBURUSiIaOeLc\nMB3X8mi9UEWbcztTkTKyqxdZt3OG3Fo76SorSrEfVThI2fw4NWeHiDmlWIuMBDwqxubKcWr0qJM8\nFAkn8Pp1nF/Zy4o4HUVOkLhJjCieYKugjbwsG2NJ9SiFIbabzxPyy5gZLiBz1cpG4RDrHHPICWGq\nX2Nj8jiFwSUWApkkhAIaszrY6rhE/fU+zg7u5r2Fu2n3lhDqi7Hh2nU2jZ8hf7EP51QES1RN94Ft\n9NVvZmIyi8WRDOat1YxeTGdh19Jw7gAAIABJREFUJo3ovmykzTJsQRNBi5TmUDfblCM06Mdw7Ehl\n+cM/8/eQ/2/vCJ577jneeOMNhEIhVVVVvPzyy/h8Pu677z5mZ2f/79uHer3+v2SvjNfAUCpsA+X2\nIFfzd5JQrCE+cw2ju5fs4DQZGauIzRHc0S8zN53LE+L3cKoMfKv5OVYqDEha/BCBnMQcdpmWhVMZ\nfPiNQyyUpaF8yM6mLedp2NTBi7OPcWOpmKjEjjYhINkhZ8mXQ1iuYuq2zciqA1iC+TguS/A/G4R7\nMpHeoyCWMY1EHURb5mEkUkKPvJovxP+Dh/2vUJ5+kxLhEOsCPSxj4rJxCxXtA5gvuUkuMaPfUYK6\nPBvlWRvK73mRHwwjeVKEMCWLFKeezUdPYVy1EzYq+XXGF/hd9N+QfHgFOmY4Obwd8bKKvVknmeoo\n5PTvbsX0zTnq13fw5LUXWQsb+WL5r9h8VU/VH4+jF8yTLFtCkyXjunI7bwTvZ1vBRR6IvsOFuq3M\n1GbxwNSb7HBeICqV8Erocc7Pb6Pqzl4O3HWM2o97yL0+h3I+grdFjrVEz7GuOo6+uw5ZLM6OWB+V\nbRfJioXxxfys5G6h+2ATt9RfojJyk8mBFBx6Jf6CZKSnnBg+mePu5+aY2BPiS9Of50p7Ngx0cGdB\nN7eWnOL7/h9z2VXPI8ExShqW8d+uIDkaImM0DKd8JD6coHh5gkz9ImMNpfR5S7ju+xItTTfJuXMF\n79tKUmKrKO50E8hJ54biU5QiRW3tQiSP4HVoOHnuNuYSOazm6elc09M7ZkayUkLjplm+8c0fk1Ey\njx0DCQQ4E3oagr3op9xE3pGiL/VQ19LDGetePu6/iwb5NTbpLiCOR7E7Ujg1vZ/09AXWVXfSMbWR\niFfBv7b8mVV7Mt/q+ip3Vx7myQde5Ls/f47O1iaer/gmO6IjKI6GqDm0QM4XHRg+8rLUmcncajar\nQgP76j+i5mY38dMJTgT38pvQUyiPjbMtdIqHQi+SIZjDKQZbVMZCWhq9B+7iZlId0R+OEfalcb20\nkcT4IlLlHOXbPURrBLx85HEOrR3jV02vo55zY3Mb8KQmc/Mf8PzfEsHMzAx/+tOfGB4eRiaTcd99\n9/HOO+8wODjIrl27+PrXv85Pf/pTnn/+eZ5//r/2Nr9w4I9w0wAGKVGVjKmSXPwyAQU5nayJTbwz\n9AQ1kT7Wmy5R1HWUrPYIhsQEOVEZz175PnPyNCwtRi5+vIPJsVJe2voZKn2DfMn4K6xzfgZ+D8HP\na5hsLOLexo84kClmstpM/1oeN79UhC8RI4aMqZeLqSro56v7f0l/3MzrbCcqUZGQKwkLpRSLxvi0\n7FWGZyt4feghnJYAs1E3kw8bkCSpqX6jk0LFOMlbPYTTJYzem89tfR9Rc7Kdc4crOTq9FUlQz12O\nDzk4+Fdig2M4x6T0zbhZK13H9b0P4DXqOcRhKgYvkxiJ8MZyJYI+EJ0DwQQIBTE2Hz7OgQuHyfRP\nsWYygg0UlZD8c1gUpzHlLqLj7A7sw2r2u19EUx3hlV0PUbg2QfVgP+FMMW8m30fvdAMzVTkUrBuh\nUdJJ2ZlBPjhdjGAmj0fSbzA+ksnLX99OT24DwR8V885EMeen3QTnIHvpCg0rr1M6MIrm/TDnErdg\nDZi569aTlC/28drbewhN2PAAWgHEk4FdQEYyuBtoXfZhGxARv2eN7RsuYvJJUQiN6EfWkPeHiF0H\nYR0IHgbh+1BtHeAHs8/wvq2J1xPrmThjJO7Scy9HKBaMIX4zjGO9Fuv+FDKEFiIaCY8ceJ1q8wBv\nXH+EhbYszq7sw5A5yf5/H6d+6QzFhnnk2iAz5DJLDk70BAVy8pQzCEwiuisa8WYpUYpdhKUSonIx\nE+4SPJM6+u11+CUK5MVeNs5d4UDHJzgUZto9G/hVxxcJzcZwuX2cTdRhW/ccnaoNrKaY+LXuC4y7\nC3jQ9zodl5t4N/QAjwdepUo9wMYNl5ioyGNelclEIJ205UH8dZBS6uRRyVFuWTyO+cIaKiVoi+HE\nxCbeW93NzEvVGGpibHusl2XTGlf0+fj+tED09CJzw4WIhFkEokoIAqtgm4Ilt4+DK+/w4v+kCLRa\nLRKJBL/fj0gkwu/3k56eznPPPcfFixcBeOyxx9i2bdvfFcFd5gvI58EizWAmnE0k5CKuiLI/vYPe\nWBNH/fkYgk5K4zoksw7EXWsEUvyYdE5aEpfQh8qI2tZRMDhDokOKX6JCGg6xJ/4Ro+EES7YcFhf3\nYCvIY3/eadIy7fjzDYxeFuPoVJLU6EKRk8DRmYwwBHsrTqMQlPNmxXZ0uS4MSV5ywtOUusYoiYwh\nCEOdu4eUxUX8njjzS2kIwqk4p3Qk6TykulcZKytgNiWLipVRRH0eXm7bybyvkJysGE5VG4mVOLEL\nVnztMAuM5phoLdzCemU/t9uO06juwa/V0Bpw43AauDmzjgVnFkJJnKqbfTTF21grSWU2zYBa5MSQ\n5kFrFNApLebq8k4+On8Qic/B/dIrBMxJ9FS0cH/PEZqd7dxIqWNSlstNRxVekQqF0YtsIQSLCawB\nDYFIHEtEzEgwlfP2JsjTYy7y4lMmM5ZSgCU7jZV+NTr7OBm+WcxLy9y0r2NOmsPDVW9hEM/wXnsT\nVmkKN8vrkHnniFsgUQQGZZicCSdhoYLr0mIqUgSkZfmxpZYx4dEhGJlCP7qA4sISiu0KxM0ioheC\nqCyL3JZYJKhfZrQoCitlpHeI2F3fSmPsKr4rf/t+5vYVMuPJY8xfQkNFD43ydoZWylicyER0Pk75\nPgv1Nb3UR6+iSPjptrUw7ilgzp3GqjqNkE4NWgFymZ8hWR6+gJrU6TVEkRhZplkc4iSmA/kEPQrk\nyT606XZKx0bYcrONDvUGlsOprFhTCPoiGAQWFgUmxhO1ZKmXKTBPsJRr5sZSNQXhEtpXa7g+10yZ\naQRVpo+S/GHE6UF6JdWkJmXgKZUSqRShLPNTE+qkMDjCnCSLVKOX0vpFVpKK6ZveTGg+iwzFFNu3\nj2DNWmI5msKsJIA9GiRpwYEiVQ4pIHD5WetPELFDVBShlMF/yPR/SwQGg4GvfvWrZGdno1Ao2LNn\nD7t27WJ5eRmTyQSAyWRieXn57+aXz0CmCE469/LHycfwXRigyHWDRoWU9I2L7Hr4EzRpbvwJDb3C\n27F5ZJjO/ifWLSu4P7OONtEuLl3bzkPat/hCwW+JXADVvAPxsp2hLXfyyT1fIuxMRnhGwUBqHWJ7\nFM97KhwpahL/omZd1SUK02c5u2E3LAAjgEoH/1ZAaVk/zRkdbJ69RtQl5WXRpzBlWPhO+Q9ICs4T\n9CdhD5WwFM1l+LM9rCRlsppqwq3S4JcqWNuWzLLeiCNYQUNigi83/o4K/RDEIS6FKBAGNGI71eqb\ntHR1sO1IGzq7m4k8DYSgL6uaH6Q9w1ogGYEadAKI+/W8NLWHwfRq8qsHKLSOI35VQId0I+9778U2\nokegNPDqpu9gXreCSWBjtdjAUnYqBu0qm4WXKa4Z40rnVj768x20btqFb6+UXcZO9FfHsZ51YauG\nkq/HSWk9Se6PrlPfKCRQU8rLtzzKzPlijnQ9R7jyJKH97The0ME4YALStXBnMR3GImypLSjO/pLM\njmUSe2AdvXxl8j9ZaxRzc1c23UdbOPp0OZLbRWhrvBjrlijpPUpF8A2MYhNqhRZneJZkhYv6etia\nMkyOeBUmVcgdAnJFM7jcMGwBp0NMKKHi+ngL3fON9NdWU140wMOfehnDeReqoyHUx11IPnJhsTu4\nbCjno+WHmHVlE2x3E64xo9kg4a76ozSvnaO84xIX/Ls5tvppyrcMsan5EpfFmwkJZDyU9SYRiYjz\nku3IVUFkhhD3db9FS+w8nvu1zBWnM5QopNdQz7AkmQeS3uaW3PPMtmQwNG7ihaO3oqsTcfArR+iK\nVNObqOTTiZfJt81y3LSPtC1ZCLM1CBakBMah7yIsDBZwzvEUO0r7KSr4I4YtCTJlCZYugXIySN6b\nC2R7ruF1dnF69nZGE408ET1CrtbL++tuRyRY4MaROKUZkFsnZrwwH/7B4eC/JYLJyUl++ctfMjMz\ng06n45577uGNN974f60RCAQIBIK/m/9pAEQOmA21IdekYVtcx8JkBcH8a+TYrexYuMBgtJwOmgn7\nhJgEC+gkQTShKMx5iKSKWdKZkVSFSNKuMtZdxEQwh65NFVzN2snCYh31wh7MghW6LPWsuQyoTB5y\n5LPsWB2jebqNnOgMSUYPyoCH+DkXxop5DracoWiul8LWYfwROVaPicjqKmm5g6xvvs5sdg5j6kpS\nZ5yow2OIc2PMruVxrX0j4YCMRDSOILGKy63GlZGMV+lkxpRHmttK6sICEg9ExAbmNfXY3E04PzHh\nD6rwZyuYLsmlR1DLmtOAHQN2n4F00yLrb71Klm8J7VqUjCEHyzoPPoOJ/qEi3r2wm3CxgJak65Aa\nIZwsxrddR6ZqgfIzg+RE5lBE/EjCLgKAXVFG3CUkxzTNSjyVC0vbiS/FyEkICNRZUZkjbJi/Tpbz\nBtnim2jSM5lOLSJqESGTgumQn8hmDTOF2ZjrLWTHpxFPuFHlaqhpGUcUSpC2tkTAoMUWD1MxfpUc\nn4VmYRvudA3GcjfaVjk90QQT0jIE/gRpvVacIjMn99+PQZeBwqvGW2GhPG+UsrouDB4XigkX8jUI\nBzR0yeoZykpnagME0SF4N5VBZTEzklxC7RJcKh2JfBEbVddplNzgprSKPkET+lAXgoCKufZ8JikA\nkZ0a8Sy10Smc08l0zG7APRtEJvWzmSuI16J4RrTUiPtQRBxEV5aQJifYVn2BYtkEIlOMAvkkWb5Z\nfDIVvWk1zKTlU7EyTsXIBFqXmxlJLha1kTWlFH/CTtnqMluHrYwpP0MHdRTPjpAUmScpuY94hYf+\nDfWkvzXPppFzLOjy6S5q4IZgKwlVOhl4GVirQCgOU13aTb24C+kxK6sCMc6iDIIuFTFHguUpLfIC\nIWyNYdVk8IqzBY3+JnmrDlyvLf5Dpv9bIujs7KSlpYXk5GQADh06xLVr1zCbzVitVsx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Bm8rYlc6zw7J8+itAWY9ur52gef59TAbpLZJRhTl9Eyi31rNnZ9PscXwuBPwnwDdEbgD4ts\nLG6nrnKc8dFSwkoNDxUeo1bSi3g6TjChYDrDyITDzJwog5S7U/HWqhmSVNBk7qYu3kN2YvY/NvmZ\nQCD7Vy8yfwRREbBOQOiSCveH6QTQUjUxzUO+9zEX+Tm/pZnFT2JwbgzmgwQKFFjSDVyQb+eEZw+W\n2ADC1Bnu2/gJ+as+QTiXZOq6ia+f+wUdDc2w1g/He/DrB5m8V49+zTIPJd+kvGMCwScgssCUycTJ\n7XuZdhXhxICi2YeiwY8wkiBzyUb9v7owjkywPBVDswzSIhifgAEl+CsFUJgC5gLaTLcSMeciLIsR\nDChIDIto6rvBg47XOWvdwSHDQYgWYctS8uG+Iuoqxnkm9Vc0THUStYFYzP88td4H1lFo64XupAor\nubym+xzjmnIeFr0OUWABvFM6pqdLyN9lpbE8iX4e7BNOGGzn4rKar688zc3yWuJF5bguaOi6qMbj\nbeSOhI98oQVRgRnFsw3oqoNoFCukfUfD7OwuvuO4hd7RelI6fTx0x9scMH5KbtoCcneE9A/cnNG3\ncOELm8gss7B99gobsNBV38iRe1YjqNSS0AhZkmThlygoNMyh1AUJKsVIE3HQQo90I4PWcm7550eE\nLTdpf3AbxtsGqSm+yAhhkgoNVdpxXNMG/jn/JdSuFf4U+gqZvRZUQS8t2jaubqnl9Zn9pKX6qOIY\noYgNs1yL8f4QcaWQTR/8AY+gkG+W/ApfmgqpN0DZO2OkDQcQ79dS/dAMt6ScIctmR9gp4HX7wzji\neu5LvI4g6eM1/puMsiDm/R6aYh2scnVwNG8XPXP57LzyAaXXTvLkqQW2bB1CUiKnz9uIICbmB3U/\np3VxA4f79hJHhjcKw3EI9bmp/Hk3nWzmFe8TPGx7HbNxHNkBI9I6LZFPF8ixj7KBGwQlSiyqdBL6\nIpJeIVyXw3gEEuDbrMS+Q0P4VARfVMDAvmI0Cy4Kj89yOO9W3rvrLrr85fgDCv76dz2S3ATuhlSs\nlTn4MqXE3pr/j01+JhBoXS5CNbmEjV5iiSTYQDgpQACoHHHypBEQiJnxq0npmaT4ShQLRhIBGeEF\nKb1U8NbM7eApoCT9OuaNx9hR3k+a28W/Vu7lb+qHcGZmQG4cqj04U6A9tYIyv5XsISciSxzCIMqE\nUIWKiboCLk2vok9WRpl5irxVNuaTRkp6x9lyupXkQoJrnkbyXF6yXG5ccS+BFIgXAxVyyJXjKsth\ntshGpmwJ2XyIFKmbTM80RV3XmTPqKVtbiWIqhCA9gXtXOSsGN6bujwnb/bR7ishN8eI3KIhNiog6\nwC2EWCiKcjFA12wRLnmcHcVH/2cLyAkxj5hQUIk5zU+VwI19wcx4JBWUHiYdxYws15BWqCBvk4xY\ndwLheAyPU8TKsoikE0yfi7C2xI1JOYNMF8Z6SwZjfZVcPFuPIJrAuDzH2oHL1Ds6WLCZkcfC5EQW\nmKvM4/KGZtbZr9Nkm2O9boScXDHLt0ySG3cRWxIxGizD7jOQnuNFmRYkGRKyGMlkUWJkuaAEnVXC\nNvslZOM3SHHHUag8GMtWSEkbQS6EouEZhpcqOS7azQbVZR4TvorcEkJgT6LOCLGQX0RKlgFlcQpq\ngZ969QQ5xiTTTXuJy6SsO32c6/FU3pHfQ6F9glV911CNBIh7JIjWK1GZ3eQq51jdexODw8mFzI0E\nRVJMgRn8Uhk+Xx7zWekMFiZ5ouF/U+CZpsNYh2OmGMWYGZN/ntvS+jHmC5gz1GFzZSFRxjDlzFKp\nS2Nckk88G6Zk2UyPu0hZ9FN3zM9JVSZvpTzAWsF1CtMn0GyQkFu1gmp0gjyhFdFEkhS1l2ytFV3x\nChGPkJn+KKH5GCSThEqlRJpF5H86jtAXZrCqjERUjGMii77SGiabilkJ5uAcTuP8R4UoloJojB7s\nukxcAR3O8Qgw8W+b/ExuKPqvX5Zy44EDpM7HyfzQQvu21Tge05O/fZKYQcrluV0IZl1UnTtMRf8o\nBQEb05SRFQlwx1wbk+czOXe2Ahx6JHl5SG+TIi2Ikbe8QKJYhORgFHe/AcsVE4JdGoLGLCbfMdLe\n1sz5kdvILl6icl8/whIQViZJKfPgvRmg51UhDxQe4qHS4wz6a3BIMyiqH6W7eBUvpzyNfbcRwaYk\nefNeZH4tF3P2YnNlwzW4S/ghT6W/iE7mJKEVEDFLsQXh/OUMlLsD7P/yRQ7OfMzOlTNk75rHZRPw\nyZ9qOdbVwPlwNdKDIuLrNbQ5t5Eh9/JYxQl0OyREd8vwXZlEdH6IPZp2YiEdJ2b2QnUc0+5Jmt85\njPGvfRyafJDTxtuJfD6DpCqdwLV01h3spuWeNnJrZ1nT3MXnNhxji3gEbW+QrGUbzVMdbNJfISPD\nzrTEzPxoHvazWWibnOQdmKDh8nl8H4n5S89/M5ZdSsEjYzhK9XjRMnSyhoX2fJqzuqmrHGWNoZv6\nzj6kJ2K80fMIVxY30VjahdG4iMQZ51PHbfw29C2aant5qOUolYVWctK8lHqmGD2u5e+vbGGNb5Bb\n3ZfIOGNnbiWXtl0b8TfJcdVoWVptYKosn9c7HuVUcCfhR8RkrreQqnGTs+wlKyng0/gjLCyVcWu0\nnUgin9bFvdwz/j5fmXwR0/ppLKuyOT28h/ErpQyerSYl10vWbQtcaljPQHElAym1qNUhnpS/is+k\n57J2MxqTB02dm9qMHpIjDt49WYchPcaOpjnEe8XYN+rpKqpjXFXMlcWNpHbMc0//iwQeVTN6ezHR\nETvpDi/5augSrudsbAd7956g8vZBxvKLycqycX/9YaKCdN4/8zDStAgNBTd5VP4m1ZE2+trDeOaS\nEFGzYVc36xp7qR0fIVPj4mLzJs5bt3Py+i1UlQxyv+ltph0FTDkLAAGm1TM0336R3ZOnWNPayZXV\nO7n4/tX/f28o+v8yIhIpwkYNS44Cum1gr9YTLRMT9CpRVITQ3bqCZzCLzsmt5K5xI9MqKcBHmjzC\notZIxqCD+5c+IiZKJ5rQYk0xMiipYKPtKrEyMeF1cionBtCGvAxEK4gndKRlT2CQOtGmLzPpK+Sd\nufsQKASENHKWPTpsliySC5AZWKI4Moy8fRH/UgJHSgBR2El57hROr4zzXeVkW8fJ9NhpGj9BNCvA\nbEEtKxlKZoRmhvtLcZLKqoKbeOvDTB9MUpU1xbqZafwlqYTLFTQoe1CMpmIfMTNkK2FJVkFL/xgZ\nqU4E4gSOlFSuWyrRW8Pcsngc1awQ75gPRaoDTaaMA9mHmTSacauV6BatpPfMk1cyzXLYzMq8mbhW\ni+CggkiDkpUsJV6lBl3UhmHchkgbZeZgHnPeAjxBHbVXblK6PEhzWQb6BS91oSE0ShepOXYWss10\nFzTSq6rG2phFRskedDMeNg1cZsmbi02jx7skQT3tpAEnwh7w9miwVmTRX1zNhdKNuLQahM4E44lC\nIkkhGQ47er+Hm/nNuGyZcBN6Ohrp61xHJ1aMVXbylUrE2VF2h08xKc3nQvZGDPPLCBNwLbYB+0oK\nqaPjSFPspCucZETCEBHj7s7CpjDgayrAtOLm3rH32D51liLHGMN3lGAxZbHp1EVmO9NYnofJNWmc\nNG1n/oaK5IAF4cIi6a5hagOdnLNsJzCiQpoXRZqIYotmM67OZ2hfNVes6WR7DYQXFPisWgoKJgjK\nFFwPrUdXVUKeq4ghSS0rsyKag12ohXOIpVApGeJeybt4NFoOq25lPFZCpnyJhvxxFEEJ0/YKfAIV\nsYCMIsUkNYpZJv1Bxo1V+BrL8KUbuDSzlZZEG42qXvpFVSzGMrkRqOS2KT8b0i4zllWEodyKMANy\nxDOUXe8lfcWKM0dPevV/PlH0mUAwf8FP5r4FrHtyGNhcw5CgmoVlIwujBTTLr/Do469yYXAzr3b9\nkfJ1/RSUTlDIMhpBgMvCZhre7eEF229ZWRAyslzES5GvMe0zEx2RMqfJo1W4jfvveJvbqw/xu99+\nh4BAwT3fPM/arJtUhCf4xW+/wx9P/AFuF5FsEpJICgnbAsTxEFKk40NI7NAs8WN2goJh1tct8vD2\n4/z20wO8d2U1q0JKmmND7J/+Ncq7xzn01Pe4bFhDb7gC+/s5FE9N8ZN7n6W8tA/3L+Qo/ncAwU8F\nvPLdW5jfa+YJz9/Z57xKbkLMezzEkUg9mrf96NsXkTwQ4mYkm+eO7eIJ3Rm+V3GczCFY9CXQXoqS\nVeXiuf0/4kjhPv7Jg4hIkCWx82TmX7nunOSZX/4c7x4Dgh9HGc6uZi5aiHdOT9l5HWWvvoNhoxj/\nT+o4PHI3/R01fPfw99l19Dh37PsIpBLiKjFCV4LlaT0/3vgsZ27ZhiFvCbtOx/9WPM4TZ17mvjfe\nZ/CblYyWG1l+NoJjARRlILQDTqAGbPsMvJd1J2eVm5AURMlMWtkXPIT+uUkGr2bzwte+QpdwI9yA\n4KSUeELCuzcFXA+YuefHXWwyD/LVK3/hff8d/NzwHXpvKomeVxAWyEh4HTh+5US9b4xNX72GfDzM\nQncuTIO7WkPnk7WsV17lt/3fRPbBCp5eJWeiLbijaXxh+WWiSwt0WqBt5Qu8vPJ5ll/zYPiwg13R\n11iTGCKaDBCVgSQlRu3QIHmaJV4uf5J2UwOR78c4/XIdbS/eSjKWRrF7gafuex5z0TQLmmx6MtZy\nvX4b0X/6yT3Zxya/mjQpiMTQojnHKu0Nfhb8Ab+Z+RbhmJSditNIw0l2ZJ+l6ZHr/ML5Qy7Nb8G9\nqKPedYNvcYbZLaNM/rScj4bv40/XbsVosbIh4yL3Jt6FgIceSxp4V9DYPTzy9D+4Z+ObSN1Jkkdi\nRJ+J0vF4M61PbaRZeuU/NvmZQNA/som63w4Q21WEfHMKB+zHuNP+KYlFMfnhaVYvduLVpTK1uQiD\nyYo+tkT2iW7c0+lcZA9SdZKaLw+gWvZiVFopNk+wkMznxYonmcrJI4CCq+JmlrTZbKs5j2lhnsYz\nHeQ2zKNZ46R6Zy8teWeIVkuxpmXT56ujsHqRjc8cxx3I4Z2XHqMhuEjFmij9mXfiqA4zvdpGzlSI\nR+KXmVA30yvagzcK8Yko2//2N1SyKMKwiPOX1zG5ksabmoNkdrQQjolYdaGVGsc1xKIVNHIviokA\nkgwZfM1IfesshnP/oF40RK7fyd62f5KXm4f/oQiFKUHkyjCTFTvot+dRNnOGRK6TpfVZZBksPLLw\nFqqVUSbiWto8e/CKSrjP/zZTUhN9eRXYL3gJj3hpXuUjSzdPW6COSHsGkRcbGHDXErCnIhRIWRan\n0XqlDns8FwJ6BEsywgtKQrukmHJmWfbqKRydYtPsx+S1djBl8WJKdKORzyONeLgZruYd9rAu7SbN\nuqtsNp9Hq3ZgnphBa/UitMYZTFZyRbgVa74WrWYefec1TNEQQ6v30JDZz/bJM5ytr2Sx2Ii9q5XO\nS3Iu2x/gen8ZK5cnSOaaEW8VkRdZJNkbwPKuEaHfgHwljNgRRx30sWPvSapz06k+ehNdkZVoIwjO\nwYpDxMS76Qzk1hKMqyjY3E2msYOE0oDtvTwiVh3qYgs19SHSDVJGJYUUGCd4MuMFage60UR91GR3\nkx2dp+DMJJenVnNWtxkqZIRMDtJuehDNQcCswdObRvCiCsJKoqszEJZnIspIJS7z0d1TwZlrm1Er\nbXwx9SWkbXEyZ4eZDjhpN1czWH0L3cFGvMsyBm6KiIzlM5CyB8uikuWXvCjnO9nq7SLcFMVSk0lG\nbJkMrR/BXh2jxhqOlO5hKqsQZygNkSKGqsFLyheXiYZ9pPzuBgn+D9s16F3cQf3fzqBRyRCu0bHJ\nfY1y+xjYQWBJIPIkKG0ZY+26yyhlQdRDC+j/eR3fxUbGBKVkPOWk9rlCSuKTpApDVKjHmPUV8VLj\nE0jTA+Qmp+gO1DMXNfFs1c/Y7L8MH4pI+hN4NyipvaUL/S02QmEFvZZabNY0Gip7ePyRt3j5h09y\n5vUWftzyQ5Rb/Xy78Bu0mlLpyBvmiUt/5159K19X/p4PhQeRhcJsGX+TB69/lwyRl4RUzbTIxzn1\nXt69coC4L53AmIgvJsPkp19BkfCgC6lJuoT4M9Px7SylTDRLS9c5MlNFiAVCNrZ9SP4uM/bvraFQ\nGiW2IGIgfQeXExvYe2UWjXiKjvIGiqLTPDb7BpfjEs6rynjX9yg5EgG/0X2TJb2Bj1QH6LgaxH10\nmT3lXcjzhPwhfRPTvbUIbpQRTckgMz2AogpcaWm81b2JAddqBIkimEtBYYmzbk0rJdE+rH1bMXfN\n8NXul5ic8tCNBCP9lAhVKOQe2uTr+bP023wl5a9s1rbRknaGddFLZI4uI+uIEr0hZSpWzGn1bqae\nMlNWeIWa776K1j+M7cEqmo2X+KryT4QeepqTpnp8T9u50anmaslD2F0xQlO9KH4pR/NQElNykni6\nBE9HHVFpLi6bFrEtSjSWZO3tlwkpRei+Z0GwVojtrjQkqVKcLhUzH+m5klPLlebdbNl0mcf2Rgkf\nNhE4rAN0qNbPUXKvHG1ZKu2qcvKFs2wJXcQQchGyymk2nEczscLmIx284P4aV0u2I14bQFvmQn08\niG9Mhw8dyS4RqSc9xBrESLalENmehzfHzHLEwoW/rubP7z3MM+oXeMDwCaJLAmZPC2gLQVtNM232\npwjIVKj9Cwx1iJhyFnDK9CjLcwG40Mb9oXNszxzAdVstY7X5bLDeQCNLIGjJYLK+klNlUVptOxn3\nliDWBcmotWJqnKXpd+9Q/6cLBML/h30aTBdVcujOb7PTeI3NnVc579/Oi56vQAJSijxkli0wNlPK\n2K/LeLjldbZJryNTuKjM6qFR8RydXQX8+Wu383jeIdaXDWDeOotJP4Msa4XVqnYeSvyLxJQUX6eU\n1rZcPph8CqKF5MmXKBX3UxqapGFpgPgJEfqbM8QWBzDsDhJslLNv91HW5XZgLdIz6NuE80Mdnlgq\n46ZKnEsGKInBtJUc+0X2xE+yIXGFJlkQ9SoIrhVQlKciollml/+PTF9P5Y2ZNWjMs2Q2iZmnim7L\nasRFCbJGugn8aACNR+UAACAASURBVJBApg7Lb/Yy9aGZ+UvpeMNx/EEDoaVyYoMfUnXkOtmfn8a0\nxUhncy3Xehu48usm9mWeYXP9NfwHKnCv2kD0WDpLAhEnb91O3rpp9giPk3aHCVujiiqjE5koTsu3\nl3EeOY/iX2/SkfkgrtImKAdznoPHbzvLir8TtVWCYEKMf1HJ2XdKGH9fRqPjBZqKehA/HMS0AAor\nOAvCJPRCsnfEGbuaRDQYh5wkQlkSfasbwWQq/RUVDASq6by5hpu2YkSLk6z2nGNdzg08Xy3A0A4P\nHv4lPgr4b93vMEqnuF3wMe1kojTE+Oam39DuXMfrKbsxZHpJDy1huZpLfElC5hcXGR428vSL34Ix\nL4lkAr+1nHi5DuldAdYVX2O/+DBnpbdwUr+LgcoqMKWDVsJIRwmvnn+Y2eISuDcJfgFEQNgO2eN2\n1ps7OKXexb8Un2d72RkK1D0E/jmBNuGB/VHKdIPcpX6bSsUw+dFpAttljKnzEWWE2Kk9xt7644zp\ni5lP5tF6bjtt1u0oYwHyb47wa+lPyRcuMEoJp8T76M0uZskIVdWzvFD7Nd5wPkS3qhL9l7Op93ex\nfujXJBMB2L7EjQvZ/Hn4IJGXc6g7baNKPvA//xAsQHX7ELfVHMW/QUNKiRuTbAZ3l47hI1X40u5n\n/pelPND6Ibxz8982+ZlAkKO34izKQWmNUNQ1zl+yn+It9f1IM8IYTXOUN/fh92pITgmJdstIquVk\nKkXoS3yI9ZMMj6VhuVzE7I5sSvR20r3LVCsHWB1rp3xxmIxxB7IbYRa7Urk+doA+ZzVZUhXuhAaE\nEWQzLiQ9VoRDKuQLftasnGHFYqKvpxFjip2sdQt8EttE57gRzcVJdC4ZSiN408X0yTKR+GcodDkw\n0YpJMYFJE0eeA4HSBA0VTnT6Warc4ygiUmrmIhSkzaDNEOCaUTAe1lFTmobcoSI5EUFoEJMoTWe8\npJgBh5kECaQGKaoxGYtXc7h2djXuplRE1Ummc8yEZsSE+2Qk5wUgB/vGShbr1pK55CUWFnGlYS1b\n8oIcEBwm2iRlpjyP4E0FPq8YpVmHsHIZtXkOVYEXq1nKuLiYDI0F/S4xWe5lNFe96GUeImliTgwV\nM2fRU5i8RLg2Rte6ejK8LtRLfiaiJpwTGqKyBP4MNTr3MqrUFRIyIU6fntGlQq7n1NEhXUNn8QZk\nYgurXBfJs4ySMutFWJuGKhaCi1P0ac30l1WyRnCJKvtFpsPlaJV+bsu7hsIk51za7dQKB8i4aeX8\n6Z0sK/XkfnGKpaSeoZObSNNModUukxRn4E2amDUYUKW4uSf0HiMJE0fFG0ArRKl0k+2dIDSj4PLg\nBhIHhEjqnJiMNsocQwhO+BFGfGQX+ljQZXNIdxfRYjG1ETnBtkkkOXHi9ztRKvxkrFgxO2fRRTz0\nVFQxYcgnWzbPRmEbdyff4nqyhmvLq7m6tJelvjyk0xGMy7MUiS1EhTKmyKY/UcxkSimyaqgs6ebB\n1De5sbSKm54GPGUlCDXj1A+cIkvlRVsG3YOPciG0newLdko6F0mYkqhXbOTN3aAw1k9J6ijl09eJ\n4UDPMr7+BqwD2UhvjRDb5QRxNrzz75v8TCD4QeR7TP4wTrHPgg+I3g/SqjAZ6YsUpQ1TL+smZ4MF\nTY6P1rda6LxZxzcK/oh7g44/mL+OWdTJt0deYn59PUf372FH/Dw7R1qpnRviVMcuvnP59wg9HsKS\nBNPriiiITHD/ud+Q5gwRJZtTx/P48+kyFPdVU/Y5F7viH7DY2cQ7P/8y4qoEghw/totz6Hr62L34\nKnUSF6UBODGWzy+CLVSt9KFnhZMUE1AoWZV5DYElguJoiN19Z+gy1fNW/kNIa108vvYjms5OwIkY\nrPSiS/rYnHkSQ2OI9u+uJee6hYpnT5J5cJ4195kJI8Uw4aa6bYpL4xv4UcbzuG+kIRVFWX/wAmtz\nb/L5296ksG+G5GEBw9IqhjfXcOdd74IN3r90H0bnEvG9R1EJ/CTdYV77cDXjnUV4zJuIa+SID3hw\nKvMIxLW80v4YH03th9o4DAcQvORj2x3tVD0+w/J0Lcu2BlqjtXRlhNAEYZO+jXrdDY69fiv950zI\n7TdQNcSp/GEXea4ZwvNSPqy5jSPJ7VjeVCBOSVDxSBdbIhfYuNDKuxd28sH1LXzpgU9ZMaXy16e/\nQbFmmu8afkr12BDpNxb4/PIy4kQC3UKA/NWT7N15mJYP29D/1cX4XAVzNSZmrfnkNk2y9vlBdlpP\nsSZwg0Slko6FSl54/1Zk5TPozD7krkFwnoNrcvJkFh6In2Y2ZRVvln+N0IAb9cQYj33hI3Zq2khO\nzDI+BvJr4FVCRCPjYvE2ukR1JFaC7FOfYLfi94ycr+SDD+7nVNN+JDVRfAo1OYk5thrbqGntRfK3\nCMroCHmZToz3z6PMlaJ/fol27wa+Lf4DtXRSHO+jxfcGd0c8GPVgSi4hmErCDfD0qzh2ppmZqJeW\n5WE2GLw0LwGWAjTyCu4In2Kf6BQGuYv86FUO8E2062HyrkzS3r6G7vkxLrOWmepUYvcL2KU9y6MT\n/8DZ+P9+Y+T/GZ8JBIJcEWuv92LVl3KocB3TllySF3xE0+eIlNmJNAlJZAhAmWTSX4BtMIMZkQZB\nfoJEVQzfcBqzgmqGM+oQZkvZ1n8RQ58FRZeFmGUbNyRraBK0khuZxeqqQJSWgnxLgni1FIcgnVhm\nCGGlnLm6fNRVGnReEclRLUOeKjRiN+kZc5hNNgy2aYKLPhbECiQpmQgSUvICPmK6LCLxGHpXjGie\nmit715E+NI22b5YM9yIGXx4TsSKi6SEK5dcIxdKQGyVkelc44GqlenyYZIEOVY2S9OkI+b45oosJ\nUpctyOulGJRe8i0zzDpSUUzWYnHpCdm1aMMetEYPKxtTWLEqiR4DV5saZ1JN6r5lpOlRhM44InGC\nFFsIVUoAVWQFxXSU5FgYqyaDgCYHtMAysJxkVF2GQFKA6LyP5FiU+GKSxLiOhaE5FlWlaIok1Kqt\npEWWESyAXuFAmB5ncSWHvpk6sIpYk9fLbbQRjUp5L3Av7emrmUrmYZ0SUC6YoGXzBTJSvEwra/Bn\n6QknlAx0FGIbz2FQWYmgRMJczgxetx6ZM4TXBPijTA0FcZUaSCt1YMqYJVO9jDrLjzAjgUQeJX1l\nhtLJ88hMflbqssjWLJJhsyPxiRAti5EtRyksWWT9nb0IkJKDBy1BRALB/yzjz8mI+9XYBFn0CYqw\neNVEAzJkKdkIXXDHwrvYHRCRQWYQdIo4ncpGhjNKsZkNzMfzkDij5BXMYlyapubmVXLHphAZksx1\nGuhzF6LRZpFv9pF+i53AiIobjjVUSAYwuWdQBnogIsQSbUKigjStnfCohKhEyvy4iah7FQkW8OVY\n8FREmbLmIJv0U6Wfp0Y5jywI2WEbO+U2lCuZCKbScfSI8VxLkM8MycQi7pIEGpmH9Ew77bmN/7HJ\nzwSCIw0P8OOOUc5v28uvd3ybwF+SRP/lwCacQXowQEppIYtyEyISLIhMJIIRFjoEFGRPc/sX3uGs\nYTe/4FfocVEf6SWyKMHfBTNHwdEAws8nuLvzGFv7Wvne9Vwm6/Pp+uFuhOUJnIJ0Vt9xhc0HJvlE\n2UCK30/W1DKpCR+C1UnMGydZvf4q67ZfxHvBx8vfW8ecfx3i3I18Q/M7vq/7gJ9ofsRkKIMvDPyQ\neKWKN5+8H+OLJ6g4Oct6BaAA4tBvNzHRfQBRSx6a27N4Jvkj7pq5gvzDCAsRJek40BWuoNqcQHFh\nkdSLVoqeBfU6IZEviigNt/HI0XbeLv6/mNZvwShbIKBT8GH9nWy+0kqub4zY6RCx6TDLVVpSW1bI\nun2eXNschgkXlnw/abjZxwhmjZN/Vu8nIM8BK3CT/5m/JCCpTRJ7NQTLUshIo/uSnoErccL7ZDRu\n6eSbxt/Q4OiGUYhoBNgy09BluCFXDh4T5tlODh4+wVuRh/lD6OvkeibJ1Fhxo8E8Ps2drxzmUPa9\n/C77G2zdeYoK+QRv/2Q/k9eN+AVJru5roPv+TQitSZAnSW4Cpn0Ijy5RXLpAnWCecLMMdMA8KNID\n5BpnMX1yjZwffEj7sw9zdO197BKewp+qIpxfDVoruIU07nIjengaEXFW0DDBVob61hA/IwZNOiui\nVP6RVoo0NEM8eYFklh7Bpm08NfIy3+j5KldnwSOETSlgkdbwsXo/A/tL0G204f7YgGbJxybDedaN\nnKLgRx2ktXgQfVdMxy838q+BW5DHiqgvnyP+nBjrlRwEp5PUyfrZtthKTzDK+XALh5afY1vjNZ7e\n/huCCTlEheBLweGu5yL5jNV4uPSUl4nlGKL2ftLyPOhkEB0HQxgyNSC85sDaH2VgfDczpPA0Zxga\nSmXkpftwPJ3GwP5S2tgM/O3fNvmZQLB4XMvkopDFawp8fi2GkjmkZR7sgWIMteNsll9l9FIZV8+t\nJZwrRP39MKPx1QQrskGXSsmmMTK/ZyUjd5mUgSDnLu+gt7OYOucJZA5ITgm4MFXGvCvMbJWeaLMU\nb46OqvggLdOXGO0Qc2okjwllFrJICi9anmIirYh4lYjalX62tV5k0F1J93IhtrsyKYm62Sl6mU0T\n10idCpBYjoIvhlkUIm3ahuqV0/S0Jzgm3oxx3SjyfEheTFDmn2fP2tNYm0ux1VaQu2hFuxQiqQHt\nrIeSF4YRWN0Mjkg4Pb2X4Vg9+tehyDFH3YGbyOULNCbtBGY+ZLp/BMUOF1JhlC3SVqpVA0i1IGoU\nE1yvZSC3Fo3Ih1+ppF2+ml8In8Eyn4HTqkbstmFzZeC7pidnzQLFu0ZIsU0imnczlrUeSa2YDaHT\neMNpdOnXowu40cS9TFfmE8oWc8h9O0u9Kaw5fYLunjou5mxjZNSMJDWB5tEI6dogavzo0h0YsheJ\nmYUI5ArKH1ui7MIc4nYvgXkRbm0qWfZ5ymVdtI6aMPpD7FF0IxuAwFspjK6pZm57ESGlnJgzDkVC\nXBojN47nEu7XoB11MOYQE9ZbsEd0DCxvw7lZxbK5moQ4gxlM6CKLbLK9hl7j4pR5K12BJvoGahCm\nJjAEbTQMdRHvS+FG3waqA5colXTSnrobLyKafMPofW5EfcPU+S+Rqg0wG9vDfJqezc0niBjkdL/S\nhHWDAdG6OKLGMIqhZXKOdCIMurhx1xbGVsfRmOKMF27E52kgENMxHlZyTredbLWdZ4Q/xzOZyj9U\nXyZzXweFSyHuHn+HzBIXs4o8QjViFEEXkR4b8WCS+JpMTOvmuFv4MYdWbeBGtJZPyu8nLM5jy0gr\nKX0O/N0QXoyzlJokuSeL1JxUjMsJ7KNuhD0Wbh7TsOJpoE+T/x+b/EwgCJ5O0u3WsDwHaX0u8n5n\nQX53gISzhALRHJuV13Bd0DL/m3R0fwyiejTIYrCWsMCIWrFCzeo+Gqs6SZvysdBp4pn+nyNeWKFK\n0Y4ikkQ4GeP4bBOE8pBv0JC5OYJfqMLosHDHwmGePbKdI0fXEFXmIBTnM0Uxse1i4i0iCgdnqOkf\n5tD8PVzOaUH8aIANsvf45uTv0cR82O06BA4vYr8KRaqMglknOb87Q1e8hTOpu9m1OkCBCRJHkpSL\nF/jSlvNMNk3SnTNH5vw8iSAI00ExHCLjj1amkgJGpOmcTdzCVe4heUxAY+I6wkY/1UQxpQXZYTmB\nrfMyc4vNyNxitiU7yIg5SGpBuEdI7A4VA7IqtF43ApmAXkEtxxW3ERmXkugVonQHEPniRHskmKsH\n2br+KMbWNqSCeT6VfhtZSSpfLH0bKxn/N3PvFRzXeabrPp1zo7sBNNDIOefETIAUgxhEkRJFipJo\nJW9bs53lIFuW5aSxZc+MZXss27KtbFJUosQkkiJFghmBAJFzbjQaqRsd0OjcfS6maurs2vZU7al9\nyue7/Naq92JVPU/Vv/61/g9hRIiJWWKFdm4q19DnKuG9rgPMDEuJa7/MOU8Vb/AQUp0ebZ2TuMMe\nVJowgWYZ2kwH2cVDzEmNCMVRqh8eJ1cxg+28AueiHKEkQkr7BHl0oZKsIjUuwBdi30e9aGfhhJTz\nq++neXUDjpCasEiMdEeI6asZtH9QQndvFZLRJSTuFqSxYyytrMJZXM/4g2uRpS2jX5pnET3xSz3U\nO06yFMnggn47N/o309lWiyI9QIPjGk+deBXlZJBTvr2sjZ5li+wIC7pUpuQZrPMNkr10B0kbZJjA\nbkqkX3gPo5nZzOwaY2YymYGXSxB4A8SXWBDmhzAGpjC90UUgXsy1F3YjSYyg9y8yH1uAyhgLDliy\nGLgW2cjjztf40vKv+d78LzlneIzPP/kKG2cvsf5bR5idSGaQEoJJUfSFkyxp5wimSFE+EENFyTCP\neI5hLs2iLfseTmVnYCeZgr4eohInln4pTpcYq9yAYE8iCXdp0PdJkX+4guD2DIONUiZaslkxxv1d\nJv8hIqg+NMTxN39AiXaKn+d/j8v69cyLY3ki5i/EC+dpFq7CiJVn+TG3acAzo2fLkfPEauzMPpRE\nV08lJy/ux7huFsmGAOJkL6lL03gi8UjUkBMzwFyfGpFTwL0bTiIXwNWj9XyWdheitSGET2rYVOmj\n4/0wsSuDHN75NgPGAt7uOszH03uZCqRRV9TCPbqTCFtCCDNCXCrbQImuH32hA5VAyJIojdckj6G4\n5sT5ppDO1HyiufH4Lt7EyyLhWCG3fXV87/jPaei/zLb8z5C2WTF7wbBFQGdWFX/9yyPMr5MRvs/D\naucgDwSfZkmnwexQcvylfFqTkin+lyVUbzahax+j4C9dzJxJ52V/A4mjQ1TP3SQiMJMT6MP+toBE\nzwwPNXyKMy2W2ylVtF+vxd4Sz2HB2yRvmObOzjKU/kFU37lIzO0ZJA4xK39RMjuZSNdDxZgmzOw7\neoSr4W0cNxwg8KCQlDIzuwrOUKu4Tl6pD51Ni2xeR33jB8RZFmid2EtPSRkX6jZx69paBl8tYe/a\nj1ifd4O48CJjA0n8NvAN2nLXIyj9j61HDQrEVTmMZifyaoIUX4+M6SYTFksm86/qCIzMkJE+wYbD\nw6iifoaiJRTv6aJc10JpzzX8MhnX1gcwJLspi+nkwtl6Bm+mEksXcYZRfI8VEHKEUT9zCul0mJgl\nF4dNTeyKNpM8P40yZYV/qfg2isxZwmmpiPUiHNZETtueRqNZQiCGmArQV4dITxgjX9jHBwP76Rkr\nZTlNzab2k9z7rSPIt4sxGNzk5o7TNlfL6EslZEVuUej5kNiR62y065GYYUJfxlXlAT6ZL2dyaje3\nQ2qCGbMsuNRc0zQwWpCDw6RjJaQg6YMmth//jMbeapTZEg4lHyclycFpxXZKpjv5keXHHNUeZGZB\nxfW/ipApChj7xWamz6Qx251IRKyixDGM8HYYBnwQWmRXQzObV3fz9iUTtwb/NpP/EBFIN4hoOX0f\nGbL3yFFfpE+SgyyywnbPOUIiEeek28lN6qKi8hZCv4qZ1ixiTrlRpPuI2eskZJEwfTON+TgpYp0T\nkcGLIsGFUCEmNjhLsbeRmOpiZAjZLm0k3B2m/ZNMrJtV3NxXS050nC3BScK948gdK+QmteNSilEu\nu+lyFDJjj6eopovaxBbixhZp91fwofJelNl+qpPvIAxKWSKZi2IjIXcAf5cPTb6Q7Bwvi2ez8FiM\neCo1uMQxjPVls1VxkSLBIDc70pmLJlG41cxMnIaJhCxG87Jxr1JQ43uFokAjIVyIOtNoMjcgKY4j\n4T4Zlmu5xFyOknx5Bg9uPvXloIuIWRTOEsJDnniAgdlYlJNOYuV2pDYvcRkqjG4jcrWfvJIBMsrG\n8B0U4DjlYP5dJRkRCYnyIJkzFsYH9HROluNuVZD2zhBCFQhzBaTWzJBqmiTTO0ayYRHj+iiJE3bS\nBwbYkHoeg8dO+8I92BfisCXqEdiiJHXOsY5bNJgvYQ4ZcVsymclKR1USoKqunWCLkhF7MZ5kHcvl\nGiaqUvHHaZhzpTBl0THfIoIeIVkVS5Q33EGzFGXOmUqKfoKkvBlqHJ34wnL6FWvI0Y+xM/UUy3Yx\nkZtVCEQB5koSWKwvRO0ZIrPvJjPRWBRSP7sdZ1gvHmMuPR5hSYj6zZdxZauZTElBGhHgHjDQmXo3\nKoUHY9I8tpoVDDV2alNvonG4ODr4CKOCXAx1i6RPjpEzPEBBwRzJQicYoGdOyfytZLQOPR6PgPj4\ncXL0nSQ7bdyZcXPNcYBZjx6xNxYR06SL7MQ45rHpEjkvX4tUGCV3xYz8lhntiTYSUZFaGOIew2nM\nwSzeG9/N2q5esqxjxCS4sAZgzi3DHZNMc84mvBUJCARSCl29JN6ZZ6wrG7dbQ0VOL2vqR6jbOsfZ\nO56/y+Q/RARn1Tvwpcs5N7WFoZ5UKpc62Oy8TEqzFbdKTcrqaQRbJcxl57K+sQ37aTNHZx4ilCVg\no+AyW2MvcDDnfTo+DXDnqJFmDhFnUGPKmkNuG8U92UnlP5WhqdVTfGwAf6Odrf0WrBXlBKil4NNh\nSm70o97kod2fze/evRt7RRoJ/2OaxTENzgEh7z2wB8vqRHaVnaFrsZQL7TspNo5QJe9k5ZSWpUEj\nAkOU2Bwr+T+dojb2DgWMcnV5I3faq5iOprMh7gbfuOclitN78ccp+HTiYdpas9n9+5fIN7Xzw5of\ncNT3GH9+8/O8nfEYzYIi1lz8d+L1Ezz49VayMpzkOmd5ObCXxugWBP7XcBHAF3UxTiYOHmU9yxTH\nD5DwVAzzzSZeOvFtls76cIZnydg/R8FzC1wU1iNVr0YXu4SFMrp5knT1H9iW9h5feeAozTlreavj\nUc617EEe8nBv/mleWv80Uruf0dNZvD1+mFl5GhXpveQ3n2XbnTus3j5BND8LlRNMLbPcFbnG3bpL\nLH9NQ/L5Ccbfj+ffg/ciLBTz7c//O9p0P0GNnOPX7+H1W7uYGLRRMt3EvpxbpEjdiNTwUsd6jndW\nwLIOjTNKXs8ktRPDbBq+xuuOxzgj20p47AZRP9w+UYbkAQG+L8nZU3We1cEOjqj2c2KpgOCf3OzO\n8PHodwTk6CeYE/vJ7V3EGkzkeOk9BJPFpBsmKXQMkTVkRq4KE/QoiCqE5FYOsmfPcbLjR4nXzjOl\nSKU5XIezKoakymnW5zQSDCk5tvwFDls+IHmyCZYBFZADXd4tzAVzyN3TT2VlG8X2cyQPgOQmrB4c\n5QtjJxkLy1gJiVizuMKgPY9T3TGs0k7yzfnPeMubzHkqyaWDaoEFuXgZ800Fl3+t47bjIGpdAp5a\nKZl1feSbNPRdNjD2PSUNO5vY+k93yDs9irNTz2uBJ0jMnOLH9/+VmVXpHE/cw6Qy9+8y+Y85oUii\np3JHG4bPFkjstFBl6aDS3I3B7WQ2ZGJoupDU+VHSRofRtU0THPNjq9LgXGvArEhBlhpEtDFEqDtA\ntF+La9KI1xxB6fdhtYQYHZIgzJOiCWrppJC4/BHqDAPcjiRy+ZgRbgWJm7ERQMZUxET7sB5Rpobk\nVA/iNCUigwjtgB15aI5FVpgOy7GEUlic1hL0+KiabWEhYqTNVY1UESW5ZpFy+qh1tjG9Kh270UhO\neJxNwUbW+G4hSQtgy9GT0OCkgGkSen3oV2zopTZqosnYhbF4FTKUuiU06WHikoMoam1kR+bImpwk\nYWUCoirawonIkj2sqZ5kWl3EhKAMk+4zaoe7cE/J6fcUYc7OYTlWi28lQKDIB2VeYkMOIn4RZmcG\nozG5TGwrwWs/gTQqYNGhYHkhTL5oAFP6PKyH+HWLUOln+rYQS28QjWUcXb6FaHGUbLmFUGSeRXsR\nNlsBdZpWlEt+2lurkSYFEGRGsCbEslwH8kCYzCIztas60DuXWRlUcMT0AL3rS4g4hojKLIQEUrRG\nB0nls1S0ClhYniXWaKQo3cOcLgm9cJxVnmaGXHn4JGH8aXqcei0ScZBZfyJnr+9m+/xnVMd0c7lk\nC6YZN/EX+6nQT5EaE2V8RYTVK8OersAj1NFsSybG5aVIvcyEMBNn2EBG3xR7R99jcQy0Pg/2ET06\npQFl8jJyvKQrJlmVfwuJ1U5OzyUWCrKYrSnmhmMDK14dVdp24nxT1No+xpFuQlKmwLEhnZEkITQ3\nkamdZdNdl6gUNVM0PoW0RstSkQzJqIuARU/UsoLHJ2VGbmJClMuEOh59pRJfgQRuLeMbiGF2KR+z\nPQ9tQMPWhXPUcht5kRbPTBpOnYkU32VqJ1uwdOiY6tVgSJ2nSD3KqsRh3lmu5FbPGkxLE3T9HSb/\nISKIE9jZ/+Q71AbaKe4bQD7hRzoYQKwNYxZkcLL3fnadOcLO4314vGHmc3UYnpjCVy9jUJlHr7oY\nX5oC1c4lAmYp/g+LoNMPLhEDoQyORO6C98tQ9aTS8oMF7vr8LR5Z+CsjH+po+VoO+zN0eLLVXB3Y\nzGVbHQHPAHpCQBRK5KitEXadOUnd6Ekm8LO0qo7o3ijui+C77eLQwdcprBnhe/M/JxgvJE5ow+SY\nI8c+zv3VH7Bh4yXU0WVMdxZQnlhhUaDDlqtl34MfoslZRvYnD9YO6GqEgk0X2LXhJtbKONw5ChQb\nXYREapwqIYyCcDxMqesMFm5zhTUklxj5yo866U3T8V50A+XdVtZ/cgvnSQGxCRbCPxAzlJ6HOZqE\nWxXPbMTNQ94PwC7kBzM/ZTixkMg3hXAOHJ+pePW9u5nL0fPc03+moNoCtXCtcDVHDXu59aEYyaez\nPBb+C3UF07A+QKZKgCGq4Plb9zLWmclz3/wT0ys5vHDnOeyNBsTaEHnP91Kz7xb7Q8cpYgCN2AfN\nwDEQPxpC+oSMQF81Cxo9t1RCQrEaQsZOCs6Pkq7soLpciHN9AWdq7sbt1JMhnuR+/4es0V/n3P/Y\nSM/6QooEo4xfz+Wl179DbNhNoWGA9euuUKlpYVV2KyYWEHeEuHo1iaMTpah+skJMbJTBXwSpWTCz\nLrOJlzd9g8EcTQAAIABJREFUjaPFD/P9C//Mk1dfpHUJLin3caz1h+j/p4Pc/D4eC7/BfukHlGd0\nMtfpxfn8AKLPVaIoUHImsotPJTv5WdqzpLvbONA9jKjCiOLxNF4LP87wWC7LJ9QUq3p56qu/Q+Cx\nYT8lIPxAIuF6PQM/Hab/khyf10iTqg6zaRczKife2CBdT27BqOli/29fQEwc1NdBrwKt3cp9Myep\nHLvNrcJarKV5RL+WRdyZKHH/ZuM307uxCuN4Tv4aFeER5LM+pvoy6DOX8uWx5zn/d5j8h4jgScer\nlEjvoBFYcK8sIZ2ApdR4rq5fxydz1SyeHKVpNANZ7LfY7DuLLmzDfWoGz4qctO0+LLYchkcK2FDQ\nSEailcG8ajwaFVPaZKyfFeMZ3U5x/RxJtaPM3UnnQst6HCtC+s2F+NKSOOPfwdRMJraNKuKT3cx1\npRIXZ6Vc0EXYr2SaNJq3bsRSqGHyXISB8DpCcWIUajBIIqhNbrSxLsRdIdAK0ITdNLesou1qLcoD\nbqS1XqIIcGWNo9vq4NbUGs6/tA1dnRNFuhe2ikjO6qPEdQ6zv5TzFzcRzBQiLfWTpJhh2auhb74U\nuS9IvG6R3NqzbAsOIOzuZYECzkUfQNPpZd+1txifTOCP4w9TMHwZ27yPrtf9xCf38YTqCj6DHK9M\nSs+IieCKhH2G99mXJoEsWFdxg4hEins4kwFVBUf8KhL6zXDNzlCTCbNSReVMPxUpfdQpLCylpnNK\ntJUgSgQKIZrtUVandLJQGIfd4SfH/ybxYgkZsQJWDGLio/Mk3FrCJYujcVUlCZ5xUscHMQom2WRs\nJMcyScAro7+njOlAJk2udYTLZxGlueiLUZIQ46W0tZ+ARMorTz2Je0FHOCQiLTzB7sHLBIVSzIpU\nhu7OZUGg5ygHyRwawxSaZXariX5tEfPxRhTxy+xbGKY/fh3hOTkbFvtYTR/qVA8ZaWNUJzeTLhsH\nvZKOVdvxpmVxb9JHDFNAz1ulvL9Qg9mzTJ18DmdfImfmn8Ixm0tkIpmchGHifVOcbUwnxiMh7kkH\nhgYBGo2LjYGrmBPSuLZjLTfkq/AkyKlIbmZj6RQqwxJirYqTm+9nPlPPY9LrDOQGudK2B2/OCjEP\nz7M7/xa5smne2/0gYZmIb2X9is8KtzE4X8ip+Hu43Z/N6OUQw3YIO2yMxifQvr+C4o8nSXQ7ubDu\nbj7TShFMwuWpzbimNFxzVAF/ewrqfymCJ554gjNnzmA0Gunu7gbAbrdz8OBBJicn/3O+oU73H58u\n/vznP+e1115DJBLx29/+lm3btv3N3IeXjuBaVLLg8WMOCYjMKVmYz+BUzE5apnXILjXRq11Nf9l9\nZDjdVNrP4z5tx+eVErsuhG0yDe9nahJFFnLyB2hM3ownS8lYaRquYAH6C+WU7T1J9sZBzn0zhfYL\nRVwnF0mJEt0aCc2ta+i25VCUYCY3fhJXRh2x+jnKBV1M+zMZFubTtG8jIkc50+1ivOJ4UEoQaeUI\nY/WsxIJXokA5vgKKMIrICre76mh7r4bi4m4SC2YIKqS4UzSkpUzQ/C91vPOHRxBqggi2R6BBzM6K\nc2x0dXH2w7v46cnvYNjmIDU8Td1KM0uLsZw37yQgUBAT4+bnVRa2K9qRLndzVpjBu5ZH2HTlHF/5\nzSv8RvYsN6XreBQrQpuZ9r+K2ameZE/MJUQ5Ucx6I8/ffgIfEv55418oVo0hT/ThTVcwlpBJwGRi\n2lXJEVcJ0c4FAp9OI/DPkSQZ52tpndyf3wZaeCepgFdCT7HgMSHzB/na9l9SW3uHbkUFjs4FSsWv\nU69eYkNqiCb1KpaWYlFcCjKkzuZYwX2U+q9wV3Aco2CCbSIf90TO02Wv4JPF3VhtJqTzARR3u5CU\n+AlMK2nou8IPLv6QzlWlHPn8QcatucinQ/xs5Husb26CiABXuRLbAS3Hwof4yLqHb7z+7+hXnFw/\nvJo7KaX0Rgu5r+wjGkLt/HL+EWz9Gr7FccriB1kpVpOUamaNphGV1sV0dj4t+x/FVGbmceMvOXr6\nc1x/fzVTXasZsS5Tqf6IoCyeG8ovM+s2Iu/28MOqH1Esu8rP37gLdNk89MQImlQX3qiXNaEb5GkG\n+MPeL9EvLEQdWkat9bAl/zrSsI9Ft5j2DQ8i3+bled3XOTcp53THY4gLtCRtdXFIcxqxJMg3Hv4l\ndbJmfqT6IUs2A7cXazlpvQdBUxm+IzcIT0QgaKbv5ybiH13DrpGPWZ7S8uzGXzHgKEHa7Mc/Jcc/\nJ+RTf/3fZf2/FMHjjz/OV77yFT73uc/9Z+/FF19k69atfOc73+EXv/gFL774Ii+++CJ9fX28++67\n9PX1YbFY2LJlC0NDQwiFwv8tty1Yy1v/+ghrFy6wcf15TiY/wlhiEfWeG+xRTuHf7uREnI7j2Zs4\nuvIQN8MV6LQLmLJ9hHVSNqivctD4PuFTg0yfUhDe7sCXHcOEJJ3C+l5+8W/f4FbBOlrc69lrOk99\nhYa/yg9SVtXJI/UfIkj3IZ72obvloXcujdHWAKGkZbxROYLKMMaEGQ5J3kVhdfCXwD4m+lPhT3By\nYQ9j4SyYBJ3dwaHFdwj5BJijCWyqaeRe+xnONd+NZTqdHQ+dRpe1xGl2E3P3Es+l/JgYowulzQsr\nQmbtJn4x+Qzti3nAMlFCKGeWqXyrhYVODVc8eWzIHOThwnPUuG8jiYqJfdKAwRmH6B0Rt80N/CL7\nNwxtzUdUI2NeWIdWkEYeiUzM5fP1yXsRpEA0IYx6k5N4tYcPEu9nZqyNHUfOc0J1L++L76GzM0pO\n5CiHV3WwUqLlxqpiDMyQHRwlfcwNE8AkJGfOsEV+kdaZaoYvpnNqqJqBWiWph/04YhLpKtiK13ya\n4NDH4HSgSFDQ91AubZ1F9P08DoGpmMR/s6OuCZO90o+m1w2+CKwOUVjSRW2khdstdSyf0fKY8S/E\nz/fxclMVqQovz6z9FccUB2jKWsWbmoc4PbCDyHUJ8cZZ0hilpLufVcNtzNfE4XKoKHyzn3jbIMlB\nHQOUcyvmn8jcN8TOvAEKK+YZYDXv+w4zfVyGczbKRVED8eU+1uZeoXy6g7Q/WkjPniD788PMvmzA\nZSmkdeVTBOXzVDx8k/4RA9bXQoypVGjqCin+mhDTtIXV57oYrszjYs1a6o59in7YSu7hHrLiJlg1\ncJuZGz6+2rITbGW4blbQLymkKq0dQS1I471o6hep0txhnfImSZhZFqooEPezKI7lJfE3aFeVEcss\n9+hOIRU5OGFbg/VmBDpGsRBDl6KcTWuuElOwjCnTQnLrOA0D57mQuIdPS+6ByWS4+t8QwYYNG5iY\nmPhfeidPnuTKlSsAPProozQ0NPDiiy9y4sQJDh06hEQiISMjg5ycHFpaWli9evX/lts8W8GNk/lo\n8+dIX71ER9JqHEYdjy28SW5kiIWGeDqNHiRJflqdNQwG86jT30QlnMfaJ6HG0cuDpvc5fjmNYWsN\n/m0QI/YhGQhRqO2j+uBtLO4UzDPppJZaiRHbkS66SJP1sC10hCVNEn61lvQ2C6LBOZTWPNzzcUzY\nMyEhSnKsmaQ7VoTjIcRaOSKfBLHLR7eohB51MUljk9QrGtkQf5mwSsSl0XpWidvIzxnhykebcA4b\nyN84zEqcghPCfWzUXGJn1sf4l2OIOGRIFX58ChkDsjxmxHrAQRQhEacQ/zUp4stOTMEWMgv6yXF0\nEiNYJBinxF5RSiSUTMG1QUb9eZxJeRiZyY2hZJ7FrELCQhOyyTiWJ3UMyzRYMxLxp0hYq7iGUO7i\nkjQf13gsscMOPhHt5KR4F/HuU1RrL3MfF5lMKqOvsoxyhY21oUGSrzrxB2XYfXqCOjE58hHsDiHu\nQRtLPiHDCWmofIuE5TpksTlMh4q4LBwjUZhCXDAGZZyHaDRKYus02gYv0QI9Kt0sGoeD0HAQ6fIS\nuRl3SKu0sDa3ifFzOQSuKmkov0IguMDLnsPUOKxsn7lCSUYvK3Fy7EodU94yFnpMmGQWitGzof8a\nya2LdB/eQCBGhvJVL6GxJaRyFza5lgljGmmjfWjTzCxUJNMcqOeY5BCezjm4YYUtuZRXWfiO6Ges\nXbxBXLedrJJRahtuM3dVR2gsSo84H3GVAtU+J4q/Rgl9IGaoIwtBagLRaiG6eA+asz7EQ2F8Ahkz\nPQn4x4KktA+TpFqiof06b3au5sjiAdK0yWhJZsWtxLGoo0tZhjkrBXFSgDJvJ3fJL2JUz+GNmFDa\nzUzIKujW74N5F9nL19mSfQqhSUxj/GZW1C6MgnZWbBsYseYzkpdPomgOdAJyhYPcL3qXUI6SqdU5\nWK+ocP4d1v+P3xHMzc2RkJAAQEJCAnNzcwDMzMz8L9CnpKRgsVj+Zsblm4U0OF9gQrmB5zN/TEFZ\nL3fLThF/eZ5RdSYnNu9mQJ9NkniShRUT7kktt6+tRjrqJzIqpGTDMOGdYjqyDnJZsp9wjIqKkVa2\nvNJIVt0IoifCbFd/ginLQpO+ms4YI/bf9mFt6+fWuSifBXcyE17FN+R/AqkTBF6mbEm4uorJyx8k\nRzHCRzf3MdOdwkxFCrLiCDGr5lnu0SFqC7Ov+w3u1p5E+8Qc8gjc99YpdAEXoeB/PE6JNozB6mJF\npWVIVkzB6Ta8Z1d4Z82TtNdtxJhjIa1ggs+Vv8qFmSKOX60DYpgVJ/Nq7Fcw6ZtZbTvG3FQOTzuf\n5umYI5TFTvKR9QDOJSVfdP+Gq767+OPI1wm+IWalXc7SN3UsC7QM/6qQzbJGvlD5Z44aDnA2bjsd\nl6sJzMrwJKqY9aTRtno15qFUVI4QWx6xsqPAgsHtp81rpP3aKqrjuqiM6UEt9LBYHsulXfWMp6bh\nkmvI4hqZqlGEh/Pw3Z+LJSOT7Bkzj7qO0lhfwYmdPyIijCOnZZYvNb7M6oVWtq9tRLEQRPW8n6F/\nymY8PYXo9By69h6+0PUC4cd1kKtDHB+CdEALyGNBs4YraQYmHfdx0HWM5zQvsiKWMpidy/lHdjCv\njmdCkM7YiBHBTT+aHREwyLmacBeeJAneWticfoWNije4eDyZizd3EXs4kTlVAX6zDFR+iCyDM4h8\nPECydI4ElR3JIxEKKoe4T/oRMatcWNUJvK57gJHCPERSGY40FaFVSjquBhnuDxF9LMpMbgraB5yk\nnZ3g0R++xcndO2jZuJ6G4ycwdXdjc3lxSDMR5m3l/vtep6awn19ffJreuWJ+OvgDXE0qnGY9CbGL\nlGb0o9ruoc9vZPCoj6E4KcGNKVRf/yNrzX8l+6kZ7OJsRJcHKOyaYF/4NBdbs2jWbuLEpj0oMz10\nuytI1ljR7hayZdUV1Pkujlwuoun/lgj+3yUQCBAIBP/l9b9VN6xtjAsdeOZuI/IncXfGKJmRMZpj\n63CuxKAZcFOT3UZO8igXh+5m6mYGFbZOggExHaYy7iSXczx+L6RrqPBMMdhqImB1ErhqpX9JzoQ6\nl7yCJVbFN2EbVjLcF0A4E2FiJZHTHKI7Yz0r+hSGplSY7FNslXWxKIrAigOfW4ITA6KEEOoKF8os\nL/GF85SUdDCuzcGpjiFBOwseP42LmaQ5HKyzThCxhVl0aqgJ3SRVY0YkC2OX63GJ1ERixSjSgyQK\n58mwTmAQzJOvHma1oAmZ0op0nR1vYgYhbSyKtSFiYmzEuOUM9xpo7kqiqzADTSWsyOTo/XbKkjoZ\nX8lB4A9T5bhDhmeMue5EggEpqyZayS0Ywp8nptjRg3zWy9JkHBPuLNqi1Uy5Y5iy+JDGR0kuWqIy\neYocnZMxQx699iLmZxLpdpdz3rUD+qPML2tpdmchEkN2kg1Hihp7ZR7CaC4SSyxpiTNk+zqIW2gm\nZVpC7mQOY0kGXEE5vm43qjkzxiwIq+UsJ2sZ7dMy0BVLYExGpnCe0kw3PYL13OxdS+rKNGWyXkyL\nVia16ZAYT4wxRJZijMLpAXIXR+jIL8YfLyNeP4fdpme8M5sE7RhxaxZQxgnwxopxN6jwqRQIyyJk\n2qzUDbfS1elhQRDCFA0SiYtDIvRSWjRM5lQzbV4xotFllDoXimw/ZMCi30h/VwlxZgeLESOzZSVI\n80RUSVqxZiczuS2L7GtjSGf9tF8qxT6kIqrystiuZ7Ini+jDcmIKAkx6CliwmRBooEuxERQpOLNM\nuAtHybt4gxXzAoOWZLxSNegU9E+XcG1xI6XrO1kWxbA0n4beskyJ5zSSLhcOTyYhtwO9eo7quUtE\nDMC2NNLSnKzou5hdTCS4FMIw1g6yWf6ky2egcY5gayPepd6/y+r/sQgSEhKYnZ0lMTERq9WK0WgE\nIDk5GbPZ/J/3TU9Pk5yc/Dczlr/yBybe9VB0oJeaJ9uoFrUi8wX419VfJPmWlR++9mMMW2y496iZ\n+yyF8FkJX837Le7NSl44+F2ux9bRES3imfSX2D1xnO+/8UWmxgLcXonSM5/Hqabd/PNjp3iorIm9\nb1iQt+YxsLKFoex6JqrWELd/kdTMHkZ+4Ce2f55vxHyKRn8ZtBJe8D3HB6L7eGLXK+iUdo6JD5Eu\nmeCA6D1u5K7nTkYFnrsTuNmyho9fSGSDb5Dq2ml8U2HcXcscUL5JMMHAWFIq5uxERAE/iv1h4ndF\nePzKUYStxxBejiDxBJFK/GQWTLHj0Rba8yuxxRmour+b2XvjOS7fwdLLBqK9ZgZ3JaB7XEqWYICU\n6DSKSADcQgRLYfYPfMRdI5d5rvMFovYw3zH9C+a6JP606TH2/+UEh85/QKRQSGNCA8+KfoZzJAxn\nr6F6JhPj52Tk3hhB1eXl+Kat3IqvIxQj5pTlHi4Ob4ELUcIdSwTEfax+sIdNRRMMlu3glGMPgk/E\nFN3s4+mf/YJE703aZ3zEDl7hC5e7+fSnj2DNT2JmxcNwL6QOgPfLWua+nkLjCynceUeL3CPDsFVI\n2o+lDC/V8Oa5J/mR5cd8zvsWqkkPk/p0MEJ9zBV+lvgsqiseFi2xfGDYT29iAZlMEBmTMHM2nd3r\nTrPzsRP0qkqZFqZgengGgRAkkgAl/9pD2h+mWWefpSz7KpsnRDQa99OVXcS962/xYPhtvvepkLkR\nPVR6/2PITBBu9m7gl8PPIGyMIFSEYVeQ7Unn+LbglzQVrObT9G0cjj2GvnGJZ09+D415nlxhO5+K\n7+dV9Tf5kvjX1Ehb+W3KN7gTqoZM8M8qiIwKOSXYTR/J3D3xOxLvvM/bbMd77yb4YiIf/nE/nZ1l\nPKP+ZyKJUcLFq6hpuck3Tz7PnwTf4lLaT9ikfZ5qxafcK/yQjrX1XP35E5SqBqhYPsLRdz/H7EWo\n7/0Nkh1qWl74CqX6AQqE/ZSvJNKp+b90QtGePXt48803eeaZZ3jzzTfZu3fvf/Yfeughnn76aSwW\nC8PDw9TV1f3NjC/MvozjwTgWvVK6fpOAdvsapDkixgWZxMqWUOpXiJlyITsTYEPeFVTZLoYTMphM\nzcAhMpB6c4DaGxfImLqNfnmWDfXtBGoDlHf4ME1PY7Rdw6NO5GjRIeYPKOkqSsMzXEmm00nNwO8Z\nOV/HYn4GS2sqmF0TRawQkWOaJVc2gvhWiPCMmKR9s5TrbiO+6GXKn8knxj0sVhhQ5XnIdJlJ8/Wj\n103iNOt56c4j1NruUBPXjmyLD0/BEpHzAhI/ucrdoSWW1qh5efVX2ZP1CUk2K+faN2FTxKLb6qYy\npZtyXSfaS0OYPRmcLLmLMWUZHUtlaExj3PdcGyUbFpFoxIx5cvAK1GzQXiOnp5X7P36BsqUrZDon\neMRzBEKQJRpj8E4udzy1rJc3Y9+q5fpAPePzWTyYdAw0c1A9wY3RAwy+Ws974kPEYKfz1XxC+QK2\nrDvL0HIBozG5iO4OQqmQiLkAWxoMi/zMulJZsRogDBGZmDiHA02yhOWvVSH7dBZZ0yzrmxqZWSik\nM7KTJtUWdK4ZYkUi1PFqZrdUYJMlcX3WiCq1hXLbBco6Wrj//L9jluk5KniI7Yrz4HPAzW4WYjzc\n2ViCotpLxCCg7LM7SG746cyvIm5pie/Kf4Gn2cqHzTlYddU4MzKIVIHIEEIqCBColnHjoRpGeiQE\nlSLG1aAJhPiK7fdEpsUcm3mMstx5khNaiK+1055UzoXoVvz9Ur48/jusa4xMpqQxYC1E+LGA+BkH\noWI5ExXZ2HINZCyPcrj9PZQKJwnr3YgMArwyEfZJF/O3F1lqjeLUqiBDhrbESUKDlWWFCrs5idjt\nIjRaBZLzRdCvhffG8Rhjmbg/g/c8DyLsjrDkSEIrDZNlsrM37iJpCW66L2bQxOexpWqIL/ZyT8x5\nsrsnkd7yc7pxEo9ZwapEG7PKWD4bKmNhUsy42cPGVZN/l+v/UgSHDh3iypUrLC4ukpqayk9+8hO+\n+93vcuDAAV599dX/3D4EKCoq4sCBAxQVFSEWi/n973//d5cGX5z/LYNfLeXdP2/m8h/WETBp0KdC\n2CNCJfAgTIzgm5WzPK6m6JFuIhtDtFBHp7OKJUs8az75kN1/fBmtXIKnTEvl0yNodAHKj4CgeYHV\nwVu8mfg475btZKVSydJYLMvnU6k7+wb3fvJvHJ39CZOTFTi+XcdEfRxDQgkOaxexI0v4b8sQtwSJ\nK7ZTou0l6a1Rjjge5+Wsb2MMWyg33CZ9cJIGazsbs8S87djNN1sf51sxIrbndLCwU4XNICL6vRlS\nOqzoxG2c/MK3OJb7FIlGOyWZnbyzch+D8XkY9zh5QvpXyiwdBM4vMtmeyvuff5Bh/Qa4AzvW29jx\n9RnUEQ/2xVjMtix8UjUBmYyM9g52vNRNWpwPbUyQe2UfElKIQStitjuJ8Ut5jH4zg67NBfyl6TFE\nA/BL9XcpSBpAkBPF+cldXPwwhbf3PQLqCJxYYcO2a9yz9SPOSPcwJstEuDWAQCgheicHW6yULk+Q\nhblYZFYfUkUQnd6JzraMpFSH+wsbkAfaocPMqvYW5i0BTih/ydWUTAJjvVQF5li17CTckIG4Op/O\n3g2YFlMJT92iuKUVcWMfv896gZPJ+0nUzBCwLxC5MYylUMyVuHUo9F5iDYtU/KAd5eQKn+3YSbmx\nn+d1P+GZ4zt54/pOlOmVCOvT8ScIEUjCSEVBWlfVEC0SIvhEQsQmJmQUczD4Hv889n1+3ftl3hp6\nmBc3Pcd9G5qJ1oQ4Jy7nRevX+LLvFZ7xvEjbjnKuFG3AdsqI5GoEYWsY+/44BtJK6IsrpKCgh/2J\nH6PWe/B+SYo8IYzcv4z1uQiRNwUs+5aRrLajSJKTWG0lOXOSofP5+AfUiDbGIzWKEDRVIuuLoBzs\nJPr9XMJ7Uzl9cwehdgnMSBAoZAhS9GxMbCJLMsp3r3yZ876tyNbG8mDqR2z3HSO+eQnnOxq049O4\nZPFklYjxquKZvpPNyBkPU00jbP96839PBO+8887f7F+8ePFv9p999lmeffbZ/yoSgM5DVXwgOEj3\nmnzkCh13yc/QcPMWrjkdSUNWNO1uPi3exrubDuDs1qK66mar5AKpsnnM0izaJ7Nwi/cg21aKcmcy\nMelOUrQW7AdimcjPoO1ODcPSPPzdUu7JOoFPJ+dYxefo6S/hjzyFeKeM0nvbGRPl0NddSsQgpK1z\nLWfP7qE3vwDlXS4k0SCzrhROfHk7jZNbCPWKsZ2VMHJWwZRTxHB8DJadhfTGVhOaSieapmUlX8a7\nZ+qZWtKzz3MBW0kG72cdpk++Bu9lNX+NHqbcXMyulYuU2oc5ce1+7FlxhNQSmoTbOSPdznxSIoJo\nkOigmG57OcuTKiTzIQxOO/dwmsryNkyHZrkQWs0bgn0I9pwmWtXHx+/WMegvhPpUZlLSMWkmudVb\nSNcPYxnuiEWeBH+56xGSsmdRilfoGIiDjj4IpIEoBjLkJMidrBm4TdfFLKKXywjrDGBSE80VsjSn\noPuEkdgyGw2HL7LmVAurxlvJaJ7gdm81F/w7uVvoJf+LjcRMQ0hoY9vuT4ja13PzdDGp3WM0PP0+\nGzdfYqE8g8bsjRjz5pmJxtM1UMfH0Xr6Fwy4/FP8Xn+IcEDJoiCdYuEtqjnHBcsOPrHu5cymRdL9\nU3wx5c9UudsRmcMgz8eQXsJh48fIJRKOLD6EZSmJsDmMrtJGQp6VivouFD4ffcYipjqTeeajF+nu\nkLNs6eANy04GFgpZH7qMtU9A+NUbWPJ9NH2thqtF65mVJvJE8mtk6juZWV5mfgaCwzIuBHbgHFNx\n/8grlEl70R8NIDCJcSrjac59FMXheubPBSlWf8CDGf2sxOsZiWZi6Qwzc1LL25/dT9QZwr44RH1s\nJ4dTLrGcbsQsT+ajrgL6bxTBcinXa9bzjV0vIbyxiKcjQveadSSLnGzueAO5XMT3017kYOoH1Oxt\ngQ/1jA6W8MuWB1hypeOrlFDk6aZSdA5zYuJ/TwT/X9UdYzm9FCE1CagLj7Bx4DYNC9eY0xpxrWjp\ntpbQsqaW1rpqIjd85Db2k507TELqAjeNHSwmOFmoTWS6qJ5oUgGbohdQ+l1cFRfRkbCWG0W7YCFA\n8q1JNFon8SkLpGePsZznxZOTROoGL7qGcaauZxNakOEvFDPmzaLdUwXGMMbcGUYvJeJYEXB+3UZm\niaNiroPp60IWupUsJ4tx14iZEmoI68JUpk9AgpJbstWc7djKgk3DloIR7MllNBsOIFKHyF0Zwh7S\nMbti4jHR6xQ6x+hrrsRrF9JqyuW2fh0j+dVo/HMY/XOoY6SI7eC/osI7BErnCkn50yRnTBMKifDG\nxOPPzca7KQ5bvZb24XyaXGuIrMlFVhxFleTE1ysmclNBWmCMYLqcq9ESJKpS1PEQ0JgpE15GoC0g\nkJGKS6ZFbgzi9mjRDc5Q2HiLGdEaVkpUqEuWUATtSMY8JK6bpWS9hYKefmLnbUyE0mgfq6Kzu4qS\nnX2s3JPF/LtqnHYtadXjrHJo8N80UDU0SkVfG6HsRMylQXqFhUiFfvxhKYuJSUzUluGenCRgtzNt\nyCQBdw4hAAAgAElEQVQqMxEQ6gjOa/C0aAhOyPDOy+mXpSBNXeRgWgcmmwWrOwGlSkKxeomGrCY0\nyT5GnTl0+UuxLRpICVjIV/SxJekChvASJvkMNwUb+HBpPwpVE8a02yy4cumezCPeN82sRUzk3DLe\n3CiLm3T0T2XjmVbxkOEYKQU9mIvFYPQTF55nzJFFyB5knfA9siMSxAsqPHNCvMs+xgqrkJdkkbzw\nIcW6FnIXx5iaKiagKiS8FGLZIeDm/BpiPPNkBY6zNu8Wm+r7IK2bSZGOFlGUAXESUVkIh0nHcFUu\n5itFLI5LMTwkIV89QMpnYwx3V3E89yB6kxtFnhdDZoDYGQ+NlkpWlEmQJUYZBK1IwkTM/89+Omp6\np5jCr/axcaqJTRevkzE8wbwynvcf3UdbQjXTvRlkxw7zecPLBIKTKMVzBO/3o60N80/S3yLyOhE6\nHLxyYg1DbyWwa9958M/w+w/WMyxXQybQ5cQeDfFeyR6yMmZYq79KUU03pZ9r52rhZsaDuTxueZ14\n/yKWVQm0NVRxtXgj1pvJWF9J4pWenUjtc8ycU7Mp/ypfqj7L6317+VS9CsNBMaZ0JwtH2tgSGuXB\nstOct+7i2bZfMKlMQFeySPt9q5h1pBA8I2bDxkb27vqAlagS3YCT5AEr2ROTvND9A862F/Dt8CPY\nDxSTcred8vePUqEYovwpIdpxEF+LErDAnDKWy09upnlLFZuMlygoG+XXjz2LrsCOL15M7VOzqEN9\neE1zWNXJTAjSeSzvQ3Y3nII5Ee2eTH73b3cxsaYC0b587rdd5275Owgr41jclsltfw1WoYkfyJ6n\nTn+Wr0b/zFthPYNyDSV5XWRmDZHcMIbcFCYikvJB+n5mhClIqlZY7DeyPKCiS1/MGxkP0+8pxTcp\nZ433GsXuLjaOXiJNYia+Tsyxkm2cjdlF/40SqsfbSVpe4K6c6yh/5eGjl+KwXBXwP0vOEIwoecmy\ngbZLmVhHv89Bxfs8IHqLVgc4yjXcerqI7oxilIUrpAze5kHHJQRrokjK4OtDL3HHUMb5dXeRETdO\nYaCfipEeklZmKUwdQZYS5dYjG8hXelkdnib71UY0t/3M78lmQVRFWFGPUWImf6Uf4dv1WEbj6Hkq\nm/DeEMK1HnI0Nhr0F7jtrEGeHCQ5HEWsMdBxdwGTp6Pw0TUYr8RYGuHwg13IHUF+/7snsRgLWarI\nwJ0phq9KYF5FUV8XX7t+AWVlkHP/4y5qxHfQh73IHixAWFNGpEXJ+pxLfEP6Er8XHeaMeAvr4m6g\n07s4H/MU5rksQucknMq7h+HUHO4rOE6l+Ca/atzPkEADEgH9ol3MC0tJZRH417/J5D9EBJE7AjYP\nN7Kxr4ny3h6G47PpLC5hJDuTgEBCqnIa6UyAuSYTQX0Udb0MR5UNk3qR9LYJYuNX0GTCrfxhViRx\n+AxSlHYhlYIRcu0Rol4LgpUlQlIPy9dkhC1ilsRapGoo3DzNlQUXkyNRXEIBWUlOaoNTGOyL6EZt\nXGitpelGLoOzSsSKBBQFCkyxNupCrXyavRGfOpFW7SYCERW2RDf5MjPVGe2cD+yiba4WwqB2eXCN\n+AkonahKHPgjIaytcpTVYZZTVFzSbkbk98GEhx5FAa26TagEenKCZmrmRtioaKZgRcCiKY2Jhkyy\n9MPow4t8ULcfZ46OYnrISDNT2tDHYEoO/w9zd/lgB2Hmb/867jbnzJwzPmfcNZmZuCsJIWiRAGUp\nW13obrtbWkqhFCrUti1boS2FFG2weIjbZCbj7u523PV58Xu7ffuw/8N9fV7e32F5Ac7CZKSISWAO\nb0yNP6wgXiBB6JKw+JmBiEvEJm07HUsiWo6bUQ/PUhAZQL8gZ2IySLOwlkmXleGlXGq016g8PMxQ\n63nEHjeCYQ0OYyLSrDglkl6SPRNcleymUbgW8Yid+JCAiD2COO5HrAkxIcnE5dazq/0sxcFBsjRj\nTGhyOZH2AN2mCpbEiayMm1iaMzOdno4630FB3Qj6lDTmtClIyvsRJoaR60TQ4kTeaCdjRyd52f20\nXLAy3ltIX+8WiuZn2eC4SU58CHnRCsMF65hNTkPVH2Lek4LbrWNhTIVmUULcHSRRukpieJUKUyd1\nVQ1IJ514e41kL13FIHTRF61nLp5ILOZlfDWNm1NbiClkmDNX8ScqGDYXsaRKZnYuFXe3FkOmDYt5\nFnHEx/SsmvPtOQiWXTyYcAp7xiraFAXlzgHmBhPpb69iLisPUjQg8aBhhSrHbfZFL7IxcRCfUknY\nbUQl8aGNedgW6EajleHbqCI/uR+XQUW2bITNPiGK2yHm01IYya1CK3Cz33KSqfQMxvVpSFcXyPd1\ncac6kRsxGa2jqWQk2CnKmMaWrPmnTX4uEGQtLHDP9RNYepYIzsu4+OBWbhysR6Xwsm3sMgcFZ3mr\n+TFe7f8W8T1RrBtGsWT8CdPVOULPO9DVhdA+JqJ4Ww9L9xm4KVtL8Yyab4WPkXDxOPGLYgTrY7hT\n47R9JODK7DaOS19A/JiI+uduMPyCgKbPhER+sAlnrYUHZ46x7dR1at9qYsW+n0bPZogJkJZrMH3H\niC4gQ3BGAGVKXLszeeOzL2OI30vKk6PcpTjBjoEJCAogCrSB6IoHxdUuYoeMmL4/TeffM2n51YOU\nv7yErlxEj6wKW1QEi9P465OJb8/Guygk1u+jQiClyg/ij+Nc2FfJ0Uce5Kmav5LnGEWWFkFIlChC\nwkYhQbWQLnkJF9nBMLlICVNJB5JIiEBARkdhGT6VhOtny0gRz/G9h3/HzYEFOv6sw++ZxymOk3Qu\nQGxQTqdsLQMzZUS6xcS/oUX1soht330X1fUBPvjgh9xeLEW0N8Z/an7KvfFP+di7TGwsSviyF6Zi\nxP1xcnz97BBcpFVdRzQipez0AMUJo4hKI1yR7eIV1bNUS5rICo8zvZzNoiaRyw9sIC9tiESWQVKM\nO6GOm5Ug2hgmfriW0j+cYWfbr0jbHWdhl4Ub44e4MrSd+Klispzvsb33GrJ1ARwHtTSkWrkQ30VP\nrJrVCRPBcSmia0bSegSsremisggIQE7ZMF/I/TvH36znwq8r2S26QnJtlAlRHlNRCVF/AxfGC2nu\n3U/tvpvUZnSgUQUZcJbw4eQXcF7UI2iIsvbLtyhJasPf4WSoUcVH76Vyb1kX395ziZ59gyyo9eh/\nOMzozQRiYQMoVaCKw4VlkjraeEr8R/YYG9FbfeASkHZsAUlNGEFijKcuvMH98mMsP2LgevoGfiX+\nGjsV7/O4/TR/eP1ZbpRuIXSflq21l/lW0St8KrmLa8v1rJ7xIWqc4WnzG2SFw/Te3MyBB07yxQf/\nyk/zv/lPm/xcIDhQd4lwlYhzU/V0LuZjHPRxZ9ZplEk+EmZm8M8s4PRHCRrl7BWdpcTfRN/foe3G\nVuJzJuqam9jINUYfNjKqTcd+PI7cN02sTMCAroCGkvWEDXIEXg+JsbMoxAGClXIEYQGGdzyUJa7i\nvmea2uVlEj4M8vbAHkpmh9iV3ED25gDFBiGz57JI8gW5b/AshpV5Xm06yOroPAd0LxPpFeDRJjLf\nX8tniQdwB5Lwz0R4eOBVGpP34SxN4rblMOK6OIqkIBs3dJIZ66c3YQPdM5XMu1PxIgKNCmvhPLk7\nLuDxqkmfmMG44EY6B8Qhv22Eg6LT9PdlcS1aRThFgC7JySSZzMdTOB45hDemRBnwsWf8Ar5ZNd3L\n5fgzpWytuor/moobrVuZrU5AlCylrbaO0eUM4p4CWkvvJ1JVhTHt/93m/uEz7HecgRCU3byJ2O0i\nJ+RHl9KPafkv9DsqGUmwMtZm5c/Xv0SBZ5AfhF+BNS56Mks4M3gIrS9Iasciqhk/i0tm3vY+jM2c\nwF7rWSI5QnzlYsba42TOznO38G8IstX064twS9UUxgZw1ycgNQlIy5wnPmsnes6EeWKGLTtWMfsl\n2G4lErFl4VPlQbqOqfJ0btSvI2V8EPWlZUpDHehNXtY5WgnMKoiNxBEYl9AcmCFpfI6Ojkxuspch\nZx6zVxJZjBlRPQFjl8HulTDpNpKe6+EL/3EepaMFSUcKymwHYl0QOwaMglWeFP2ZcLGIeEKcXMcI\n1ulhUpJtzG3JIKBbi0iwin4xwIivkMvyGhSuDDwWPZkHRhEliZiVZYBYhyNs5ZR/I4MSKyK/CcGM\nhPgYMDEOiiUYVBGWmPC4M4ltl1K8r4+iLVOUeKZ5uPFjUha9nL28i+H5TN7pO0KvpJRZdyqfja7H\na/Bx/91DlM628OQHP2HT1A0S+5bJSxr5p01+LhBsWXuDkbXZHGvawwnfen7d+QsekJ3FV6pkeSbG\n1EoEv8RPkmSJ+8MfUDp+kaf/so9r/XsQ6nfjm/sjlk+uM1CXwlBCDqG3pllVxXHtkdFYU8VvtjxB\naEaPvmOJu6XjCJIEaLe7Udn8iN4XUPFFJ4m141S+3cfoJQuvDT/JtNlMXfkghkNaCgq0xObTyO0b\n58HuE7TNmPhlx8PsDB3nfn5HUAgjGfUcbyimKWM9l5U7eWriBZ4Y+in+O/Vc3XOIrs2HMJqWSYnM\nsb2qma35V/nJTBU3O1IIuuUgFYNWQUZRH5vXX2IxYkY96EPYFMU9ryEaEZHTPUZ+3xBP9z/LMeVe\ndta2Y8ryMK7MZsRrZWghlxptK5siDdzRfIr57gw+mbyXhB1LbK+/wIXLuxk6XoDmJRfh3RouRrew\nLEtGK86kv66QjoekoIH10w28MvU9KqUdyFMCzHeLWW0Rk1wQJzdtio2Tf6InXMh52RYu9ezl6tGt\nPG/4AYcLP0R6OMgxzwNcDR9GEYxj6HMhWwyz6DbzTvBBPEIVZaou0ITQlU+z8qkb3bV57r7nNJ7s\nXJ71P4/brcAgccAaMBcuUZzQR7hhFs0fJFiM01TsgNC8hJVGFfGFVEQZSUhKQizXGrlq3UDutyLk\nveOgxN7O9oLboAf/ogzPbQXKbwQR3xFk+mdiLvds4jdJ/8rQcjE0hMj87jQ5X+9jxhZnakiEc0lI\nbd0KT327GePfXcQ+ltCzuZDeQCFzoRRy/BPcK3obdZkdQV0YxfEIst4o8UIYMGlISMpG0Woi2iii\ndbqKf4TuQhnOILVwkZKvjiN2SvDd0uCTS1lVWnk3eBCJIIYynIrAIye+CoLe6wi8fRA1Ehbm4G+v\nYVfoOk+u/z3W2hWMZiH3x86QfnWJ4ZupDHSX8U76Y6ASEBP4ueGoxZ0XoereEPm9Yzxx4eeYZiLE\nr8pJyfg/NoJ6/FYJF4qeoietCO5Xg0fF1HAGR8NHUEhs3PnQUR4xnWdz4hxr+lrwnBbCjBJFWRzD\nY7PkRuyULQg4maBGNQFPCM6xSX2NRKGDjMs32fb2EkUpUrQxKQ1OCwIRPL3yaxyp6TzzwK/ZvXiW\n/HeHOBa5j8XdBu4/chH3oIlvXf0F465MvKlK7s7+mE0bb6AvX6G6w8XPZ94kxT1FagyiSVCuHWXt\nws84N3cnR0NH0Oug8E4PTy29ydZLLUyXppIQcVDW209fWzEv9/yAkqR+vi14laPKIwxV5kM+jBbl\nEPLvx92vRzXjJ3ZQjHKnnylHNltuXeDAlX8QsU+idN2i+LWrKMe1DB4pIKmpi4o3fs+4dA0NMStl\nCyrkqTbKDt/GV6VhQFBIXuQqNY63yW4MY5u1cLJ/PemjgzyR+ncu5m7jet56kIArquSXGd9kk/Ua\n+4pO0horoctupfD0eYpsfRQdBp1ogdznr5IqH8H/SAp9jUo6ew6wzn2dITFE3BBIkuBaryQ0LcaQ\naKO+9gbKJRs/OvE0sb9PsKXxJeRFatJfCqDKEjA7F8b74xEyUvq5q+oM9ZJ2wjEpJb3dxOZ8JGxz\nIJl20PapgOvBe7jpu4deZwlm+Ty1GbdYm9jEGsFtrrKWa9F9fNX2DmZ3B6TDLdV63ooeYb/8JBus\nV+n/ShH9TdX4LnggbIe9BlQFAcxqGxmPazC3rZJ/5n/wziTx7r98AZvEzKrIjH1Sj+MzHc5GHS1y\nN03Vmzi88hGHZz9EmB0jfFhAWCOiaLyHH158nmzhGKyLQcsEyXM3OBI4hcig5YzrQYoHhnnqyl84\nOlbJBWEJbMqkJG2eR2RHMaQ48VslKLpmkPYtw5ScHnU9R9flYNLaqPyfXs7rd9OurWbNnibMm+f4\nt+m/EPFJISYmliXAnSrlhi+fRX0WH6U9QpF6kKqfNeE9PUP0RoDrqxn/tMnPBYIRbyoXB3ehDQWo\nSB7G1ZFA40IZ11YLMaWtUmktpihtjHWG94mcgK7WdORmFaqNMmS7QiT4ImSMxcnSrWJ3DnLAeItC\n0zDTYgsSu5vKoctsd/jRqnQ0pt6HUB9nn+YEzUlbOZ5+J3ecPkVK6xwLqXpmq8ys2dbMaljC1bdy\nsS1aUa9A4qMrWNeNIFUGyfI6KNgyx3y/mdWZYjL1c2RJlrFOnsYuVHA9YT3J+Q4SC6JkfrSAZCKO\ndXQcqSCM9qoL/2AN09NZ1M20kqydo1rTRixRwKQ+nYWQmdUpHeGmGPJZF6G6GjR5IYJiE5neQRaH\njPhEYmRuD9aREdSDGvRBO4kj3dR89D5T+hyGdGVM6CwU5ExRZ7zFqsiCc1FHqb6Rkuxr5M/BxHwJ\nneMppMTt5FmHWUSLbzqMTAH2cBKNObW45SKU6aPMpWdjk6QyPZWIcMZCZKce9ZSXzCvDRIvdOEpD\nnHduZECcSSRiZzGUSlggZiaQTLOjDJHISbnxJtusZ5lTWnjD+Ch1y29yt/1TpPsqUN1tIYwGn1tE\n0vQo+ZEeqlM78fk7CQZExMJKvCIdWUViRpcSudiu51TGHtqNe8gQTVGR3M4W8wUK/J2kDwzhk2yn\np3wjrZpxRAEB2Lw0OXO5Fc6nZMFI2aSSvrQaBkOVSDq8qL12vIkm4goxyESY62RUxN2ojp1nwFnK\n+bX3csu+gVbJWhj0Qm8UrqvQZPtYLTeSaZvAOjqOYG0EeaWPxPAiqhEP1o4x9Ekr+A1xIhNBZIMe\n0jReZEIhiT4nVeEO7he9xy0EXJBnQakGVeo81pku8vJGUR0OY5S7kHsiTIoziemWqCnopHS+l8ym\nGUaS83k390HmtiexXnOTGmkHeq8LpAKSi6eRZ3mQLu3jknsTzSMVrJoS0N7rQ2eXEutZJdj9z5sU\nvfDCCy/8/wUAwIsvvsiRZ8q5MrKXg60n+UbTb7kxVM7Z2XJS5k8SG1vkZPcB5ENRakb68XbCgiqB\n/m+sZ2VbKSvubCometkxewl1tpPC9D6KHAOsmE2crdyDLSMVU72ELHsA3UoI5wMppB3yUZk9QoZg\nhnXzt6ic6EQzZ0M+MErQFuJ0xhdYGghQefm/8WWbWKitwLZZi1enpLhxCK3AhX+nhHdGDvGXq18g\nyzGDfnGa9nlYqFRi+hbU1bSQlLDCH71f5kpwJxuWG1lt0PHLS0+RWjbH44+8w83hDTSOreOukk/I\nDI3RfqYEn01FLCIhdnaE6KVhPI1h8p2DfLP6DazFU0xsttIgPIhbXMqhzU3kb5lFVhQmrWMI02cz\ndFXfyezuegoeniEna5Gak33UDHRRE+wgP3sC84YAWjvopX7K7hrBVpzBb0XfJKF7hH2fvM22vias\noXlma5OZmRXR8gsFpfIhHiy/gCJbzOy6Ks7k3Yc7xUh9ZQ+fxQ/zu+F/R7k1SupDAeb3VTOSuYFl\nWxaLgzp6T2aQ19TAvoF3qGntxBtK4Madu9lY2cNj1svE6pNwphqxYyCkU5FR5WFNzTjZqXNMXYCR\nWwpmduVyo2gnf1r8MqcHd9E8VM3kvTXoHwzzDfPveCj/HXKrhxDdWGDlBRfe8gSER9S0KOv4dHEr\nZ29l4upbZoP7XdbMtaJugU9m7qdLuoaMnQvIxUJWP7IQyRZCtYCtzpvUTnegGwhhXnZT2jfE7FI6\nzZIq6B6G3jmQ6yhdM8xDd7zFYrKZt5K/yLXijUxK0ynoH2Xydg4/afsO9kkxqf0dXDTcQ2v6AYbm\nihCqojy+/m3q8hoR1Qb5bPk+2u0HoToBdzBG12khLp2M5K0h1Bf8+BrU/C7pG/RJSni4/W22r14m\nIcnOec1ubkg3sSo3MTBcxI2j6/jMsYuzFXdgMqxS6W8n/pdlvK+76TibhiegRLpWgSfdSKxSz0b3\nAO91TfK/Jf/5rCFXO9ksuophaICR4TCd/hzmI4nskb7BilvDuWAy09IEIhYxzZIaurRFWEvtKIua\nmZpeIdM1gUAZI0mzgMASZqI2nWlROkuKRLyBLCb9pawut5PkmEaRFYZsJZ/176XIO0ht/DYCX5yQ\nH8osg6xYzZzT6EgQhSkK32Ys6S4m8iOYZKuYg0soREHE0hgRcYyZiJzGcCLX88pZVSlobDCw4ssk\nHrDQ5KhjIpjN5ext+IwKbBEDCwoz7YIqytcPYl0/grV3lAhibOkJyF1B7lSexDetIhYQIgoO4c/0\nMGYoJBpXMH01A41mkaDYQ9ipJho247TL8ffa0Xja0cy5SK2Ns724m/QsBSWWSYTLcnrslcjUAXQ6\nB4H8NBCHyTo/gNbnRlfixRfQcttfi3JumgT/PDZxHIRKNiw3cHswkQtNqSwbGgiJxhiVrOO2qJi2\n7nqUSVEUVhGqZjvqvlHK9vUhq41xIrIGqUfGIdEJJGE3Ar+buFLOnMKKJlXJakEisWoxAtQInYmY\n3AG8t7w0WNcR94Yom7yF2bJIMENEu9tK74yZIk0ct0JN51AFmvl51kg7INGDriCGybSIQB3BKFuh\nR5zCee0OVjylLC1aGEgrYM4pJHYtwB2BLrZbm3HGSji9sIHelXLwSlgv7oJQmOyZEYavp7OSIEUg\nCSCf8eG2g8ztpEDiZLviMssyA2PpcTxFAtKTZ6lI7KGo4RLBjI0MVeYzk5CM3aHF0mQjsiDBuUlD\nqF0ILTHWWvtwp3TSuljCrM9CyqU+kkrmiFsVCFJ14DVDAvhFambW5OKRTKK75EE2EMbpVDOYW4A7\nSY1OY2fFlkDTXB0KpZ87HKdoXalh2FsA/iishOBWiDzZBhIyF7As9lC+MkJF4RDigJKCM7Po890Y\nCu1Upo390yY/FwgSihd5IvePfDqcyneO7cUrKKREGqFWJ2YmEkQadkCmj9BWGZ/4H6YpsIZ/l7zK\nQd1pVtRGMtVzoBAQ1MmZ1aTQvr4KJzr0UieDV4q5+OZuxJ23SRW1cMTfQXw1kb/f+iIHlKeoKmxH\n4I8j8ULKvZC73Yc1awJt9yJmAagSQZvp5j7XR9wV+RRFiR+hPYq6JYZ0dgqfZpgLD1fQaNlL90QZ\nzpEE4u+IkSREERmj+OolZG4eY0CVzXLchMgnw6k2MidL4s5tnzBrzeRlw/dI8izxnOQltI0rhC4L\nkB0Os7jLwrulKm62beLlH93DpoU3OCD4GSLJEgFBHqPtIIh5WBKNkFAXo+jBONb4P4jEP0U6EOS6\nZxM/T/k2oWohBVt68UhVyKad3DH9JzK949ikJYwpMoklCrhWeYimvN2IauPUiW/z/PUfYryVxNXI\nwwyflXD2qpBzAhM9pBPCjH+bFuGXhNQOnUHfc4VMu5DZWCUfeu8hxznKjxwvYkxbxbNBxYt8nz+b\nv8y67TdxJWsIhMWsKHQMiHPIfWOStIFF2h5YQ2TWReVP3kd81wL+70q4KlnPTQoo4TjpS9NIrofY\nMt7Cy8qfIAgIWPYn8mHZIaYNFh7kXYZL6/nzN18mdFRO/LgIwffCSGrmCH3ow6wMUb8Ofh66k9/G\nnsGfqWJtvJk7Gj4jvbOXxYiY/zn1AKdubCVa6icggeleUJhAmQv7F06zdvk273z1biZ3pbBfdA7j\nqUHsL65QfcRN0XcmeU/0BRomNvLrK0+Toxtl03MXqXivG11LmEeE71GnHuC7Ra/gn1Qw/3shppIo\niVuDSKRRqAWMoEoTYd2voOCGn6LXRhAvR3EKDCAET46KwQ3ZDJ0o5OgPnuDbCa/yovYHfHflFWb0\nabBZAPMueH2W80lrma1K51upv6RiQw/ur/ZiGHFR94sOZIdCCPfEkYpD/7TJzwWC9MZZEmuXqBAv\nsyhbwrPXQFJ1AguKVCZHzUSuFjJS7OJSjY2C/gEyFybQiN0IF+Lk94wj1wSxZWtRRnzk94+jHAzT\nLS3jRvlG5OYgTxx4nSv2fGxT1cTjo8TkAhzJeq64t/LS6POwEEc/b2fbpbN47XKWy8yMD5TjjO1m\n2Gsl6JDSl1dItmSE0uE+Rn25nBXsZXqnnLXVLtKnBjA1hKm0N9KbWcf5yn3ULrawfuAqU/NR7OoI\nTTIxsrIwe3edYl24idS5BYy9DgTLQmpzGpDn+Yllx2mer6X1UhXr288i8y0yedvF5EqEZaEO1mux\n1Mgwyzw4o0tkzQQpDsfI1MaYzKzjx9l72HTjDIWznSzckYIgPcp9wg9o11XS6llDRAWyqIdI+AiV\nrg62TPSSbFtAeCJKwBUlrgyQXb+A2BTkw/g9iMJzPBo/QeK6ZWQbioiEatDFjWyUnqIiv5u+rDzE\nqikyXEtYzonRrQ7zJG9ijNtI3T+LSuFDIokQb5AQtKvQ7vRQuNBH8addlOn7SbUu0m0upSVxDRPR\nbDLjg6TXRojn6ekVpzOfWc20uYZjx0Vks8Sjur8h3CDktdSnoQikkRBJ3QvkCEfRqzyUDnXzROP/\nIIkIkVXHEQtCzGsV3PpSChJZMpOZOZQEBviK7TeMtoG+eRJPcJyhJAPD36qgoMtG+uBpBnQ1dGdu\nxFURRKwVok2QsT7eQEm0m2VHMtPX8nGvb8JhLudqfgVZQ/0U/ryXrL2dLJHEgLcQ5aqTjZcbyRse\nRYiAa6MZdLqMlPo/wWxbQLzq5MZALX3hnbTsrUCfusjG25+hnPAy/IVSfGYdkpwIgtI4er2de3I/\nYAo10WPdLI5lsFqfwIXsHXiTFay13cbkWOHswl7SZobYK3ifLmEtS8pM4rViDP0eyi/0oZwMoLIF\niU0AACAASURBVIu5iCkFODVKTtjrgCv/a5OfCwTmKys4KpSUK1aQWsaYuc+A/74iZiMWZi/no5xJ\nYTKtkjOpAe5P+IhSVz89wmK8ixrSri0RXiNiuU5PYu8q5rYlcq+MEdSpeNP4L9Sn3eCLWX9isen7\nXF/aSsBvIhYREDfF6PCWMzhdgN8nx+KfxXhxDHWfB+86LSNLagZ1Owh5UpFOC2jLK8IQmcfQsspt\n0Rp+V/w1Kna3slV/Cc13b5Ly0TCVUTnn19i4WrGRjS1XeKb9Va41irm4nMlJwQHy7gnwlfWfUGYb\nIblrjuBtJaJQhI13XcVfKmOORE7dPsDbyiP4h5cpGjrNTNSBJ2EZtdWG8RAkPZlAKi5C3hnSBiHP\nr0RuCtAaq+WVwLN8Z36ZhK5+Jh5NR1YR5RGOohAFOD+3h7AeYl4T47JsfIIM7hjrxNhjQ/BxBKJu\nZOlzlB1pQaEVcVT+CBsUp/mO9hm8u1IZ/Vo1SkcJ5riKg0mfYZDZaY5VkaaXkxMJorjgw9w2w4O6\nfxDZKCb4oIxQVIR7VkFwUYx4KYx+boXK1dvkvjOESedCXhHnxP37OVe2E1+bGqMoiPmAFG+ehlFB\nLv68HLxDJXx40sjO2Hl+XPtbzlUe4tmyV0EpwOof4yeN/0mVtwOsIrLbh3ni0yHMd/lJ2B4kFoL2\neDnhJ55EoTExFsmiPNzO1pmTXLoiYO52jBXCLD5UT8d/7WLvsSus+fAmL6Q8z5nCPSg2uwkKFdgm\nTDyd9StSzBMs/9bMTHMmY8k5rMiMnKp4kLqGtzBduEVqQj8VxSbm4kZMcwus/bQN1bydOYmRs7PF\nNM8l82j4ApXxcZRCH585Kni9918R7BGRoRnhrs63QAYTm/IJaORE60UETWIESWH2p37CVCPc/EsM\nu3Y98f1+LtVtZLQok5d8L1Ld1ErLa+XULHbznOUv/EYs5MNAMt5cGRGbgNQ3Z8AOrkQ5PoWMOYGR\njxyb+D8FwZgtiyvRjQj22RBmOhgo3kBwKpFNnbeov97C+pGbXLMVc+OWlYwhJUqNn+SVURYK8nnj\n0KMkJS2QH+nHdcmDqDGEcKsFbfEq3zP8iL6rBbx47gW6NDU4tms5fnIH8XcCOFx+NtSe5IFDpzlW\ncy/jU1aU4zGqlgb5vvtlJoq1zD8El67D8LFprA0nibPEf08ewpAU5xXvsyQmLqBPXSDyuIvZ9DJ+\n//adNLcVEXi1h+6Net59+iFuzhbQ680gotWRor5NYfsU8aZVWhsUXJE8wmRhBVnyIUREWMXI3G4L\nyYnTCNGjC1j46mwX0dUJcJ9BrfayRCLF9JEgs3E+cxtjPVYOHz1BYtEilXtbyN6yRHJqhEjyMpPa\nbN4ufQDJVIxf9X2T9+z3c9W/iaw9E8QQ80v7fzC5kEokHgGjCnGmkTTVMhppALkhgHIDJG8FkXoB\n1bud6HtyGdKW8d4X9yO2qFmyp6CUOEnOmeZLkqOkqxd4y/Ioows5xH8DuEeJeKdpTstgNV/N6X+U\n0q41YXhaQn3beTbcPEFt8gWKHCP4utTE9QI6dhaTlLTMukAL9opERDEnrf0Genp0vNi6k9nxErga\ngBIJKwl6/tB4FycUa6Eyieg+IZG6MA9nf8BuxXlWPwCf2EVhThcWj52K/l5GsnK4mLyJ8afkSPfb\nKaCV9MwFKqPv0mKp52zpflJ6u3h27CxJzjC3vRX8reUAnvvDuO9Tc+/GY1RrOrn1Xh3+CQ93TLyE\nrTCFf+z6MYf6LlJ/s5FG8wZIlAMCrrKT91T3UFp8mSrdIM3tD7LqW+bRxKMcLrpOfumzSBbiSG+E\nURyKIIjAf3zwW5KKl5nZkcTpj9fSOZRN6ZML+IhwgQQGx9OIf3qRsnQ/hesDnGQ/iyI5c4EFyByB\nO2L4xucZf2aK3/s2ccuQQ/XDIzhGhLR+nEjgTQ2iG1KqTMO8/0+a/FwguO7axNnFfRRZ+ygu7cXh\nScc/ocQysUShox99yjId/iQWRiqxLUkJBMMkdy4TMVrpL8tlVmDB61BgmQ6iWw0QLDUirxFS5Oyl\nw1PCicGDxNbLiCdEaT+fBgM+kIC+eImCyj4qC9uQL/hwXlSzGDJhWZ7HpJ0jmBdh7LNERrsUKJ1L\nCKRepudNpAcGuV/9AQvdWiZVSXiSc1mqKWDyRCHxyShVo9eQVgUYSExmIrsajyiVFPcYmTYH2mUP\n9r4wMzdljO2xMJeeRtr0EEGpjKHMPFYsRmRrAii0YiwIKJyYQDXjwzunopMCmptLUSojRCVibnjX\n0zdRjLlvhaBFQm1CA5bEeQT2OFJZEHtIxfnVNWxbusVDi+8wYCugTVyNZoeXYFxE+9EM/CE5iTXz\nCMNBDPIVVEIXKkWIrMxxUnULKAQxXP0inD1B0ga6mUpW0rF+Fy5XNt4ZHSbZCv7NepYjFiThIC3h\nLIZm8tFNgk8qwZWgxl2qImiI0f3zBHoSk5AeKMLvDGDsbaM6NssG1yBif4xOSQHvLe+nWhGiTtZC\ninmOlNw5QklLLOmi9OtTsIVMMBwDcRy3QcnloTWQUAOOLBILnKTtGqfLVYl2yM5ir48Vtx5frYRI\nohivTcNMWip9ulKmN6ehjPhJC6gQOqcwjjuxKw2MlmeSdusS1ukmUgtkBGxx1t7WEc8M0pNXwGZB\nM8myJT64cT+SyUke0B7nrOkpzpV8gTXjsyjGe4lleLAJY3SsptEryWE2PYcHCj+lJGWC26oUVlZ0\nCMJSjNkucqvHyTs9jtrho+HuNQjtcbZ9chGHQcNAZg7nJ2u5dLKcDfmj6IwuXHkyTCoPydHPqJLE\nSFVpuRzeRY/OgtcyzZJZRUtBDZMLSdi6hVyZKWMos4jl7UnEzW7G5CKcPSrknXHuf+B/fx8AnxME\nv/M+w/JNE+m1sySVLZGoWMRt0iPJDjOSnsv7KYcZElnJCUD5+5DbACvvQHhpmbpv3eR2ZAuvT3yd\np7PdrLXMMJQkZUiaxxlDLq17q5HmhwhfFRO5FoSVUZDFwLiWJu1uXohlczB0hq22K3x0o54/jR+C\ntamsn7rAPf/xGpIJPWHDBqb/JUi6uZln/v4JOZ4ZZPEwZ07W8rdP9xPdmINV6OJI7H3Sq4dhr4+l\nwShzzymQf92NJ9VE2m+vkZW4wMwTiQTb4qglKzxS83dkOccRHbUxmFXI2DdzGG/OY/FkOqY1Doqz\nh1HFfKxkJtBbXciZM7Wc/M06RIVJRPQmVgZ1SCQRpktzWF9wnZ3x00jaZhi+Jsax1siMTc/4L8OU\nqULEayF1yxS5Gf3MKtJQtnnYdPuXSHNVuJ6tR/ZmG9KbIyz7KvEazOyrP0HuyQ6W/xDm45VSLgeK\nOZzQT42qid807GQxqiY+JWTdxhsc/sYxDPElPAMh1r72GltnRKw1wcieNFr359BuXmJ4UoRbPkN4\nwkDwmJyF6nJ6f3kYs/EWFlEfamuAxRYlV36aimzfPHu+IqZ1bg09c3U8lvBHUvaOMnA4g0v2LM60\nycEmhPk4BGWwBFwSUq1t47HK17neuI3/+uxVgnPDhOfjhH9cTMohNzeeHCXZOEd6fJq+UBGdjip6\nZ6qQD4WQ9ETYue4s36z6MZ8YrZz3PkbSFgvVzlFemPwbZ5s389eOezFnrpCMCxyQkAvVe6A3DMEL\nMv5R8hDKwhmmPl1kZsTO86H97E0d4efWb5E1NIEwFOWOrcfxTIRYeM1Nw8hBTvR9jf+w/JLt6y7i\nS1Ah8MaIGYUsqC00UccCqfhWFDT/0UxFvYtHnrxOjmaGBLeNmZI8ZoQ51Glvoi3I48ahNdxazOdb\nLXcwXy5DvE5C5H98rLa7ufizfHZZm3h5zyeMN0aYbomTdmXxnzb5uUCQXTGGxugiMzhFyvg8srFp\nxh0SrpuqMEr8GMNOVLI4s0lmFGsVCOJ6mlrX4OhTkPHRCHlhDcFlBeQqGMmuorm7lq7WUobjOSxE\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dvZVUD/WTZHTRWDlJUC8lZcWNIl1IplaEdE2ApBIzTEWJzgtJky3jz5DiTRSjdjso9k3y\nkfUWjs3dAnMh0m3T6HtWKEqboqG6lZmELKbj+XTZ67m5UoNKPIa2Ikx97SCC4ighmZA5ayayYRt6\nvxGV3ouvJBGLVYclO52sJDeZBWZKyhaQVkUY1xajci2yo2WA8Uw1Hcm5nGQ9XpmKbP0EcmcqJOQS\nW5WKrzYfj0qA0jdKnmYSj12OoTeT9EIL9XmDcEOCfTKZ06P7KHZNcIf3A5RCHwptCGNSJillVjLL\nrZijKRiWsli7MEeReRhxuwy5PAIbBGiyhZSmA2MKrAYF1uQ0olUQ3iAgs89E8RvNnLceQCKOU5k9\nSrF6nNkeHUOFBSytziSpaxHtqQTmvlNCbrWArfJmYgEJfdpqNDo7riUlrptSrFNKHOYCSpqM+A4m\nYDakMXWjFFdEg1QTQq9fQRexYB7Qo1O6KMidQ6MMEfaLyZozo21x4LuqROCNk5q6zJa8KxzN/ZAJ\nuY6geyOy+SZk+jDKZC+hWTnLAxn0S4vxpiiYMhcSyJeR3rCI0BbB404kQeojlCZlYm8hmqCLdSe6\nuZK/iKwkgLBfSHjeh8BlIDG0Qg5RqgO9FDuXkZikOIQyKj1tzFpzaDMdxeDLpEO+mrBDiNQdpFNR\nTzQY44HuIeSGAPGQCkunkHFfDt15+Ri0mcRqVcxEcjBcriZ+BHJrJznbsx3zWCrpy06SXUs0OJWY\ndQJslSlESkUI41Ei41JyYvOsl7ZgE2owJaeiWK1EbhKjPb6C3y1iQJLPzkA/UusiJdYplNFEtGI/\nkflETB9kIE4qx1IlpcvTiM0zz23S00gjEjzzGhonR9m3fAIO5/1DJv8pIsjIWmC76DLNSdv596Lv\n4hVCRAwKlwDXeQnO96OYtiUw9kgh+j4LeZMLJJb5ydi8wG7tGToupnHhu2mU3pmO9jkvW6KXEQ/4\nKflNH9dNu3gr9GVmPXoIOEFsQpi0gKwkRMAJ5pPQ8OhFpOtDvNX2IEZlGqu/Nsem7DZ2RNoRaqKY\nvFpeMN4BngjiVUG8W5UsHMhknbyNJm8nsdoIk6Ii3n/jPrwbEsn6goFiy0fkjF3E88UKYltKWUsC\nk10B+p+LY4+EkCsD3HrpFJutV+nfXcHQWAk//N232OK8yEH1R1R+o49YhRJZZ5zmmxv5leWroI8g\nLoxw16b3WL/mJiZ9MkPhCn43+w0abrbxpUt/JBJ2sCLTcKzsi9jW5lCV3osprGdwdjV4BCADkkGX\nuUKVtJst+hsUrTNAN7DyH3/ApD+DFwZu5WZsC4GwlH2ifg5HzvPxxYfojR7ia7f9mtLcIb6e+QLa\nsIO5QA5Fm31sHl/k+ukgCf0BMuIWVDYf5AL1kFpu5nDGMcRDAT4avJNdxgt82fUHlrak4V1SIPhN\nCPGED8GqOPp6K+Vrh9FN2jC1aPmLeT8ehZqvBn+FTaCnW1VNnXKAJK+ZD/7SQHd+LaHPR8gtmaFY\nMMH6znZqRwYx7k9nZNUq+lhNUoGHxtt6cdiTcZ1K5o7176Iomuatnxcy6FrLrznC12V/pjb1DYy7\ndSxs1+Pw6Qi2lBH/MIFrk4cYs9YRvpiAIAbqHXZSpmYw/iyCaV0Btp+tpusFKb6JCPse7aa2NsI7\n8XxWTiYSOq5gqK4Sw6osrLfkUyQf4t5TL7JoKec78V+zKnUYZa6boUAN8+25LP81l6rgGBv1HdhW\nRFgVCiyP5eGvTWD0rmLS9gf4rv84q9+cIqnVziPJfyEoF6HPXMbgiSNwxMm9O0z2/gBDr0ahFQhB\n3bpOnvrSD8g4Z0B1WckfSj8DPPV3mfyniKA6MMhSPIMBew1tc+vAAwkpPjLli6SkGigs7iaeLuaU\n5BAHxk6jGHDRVrcNp0xDY3YHi7F1XDJu57ophte0CrdfhcLjJJYRodW3nraZJhAHSZCukG9uoZwW\nFOUeZpOLuKBfTdSXgK8jQqXlOqtVUJ89SENWL03eLuYTsnHFZWxObEMi8aHwupiw5XPevI9Nxmbq\n57voN9XjcGaQ4TETd5tIdaygU7pJLo6jlfiQhWMspBYQD/vI7O4npizBVxgl1zvP6uVe1pxp4QAA\nIABJREFUROd8WBwKmvMbiQQl6EUWRsay8C8p2eDqQaoPspihp8Tfi3EB4lov8lwvjtFCAtMKshaW\nUNq9WAtSkEfCBGNyxnw6nFYN9TEP7rlc2k9XEEtMQKgTIyv0s4o4NeddeOacDBviWOZ8+OxBmqca\n6aWYa8vbiRYksk12Fa1ogRmBivGkXMTaRIJuGfm2WTaKb9LhXUurZz35gTk2y/pYqKrENSvk3PVq\nBEEbh4Xvo5ybxiCMsWQIol6Z4nDCMQ6EztEYbKertZjxvmxar9TSJajDv1ZOXA1uZwo3BxsY7cjg\nqnc38swIG7JXcKtSsHrTWFxSYZ0M0j9fw4ymnPSkEAqNlzgCVHMeklocuJVapkylhCRSkmfmWWMQ\nYLTKCIoTUO1zU5w3wy2bXPQUauhV7+GmYispKgfzkRR8izKS67yUuJe548ZxeiR19EWaIA0S/R5y\n5mawDMqY7hQwo0xjoKQSRYmB/FIr2Zt9hFQB6AxRODNAsWGaZIcLqS9EtnUMvWyGzB1GVqKrsAs0\nEAwhv2onmh9HII+RnG3FMRzn0rVcim0GkpUOuq5vx+PRssbZSbRAinttOsNGJQtiD06HgpBQyuj6\nMobL1hAzCynfaGBdXTf2mVLMoWTO9u5mk6WZOkM3M+lFdO/YQDQm+4dM/lNEsMHQxptZd9E9Xgef\nAArQ1DlYn9ZMfWU7ZTuHODl9lJfbv0x62zShjgAvjD1K2piDH+55msFILZHqJs731nH1epTYsoDE\nBhdp31vAfkMHvwUKpKgSguxsOcPO2DnU4QCfbjnEr/b+gNAvHaT+cpLPe19j5+oO8mbDqMUhhNIY\nnSmr6Uio42DBJ5SNDBAZDXDWso8/TH8d4WyQjLkJ/iR4GHNmNk9ufpaqzEGEszFE+QFER8PQPUxX\nbyJ/XfsIoakZ9kZ+R0u8nE72QQYoRF5KXxrAvErF6JM1ZKc6EfsVfPTsrQxfLSd9509Ye3sfPyp7\nDtOHIUZfEmB0FzPm2cqpl28jr32BpxJ/xOQdBbz69MNsjV8heXYBnjaSfmWGfbs/Qtbn4IM/bYbV\naQg3JqBZbyXXPkrtz84jHp3gYiTGTMSOQ+7h5ev3IZCp8Lrl7JJ+yr9ofs9H4lReFW9DvkfOuu1W\nEkeDaBe9qBP8vDFfzd9mP8+z5ifZknKDvjurae/L5skTd/Kw/Tw/FP0/GG56uSrQ846gnOJ8N083\nPUNeogmhM0rSiSnicyJe936Lnuod+FI1CMaSsb6to38yB6HJg1eZQ2JOlN/WFeFVa1hezOFkTzmC\nLhPenDUkZslRS6aIIGKOXFxWNaFhGRPuMrrT1hFXgXKyl9xWIXMlEG0QsSLUU5sh4VtHr3BBLmam\nZBeX5Rtpd1bj+HGMrLMG7v+3T9guvswXlK/xQvG3GSipBgX4XIlMnSsjoaeQHL+Y3nNJdHZl88zX\nLrPnyADdmXXMXszG9bSAbdOf8rX4b0gNxhAtxRk9LmRRkYX56dVIs6Rsjl5i+7Mfo/3QRPvjjUQ2\nidjUcJXll4M8+oPb+QGn2C5c5srZvQQvSdkx9gljd67ildqH0exxIyv2MfZMJnZXErLvi/HlaAk7\nhdSq+zkiOEXrgXWcSjrAzya/yWJbOv+2NMiFB3fyxh338t0/vfAPmfxvRfC5z32O06dPk5aWxsDA\nfzxv8p3vfIdTp04hlUopKiri1VdfRaPRAPD888/zyiuvIBKJePHFF9mzZ8/fzU27OErWtTNEr/eQ\n60hG7xeQv+BkzcAYSRonkQIh9dIe0n1m9IEhAj4rey3HUQ/48Hus1Giv82TGzxnOWYVdkETF5VG0\nK0uE3nDjTUnB8y85tBvXszCto0e4n3SBgDWKyxQmz3JbxikCrBCx2xinkEVfOep5JZJUARREGQhW\n4wpp2FNxFZc9jROBfbQXNaE7ZGTAVU3Y9iCF8RmK3dNcmlvDtdRaJJslbJptoamrA3OBhqA4xJru\n91BFltn+iI1A2gpGzRT2MRfXB/R0qfbRl7yBMUs19ZYBEkxRgjNxJhaT+EvHbq6kVCPbosCi9mMM\nucmOKUgSuLkt/hGFwVlywwZsLT6SYlJ6thcRz6ul4Y45MoVLmHSZZMTNfMf3Gy7ab6drajPe19VE\n3UoyXGHmYlmc9dSQttPPV9a9jdItxRTL49IXtqFs9KCWO9m61UlS3Evz7BpmTxVwpnofi9kZZEoW\nKY6M8SXfS+QVzqBIdnMoeobk9EoufKUS3WKUFLOXU9F9fBLbyhw1FGZ3o6314Orz0T8i4YZvH215\n27E0FVFZOcXGgg9x5CQxU5BL8fUBMifmQa9kJruY5pkNmPwZ+GYTkZemk/ZFH4cnTyK1Smm70kDq\nGhsNVW2M+ssYMVeQ5R3iC4tDqLVQ4OmiLDGCzH6RVQMxTK3ZfOC4h077RlQqN1+3/gFBWhSvRs7J\nuj3EDUnkzq6gVPmYPJCPWZgM0Sj0rpBhnGGfrgfZKiudy19h0laJb6UI96VETGIhI7cVESpOYM9n\nmxFeUfBm91coVs6jFHvpC+UwTy4uXz7VwmH2qz9hKlLA+YV9LFwtxC1JYrCuBpvAhzUCp/XJzKcs\nk7g8RKreQ+fttQwl1jH9b/kcSj9LdXyIhdm7sCiVFKrGsC/7sJ9KozlcjzshylhtERmJJvYePsfm\n2HX86RLyfH1sekvCRGbG/0wEDz/8MF//+td56KGH/nO2Z88efvrTnyIUCvne977H888/z09+8hOG\nh4d59913GR4eZnFxkV27djE+Po5QKPwvudI2E1VdH5ARlRBIkFIeEFGyEid3OIA9PY1+XSVrox08\nKPobHUJYQcQBPkBsguWlAIVVXTStG+LT9TtZ0mdxi+U0mksGxv6owPdwOpFnc/GfTGRlfhfDst1k\nCcEv66JYMsXt4mMgnsEkhZdl36ZTugeRPRUCIuKqCBFPAnq3FWNxBr5oIn9d/hdCa8TU397ChKKU\nvvgannM8RWrLAt/+9ycYWqomIVHG92aj1F7uZnpjDqYMGWvffoOsfC9VX5Izn2Zj2tuHvdvDlZF8\njm97gLnctYgmonhtWuLzEjCHMYclvDe6mcRSMRqxFo/GjVNrZr9whr2xEQ6mniNfb0DsiaDpd1DS\nOsXpxOdYKtnBY7f8gkLZHP2KWvISZ3gs9c8EBVmMG9bgu5CIP5iEKluMX6On17ubB3Z1cvdnz6B/\n18aIu4Lxo/kIcmN4hCrWrx1la+oshufu4YqtkIt12zHnq6gOd7PG3scR93GWK1MIqyXsmz5PutaE\n+0uZ5MyGEY7J6I3exlUeRCH2IUk04tIm4Rj00LMg5OP0W+isuZeCR8bZVHyOL6+8zJJST3tSHdtF\nV6mRDyDIgDOJh7jevZmwVYrG6ER6t4zMMhFHf3+a8IicK1e3oZZ4OLjqNL8Lf5PLga18K/htDgZO\nkR2JIVMJiJbKKFu6QsPsME+1/oozlttBLOSh5L/yQuQx1BEn5rRkZtaVMZ+fR+aKFYEM+rZVYTSn\nIRkJIG6bpNhwiS997U2mNQ18OPhrzPFUEv1uFq6k07uip6eyAuUWAYe/dZZPk/bzG+O3WKdtJlOx\nSJ+qAXNYDyZYnTbEDuklLsqf5X3BZ5C0CCEeZ0hZStgVhYQY59Oa6NEZOTrzfdJTFmj78p0MNzdh\n/l4K+bkz7NRd5op9F970NGpEQxjG8hl+bTUX3Ju4qKpC/qCGDVsGePDQO+TppzBmppL78yH2vjvK\nq889+T8TwebNm5mdnf3fZrt37/7P76amJj788EMAPvroI+69914kEgn5+fkUFxfT3t7OunXr/kvu\nuQMPsaf4ddqzaujQb6HrVCYZHh+3rz9JQaqB6psjqJUuorXg+QRGtFmc2/YgKVXQlHOOzm4dvedK\n2eKZYmfaBYQtJtrd1bxb+SBWXw68oaBR3MnhhpMgj5C1sIhuysrl/u38seyL3C36C42rr+Led53t\nNbNkprgIZMowa7TMKfIxyTI40XoYvyeByP3QFGvhgRNv8t6ao1zJ3sji23EC4wIk9wuReXz4n1nB\n2O9k2CjjxF82YFCksXnOiKckn3PKW8hvN/L55rcYqE3HsK+azekd3Oa/RuGMAUFBhPeabmdK0Ehq\nloY9m5uprRgly+5hoKaA6y9UsSip5YORGlZ2Z1C3q4uy8CiagI3tPh9Jay8zEvBz7bUmLok2oXnQ\nS2STENkvgmwcuUnO4ApvzD+A0wk9y5Aum+AX6W/S69jOs92HSWgN4jBomVgpZ6EhD2dDKgeuvErN\nxWu4FyFdY+Jo5wnS2rtY6rNisdixemLEtG6kJQIEe8IUuaZ56JW3yFQsgwZwQbZ4nvsr3kCzYOFn\nb3+NNZlXaPrpp1y8JkDnsnD//HvoU5f5c+rDlBgn2dN9CZqXGW0FeQKYJRCJQ9PmVg4/coxPrAcZ\nPFHOb7ruIxYRYtqUjEbloXBmAU2Sm9CuBGYa6ujJ82CSOXBLtMyJ86jxdZJkNiG85IZPHZCkxro5\nmd49lawanSCx2U9VYx/SNT5G/MUULs6w6YNWjMXZGEr1lKtPss57kbRhE9MRIA6q/S60G8y0vH8r\nXTN7WTyxiiZXF8Vbp+nPNSHaH6M4d5Jtmkvcsu0sgpgA8mBVdISEuQhr9vYTzH+XqovjxDstDM3G\n6ZXuoPvgPTC7gteyQvt9t5C5zYVKLyFhQwDZTyOcPLeekREFwrts5K2bpo0NGDWZRI+IwKVCKQ2x\nb+NZCnPmeW3qM3hWlAQNErJXj5BUusSSvux/JoL/r/XKK69w7733ArC0tPS/QZ+dnc3i4uLfvTck\nK2GtrpRQbj6C3Gxc6fmEVmJ0iEyIjHHWNndjy9UyUlSLJ7KAVCHDqM/BWpaEZrWX4UkZ7YPJlGlX\niLtFhHwCXNJEllQFLESLCRmVbFBdoFTY8R+lEjIpI0sVNC9s4dzEAfYELlKQehZBeR+uuhkkyigW\nUpA5soioxfgT5LTONBKwyWlo7KHB20nZ7DhJy078URmDl9PRWIT471ejGHKiPt6FU+Wnr6yEqUAO\nHocKpVjCUjyPj+dv5b6bb3DryRuYHzlAoFZMaWobBZZZ8pwLnJdv4BPhAUxlmWQUhNm+Z5hNSdfR\nLduQlGxjaks11mMCYsMiHHu1zKTn4zSrSQo50cRc5AWNSLouMf7x7cxKi9A1hPCkqzEVplMbHaYg\nPE3iiod5n5oWZwUN8Vmq1VHOzeXzyZUtZPSZUFl9JCXbcSRouOjfivzcBOErNtSFdlJS+8kOLKE0\nreAZNuKKqJkQ5ZHstaMVeAiUxYgEYyT0BTEX6JlNLsQc0JEssLE79inukJz3LYeQ51koKx4mc9xC\nXbSH4ugU7riCFnUjKpObbNsibfMpLM7oKUtZRKrxUJA4RUHqHPnVM8gvB3EtajHIMlBmecgqN5CX\nNEuaxcoq2QTVeQMIiiJMlecxk1bOciCDiZU8VCIL+hQTwhtREoNOMmTLpMSWsZoTmDFlIzNBdFlE\nVBqhI56HeNlCU38nmdolEot8pOb70c6F8EzHEIus1Op6EJZFUa+1MT1fwlS0AkevlLAihK7BhFrs\nIq6A2KIYkSdGlnCJ7KQlcpIM2JeT6euvQ55loSH3DJuj3QRMXryhLKaLa6FSQW50mhTPOKaqRsIZ\nUTZMXCJNu4D27gIMtnRcvgB7dg2jqp+nxbQJrciKKsWMqSCTiE6GTmlHsBzj/PBu3FIVWUULyCt9\nyHMhcCPp/74InnvuOaRSKffdd98/PPOPatHFv23hh6HbWa0Y5hbF79CFE7DJS3j73btZ8aZTNDHG\nWdE23pTfw+dtf+COeDM7e17kpnc7by3cQ/rIVe4Rvk5P1v301h3mq5m/pHF8EGHPM0ytqsVwXwPt\nf07l7MnPQaASEnSQCUbSiY0IYR6kc2FyTizTa03lr7fdw4irAnN3OvI1XtAG8Ix4KBjr52Ht39Ct\ncdJ6pJ6pmWIsl/ScXd6OOOLGOldEjrGdptibCA6UcfNzd1OKlRJDO+s+snHdJcf5ahrTgypG50JU\nH7vBRmMPifu8uPMTGVlfQvvxLDrfl6B60EzBAQdF4lnUMS9L/28rriWeyuGlk2wcbEOV5aK/q4a/\nnHwESzgVqTbMZttvqTR/ws5ZM6P6rZz/5EsMuuqR9fs5e6sH0fY4s7JCAko40/ZtbgRDKJ1S5m8I\nUIm6OGL+kNqyabyfyebG0jqO/3kXra4dmNPLuDf/ODlVS1zctoUceQaHd7xFr6yGTxWNbOEairlh\n3B/4aY3V8XbdY7gztERlImYl+eQ45xF2Qk7aMgd+cJ6RqxJ++MQ+jpa1smfddS5XbGIuI4dksZV4\nbow+ZQUfXd7DnDqNf931ZxrWzRAu+xtt0g081/cMC/JsUjfb+fLWD6lIGcRQVEBJYBqBMc4R33HW\nTF9nrtvBQE45zbd/laUZPb5jLg4mylCkxBFlKsnZ4OQzVX+jeqgN3ZMTtB7cxc3b9zP2djmmXwoQ\nxm4gz5RyWwXY/amMTdayvCmdcV097mMvsErazQ+3fh+ZIw5/i3O9cT03yqu5/ts04itThEVRoqNC\nIq+KuajcQ69kDTKXl60brvDo+t9wdWA7f/j9V6mSvExF+BTziy6WKqq49vVHGA9thCEBR3e1sUl1\niV9Pl+K+KKRs6gLJewtY/mYeuqMLFG0b50D6dXIwsj39Eq6bInzvBzn2wMOcrzrKmRMHEbQLMDnS\nWL/1Bp+74yVS0yyEglJ6ehoZ/L8pgtdee40zZ85w8eLF/5xlZWUxPz//n/uFhQWysrL+7v0ZURum\nhGXG7As0LM/SuAaWCvx8mrSLYETKbGoeoT4R2f0Gsuo8aPOkTAYzmfQWMzpeQaH0Jtu2TTDVJGC2\nPJ24SI46EiFZ5SbeOEtacYyTlTXMG/KpcPgI+wJ0+epRil1szL+GcT6ND/1HSckw40hRoZS70ETs\nOLRJFHqnSfGs4JSuRqkIU2KaJtnpJqiU0CDuQi72Qd0UZjS4YkJ87kQccS3+7Dwia0op7PoI5cIi\n3c4NjIgbSND7mRMUcVJ9F/krPop7LFRuniFo8zM/4SXYbCCjM0bpg8nUZ3vIMi8RiksZVa5ifCKP\npW4VacuLlCaN0aesYVmhJz1vGeNQGj0365EVbSKY40Ri8iAMumgytxKxa8AYo3uqjnlFDnXmHrKY\nR5AeRiAWQrKE5BkjoZllIMCiMBO/LJOUFDt3FH3EhLIWS3IeC+STIAihNboIx6FlqZJO2QaGFRsp\nj42RMjlPe28N06Ii0lOXWRFlMkI59eYuVi/20u+sJZQpZkmUh1UsxFoQYCESQL1sQ+VzUmHykrpk\nwWOTc9KxF0tWJtJ90CVfhS82QXVxL2NzlfQ2r6E+pYuGzE7UZQFimTIyEpdImbIgmI1jNacw5chn\nbHiZZb8IXWwRtcNItGOe9AwDovwwQpEVtWyKGlk7Oe5ZbLlKJnxJdAwp0YlXKCwMshITg14BmQLy\nkmfZqLmBO1dFRJJM6/t7kC63ced8F4vJRfQr61i05BAUJpC12oK+yE4gMQF5ppfiunHylufReU3E\nC6OkpZqQTEbwhxMw1aYQjioROKRYVoSEBQGqtUsEpFaM4TDaMh/6JC9Soognw6QMLOLOSsG3oKLY\ntsx61xA2ZQZel4zkiQFylxwo66PcjBxE0AdqgYu0YjMV8UGyiha40RbFd3UKjdOJcPjpf8j0/7EI\nzp49y89//nOuXr2KXC7/z/nhw4e57777eOyxx1hcXGRiYoLGxsa/m/GvV3JJIEDeuyHyToBwGyQ1\nWdlech6zOpnhWCUlv5pg3/gZdFt9zG8u5sWpR7li2IZ3KRF9CdTVxWmtXAL5JIpRD56CVKb/ZRPK\ndA/lwlEG7qwgtiPGoyMv47qUzBNvP0dBwyT3HnqdU+IjvJN+L2v2t7G+4gZH5R9g1aTQkbaWjYNt\npI9amC/8NzwpmRCXkmFbIXXGSpO4h3CtGNZFaIs28bhtN8PBAlbiR0kV6Mj2ygn8dYXJ9+J8GPwK\njtvySH94noVoHZ1T+xH/Zp4KUzffTHybpOlBVp4aQz83zn6BmFpSqEBCGitMU0gPaxi4WIThxwrc\n+8SYdqbxcu1nCKeL+NKuP3Duj3vof7qEwa/vYXZbLZ7H51g/18nTyhcpUFlABk+O/pgzzQd4xPtn\n9inOIiyMI8gH8gUMfRKlYz6VEzzGgHMv8R4Nd605xvOPP8OHybfyjvBu3j97lNK+KX4w+WMmZiM8\nPbgDCztIEFdijSSyFNTwlvde8mU2njr7Y15d8wVmagp4YOINaqb6eW7u+7TFmwhfEFP64ABrftrD\npWcO0Hraww9WvcT6+V6E52O81P8Qr07dyx1PnqT01n5OPFNG95KGH6/+GP2wGcHZOHcUfsAt1R/z\natkDXFWuY72wBf2KGTrg+OIRfh+8n0jsAlWKTr5Q+BbpK4v4BEaqUvz/AXf7GOKTy6hki0RvT2Dm\nx2uY/ZME5/N93PXkGCXfjXODRvLMKkQzQrbkXSG3eJJpYQE9tjWclTyIYK6cHcYxTj60l1/d8QTh\n30nQzttY9/3LrNoYxJOoRbvbzqamy9z+6UnWzbcT2RZHGgqjvuIlq2KRxhdukhuXIpusxvl4Lxm2\ncQ50/Zp3G7y076jDqE5lXFaIVyNFggv51Tg+SxKT/eU8ePNd6mf7efqeZxiNKNj1i++zvt5G/U/k\nCP+iQPxH2Pmv59m+6yKpmLli38nvll7kGyW/5ludv8SfOIT6H3D93/Ya3HvvvTz11FPMz8/zpz/9\nCa1WyxNPPIHf7+eDDz7gpZdeor+/n4MHD6LT6bDZbDzyyCO8/fbb/Pa3v6W4uPi/ZD7zzDPsfqAC\nyftTDF3L4sJ4I1GXn4QFB/IRP1ZDGpd8+xhtK2W2N5dUpYt0kQVxZoTSonEaS1vY6L5Kzo1pzo9U\n09KRx0qLmi5bPa2KbYQT5CTrbIxLy7Ek6FElejBY1XRc1+JzGfGaLWT5LdQkz7IUzCJqkbDFdZ1s\ni5HUFRvFw7OkTtpI8AYpsg2jH+nDO+XGOhZlLiWPkeI13GjdysREGYXFE1TmDZBVbKE8NknplQ7S\nLvcRicYxHakhp2CZ7f0nWeMYoEY8jrlNwZwxG3dWNj65Gn2OnWVRPV3mO1iM1jM2UkJHcxLmMQml\n9hVEEXCv0hCqTqZX30hzdDOaSQ+HL59CFIwS3CClKnKTrLZBjF0aEm1+1gdaSBebSMn2Y5iP4rcu\nkrHbwsqOfK7m7KCrooHh+lLCuSLkBQI6nTsJe1Qc0Zwh02mid6qOhNkgVYZBZlZKMCTkEq0U0JdV\nRZd2I15PEsIVJ8U7Zghv0NIc3UtKaZC9t10icYOX7Mp5qvP6UWW7UEm8CHPiTDUVE5QkEBhKZOFa\nFIlvjgO7bpBYEuNm0nouGnfQ1dkEGgkhUQIp6TY0yV4mBnOJzEnYquiksaEVYbGXT7qr6fs0HdNV\nJSZPGsurC0AgIN87zYpJgcAdp97VRU7MQG6jm4XtlbSs3cJsaS4ZeR62iwdZjhfyrvF+0kbNHPQ0\nsyU2RcnMMhn9VkLtMi62bKR5ZBMjY7VkKxZIkdlpW95EPF9E/r5p2is30iLZQmhUhk5g4fCuk2xI\nbCN72MhQTzVXWndS3dJBnnmMhXXZ+OVKkvrdSMNhdHEL+RoDySIrkvMWbIEsWpvuwGdT0XShGaXW\ngyEnj4HgahKn3Gy98ilZETslshUae66SOjOLOC2GQhNgKTMbw/oGplevRXw5TN356+SErpJkGKNo\n2Mz8RCmnF2/B3ynGOqBCl2HlD+OW//Neg7fffvu/zD73uc/9w/NPPPEETzzxxH8XCcByVwKRP3g4\n78ijOb4Hz5yfvREXKVIT4UoxV3fuwL0iJSlxmewRO6Wx4zxY/Dr+aimmjGQiv42w+JYWszjCjCDO\nVGgT4dxcgsJSQuJEigvH8YZkuKMy2rVrcGd48GrnWB4JMzlSxL/df54DW4w8M/YUi9E8vPkaMrBQ\n7pkkYcaPdDHIIdFxTDYJ42NCDPYEhNEAw4UV9Dccof36RtKsZn68/3ukblth4mApoqfHEf98DIdC\nhGVtPuUP9JE1bmPDE6fJ3eJGfjARu+jrvCM4wunh27Dqcvj8Z1awyvdzaeAL8IkT0QfzKETXuaX0\nCj/acRnVvhDeB5K5OruTwbnVMB6nqHsG2bkolXeNccczx1A/1ovjD0Gmot8gEFdi7BShl8kQNCko\nVF6mSdnB7L7bOdewn5bxrYS1IrSlZu6reoumTS3IVqTkXp/lgdlX6Jtt4Aeun/Co/EXuU79NV846\npmvyObHhEH6RgmBNOnLhIEp3H7bbVQiqdUjEyUhT5bgfklOl6qZcMIBRridmEXFfyptkhxcZrKpi\n7pM8TC/rSHB9giJ3Eq9MwGBeGe9W3073XD2x96R03VyHJZTFo9/4Bdjc/OV7O7hVe5Pnt/+OuXWZ\ndOuKsP/ZzcL5IEsJ+Yx9ppSWu0Q8Gv0TR83vMDfzbcanslj+m5jCB4SUPifnmqqG85I9qHBRNb2C\n9kSMzmt5nPv9Ib6ROcmXM/qQtfiRnA5TGR3nncit/DH8KO5YBinqIGu0vVRsHUW/YQVfooLexiYW\npvIQdMdQqP3oU1YoF49SbJxG0hvD261hsr+UmeVMJvQZ9LibSNF4yYkZKZicJnd+GqdQhSNNii3B\nh0FTwBvqz7K36zxPvvw4b6Z+luulW7GbtCTZlwlH1dTYJ9ky+hvMS2C1C9gxcob09AZ+dse/YkjP\nISHm44HAX9hneYuht4O45HGiqVKk2UG0BQ56hqswLGrJ2zIPjP5dJv85rxgPLxD+QS1NkgDlvmZm\nXt/GTwceQFYJ9i06FAfdbN3VwpY7LzO2Us5PTKXcf/51nP0aXmv8DDa7FtGqENWrbvBCwqtYr8no\nFW3itPzLZJsX2X79Or2tcvxzbraUthJSJjPywH24oqlAjGPqKibii5StH0KgivKp8ctkAAAeOUlE\nQVRH0ecJTSbAkIh7Z9+myXCN170baUuvIvC1dBrj3Ryd/pCeqQI6nluPRatDui5Me2ojGSziJZFk\nFkhSiuHBEpQVOho+aGGxV82LngPUZLtYu9nO9sxWCl2L3MjciXVRzl9/vIXZ/lXgjkPITaZ8jody\nr1G5YZ7uw2tRpwa4e/QYSxMFDI7UQFcQgSmIIC1GSpqVSoZR5dsxrVajnEoGv4D8JDGjxVv4Se0D\nbE98m8aZAV65XstARx3ezERiPgH2N9JQFgQpyZ6j8OASPWU5vHCyiWh+KhVf6aZzqI75nlwqx/vY\nNXcG2WSY6xmbeU3zebZuHOCWHe9SIrOiGo+zobYD6ZKN6A9NnErYS6tuP0fWnWBD6k0UMT9r5nt4\ntvtJ3o/dyfHdt3Kvq4/62BgXLt3LpKua2X1ZWMNBiLVCZjHS/BBZthUqg13kb5on12iCNvDVK0An\nYC/NbCm7QeoDcrrDjZx5bC8OhQShRoj8YQUO4Ro+NnyfxdphlhR9KEQ+buMEEsKka02oGt0gATRw\namoX1hkR9xw6RoOym/i7cbLS57nzrtPobrgo6likWDGCXOfji02/p3OggSu/2MXMTB4ys5P71r3D\n6toBznoPckxzO9oNdpRVHr5l/TmzhhJ+6t+GLZBO00onOx3N+KedGIyQIQmgrwuhORRB4RiioP9H\nZHkXUR6KYx7LYeyHJbisRqZm4feWb5O8PoTsrhgBi5DwfJSEQROO15MZuVJKffUgD659naGlJH7B\nZ9nERRLDLl60byVBLONH4u8jloeQVAbIa/j/WQmqWhQkvkNHfpobSciKZbyeUW8pFIA01U1prIOG\n8ps0bOvl4s1DDDbXkN1iwDGp5aPQUUSBKAXl06zL6aZU7sZW48CfMMNU8Sjl/lHKr4yzqiuNlQUf\nWqsd+6osBGUFqPI1JGXbCAxnMb8cozClDW9cztnw/ZiE2UhVERqCHVSbO7gWauRKwXpWNcaQxefJ\nRUj6lSC6cTu6h+yk1RshICRilqJM9OENa7DJyvBvrEKwSo30+CTeXhnLER0av4Q0V4T0Cjd5CRZG\nPE4MsxqGZyrwLacgDDjJzRpnva6DI8puklOD3EjJRmE1UT3cR/LcBEwYoTMOCi9sjyHPDZAssKFY\nFSeyVUKFwoLHqMQqLaNFuJU3HXeRIp8hSeXFcLUUZ0xL6ZFx4n5wz6gIrEhY0usIb5JiT9My4thI\nWdTMnsJrTMUqmPHlcaTrXfYsfIrYC+pVPkYaqqjMmqdIH6CsZ4E0j5WiNdN4QlG8x7w4g2rm1QVI\nEmIklviYXsolOhJmfe/HTGzQ07G/jrVDI1TML/O2fS3D5ioKwpMo9dNkNBhY0isQBxQkjTrIxkIs\nQUYgKKN9ZBVuexJxiZjkrAAiRZiMsjDj/XFMN5MYTS8lv9KO5miUpAwRw1f3IhToyO5doi55kELF\nAgZHDg6HhkFnFTPKAqKlInpni5gzC9CVG4mVioj2QiRDwq6NzajjPtQRNwqxhwSbl12STwnHJBw3\n3IF8PECTtYXtBy+RXbXMGf9+ugMViEV2DuSe59b11xlYqmFgoYmkGTuecTV9tlpCUR0LiX5yxxXk\nRmPk7TWQo/ESvzGDRupEVgqeRS32BS2p7j7i9iDXI42EdGlQrwCPEKbC0DqNutVObq+F4oUZskJG\n+mOpLK3OJiTOQRZwMW/MpFhuY216K3GvEHdUwWIoC+j7u0z+U0QQuSWL9bP92ONqjOk67tr4Pg+I\n3wEjOC9HWDoWwvtgDhcf3sHiXCZLU1n8VvsokYAYx6CWu3Le40s1L3GsfS8fen6E8gEPWfVGjmo/\nYN0nnQjei3NnzSBljUGOmR6mdXIb5nYdZfuH2FZ/gTVjAyTPLvLu6SJuhKuxlqlR1nlJu3sJld0B\ng1KoLKawSMY3Jn7PuqUOpDdC3MJJ6tZ2/0fZRkIQbacDiTKEsDTGXz2beYcjZCNFnqBiqfBuyhe7\n+Y7hdaKfWjFPSfjgri/QV7IfS3sqyIKkP7GE7X0Xrtfc3HP0Q+6q+ZTCE0akA1F2iq/hn/Ez3xXH\nExgG71VwZUKhA/ZE8VUrMAnSEKwFQaaAB6veZaSrkj91fJmB1lLinV6OR6q4Gkxg1pxBafUI36j9\nNdFcIV1HVjP0WibnPvo6y9cqsUd0hBaLKeEU9734Ms5dF3A+oKImdRDxEAiBteXt/GTDd/m4+zDP\nf/gDvpPw7zQWtNGlq0W+1keVr4+7e8+xc2qAfNssCyMZ/PrCZ/GNO9jufhlSJ9mx4QzhmwYGJmJ4\nPx+kcnM/jyT/GdVuE+4SIX97pYzFMyWQHmcovopfrjzColMBHhuHAl2UaKyc3f8Ao+0VyF4BiyKF\n2NF0jnc8xGDXZjYducxByWneuqZDPTFPUeBTctfbUaxScObaAa5NbkESEWLN0hGskEF0Fk98hTf9\nt/KJ8h7itwjZYbvMl8++xKWCbVz65jY+M/46W45dRySNkqazUPFYP3UXe9hz6SKe4gRmCrIpio3i\navYy8MdERvankPLVTahSHRwOfMjWmzdwTiTxovFR2Ogh9dZpTr5cjPpyhMfUvyQiEvEr67dYL27h\nW9JfItkcQFdrZX98GNqsnHghiilcA54qcAvBDYSFlIim+Jb8OGZ7Ho+3Ps/edR/x5P3vMqZahdOQ\nzr+8fRV3voaPP3OI4bfymXgrm6AxDTjzd5n853QfjlhRj5uY265nVF+GOhpALfOiLrcTc7tRzEeZ\nSMyj17mF3KUF0lxW+tdUYU1MQeIJkCeYoTHazs2cWoTKGmJNCfiSkljpSsc1roGAAGdRNoaqWvpO\nlDM6lgVLEgJVcqyxFPRiIyWCUZzzWzHFC8lqWqZMZKDG0IPXouQT715kLguVcxOkWjuIrBiYH4W5\npmQW1hSCE5QtZuI9c/jFQhbz8+gWVDG2fwOecBTlkhhvvZKytEl0yzGsoURsUh2zKUVMKooJKuVk\nB0apMrQgc7kRSmOo7TGmDKsILbuJiWQsUo8nPY5/tQdhp4Ma8xni6NEpbZiKtVh8ObSdXktdbjeV\nimEyhGbsiWmMFZWxGM5AKbaxPFqA1aKmrnCE2tULuDMTScm2slt6kVZxIS5TJYv6SqSFAsqrl6j3\nTVE6PU33aC0GeTaK5RmMLh1L8jr80iTQRRmJl9O/tBqrIIWYBGLAvD6PhfIiShPGKMkfZUBSw5Cn\nHElKnOwsC0krUVJUMxQrzqMqXMDoSERkBUWLg4LOPjzyFMal6ymPzlGpmkFYFMGkTiHiFCOKgggI\nO9wETxhJWZgjYS6f4d56vKlCkNuJmRcJOZcw2VSE9RpUxU50YispTi/qsB/VdJz8yDzTKYvMKwvw\nZicQzxWATkHYkMaUs4AFv4qUGhsT/aVcPr2TUUsydpeJ1rZSIi4hletHEOZE0K9eptw+Sr21l4uZ\nm1mOJKHr6qfp2hAFw2oc+hLaLm+gxNlKqbuXzMRZbDlN9C/XUKSaYGtWC0OrdThFSVzVbMHi1tEs\n2Iw8zc/ImlKy6gzsqr1ImsiPXZFIqsGPLjBA5idDKLUgFcfwrPdRkT/IhnAbs0EHvcHViLJFLNSv\nomdqI+6wCkGlGrM6hRuL60hwWimKGWDBQe8/YPKfIoKqX/biWQwzocmmeeNmxoer8M6qKP3KIHll\n02RG5+n1b6LdtJEfGZ9mdaybn+7+Nl2r1yCIx1Cc9yA6FWX97R0ot3ixJyYxcrGad55+EHFYyN78\n85wsO8BrhXexYhTCiBmiWUy5ipm3Z9Ek6aYkbRpSq0lOyqHptmb2zV7g0B/P8rOB7/Ke4yC7ep+j\nXHCGSaGXpSiownBMfwufrP4mnICcG23sW3gcU1DGWdER3F9pQvDlXBavJKDt9VBxsB9Npo2xaDmW\neBILghyQq9BELdh0aejOTLDu2b+w1rVCoULBsx8/xeuh+9kXeJzAhiTO1j2DvCxOdtIEeY//jYOT\nx4khRC1JZUJdTHPHLbz34n08f+sTHCr7FNHNKHiAekioDZFa48L6x0w0F1V8cedTqNe6eDbxe6zz\ntfMj/48oC96gTp3Li3fmsHIgzpHk99l8/QqSV8NcurabP529n73LdtTxRc4XfIeVykqE8SBejQaZ\nPkqsD6ThINmeBfo1q3kp4WvcueUdHkx9hVfOP8zUZAnfP/Qs64w3kTb7Qe4jbl1AuCPMUHYZij9K\nCfQFcAiCNCev5RX94zyV8mNu23WMyaN5+Fdp2Re7gpgI8rgX33PD/K/2zjyozfPO4x8JiftGCIEB\nI8uI2xyG4BgfMQTfYMd2ndgJ7iap2zjdbZprM+2m3U5bH2w2mzibtsk0duI4cdw4cS4fGDA+A8bY\nXOYyhyXAHAIE4gYh6dk/MvE02zg729rQmeoz8/zxPnpnfp+Z932+mvd6nrGX21hrbSZc1LJXupvR\nARk01JBg/oB4/xpOGX9Bh0cqgY+2EygbwsUWiOyUFa/L/Tye8S6LIkr4Qr2Ks2IZxgEFNl0wNAdC\nnwz3nn6ik6/R1+jDz6t3knTuD8Rbf0e++VkK4tbwzA9fRzbfirt8BOegSWxJEowKBX033Qjee475\nFW3M85Kw/8ZPeOl3OYQ1fY6XWyktv4qhITycyZuOzBu5xhO1+7iWEcnljSkc7XiQ6isJjHm60p4Y\nROHjS4hyrmW+rIxPeICmyGhUL0LCu+fI/PUHhC6z4pMhR79NhdTNiqJ+kKD6YuIbqnjT/zF2Sl6g\n96MgxprcOZG9DmuPFPNLTjw/kctTIa/ChJRD/d8+JmckCHZJtKz70STme91QyIxowk/iIwbxv2HA\n2WkUoqyIK+P4nqii3x+KF9yDg2YKbcclfPKqmF31JdJmG3POtMG4lEtpKQxLPRhy9OKcKZ7ftDxI\n0UcBmP1b2NB1DeZ5c27RGnxdO4h79Tw3/P15zeEpGl1ikY5dJMqjgQE3b153+xcuLFrM4D1eOF+c\nZKrVlzLHrWidOsj2yMNZ4sqIyYno9jxUbeWUD6eiEMM8Ki/ly1IXLpr9sTb44ikzkOmZT1JkJf4+\nvdxQhTHh744rZQT29nLhWjouFgdUjzjTVBrMmVIVfvfVkOR+DlWYC1MTVtYVHMJ30EzAfQNUpofR\nKHuQ2MIiApv68H7dgRTVaXy2DjDPpZIhvRt5zWmcNKRjHLBgbhjGdEHCgpIiYruaaGiMQAQ4sFBb\nTIprGY5iHMeVLrip5SiWmhgIHafBNYKBQF/y1auhU0JG26/RbLBwUxqLqSIQ+fAUkU41+MUM4OM0\nRGOKho/GNpBSfZXwc5dJbHgL9f2V+GaYSGk5hfJcGVO+jVxUzqbm3pUky6tYcuo8ecZVnB5NZ+Je\nFxyXTHKRLFwdYYfL77EqBJ/4ZVNvi6K3yh9blxSHvl76r36Jx/UH8RoZ4mHPPJaGVuKQ8Brtfu4M\nuIyzsLGBAMMg5+vkuPQNsVj6BQvlpYQ69qBXqDl/70K6rk0hrluJiypjaExQ1uLGYrmORWuaMXcK\nXC6MEjK3ncrIJGq3x9BiSMMy7IC3pwxXdQ95qpUMVztT/ZYBQ0oW1WFJBDq1scT5Ej5ZBvxTx3H3\nccDZeQy5bILamnQsNm9ignoJ877BDza+wcLuUjwqhxHlOpwcp1gdZuHe0SKMXeB3phWH8TpK8WbQ\nNxTryjFSvMoJrbqJ6bozn5ifICG4mdikGyhUfQwZvfj5seXEa3zRZtfTMjSX/vf9SLxSiJxJShWZ\nzFL3ke59BmuplD82PEHM8mq4/nd0afCJ3APtc9GM+3mjsnSTHXeCBFkNY3Wu9Iz40aYIxPtKFVEf\nGSn+5TaaVy/C1XWc6EulhP3XIcJGB5AoYNaZbiTdcGVOEpNOcoTCTEmHhhL9FmRtvUS4VrDF/RNc\nl/nS/6PZBJ0sZ83P3uDdrF9xPPkH2HwdUDblorX2Uy5N5BWfZ5AlQKCyDfcBG+bB2VxzeARPjxpU\nvlfxdwSlsYMk46c4j+r51Gk76bImnpa+ikuJnMozQViECqV/BwtECcn6KuTqMczxDvS6+REl6lF3\n6ak7F4fEzxXZ9jmUS1349MIsnlt5GaO+joBn0pGdGGXVb/5I4MggnoFSquJ2Uhm8gHltzQRd0uO3\nf5jIx3Ss+8VJpMVyWq+HcqR7OYU3FjCutyKko1ikkGbNY4XjeXY15zI4S8E/j73CPI8KJq02JtPd\nkaz1JNDSQ6clmKsjiQzLfRiJ8OFFj98SaDhC3KPL6BFaPJts+E20sNTpHBHqenwDjbzpsIPS1nuI\nOdTInOIK0hpqiVRO4n/PBPe3fYy+Qka71ZmLS5bxybrn+FHtAVJOlnC8dRVH/L6Hdmct7gumqDZn\nsMp6ik3idd6Ub+fT4fX0fhnI4BUvxqtk2Fra4MZ5kD6K2knCGj8DC2NOkJj9ezoilNQpNWjP9yC5\nKMe7VsJoQS9JHaeJlDfg4Sej9l9j+fT+dJqO9TGnuIF/bz5NuEmCQ1UYi7YX8EzWKUZ2S5lqkeCc\nbUMaLygKvY++vmjqBmPIDvsYX68bfDC4hRtnAuC9X9HAdrxSHPmt5TmWuJUg32zD7OjMoIsHkkkz\nytGbXNcupW1oId7eh1ikPEfG+gJ8Tg0zUOKNqWIIh9FqsnKa8LRBR98YXdVyDAWuXHReQ334fNaG\ndTA/oJ77jn3J4cotvO38ImnBZ+nT5JM5foZevYojJUouh32fjIw8Gg5FYj3qQFrTp/hr2unyCSUh\nqZGnl/4Hb4ofs/fmT9i49BD8999REITMneTo0CNIHAUKtx6GFJ60qkN5z5xDTUs0I790JjikDvXO\nKmLrm0nKbaE3JxhXOgnDgjIKuB9wAs/AYRYpLzJYP05l5wDDAwokUmdmf8+R2HkypEckqK418+MP\nfs/ljgRen70Xp2QHMlefwJAWwMQbfWiq2mm/LIXTJ1k/2cCalFocv2dEkmrlZ8dfoc03nJ8v2sUc\nzQ1edHuJ88EJFFsewpQayXjnEN0FDiwSV1A6G6k1u9A1rGJf5YMUtcaT6P4eZYnxXIl/iMem9qO+\n2YzrpSYaFXN4M/AJklqL2OVXxDVTJmfbVdTn7mD2ZDPWp6UM3KjF/ZV2el06kHk0okkYIXYJODnA\niNFG61MWzvo8RJM8laVxVwlPMHBQncO4vxP+PgYCbpoJGTGxOeoIFdZEjvxhAzUmFctt7+K72URE\nRhPuug9R17XzTskjzAluJjPjNIm2y3xhdKJTrcKtb5Sn4l/DFgx9Mk8sJ8cQnxuYDJLQG6yiJi2K\nyYVq8oypuAeeIW3kOGKjAmOKlsLWVVTOWoA1TMZQ+SS6JiuKrCYyluST6VTI7II2uAi16lieSd3L\n9S4t5hZHtlx8h/HrQxwxzqPXUQ3KQLB502ey8cbgdvQ3wsg5e5Cqs1r+0LeZTUsKiFnXxkSII62e\nKt4aXINi9ipcVmnQh6oZGnVhY9bbzF6i53jIJsq64zFXxHMxzRtrQDTdzv5M4oSPbIAgnYGfnflP\nJBYJZndn2jxU1HlFMI4reLmAwp3gcBMRiiZGPuqmVi+IWA6dsWHkqTIZL5xgy5FcjsX8gNrZiyks\nW8mNWRo0sY1MOTkz5ONLRtJBIqdq+XA4B2dsrEs7SH1bPO/rcgjKaiVzYQ8t/anIK2eR1FADfYAV\nrh+NZqjCgzLLPJyVY7hrz+AYO0leQRZdigDE9yfpf9tGkEMnG9wOEzHSi2/tCGkRX8IOC5FXv31N\nA5ihIPBVTnF+fCEuE0NEe1Qz4eGM0d+PU6YVFJcvgKNW5v9YgcdGK/FPfYb6ajMNy+cjYZIgrHgG\nAqmAE7h4jaP1bOK6WYKjyRvGosFhLooUGbMyHZAUSPCq6CHjUhFNkhhO+21lufo4CfOu4IyGjiMj\nKNuNuFfLkNRdIymoiA3acq6tjGQqUk7clXw+VPjw0fxN/JvfTrKmTnDadzn1trWwGMw1ngyekqBF\nT6JcT6EFCiZSOdb2Q+ra/JByhPKxUK6O30POxCH8DQYcWwx0D8bSXZ3OAmMFm9wbuDC+g+v9Tlyv\nWkaCxpPIp+qRHO7G7VQ7wwzgoDagfHaSWfcCchh7X9D/vo0L982nPiGD9bM+J3LWTT5L/idQO6II\nmsCr1oJv9zgpsVcYqPHg4zNZmOqGiUWKT8wYgWljhHR1Yyl34uDRrfivMLByx3G83QcwH5NhUnjh\nNGImMySfQaUHx6WZ2KrNcGiQqSgJw8u8uJkehGmuF9Xm9Szu6UHSdwKS3RldoKauYjXNsiic/EeY\nsFjoNQjcY7uJWlHD4oaLRNY0wRdQvSCRw3O3QiNoK+tZUHaRqVY9J2xmetX+4OENFjeGjRbOjN2H\nY4+VdQ2f0daloqD6XuISmwhJMjI1JaOv04vzzgmg8oeFqeDjwCxzB8kJBkI8+jjml0zFzQVYXANp\nnOvMgGcwbXI1Y7iicuhkc9/HPFn8Fp5OQ4wEu/H2SA49KJnEEVwdwcMJn4AxwjyamawcpONLgXou\n9IcpKLWlEllbxOLDxynLyaRMuYLam3F0WQLpiFBhlCnpcA1jgVcpgaKdMmM6MqmFB8M/ZVQWScnA\nwzyc9g4xy69S/uEcuObJeKfLV/d+bNB1NYiuYiXlqJmV3oq39jiyWVNcO5GASLXiHd3K6HEb0gET\nyY6lqCfGcNNPEB7XhDRpDPfPGm8/KMU0s3TpUgHYm73Z2wy0pUuXfuu4lAghBHbs2PmH5i/nEbNj\nx84/HPYgsGPHjj0I7NixMwNBkJeXR2RkJOHh4eTm5k5r7fb2dpYtW0ZMTAyxsbG89tprAPT395OZ\nmYlWq2X58uWYTKZp8bFarSQmJpKVlTWjHiaTiU2bNhEVFUV0dDSlpaUz5rJ7925iYmKIi4tj69at\nTE5OTpvLY489RkBAAHFxcbf6vqv27t27CQ8PJzIykvz8/Lvu8vzzzxMVFUV8fDwbNmxgcHDwzrlM\n5xMDi8UiNBqN0Ol0wmw2i/j4eFFXVzdt9bu6ukRFRYUQQojh4WGh1WpFXV2deP7550Vubq4QQog9\ne/aIF154YVp8Xn75ZbF161aRlZUlhBAz5rFt2zaxb98+IYQQU1NTwmQyzYiLTqcTarVaTExMCCGE\n2Lx5s3jnnXemzeX8+fOivLxcxMbG3uq7Xe3a2loRHx8vzGaz0Ol0QqPRCKvVeldd8vPzb9V44YUX\n7qjLtAZBcXGxWLFixa3t3bt3i927d0+nwjdYt26dKCgoEBEREaK7u1sI8VVYRERE3PXa7e3tIiMj\nQxQVFYm1a9cKIcSMeJhMJqFWq/+ifyZcjEaj0Gq1or+/X0xNTYm1a9eK/Pz8aXXR6XTfGHy3q71r\n1y6xZ8+eW/utWLFClJSU3FWXP+fo0aPi4YcfvmMu03pp0NHRQUhIyK3t75ry/G6j1+upqKggNTUV\ng8FAQEAAAAEBARgMhrte/+mnn+all176xgIwM+Gh0+nw9/fn0UcfJSkpie3btzM6OjojLr6+vjz7\n7LOEhoYSFBSEt7c3mZmZM+LyNber3dnZSXBw8K39pvtc3r9/P6tXr75jLtMaBLeb3ny6GRkZYePG\njezduxcPD49v/CaRSO6657Fjx1AqlSQmJiJu8xrHdHgAWCwWysvLefLJJykvL8fNzY09e/bMiEtL\nSwuvvvoqer2ezs5ORkZGeO+992bE5dv4v2pPl9ffspTA7ZjWIPjfU563t7d/I8mmg6mpKTZu3EhO\nTg7r168Hvkr67u5uALq6ulAqlXfVobi4mM8//xy1Ws2WLVsoKioiJydn2j3gq3+P4OBgUlJSANi0\naRPl5eWoVKppd7ly5QoLFy7Ez88PmUzGhg0bKCkpmRGXr7ndMfn/TN9/J/l6KYH333//Vt+dcJnW\nIEhOTqapqQm9Xo/ZbOZPf/oT2dnZ01ZfCMHjjz9OdHQ0P/3pT2/1Z2dnc+DAAQAOHDhwKyDuFrt2\n7aK9vR2dTsfhw4dJT0/n4MGD0+4BoFKpCAkJobHxq/fQCwsLiYmJISsra9pdIiMjuXTpEuPj4wgh\nKCwsJDo6ekZcvuZ2xyQ7O5vDhw9jNpvR6XTfOX3/neLrpQQ+++yzv1hK4G92+SvvY/zVnDhxQmi1\nWqHRaMSuXbumtfaFCxeERCIR8fHxIiEhQSQkJIiTJ08Ko9EoMjIyRHh4uMjMzBQDAwPT5nT27Nlb\nTw1myqOyslIkJyeLefPmiQceeECYTKYZc8nNzRXR0dEiNjZWbNu2TZjN5mlzeeihh0RgYKCQy+Ui\nODhY7N+//ztr79y5U2g0GhERESHy8vLuqsu+ffvE3LlzRWho6K1zd8eOHXfMxf6tgR07duxvFtqx\nY8ceBHbs2MEeBHbs2MEeBHbs2MEeBHbs2MEeBHbs2MEeBHbs2AH+B220ECIfMABSAAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce5663d0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Color Bar"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(noise)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tJT/74EmCX5IgXuPhwr5Kgkcd1F5ziMXCZBwJsVwI74bsWrgmTFLRRYJzIPK4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BKbhDS3g+iCB6XGSUWDmUVfHovWDNwTCkyM0ZAyxEb3NdJaHYxSTjQmY0POFbQZM9hnVlDN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\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2jvF5JqPZBPxy2lYa+PriD/hs5ot8P+NVvi4s5bhyPz+55d8Qa6PgkvBV3Y942Pwyf+mr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\ndxpnEnPixMl3GmcSc+LEyXea/wYnBeSBbvNIuAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce4d91d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Color Maps"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(noise, cmap=plt.cm.gray)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9nfHxcSqVCvA5ltXY2Mi+fftwuVx4PB7W19fZ3NzEarXy+OOP43K5UCgUfPDB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U5zbB1lKlCwr1ar5eBoAiXmcjm43W6eOdL3IhQKeTseDodx5coVDstZXV2FSqVCb28v8vk8fvaz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SX2V1JSEtMjAHDDnhhLZGUhTDJNyhYXF1FQUABBENDX18eAQ/LHkceTmvChoaGszB4fH0db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\nvtXl38T85S9/favr/wGNa0I+WKsOFwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce545b10>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(noise, cmap=plt.cm.Paired)\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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phEoRWfwi9wQVbapz5XjGnD4hFhoUvBrFVpdH4jnqyTniWw5erc3s6hB9UsBs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G0PfzjGPzwmpdlsGSYyaRYLHWscIUgJelmJRUKkndNYE2cgdWkHMQ/CmJuBz5m5w0uuQSdKc19I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Cq7ixtn6XCJY+sqh+Ihe+N7XMvovJDO0NlpMWuP0M8ukOx4hN8acqrWYUNs8428Ri9V4qmJ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\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KcxaVRQJYIrcyr1doav9BoWeGjXAS3ZnC762QiA3hDRk0AnnyhQGWy72cEBpZm4nfLqHb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\nEuvSpcvHmv8HTdW41mJHLxcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10ce4ed310>"
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/6 - IPython Widgets.ipynb
================================================
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 "metadata": {
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython notebook provides a variaty of web widgets that can interact with python code running the the background kernel.\n",
      "\n",
      "Finding Help:\n",
      "\n",
      "- http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>NumPy</h4> Base N-dimensional array package </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n",
      "</tr>\n",
      "<tr>\n",
      "    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td style=\"background:Lavender;\"><h4>IPython</h4> Enhanced Interactive Console </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SymPy</h4> Symbolic mathematics </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>Pandas</h4> Data structures & analysis </td>\n",
      "</tr>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Why do we use widgets?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> ##IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to explore their code and data interactively.\n",
      "> *http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb*"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact\n",
      "from IPython.display import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Build-it Widgets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tab1_children = [widgets.ButtonWidget(description=\"ButtonWidget\"),\n",
      "                 widgets.CheckboxWidget(description=\"CheckboxWidget\"),\n",
      "                 widgets.DropdownWidget(values=[1, 2], description=\"DropdownWidget\"),\n",
      "                 widgets.RadioButtonsWidget(values=[1, 2], description=\"RadioButtonsWidget\"),\n",
      "                 widgets.SelectWidget(values=[1, 2], description=\"SelectWidget\"),\n",
      "                 widgets.TextWidget(description=\"TextWidget\"),\n",
      "                 widgets.TextareaWidget(description=\"TextareaWidget\"),\n",
      "                 widgets.ToggleButtonWidget(description=\"ToggleButtonWidget\"),\n",
      "                 widgets.ToggleButtonsWidget(values=[\"Value 1\", \"Value2\"], description=\"ToggleButtonsWidget\"),\n",
      "                 ]\n",
      "\n",
      "tab2_children = [widgets.BoundedFloatTextWidget(description=\"BoundedFloatTextWidget\"),\n",
      "                 widgets.BoundedIntTextWidget(description=\"BoundedIntTextWidget\"),\n",
      "                 widgets.FloatSliderWidget(description=\"FloatSliderWidget\"),\n",
      "                 widgets.FloatTextWidget(description=\"FloatTextWidget\"),\n",
      "                 widgets.IntSliderWidget(description=\"IntSliderWidget\"),\n",
      "                 widgets.IntTextWidget(description=\"IntTextWidget\"),\n",
      "                 ]\n",
      "\n",
      "tab1 = widgets.ContainerWidget(children=tab1_children)\n",
      "tab2 = widgets.ContainerWidget(children=tab2_children)\n",
      "\n",
      "\n",
      "i = widgets.AccordionWidget(children=[tab1, tab2])\n",
      "\n",
      "i.set_title(0,\"Basic Widgets\")\n",
      "i.set_title(1,\"Numbers Input\")\n",
      "\n",
      "display(i)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Simple Example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will define a function that print the factorial.\n",
      "\n",
      "$f(x) = x!$\n",
      "\n",
      "$f(x) = x \\times (x-1) \\times ... 1$\n",
      "\n",
      "$f(3) = 3! = 3 \\times 2 \\times 1 = 6$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def factorial(x):\n",
      "    print \"%s!= %s\" % (x,np.math.factorial(x))\n",
      "\n",
      "def factorial2(x):\n",
      "    if type(x) == int:\n",
      "        if x >= 0:\n",
      "            print np.prod(np.arange(1,x+1))\n",
      "        else:\n",
      "            print \"ERROR: Number must be positive\"\n",
      "    else:\n",
      "        print \"ERROR: Only interger is allowed\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will test it using a code cell"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "factorial(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3!= 6\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Using interact function"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will link that to a slider to make the x a variable that we can control."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = interact(factorial, x=(0,100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100!= 93326215443944152681699238856266700490715968264381621468592963895217599993229915608941463976156518286253697920827223758251185210916864000000000000000000000000\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Controlling a Chart"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#This function plot x, y and adds a title\n",
      "def plt_arrays(x, y, title=\"\", color=\"red\", linestyle=\"dashed\", linewidth=2):\n",
      "    fig = plt.figure()\n",
      "    axes = fig.add_subplot(111)\n",
      "    axes.plot(x,y, color=color, linestyle=linestyle, linewidth=linewidth)\n",
      "    axes.set_title(title)\n",
      "    axes.grid()\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will define a function that return the following:\n",
      "\n",
      "$f(x) = ax^3 + bx^2 + cx + d$\n",
      "\n",
      "where a,b,c and d are are constants."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(a, b, c, d, **kwargs):\n",
      "    x=np.linspace(-10, 10, 20)\n",
      "    y = a*(x**3) + b*(x**2) + c*x + d\n",
      "    \n",
      "    title=\"$f(x) = (%s)x^{3} + (%s)x^{2} + (%s)x + (%s)$\" % (a,b,c,d)\n",
      "    \n",
      "    plt_arrays(x,y, title=title, **kwargs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Define Constants\n",
      "a=0.25\n",
      "b=2\n",
      "c=-4\n",
      "d=0\n",
      "\n",
      "f(a, b, c, d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gGPDvf4PbXf+zykrzvuanT9d+dekC77xTf/4jR2DQIHMel8tcZmys+Xr1VfO9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\nBUBExKFUAEREHOr/Awq5PBBPvRmmAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0eae068590>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i = interact(f,\n",
      "             a=(-10.,10),\n",
      "             b=(-10.,10),\n",
      "             c=(-10.,10),\n",
      "             d=(-10.,10),\n",
      "             color = [\"red\", \"blue\", \"green\"],\n",
      "             linestyle=[\"solid\", \"dashed\"],\n",
      "             linewidth=(1,5)\n",
      "             )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sLPTu3Rt3795VK1BdsO/2Poz9fSyGuQ1DWEAYPbCMEC17+OIh7jy5g9ySXOSV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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7b76843050>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Displaying a widget from interact function"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i.widget"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sLPTu3Rt3795VK1BdsO/2Poz9fSyGuQ1DWEAYPbCMEC17+OIh7jy5g9ySXOSV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\n05U3WlRSXqKwfZH/IjQxbSL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       "text": [
        "<matplotlib.figure.Figure at 0x7f7b76831390>"
       ]
      }
     ],
     "prompt_number": 17
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/7 - Pandas.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas is powerful and easy-to-use library for data analysis. Is has two main object to represents data: Series and DataFrame.\n",
      "\n",
      "Finding Help:\n",
      "\n",
      "- http://pandas.pydata.org/pandas-docs/stable/10min.html\n",
      "- http://pandas.pydata.org/pandas-docs/stable/tutorials.html"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>NumPy</h4> Base N-dimensional array package </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n",
      "</tr>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>IPython</h4> Enhanced Interactive Console </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SymPy</h4> Symbolic mathematics </td>\n",
      "    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td style=\"background:Lavender;\"><h4>Pandas</h4> Data structures & analysis </td>\n",
      "</tr>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Working with Series"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Series is an array like object."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "pd.Series(self, data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = pd.Series([1,2,3,4,5])\n",
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "0    1\n",
        "1    2\n",
        "2    3\n",
        "3    4\n",
        "4    5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice that generated an index for your item"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Basic Operation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x + 100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "0    101\n",
        "1    102\n",
        "2    103\n",
        "3    104\n",
        "4    105\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(x ** 2) + 100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "0    101\n",
        "1    104\n",
        "2    109\n",
        "3    116\n",
        "4    125\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x > 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0    False\n",
        "1    False\n",
        "2     True\n",
        "3     True\n",
        "4     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`any()` and `all()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "larger_than_2 = x > 2\n",
      "larger_than_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "0    False\n",
        "1    False\n",
        "2     True\n",
        "3     True\n",
        "4     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "larger_than_2.any()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "larger_than_2.all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`apply()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    if x % 2 == 0:\n",
      "        return x * 2\n",
      "    else:\n",
      "        return x * 3\n",
      "\n",
      "x.apply(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "0     3\n",
        "1     4\n",
        "2     9\n",
        "3     8\n",
        "4    15\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Avoid looping over your data**\n",
      "\n",
      "This is a `%%timeit` results from `apply()` and a for loop."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "\n",
      "ds = pd.Series(range(10000))\n",
      "\n",
      "for counter in range(len(ds)):\n",
      "    ds[counter] = f(ds[counter])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 241 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit\n",
      "\n",
      "ds = pd.Series(range(10000))\n",
      "\n",
      "ds = ds.apply(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 40 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`astype()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.astype(np.float64)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0    1\n",
        "1    2\n",
        "2    3\n",
        "3    4\n",
        "4    5\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`copy()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y[0] = 100"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "0    100\n",
        "1      2\n",
        "2      3\n",
        "3      4\n",
        "4      5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0    100\n",
        "1      2\n",
        "2      3\n",
        "3      4\n",
        "4      5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Avoid using copy (is you can) to save memory**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = x.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[0]=1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0    1\n",
        "1    2\n",
        "2    3\n",
        "3    4\n",
        "4    5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0    100\n",
        "1      2\n",
        "2      3\n",
        "3      4\n",
        "4      5\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.describe(percentile_width=50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "count    5.000000\n",
        "mean     3.000000\n",
        "std      1.581139\n",
        "min      1.000000\n",
        "25%      2.000000\n",
        "50%      3.000000\n",
        "75%      4.000000\n",
        "max      5.000000\n",
        "dtype: float64"
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      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "DataFrame"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "pd.DataFrame(self, data=None, index=None, columns=None, dtype=None, copy=False)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = [1,2,3,4,5,6,7,8,9]\n",
      "df = pd.DataFrame(data, columns=[\"x\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
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        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
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        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
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        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 7</td>\n",
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        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "   x\n",
        "0  1\n",
        "1  2\n",
        "2  3\n",
        "3  4\n",
        "4  5\n",
        "5  6\n",
        "6  7\n",
        "7  8\n",
        "8  9\n",
        "\n",
        "[9 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Selecting Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"x\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "0    1\n",
        "1    2\n",
        "2    3\n",
        "3    4\n",
        "4    5\n",
        "5    6\n",
        "6    7\n",
        "7    8\n",
        "8    9\n",
        "Name: x, dtype: int64"
       ]
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     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"x\"][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "1"
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      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Adding extra columns"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"x_plus_2\"] = df[\"x\"] + 2\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>x_plus_2</th>\n",
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        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
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        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>  4</td>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>  5</td>\n",
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        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 7</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "   x  x_plus_2\n",
        "0  1         3\n",
        "1  2         4\n",
        "2  3         5\n",
        "3  4         6\n",
        "4  5         7\n",
        "5  6         8\n",
        "6  7         9\n",
        "7  8        10\n",
        "8  9        11\n",
        "\n",
        "[9 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"x_square\"] = df[\"x\"] ** 2\n",
      "df[\"x_factorial\"] = df[\"x\"].apply(np.math.factorial)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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        "  <thead>\n",
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        "      <th>x</th>\n",
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        "  <tbody>\n",
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        "      <th>5</th>\n",
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        "      <th>6</th>\n",
        "      <td> 7</td>\n",
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        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 64</td>\n",
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        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 81</td>\n",
        "      <td> 362880</td>\n",
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        "</table>\n",
        "<p>9 rows \u00d7 4 columns</p>\n",
        "</div>"
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       "metadata": {},
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       "prompt_number": 28,
       "text": [
        "   x  x_plus_2  x_square  x_factorial\n",
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        "1  2         4         4            2\n",
        "2  3         5         9            6\n",
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        "\n",
        "[9 rows x 4 columns]"
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     "prompt_number": 28
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     "collapsed": false,
     "input": [
      "df[\"is_even\"] = df[\"x\"] % 2\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>x_plus_2</th>\n",
        "      <th>x_square</th>\n",
        "      <th>x_factorial</th>\n",
        "      <th>is_even</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td>  1</td>\n",
        "      <td>      1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  4</td>\n",
        "      <td>      2</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>  5</td>\n",
        "      <td>  9</td>\n",
        "      <td>      6</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>  6</td>\n",
        "      <td> 16</td>\n",
        "      <td>     24</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>  7</td>\n",
        "      <td> 25</td>\n",
        "      <td>    120</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "      <td>  8</td>\n",
        "      <td> 36</td>\n",
        "      <td>    720</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 7</td>\n",
        "      <td>  9</td>\n",
        "      <td> 49</td>\n",
        "      <td>   5040</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 64</td>\n",
        "      <td>  40320</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 81</td>\n",
        "      <td> 362880</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "   x  x_plus_2  x_square  x_factorial  is_even\n",
        "0  1         3         1            1        1\n",
        "1  2         4         4            2        0\n",
        "2  3         5         9            6        1\n",
        "3  4         6        16           24        0\n",
        "4  5         7        25          120        1\n",
        "5  6         8        36          720        0\n",
        "6  7         9        49         5040        1\n",
        "7  8        10        64        40320        0\n",
        "8  9        11        81       362880        1\n",
        "\n",
        "[9 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "`map()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"odd_even\"] = df[\"is_even\"].map({1:\"odd\", 0:\"even\"})\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>x_plus_2</th>\n",
        "      <th>x_square</th>\n",
        "      <th>x_factorial</th>\n",
        "      <th>is_even</th>\n",
        "      <th>odd_even</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td>  1</td>\n",
        "      <td>      1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  4</td>\n",
        "      <td>      2</td>\n",
        "      <td> 0</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>  5</td>\n",
        "      <td>  9</td>\n",
        "      <td>      6</td>\n",
        "      <td> 1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>  6</td>\n",
        "      <td> 16</td>\n",
        "      <td>     24</td>\n",
        "      <td> 0</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>  7</td>\n",
        "      <td> 25</td>\n",
        "      <td>    120</td>\n",
        "      <td> 1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "      <td>  8</td>\n",
        "      <td> 36</td>\n",
        "      <td>    720</td>\n",
        "      <td> 0</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 7</td>\n",
        "      <td>  9</td>\n",
        "      <td> 49</td>\n",
        "      <td>   5040</td>\n",
        "      <td> 1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 64</td>\n",
        "      <td>  40320</td>\n",
        "      <td> 0</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 81</td>\n",
        "      <td> 362880</td>\n",
        "      <td> 1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "   x  x_plus_2  x_square  x_factorial  is_even odd_even\n",
        "0  1         3         1            1        1      odd\n",
        "1  2         4         4            2        0     even\n",
        "2  3         5         9            6        1      odd\n",
        "3  4         6        16           24        0     even\n",
        "4  5         7        25          120        1      odd\n",
        "5  6         8        36          720        0     even\n",
        "6  7         9        49         5040        1      odd\n",
        "7  8        10        64        40320        0     even\n",
        "8  9        11        81       362880        1      odd\n",
        "\n",
        "[9 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "`drop()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.drop(\"is_even\", 1)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>x_plus_2</th>\n",
        "      <th>x_square</th>\n",
        "      <th>x_factorial</th>\n",
        "      <th>odd_even</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  3</td>\n",
        "      <td>  1</td>\n",
        "      <td>      1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>  4</td>\n",
        "      <td>  4</td>\n",
        "      <td>      2</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>  5</td>\n",
        "      <td>  9</td>\n",
        "      <td>      6</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td>  6</td>\n",
        "      <td> 16</td>\n",
        "      <td>     24</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>  7</td>\n",
        "      <td> 25</td>\n",
        "      <td>    120</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "      <td>  8</td>\n",
        "      <td> 36</td>\n",
        "      <td>    720</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 7</td>\n",
        "      <td>  9</td>\n",
        "      <td> 49</td>\n",
        "      <td>   5040</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "      <td> 10</td>\n",
        "      <td> 64</td>\n",
        "      <td>  40320</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "      <td> 11</td>\n",
        "      <td> 81</td>\n",
        "      <td> 362880</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "   x  x_plus_2  x_square  x_factorial odd_even\n",
        "0  1         3         1            1      odd\n",
        "1  2         4         4            2     even\n",
        "2  3         5         9            6      odd\n",
        "3  4         6        16           24     even\n",
        "4  5         7        25          120      odd\n",
        "5  6         8        36          720     even\n",
        "6  7         9        49         5040      odd\n",
        "7  8        10        64        40320     even\n",
        "8  9        11        81       362880      odd\n",
        "\n",
        "[9 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Multi Column Select"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[[\"x\", \"odd_even\"]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>x</th>\n",
        "      <th>odd_even</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 7</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td> 8</td>\n",
        "      <td> even</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> 9</td>\n",
        "      <td>  odd</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "   x odd_even\n",
        "0  1      odd\n",
        "1  2     even\n",
        "2  3      odd\n",
        "3  4     even\n",
        "4  5      odd\n",
        "5  6     even\n",
        "6  7      odd\n",
        "7  8     even\n",
        "8  9      odd\n",
        "\n",
        "[9 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Controlling display options"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.options.display.max_columns= 60\n",
      "pd.options.display.max_rows= 6\n",
      "pd.options.display.notebook_repr_html = False\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "   x  x_plus_2  x_square  x_factorial odd_even\n",
        "0  1         3         1            1      odd\n",
        "1  2         4         4            2     even\n",
        "2  3         5         9            6      odd\n",
        "3  4         6        16           24     even\n",
        "4  5         7        25          120      odd\n",
        "5  6         8        36          720     even\n",
        "  ..       ...       ...          ...      ...\n",
        "\n",
        "[9 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Filtering"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df[\"odd_even\"] == \"odd\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "   x  x_plus_2  x_square  x_factorial odd_even\n",
        "0  1         3         1            1      odd\n",
        "2  3         5         9            6      odd\n",
        "4  5         7        25          120      odd\n",
        "6  7         9        49         5040      odd\n",
        "8  9        11        81       362880      odd\n",
        "\n",
        "[5 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.odd_even == \"even\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "   x  x_plus_2  x_square  x_factorial odd_even\n",
        "1  2         4         4            2     even\n",
        "3  4         6        16           24     even\n",
        "5  6         8        36          720     even\n",
        "7  8        10        64        40320     even\n",
        "\n",
        "[4 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Chaining Filters"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "`|` OR"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[(df.odd_even == \"even\") | (df.x_square < 20)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "   x  x_plus_2  x_square  x_factorial odd_even\n",
        "0  1         3         1            1      odd\n",
        "1  2         4         4            2     even\n",
        "2  3         5         9            6      odd\n",
        "3  4         6        16           24     even\n",
        "5  6         8        36          720     even\n",
        "7  8        10        64        40320     even\n",
        "\n",
        "[6 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "`&` AND"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[(df.odd_even == \"even\") & (df.x_square < 20)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "   x  x_plus_2  x_square  x_factorial odd_even\n",
        "1  2         4         4            2     even\n",
        "3  4         6        16           24     even\n",
        "\n",
        "[2 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Furter Chaining"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[(df.odd_even == \"even\") & (df.x_square < 20)][\"x_plus_2\"][:1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "1    4\n",
        "Name: x_plus_2, dtype: int64"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`scatter_matrix()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.scatter_matrix(df, diagonal=\"kde\", figsize=(10,10));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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Szz2X6M23IDbhzSnS/Px8Jk+ezejRk4pHjqX8yMrK4rvvpvL999PJyckxHUf8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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ffb8c0bea10>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "              x  x_plus_2   x_square    x_factorial\n",
        "count  9.000000  9.000000   9.000000       9.000000\n",
        "mean   5.000000  7.000000  31.666667   45457.000000\n",
        "std    2.738613  2.738613  28.080242  119758.341137\n",
        "min    1.000000  3.000000   1.000000       1.000000\n",
        "25%    3.000000  5.000000   9.000000       6.000000\n",
        "50%    5.000000  7.000000  25.000000     120.000000\n",
        "            ...       ...        ...            ...\n",
        "\n",
        "[8 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Reading Data from CSV/TSV Files"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"http://www.google.com/finance/historical?q=TADAWUL:TASI&output=csv\"\n",
      "stocks_data = pd.read_csv(url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stocks_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "       \ufeffDate      Open      High       Low     Close     Volume\n",
        "0  11-Aug-14  10579.12  10603.30  10547.21  10596.55  197234714\n",
        "1  10-Aug-14  10552.48  10614.11  10551.77  10579.12  199773735\n",
        "2   7-Aug-14  10478.34  10585.38  10478.34  10552.48  202329194\n",
        "3   6-Aug-14  10450.52  10494.12  10398.25  10478.34  192868941\n",
        "4   5-Aug-14  10405.81  10501.38  10405.81  10450.52  287651475\n",
        "5   4-Aug-14  10302.88  10409.47  10290.95  10405.81  223099538\n",
        "         ...       ...       ...       ...       ...        ...\n",
        "\n",
        "[241 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stocks_data[\"change_amount\"] = stocks_data[\"Close\"] - stocks_data[\"Open\"]\n",
      "stocks_data[\"change_percentage\"] = stocks_data[\"change_amount\"] / stocks_data[\"Close\"]\n",
      "stocks_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "       \ufeffDate      Open      High       Low     Close     Volume  \\\n",
        "0  11-Aug-14  10579.12  10603.30  10547.21  10596.55  197234714   \n",
        "1  10-Aug-14  10552.48  10614.11  10551.77  10579.12  199773735   \n",
        "2   7-Aug-14  10478.34  10585.38  10478.34  10552.48  202329194   \n",
        "3   6-Aug-14  10450.52  10494.12  10398.25  10478.34  192868941   \n",
        "4   5-Aug-14  10405.81  10501.38  10405.81  10450.52  287651475   \n",
        "5   4-Aug-14  10302.88  10409.47  10290.95  10405.81  223099538   \n",
        "         ...       ...       ...       ...       ...        ...   \n",
        "\n",
        "   change_amount  change_percentage  \n",
        "0          17.43           0.001645  \n",
        "1          26.64           0.002518  \n",
        "2          74.14           0.007026  \n",
        "3          27.82           0.002655  \n",
        "4          44.71           0.004278  \n",
        "5         102.93           0.009892  \n",
        "             ...                ...  \n",
        "\n",
        "[241 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 43
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/8 - SymPy.ipynb
================================================
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  "name": "",
  "signature": "sha256:a8af92a8c641e7042d9850c27a420d6398556a582dddc3d5298f6d06777a7900"
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "SymPy is symbolic mathematics library written completely in Python and doesn't require any dependencies.\n",
      "\n",
      "Finding Help:\n",
      "\n",
      "- http://docs.sympy.org/latest/index.html\n",
      "- http://sympy.org/en/features.html\n",
      "- http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-5-Sympy.ipynb"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/numpylogo_med.png\"  style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>NumPy</h4> Base N-dimensional array package </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/scipy_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/matplotlib_med.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>\n",
      "</tr>\n",
      "<tr>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/ipython.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>IPython</h4> Enhanced Interactive Console </td>\n",
      "    <td style=\"background:Lavender;\"><img src=\"http://www.scipy.org/_static/images/sympy_logo.png\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td style=\"background:Lavender;\"><h4>SymPy</h4> Symbolic mathematics </td>\n",
      "    <td><img src=\"http://www.scipy.org/_static/images/pandas_badge2.jpg\" style=\"width:50px;height:50px;\" /></td>\n",
      "    <td><h4>Pandas</h4> Data structures & analysis </td>\n",
      "</tr>\n",
      "</table>\n",
      "\n",
      "> ###SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries.\n",
      "> **http://sympy.org/**"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import SymPy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sympy import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "3 + math.sqrt(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "4.732050807568877"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = 3 * sqrt(3)\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "3*sqrt(3)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "init_printing(use_latex='mathjax')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$3 \\sqrt{3}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "    ___\n",
        "3\u22c5\u2572\u2571 3 "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = sqrt(8)\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$2 \\sqrt{2}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "    ___\n",
        "2\u22c5\u2572\u2571 2 "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`symbols()` & `Symbol()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x, y = symbols(\"x y\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = x**2 + y**2\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$x^{2} + y^{2}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        " 2    2\n",
        "x  + y "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = (x+y)**3\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\left(x + y\\right)^{3}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "       3\n",
        "(x + y) "
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Assumptions for symbols"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = Symbol(\"a\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.is_imaginary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = Symbol(\"b\", integer=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b.is_imaginary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = Symbol(\"c\", positive=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.is_positive"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c.is_imaginary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Imaginary Numbers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$i$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "\u2148"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "I ** 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$-1$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "-1"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`Rational()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rational(1,3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{1}{3}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "1/3"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rational(1,3) + Rational(1,2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{5}{6}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "5/6"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Numerical evaluation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = Rational(1,3) + Rational(1,2)\n",
      "N(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$0.833333333333333$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "0.833333333333333"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N(pi, 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "3.1415926535897932384626433832795028841971693993751058209749445923078164062862\n",
        "08998628034825342117068"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pi.evalf(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "3.1415926535897932384626433832795028841971693993751058209749445923078164062862\n",
        "08998628034825342117068"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`subs()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = x**2 + 2*x + 1\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$x^{2} + 2 x + 1$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        " 2          \n",
        "x  + 2\u22c5x + 1"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr.subs(x, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$4$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "4"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = pi * x**2\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\pi x^{2}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "   2\n",
        "\u03c0\u22c5x "
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr.subs(x, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$9 \\pi$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "9\u22c5\u03c0"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N(_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$28.2743338823081$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "28.2743338823081"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`factor()` and `expand()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = (x + y) ** 2\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\left(x + y\\right)^{2}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "       2\n",
        "(x + y) "
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expand(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$x^{2} + 2 x y + y^{2}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        " 2            2\n",
        "x  + 2\u22c5x\u22c5y + y "
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "factor(_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\left(x + y\\right)^{2}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "       2\n",
        "(x + y) "
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`simplify()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = (2*x + Rational(1,3)*x + 4) / x\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{1}{x} \\left(\\frac{7 x}{3} + 4\\right)$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "7\u22c5x    \n",
        "\u2500\u2500\u2500 + 4\n",
        " 3     \n",
        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "   x   "
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simplify(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{7}{3} + \\frac{4}{x}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "7   4\n",
        "\u2500 + \u2500\n",
        "3   x"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = \"(2*x + 1/3*x + 4)/x\"\n",
      "simplify(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{7}{3} + \\frac{4}{x}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "7   4\n",
        "\u2500 + \u2500\n",
        "3   x"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = sin(x)/cos(x)\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{\\sin{\\left (x \\right )}}{\\cos{\\left (x \\right )}}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "sin(x)\n",
        "\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "cos(x)"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "simplify(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\tan{\\left (x \\right )}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "tan(x)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`apart()` and `together()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = 1/(x**2 + 2*x)\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{1}{x^{2} + 2 x}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "   1    \n",
        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        " 2      \n",
        "x  + 2\u22c5x"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "apart(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$- \\frac{1}{2 x + 4} + \\frac{1}{2 x}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "      1        1 \n",
        "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\u2500\u2500\n",
        "  2\u22c5(x + 2)   2\u22c5x"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "together(_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{1}{x \\left(x + 2\\right)}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "    1    \n",
        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "x\u22c5(x + 2)"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Calculus"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff(sin(x), x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\cos{\\left (x \\right )}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "cos(x)"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diff(log(x**2 + 1) + 2*x, x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{2 x}{x^{2} + 1} + 2$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        " 2\u22c5x      \n",
        "\u2500\u2500\u2500\u2500\u2500\u2500 + 2\n",
        " 2        \n",
        "x  + 1    "
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "integrate(cos(x), x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\sin{\\left (x \\right )}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "sin(x)"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Integral(sin(x), (x,0,pi))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\int_{0}^{\\pi} \\sin{\\left (x \\right )}\\, dx$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "\u03c0          \n",
        "\u2320          \n",
        "\u23ae sin(x) dx\n",
        "\u2321          \n",
        "0          "
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N(_)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$2.0$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "2.00000000000000"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`Sum()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = Sum(1/(x**2 + 2*x), (x, 1, 10))\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\sum_{x=1}^{10} \\frac{1}{x^{2} + 2 x}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "  10          \n",
        " ____         \n",
        " \u2572            \n",
        "  \u2572      1    \n",
        "   \u2572  \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "   \u2571   2      \n",
        "  \u2571   x  + 2\u22c5x\n",
        " \u2571            \n",
        " \u203e\u203e\u203e\u203e         \n",
        "x = 1         "
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr.doit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{175}{264}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "175\n",
        "\u2500\u2500\u2500\n",
        "264"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`Product()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = Product(1/(x**2 + 2*x), (x, 1, 10))\n",
      "expr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\prod_{x=1}^{10} \\frac{1}{x^{2} + 2 x}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "    10             \n",
        "\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c         \n",
        "\u2502        \u2502    1    \n",
        "\u2502        \u2502 \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
        "\u2502        \u2502  2      \n",
        "\u2502        \u2502 x  + 2\u22c5x\n",
        "\u2502        \u2502         \n",
        "  x = 1            "
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr.doit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{1}{869100503040000}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "1/869100503040000"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "`Solve()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = 2*x + 1\n",
      "solve(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\begin{bmatrix}- \\frac{1}{2}\\end{bmatrix}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "[-1/2]"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = x**2 - 1\n",
      "solve(expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\begin{bmatrix}-1, & 1\\end{bmatrix}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "[-1, 1]"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr_1 = 2*x + y + 3\n",
      "expr_2 = 2*y - x\n",
      "solve([expr_1, expr_2],(x,y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\begin{Bmatrix}x : - \\frac{6}{5}, & y : - \\frac{3}{5}\\end{Bmatrix}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "{x: -6/5, y: -3/5}"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Units"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sympy.physics import units as u"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "5. * u.milligram"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$5.0 \\cdot 10^{-6} kg$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "5.0e-6\u22c5kg"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1./2 * u.inch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$0.0127 m$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "0.0127\u22c5m"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1. * u.nano"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$1.0 \\cdot 10^{-9}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": [
        "1.00000000000000e-9"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u.watt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{kg m^{2}}{s^{3}}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "    2\n",
        "kg\u22c5m \n",
        "\u2500\u2500\u2500\u2500\u2500\n",
        "   3 \n",
        "  s  "
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "u.ohm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$\\frac{kg m^{2}}{A^{2} s^{3}}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "    2\n",
        "kg\u22c5m \n",
        "\u2500\u2500\u2500\u2500\u2500\n",
        " 2  3\n",
        "A \u22c5s "
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Converting from Kilometers/hours to Miles/hours"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmph = u.km / u.hour\n",
      "mph = u.mile / u.hour\n",
      "\n",
      "N(mph / kmph)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$1.609344$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "1.60934400000000"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "80 * N(mph / kmph)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$128.74752$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "128.747520000000"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Working with NumPy / Pandas and Matplotlib"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def sympy_expr(x_val):\n",
      "    expr = x**2 + sqrt(3)*x - Rational(1,3)\n",
      "    return expr.subs(x, x_val)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sympy_expr(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "latex": [
        "$$3 \\sqrt{3} + \\frac{26}{3}$$"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "    ___   26\n",
        "3\u22c5\u2572\u2571 3  + \u2500\u2500\n",
        "          3 "
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list1 = np.arange(1,1000)\n",
      "list2 = pd.Series(list1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit [sympy_expr(item) for item in list1]\n",
      "%timeit [sympy_expr(item) for item in list2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 103 ms per loop\n",
        "10 loops, best of 3: 105 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit np.vectorize(sympy_expr)(list1)\n",
      "%timeit list2.apply(sympy_expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 776 ms per loop\n",
        "1 loops, best of 3: 1.09 s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "expr = x**2 + sqrt(3)*x - Rational(1,3)\n",
      "\n",
      "lf = lambdify(x, expr)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit lf(list1)\n",
      "%timeit lf(list2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1000 loops, best of 3: 242 \u00b5s per loop\n",
        "100 loops, best of 3: 4.8 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "x_vals = np.linspace(-5.,5.)\n",
      "y_vals = lf(x_vals)\n",
      "\n",
      "axes.grid()\n",
      "axes.plot(x_vals, y_vals)\n",
      "\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fc397382a90>"
       ]
      }
     ],
     "prompt_number": 67
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/BLEGH.md
================================================

# Tutorial Brief

IPython notebook provides a variaty of web widgets that can interact with python code running the the background kernel.

Finding Help:

- http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb

<table>
<tr>
    <td><img src="http://www.scipy.org/_static/images/numpylogo_med.png"  style="width:50px;height:50px;" /></td>
    <td><h4>NumPy</h4> Base N-dimensional array package </td>
    <td><img src="http://www.scipy.org/_static/images/scipy_med.png" style="width:50px;height:50px;" /></td>
    <td><h4>SciPy</h4> Fundamental library for scientific computing </td>
    <td><img src="http://www.scipy.org/_static/images/matplotlib_med.png" style="width:50px;height:50px;" /></td>
    <td><h4>Matplotlib</h4> Comprehensive 2D Plotting </td>
</tr>
<tr>
    <td style="background:Lavender;"><img src="http://www.scipy.org/_static/images/ipython.png" style="width:50px;height:50px;" /></td>
    <td style="background:Lavender;"><h4>IPython</h4> Enhanced Interactive Console </td>
    <td><img src="http://www.scipy.org/_static/images/sympy_logo.png" style="width:50px;height:50px;" /></td>
    <td><h4>SymPy</h4> Symbolic mathematics </td>
    <td><img src="http://www.scipy.org/_static/images/pandas_badge2.jpg" style="width:50px;height:50px;" /></td>
    <td><h4>Pandas</h4> Data structures & analysis </td>
</tr>
</table>

# Why do we use widgets?

> ##IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS running in the browser. These widgets enable users to explore their code and data interactively.
> *http://nbviewer.ipython.org/github/ipython/ipython/blob/2.x/examples/Interactive%20Widgets/Index.ipynb*

# Import libraries


```python
import numpy as np
import matplotlib.pyplot as plt
```


```python
from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from IPython.display import display
```

## Build-it Widgets


```python
# had to rename the modules from the widget library e.g. widgets.ButtonWidget is now widgegts.Button
# also had to conda install ipython-notebook to get the widgets package (for those of you who still get errors and aren't sure you've installed it.)
```


```python
    tab1_children = [widgets.Button(description="ButtonWidget"),
                 widgets.Checkbox(description="CheckboxWidget"),
                 widgets.Dropdown (values=[1, 2], description="DropdownWidget"),
                 widgets.RadioButtons(values=[1, 2], description="RadioButtonsWidget"),
                 widgets.Select(values=[1, 2], description="SelectWidget"),
                 widgets.Text(description="TextWidget"),
                 widgets.Textarea(description="TextareaWidget"),
                 widgets.ToggleButton(description="ToggleButtonWidget"),
                 widgets.ToggleButtons (values=["Value 1", "Value2"], description="ToggleButtonsWidget"),
                 ]

    tab2_children = [widgets.BoundedFloatText(description="BoundedFloatTextWidget"),
                 widgets.BoundedIntText(description="BoundedIntTextWidget"),
                 widgets.FloatSlider(description="FloatSliderWidget"),
                 widgets.FloatText(description="FloatTextWidget"),
                 widgets.IntSlider(description="IntSliderWidget"),
                 widgets.IntText(description="IntTextWidget"),
                 ]

    tab1 = widgets.Box(children=tab1_children)
    tab2 = widgets.Box(children=tab2_children)


    i = widgets.Accordion (children=[tab1, tab2])

    i.set_title(0,"Basic Widgets")
    i.set_title(1,"Numbers Input")

display(i)
```

# Simple Example

We will define a function that print the factorial.

$f(x) = x!$

$f(x) = x \times (x-1) \times ... 1$

$f(3) = 3! = 3 \times 2 \times 1 = 6$


```python
#new version of Python changes Print from statement to function. () needed now.
def factorial(x):
    print ("%s! = %s" % (x,np.math.factorial(x)));

def factorial2(x):
    if type(x) == int:
        if x >= 0:
            print (np.prod(np.arange(1,x+1)))
        else:
            print ("ERROR: Number must be positive")
    else:
        print ("ERROR: Only interger is allowed")
```

Now we will test it using a code cell


```python
factorial(3)
```

    3! = 6



```python
factorial2(3)
```

    6


# Using interact function

We will link that to a slider to make the x a variable that we can control.


```python
i = interact(factorial, x=(0,100))
```

    50! = 30414093201713378043612608166064768844377641568960512000000000000


## Controlling a Chart


```python
#This function plot x, y and adds a title
def plt_arrays(x, y, title="", color="red", linestyle="dashed", linewidth=2):
    fig = plt.figure()
    axes = fig.add_subplot(111)
    axes.plot(x,y, color=color, linestyle=linestyle, linewidth=linewidth)
    axes.set_title(title)
    axes.grid()
    plt.show()
```

We will define a function that return the following:

$f(x) = ax^3 + bx^2 + cx + d$

where a,b,c and d are are constants.


```python
def f(a, b, c, d, **kwargs):
    x=np.linspace(-10, 10, 20)
    y = a*(x**3) + b*(x**2) + c*x + d 
    
    title="$f(x) = (%s)x^{3} + (%s)x^{2} + (%s)x + (%s)$" % (a,b,c,d)
    
    plt_arrays(x,y, title=title, **kwargs)
```


```python
#Define Constants
a=0.25
b=2
c=-4
d=0


f(a, b, c, d,)
```


![png](output_24_0.png)



```python
i = interact(f,
             a=(-10.,10),
             b=(-10.,10),
             c=(-10.,10),
             d=(-10.,10),
             color = ["red", "blue", "green"],
             linestyle=["solid", "dashed"],
             linewidth=(1,5)
             )
```


![png](output_25_0.png)


## Displaying a widget from interact function


```python
i.widget
```


![png](output_27_0.png)



================================================
FILE: notebooks/IPython-Tutorial/Indian General Elections 2014.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:aedd7fdf9fbe39328e0b0c1093fb2872e62db2ec9bb7a30d8a84c29e3152db3d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is an a review of how to use IPython Notebooks."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data Sources"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- http://en.wikipedia.org/wiki/List_of_constituencies_of_the_Lok_Sabha\n",
      "- http://eci.nic.in/eci_main1/GE2014/ge.html\n",
      "- http://eci.nic.in/eci/eci.html\n",
      "- http://eci.nic.in/eci_main1/current/ImpIns04042014.pdf\n",
      "- http://eci.nic.in/eci_main1/GE2014/PC_WISE_TURNOUT.htm"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Loading the data using Pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "#This line is to make float division the default. For int division use //\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = \"data/2014_indian_election_turnout.csv\"\n",
      "csv_data = pd.read_csv(path)\n",
      "csv_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>PC Name</th>\n",
        "      <th>Male Electors</th>\n",
        "      <th>Male Voters</th>\n",
        "      <th>Female Electors</th>\n",
        "      <th>Female Voters</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td> 1 - Andaman &amp; Nicobar Islands</td>\n",
        "      <td>  142782</td>\n",
        "      <td> 101178</td>\n",
        "      <td>  126578</td>\n",
        "      <td>  89150</td>\n",
        "      <td>  269360</td>\n",
        "      <td>  190328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 1 - Adilabad </td>\n",
        "      <td>  687389</td>\n",
        "      <td> 519364</td>\n",
        "      <td>  698844</td>\n",
        "      <td> 526475</td>\n",
        "      <td> 1386233</td>\n",
        "      <td> 1045839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               2 - Peddapalle </td>\n",
        "      <td>  725767</td>\n",
        "      <td> 520598</td>\n",
        "      <td>  699594</td>\n",
        "      <td> 501586</td>\n",
        "      <td> 1425361</td>\n",
        "      <td> 1022184</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               3 - Karimnagar </td>\n",
        "      <td>  777458</td>\n",
        "      <td> 552489</td>\n",
        "      <td>  773376</td>\n",
        "      <td> 573202</td>\n",
        "      <td> 1550834</td>\n",
        "      <td> 1125691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 4 - Nizamabad</td>\n",
        "      <td>  724504</td>\n",
        "      <td> 469712</td>\n",
        "      <td>  771689</td>\n",
        "      <td> 564212</td>\n",
        "      <td> 1496193</td>\n",
        "      <td> 1033924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 5 - Zahirabad</td>\n",
        "      <td>  717811</td>\n",
        "      <td> 546746</td>\n",
        "      <td>  727435</td>\n",
        "      <td> 548060</td>\n",
        "      <td> 1445246</td>\n",
        "      <td> 1094806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                     6 - Medak</td>\n",
        "      <td>  775903</td>\n",
        "      <td> 606863</td>\n",
        "      <td>  760812</td>\n",
        "      <td> 584233</td>\n",
        "      <td> 1536715</td>\n",
        "      <td> 1191096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                7 - Malkajgiri</td>\n",
        "      <td> 1723413</td>\n",
        "      <td> 878093</td>\n",
        "      <td> 1459912</td>\n",
        "      <td> 742304</td>\n",
        "      <td> 3183325</td>\n",
        "      <td> 1620397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               8 - Secundrabad</td>\n",
        "      <td> 1012378</td>\n",
        "      <td> 545749</td>\n",
        "      <td>  881269</td>\n",
        "      <td> 458020</td>\n",
        "      <td> 1893647</td>\n",
        "      <td> 1003769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 9 - Hyderabad</td>\n",
        "      <td>  961290</td>\n",
        "      <td> 526510</td>\n",
        "      <td>  862374</td>\n",
        "      <td> 444911</td>\n",
        "      <td> 1823664</td>\n",
        "      <td>  971421</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                10 - CHELVELLA</td>\n",
        "      <td> 1153049</td>\n",
        "      <td> 696749</td>\n",
        "      <td> 1032130</td>\n",
        "      <td> 619113</td>\n",
        "      <td> 2185179</td>\n",
        "      <td> 1315862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              11 - Mahbubnagar</td>\n",
        "      <td>  709711</td>\n",
        "      <td> 511764</td>\n",
        "      <td>  708961</td>\n",
        "      <td> 503036</td>\n",
        "      <td> 1418672</td>\n",
        "      <td> 1014800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             12 - Nagarkurnool</td>\n",
        "      <td>  745038</td>\n",
        "      <td> 570342</td>\n",
        "      <td>  732300</td>\n",
        "      <td> 538626</td>\n",
        "      <td> 1477338</td>\n",
        "      <td> 1108968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 13 - Nalgonda</td>\n",
        "      <td>  747281</td>\n",
        "      <td> 602193</td>\n",
        "      <td>  748299</td>\n",
        "      <td> 587206</td>\n",
        "      <td> 1495580</td>\n",
        "      <td> 1189399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 14 - Bhongir </td>\n",
        "      <td>  756963</td>\n",
        "      <td> 622333</td>\n",
        "      <td>  735288</td>\n",
        "      <td> 589610</td>\n",
        "      <td> 1492251</td>\n",
        "      <td> 1211943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 15 - Warangal</td>\n",
        "      <td>  771756</td>\n",
        "      <td> 592484</td>\n",
        "      <td>  766025</td>\n",
        "      <td> 582147</td>\n",
        "      <td> 1537781</td>\n",
        "      <td> 1174631</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>            16 - Mahabubabad  </td>\n",
        "      <td>  688398</td>\n",
        "      <td> 562073</td>\n",
        "      <td>  698945</td>\n",
        "      <td> 562297</td>\n",
        "      <td> 1387343</td>\n",
        "      <td> 1124370</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 17 - Khammam </td>\n",
        "      <td>  712329</td>\n",
        "      <td> 588728</td>\n",
        "      <td>  727960</td>\n",
        "      <td> 594169</td>\n",
        "      <td> 1440289</td>\n",
        "      <td> 1182897</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   18 - Aruku </td>\n",
        "      <td>  622416</td>\n",
        "      <td> 451350</td>\n",
        "      <td>  650308</td>\n",
        "      <td> 458264</td>\n",
        "      <td> 1272724</td>\n",
        "      <td>  909614</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               19 - Srikakulam</td>\n",
        "      <td>  706828</td>\n",
        "      <td> 505010</td>\n",
        "      <td>  707161</td>\n",
        "      <td> 546436</td>\n",
        "      <td> 1413989</td>\n",
        "      <td> 1051446</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             20 - Vizianagaram</td>\n",
        "      <td>  700837</td>\n",
        "      <td> 555005</td>\n",
        "      <td>  703290</td>\n",
        "      <td> 565311</td>\n",
        "      <td> 1404127</td>\n",
        "      <td> 1120316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>            21 - Visakhapatnam</td>\n",
        "      <td>  875187</td>\n",
        "      <td> 585270</td>\n",
        "      <td>  847850</td>\n",
        "      <td> 578288</td>\n",
        "      <td> 1723037</td>\n",
        "      <td> 1163558</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               22 - Anakapalli</td>\n",
        "      <td>  689132</td>\n",
        "      <td> 563818</td>\n",
        "      <td>  712342</td>\n",
        "      <td> 584254</td>\n",
        "      <td> 1401474</td>\n",
        "      <td> 1148072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 23 - Kakinada</td>\n",
        "      <td>  709101</td>\n",
        "      <td> 558689</td>\n",
        "      <td>  709189</td>\n",
        "      <td> 541310</td>\n",
        "      <td> 1418290</td>\n",
        "      <td> 1099999</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              24 - Amalapuram </td>\n",
        "      <td>  682607</td>\n",
        "      <td> 568534</td>\n",
        "      <td>  675259</td>\n",
        "      <td> 552393</td>\n",
        "      <td> 1357866</td>\n",
        "      <td> 1120927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              25 - Rajahmundry</td>\n",
        "      <td>  701707</td>\n",
        "      <td> 577999</td>\n",
        "      <td>  719581</td>\n",
        "      <td> 576382</td>\n",
        "      <td> 1421288</td>\n",
        "      <td> 1154381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               26 - Narsapuram</td>\n",
        "      <td>  652668</td>\n",
        "      <td> 539357</td>\n",
        "      <td>  672475</td>\n",
        "      <td> 549594</td>\n",
        "      <td> 1325143</td>\n",
        "      <td> 1088951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   27 - Eluru </td>\n",
        "      <td>  706916</td>\n",
        "      <td> 597603</td>\n",
        "      <td>  720844</td>\n",
        "      <td> 604093</td>\n",
        "      <td> 1427760</td>\n",
        "      <td> 1201696</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>           28 - Machilipatnam </td>\n",
        "      <td>  675774</td>\n",
        "      <td> 567908</td>\n",
        "      <td>  693537</td>\n",
        "      <td> 573157</td>\n",
        "      <td> 1369311</td>\n",
        "      <td> 1141065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               29 - Vijayawada</td>\n",
        "      <td>  781156</td>\n",
        "      <td> 602198</td>\n",
        "      <td>  783357</td>\n",
        "      <td> 592877</td>\n",
        "      <td> 1564513</td>\n",
        "      <td> 1195075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   30 - Guntur</td>\n",
        "      <td>  773027</td>\n",
        "      <td> 617483</td>\n",
        "      <td>  798989</td>\n",
        "      <td> 627443</td>\n",
        "      <td> 1572016</td>\n",
        "      <td> 1244926</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             31 - Narasaraopet</td>\n",
        "      <td>  748465</td>\n",
        "      <td> 638080</td>\n",
        "      <td>  766396</td>\n",
        "      <td> 643978</td>\n",
        "      <td> 1514861</td>\n",
        "      <td> 1282058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 32 - Bapatla </td>\n",
        "      <td>  686482</td>\n",
        "      <td> 587223</td>\n",
        "      <td>  706483</td>\n",
        "      <td> 597411</td>\n",
        "      <td> 1392965</td>\n",
        "      <td> 1184634</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  33 - Ongole </td>\n",
        "      <td>  736245</td>\n",
        "      <td> 603025</td>\n",
        "      <td>  734238</td>\n",
        "      <td> 605200</td>\n",
        "      <td> 1470483</td>\n",
        "      <td> 1208225</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  34 - Nandyal</td>\n",
        "      <td>  783266</td>\n",
        "      <td> 603562</td>\n",
        "      <td>  793862</td>\n",
        "      <td> 601394</td>\n",
        "      <td> 1577128</td>\n",
        "      <td> 1204956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  35 - Kurnool</td>\n",
        "      <td>  738791</td>\n",
        "      <td> 544802</td>\n",
        "      <td>  743016</td>\n",
        "      <td> 520930</td>\n",
        "      <td> 1481807</td>\n",
        "      <td> 1065732</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                36 - Anantapur</td>\n",
        "      <td>  775509</td>\n",
        "      <td> 612890</td>\n",
        "      <td>  761403</td>\n",
        "      <td> 592164</td>\n",
        "      <td> 1536912</td>\n",
        "      <td> 1205054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 37 - Hindupur</td>\n",
        "      <td>  734638</td>\n",
        "      <td> 601932</td>\n",
        "      <td>  711865</td>\n",
        "      <td> 575325</td>\n",
        "      <td> 1446503</td>\n",
        "      <td> 1177257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   38 - Kadapa</td>\n",
        "      <td>  765036</td>\n",
        "      <td> 588805</td>\n",
        "      <td>  785543</td>\n",
        "      <td> 611857</td>\n",
        "      <td> 1550579</td>\n",
        "      <td> 1200662</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  39 - Nellore</td>\n",
        "      <td>  796583</td>\n",
        "      <td> 593468</td>\n",
        "      <td>  809557</td>\n",
        "      <td> 594180</td>\n",
        "      <td> 1606140</td>\n",
        "      <td> 1187648</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                40 - Tirupati </td>\n",
        "      <td>  778778</td>\n",
        "      <td> 604834</td>\n",
        "      <td>  795766</td>\n",
        "      <td> 608230</td>\n",
        "      <td> 1574544</td>\n",
        "      <td> 1213064</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 41 - Rajampet</td>\n",
        "      <td>  735347</td>\n",
        "      <td> 573019</td>\n",
        "      <td>  752151</td>\n",
        "      <td> 585298</td>\n",
        "      <td> 1487498</td>\n",
        "      <td> 1158317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                42 - Chittoor </td>\n",
        "      <td>  723996</td>\n",
        "      <td> 598259</td>\n",
        "      <td>  728145</td>\n",
        "      <td> 600656</td>\n",
        "      <td> 1452141</td>\n",
        "      <td> 1198915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>            1 - ARUNACHAL WEST</td>\n",
        "      <td>  219334</td>\n",
        "      <td> 159978</td>\n",
        "      <td>  227181</td>\n",
        "      <td> 175687</td>\n",
        "      <td>  446515</td>\n",
        "      <td>  335665</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>            2 - ARUNACHAL EAST</td>\n",
        "      <td>  160293</td>\n",
        "      <td> 129313</td>\n",
        "      <td>  152579</td>\n",
        "      <td> 131978</td>\n",
        "      <td>  312872</td>\n",
        "      <td>  261291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                1 - Karimganj </td>\n",
        "      <td>  615198</td>\n",
        "      <td> 482484</td>\n",
        "      <td>  550799</td>\n",
        "      <td> 404436</td>\n",
        "      <td> 1165997</td>\n",
        "      <td>  886920</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   2 - Silchar</td>\n",
        "      <td>  554540</td>\n",
        "      <td> 428949</td>\n",
        "      <td>  505635</td>\n",
        "      <td> 370881</td>\n",
        "      <td> 1060175</td>\n",
        "      <td>  799830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>       3 - Autonomous District</td>\n",
        "      <td>  358880</td>\n",
        "      <td> 279409</td>\n",
        "      <td>  343010</td>\n",
        "      <td> 263871</td>\n",
        "      <td>  701890</td>\n",
        "      <td>  543280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    4 - Dhubri</td>\n",
        "      <td>  798124</td>\n",
        "      <td> 709191</td>\n",
        "      <td>  754430</td>\n",
        "      <td> 660433</td>\n",
        "      <td> 1552554</td>\n",
        "      <td> 1369624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 5 - Kokrajhar</td>\n",
        "      <td>  776071</td>\n",
        "      <td> 636657</td>\n",
        "      <td>  729405</td>\n",
        "      <td> 587212</td>\n",
        "      <td> 1505476</td>\n",
        "      <td> 1223869</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   6 - Barpeta</td>\n",
        "      <td>  755559</td>\n",
        "      <td> 634405</td>\n",
        "      <td>  674616</td>\n",
        "      <td> 571458</td>\n",
        "      <td> 1430175</td>\n",
        "      <td> 1205863</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   7 - Gauhati</td>\n",
        "      <td>  988067</td>\n",
        "      <td> 785494</td>\n",
        "      <td>  934203</td>\n",
        "      <td> 726235</td>\n",
        "      <td> 1922270</td>\n",
        "      <td> 1511729</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 8 - Mangaldoi</td>\n",
        "      <td>  791539</td>\n",
        "      <td> 651272</td>\n",
        "      <td>  724137</td>\n",
        "      <td> 581965</td>\n",
        "      <td> 1515676</td>\n",
        "      <td> 1233237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    9 - Tezpur</td>\n",
        "      <td>  654866</td>\n",
        "      <td> 505643</td>\n",
        "      <td>  604702</td>\n",
        "      <td> 475045</td>\n",
        "      <td> 1259568</td>\n",
        "      <td>  980688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                  10 - Nowgong</td>\n",
        "      <td>  792424</td>\n",
        "      <td> 639392</td>\n",
        "      <td>  731457</td>\n",
        "      <td> 590682</td>\n",
        "      <td> 1523881</td>\n",
        "      <td> 1230074</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 11 - Kaliabor</td>\n",
        "      <td>  752785</td>\n",
        "      <td> 605292</td>\n",
        "      <td>  705080</td>\n",
        "      <td> 561198</td>\n",
        "      <td> 1457865</td>\n",
        "      <td> 1166490</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   12 - Jorhat</td>\n",
        "      <td>  634236</td>\n",
        "      <td> 483829</td>\n",
        "      <td>  600212</td>\n",
        "      <td> 447507</td>\n",
        "      <td> 1234448</td>\n",
        "      <td>  931336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                13 - Dibrugarh</td>\n",
        "      <td>  579657</td>\n",
        "      <td> 462412</td>\n",
        "      <td>  544648</td>\n",
        "      <td> 428556</td>\n",
        "      <td> 1124305</td>\n",
        "      <td>  890968</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                14 - Lakhimpur</td>\n",
        "      <td>  735263</td>\n",
        "      <td> 572334</td>\n",
        "      <td>  695731</td>\n",
        "      <td> 539641</td>\n",
        "      <td> 1430994</td>\n",
        "      <td> 1111975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td>             1 - Valmiki Nagar</td>\n",
        "      <td>  786297</td>\n",
        "      <td> 484621</td>\n",
        "      <td>  670279</td>\n",
        "      <td> 415493</td>\n",
        "      <td> 1456576</td>\n",
        "      <td>  900114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>543 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "                        State                        PC Name  Male Electors  \\\n",
        "0   Andaman & Nicobar Islands  1 - Andaman & Nicobar Islands         142782   \n",
        "1              Andhra Pradesh                  1 - Adilabad          687389   \n",
        "2              Andhra Pradesh                2 - Peddapalle          725767   \n",
        "3              Andhra Pradesh                3 - Karimnagar          777458   \n",
        "4              Andhra Pradesh                  4 - Nizamabad         724504   \n",
        "5              Andhra Pradesh                  5 - Zahirabad         717811   \n",
        "6              Andhra Pradesh                      6 - Medak         775903   \n",
        "7              Andhra Pradesh                 7 - Malkajgiri        1723413   \n",
        "8              Andhra Pradesh                8 - Secundrabad        1012378   \n",
        "9              Andhra Pradesh                  9 - Hyderabad         961290   \n",
        "10             Andhra Pradesh                 10 - CHELVELLA        1153049   \n",
        "11             Andhra Pradesh               11 - Mahbubnagar         709711   \n",
        "12             Andhra Pradesh              12 - Nagarkurnool         745038   \n",
        "13             Andhra Pradesh                  13 - Nalgonda         747281   \n",
        "14             Andhra Pradesh                  14 - Bhongir          756963   \n",
        "15             Andhra Pradesh                  15 - Warangal         771756   \n",
        "16             Andhra Pradesh             16 - Mahabubabad           688398   \n",
        "17             Andhra Pradesh                  17 - Khammam          712329   \n",
        "18             Andhra Pradesh                    18 - Aruku          622416   \n",
        "19             Andhra Pradesh                19 - Srikakulam         706828   \n",
        "20             Andhra Pradesh              20 - Vizianagaram         700837   \n",
        "21             Andhra Pradesh             21 - Visakhapatnam         875187   \n",
        "22             Andhra Pradesh                22 - Anakapalli         689132   \n",
        "23             Andhra Pradesh                  23 - Kakinada         709101   \n",
        "24             Andhra Pradesh               24 - Amalapuram          682607   \n",
        "25             Andhra Pradesh               25 - Rajahmundry         701707   \n",
        "26             Andhra Pradesh                26 - Narsapuram         652668   \n",
        "27             Andhra Pradesh                    27 - Eluru          706916   \n",
        "28             Andhra Pradesh            28 - Machilipatnam          675774   \n",
        "29             Andhra Pradesh                29 - Vijayawada         781156   \n",
        "30             Andhra Pradesh                    30 - Guntur         773027   \n",
        "31             Andhra Pradesh              31 - Narasaraopet         748465   \n",
        "32             Andhra Pradesh                  32 - Bapatla          686482   \n",
        "33             Andhra Pradesh                   33 - Ongole          736245   \n",
        "34             Andhra Pradesh                   34 - Nandyal         783266   \n",
        "35             Andhra Pradesh                   35 - Kurnool         738791   \n",
        "36             Andhra Pradesh                 36 - Anantapur         775509   \n",
        "37             Andhra Pradesh                  37 - Hindupur         734638   \n",
        "38             Andhra Pradesh                    38 - Kadapa         765036   \n",
        "39             Andhra Pradesh                   39 - Nellore         796583   \n",
        "40             Andhra Pradesh                 40 - Tirupati          778778   \n",
        "41             Andhra Pradesh                  41 - Rajampet         735347   \n",
        "42             Andhra Pradesh                 42 - Chittoor          723996   \n",
        "43          Arunachal Pradesh             1 - ARUNACHAL WEST         219334   \n",
        "44          Arunachal Pradesh             2 - ARUNACHAL EAST         160293   \n",
        "45                      Assam                 1 - Karimganj          615198   \n",
        "46                      Assam                    2 - Silchar         554540   \n",
        "47                      Assam        3 - Autonomous District         358880   \n",
        "48                      Assam                     4 - Dhubri         798124   \n",
        "49                      Assam                  5 - Kokrajhar         776071   \n",
        "50                      Assam                    6 - Barpeta         755559   \n",
        "51                      Assam                    7 - Gauhati         988067   \n",
        "52                      Assam                  8 - Mangaldoi         791539   \n",
        "53                      Assam                     9 - Tezpur         654866   \n",
        "54                      Assam                   10 - Nowgong         792424   \n",
        "55                      Assam                  11 - Kaliabor         752785   \n",
        "56                      Assam                    12 - Jorhat         634236   \n",
        "57                      Assam                 13 - Dibrugarh         579657   \n",
        "58                      Assam                 14 - Lakhimpur         735263   \n",
        "59                      Bihar              1 - Valmiki Nagar         786297   \n",
        "                          ...                            ...            ...   \n",
        "\n",
        "    Male Voters  Female Electors  Female Voters  Total Electors  Total Voters  \n",
        "0        101178           126578          89150          269360        190328  \n",
        "1        519364           698844         526475         1386233       1045839  \n",
        "2        520598           699594         501586         1425361       1022184  \n",
        "3        552489           773376         573202         1550834       1125691  \n",
        "4        469712           771689         564212         1496193       1033924  \n",
        "5        546746           727435         548060         1445246       1094806  \n",
        "6        606863           760812         584233         1536715       1191096  \n",
        "7        878093          1459912         742304         3183325       1620397  \n",
        "8        545749           881269         458020         1893647       1003769  \n",
        "9        526510           862374         444911         1823664        971421  \n",
        "10       696749          1032130         619113         2185179       1315862  \n",
        "11       511764           708961         503036         1418672       1014800  \n",
        "12       570342           732300         538626         1477338       1108968  \n",
        "13       602193           748299         587206         1495580       1189399  \n",
        "14       622333           735288         589610         1492251       1211943  \n",
        "15       592484           766025         582147         1537781       1174631  \n",
        "16       562073           698945         562297         1387343       1124370  \n",
        "17       588728           727960         594169         1440289       1182897  \n",
        "18       451350           650308         458264         1272724        909614  \n",
        "19       505010           707161         546436         1413989       1051446  \n",
        "20       555005           703290         565311         1404127       1120316  \n",
        "21       585270           847850         578288         1723037       1163558  \n",
        "22       563818           712342         584254         1401474       1148072  \n",
        "23       558689           709189         541310         1418290       1099999  \n",
        "24       568534           675259         552393         1357866       1120927  \n",
        "25       577999           719581         576382         1421288       1154381  \n",
        "26       539357           672475         549594         1325143       1088951  \n",
        "27       597603           720844         604093         1427760       1201696  \n",
        "28       567908           693537         573157         1369311       1141065  \n",
        "29       602198           783357         592877         1564513       1195075  \n",
        "30       617483           798989         627443         1572016       1244926  \n",
        "31       638080           766396         643978         1514861       1282058  \n",
        "32       587223           706483         597411         1392965       1184634  \n",
        "33       603025           734238         605200         1470483       1208225  \n",
        "34       603562           793862         601394         1577128       1204956  \n",
        "35       544802           743016         520930         1481807       1065732  \n",
        "36       612890           761403         592164         1536912       1205054  \n",
        "37       601932           711865         575325         1446503       1177257  \n",
        "38       588805           785543         611857         1550579       1200662  \n",
        "39       593468           809557         594180         1606140       1187648  \n",
        "40       604834           795766         608230         1574544       1213064  \n",
        "41       573019           752151         585298         1487498       1158317  \n",
        "42       598259           728145         600656         1452141       1198915  \n",
        "43       159978           227181         175687          446515        335665  \n",
        "44       129313           152579         131978          312872        261291  \n",
        "45       482484           550799         404436         1165997        886920  \n",
        "46       428949           505635         370881         1060175        799830  \n",
        "47       279409           343010         263871          701890        543280  \n",
        "48       709191           754430         660433         1552554       1369624  \n",
        "49       636657           729405         587212         1505476       1223869  \n",
        "50       634405           674616         571458         1430175       1205863  \n",
        "51       785494           934203         726235         1922270       1511729  \n",
        "52       651272           724137         581965         1515676       1233237  \n",
        "53       505643           604702         475045         1259568        980688  \n",
        "54       639392           731457         590682         1523881       1230074  \n",
        "55       605292           705080         561198         1457865       1166490  \n",
        "56       483829           600212         447507         1234448        931336  \n",
        "57       462412           544648         428556         1124305        890968  \n",
        "58       572334           695731         539641         1430994       1111975  \n",
        "59       484621           670279         415493         1456576        900114  \n",
        "            ...              ...            ...             ...           ...  \n",
        "\n",
        "[543 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Understanding the data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **State**: State Name.\n",
      "- **PC Name**:  Parliamentary Constituencies Names.\n",
      "- **Male Electors**: The number of elegeble male voters.\n",
      "- **Male Voters**: The number of male voters turnout.\n",
      "- **Female Electors**: The number of elegeble female voters.\n",
      "- **Female Voters**: The number of female voters turnout.\n",
      "- **Total Electors**: The number of elegeble voters.\n",
      "- **Total Voters**: The number of voters turnout."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![](http://upload.wikimedia.org/wikipedia/commons/3/3a/Lok_Sabha_constituencies_of_India.png)\n",
      "![](http://upload.wikimedia.org/wikipedia/commons/c/c1/State_and_union_territories_map.png)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Calculating Turnout"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data[\"Male Turnout\"] = csv_data[\"Male Voters\"] / csv_data[\"Male Electors\"]\n",
      "csv_data[\"Female Turnout\"] = csv_data[\"Female Voters\"] / csv_data[\"Female Electors\"]\n",
      "csv_data[\"Total Turnout\"] = csv_data[\"Total Voters\"] / csv_data[\"Total Electors\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Statistical Analysis of Turnout"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Male Electors</th>\n",
        "      <th>Male Voters</th>\n",
        "      <th>Female Electors</th>\n",
        "      <th>Female Voters</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "      <th>Male Turnout</th>\n",
        "      <th>Female Turnout</th>\n",
        "      <th>Total Turnout</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>     543.000000</td>\n",
        "      <td>    543.000000</td>\n",
        "      <td>     543.000000</td>\n",
        "      <td>    543.000000</td>\n",
        "      <td>     543.000000</td>\n",
        "      <td>     543.000000</td>\n",
        "      <td> 543.000000</td>\n",
        "      <td> 543.000000</td>\n",
        "      <td> 543.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  804883.127072</td>\n",
        "      <td> 540030.900552</td>\n",
        "      <td>  731215.360958</td>\n",
        "      <td> 479861.918969</td>\n",
        "      <td> 1536098.488029</td>\n",
        "      <td> 1019892.819521</td>\n",
        "      <td>   0.680646</td>\n",
        "      <td>   0.663664</td>\n",
        "      <td>   0.672688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  165950.096614</td>\n",
        "      <td> 105316.219120</td>\n",
        "      <td>  127159.414206</td>\n",
        "      <td>  96137.678162</td>\n",
        "      <td>  288064.231735</td>\n",
        "      <td>  192716.272135</td>\n",
        "      <td>   0.100641</td>\n",
        "      <td>   0.111115</td>\n",
        "      <td>   0.102917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   25433.000000</td>\n",
        "      <td>  21585.000000</td>\n",
        "      <td>   24489.000000</td>\n",
        "      <td>  21654.000000</td>\n",
        "      <td>   49922.000000</td>\n",
        "      <td>   43239.000000</td>\n",
        "      <td>   0.283097</td>\n",
        "      <td>   0.232644</td>\n",
        "      <td>   0.259047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  728644.000000</td>\n",
        "      <td> 491359.000000</td>\n",
        "      <td>  691996.500000</td>\n",
        "      <td> 430761.500000</td>\n",
        "      <td> 1425052.000000</td>\n",
        "      <td>  940426.500000</td>\n",
        "      <td>   0.608490</td>\n",
        "      <td>   0.578545</td>\n",
        "      <td>   0.592252</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  814968.000000</td>\n",
        "      <td> 549484.000000</td>\n",
        "      <td>  740045.000000</td>\n",
        "      <td> 484001.000000</td>\n",
        "      <td> 1555779.000000</td>\n",
        "      <td> 1033783.000000</td>\n",
        "      <td>   0.680857</td>\n",
        "      <td>   0.656567</td>\n",
        "      <td>   0.667207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  899919.500000</td>\n",
        "      <td> 597803.000000</td>\n",
        "      <td>  791426.500000</td>\n",
        "      <td> 540898.000000</td>\n",
        "      <td> 1692364.500000</td>\n",
        "      <td> 1129424.500000</td>\n",
        "      <td>   0.763453</td>\n",
        "      <td>   0.756988</td>\n",
        "      <td>   0.755237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1723413.000000</td>\n",
        "      <td> 878093.000000</td>\n",
        "      <td> 1459912.000000</td>\n",
        "      <td> 742304.000000</td>\n",
        "      <td> 3183325.000000</td>\n",
        "      <td> 1620397.000000</td>\n",
        "      <td>   0.888572</td>\n",
        "      <td>   0.892712</td>\n",
        "      <td>   0.882175</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "        Male Electors    Male Voters  Female Electors  Female Voters  \\\n",
        "count      543.000000     543.000000       543.000000     543.000000   \n",
        "mean    804883.127072  540030.900552    731215.360958  479861.918969   \n",
        "std     165950.096614  105316.219120    127159.414206   96137.678162   \n",
        "min      25433.000000   21585.000000     24489.000000   21654.000000   \n",
        "25%     728644.000000  491359.000000    691996.500000  430761.500000   \n",
        "50%     814968.000000  549484.000000    740045.000000  484001.000000   \n",
        "75%     899919.500000  597803.000000    791426.500000  540898.000000   \n",
        "max    1723413.000000  878093.000000   1459912.000000  742304.000000   \n",
        "\n",
        "       Total Electors    Total Voters  Male Turnout  Female Turnout  \\\n",
        "count      543.000000      543.000000    543.000000      543.000000   \n",
        "mean   1536098.488029  1019892.819521      0.680646        0.663664   \n",
        "std     288064.231735   192716.272135      0.100641        0.111115   \n",
        "min      49922.000000    43239.000000      0.283097        0.232644   \n",
        "25%    1425052.000000   940426.500000      0.608490        0.578545   \n",
        "50%    1555779.000000  1033783.000000      0.680857        0.656567   \n",
        "75%    1692364.500000  1129424.500000      0.763453        0.756988   \n",
        "max    3183325.000000  1620397.000000      0.888572        0.892712   \n",
        "\n",
        "       Total Turnout  \n",
        "count     543.000000  \n",
        "mean        0.672688  \n",
        "std         0.102917  \n",
        "min         0.259047  \n",
        "25%         0.592252  \n",
        "50%         0.667207  \n",
        "75%         0.755237  \n",
        "max         0.882175  \n",
        "\n",
        "[8 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Quick Facts"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Total Electors"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print '{0:,}'.format(csv_data[\"Total Electors\"].sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "834,101,479\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Gender Ration (Men to Women)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print '%.2f%%' % (csv_data[\"Female Electors\"].sum() / csv_data[\"Male Electors\"].sum()*100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "90.85%\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Total Voters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print '{0:,}'.format(csv_data[\"Total Voters\"].sum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "553,801,801\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Total Turnout"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print '%.2f%%' % (csv_data[\"Total Voters\"].sum() / csv_data[\"Total Electors\"].sum() * 100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "66.40%\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Analyzing States"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "states = pd.DataFrame(csv_data[\"State\"].unique(), columns=[\"State\"])\n",
      "print \"The number of states: %s\" % len(states)\n",
      "states"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The number of states: 35\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                     Assam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                     Bihar</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>                Chandigarh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>               Chattisgarh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>      Dadra &amp; Nagar Haveli</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>               Daman &amp; Diu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>                       Goa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>                   Gujarat</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>                   Haryana</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>          Himachal Pradesh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>           Jammu &amp; Kashmir</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                 Jharkhand</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                 Karnataka</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>                    Kerala</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>               Lakshadweep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Madhya Pradesh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               Maharashtra</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>                   Manipur</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>                 Meghalaya</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>                   Mizoram</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>                  Nagaland</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>              NCT OF Delhi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>                    Orissa</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>                Puducherry</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>                    Punjab</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>                 Rajasthan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>                    Sikkim</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>                Tamil Nadu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>                   Tripura</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>             Uttar Pradesh</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>               Uttarakhand</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>               West Bengal</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>35 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                        State\n",
        "0   Andaman & Nicobar Islands\n",
        "1              Andhra Pradesh\n",
        "2           Arunachal Pradesh\n",
        "3                       Assam\n",
        "4                       Bihar\n",
        "5                  Chandigarh\n",
        "6                 Chattisgarh\n",
        "7        Dadra & Nagar Haveli\n",
        "8                 Daman & Diu\n",
        "9                         Goa\n",
        "10                    Gujarat\n",
        "11                    Haryana\n",
        "12           Himachal Pradesh\n",
        "13            Jammu & Kashmir\n",
        "14                  Jharkhand\n",
        "15                  Karnataka\n",
        "16                     Kerala\n",
        "17                Lakshadweep\n",
        "18             Madhya Pradesh\n",
        "19                Maharashtra\n",
        "20                    Manipur\n",
        "21                  Meghalaya\n",
        "22                    Mizoram\n",
        "23                   Nagaland\n",
        "24               NCT OF Delhi\n",
        "25                     Orissa\n",
        "26                 Puducherry\n",
        "27                     Punjab\n",
        "28                  Rajasthan\n",
        "29                     Sikkim\n",
        "30                 Tamil Nadu\n",
        "31                    Tripura\n",
        "32              Uttar Pradesh\n",
        "33                Uttarakhand\n",
        "34                West Bengal\n",
        "\n",
        "[35 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculating constituencies\n",
      "seats = csv_data[\"State\"].value_counts()\n",
      "states[\"Seats\"] = seats.sort_index().values\n",
      "states.sort(\"Seats\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>Seats</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>             Uttar Pradesh</td>\n",
        "      <td> 80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               Maharashtra</td>\n",
        "      <td> 48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>               West Bengal</td>\n",
        "      <td> 42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td> 42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td> 40</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>                Tamil Nadu</td>\n",
        "      <td> 39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Madhya Pradesh</td>\n",
        "      <td> 29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                 Karnataka</td>\n",
        "      <td> 28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>                   Gujarat</td>\n",
        "      <td> 26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>                 Rajasthan</td>\n",
        "      <td> 25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>                    Orissa</td>\n",
        "      <td> 21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>                    Kerala</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                 Jharkhand</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>                    Punjab</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>               Chattisgarh</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>                   Haryana</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>                  Nagaland</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>           Jammu &amp; Kashmir</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>               Uttarakhand</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>          Himachal Pradesh</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>                       Goa</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>                 Meghalaya</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>                   Manipur</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>                   Tripura</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>                Chandigarh</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>               Lakshadweep</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>      Dadra &amp; Nagar Haveli</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>               Daman &amp; Diu</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>                   Mizoram</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>              NCT OF Delhi</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>                Puducherry</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>                    Sikkim</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>35 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                        State  Seats\n",
        "32              Uttar Pradesh     80\n",
        "19                Maharashtra     48\n",
        "34                West Bengal     42\n",
        "1              Andhra Pradesh     42\n",
        "4                       Bihar     40\n",
        "30                 Tamil Nadu     39\n",
        "18             Madhya Pradesh     29\n",
        "15                  Karnataka     28\n",
        "10                    Gujarat     26\n",
        "28                  Rajasthan     25\n",
        "25                     Orissa     21\n",
        "16                     Kerala     20\n",
        "3                       Assam     14\n",
        "14                  Jharkhand     14\n",
        "27                     Punjab     13\n",
        "6                 Chattisgarh     11\n",
        "11                    Haryana     10\n",
        "23                   Nagaland      7\n",
        "13            Jammu & Kashmir      6\n",
        "33                Uttarakhand      5\n",
        "12           Himachal Pradesh      4\n",
        "9                         Goa      2\n",
        "21                  Meghalaya      2\n",
        "20                    Manipur      2\n",
        "2           Arunachal Pradesh      2\n",
        "31                    Tripura      2\n",
        "5                  Chandigarh      1\n",
        "17                Lakshadweep      1\n",
        "7        Dadra & Nagar Haveli      1\n",
        "8                 Daman & Diu      1\n",
        "22                    Mizoram      1\n",
        "24               NCT OF Delhi      1\n",
        "26                 Puducherry      1\n",
        "29                     Sikkim      1\n",
        "0   Andaman & Nicobar Islands      1\n",
        "\n",
        "[35 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculating Total States Electors and Voters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_electors = pd.pivot_table(csv_data, values=\"Total Electors\", rows=[\"State\"], aggfunc=\"sum\")\n",
      "total_votes = pd.pivot_table(csv_data, values=\"Total Voters\", rows=[\"State\"], aggfunc=\"sum\")\n",
      "states[\"Total Electors\"] = total_electors.sort_index().values\n",
      "states[\"Total Voters\"] = total_votes.sort_index().values\n",
      "states.sort(\"Total Electors\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>Seats</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>             Uttar Pradesh</td>\n",
        "      <td> 80</td>\n",
        "      <td> 138965820</td>\n",
        "      <td> 81092302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               Maharashtra</td>\n",
        "      <td> 48</td>\n",
        "      <td>  80717283</td>\n",
        "      <td> 48718844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td> 42</td>\n",
        "      <td>  64938750</td>\n",
        "      <td> 48358545</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td> 40</td>\n",
        "      <td>  63761796</td>\n",
        "      <td> 35885366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>               West Bengal</td>\n",
        "      <td> 42</td>\n",
        "      <td>  62833128</td>\n",
        "      <td> 51622555</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>                Tamil Nadu</td>\n",
        "      <td> 39</td>\n",
        "      <td>  55114505</td>\n",
        "      <td> 40620440</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Madhya Pradesh</td>\n",
        "      <td> 29</td>\n",
        "      <td>  48118040</td>\n",
        "      <td> 29639796</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                 Karnataka</td>\n",
        "      <td> 28</td>\n",
        "      <td>  46212109</td>\n",
        "      <td> 31038888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>                 Rajasthan</td>\n",
        "      <td> 25</td>\n",
        "      <td>  42969447</td>\n",
        "      <td> 27110044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>                   Gujarat</td>\n",
        "      <td> 26</td>\n",
        "      <td>  40603104</td>\n",
        "      <td> 25824003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>                    Orissa</td>\n",
        "      <td> 21</td>\n",
        "      <td>  29196041</td>\n",
        "      <td> 21532275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>                    Kerala</td>\n",
        "      <td> 20</td>\n",
        "      <td>  24326649</td>\n",
        "      <td> 17975893</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                 Jharkhand</td>\n",
        "      <td> 14</td>\n",
        "      <td>  20326743</td>\n",
        "      <td> 12982940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>                    Punjab</td>\n",
        "      <td> 13</td>\n",
        "      <td>  19608008</td>\n",
        "      <td> 13845132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td> 14</td>\n",
        "      <td>  18885274</td>\n",
        "      <td> 15085883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>               Chattisgarh</td>\n",
        "      <td> 11</td>\n",
        "      <td>  17623049</td>\n",
        "      <td> 12255579</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>                   Haryana</td>\n",
        "      <td> 10</td>\n",
        "      <td>  16097749</td>\n",
        "      <td> 11495150</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>                  Nagaland</td>\n",
        "      <td>  7</td>\n",
        "      <td>  12711236</td>\n",
        "      <td>  8271766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>           Jammu &amp; Kashmir</td>\n",
        "      <td>  6</td>\n",
        "      <td>   7202163</td>\n",
        "      <td>  3566863</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>               Uttarakhand</td>\n",
        "      <td>  5</td>\n",
        "      <td>   7129939</td>\n",
        "      <td>  4391890</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>          Himachal Pradesh</td>\n",
        "      <td>  4</td>\n",
        "      <td>   4810071</td>\n",
        "      <td>  3098501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>                   Tripura</td>\n",
        "      <td>  2</td>\n",
        "      <td>   2388819</td>\n",
        "      <td>  2023859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>                   Manipur</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1774325</td>\n",
        "      <td>  1412637</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>                 Meghalaya</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1567241</td>\n",
        "      <td>  1078058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>              NCT OF Delhi</td>\n",
        "      <td>  1</td>\n",
        "      <td>   1182948</td>\n",
        "      <td>  1038910</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>                       Goa</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1060777</td>\n",
        "      <td>   817000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>                Puducherry</td>\n",
        "      <td>  1</td>\n",
        "      <td>    901357</td>\n",
        "      <td>   740017</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>  2</td>\n",
        "      <td>    759387</td>\n",
        "      <td>   596956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>                   Mizoram</td>\n",
        "      <td>  1</td>\n",
        "      <td>    702170</td>\n",
        "      <td>   433201</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>                Chandigarh</td>\n",
        "      <td>  1</td>\n",
        "      <td>    615214</td>\n",
        "      <td>   453455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>                    Sikkim</td>\n",
        "      <td>  1</td>\n",
        "      <td>    370611</td>\n",
        "      <td>   308967</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td>  1</td>\n",
        "      <td>    269360</td>\n",
        "      <td>   190328</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>      Dadra &amp; Nagar Haveli</td>\n",
        "      <td>  1</td>\n",
        "      <td>    196617</td>\n",
        "      <td>   165286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>               Daman &amp; Diu</td>\n",
        "      <td>  1</td>\n",
        "      <td>    111827</td>\n",
        "      <td>    87233</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>               Lakshadweep</td>\n",
        "      <td>  1</td>\n",
        "      <td>     49922</td>\n",
        "      <td>    43239</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>35 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "                        State  Seats  Total Electors  Total Voters\n",
        "32              Uttar Pradesh     80       138965820      81092302\n",
        "19                Maharashtra     48        80717283      48718844\n",
        "1              Andhra Pradesh     42        64938750      48358545\n",
        "4                       Bihar     40        63761796      35885366\n",
        "34                West Bengal     42        62833128      51622555\n",
        "30                 Tamil Nadu     39        55114505      40620440\n",
        "18             Madhya Pradesh     29        48118040      29639796\n",
        "15                  Karnataka     28        46212109      31038888\n",
        "28                  Rajasthan     25        42969447      27110044\n",
        "10                    Gujarat     26        40603104      25824003\n",
        "25                     Orissa     21        29196041      21532275\n",
        "16                     Kerala     20        24326649      17975893\n",
        "14                  Jharkhand     14        20326743      12982940\n",
        "27                     Punjab     13        19608008      13845132\n",
        "3                       Assam     14        18885274      15085883\n",
        "6                 Chattisgarh     11        17623049      12255579\n",
        "11                    Haryana     10        16097749      11495150\n",
        "23                   Nagaland      7        12711236       8271766\n",
        "13            Jammu & Kashmir      6         7202163       3566863\n",
        "33                Uttarakhand      5         7129939       4391890\n",
        "12           Himachal Pradesh      4         4810071       3098501\n",
        "31                    Tripura      2         2388819       2023859\n",
        "20                    Manipur      2         1774325       1412637\n",
        "21                  Meghalaya      2         1567241       1078058\n",
        "24               NCT OF Delhi      1         1182948       1038910\n",
        "9                         Goa      2         1060777        817000\n",
        "26                 Puducherry      1          901357        740017\n",
        "2           Arunachal Pradesh      2          759387        596956\n",
        "22                    Mizoram      1          702170        433201\n",
        "5                  Chandigarh      1          615214        453455\n",
        "29                     Sikkim      1          370611        308967\n",
        "0   Andaman & Nicobar Islands      1          269360        190328\n",
        "7        Dadra & Nagar Haveli      1          196617        165286\n",
        "8                 Daman & Diu      1          111827         87233\n",
        "17                Lakshadweep      1           49922         43239\n",
        "\n",
        "[35 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculating State Turnout"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "states[\"Turnout\"] = states[\"Total Voters\"] / states[\"Total Electors\"]\n",
      "states.sort(\"Turnout\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>Seats</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "      <th>Turnout</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>              NCT OF Delhi</td>\n",
        "      <td>  1</td>\n",
        "      <td>   1182948</td>\n",
        "      <td>  1038910</td>\n",
        "      <td> 0.878238</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>               Lakshadweep</td>\n",
        "      <td>  1</td>\n",
        "      <td>     49922</td>\n",
        "      <td>    43239</td>\n",
        "      <td> 0.866131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>                   Tripura</td>\n",
        "      <td>  2</td>\n",
        "      <td>   2388819</td>\n",
        "      <td>  2023859</td>\n",
        "      <td> 0.847222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>      Dadra &amp; Nagar Haveli</td>\n",
        "      <td>  1</td>\n",
        "      <td>    196617</td>\n",
        "      <td>   165286</td>\n",
        "      <td> 0.840650</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>                    Sikkim</td>\n",
        "      <td>  1</td>\n",
        "      <td>    370611</td>\n",
        "      <td>   308967</td>\n",
        "      <td> 0.833669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>               West Bengal</td>\n",
        "      <td> 42</td>\n",
        "      <td>  62833128</td>\n",
        "      <td> 51622555</td>\n",
        "      <td> 0.821582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>                Puducherry</td>\n",
        "      <td>  1</td>\n",
        "      <td>    901357</td>\n",
        "      <td>   740017</td>\n",
        "      <td> 0.821003</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td> 14</td>\n",
        "      <td>  18885274</td>\n",
        "      <td> 15085883</td>\n",
        "      <td> 0.798817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>                   Manipur</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1774325</td>\n",
        "      <td>  1412637</td>\n",
        "      <td> 0.796155</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>  2</td>\n",
        "      <td>    759387</td>\n",
        "      <td>   596956</td>\n",
        "      <td> 0.786102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>               Daman &amp; Diu</td>\n",
        "      <td>  1</td>\n",
        "      <td>    111827</td>\n",
        "      <td>    87233</td>\n",
        "      <td> 0.780071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>                       Goa</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1060777</td>\n",
        "      <td>   817000</td>\n",
        "      <td> 0.770190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td> 42</td>\n",
        "      <td>  64938750</td>\n",
        "      <td> 48358545</td>\n",
        "      <td> 0.744679</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>                    Kerala</td>\n",
        "      <td> 20</td>\n",
        "      <td>  24326649</td>\n",
        "      <td> 17975893</td>\n",
        "      <td> 0.738938</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>                    Orissa</td>\n",
        "      <td> 21</td>\n",
        "      <td>  29196041</td>\n",
        "      <td> 21532275</td>\n",
        "      <td> 0.737507</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>                Chandigarh</td>\n",
        "      <td>  1</td>\n",
        "      <td>    615214</td>\n",
        "      <td>   453455</td>\n",
        "      <td> 0.737069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>                Tamil Nadu</td>\n",
        "      <td> 39</td>\n",
        "      <td>  55114505</td>\n",
        "      <td> 40620440</td>\n",
        "      <td> 0.737019</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>                   Haryana</td>\n",
        "      <td> 10</td>\n",
        "      <td>  16097749</td>\n",
        "      <td> 11495150</td>\n",
        "      <td> 0.714084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td>  1</td>\n",
        "      <td>    269360</td>\n",
        "      <td>   190328</td>\n",
        "      <td> 0.706593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>                    Punjab</td>\n",
        "      <td> 13</td>\n",
        "      <td>  19608008</td>\n",
        "      <td> 13845132</td>\n",
        "      <td> 0.706096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>               Chattisgarh</td>\n",
        "      <td> 11</td>\n",
        "      <td>  17623049</td>\n",
        "      <td> 12255579</td>\n",
        "      <td> 0.695429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>                 Meghalaya</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1567241</td>\n",
        "      <td>  1078058</td>\n",
        "      <td> 0.687870</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                 Karnataka</td>\n",
        "      <td> 28</td>\n",
        "      <td>  46212109</td>\n",
        "      <td> 31038888</td>\n",
        "      <td> 0.671661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>                  Nagaland</td>\n",
        "      <td>  7</td>\n",
        "      <td>  12711236</td>\n",
        "      <td>  8271766</td>\n",
        "      <td> 0.650744</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>          Himachal Pradesh</td>\n",
        "      <td>  4</td>\n",
        "      <td>   4810071</td>\n",
        "      <td>  3098501</td>\n",
        "      <td> 0.644169</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                 Jharkhand</td>\n",
        "      <td> 14</td>\n",
        "      <td>  20326743</td>\n",
        "      <td> 12982940</td>\n",
        "      <td> 0.638712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>                   Gujarat</td>\n",
        "      <td> 26</td>\n",
        "      <td>  40603104</td>\n",
        "      <td> 25824003</td>\n",
        "      <td> 0.636011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>                 Rajasthan</td>\n",
        "      <td> 25</td>\n",
        "      <td>  42969447</td>\n",
        "      <td> 27110044</td>\n",
        "      <td> 0.630914</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>                   Mizoram</td>\n",
        "      <td>  1</td>\n",
        "      <td>    702170</td>\n",
        "      <td>   433201</td>\n",
        "      <td> 0.616946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Madhya Pradesh</td>\n",
        "      <td> 29</td>\n",
        "      <td>  48118040</td>\n",
        "      <td> 29639796</td>\n",
        "      <td> 0.615981</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>               Uttarakhand</td>\n",
        "      <td>  5</td>\n",
        "      <td>   7129939</td>\n",
        "      <td>  4391890</td>\n",
        "      <td> 0.615979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               Maharashtra</td>\n",
        "      <td> 48</td>\n",
        "      <td>  80717283</td>\n",
        "      <td> 48718844</td>\n",
        "      <td> 0.603574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>             Uttar Pradesh</td>\n",
        "      <td> 80</td>\n",
        "      <td> 138965820</td>\n",
        "      <td> 81092302</td>\n",
        "      <td> 0.583541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td> 40</td>\n",
        "      <td>  63761796</td>\n",
        "      <td> 35885366</td>\n",
        "      <td> 0.562804</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>           Jammu &amp; Kashmir</td>\n",
        "      <td>  6</td>\n",
        "      <td>   7202163</td>\n",
        "      <td>  3566863</td>\n",
        "      <td> 0.495249</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>35 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                        State  Seats  Total Electors  Total Voters   Turnout\n",
        "24               NCT OF Delhi      1         1182948       1038910  0.878238\n",
        "17                Lakshadweep      1           49922         43239  0.866131\n",
        "31                    Tripura      2         2388819       2023859  0.847222\n",
        "7        Dadra & Nagar Haveli      1          196617        165286  0.840650\n",
        "29                     Sikkim      1          370611        308967  0.833669\n",
        "34                West Bengal     42        62833128      51622555  0.821582\n",
        "26                 Puducherry      1          901357        740017  0.821003\n",
        "3                       Assam     14        18885274      15085883  0.798817\n",
        "20                    Manipur      2         1774325       1412637  0.796155\n",
        "2           Arunachal Pradesh      2          759387        596956  0.786102\n",
        "8                 Daman & Diu      1          111827         87233  0.780071\n",
        "9                         Goa      2         1060777        817000  0.770190\n",
        "1              Andhra Pradesh     42        64938750      48358545  0.744679\n",
        "16                     Kerala     20        24326649      17975893  0.738938\n",
        "25                     Orissa     21        29196041      21532275  0.737507\n",
        "5                  Chandigarh      1          615214        453455  0.737069\n",
        "30                 Tamil Nadu     39        55114505      40620440  0.737019\n",
        "11                    Haryana     10        16097749      11495150  0.714084\n",
        "0   Andaman & Nicobar Islands      1          269360        190328  0.706593\n",
        "27                     Punjab     13        19608008      13845132  0.706096\n",
        "6                 Chattisgarh     11        17623049      12255579  0.695429\n",
        "21                  Meghalaya      2         1567241       1078058  0.687870\n",
        "15                  Karnataka     28        46212109      31038888  0.671661\n",
        "23                   Nagaland      7        12711236       8271766  0.650744\n",
        "12           Himachal Pradesh      4         4810071       3098501  0.644169\n",
        "14                  Jharkhand     14        20326743      12982940  0.638712\n",
        "10                    Gujarat     26        40603104      25824003  0.636011\n",
        "28                  Rajasthan     25        42969447      27110044  0.630914\n",
        "22                    Mizoram      1          702170        433201  0.616946\n",
        "18             Madhya Pradesh     29        48118040      29639796  0.615981\n",
        "33                Uttarakhand      5         7129939       4391890  0.615979\n",
        "19                Maharashtra     48        80717283      48718844  0.603574\n",
        "32              Uttar Pradesh     80       138965820      81092302  0.583541\n",
        "4                       Bihar     40        63761796      35885366  0.562804\n",
        "13            Jammu & Kashmir      6         7202163       3566863  0.495249\n",
        "\n",
        "[35 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculating Electors Per Seat"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "states[\"Electors Per Seat\"] = states[\"Total Electors\"] / states[\"Seats\"]\n",
      "states.sort(\"Electors Per Seat\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>Seats</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "      <th>Turnout</th>\n",
        "      <th>Electors Per Seat</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>                  Nagaland</td>\n",
        "      <td>  7</td>\n",
        "      <td>  12711236</td>\n",
        "      <td>  8271766</td>\n",
        "      <td> 0.650744</td>\n",
        "      <td> 1815890.857143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>             Uttar Pradesh</td>\n",
        "      <td> 80</td>\n",
        "      <td> 138965820</td>\n",
        "      <td> 81092302</td>\n",
        "      <td> 0.583541</td>\n",
        "      <td> 1737072.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>                 Rajasthan</td>\n",
        "      <td> 25</td>\n",
        "      <td>  42969447</td>\n",
        "      <td> 27110044</td>\n",
        "      <td> 0.630914</td>\n",
        "      <td> 1718777.880000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>               Maharashtra</td>\n",
        "      <td> 48</td>\n",
        "      <td>  80717283</td>\n",
        "      <td> 48718844</td>\n",
        "      <td> 0.603574</td>\n",
        "      <td> 1681610.062500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Madhya Pradesh</td>\n",
        "      <td> 29</td>\n",
        "      <td>  48118040</td>\n",
        "      <td> 29639796</td>\n",
        "      <td> 0.615981</td>\n",
        "      <td> 1659242.758621</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                 Karnataka</td>\n",
        "      <td> 28</td>\n",
        "      <td>  46212109</td>\n",
        "      <td> 31038888</td>\n",
        "      <td> 0.671661</td>\n",
        "      <td> 1650432.464286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>                   Haryana</td>\n",
        "      <td> 10</td>\n",
        "      <td>  16097749</td>\n",
        "      <td> 11495150</td>\n",
        "      <td> 0.714084</td>\n",
        "      <td> 1609774.900000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>               Chattisgarh</td>\n",
        "      <td> 11</td>\n",
        "      <td>  17623049</td>\n",
        "      <td> 12255579</td>\n",
        "      <td> 0.695429</td>\n",
        "      <td> 1602095.363636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td> 40</td>\n",
        "      <td>  63761796</td>\n",
        "      <td> 35885366</td>\n",
        "      <td> 0.562804</td>\n",
        "      <td> 1594044.900000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>                   Gujarat</td>\n",
        "      <td> 26</td>\n",
        "      <td>  40603104</td>\n",
        "      <td> 25824003</td>\n",
        "      <td> 0.636011</td>\n",
        "      <td> 1561657.846154</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td> 42</td>\n",
        "      <td>  64938750</td>\n",
        "      <td> 48358545</td>\n",
        "      <td> 0.744679</td>\n",
        "      <td> 1546160.714286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>                    Punjab</td>\n",
        "      <td> 13</td>\n",
        "      <td>  19608008</td>\n",
        "      <td> 13845132</td>\n",
        "      <td> 0.706096</td>\n",
        "      <td> 1508308.307692</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>               West Bengal</td>\n",
        "      <td> 42</td>\n",
        "      <td>  62833128</td>\n",
        "      <td> 51622555</td>\n",
        "      <td> 0.821582</td>\n",
        "      <td> 1496026.857143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                 Jharkhand</td>\n",
        "      <td> 14</td>\n",
        "      <td>  20326743</td>\n",
        "      <td> 12982940</td>\n",
        "      <td> 0.638712</td>\n",
        "      <td> 1451910.214286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>               Uttarakhand</td>\n",
        "      <td>  5</td>\n",
        "      <td>   7129939</td>\n",
        "      <td>  4391890</td>\n",
        "      <td> 0.615979</td>\n",
        "      <td> 1425987.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>                Tamil Nadu</td>\n",
        "      <td> 39</td>\n",
        "      <td>  55114505</td>\n",
        "      <td> 40620440</td>\n",
        "      <td> 0.737019</td>\n",
        "      <td> 1413192.435897</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>                    Orissa</td>\n",
        "      <td> 21</td>\n",
        "      <td>  29196041</td>\n",
        "      <td> 21532275</td>\n",
        "      <td> 0.737507</td>\n",
        "      <td> 1390287.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td> 14</td>\n",
        "      <td>  18885274</td>\n",
        "      <td> 15085883</td>\n",
        "      <td> 0.798817</td>\n",
        "      <td> 1348948.142857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>                    Kerala</td>\n",
        "      <td> 20</td>\n",
        "      <td>  24326649</td>\n",
        "      <td> 17975893</td>\n",
        "      <td> 0.738938</td>\n",
        "      <td> 1216332.450000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>          Himachal Pradesh</td>\n",
        "      <td>  4</td>\n",
        "      <td>   4810071</td>\n",
        "      <td>  3098501</td>\n",
        "      <td> 0.644169</td>\n",
        "      <td> 1202517.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>           Jammu &amp; Kashmir</td>\n",
        "      <td>  6</td>\n",
        "      <td>   7202163</td>\n",
        "      <td>  3566863</td>\n",
        "      <td> 0.495249</td>\n",
        "      <td> 1200360.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>                   Tripura</td>\n",
        "      <td>  2</td>\n",
        "      <td>   2388819</td>\n",
        "      <td>  2023859</td>\n",
        "      <td> 0.847222</td>\n",
        "      <td> 1194409.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>              NCT OF Delhi</td>\n",
        "      <td>  1</td>\n",
        "      <td>   1182948</td>\n",
        "      <td>  1038910</td>\n",
        "      <td> 0.878238</td>\n",
        "      <td> 1182948.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>                Puducherry</td>\n",
        "      <td>  1</td>\n",
        "      <td>    901357</td>\n",
        "      <td>   740017</td>\n",
        "      <td> 0.821003</td>\n",
        "      <td>  901357.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>                   Manipur</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1774325</td>\n",
        "      <td>  1412637</td>\n",
        "      <td> 0.796155</td>\n",
        "      <td>  887162.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>                 Meghalaya</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1567241</td>\n",
        "      <td>  1078058</td>\n",
        "      <td> 0.687870</td>\n",
        "      <td>  783620.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>                   Mizoram</td>\n",
        "      <td>  1</td>\n",
        "      <td>    702170</td>\n",
        "      <td>   433201</td>\n",
        "      <td> 0.616946</td>\n",
        "      <td>  702170.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>                Chandigarh</td>\n",
        "      <td>  1</td>\n",
        "      <td>    615214</td>\n",
        "      <td>   453455</td>\n",
        "      <td> 0.737069</td>\n",
        "      <td>  615214.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>                       Goa</td>\n",
        "      <td>  2</td>\n",
        "      <td>   1060777</td>\n",
        "      <td>   817000</td>\n",
        "      <td> 0.770190</td>\n",
        "      <td>  530388.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>  2</td>\n",
        "      <td>    759387</td>\n",
        "      <td>   596956</td>\n",
        "      <td> 0.786102</td>\n",
        "      <td>  379693.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>                    Sikkim</td>\n",
        "      <td>  1</td>\n",
        "      <td>    370611</td>\n",
        "      <td>   308967</td>\n",
        "      <td> 0.833669</td>\n",
        "      <td>  370611.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td>  1</td>\n",
        "      <td>    269360</td>\n",
        "      <td>   190328</td>\n",
        "      <td> 0.706593</td>\n",
        "      <td>  269360.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>      Dadra &amp; Nagar Haveli</td>\n",
        "      <td>  1</td>\n",
        "      <td>    196617</td>\n",
        "      <td>   165286</td>\n",
        "      <td> 0.840650</td>\n",
        "      <td>  196617.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>               Daman &amp; Diu</td>\n",
        "      <td>  1</td>\n",
        "      <td>    111827</td>\n",
        "      <td>    87233</td>\n",
        "      <td> 0.780071</td>\n",
        "      <td>  111827.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>               Lakshadweep</td>\n",
        "      <td>  1</td>\n",
        "      <td>     49922</td>\n",
        "      <td>    43239</td>\n",
        "      <td> 0.866131</td>\n",
        "      <td>   49922.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>35 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                        State  Seats  Total Electors  Total Voters   Turnout  \\\n",
        "23                   Nagaland      7        12711236       8271766  0.650744   \n",
        "32              Uttar Pradesh     80       138965820      81092302  0.583541   \n",
        "28                  Rajasthan     25        42969447      27110044  0.630914   \n",
        "19                Maharashtra     48        80717283      48718844  0.603574   \n",
        "18             Madhya Pradesh     29        48118040      29639796  0.615981   \n",
        "15                  Karnataka     28        46212109      31038888  0.671661   \n",
        "11                    Haryana     10        16097749      11495150  0.714084   \n",
        "6                 Chattisgarh     11        17623049      12255579  0.695429   \n",
        "4                       Bihar     40        63761796      35885366  0.562804   \n",
        "10                    Gujarat     26        40603104      25824003  0.636011   \n",
        "1              Andhra Pradesh     42        64938750      48358545  0.744679   \n",
        "27                     Punjab     13        19608008      13845132  0.706096   \n",
        "34                West Bengal     42        62833128      51622555  0.821582   \n",
        "14                  Jharkhand     14        20326743      12982940  0.638712   \n",
        "33                Uttarakhand      5         7129939       4391890  0.615979   \n",
        "30                 Tamil Nadu     39        55114505      40620440  0.737019   \n",
        "25                     Orissa     21        29196041      21532275  0.737507   \n",
        "3                       Assam     14        18885274      15085883  0.798817   \n",
        "16                     Kerala     20        24326649      17975893  0.738938   \n",
        "12           Himachal Pradesh      4         4810071       3098501  0.644169   \n",
        "13            Jammu & Kashmir      6         7202163       3566863  0.495249   \n",
        "31                    Tripura      2         2388819       2023859  0.847222   \n",
        "24               NCT OF Delhi      1         1182948       1038910  0.878238   \n",
        "26                 Puducherry      1          901357        740017  0.821003   \n",
        "20                    Manipur      2         1774325       1412637  0.796155   \n",
        "21                  Meghalaya      2         1567241       1078058  0.687870   \n",
        "22                    Mizoram      1          702170        433201  0.616946   \n",
        "5                  Chandigarh      1          615214        453455  0.737069   \n",
        "9                         Goa      2         1060777        817000  0.770190   \n",
        "2           Arunachal Pradesh      2          759387        596956  0.786102   \n",
        "29                     Sikkim      1          370611        308967  0.833669   \n",
        "0   Andaman & Nicobar Islands      1          269360        190328  0.706593   \n",
        "7        Dadra & Nagar Haveli      1          196617        165286  0.840650   \n",
        "8                 Daman & Diu      1          111827         87233  0.780071   \n",
        "17                Lakshadweep      1           49922         43239  0.866131   \n",
        "\n",
        "    Electors Per Seat  \n",
        "23     1815890.857143  \n",
        "32     1737072.750000  \n",
        "28     1718777.880000  \n",
        "19     1681610.062500  \n",
        "18     1659242.758621  \n",
        "15     1650432.464286  \n",
        "11     1609774.900000  \n",
        "6      1602095.363636  \n",
        "4      1594044.900000  \n",
        "10     1561657.846154  \n",
        "1      1546160.714286  \n",
        "27     1508308.307692  \n",
        "34     1496026.857143  \n",
        "14     1451910.214286  \n",
        "33     1425987.800000  \n",
        "30     1413192.435897  \n",
        "25     1390287.666667  \n",
        "3      1348948.142857  \n",
        "16     1216332.450000  \n",
        "12     1202517.750000  \n",
        "13     1200360.500000  \n",
        "31     1194409.500000  \n",
        "24     1182948.000000  \n",
        "26      901357.000000  \n",
        "20      887162.500000  \n",
        "21      783620.500000  \n",
        "22      702170.000000  \n",
        "5       615214.000000  \n",
        "9       530388.500000  \n",
        "2       379693.500000  \n",
        "29      370611.000000  \n",
        "0       269360.000000  \n",
        "7       196617.000000  \n",
        "8       111827.000000  \n",
        "17       49922.000000  \n",
        "\n",
        "[35 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Reading the Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = \"data/2014_indian_elections_results.csv\"\n",
      "results_data = pd.read_csv(path, sep=\"\\t\")\n",
      "results_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name of State/ UT</th>\n",
        "      <th>Parliamentary Constituency</th>\n",
        "      <th>Candidate Name</th>\n",
        "      <th>Total Votes Polled</th>\n",
        "      <th>Winner or Not?</th>\n",
        "      <th>Party Abbreviation</th>\n",
        "      <th>Party Name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>                 GODAM NAGESH</td>\n",
        "      <td> 430847</td>\n",
        "      <td> yes</td>\n",
        "      <td>    TRS</td>\n",
        "      <td>             Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>             NETHAWATH RAMDAS</td>\n",
        "      <td>  41032</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>                RAMESH RATHOD</td>\n",
        "      <td> 184198</td>\n",
        "      <td>  no</td>\n",
        "      <td>    TDP</td>\n",
        "      <td>                          Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>              RATHOD SADASHIV</td>\n",
        "      <td>  94420</td>\n",
        "      <td>  no</td>\n",
        "      <td>    BSP</td>\n",
        "      <td>                   Bahujan Samaj Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>             MOSALI CHINNAIAH</td>\n",
        "      <td>   8859</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>                       NARESH</td>\n",
        "      <td> 259557</td>\n",
        "      <td>  no</td>\n",
        "      <td>    INC</td>\n",
        "      <td>              Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>                PAWAR KRISHNA</td>\n",
        "      <td>   5055</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>                BANKA SAHADEV</td>\n",
        "      <td>   4787</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>   Adilabad</td>\n",
        "      <td>            None of the Above</td>\n",
        "      <td>  17084</td>\n",
        "      <td>  no</td>\n",
        "      <td>   NOTA</td>\n",
        "      <td>                     None of the Above</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>                  BALKA SUMAN</td>\n",
        "      <td> 565496</td>\n",
        "      <td> yes</td>\n",
        "      <td>    TRS</td>\n",
        "      <td>             Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>            None of the Above</td>\n",
        "      <td>   5361</td>\n",
        "      <td>  no</td>\n",
        "      <td>   NOTA</td>\n",
        "      <td>                     None of the Above</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>                G. VIVEKANAND</td>\n",
        "      <td> 274338</td>\n",
        "      <td>  no</td>\n",
        "      <td>    INC</td>\n",
        "      <td>              Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>              KALVALA SHANKAR</td>\n",
        "      <td>   9290</td>\n",
        "      <td>  no</td>\n",
        "      <td>   PPOI</td>\n",
        "      <td>                Pyramid Party of India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>              MOUTAM RAVINDER</td>\n",
        "      <td>   2674</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>            VELTHURU MALLAIAH</td>\n",
        "      <td>  45977</td>\n",
        "      <td>  no</td>\n",
        "      <td>  RP(K)</td>\n",
        "      <td>           Republican Paksha (Khoripa)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>       DR.JANAPATI SARAT BABU</td>\n",
        "      <td>  63334</td>\n",
        "      <td>  no</td>\n",
        "      <td>    TDP</td>\n",
        "      <td>                          Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>             KAMILLA JAYA RAO</td>\n",
        "      <td>   2742</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>         THALLAPALLI SRINIVAS</td>\n",
        "      <td>   3699</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>          VENKATA SWAMY INJAM</td>\n",
        "      <td>   3866</td>\n",
        "      <td>  no</td>\n",
        "      <td>   MaSP</td>\n",
        "      <td>              Mahajana Socialist Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>                 GORRE RAMESH</td>\n",
        "      <td>   2361</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>             BUPELLI NARAYANA</td>\n",
        "      <td>   7258</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>       BOTHA VENKATA MALLAIAH</td>\n",
        "      <td>   2245</td>\n",
        "      <td>  no</td>\n",
        "      <td>   BCUF</td>\n",
        "      <td>                    B. C. United Front</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>               JINNA RAMADEVI</td>\n",
        "      <td>   9199</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>                    J.V. RAJU</td>\n",
        "      <td>   3020</td>\n",
        "      <td>  no</td>\n",
        "      <td>    NIP</td>\n",
        "      <td>                       New India Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>          GADDALA VINAY KUMAR</td>\n",
        "      <td>   3266</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>              KALVALA SANJEEV</td>\n",
        "      <td>   8609</td>\n",
        "      <td>  no</td>\n",
        "      <td>    RPI</td>\n",
        "      <td>             Republican Party of India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Peddapalle</td>\n",
        "      <td>          TAGARAM SHANKAR LAL</td>\n",
        "      <td>   9449</td>\n",
        "      <td>  no</td>\n",
        "      <td>    BSP</td>\n",
        "      <td>                   Bahujan Samaj Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>       VINOD KUMAR BOINAPALLY</td>\n",
        "      <td> 505783</td>\n",
        "      <td> yes</td>\n",
        "      <td>    TRS</td>\n",
        "      <td>             Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>   SRINIVAS REDDY LINGAMPALLY</td>\n",
        "      <td>   3865</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>            RANAVENI LAKSHMAN</td>\n",
        "      <td>   2062</td>\n",
        "      <td>  no</td>\n",
        "      <td>    NIP</td>\n",
        "      <td>                       New India Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>          MALLESH YADAV BARLA</td>\n",
        "      <td>   2760</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>         REDDI MALLA SRINIVAS</td>\n",
        "      <td>   8040</td>\n",
        "      <td>  no</td>\n",
        "      <td> RPI(A)</td>\n",
        "      <td>         Republican Party of India (A)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>                  BURLA. RAVI</td>\n",
        "      <td>   4769</td>\n",
        "      <td>  no</td>\n",
        "      <td>    NCP</td>\n",
        "      <td>            Nationalist Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>             KANDEM PRABHAKAR</td>\n",
        "      <td>   4731</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>                    B. LAXMAN</td>\n",
        "      <td>  11333</td>\n",
        "      <td>  no</td>\n",
        "      <td>    BSP</td>\n",
        "      <td>                   Bahujan Samaj Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>                SHAIK MAHMOOD</td>\n",
        "      <td>  39325</td>\n",
        "      <td>  no</td>\n",
        "      <td>   WPOI</td>\n",
        "      <td>                Welfare Party Of India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>             PONNAM PRABHAKAR</td>\n",
        "      <td> 300706</td>\n",
        "      <td>  no</td>\n",
        "      <td>    INC</td>\n",
        "      <td>              Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td> CHENNAMANENI VIDYA SAGAR RAO</td>\n",
        "      <td> 214828</td>\n",
        "      <td>  no</td>\n",
        "      <td>    BJP</td>\n",
        "      <td>                Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>           MEESALA RAJI REDDY</td>\n",
        "      <td>   3080</td>\n",
        "      <td>  no</td>\n",
        "      <td>  YSRCP</td>\n",
        "      <td> Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>     MOHAMMED AMJAD MOHIUDDIN</td>\n",
        "      <td>   6720</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>              UPPULETI.LAXMAN</td>\n",
        "      <td>   2448</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>           L. PRABHAKAR REDDY</td>\n",
        "      <td>   3944</td>\n",
        "      <td>  no</td>\n",
        "      <td>   PPOI</td>\n",
        "      <td>                Pyramid Party of India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>              PULIKOTA RAMESH</td>\n",
        "      <td>   1777</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>      DR.DHARMAIAH VENDAVETLA</td>\n",
        "      <td>   3773</td>\n",
        "      <td>  no</td>\n",
        "      <td>   BMUP</td>\n",
        "      <td>                   Bahujan Mukti Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td> Karimnagar</td>\n",
        "      <td>            None of the Above</td>\n",
        "      <td>   5747</td>\n",
        "      <td>  no</td>\n",
        "      <td>   NOTA</td>\n",
        "      <td>                     None of the Above</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>          KALVAKUNTLA KAVITHA</td>\n",
        "      <td> 439307</td>\n",
        "      <td> yes</td>\n",
        "      <td>    TRS</td>\n",
        "      <td>             Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>               BANJA VEERAPPA</td>\n",
        "      <td>   3651</td>\n",
        "      <td>  no</td>\n",
        "      <td>   PPOI</td>\n",
        "      <td>                Pyramid Party of India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>  CHIRULINGADRULA VENKATESHAM</td>\n",
        "      <td>   3091</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>             KANDEM PRABHAKAR</td>\n",
        "      <td>   3990</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>          MALIK MOHTASIM KHAN</td>\n",
        "      <td>  43814</td>\n",
        "      <td>  no</td>\n",
        "      <td>   WPOI</td>\n",
        "      <td>                Welfare Party Of India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>                S. CHANDRAIAH</td>\n",
        "      <td>   2817</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>                     BAGWAN B</td>\n",
        "      <td>   8129</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>                   ABDUL GANI</td>\n",
        "      <td>   4791</td>\n",
        "      <td>  no</td>\n",
        "      <td>   BCUF</td>\n",
        "      <td>                    B. C. United Front</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>       ENDALA LAKSHMINARAYANA</td>\n",
        "      <td> 225333</td>\n",
        "      <td>  no</td>\n",
        "      <td>    BJP</td>\n",
        "      <td>                Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>     ABDUL KAREEM KHAN (BABU)</td>\n",
        "      <td>   1601</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>                TALARI RAMULU</td>\n",
        "      <td>   7421</td>\n",
        "      <td>  no</td>\n",
        "      <td>    BSP</td>\n",
        "      <td>                   Bahujan Samaj Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>                SAGAR SUDDALA</td>\n",
        "      <td>   1978</td>\n",
        "      <td>  no</td>\n",
        "      <td>   BMUP</td>\n",
        "      <td>                   Bahujan Mukti Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>            MADHU YASKHI GOUD</td>\n",
        "      <td> 272123</td>\n",
        "      <td>  no</td>\n",
        "      <td>    INC</td>\n",
        "      <td>              Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>             RAPELLY SRINIVAS</td>\n",
        "      <td>   2621</td>\n",
        "      <td>  no</td>\n",
        "      <td>   AAAP</td>\n",
        "      <td>                       Aam Aadmi Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td> Andhra Pradesh</td>\n",
        "      <td>  Nizamabad</td>\n",
        "      <td>     KOTAPATI NARSIMHAM NAIDU</td>\n",
        "      <td>   2462</td>\n",
        "      <td>  no</td>\n",
        "      <td>    IND</td>\n",
        "      <td>                           Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8794 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "   Name of State/ UT Parliamentary Constituency                Candidate Name  \\\n",
        "0     Andhra Pradesh                   Adilabad                  GODAM NAGESH   \n",
        "1     Andhra Pradesh                   Adilabad              NETHAWATH RAMDAS   \n",
        "2     Andhra Pradesh                   Adilabad                 RAMESH RATHOD   \n",
        "3     Andhra Pradesh                   Adilabad               RATHOD SADASHIV   \n",
        "4     Andhra Pradesh                   Adilabad              MOSALI CHINNAIAH   \n",
        "5     Andhra Pradesh                   Adilabad                        NARESH   \n",
        "6     Andhra Pradesh                   Adilabad                 PAWAR KRISHNA   \n",
        "7     Andhra Pradesh                   Adilabad                 BANKA SAHADEV   \n",
        "8     Andhra Pradesh                   Adilabad             None of the Above   \n",
        "9     Andhra Pradesh                 Peddapalle                   BALKA SUMAN   \n",
        "10    Andhra Pradesh                 Peddapalle             None of the Above   \n",
        "11    Andhra Pradesh                 Peddapalle                 G. VIVEKANAND   \n",
        "12    Andhra Pradesh                 Peddapalle               KALVALA SHANKAR   \n",
        "13    Andhra Pradesh                 Peddapalle               MOUTAM RAVINDER   \n",
        "14    Andhra Pradesh                 Peddapalle             VELTHURU MALLAIAH   \n",
        "15    Andhra Pradesh                 Peddapalle        DR.JANAPATI SARAT BABU   \n",
        "16    Andhra Pradesh                 Peddapalle              KAMILLA JAYA RAO   \n",
        "17    Andhra Pradesh                 Peddapalle          THALLAPALLI SRINIVAS   \n",
        "18    Andhra Pradesh                 Peddapalle           VENKATA SWAMY INJAM   \n",
        "19    Andhra Pradesh                 Peddapalle                  GORRE RAMESH   \n",
        "20    Andhra Pradesh                 Peddapalle              BUPELLI NARAYANA   \n",
        "21    Andhra Pradesh                 Peddapalle        BOTHA VENKATA MALLAIAH   \n",
        "22    Andhra Pradesh                 Peddapalle                JINNA RAMADEVI   \n",
        "23    Andhra Pradesh                 Peddapalle                     J.V. RAJU   \n",
        "24    Andhra Pradesh                 Peddapalle           GADDALA VINAY KUMAR   \n",
        "25    Andhra Pradesh                 Peddapalle               KALVALA SANJEEV   \n",
        "26    Andhra Pradesh                 Peddapalle           TAGARAM SHANKAR LAL   \n",
        "27    Andhra Pradesh                 Karimnagar        VINOD KUMAR BOINAPALLY   \n",
        "28    Andhra Pradesh                 Karimnagar    SRINIVAS REDDY LINGAMPALLY   \n",
        "29    Andhra Pradesh                 Karimnagar             RANAVENI LAKSHMAN   \n",
        "30    Andhra Pradesh                 Karimnagar           MALLESH YADAV BARLA   \n",
        "31    Andhra Pradesh                 Karimnagar          REDDI MALLA SRINIVAS   \n",
        "32    Andhra Pradesh                 Karimnagar                   BURLA. RAVI   \n",
        "33    Andhra Pradesh                 Karimnagar              KANDEM PRABHAKAR   \n",
        "34    Andhra Pradesh                 Karimnagar                     B. LAXMAN   \n",
        "35    Andhra Pradesh                 Karimnagar                 SHAIK MAHMOOD   \n",
        "36    Andhra Pradesh                 Karimnagar              PONNAM PRABHAKAR   \n",
        "37    Andhra Pradesh                 Karimnagar  CHENNAMANENI VIDYA SAGAR RAO   \n",
        "38    Andhra Pradesh                 Karimnagar            MEESALA RAJI REDDY   \n",
        "39    Andhra Pradesh                 Karimnagar      MOHAMMED AMJAD MOHIUDDIN   \n",
        "40    Andhra Pradesh                 Karimnagar               UPPULETI.LAXMAN   \n",
        "41    Andhra Pradesh                 Karimnagar            L. PRABHAKAR REDDY   \n",
        "42    Andhra Pradesh                 Karimnagar               PULIKOTA RAMESH   \n",
        "43    Andhra Pradesh                 Karimnagar       DR.DHARMAIAH VENDAVETLA   \n",
        "44    Andhra Pradesh                 Karimnagar             None of the Above   \n",
        "45    Andhra Pradesh                  Nizamabad           KALVAKUNTLA KAVITHA   \n",
        "46    Andhra Pradesh                  Nizamabad                BANJA VEERAPPA   \n",
        "47    Andhra Pradesh                  Nizamabad   CHIRULINGADRULA VENKATESHAM   \n",
        "48    Andhra Pradesh                  Nizamabad              KANDEM PRABHAKAR   \n",
        "49    Andhra Pradesh                  Nizamabad           MALIK MOHTASIM KHAN   \n",
        "50    Andhra Pradesh                  Nizamabad                 S. CHANDRAIAH   \n",
        "51    Andhra Pradesh                  Nizamabad                      BAGWAN B   \n",
        "52    Andhra Pradesh                  Nizamabad                    ABDUL GANI   \n",
        "53    Andhra Pradesh                  Nizamabad        ENDALA LAKSHMINARAYANA   \n",
        "54    Andhra Pradesh                  Nizamabad      ABDUL KAREEM KHAN (BABU)   \n",
        "55    Andhra Pradesh                  Nizamabad                 TALARI RAMULU   \n",
        "56    Andhra Pradesh                  Nizamabad                 SAGAR SUDDALA   \n",
        "57    Andhra Pradesh                  Nizamabad             MADHU YASKHI GOUD   \n",
        "58    Andhra Pradesh                  Nizamabad              RAPELLY SRINIVAS   \n",
        "59    Andhra Pradesh                  Nizamabad      KOTAPATI NARSIMHAM NAIDU   \n",
        "                 ...                        ...                           ...   \n",
        "\n",
        "    Total Votes Polled Winner or Not? Party Abbreviation  \\\n",
        "0               430847            yes                TRS   \n",
        "1                41032             no                IND   \n",
        "2               184198             no                TDP   \n",
        "3                94420             no                BSP   \n",
        "4                 8859             no                IND   \n",
        "5               259557             no                INC   \n",
        "6                 5055             no                IND   \n",
        "7                 4787             no                IND   \n",
        "8                17084             no               NOTA   \n",
        "9               565496            yes                TRS   \n",
        "10                5361             no               NOTA   \n",
        "11              274338             no                INC   \n",
        "12                9290             no               PPOI   \n",
        "13                2674             no                IND   \n",
        "14               45977             no              RP(K)   \n",
        "15               63334             no                TDP   \n",
        "16                2742             no                IND   \n",
        "17                3699             no                IND   \n",
        "18                3866             no               MaSP   \n",
        "19                2361             no                IND   \n",
        "20                7258             no                IND   \n",
        "21                2245             no               BCUF   \n",
        "22                9199             no                IND   \n",
        "23                3020             no                NIP   \n",
        "24                3266             no                IND   \n",
        "25                8609             no                RPI   \n",
        "26                9449             no                BSP   \n",
        "27              505783            yes                TRS   \n",
        "28                3865             no                IND   \n",
        "29                2062             no                NIP   \n",
        "30                2760             no                IND   \n",
        "31                8040             no             RPI(A)   \n",
        "32                4769             no                NCP   \n",
        "33                4731             no                IND   \n",
        "34               11333             no                BSP   \n",
        "35               39325             no               WPOI   \n",
        "36              300706             no                INC   \n",
        "37              214828             no                BJP   \n",
        "38                3080             no              YSRCP   \n",
        "39                6720             no                IND   \n",
        "40                2448             no                IND   \n",
        "41                3944             no               PPOI   \n",
        "42                1777             no                IND   \n",
        "43                3773             no               BMUP   \n",
        "44                5747             no               NOTA   \n",
        "45              439307            yes                TRS   \n",
        "46                3651             no               PPOI   \n",
        "47                3091             no                IND   \n",
        "48                3990             no                IND   \n",
        "49               43814             no               WPOI   \n",
        "50                2817             no                IND   \n",
        "51                8129             no                IND   \n",
        "52                4791             no               BCUF   \n",
        "53              225333             no                BJP   \n",
        "54                1601             no                IND   \n",
        "55                7421             no                BSP   \n",
        "56                1978             no               BMUP   \n",
        "57              272123             no                INC   \n",
        "58                2621             no               AAAP   \n",
        "59                2462             no                IND   \n",
        "                   ...            ...                ...   \n",
        "\n",
        "                               Party Name  \n",
        "0               Telangana Rashtra Samithi  \n",
        "1                             Independent  \n",
        "2                            Telugu Desam  \n",
        "3                     Bahujan Samaj Party  \n",
        "4                             Independent  \n",
        "5                Indian National Congress  \n",
        "6                             Independent  \n",
        "7                             Independent  \n",
        "8                       None of the Above  \n",
        "9               Telangana Rashtra Samithi  \n",
        "10                      None of the Above  \n",
        "11               Indian National Congress  \n",
        "12                 Pyramid Party of India  \n",
        "13                            Independent  \n",
        "14            Republican Paksha (Khoripa)  \n",
        "15                           Telugu Desam  \n",
        "16                            Independent  \n",
        "17                            Independent  \n",
        "18               Mahajana Socialist Party  \n",
        "19                            Independent  \n",
        "20                            Independent  \n",
        "21                     B. C. United Front  \n",
        "22                            Independent  \n",
        "23                        New India Party  \n",
        "24                            Independent  \n",
        "25              Republican Party of India  \n",
        "26                    Bahujan Samaj Party  \n",
        "27              Telangana Rashtra Samithi  \n",
        "28                            Independent  \n",
        "29                        New India Party  \n",
        "30                            Independent  \n",
        "31          Republican Party of India (A)  \n",
        "32             Nationalist Congress Party  \n",
        "33                            Independent  \n",
        "34                    Bahujan Samaj Party  \n",
        "35                 Welfare Party Of India  \n",
        "36               Indian National Congress  \n",
        "37                 Bharatiya Janata Party  \n",
        "38  Yuvajana Sramika Rythu Congress Party  \n",
        "39                            Independent  \n",
        "40                            Independent  \n",
        "41                 Pyramid Party of India  \n",
        "42                            Independent  \n",
        "43                    Bahujan Mukti Party  \n",
        "44                      None of the Above  \n",
        "45              Telangana Rashtra Samithi  \n",
        "46                 Pyramid Party of India  \n",
        "47                            Independent  \n",
        "48                            Independent  \n",
        "49                 Welfare Party Of India  \n",
        "50                            Independent  \n",
        "51                            Independent  \n",
        "52                     B. C. United Front  \n",
        "53                 Bharatiya Janata Party  \n",
        "54                            Independent  \n",
        "55                    Bahujan Samaj Party  \n",
        "56                    Bahujan Mukti Party  \n",
        "57               Indian National Congress  \n",
        "58                        Aam Aadmi Party  \n",
        "59                            Independent  \n",
        "                                      ...  \n",
        "\n",
        "[8794 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Analyzing The results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "winners = results_data[results_data[\"Winner or Not?\"] == \"yes\"].sort(\"Name of State/ UT\")\n",
      "winners"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Name of State/ UT</th>\n",
        "      <th>Parliamentary Constituency</th>\n",
        "      <th>Candidate Name</th>\n",
        "      <th>Total Votes Polled</th>\n",
        "      <th>Winner or Not?</th>\n",
        "      <th>Party Abbreviation</th>\n",
        "      <th>Party Name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>8548</th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td>                     BISHNU PADA RAY</td>\n",
        "      <td>  90969</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0   </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Adilabad</td>\n",
        "      <td>                        GODAM NAGESH</td>\n",
        "      <td> 430847</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9   </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Peddapalle</td>\n",
        "      <td>                         BALKA SUMAN</td>\n",
        "      <td> 565496</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27  </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Karimnagar</td>\n",
        "      <td>              VINOD KUMAR BOINAPALLY</td>\n",
        "      <td> 505783</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45  </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 Nizamabad</td>\n",
        "      <td>                 KALVAKUNTLA KAVITHA</td>\n",
        "      <td> 439307</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62  </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 Zahirabad</td>\n",
        "      <td>                          B.B. PATIL</td>\n",
        "      <td> 508661</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>73  </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                     Medak</td>\n",
        "      <td>       KALVAKUNTLA CHANDRASEKHAR RAO</td>\n",
        "      <td> 657492</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>87  </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Malkajgiri</td>\n",
        "      <td>                      CH.MALLA REDDY</td>\n",
        "      <td> 523336</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               Secundrabad</td>\n",
        "      <td>                  BANDARU DATTATREYA</td>\n",
        "      <td> 438271</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>149 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 Hyderabad</td>\n",
        "      <td>                    ASADUDDIN OWAISI</td>\n",
        "      <td> 513868</td>\n",
        "      <td> yes</td>\n",
        "      <td> AIMIM</td>\n",
        "      <td> All India Majlis-E-Ittehadul Muslimeen</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>166 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 CHELVELLA</td>\n",
        "      <td>             KONDA VISHWESHWAR REDDY</td>\n",
        "      <td> 435077</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>182 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               Mahbubnagar</td>\n",
        "      <td>                  AP JITHENDER REDDY</td>\n",
        "      <td> 334228</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>192 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              Nagarkurnool</td>\n",
        "      <td>                      YELLAIAH NANDI</td>\n",
        "      <td> 420075</td>\n",
        "      <td> yes</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>199 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Nalgonda</td>\n",
        "      <td>               GUTHA SUKHENDER REDDY</td>\n",
        "      <td> 472093</td>\n",
        "      <td> yes</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>209 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   Bhongir</td>\n",
        "      <td>             DR. BOORA NARSAIAH GOUD</td>\n",
        "      <td> 448245</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>223 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Warangal</td>\n",
        "      <td>                     KADIYAM SRIHARI</td>\n",
        "      <td> 661639</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>236 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               Mahabubabad</td>\n",
        "      <td>         PROF. AZMEERA SEETARAM NAIK</td>\n",
        "      <td> 320569</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>254 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   Khammam</td>\n",
        "      <td>          PONGULETI  SRINIVASA REDDY</td>\n",
        "      <td> 421957</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>282 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                     Aruku</td>\n",
        "      <td>                   KOTHAPALLI GEETHA</td>\n",
        "      <td> 413191</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>294 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Srikakulam</td>\n",
        "      <td>            RAMMOHAN NAIDU KINJARAPU</td>\n",
        "      <td> 556163</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>305 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              Vizianagaram</td>\n",
        "      <td>       ASHOK GAJAPATHI RAJU PUSAPATI</td>\n",
        "      <td> 536549</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>315 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             Visakhapatnam</td>\n",
        "      <td>               KAMBHAMPATI HARI BABU</td>\n",
        "      <td> 566832</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>338 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Anakapalli</td>\n",
        "      <td> MUTTAMSETTI SRINIVASA RAO (AVANTHI)</td>\n",
        "      <td> 568463</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>347 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Kakinada</td>\n",
        "      <td>                    THOTA NARASIMHAM</td>\n",
        "      <td> 514402</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>370 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Amalapuram</td>\n",
        "      <td>            DR PANDULA RAVINDRA BABU</td>\n",
        "      <td> 594547</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>385 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               Rajahmundry</td>\n",
        "      <td>                MURALI MOHAN MAGANTI</td>\n",
        "      <td> 630573</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>400 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Narsapuram</td>\n",
        "      <td>                 GOKARAJU GANGA RAJU</td>\n",
        "      <td> 540306</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>415 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                     Eluru</td>\n",
        "      <td>     MAGANTI VENKATESWARA RAO (BABU)</td>\n",
        "      <td> 623471</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>431 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             Machilipatnam</td>\n",
        "      <td>              KONAKALLA NARAYANA RAO</td>\n",
        "      <td> 587280</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>443 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                Vijayawada</td>\n",
        "      <td>                   KESINENI SRINIVAS</td>\n",
        "      <td> 592696</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>466 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                    Guntur</td>\n",
        "      <td>                       JAYADEV GALLA</td>\n",
        "      <td> 618417</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>479 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              Narasaraopet</td>\n",
        "      <td>              SAMBASIVA RAO RAYAPATI</td>\n",
        "      <td> 632464</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>491 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   Bapatla</td>\n",
        "      <td>                     MALYADRI SRIRAM</td>\n",
        "      <td> 578145</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>506 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                    Ongole</td>\n",
        "      <td>                     Y.V.SUBBA REDDY</td>\n",
        "      <td> 589960</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>522 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   Nandyal</td>\n",
        "      <td>                         S.P.Y REDDY</td>\n",
        "      <td> 622411</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>537 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   Kurnool</td>\n",
        "      <td>                        BUTTA RENUKA</td>\n",
        "      <td> 472782</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>550 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 Anantapur</td>\n",
        "      <td>                  J.C. DIVAKAR REDDI</td>\n",
        "      <td> 606509</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>564 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Hindupur</td>\n",
        "      <td>                   KRISTAPPA NIMMALA</td>\n",
        "      <td> 604291</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>577 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                    Kadapa</td>\n",
        "      <td>                  Y.S. AVINASH REDDY</td>\n",
        "      <td> 671983</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>592 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   Nellore</td>\n",
        "      <td>            MEKAPATI RAJAMOHAN REDDY</td>\n",
        "      <td> 576396</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>607 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Tirupati</td>\n",
        "      <td>          VARAPRASAD RAO VELAGAPALLI</td>\n",
        "      <td> 580376</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>622 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Rajampet</td>\n",
        "      <td>                    P.V.MIDHUN REDDY</td>\n",
        "      <td> 601752</td>\n",
        "      <td> yes</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>632 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  Chittoor</td>\n",
        "      <td>                NARAMALLI SIVAPRASAD</td>\n",
        "      <td> 594862</td>\n",
        "      <td> yes</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>640 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>            ARUNACHAL WEST</td>\n",
        "      <td>                        KIREN RIJIJU</td>\n",
        "      <td> 169367</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>649 </th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>            ARUNACHAL EAST</td>\n",
        "      <td>                        NINONG ERING</td>\n",
        "      <td> 118455</td>\n",
        "      <td> yes</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>653 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 Karimganj</td>\n",
        "      <td>                   RADHESHYAM BISWAS</td>\n",
        "      <td> 362866</td>\n",
        "      <td> yes</td>\n",
        "      <td> AIUDF</td>\n",
        "      <td>      All India United Democratic Front</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>669 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   Silchar</td>\n",
        "      <td>                        SUSHMITA DEV</td>\n",
        "      <td> 336451</td>\n",
        "      <td> yes</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>687 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>       Autonomous District</td>\n",
        "      <td>                   BIREN SINGH ENGTI</td>\n",
        "      <td> 213152</td>\n",
        "      <td> yes</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>693 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    Dhubri</td>\n",
        "      <td>                     BADRUDDIN AJMAL</td>\n",
        "      <td> 592569</td>\n",
        "      <td> yes</td>\n",
        "      <td> AIUDF</td>\n",
        "      <td>      All India United Democratic Front</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>709 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 Kokrajhar</td>\n",
        "      <td>           NABA KUMAR SARANIA (HIRA)</td>\n",
        "      <td> 634428</td>\n",
        "      <td> yes</td>\n",
        "      <td>   IND</td>\n",
        "      <td>                            Independent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>716 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   Barpeta</td>\n",
        "      <td>                   SIRAJ UDDIN AJMAL</td>\n",
        "      <td> 394702</td>\n",
        "      <td> yes</td>\n",
        "      <td> AIUDF</td>\n",
        "      <td>      All India United Democratic Front</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>732 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   Gauhati</td>\n",
        "      <td>                  BIJOYA CHAKRAVARTY</td>\n",
        "      <td> 764985</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>751 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 Mangaldoi</td>\n",
        "      <td>                          RAMEN DEKA</td>\n",
        "      <td> 486357</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>764 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    Tezpur</td>\n",
        "      <td>                   RAM PRASAD SARMAH</td>\n",
        "      <td> 446511</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>774 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   Nowgong</td>\n",
        "      <td>                        RAJEN GOHAIN</td>\n",
        "      <td> 494146</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>783 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                  Kaliabor</td>\n",
        "      <td>                        GOURAV GOGOI</td>\n",
        "      <td> 443315</td>\n",
        "      <td> yes</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>797 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    Jorhat</td>\n",
        "      <td>                KAMAKHYA PRASAD TASA</td>\n",
        "      <td> 456420</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>808 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 Dibrugarh</td>\n",
        "      <td>                       RAMESWAR TELI</td>\n",
        "      <td> 494364</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>815 </th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 Lakhimpur</td>\n",
        "      <td>                  SARBANANDA SONOWAL</td>\n",
        "      <td> 612543</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>829 </th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td>             Valmiki Nagar</td>\n",
        "      <td>                SATISH CHANDRA DUBEY</td>\n",
        "      <td> 364011</td>\n",
        "      <td> yes</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>543 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "              Name of State/ UT Parliamentary Constituency  \\\n",
        "8548  Andaman & Nicobar Islands  Andaman & Nicobar Islands   \n",
        "0                Andhra Pradesh                   Adilabad   \n",
        "9                Andhra Pradesh                 Peddapalle   \n",
        "27               Andhra Pradesh                 Karimnagar   \n",
        "45               Andhra Pradesh                  Nizamabad   \n",
        "62               Andhra Pradesh                  Zahirabad   \n",
        "73               Andhra Pradesh                      Medak   \n",
        "87               Andhra Pradesh                 Malkajgiri   \n",
        "118              Andhra Pradesh                Secundrabad   \n",
        "149              Andhra Pradesh                  Hyderabad   \n",
        "166              Andhra Pradesh                  CHELVELLA   \n",
        "182              Andhra Pradesh                Mahbubnagar   \n",
        "192              Andhra Pradesh               Nagarkurnool   \n",
        "199              Andhra Pradesh                   Nalgonda   \n",
        "209              Andhra Pradesh                    Bhongir   \n",
        "223              Andhra Pradesh                   Warangal   \n",
        "236              Andhra Pradesh                Mahabubabad   \n",
        "254              Andhra Pradesh                    Khammam   \n",
        "282              Andhra Pradesh                      Aruku   \n",
        "294              Andhra Pradesh                 Srikakulam   \n",
        "305              Andhra Pradesh               Vizianagaram   \n",
        "315              Andhra Pradesh              Visakhapatnam   \n",
        "338              Andhra Pradesh                 Anakapalli   \n",
        "347              Andhra Pradesh                   Kakinada   \n",
        "370              Andhra Pradesh                 Amalapuram   \n",
        "385              Andhra Pradesh                Rajahmundry   \n",
        "400              Andhra Pradesh                 Narsapuram   \n",
        "415              Andhra Pradesh                      Eluru   \n",
        "431              Andhra Pradesh              Machilipatnam   \n",
        "443              Andhra Pradesh                 Vijayawada   \n",
        "466              Andhra Pradesh                     Guntur   \n",
        "479              Andhra Pradesh               Narasaraopet   \n",
        "491              Andhra Pradesh                    Bapatla   \n",
        "506              Andhra Pradesh                     Ongole   \n",
        "522              Andhra Pradesh                    Nandyal   \n",
        "537              Andhra Pradesh                    Kurnool   \n",
        "550              Andhra Pradesh                  Anantapur   \n",
        "564              Andhra Pradesh                   Hindupur   \n",
        "577              Andhra Pradesh                     Kadapa   \n",
        "592              Andhra Pradesh                    Nellore   \n",
        "607              Andhra Pradesh                   Tirupati   \n",
        "622              Andhra Pradesh                   Rajampet   \n",
        "632              Andhra Pradesh                   Chittoor   \n",
        "640           Arunachal Pradesh             ARUNACHAL WEST   \n",
        "649           Arunachal Pradesh             ARUNACHAL EAST   \n",
        "653                       Assam                  Karimganj   \n",
        "669                       Assam                    Silchar   \n",
        "687                       Assam        Autonomous District   \n",
        "693                       Assam                     Dhubri   \n",
        "709                       Assam                  Kokrajhar   \n",
        "716                       Assam                    Barpeta   \n",
        "732                       Assam                    Gauhati   \n",
        "751                       Assam                  Mangaldoi   \n",
        "764                       Assam                     Tezpur   \n",
        "774                       Assam                    Nowgong   \n",
        "783                       Assam                   Kaliabor   \n",
        "797                       Assam                     Jorhat   \n",
        "808                       Assam                  Dibrugarh   \n",
        "815                       Assam                  Lakhimpur   \n",
        "829                       Bihar              Valmiki Nagar   \n",
        "                            ...                        ...   \n",
        "\n",
        "                           Candidate Name  Total Votes Polled Winner or Not?  \\\n",
        "8548                      BISHNU PADA RAY               90969            yes   \n",
        "0                            GODAM NAGESH              430847            yes   \n",
        "9                             BALKA SUMAN              565496            yes   \n",
        "27                 VINOD KUMAR BOINAPALLY              505783            yes   \n",
        "45                    KALVAKUNTLA KAVITHA              439307            yes   \n",
        "62                             B.B. PATIL              508661            yes   \n",
        "73          KALVAKUNTLA CHANDRASEKHAR RAO              657492            yes   \n",
        "87                         CH.MALLA REDDY              523336            yes   \n",
        "118                    BANDARU DATTATREYA              438271            yes   \n",
        "149                      ASADUDDIN OWAISI              513868            yes   \n",
        "166               KONDA VISHWESHWAR REDDY              435077            yes   \n",
        "182                    AP JITHENDER REDDY              334228            yes   \n",
        "192                        YELLAIAH NANDI              420075            yes   \n",
        "199                 GUTHA SUKHENDER REDDY              472093            yes   \n",
        "209               DR. BOORA NARSAIAH GOUD              448245            yes   \n",
        "223                       KADIYAM SRIHARI              661639            yes   \n",
        "236           PROF. AZMEERA SEETARAM NAIK              320569            yes   \n",
        "254            PONGULETI  SRINIVASA REDDY              421957            yes   \n",
        "282                     KOTHAPALLI GEETHA              413191            yes   \n",
        "294              RAMMOHAN NAIDU KINJARAPU              556163            yes   \n",
        "305         ASHOK GAJAPATHI RAJU PUSAPATI              536549            yes   \n",
        "315                 KAMBHAMPATI HARI BABU              566832            yes   \n",
        "338   MUTTAMSETTI SRINIVASA RAO (AVANTHI)              568463            yes   \n",
        "347                      THOTA NARASIMHAM              514402            yes   \n",
        "370              DR PANDULA RAVINDRA BABU              594547            yes   \n",
        "385                  MURALI MOHAN MAGANTI              630573            yes   \n",
        "400                   GOKARAJU GANGA RAJU              540306            yes   \n",
        "415       MAGANTI VENKATESWARA RAO (BABU)              623471            yes   \n",
        "431                KONAKALLA NARAYANA RAO              587280            yes   \n",
        "443                     KESINENI SRINIVAS              592696            yes   \n",
        "466                         JAYADEV GALLA              618417            yes   \n",
        "479                SAMBASIVA RAO RAYAPATI              632464            yes   \n",
        "491                       MALYADRI SRIRAM              578145            yes   \n",
        "506                       Y.V.SUBBA REDDY              589960            yes   \n",
        "522                           S.P.Y REDDY              622411            yes   \n",
        "537                          BUTTA RENUKA              472782            yes   \n",
        "550                    J.C. DIVAKAR REDDI              606509            yes   \n",
        "564                     KRISTAPPA NIMMALA              604291            yes   \n",
        "577                    Y.S. AVINASH REDDY              671983            yes   \n",
        "592              MEKAPATI RAJAMOHAN REDDY              576396            yes   \n",
        "607            VARAPRASAD RAO VELAGAPALLI              580376            yes   \n",
        "622                      P.V.MIDHUN REDDY              601752            yes   \n",
        "632                  NARAMALLI SIVAPRASAD              594862            yes   \n",
        "640                          KIREN RIJIJU              169367            yes   \n",
        "649                          NINONG ERING              118455            yes   \n",
        "653                     RADHESHYAM BISWAS              362866            yes   \n",
        "669                          SUSHMITA DEV              336451            yes   \n",
        "687                     BIREN SINGH ENGTI              213152            yes   \n",
        "693                       BADRUDDIN AJMAL              592569            yes   \n",
        "709             NABA KUMAR SARANIA (HIRA)              634428            yes   \n",
        "716                     SIRAJ UDDIN AJMAL              394702            yes   \n",
        "732                    BIJOYA CHAKRAVARTY              764985            yes   \n",
        "751                            RAMEN DEKA              486357            yes   \n",
        "764                     RAM PRASAD SARMAH              446511            yes   \n",
        "774                          RAJEN GOHAIN              494146            yes   \n",
        "783                          GOURAV GOGOI              443315            yes   \n",
        "797                  KAMAKHYA PRASAD TASA              456420            yes   \n",
        "808                         RAMESWAR TELI              494364            yes   \n",
        "815                    SARBANANDA SONOWAL              612543            yes   \n",
        "829                  SATISH CHANDRA DUBEY              364011            yes   \n",
        "                                      ...                 ...            ...   \n",
        "\n",
        "     Party Abbreviation                              Party Name  \n",
        "8548                BJP                  Bharatiya Janata Party  \n",
        "0                   TRS               Telangana Rashtra Samithi  \n",
        "9                   TRS               Telangana Rashtra Samithi  \n",
        "27                  TRS               Telangana Rashtra Samithi  \n",
        "45                  TRS               Telangana Rashtra Samithi  \n",
        "62                  TRS               Telangana Rashtra Samithi  \n",
        "73                  TRS               Telangana Rashtra Samithi  \n",
        "87                  TDP                            Telugu Desam  \n",
        "118                 BJP                  Bharatiya Janata Party  \n",
        "149               AIMIM  All India Majlis-E-Ittehadul Muslimeen  \n",
        "166                 TRS               Telangana Rashtra Samithi  \n",
        "182                 TRS               Telangana Rashtra Samithi  \n",
        "192                 INC                Indian National Congress  \n",
        "199                 INC                Indian National Congress  \n",
        "209                 TRS               Telangana Rashtra Samithi  \n",
        "223                 TRS               Telangana Rashtra Samithi  \n",
        "236                 TRS               Telangana Rashtra Samithi  \n",
        "254               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "282               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "294                 TDP                            Telugu Desam  \n",
        "305                 TDP                            Telugu Desam  \n",
        "315                 BJP                  Bharatiya Janata Party  \n",
        "338                 TDP                            Telugu Desam  \n",
        "347                 TDP                            Telugu Desam  \n",
        "370                 TDP                            Telugu Desam  \n",
        "385                 TDP                            Telugu Desam  \n",
        "400                 BJP                  Bharatiya Janata Party  \n",
        "415                 TDP                            Telugu Desam  \n",
        "431                 TDP                            Telugu Desam  \n",
        "443                 TDP                            Telugu Desam  \n",
        "466                 TDP                            Telugu Desam  \n",
        "479                 TDP                            Telugu Desam  \n",
        "491                 TDP                            Telugu Desam  \n",
        "506               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "522               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "537               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "550                 TDP                            Telugu Desam  \n",
        "564                 TDP                            Telugu Desam  \n",
        "577               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "592               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "607               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "622               YSRCP   Yuvajana Sramika Rythu Congress Party  \n",
        "632                 TDP                            Telugu Desam  \n",
        "640                 BJP                  Bharatiya Janata Party  \n",
        "649                 INC                Indian National Congress  \n",
        "653               AIUDF       All India United Democratic Front  \n",
        "669                 INC                Indian National Congress  \n",
        "687                 INC                Indian National Congress  \n",
        "693               AIUDF       All India United Democratic Front  \n",
        "709                 IND                             Independent  \n",
        "716               AIUDF       All India United Democratic Front  \n",
        "732                 BJP                  Bharatiya Janata Party  \n",
        "751                 BJP                  Bharatiya Janata Party  \n",
        "764                 BJP                  Bharatiya Janata Party  \n",
        "774                 BJP                  Bharatiya Janata Party  \n",
        "783                 INC                Indian National Congress  \n",
        "797                 BJP                  Bharatiya Janata Party  \n",
        "808                 BJP                  Bharatiya Janata Party  \n",
        "815                 BJP                  Bharatiya Janata Party  \n",
        "829                 BJP                  Bharatiya Janata Party  \n",
        "                    ...                                     ...  \n",
        "\n",
        "[543 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading the results to the origianl Data Frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data[\"Candidate Name\"] = winners[\"Candidate Name\"].values\n",
      "csv_data[\"Party Abbreviation\"] = winners[\"Party Abbreviation\"].values\n",
      "csv_data[\"Party Name\"] = winners[\"Party Name\"].values\n",
      "csv_data[\"Total Votes Polled\"] = winners[\"Total Votes Polled\"].values\n",
      "csv_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>PC Name</th>\n",
        "      <th>Male Electors</th>\n",
        "      <th>Male Voters</th>\n",
        "      <th>Female Electors</th>\n",
        "      <th>Female Voters</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "      <th>Male Turnout</th>\n",
        "      <th>Female Turnout</th>\n",
        "      <th>Total Turnout</th>\n",
        "      <th>Candidate Name</th>\n",
        "      <th>Party Abbreviation</th>\n",
        "      <th>Party Name</th>\n",
        "      <th>Total Votes Polled</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Andaman &amp; Nicobar Islands</td>\n",
        "      <td> 1 - Andaman &amp; Nicobar Islands</td>\n",
        "      <td>  142782</td>\n",
        "      <td> 101178</td>\n",
        "      <td>  126578</td>\n",
        "      <td>  89150</td>\n",
        "      <td>  269360</td>\n",
        "      <td>  190328</td>\n",
        "      <td> 0.708619</td>\n",
        "      <td> 0.704309</td>\n",
        "      <td> 0.706593</td>\n",
        "      <td>                     BISHNU PADA RAY</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td>  90969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 1 - Adilabad </td>\n",
        "      <td>  687389</td>\n",
        "      <td> 519364</td>\n",
        "      <td>  698844</td>\n",
        "      <td> 526475</td>\n",
        "      <td> 1386233</td>\n",
        "      <td> 1045839</td>\n",
        "      <td> 0.755561</td>\n",
        "      <td> 0.753351</td>\n",
        "      <td> 0.754447</td>\n",
        "      <td>                        GODAM NAGESH</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 430847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               2 - Peddapalle </td>\n",
        "      <td>  725767</td>\n",
        "      <td> 520598</td>\n",
        "      <td>  699594</td>\n",
        "      <td> 501586</td>\n",
        "      <td> 1425361</td>\n",
        "      <td> 1022184</td>\n",
        "      <td> 0.717307</td>\n",
        "      <td> 0.716967</td>\n",
        "      <td> 0.717140</td>\n",
        "      <td>                         BALKA SUMAN</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 565496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               3 - Karimnagar </td>\n",
        "      <td>  777458</td>\n",
        "      <td> 552489</td>\n",
        "      <td>  773376</td>\n",
        "      <td> 573202</td>\n",
        "      <td> 1550834</td>\n",
        "      <td> 1125691</td>\n",
        "      <td> 0.710635</td>\n",
        "      <td> 0.741169</td>\n",
        "      <td> 0.725862</td>\n",
        "      <td>              VINOD KUMAR BOINAPALLY</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 505783</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 4 - Nizamabad</td>\n",
        "      <td>  724504</td>\n",
        "      <td> 469712</td>\n",
        "      <td>  771689</td>\n",
        "      <td> 564212</td>\n",
        "      <td> 1496193</td>\n",
        "      <td> 1033924</td>\n",
        "      <td> 0.648322</td>\n",
        "      <td> 0.731139</td>\n",
        "      <td> 0.691037</td>\n",
        "      <td>                 KALVAKUNTLA KAVITHA</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 439307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 5 - Zahirabad</td>\n",
        "      <td>  717811</td>\n",
        "      <td> 546746</td>\n",
        "      <td>  727435</td>\n",
        "      <td> 548060</td>\n",
        "      <td> 1445246</td>\n",
        "      <td> 1094806</td>\n",
        "      <td> 0.761685</td>\n",
        "      <td> 0.753414</td>\n",
        "      <td> 0.757522</td>\n",
        "      <td>                          B.B. PATIL</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 508661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                     6 - Medak</td>\n",
        "      <td>  775903</td>\n",
        "      <td> 606863</td>\n",
        "      <td>  760812</td>\n",
        "      <td> 584233</td>\n",
        "      <td> 1536715</td>\n",
        "      <td> 1191096</td>\n",
        "      <td> 0.782138</td>\n",
        "      <td> 0.767907</td>\n",
        "      <td> 0.775092</td>\n",
        "      <td>       KALVAKUNTLA CHANDRASEKHAR RAO</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 657492</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                7 - Malkajgiri</td>\n",
        "      <td> 1723413</td>\n",
        "      <td> 878093</td>\n",
        "      <td> 1459912</td>\n",
        "      <td> 742304</td>\n",
        "      <td> 3183325</td>\n",
        "      <td> 1620397</td>\n",
        "      <td> 0.509508</td>\n",
        "      <td> 0.508458</td>\n",
        "      <td> 0.509027</td>\n",
        "      <td>                      CH.MALLA REDDY</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 523336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               8 - Secundrabad</td>\n",
        "      <td> 1012378</td>\n",
        "      <td> 545749</td>\n",
        "      <td>  881269</td>\n",
        "      <td> 458020</td>\n",
        "      <td> 1893647</td>\n",
        "      <td> 1003769</td>\n",
        "      <td> 0.539076</td>\n",
        "      <td> 0.519728</td>\n",
        "      <td> 0.530072</td>\n",
        "      <td>                  BANDARU DATTATREYA</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 438271</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 9 - Hyderabad</td>\n",
        "      <td>  961290</td>\n",
        "      <td> 526510</td>\n",
        "      <td>  862374</td>\n",
        "      <td> 444911</td>\n",
        "      <td> 1823664</td>\n",
        "      <td>  971421</td>\n",
        "      <td> 0.547712</td>\n",
        "      <td> 0.515914</td>\n",
        "      <td> 0.532675</td>\n",
        "      <td>                    ASADUDDIN OWAISI</td>\n",
        "      <td> AIMIM</td>\n",
        "      <td> All India Majlis-E-Ittehadul Muslimeen</td>\n",
        "      <td> 513868</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                10 - CHELVELLA</td>\n",
        "      <td> 1153049</td>\n",
        "      <td> 696749</td>\n",
        "      <td> 1032130</td>\n",
        "      <td> 619113</td>\n",
        "      <td> 2185179</td>\n",
        "      <td> 1315862</td>\n",
        "      <td> 0.604267</td>\n",
        "      <td> 0.599840</td>\n",
        "      <td> 0.602176</td>\n",
        "      <td>             KONDA VISHWESHWAR REDDY</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 435077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              11 - Mahbubnagar</td>\n",
        "      <td>  709711</td>\n",
        "      <td> 511764</td>\n",
        "      <td>  708961</td>\n",
        "      <td> 503036</td>\n",
        "      <td> 1418672</td>\n",
        "      <td> 1014800</td>\n",
        "      <td> 0.721088</td>\n",
        "      <td> 0.709540</td>\n",
        "      <td> 0.715317</td>\n",
        "      <td>                  AP JITHENDER REDDY</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 334228</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             12 - Nagarkurnool</td>\n",
        "      <td>  745038</td>\n",
        "      <td> 570342</td>\n",
        "      <td>  732300</td>\n",
        "      <td> 538626</td>\n",
        "      <td> 1477338</td>\n",
        "      <td> 1108968</td>\n",
        "      <td> 0.765521</td>\n",
        "      <td> 0.735526</td>\n",
        "      <td> 0.750653</td>\n",
        "      <td>                      YELLAIAH NANDI</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "      <td> 420075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 13 - Nalgonda</td>\n",
        "      <td>  747281</td>\n",
        "      <td> 602193</td>\n",
        "      <td>  748299</td>\n",
        "      <td> 587206</td>\n",
        "      <td> 1495580</td>\n",
        "      <td> 1189399</td>\n",
        "      <td> 0.805845</td>\n",
        "      <td> 0.784721</td>\n",
        "      <td> 0.795276</td>\n",
        "      <td>               GUTHA SUKHENDER REDDY</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "      <td> 472093</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 14 - Bhongir </td>\n",
        "      <td>  756963</td>\n",
        "      <td> 622333</td>\n",
        "      <td>  735288</td>\n",
        "      <td> 589610</td>\n",
        "      <td> 1492251</td>\n",
        "      <td> 1211943</td>\n",
        "      <td> 0.822145</td>\n",
        "      <td> 0.801876</td>\n",
        "      <td> 0.812158</td>\n",
        "      <td>             DR. BOORA NARSAIAH GOUD</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 448245</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 15 - Warangal</td>\n",
        "      <td>  771756</td>\n",
        "      <td> 592484</td>\n",
        "      <td>  766025</td>\n",
        "      <td> 582147</td>\n",
        "      <td> 1537781</td>\n",
        "      <td> 1174631</td>\n",
        "      <td> 0.767709</td>\n",
        "      <td> 0.759958</td>\n",
        "      <td> 0.763848</td>\n",
        "      <td>                     KADIYAM SRIHARI</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 661639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>            16 - Mahabubabad  </td>\n",
        "      <td>  688398</td>\n",
        "      <td> 562073</td>\n",
        "      <td>  698945</td>\n",
        "      <td> 562297</td>\n",
        "      <td> 1387343</td>\n",
        "      <td> 1124370</td>\n",
        "      <td> 0.816494</td>\n",
        "      <td> 0.804494</td>\n",
        "      <td> 0.810448</td>\n",
        "      <td>         PROF. AZMEERA SEETARAM NAIK</td>\n",
        "      <td>   TRS</td>\n",
        "      <td>              Telangana Rashtra Samithi</td>\n",
        "      <td> 320569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 17 - Khammam </td>\n",
        "      <td>  712329</td>\n",
        "      <td> 588728</td>\n",
        "      <td>  727960</td>\n",
        "      <td> 594169</td>\n",
        "      <td> 1440289</td>\n",
        "      <td> 1182897</td>\n",
        "      <td> 0.826483</td>\n",
        "      <td> 0.816211</td>\n",
        "      <td> 0.821291</td>\n",
        "      <td>          PONGULETI  SRINIVASA REDDY</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 421957</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   18 - Aruku </td>\n",
        "      <td>  622416</td>\n",
        "      <td> 451350</td>\n",
        "      <td>  650308</td>\n",
        "      <td> 458264</td>\n",
        "      <td> 1272724</td>\n",
        "      <td>  909614</td>\n",
        "      <td> 0.725158</td>\n",
        "      <td> 0.704688</td>\n",
        "      <td> 0.714699</td>\n",
        "      <td>                   KOTHAPALLI GEETHA</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 413191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               19 - Srikakulam</td>\n",
        "      <td>  706828</td>\n",
        "      <td> 505010</td>\n",
        "      <td>  707161</td>\n",
        "      <td> 546436</td>\n",
        "      <td> 1413989</td>\n",
        "      <td> 1051446</td>\n",
        "      <td> 0.714474</td>\n",
        "      <td> 0.772718</td>\n",
        "      <td> 0.743603</td>\n",
        "      <td>            RAMMOHAN NAIDU KINJARAPU</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 556163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             20 - Vizianagaram</td>\n",
        "      <td>  700837</td>\n",
        "      <td> 555005</td>\n",
        "      <td>  703290</td>\n",
        "      <td> 565311</td>\n",
        "      <td> 1404127</td>\n",
        "      <td> 1120316</td>\n",
        "      <td> 0.791917</td>\n",
        "      <td> 0.803809</td>\n",
        "      <td> 0.797874</td>\n",
        "      <td>       ASHOK GAJAPATHI RAJU PUSAPATI</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 536549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>            21 - Visakhapatnam</td>\n",
        "      <td>  875187</td>\n",
        "      <td> 585270</td>\n",
        "      <td>  847850</td>\n",
        "      <td> 578288</td>\n",
        "      <td> 1723037</td>\n",
        "      <td> 1163558</td>\n",
        "      <td> 0.668737</td>\n",
        "      <td> 0.682064</td>\n",
        "      <td> 0.675295</td>\n",
        "      <td>               KAMBHAMPATI HARI BABU</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 566832</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               22 - Anakapalli</td>\n",
        "      <td>  689132</td>\n",
        "      <td> 563818</td>\n",
        "      <td>  712342</td>\n",
        "      <td> 584254</td>\n",
        "      <td> 1401474</td>\n",
        "      <td> 1148072</td>\n",
        "      <td> 0.818157</td>\n",
        "      <td> 0.820187</td>\n",
        "      <td> 0.819189</td>\n",
        "      <td> MUTTAMSETTI SRINIVASA RAO (AVANTHI)</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 568463</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 23 - Kakinada</td>\n",
        "      <td>  709101</td>\n",
        "      <td> 558689</td>\n",
        "      <td>  709189</td>\n",
        "      <td> 541310</td>\n",
        "      <td> 1418290</td>\n",
        "      <td> 1099999</td>\n",
        "      <td> 0.787884</td>\n",
        "      <td> 0.763280</td>\n",
        "      <td> 0.775581</td>\n",
        "      <td>                    THOTA NARASIMHAM</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 514402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              24 - Amalapuram </td>\n",
        "      <td>  682607</td>\n",
        "      <td> 568534</td>\n",
        "      <td>  675259</td>\n",
        "      <td> 552393</td>\n",
        "      <td> 1357866</td>\n",
        "      <td> 1120927</td>\n",
        "      <td> 0.832886</td>\n",
        "      <td> 0.818046</td>\n",
        "      <td> 0.825506</td>\n",
        "      <td>            DR PANDULA RAVINDRA BABU</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 594547</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>              25 - Rajahmundry</td>\n",
        "      <td>  701707</td>\n",
        "      <td> 577999</td>\n",
        "      <td>  719581</td>\n",
        "      <td> 576382</td>\n",
        "      <td> 1421288</td>\n",
        "      <td> 1154381</td>\n",
        "      <td> 0.823704</td>\n",
        "      <td> 0.800997</td>\n",
        "      <td> 0.812208</td>\n",
        "      <td>                MURALI MOHAN MAGANTI</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 630573</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               26 - Narsapuram</td>\n",
        "      <td>  652668</td>\n",
        "      <td> 539357</td>\n",
        "      <td>  672475</td>\n",
        "      <td> 549594</td>\n",
        "      <td> 1325143</td>\n",
        "      <td> 1088951</td>\n",
        "      <td> 0.826388</td>\n",
        "      <td> 0.817271</td>\n",
        "      <td> 0.821761</td>\n",
        "      <td>                 GOKARAJU GANGA RAJU</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 540306</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   27 - Eluru </td>\n",
        "      <td>  706916</td>\n",
        "      <td> 597603</td>\n",
        "      <td>  720844</td>\n",
        "      <td> 604093</td>\n",
        "      <td> 1427760</td>\n",
        "      <td> 1201696</td>\n",
        "      <td> 0.845366</td>\n",
        "      <td> 0.838036</td>\n",
        "      <td> 0.841665</td>\n",
        "      <td>     MAGANTI VENKATESWARA RAO (BABU)</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 623471</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>           28 - Machilipatnam </td>\n",
        "      <td>  675774</td>\n",
        "      <td> 567908</td>\n",
        "      <td>  693537</td>\n",
        "      <td> 573157</td>\n",
        "      <td> 1369311</td>\n",
        "      <td> 1141065</td>\n",
        "      <td> 0.840382</td>\n",
        "      <td> 0.826426</td>\n",
        "      <td> 0.833313</td>\n",
        "      <td>              KONAKALLA NARAYANA RAO</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 587280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>               29 - Vijayawada</td>\n",
        "      <td>  781156</td>\n",
        "      <td> 602198</td>\n",
        "      <td>  783357</td>\n",
        "      <td> 592877</td>\n",
        "      <td> 1564513</td>\n",
        "      <td> 1195075</td>\n",
        "      <td> 0.770906</td>\n",
        "      <td> 0.756841</td>\n",
        "      <td> 0.763864</td>\n",
        "      <td>                   KESINENI SRINIVAS</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 592696</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   30 - Guntur</td>\n",
        "      <td>  773027</td>\n",
        "      <td> 617483</td>\n",
        "      <td>  798989</td>\n",
        "      <td> 627443</td>\n",
        "      <td> 1572016</td>\n",
        "      <td> 1244926</td>\n",
        "      <td> 0.798786</td>\n",
        "      <td> 0.785296</td>\n",
        "      <td> 0.791930</td>\n",
        "      <td>                       JAYADEV GALLA</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 618417</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>             31 - Narasaraopet</td>\n",
        "      <td>  748465</td>\n",
        "      <td> 638080</td>\n",
        "      <td>  766396</td>\n",
        "      <td> 643978</td>\n",
        "      <td> 1514861</td>\n",
        "      <td> 1282058</td>\n",
        "      <td> 0.852518</td>\n",
        "      <td> 0.840268</td>\n",
        "      <td> 0.846321</td>\n",
        "      <td>              SAMBASIVA RAO RAYAPATI</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 632464</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 32 - Bapatla </td>\n",
        "      <td>  686482</td>\n",
        "      <td> 587223</td>\n",
        "      <td>  706483</td>\n",
        "      <td> 597411</td>\n",
        "      <td> 1392965</td>\n",
        "      <td> 1184634</td>\n",
        "      <td> 0.855409</td>\n",
        "      <td> 0.845613</td>\n",
        "      <td> 0.850441</td>\n",
        "      <td>                     MALYADRI SRIRAM</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 578145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  33 - Ongole </td>\n",
        "      <td>  736245</td>\n",
        "      <td> 603025</td>\n",
        "      <td>  734238</td>\n",
        "      <td> 605200</td>\n",
        "      <td> 1470483</td>\n",
        "      <td> 1208225</td>\n",
        "      <td> 0.819055</td>\n",
        "      <td> 0.824256</td>\n",
        "      <td> 0.821652</td>\n",
        "      <td>                     Y.V.SUBBA REDDY</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 589960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  34 - Nandyal</td>\n",
        "      <td>  783266</td>\n",
        "      <td> 603562</td>\n",
        "      <td>  793862</td>\n",
        "      <td> 601394</td>\n",
        "      <td> 1577128</td>\n",
        "      <td> 1204956</td>\n",
        "      <td> 0.770571</td>\n",
        "      <td> 0.757555</td>\n",
        "      <td> 0.764019</td>\n",
        "      <td>                         S.P.Y REDDY</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 622411</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  35 - Kurnool</td>\n",
        "      <td>  738791</td>\n",
        "      <td> 544802</td>\n",
        "      <td>  743016</td>\n",
        "      <td> 520930</td>\n",
        "      <td> 1481807</td>\n",
        "      <td> 1065732</td>\n",
        "      <td> 0.737424</td>\n",
        "      <td> 0.701102</td>\n",
        "      <td> 0.719211</td>\n",
        "      <td>                        BUTTA RENUKA</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 472782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                36 - Anantapur</td>\n",
        "      <td>  775509</td>\n",
        "      <td> 612890</td>\n",
        "      <td>  761403</td>\n",
        "      <td> 592164</td>\n",
        "      <td> 1536912</td>\n",
        "      <td> 1205054</td>\n",
        "      <td> 0.790307</td>\n",
        "      <td> 0.777727</td>\n",
        "      <td> 0.784075</td>\n",
        "      <td>                  J.C. DIVAKAR REDDI</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 606509</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 37 - Hindupur</td>\n",
        "      <td>  734638</td>\n",
        "      <td> 601932</td>\n",
        "      <td>  711865</td>\n",
        "      <td> 575325</td>\n",
        "      <td> 1446503</td>\n",
        "      <td> 1177257</td>\n",
        "      <td> 0.819359</td>\n",
        "      <td> 0.808194</td>\n",
        "      <td> 0.813864</td>\n",
        "      <td>                   KRISTAPPA NIMMALA</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 604291</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                   38 - Kadapa</td>\n",
        "      <td>  765036</td>\n",
        "      <td> 588805</td>\n",
        "      <td>  785543</td>\n",
        "      <td> 611857</td>\n",
        "      <td> 1550579</td>\n",
        "      <td> 1200662</td>\n",
        "      <td> 0.769644</td>\n",
        "      <td> 0.778897</td>\n",
        "      <td> 0.774331</td>\n",
        "      <td>                  Y.S. AVINASH REDDY</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 671983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                  39 - Nellore</td>\n",
        "      <td>  796583</td>\n",
        "      <td> 593468</td>\n",
        "      <td>  809557</td>\n",
        "      <td> 594180</td>\n",
        "      <td> 1606140</td>\n",
        "      <td> 1187648</td>\n",
        "      <td> 0.745017</td>\n",
        "      <td> 0.733957</td>\n",
        "      <td> 0.739442</td>\n",
        "      <td>            MEKAPATI RAJAMOHAN REDDY</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 576396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                40 - Tirupati </td>\n",
        "      <td>  778778</td>\n",
        "      <td> 604834</td>\n",
        "      <td>  795766</td>\n",
        "      <td> 608230</td>\n",
        "      <td> 1574544</td>\n",
        "      <td> 1213064</td>\n",
        "      <td> 0.776645</td>\n",
        "      <td> 0.764333</td>\n",
        "      <td> 0.770422</td>\n",
        "      <td>          VARAPRASAD RAO VELAGAPALLI</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 580376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                 41 - Rajampet</td>\n",
        "      <td>  735347</td>\n",
        "      <td> 573019</td>\n",
        "      <td>  752151</td>\n",
        "      <td> 585298</td>\n",
        "      <td> 1487498</td>\n",
        "      <td> 1158317</td>\n",
        "      <td> 0.779250</td>\n",
        "      <td> 0.778166</td>\n",
        "      <td> 0.778702</td>\n",
        "      <td>                    P.V.MIDHUN REDDY</td>\n",
        "      <td> YSRCP</td>\n",
        "      <td>  Yuvajana Sramika Rythu Congress Party</td>\n",
        "      <td> 601752</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td>            Andhra Pradesh</td>\n",
        "      <td>                42 - Chittoor </td>\n",
        "      <td>  723996</td>\n",
        "      <td> 598259</td>\n",
        "      <td>  728145</td>\n",
        "      <td> 600656</td>\n",
        "      <td> 1452141</td>\n",
        "      <td> 1198915</td>\n",
        "      <td> 0.826329</td>\n",
        "      <td> 0.824913</td>\n",
        "      <td> 0.825619</td>\n",
        "      <td>                NARAMALLI SIVAPRASAD</td>\n",
        "      <td>   TDP</td>\n",
        "      <td>                           Telugu Desam</td>\n",
        "      <td> 594862</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>            1 - ARUNACHAL WEST</td>\n",
        "      <td>  219334</td>\n",
        "      <td> 159978</td>\n",
        "      <td>  227181</td>\n",
        "      <td> 175687</td>\n",
        "      <td>  446515</td>\n",
        "      <td>  335665</td>\n",
        "      <td> 0.729381</td>\n",
        "      <td> 0.773335</td>\n",
        "      <td> 0.751744</td>\n",
        "      <td>                        KIREN RIJIJU</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 169367</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>         Arunachal Pradesh</td>\n",
        "      <td>            2 - ARUNACHAL EAST</td>\n",
        "      <td>  160293</td>\n",
        "      <td> 129313</td>\n",
        "      <td>  152579</td>\n",
        "      <td> 131978</td>\n",
        "      <td>  312872</td>\n",
        "      <td>  261291</td>\n",
        "      <td> 0.806729</td>\n",
        "      <td> 0.864981</td>\n",
        "      <td> 0.835137</td>\n",
        "      <td>                        NINONG ERING</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "      <td> 118455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                1 - Karimganj </td>\n",
        "      <td>  615198</td>\n",
        "      <td> 482484</td>\n",
        "      <td>  550799</td>\n",
        "      <td> 404436</td>\n",
        "      <td> 1165997</td>\n",
        "      <td>  886920</td>\n",
        "      <td> 0.784274</td>\n",
        "      <td> 0.734271</td>\n",
        "      <td> 0.760654</td>\n",
        "      <td>                   RADHESHYAM BISWAS</td>\n",
        "      <td> AIUDF</td>\n",
        "      <td>      All India United Democratic Front</td>\n",
        "      <td> 362866</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   2 - Silchar</td>\n",
        "      <td>  554540</td>\n",
        "      <td> 428949</td>\n",
        "      <td>  505635</td>\n",
        "      <td> 370881</td>\n",
        "      <td> 1060175</td>\n",
        "      <td>  799830</td>\n",
        "      <td> 0.773522</td>\n",
        "      <td> 0.733496</td>\n",
        "      <td> 0.754432</td>\n",
        "      <td>                        SUSHMITA DEV</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "      <td> 336451</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>       3 - Autonomous District</td>\n",
        "      <td>  358880</td>\n",
        "      <td> 279409</td>\n",
        "      <td>  343010</td>\n",
        "      <td> 263871</td>\n",
        "      <td>  701890</td>\n",
        "      <td>  543280</td>\n",
        "      <td> 0.778558</td>\n",
        "      <td> 0.769281</td>\n",
        "      <td> 0.774024</td>\n",
        "      <td>                   BIREN SINGH ENGTI</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "      <td> 213152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    4 - Dhubri</td>\n",
        "      <td>  798124</td>\n",
        "      <td> 709191</td>\n",
        "      <td>  754430</td>\n",
        "      <td> 660433</td>\n",
        "      <td> 1552554</td>\n",
        "      <td> 1369624</td>\n",
        "      <td> 0.888572</td>\n",
        "      <td> 0.875407</td>\n",
        "      <td> 0.882175</td>\n",
        "      <td>                     BADRUDDIN AJMAL</td>\n",
        "      <td> AIUDF</td>\n",
        "      <td>      All India United Democratic Front</td>\n",
        "      <td> 592569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 5 - Kokrajhar</td>\n",
        "      <td>  776071</td>\n",
        "      <td> 636657</td>\n",
        "      <td>  729405</td>\n",
        "      <td> 587212</td>\n",
        "      <td> 1505476</td>\n",
        "      <td> 1223869</td>\n",
        "      <td> 0.820359</td>\n",
        "      <td> 0.805056</td>\n",
        "      <td> 0.812945</td>\n",
        "      <td>           NABA KUMAR SARANIA (HIRA)</td>\n",
        "      <td>   IND</td>\n",
        "      <td>                            Independent</td>\n",
        "      <td> 634428</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   6 - Barpeta</td>\n",
        "      <td>  755559</td>\n",
        "      <td> 634405</td>\n",
        "      <td>  674616</td>\n",
        "      <td> 571458</td>\n",
        "      <td> 1430175</td>\n",
        "      <td> 1205863</td>\n",
        "      <td> 0.839650</td>\n",
        "      <td> 0.847086</td>\n",
        "      <td> 0.843158</td>\n",
        "      <td>                   SIRAJ UDDIN AJMAL</td>\n",
        "      <td> AIUDF</td>\n",
        "      <td>      All India United Democratic Front</td>\n",
        "      <td> 394702</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   7 - Gauhati</td>\n",
        "      <td>  988067</td>\n",
        "      <td> 785494</td>\n",
        "      <td>  934203</td>\n",
        "      <td> 726235</td>\n",
        "      <td> 1922270</td>\n",
        "      <td> 1511729</td>\n",
        "      <td> 0.794981</td>\n",
        "      <td> 0.777385</td>\n",
        "      <td> 0.786429</td>\n",
        "      <td>                  BIJOYA CHAKRAVARTY</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 764985</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 8 - Mangaldoi</td>\n",
        "      <td>  791539</td>\n",
        "      <td> 651272</td>\n",
        "      <td>  724137</td>\n",
        "      <td> 581965</td>\n",
        "      <td> 1515676</td>\n",
        "      <td> 1233237</td>\n",
        "      <td> 0.822792</td>\n",
        "      <td> 0.803667</td>\n",
        "      <td> 0.813655</td>\n",
        "      <td>                          RAMEN DEKA</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 486357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                    9 - Tezpur</td>\n",
        "      <td>  654866</td>\n",
        "      <td> 505643</td>\n",
        "      <td>  604702</td>\n",
        "      <td> 475045</td>\n",
        "      <td> 1259568</td>\n",
        "      <td>  980688</td>\n",
        "      <td> 0.772132</td>\n",
        "      <td> 0.785585</td>\n",
        "      <td> 0.778591</td>\n",
        "      <td>                   RAM PRASAD SARMAH</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 446511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                  10 - Nowgong</td>\n",
        "      <td>  792424</td>\n",
        "      <td> 639392</td>\n",
        "      <td>  731457</td>\n",
        "      <td> 590682</td>\n",
        "      <td> 1523881</td>\n",
        "      <td> 1230074</td>\n",
        "      <td> 0.806881</td>\n",
        "      <td> 0.807542</td>\n",
        "      <td> 0.807198</td>\n",
        "      <td>                        RAJEN GOHAIN</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 494146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                 11 - Kaliabor</td>\n",
        "      <td>  752785</td>\n",
        "      <td> 605292</td>\n",
        "      <td>  705080</td>\n",
        "      <td> 561198</td>\n",
        "      <td> 1457865</td>\n",
        "      <td> 1166490</td>\n",
        "      <td> 0.804070</td>\n",
        "      <td> 0.795935</td>\n",
        "      <td> 0.800136</td>\n",
        "      <td>                        GOURAV GOGOI</td>\n",
        "      <td>   INC</td>\n",
        "      <td>               Indian National Congress</td>\n",
        "      <td> 443315</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                   12 - Jorhat</td>\n",
        "      <td>  634236</td>\n",
        "      <td> 483829</td>\n",
        "      <td>  600212</td>\n",
        "      <td> 447507</td>\n",
        "      <td> 1234448</td>\n",
        "      <td>  931336</td>\n",
        "      <td> 0.762853</td>\n",
        "      <td> 0.745582</td>\n",
        "      <td> 0.754455</td>\n",
        "      <td>                KAMAKHYA PRASAD TASA</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 456420</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                13 - Dibrugarh</td>\n",
        "      <td>  579657</td>\n",
        "      <td> 462412</td>\n",
        "      <td>  544648</td>\n",
        "      <td> 428556</td>\n",
        "      <td> 1124305</td>\n",
        "      <td>  890968</td>\n",
        "      <td> 0.797734</td>\n",
        "      <td> 0.786849</td>\n",
        "      <td> 0.792461</td>\n",
        "      <td>                       RAMESWAR TELI</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 494364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td>                     Assam</td>\n",
        "      <td>                14 - Lakhimpur</td>\n",
        "      <td>  735263</td>\n",
        "      <td> 572334</td>\n",
        "      <td>  695731</td>\n",
        "      <td> 539641</td>\n",
        "      <td> 1430994</td>\n",
        "      <td> 1111975</td>\n",
        "      <td> 0.778407</td>\n",
        "      <td> 0.775646</td>\n",
        "      <td> 0.777065</td>\n",
        "      <td>                  SARBANANDA SONOWAL</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 612543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td>                     Bihar</td>\n",
        "      <td>             1 - Valmiki Nagar</td>\n",
        "      <td>  786297</td>\n",
        "      <td> 484621</td>\n",
        "      <td>  670279</td>\n",
        "      <td> 415493</td>\n",
        "      <td> 1456576</td>\n",
        "      <td>  900114</td>\n",
        "      <td> 0.616333</td>\n",
        "      <td> 0.619881</td>\n",
        "      <td> 0.617966</td>\n",
        "      <td>                SATISH CHANDRA DUBEY</td>\n",
        "      <td>   BJP</td>\n",
        "      <td>                 Bharatiya Janata Party</td>\n",
        "      <td> 364011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>543 rows \u00d7 15 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "                        State                        PC Name  Male Electors  \\\n",
        "0   Andaman & Nicobar Islands  1 - Andaman & Nicobar Islands         142782   \n",
        "1              Andhra Pradesh                  1 - Adilabad          687389   \n",
        "2              Andhra Pradesh                2 - Peddapalle          725767   \n",
        "3              Andhra Pradesh                3 - Karimnagar          777458   \n",
        "4              Andhra Pradesh                  4 - Nizamabad         724504   \n",
        "5              Andhra Pradesh                  5 - Zahirabad         717811   \n",
        "6              Andhra Pradesh                      6 - Medak         775903   \n",
        "7              Andhra Pradesh                 7 - Malkajgiri        1723413   \n",
        "8              Andhra Pradesh                8 - Secundrabad        1012378   \n",
        "9              Andhra Pradesh                  9 - Hyderabad         961290   \n",
        "10             Andhra Pradesh                 10 - CHELVELLA        1153049   \n",
        "11             Andhra Pradesh               11 - Mahbubnagar         709711   \n",
        "12             Andhra Pradesh              12 - Nagarkurnool         745038   \n",
        "13             Andhra Pradesh                  13 - Nalgonda         747281   \n",
        "14             Andhra Pradesh                  14 - Bhongir          756963   \n",
        "15             Andhra Pradesh                  15 - Warangal         771756   \n",
        "16             Andhra Pradesh             16 - Mahabubabad           688398   \n",
        "17             Andhra Pradesh                  17 - Khammam          712329   \n",
        "18             Andhra Pradesh                    18 - Aruku          622416   \n",
        "19             Andhra Pradesh                19 - Srikakulam         706828   \n",
        "20             Andhra Pradesh              20 - Vizianagaram         700837   \n",
        "21             Andhra Pradesh             21 - Visakhapatnam         875187   \n",
        "22             Andhra Pradesh                22 - Anakapalli         689132   \n",
        "23             Andhra Pradesh                  23 - Kakinada         709101   \n",
        "24             Andhra Pradesh               24 - Amalapuram          682607   \n",
        "25             Andhra Pradesh               25 - Rajahmundry         701707   \n",
        "26             Andhra Pradesh                26 - Narsapuram         652668   \n",
        "27             Andhra Pradesh                    27 - Eluru          706916   \n",
        "28             Andhra Pradesh            28 - Machilipatnam          675774   \n",
        "29             Andhra Pradesh                29 - Vijayawada         781156   \n",
        "30             Andhra Pradesh                    30 - Guntur         773027   \n",
        "31             Andhra Pradesh              31 - Narasaraopet         748465   \n",
        "32             Andhra Pradesh                  32 - Bapatla          686482   \n",
        "33             Andhra Pradesh                   33 - Ongole          736245   \n",
        "34             Andhra Pradesh                   34 - Nandyal         783266   \n",
        "35             Andhra Pradesh                   35 - Kurnool         738791   \n",
        "36             Andhra Pradesh                 36 - Anantapur         775509   \n",
        "37             Andhra Pradesh                  37 - Hindupur         734638   \n",
        "38             Andhra Pradesh                    38 - Kadapa         765036   \n",
        "39             Andhra Pradesh                   39 - Nellore         796583   \n",
        "40             Andhra Pradesh                 40 - Tirupati          778778   \n",
        "41             Andhra Pradesh                  41 - Rajampet         735347   \n",
        "42             Andhra Pradesh                 42 - Chittoor          723996   \n",
        "43          Arunachal Pradesh             1 - ARUNACHAL WEST         219334   \n",
        "44          Arunachal Pradesh             2 - ARUNACHAL EAST         160293   \n",
        "45                      Assam                 1 - Karimganj          615198   \n",
        "46                      Assam                    2 - Silchar         554540   \n",
        "47                      Assam        3 - Autonomous District         358880   \n",
        "48                      Assam                     4 - Dhubri         798124   \n",
        "49                      Assam                  5 - Kokrajhar         776071   \n",
        "50                      Assam                    6 - Barpeta         755559   \n",
        "51                      Assam                    7 - Gauhati         988067   \n",
        "52                      Assam                  8 - Mangaldoi         791539   \n",
        "53                      Assam                     9 - Tezpur         654866   \n",
        "54                      Assam                   10 - Nowgong         792424   \n",
        "55                      Assam                  11 - Kaliabor         752785   \n",
        "56                      Assam                    12 - Jorhat         634236   \n",
        "57                      Assam                 13 - Dibrugarh         579657   \n",
        "58                      Assam                 14 - Lakhimpur         735263   \n",
        "59                      Bihar              1 - Valmiki Nagar         786297   \n",
        "                          ...                            ...            ...   \n",
        "\n",
        "    Male Voters  Female Electors  Female Voters  Total Electors  Total Voters  \\\n",
        "0        101178           126578          89150          269360        190328   \n",
        "1        519364           698844         526475         1386233       1045839   \n",
        "2        520598           699594         501586         1425361       1022184   \n",
        "3        552489           773376         573202         1550834       1125691   \n",
        "4        469712           771689         564212         1496193       1033924   \n",
        "5        546746           727435         548060         1445246       1094806   \n",
        "6        606863           760812         584233         1536715       1191096   \n",
        "7        878093          1459912         742304         3183325       1620397   \n",
        "8        545749           881269         458020         1893647       1003769   \n",
        "9        526510           862374         444911         1823664        971421   \n",
        "10       696749          1032130         619113         2185179       1315862   \n",
        "11       511764           708961         503036         1418672       1014800   \n",
        "12       570342           732300         538626         1477338       1108968   \n",
        "13       602193           748299         587206         1495580       1189399   \n",
        "14       622333           735288         589610         1492251       1211943   \n",
        "15       592484           766025         582147         1537781       1174631   \n",
        "16       562073           698945         562297         1387343       1124370   \n",
        "17       588728           727960         594169         1440289       1182897   \n",
        "18       451350           650308         458264         1272724        909614   \n",
        "19       505010           707161         546436         1413989       1051446   \n",
        "20       555005           703290         565311         1404127       1120316   \n",
        "21       585270           847850         578288         1723037       1163558   \n",
        "22       563818           712342         584254         1401474       1148072   \n",
        "23       558689           709189         541310         1418290       1099999   \n",
        "24       568534           675259         552393         1357866       1120927   \n",
        "25       577999           719581         576382         1421288       1154381   \n",
        "26       539357           672475         549594         1325143       1088951   \n",
        "27       597603           720844         604093         1427760       1201696   \n",
        "28       567908           693537         573157         1369311       1141065   \n",
        "29       602198           783357         592877         1564513       1195075   \n",
        "30       617483           798989         627443         1572016       1244926   \n",
        "31       638080           766396         643978         1514861       1282058   \n",
        "32       587223           706483         597411         1392965       1184634   \n",
        "33       603025           734238         605200         1470483       1208225   \n",
        "34       603562           793862         601394         1577128       1204956   \n",
        "35       544802           743016         520930         1481807       1065732   \n",
        "36       612890           761403         592164         1536912       1205054   \n",
        "37       601932           711865         575325         1446503       1177257   \n",
        "38       588805           785543         611857         1550579       1200662   \n",
        "39       593468           809557         594180         1606140       1187648   \n",
        "40       604834           795766         608230         1574544       1213064   \n",
        "41       573019           752151         585298         1487498       1158317   \n",
        "42       598259           728145         600656         1452141       1198915   \n",
        "43       159978           227181         175687          446515        335665   \n",
        "44       129313           152579         131978          312872        261291   \n",
        "45       482484           550799         404436         1165997        886920   \n",
        "46       428949           505635         370881         1060175        799830   \n",
        "47       279409           343010         263871          701890        543280   \n",
        "48       709191           754430         660433         1552554       1369624   \n",
        "49       636657           729405         587212         1505476       1223869   \n",
        "50       634405           674616         571458         1430175       1205863   \n",
        "51       785494           934203         726235         1922270       1511729   \n",
        "52       651272           724137         581965         1515676       1233237   \n",
        "53       505643           604702         475045         1259568        980688   \n",
        "54       639392           731457         590682         1523881       1230074   \n",
        "55       605292           705080         561198         1457865       1166490   \n",
        "56       483829           600212         447507         1234448        931336   \n",
        "57       462412           544648         428556         1124305        890968   \n",
        "58       572334           695731         539641         1430994       1111975   \n",
        "59       484621           670279         415493         1456576        900114   \n",
        "            ...              ...            ...             ...           ...   \n",
        "\n",
        "    Male Turnout  Female Turnout  Total Turnout  \\\n",
        "0       0.708619        0.704309       0.706593   \n",
        "1       0.755561        0.753351       0.754447   \n",
        "2       0.717307        0.716967       0.717140   \n",
        "3       0.710635        0.741169       0.725862   \n",
        "4       0.648322        0.731139       0.691037   \n",
        "5       0.761685        0.753414       0.757522   \n",
        "6       0.782138        0.767907       0.775092   \n",
        "7       0.509508        0.508458       0.509027   \n",
        "8       0.539076        0.519728       0.530072   \n",
        "9       0.547712        0.515914       0.532675   \n",
        "10      0.604267        0.599840       0.602176   \n",
        "11      0.721088        0.709540       0.715317   \n",
        "12      0.765521        0.735526       0.750653   \n",
        "13      0.805845        0.784721       0.795276   \n",
        "14      0.822145        0.801876       0.812158   \n",
        "15      0.767709        0.759958       0.763848   \n",
        "16      0.816494        0.804494       0.810448   \n",
        "17      0.826483        0.816211       0.821291   \n",
        "18      0.725158        0.704688       0.714699   \n",
        "19      0.714474        0.772718       0.743603   \n",
        "20      0.791917        0.803809       0.797874   \n",
        "21      0.668737        0.682064       0.675295   \n",
        "22      0.818157        0.820187       0.819189   \n",
        "23      0.787884        0.763280       0.775581   \n",
        "24      0.832886        0.818046       0.825506   \n",
        "25      0.823704        0.800997       0.812208   \n",
        "26      0.826388        0.817271       0.821761   \n",
        "27      0.845366        0.838036       0.841665   \n",
        "28      0.840382        0.826426       0.833313   \n",
        "29      0.770906        0.756841       0.763864   \n",
        "30      0.798786        0.785296       0.791930   \n",
        "31      0.852518        0.840268       0.846321   \n",
        "32      0.855409        0.845613       0.850441   \n",
        "33      0.819055        0.824256       0.821652   \n",
        "34      0.770571        0.757555       0.764019   \n",
        "35      0.737424        0.701102       0.719211   \n",
        "36      0.790307        0.777727       0.784075   \n",
        "37      0.819359        0.808194       0.813864   \n",
        "38      0.769644        0.778897       0.774331   \n",
        "39      0.745017        0.733957       0.739442   \n",
        "40      0.776645        0.764333       0.770422   \n",
        "41      0.779250        0.778166       0.778702   \n",
        "42      0.826329        0.824913       0.825619   \n",
        "43      0.729381        0.773335       0.751744   \n",
        "44      0.806729        0.864981       0.835137   \n",
        "45      0.784274        0.734271       0.760654   \n",
        "46      0.773522        0.733496       0.754432   \n",
        "47      0.778558        0.769281       0.774024   \n",
        "48      0.888572        0.875407       0.882175   \n",
        "49      0.820359        0.805056       0.812945   \n",
        "50      0.839650        0.847086       0.843158   \n",
        "51      0.794981        0.777385       0.786429   \n",
        "52      0.822792        0.803667       0.813655   \n",
        "53      0.772132        0.785585       0.778591   \n",
        "54      0.806881        0.807542       0.807198   \n",
        "55      0.804070        0.795935       0.800136   \n",
        "56      0.762853        0.745582       0.754455   \n",
        "57      0.797734        0.786849       0.792461   \n",
        "58      0.778407        0.775646       0.777065   \n",
        "59      0.616333        0.619881       0.617966   \n",
        "             ...             ...            ...   \n",
        "\n",
        "                         Candidate Name Party Abbreviation  \\\n",
        "0                       BISHNU PADA RAY                BJP   \n",
        "1                          GODAM NAGESH                TRS   \n",
        "2                           BALKA SUMAN                TRS   \n",
        "3                VINOD KUMAR BOINAPALLY                TRS   \n",
        "4                   KALVAKUNTLA KAVITHA                TRS   \n",
        "5                            B.B. PATIL                TRS   \n",
        "6         KALVAKUNTLA CHANDRASEKHAR RAO                TRS   \n",
        "7                        CH.MALLA REDDY                TDP   \n",
        "8                    BANDARU DATTATREYA                BJP   \n",
        "9                      ASADUDDIN OWAISI              AIMIM   \n",
        "10              KONDA VISHWESHWAR REDDY                TRS   \n",
        "11                   AP JITHENDER REDDY                TRS   \n",
        "12                       YELLAIAH NANDI                INC   \n",
        "13                GUTHA SUKHENDER REDDY                INC   \n",
        "14              DR. BOORA NARSAIAH GOUD                TRS   \n",
        "15                      KADIYAM SRIHARI                TRS   \n",
        "16          PROF. AZMEERA SEETARAM NAIK                TRS   \n",
        "17           PONGULETI  SRINIVASA REDDY              YSRCP   \n",
        "18                    KOTHAPALLI GEETHA              YSRCP   \n",
        "19             RAMMOHAN NAIDU KINJARAPU                TDP   \n",
        "20        ASHOK GAJAPATHI RAJU PUSAPATI                TDP   \n",
        "21                KAMBHAMPATI HARI BABU                BJP   \n",
        "22  MUTTAMSETTI SRINIVASA RAO (AVANTHI)                TDP   \n",
        "23                     THOTA NARASIMHAM                TDP   \n",
        "24             DR PANDULA RAVINDRA BABU                TDP   \n",
        "25                 MURALI MOHAN MAGANTI                TDP   \n",
        "26                  GOKARAJU GANGA RAJU                BJP   \n",
        "27      MAGANTI VENKATESWARA RAO (BABU)                TDP   \n",
        "28               KONAKALLA NARAYANA RAO                TDP   \n",
        "29                    KESINENI SRINIVAS                TDP   \n",
        "30                        JAYADEV GALLA                TDP   \n",
        "31               SAMBASIVA RAO RAYAPATI                TDP   \n",
        "32                      MALYADRI SRIRAM                TDP   \n",
        "33                      Y.V.SUBBA REDDY              YSRCP   \n",
        "34                          S.P.Y REDDY              YSRCP   \n",
        "35                         BUTTA RENUKA              YSRCP   \n",
        "36                   J.C. DIVAKAR REDDI                TDP   \n",
        "37                    KRISTAPPA NIMMALA                TDP   \n",
        "38                   Y.S. AVINASH REDDY              YSRCP   \n",
        "39             MEKAPATI RAJAMOHAN REDDY              YSRCP   \n",
        "40           VARAPRASAD RAO VELAGAPALLI              YSRCP   \n",
        "41                     P.V.MIDHUN REDDY              YSRCP   \n",
        "42                 NARAMALLI SIVAPRASAD                TDP   \n",
        "43                         KIREN RIJIJU                BJP   \n",
        "44                         NINONG ERING                INC   \n",
        "45                    RADHESHYAM BISWAS              AIUDF   \n",
        "46                         SUSHMITA DEV                INC   \n",
        "47                    BIREN SINGH ENGTI                INC   \n",
        "48                      BADRUDDIN AJMAL              AIUDF   \n",
        "49            NABA KUMAR SARANIA (HIRA)                IND   \n",
        "50                    SIRAJ UDDIN AJMAL              AIUDF   \n",
        "51                   BIJOYA CHAKRAVARTY                BJP   \n",
        "52                           RAMEN DEKA                BJP   \n",
        "53                    RAM PRASAD SARMAH                BJP   \n",
        "54                         RAJEN GOHAIN                BJP   \n",
        "55                         GOURAV GOGOI                INC   \n",
        "56                 KAMAKHYA PRASAD TASA                BJP   \n",
        "57                        RAMESWAR TELI                BJP   \n",
        "58                   SARBANANDA SONOWAL                BJP   \n",
        "59                 SATISH CHANDRA DUBEY                BJP   \n",
        "                                    ...                ...   \n",
        "\n",
        "                                Party Name  Total Votes Polled  \n",
        "0                   Bharatiya Janata Party               90969  \n",
        "1                Telangana Rashtra Samithi              430847  \n",
        "2                Telangana Rashtra Samithi              565496  \n",
        "3                Telangana Rashtra Samithi              505783  \n",
        "4                Telangana Rashtra Samithi              439307  \n",
        "5                Telangana Rashtra Samithi              508661  \n",
        "6                Telangana Rashtra Samithi              657492  \n",
        "7                             Telugu Desam              523336  \n",
        "8                   Bharatiya Janata Party              438271  \n",
        "9   All India Majlis-E-Ittehadul Muslimeen              513868  \n",
        "10               Telangana Rashtra Samithi              435077  \n",
        "11               Telangana Rashtra Samithi              334228  \n",
        "12                Indian National Congress              420075  \n",
        "13                Indian National Congress              472093  \n",
        "14               Telangana Rashtra Samithi              448245  \n",
        "15               Telangana Rashtra Samithi              661639  \n",
        "16               Telangana Rashtra Samithi              320569  \n",
        "17   Yuvajana Sramika Rythu Congress Party              421957  \n",
        "18   Yuvajana Sramika Rythu Congress Party              413191  \n",
        "19                            Telugu Desam              556163  \n",
        "20                            Telugu Desam              536549  \n",
        "21                  Bharatiya Janata Party              566832  \n",
        "22                            Telugu Desam              568463  \n",
        "23                            Telugu Desam              514402  \n",
        "24                            Telugu Desam              594547  \n",
        "25                            Telugu Desam              630573  \n",
        "26                  Bharatiya Janata Party              540306  \n",
        "27                            Telugu Desam              623471  \n",
        "28                            Telugu Desam              587280  \n",
        "29                            Telugu Desam              592696  \n",
        "30                            Telugu Desam              618417  \n",
        "31                            Telugu Desam              632464  \n",
        "32                            Telugu Desam              578145  \n",
        "33   Yuvajana Sramika Rythu Congress Party              589960  \n",
        "34   Yuvajana Sramika Rythu Congress Party              622411  \n",
        "35   Yuvajana Sramika Rythu Congress Party              472782  \n",
        "36                            Telugu Desam              606509  \n",
        "37                            Telugu Desam              604291  \n",
        "38   Yuvajana Sramika Rythu Congress Party              671983  \n",
        "39   Yuvajana Sramika Rythu Congress Party              576396  \n",
        "40   Yuvajana Sramika Rythu Congress Party              580376  \n",
        "41   Yuvajana Sramika Rythu Congress Party              601752  \n",
        "42                            Telugu Desam              594862  \n",
        "43                  Bharatiya Janata Party              169367  \n",
        "44                Indian National Congress              118455  \n",
        "45       All India United Democratic Front              362866  \n",
        "46                Indian National Congress              336451  \n",
        "47                Indian National Congress              213152  \n",
        "48       All India United Democratic Front              592569  \n",
        "49                             Independent              634428  \n",
        "50       All India United Democratic Front              394702  \n",
        "51                  Bharatiya Janata Party              764985  \n",
        "52                  Bharatiya Janata Party              486357  \n",
        "53                  Bharatiya Janata Party              446511  \n",
        "54                  Bharatiya Janata Party              494146  \n",
        "55                Indian National Congress              443315  \n",
        "56                  Bharatiya Janata Party              456420  \n",
        "57                  Bharatiya Janata Party              494364  \n",
        "58                  Bharatiya Janata Party              612543  \n",
        "59                  Bharatiya Janata Party              364011  \n",
        "                                       ...                 ...  \n",
        "\n",
        "[543 rows x 15 columns]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculaing the winning margin"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data[\"Winner Percentage\"] = csv_data[\"Total Votes Polled\"] / csv_data[\"Total Voters\"]\n",
      "csv_data.sort(\"Winner Percentage\", ascending=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State</th>\n",
        "      <th>PC Name</th>\n",
        "      <th>Male Electors</th>\n",
        "      <th>Male Voters</th>\n",
        "      <th>Female Electors</th>\n",
        "      <th>Female Voters</th>\n",
        "      <th>Total Electors</th>\n",
        "      <th>Total Voters</th>\n",
        "      <th>Male Turnout</th>\n",
        "      <th>Female Turnout</th>\n",
        "      <th>Total Turnout</th>\n",
        "      <th>Candidate Name</th>\n",
        "      <th>Party Abbreviation</th>\n",
        "      <th>Party Name</th>\n",
        "      <th>Total Votes Polled</th>\n",
        "      <th>Winner Percentage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>138</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                         24 - Surat</td>\n",
        "      <td>  803829</td>\n",
        "      <td> 539368</td>\n",
        "      <td>  680239</td>\n",
        "      <td> 408554</td>\n",
        "      <td> 1484068</td>\n",
        "      <td>  947922</td>\n",
        "      <td> 0.670998</td>\n",
        "      <td> 0.600604</td>\n",
        "      <td> 0.638732</td>\n",
        "      <td>                           DARSHANA VIKRAM JARDOSH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 718412</td>\n",
        "      <td> 0.757881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>134</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                      20 - Vadodara</td>\n",
        "      <td>  849077</td>\n",
        "      <td> 625045</td>\n",
        "      <td>  789244</td>\n",
        "      <td> 536532</td>\n",
        "      <td> 1638321</td>\n",
        "      <td> 1161577</td>\n",
        "      <td> 0.736146</td>\n",
        "      <td> 0.679805</td>\n",
        "      <td> 0.709005</td>\n",
        "      <td>                                     NARENDRA MODI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 845464</td>\n",
        "      <td> 0.727859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>139</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                       25 - Navsari</td>\n",
        "      <td>  972090</td>\n",
        "      <td> 649206</td>\n",
        "      <td>  792532</td>\n",
        "      <td> 511541</td>\n",
        "      <td> 1764622</td>\n",
        "      <td> 1160747</td>\n",
        "      <td> 0.667846</td>\n",
        "      <td> 0.645452</td>\n",
        "      <td> 0.657788</td>\n",
        "      <td>                                       C. R. PATIL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 820831</td>\n",
        "      <td> 0.707158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>278</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                  26 - Mumbai North</td>\n",
        "      <td>  972645</td>\n",
        "      <td> 527803</td>\n",
        "      <td>  811225</td>\n",
        "      <td> 418759</td>\n",
        "      <td> 1783870</td>\n",
        "      <td>  946562</td>\n",
        "      <td> 0.542647</td>\n",
        "      <td> 0.516206</td>\n",
        "      <td> 0.530623</td>\n",
        "      <td>                             GOPAL CHINAYYA SHETTY</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 664004</td>\n",
        "      <td> 0.701490</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>120</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                    6 - Gandhinagar</td>\n",
        "      <td>  900744</td>\n",
        "      <td> 620889</td>\n",
        "      <td>  833228</td>\n",
        "      <td> 514606</td>\n",
        "      <td> 1733972</td>\n",
        "      <td> 1135495</td>\n",
        "      <td> 0.689307</td>\n",
        "      <td> 0.617605</td>\n",
        "      <td> 0.654852</td>\n",
        "      <td>                                        L.K.ADVANI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 773539</td>\n",
        "      <td> 0.681235</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>355</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                         7 - Jaipur</td>\n",
        "      <td> 1047468</td>\n",
        "      <td> 716874</td>\n",
        "      <td>  910350</td>\n",
        "      <td> 579932</td>\n",
        "      <td> 1957818</td>\n",
        "      <td> 1296806</td>\n",
        "      <td> 0.684387</td>\n",
        "      <td> 0.637043</td>\n",
        "      <td> 0.662373</td>\n",
        "      <td>                                  RAMCHARAN BOHARA</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 863358</td>\n",
        "      <td> 0.665757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                       18 - VIDISHA</td>\n",
        "      <td>  872410</td>\n",
        "      <td> 621326</td>\n",
        "      <td>  761960</td>\n",
        "      <td> 452147</td>\n",
        "      <td> 1634370</td>\n",
        "      <td> 1073473</td>\n",
        "      <td> 0.712195</td>\n",
        "      <td> 0.593400</td>\n",
        "      <td> 0.656811</td>\n",
        "      <td>                                     SUSHMA SWARAJ</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 714348</td>\n",
        "      <td> 0.665455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>364</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                       16 - Jodhpur</td>\n",
        "      <td>  910466</td>\n",
        "      <td> 600132</td>\n",
        "      <td>  816897</td>\n",
        "      <td> 478466</td>\n",
        "      <td> 1727363</td>\n",
        "      <td> 1078598</td>\n",
        "      <td> 0.659148</td>\n",
        "      <td> 0.585712</td>\n",
        "      <td> 0.624419</td>\n",
        "      <td>                           GAJENDRASINGH SHEKHAWAT</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 713515</td>\n",
        "      <td> 0.661521</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>370</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                     22 - Rajsamand</td>\n",
        "      <td>  879526</td>\n",
        "      <td> 508738</td>\n",
        "      <td>  819875</td>\n",
        "      <td> 473381</td>\n",
        "      <td> 1699401</td>\n",
        "      <td>  982119</td>\n",
        "      <td> 0.578423</td>\n",
        "      <td> 0.577382</td>\n",
        "      <td> 0.577921</td>\n",
        "      <td>                              HARIOM SINGH RATHORE</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 644794</td>\n",
        "      <td> 0.656533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>415</th>\n",
        "      <td>          Tripura</td>\n",
        "      <td>                   2 - Tripura East</td>\n",
        "      <td>  581599</td>\n",
        "      <td> 489156</td>\n",
        "      <td>  558670</td>\n",
        "      <td> 461954</td>\n",
        "      <td> 1140269</td>\n",
        "      <td>  951110</td>\n",
        "      <td> 0.841054</td>\n",
        "      <td> 0.826882</td>\n",
        "      <td> 0.834110</td>\n",
        "      <td>                                JITENDRA CHOUDHURY</td>\n",
        "      <td> CPM</td>\n",
        "      <td> Communist Party of India  (Marxist)</td>\n",
        "      <td> 623771</td>\n",
        "      <td> 0.655835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>255</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                        3 - Jalgaon</td>\n",
        "      <td>  909027</td>\n",
        "      <td> 594892</td>\n",
        "      <td>  798906</td>\n",
        "      <td> 395440</td>\n",
        "      <td> 1707933</td>\n",
        "      <td>  990332</td>\n",
        "      <td> 0.654427</td>\n",
        "      <td> 0.494977</td>\n",
        "      <td> 0.579842</td>\n",
        "      <td>                                   A.T. NANA PATIL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 647773</td>\n",
        "      <td> 0.654097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>363</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                          15 - Pali</td>\n",
        "      <td>  994082</td>\n",
        "      <td> 581099</td>\n",
        "      <td>  898948</td>\n",
        "      <td> 514488</td>\n",
        "      <td> 1893030</td>\n",
        "      <td> 1095587</td>\n",
        "      <td> 0.584558</td>\n",
        "      <td> 0.572322</td>\n",
        "      <td> 0.578748</td>\n",
        "      <td>                                     P P CHOUDHARY</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 711772</td>\n",
        "      <td> 0.649672</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>310</th>\n",
        "      <td>     NCT OF Delhi</td>\n",
        "      <td> 4 - NEW DELHI                     </td>\n",
        "      <td>  830322</td>\n",
        "      <td> 546295</td>\n",
        "      <td>  659825</td>\n",
        "      <td> 423517</td>\n",
        "      <td> 1490147</td>\n",
        "      <td>  969812</td>\n",
        "      <td> 0.657932</td>\n",
        "      <td> 0.641863</td>\n",
        "      <td> 0.650816</td>\n",
        "      <td>                                          UDIT RAJ</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 629860</td>\n",
        "      <td> 0.649466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>249</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                        26 - INDORE</td>\n",
        "      <td> 1106461</td>\n",
        "      <td> 736759</td>\n",
        "      <td> 1008842</td>\n",
        "      <td> 580058</td>\n",
        "      <td> 2115303</td>\n",
        "      <td> 1316817</td>\n",
        "      <td> 0.665870</td>\n",
        "      <td> 0.574974</td>\n",
        "      <td> 0.622519</td>\n",
        "      <td>                             SUMITRA MAHAJAN (TAI)</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 854972</td>\n",
        "      <td> 0.649272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                   17 - HOSHANGABAD</td>\n",
        "      <td>  835492</td>\n",
        "      <td> 593957</td>\n",
        "      <td>  732635</td>\n",
        "      <td> 437218</td>\n",
        "      <td> 1568127</td>\n",
        "      <td> 1031175</td>\n",
        "      <td> 0.710907</td>\n",
        "      <td> 0.596775</td>\n",
        "      <td> 0.657584</td>\n",
        "      <td>                                 UDAY PRATAP SINGH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 669128</td>\n",
        "      <td> 0.648899</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>313</th>\n",
        "      <td>     NCT OF Delhi</td>\n",
        "      <td> 7 - SOUTH DELHI                   </td>\n",
        "      <td> 1005289</td>\n",
        "      <td> 638406</td>\n",
        "      <td>  747452</td>\n",
        "      <td> 464004</td>\n",
        "      <td> 1752741</td>\n",
        "      <td> 1102410</td>\n",
        "      <td> 0.635047</td>\n",
        "      <td> 0.620781</td>\n",
        "      <td> 0.628963</td>\n",
        "      <td>                                       NEIPHIU RIO</td>\n",
        "      <td> NPF</td>\n",
        "      <td>                  Naga Peoples Front</td>\n",
        "      <td> 713372</td>\n",
        "      <td> 0.647102</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                 7 - Ahmedabad East</td>\n",
        "      <td>  852765</td>\n",
        "      <td> 560074</td>\n",
        "      <td>  749067</td>\n",
        "      <td> 425451</td>\n",
        "      <td> 1601832</td>\n",
        "      <td>  985525</td>\n",
        "      <td> 0.656774</td>\n",
        "      <td> 0.567975</td>\n",
        "      <td> 0.615249</td>\n",
        "      <td>                                      PARESH RAWAL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 633582</td>\n",
        "      <td> 0.642888</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>122</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                 8 - Ahmedabad West</td>\n",
        "      <td>  800933</td>\n",
        "      <td> 534008</td>\n",
        "      <td>  733467</td>\n",
        "      <td> 430601</td>\n",
        "      <td> 1534400</td>\n",
        "      <td>  964609</td>\n",
        "      <td> 0.666732</td>\n",
        "      <td> 0.587076</td>\n",
        "      <td> 0.628656</td>\n",
        "      <td>                               DR. KIRIT P SOLANKI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 617104</td>\n",
        "      <td> 0.639745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>451</th>\n",
        "      <td>    Uttar Pradesh</td>\n",
        "      <td>                    36 - Rae Bareli</td>\n",
        "      <td>  857875</td>\n",
        "      <td> 437735</td>\n",
        "      <td>  737079</td>\n",
        "      <td> 387401</td>\n",
        "      <td> 1594954</td>\n",
        "      <td>  825136</td>\n",
        "      <td> 0.510255</td>\n",
        "      <td> 0.525590</td>\n",
        "      <td> 0.517342</td>\n",
        "      <td>                                      SONIA GANDHI</td>\n",
        "      <td> INC</td>\n",
        "      <td>            Indian National Congress</td>\n",
        "      <td> 526434</td>\n",
        "      <td> 0.637997</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>242</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                        19 - BHOPAL</td>\n",
        "      <td> 1039004</td>\n",
        "      <td> 639683</td>\n",
        "      <td>  917932</td>\n",
        "      <td> 490499</td>\n",
        "      <td> 1956936</td>\n",
        "      <td> 1130182</td>\n",
        "      <td> 0.615669</td>\n",
        "      <td> 0.534352</td>\n",
        "      <td> 0.577526</td>\n",
        "      <td>                                       ALOK SANJAR</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 714178</td>\n",
        "      <td> 0.631914</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>245</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                        22 - UJJAIN</td>\n",
        "      <td>  790889</td>\n",
        "      <td> 571525</td>\n",
        "      <td>  734592</td>\n",
        "      <td> 444880</td>\n",
        "      <td> 1525481</td>\n",
        "      <td> 1016405</td>\n",
        "      <td> 0.722636</td>\n",
        "      <td> 0.605615</td>\n",
        "      <td> 0.666285</td>\n",
        "      <td>                          PROF. CHINTAMANI MALVIYA</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 641101</td>\n",
        "      <td> 0.630753</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>350</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                        2 - Bikaner</td>\n",
        "      <td>  847064</td>\n",
        "      <td> 526983</td>\n",
        "      <td>  744004</td>\n",
        "      <td> 402768</td>\n",
        "      <td> 1591068</td>\n",
        "      <td>  929751</td>\n",
        "      <td> 0.622129</td>\n",
        "      <td> 0.541352</td>\n",
        "      <td> 0.584357</td>\n",
        "      <td>                                 ARJUN RAM MEGHWAL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 584932</td>\n",
        "      <td> 0.629128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>125</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                     11 - Porbandar</td>\n",
        "      <td>  807383</td>\n",
        "      <td> 471495</td>\n",
        "      <td>  731840</td>\n",
        "      <td> 337938</td>\n",
        "      <td> 1539223</td>\n",
        "      <td>  809433</td>\n",
        "      <td> 0.583979</td>\n",
        "      <td> 0.461765</td>\n",
        "      <td> 0.525871</td>\n",
        "      <td>                  RADADIYA VITHALBHAI  HANSRAJBHAI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 508437</td>\n",
        "      <td> 0.628140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>414</th>\n",
        "      <td>          Tripura</td>\n",
        "      <td>                   1 - Tripura West</td>\n",
        "      <td>  635976</td>\n",
        "      <td> 546566</td>\n",
        "      <td>  612574</td>\n",
        "      <td> 526183</td>\n",
        "      <td> 1248550</td>\n",
        "      <td> 1072749</td>\n",
        "      <td> 0.859413</td>\n",
        "      <td> 0.858971</td>\n",
        "      <td> 0.859196</td>\n",
        "      <td>                               SANKAR PRASAD DATTA</td>\n",
        "      <td> CPM</td>\n",
        "      <td> Communist Party of India  (Marxist)</td>\n",
        "      <td> 671665</td>\n",
        "      <td> 0.626116</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>354</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                   6 - Jaipur Rural</td>\n",
        "      <td>  906275</td>\n",
        "      <td> 554084</td>\n",
        "      <td>  793187</td>\n",
        "      <td> 459607</td>\n",
        "      <td> 1699462</td>\n",
        "      <td> 1013691</td>\n",
        "      <td> 0.611386</td>\n",
        "      <td> 0.579443</td>\n",
        "      <td> 0.596478</td>\n",
        "      <td>                        RAJYAVARDHAN SINGH RATHORE</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 632930</td>\n",
        "      <td> 0.624382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>252</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                         29 - BETUL</td>\n",
        "      <td>  836835</td>\n",
        "      <td> 574150</td>\n",
        "      <td>  770987</td>\n",
        "      <td> 473569</td>\n",
        "      <td> 1607822</td>\n",
        "      <td> 1047719</td>\n",
        "      <td> 0.686097</td>\n",
        "      <td> 0.614237</td>\n",
        "      <td> 0.651639</td>\n",
        "      <td>                                      JYOTI DHURVE</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 643651</td>\n",
        "      <td> 0.614336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>280</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>             28 - Mumbai North East</td>\n",
        "      <td>  923016</td>\n",
        "      <td> 485775</td>\n",
        "      <td>  745341</td>\n",
        "      <td> 375986</td>\n",
        "      <td> 1668357</td>\n",
        "      <td>  861761</td>\n",
        "      <td> 0.526291</td>\n",
        "      <td> 0.504448</td>\n",
        "      <td> 0.516533</td>\n",
        "      <td>                                    KIRIT  SOMAIYA</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 525285</td>\n",
        "      <td> 0.609548</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>307</th>\n",
        "      <td>     NCT OF Delhi</td>\n",
        "      <td> 1 - CHANDNI CHOWK                 </td>\n",
        "      <td>  791317</td>\n",
        "      <td> 550825</td>\n",
        "      <td>  655911</td>\n",
        "      <td> 431038</td>\n",
        "      <td> 1447228</td>\n",
        "      <td>  981863</td>\n",
        "      <td> 0.696086</td>\n",
        "      <td> 0.657159</td>\n",
        "      <td> 0.678444</td>\n",
        "      <td>                                      MANOJ TIWARI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 596125</td>\n",
        "      <td> 0.607137</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>356</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                          8 - Alwar</td>\n",
        "      <td>  871339</td>\n",
        "      <td> 587242</td>\n",
        "      <td>  756728</td>\n",
        "      <td> 475063</td>\n",
        "      <td> 1628067</td>\n",
        "      <td> 1062305</td>\n",
        "      <td> 0.673954</td>\n",
        "      <td> 0.627786</td>\n",
        "      <td> 0.652495</td>\n",
        "      <td>                                        CHAND NATH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 642278</td>\n",
        "      <td> 0.604608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>332</th>\n",
        "      <td>           Orissa</td>\n",
        "      <td>                          19 - Aska</td>\n",
        "      <td>  750999</td>\n",
        "      <td> 449485</td>\n",
        "      <td>  657781</td>\n",
        "      <td> 446796</td>\n",
        "      <td> 1408780</td>\n",
        "      <td>  896281</td>\n",
        "      <td> 0.598516</td>\n",
        "      <td> 0.679247</td>\n",
        "      <td> 0.636211</td>\n",
        "      <td>                                LADU KISHORE SWAIN</td>\n",
        "      <td> BJD</td>\n",
        "      <td>                     Biju Janata Dal</td>\n",
        "      <td> 541473</td>\n",
        "      <td> 0.604133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>357</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                      9 - BHARATPUR</td>\n",
        "      <td>  911069</td>\n",
        "      <td> 548105</td>\n",
        "      <td>  775828</td>\n",
        "      <td> 414327</td>\n",
        "      <td> 1686897</td>\n",
        "      <td>  962432</td>\n",
        "      <td> 0.601606</td>\n",
        "      <td> 0.534045</td>\n",
        "      <td> 0.570534</td>\n",
        "      <td>                                     BAHADUR SINGH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 579825</td>\n",
        "      <td> 0.602458</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>246</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                      23 - MANDSOUR</td>\n",
        "      <td>  838076</td>\n",
        "      <td> 640484</td>\n",
        "      <td>  788495</td>\n",
        "      <td> 520865</td>\n",
        "      <td> 1626571</td>\n",
        "      <td> 1161349</td>\n",
        "      <td> 0.764231</td>\n",
        "      <td> 0.660581</td>\n",
        "      <td> 0.713986</td>\n",
        "      <td>                                      SUDHIR GUPTA</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 698335</td>\n",
        "      <td> 0.601314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>256</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                          4 - Raver</td>\n",
        "      <td>  842682</td>\n",
        "      <td> 554159</td>\n",
        "      <td>  750688</td>\n",
        "      <td> 455054</td>\n",
        "      <td> 1593370</td>\n",
        "      <td> 1009213</td>\n",
        "      <td> 0.657613</td>\n",
        "      <td> 0.606183</td>\n",
        "      <td> 0.633383</td>\n",
        "      <td>                             KHADASE RAKSHA NIKHIL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 605452</td>\n",
        "      <td> 0.599925</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>369</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                   21 - Chittorgarh</td>\n",
        "      <td>  928572</td>\n",
        "      <td> 632047</td>\n",
        "      <td>  889575</td>\n",
        "      <td> 540582</td>\n",
        "      <td> 1818147</td>\n",
        "      <td> 1172629</td>\n",
        "      <td> 0.680666</td>\n",
        "      <td> 0.607686</td>\n",
        "      <td> 0.644958</td>\n",
        "      <td>                             CHANDRA PRAKASH JOSHI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 703236</td>\n",
        "      <td> 0.599709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>129</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                     15 - Bhavnagar</td>\n",
        "      <td>  834571</td>\n",
        "      <td> 519525</td>\n",
        "      <td>  759960</td>\n",
        "      <td> 397877</td>\n",
        "      <td> 1594531</td>\n",
        "      <td>  917402</td>\n",
        "      <td> 0.622505</td>\n",
        "      <td> 0.523550</td>\n",
        "      <td> 0.575343</td>\n",
        "      <td>                   DR. BHARATIBEN DHIRUBHAI SHIYAL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 549529</td>\n",
        "      <td> 0.599006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>429</th>\n",
        "      <td>    Uttar Pradesh</td>\n",
        "      <td>                   14 - Bulandshahr</td>\n",
        "      <td>  930831</td>\n",
        "      <td> 549484</td>\n",
        "      <td>  805616</td>\n",
        "      <td> 460226</td>\n",
        "      <td> 1736447</td>\n",
        "      <td> 1009710</td>\n",
        "      <td> 0.590316</td>\n",
        "      <td> 0.571272</td>\n",
        "      <td> 0.581480</td>\n",
        "      <td>                                       BHOLA SINGH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 604449</td>\n",
        "      <td> 0.598636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>436</th>\n",
        "      <td>    Uttar Pradesh</td>\n",
        "      <td>                      21 - Mainpuri</td>\n",
        "      <td>  900568</td>\n",
        "      <td> 580173</td>\n",
        "      <td>  752497</td>\n",
        "      <td> 419092</td>\n",
        "      <td> 1653065</td>\n",
        "      <td>  999265</td>\n",
        "      <td> 0.644230</td>\n",
        "      <td> 0.556935</td>\n",
        "      <td> 0.604492</td>\n",
        "      <td>                               MULAYAM SINGH YADAV</td>\n",
        "      <td>  SP</td>\n",
        "      <td>                     Samajwadi Party</td>\n",
        "      <td> 595918</td>\n",
        "      <td> 0.596356</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>115</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                        1 - Kachchh</td>\n",
        "      <td>  806343</td>\n",
        "      <td> 520905</td>\n",
        "      <td>  727439</td>\n",
        "      <td> 425335</td>\n",
        "      <td> 1533782</td>\n",
        "      <td>  946240</td>\n",
        "      <td> 0.646009</td>\n",
        "      <td> 0.584702</td>\n",
        "      <td> 0.616933</td>\n",
        "      <td>                           CHAVDA VINOD LAKHAMASHI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 562855</td>\n",
        "      <td> 0.594833</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>497</th>\n",
        "      <td>      Uttarakhand</td>\n",
        "      <td>                        2 - Garhwal</td>\n",
        "      <td>  652891</td>\n",
        "      <td> 323143</td>\n",
        "      <td>  616192</td>\n",
        "      <td> 358881</td>\n",
        "      <td> 1269083</td>\n",
        "      <td>  682024</td>\n",
        "      <td> 0.494942</td>\n",
        "      <td> 0.582417</td>\n",
        "      <td> 0.537415</td>\n",
        "      <td> (MAJ GEN (RETD.) ) BHUWAN CHANDRA KHANDURI (AVSM)</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 405690</td>\n",
        "      <td> 0.594832</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>131</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                         17 - Kheda</td>\n",
        "      <td>  833232</td>\n",
        "      <td> 542955</td>\n",
        "      <td>  766244</td>\n",
        "      <td> 412951</td>\n",
        "      <td> 1599476</td>\n",
        "      <td>  955906</td>\n",
        "      <td> 0.651625</td>\n",
        "      <td> 0.538929</td>\n",
        "      <td> 0.597637</td>\n",
        "      <td>    CHAUHAN DEVUSINH JESINGBHAI (CHAUHAN DEVUSINH)</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 568235</td>\n",
        "      <td> 0.594447</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>288</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                        36 - Shirur</td>\n",
        "      <td>  973236</td>\n",
        "      <td> 613828</td>\n",
        "      <td>  850876</td>\n",
        "      <td> 475678</td>\n",
        "      <td> 1824112</td>\n",
        "      <td> 1089506</td>\n",
        "      <td> 0.630708</td>\n",
        "      <td> 0.559045</td>\n",
        "      <td> 0.597280</td>\n",
        "      <td>                        ADHALRAO SHIVAJI DATTATREY</td>\n",
        "      <td> SHS</td>\n",
        "      <td>                            Shivsena</td>\n",
        "      <td> 643415</td>\n",
        "      <td> 0.590557</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>243</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                       20 - RAJGARH</td>\n",
        "      <td>  827001</td>\n",
        "      <td> 598652</td>\n",
        "      <td>  751747</td>\n",
        "      <td> 412072</td>\n",
        "      <td> 1578748</td>\n",
        "      <td> 1010724</td>\n",
        "      <td> 0.723883</td>\n",
        "      <td> 0.548153</td>\n",
        "      <td> 0.640206</td>\n",
        "      <td>                                      RODMAL NAGAR</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 596727</td>\n",
        "      <td> 0.590396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>418</th>\n",
        "      <td>    Uttar Pradesh</td>\n",
        "      <td>                  3 - Muzaffarnagar</td>\n",
        "      <td>  875186</td>\n",
        "      <td> 620606</td>\n",
        "      <td>  713297</td>\n",
        "      <td> 486828</td>\n",
        "      <td> 1588483</td>\n",
        "      <td> 1107434</td>\n",
        "      <td> 0.709113</td>\n",
        "      <td> 0.682504</td>\n",
        "      <td> 0.697165</td>\n",
        "      <td>                        (DR.) SANJEEV KUMAR BALYAN</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 653391</td>\n",
        "      <td> 0.590004</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>373</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                25 - JHALAWAR-BARAN</td>\n",
        "      <td>  868977</td>\n",
        "      <td> 635659</td>\n",
        "      <td>  800865</td>\n",
        "      <td> 510705</td>\n",
        "      <td> 1669842</td>\n",
        "      <td> 1146364</td>\n",
        "      <td> 0.731503</td>\n",
        "      <td> 0.637692</td>\n",
        "      <td> 0.686510</td>\n",
        "      <td>                                    DUSHYANT SINGH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 676102</td>\n",
        "      <td> 0.589780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>124</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                        10 - Rajkot</td>\n",
        "      <td>  864760</td>\n",
        "      <td> 593333</td>\n",
        "      <td>  790957</td>\n",
        "      <td> 463736</td>\n",
        "      <td> 1655717</td>\n",
        "      <td> 1057069</td>\n",
        "      <td> 0.686124</td>\n",
        "      <td> 0.586297</td>\n",
        "      <td> 0.638436</td>\n",
        "      <td>                  KUNDARIYA MOHANBHAI KALYANJIBHAI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 621524</td>\n",
        "      <td> 0.587969</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>113</th>\n",
        "      <td>              Goa</td>\n",
        "      <td>                      1 - North Goa</td>\n",
        "      <td>  255870</td>\n",
        "      <td> 200184</td>\n",
        "      <td>  259571</td>\n",
        "      <td> 206447</td>\n",
        "      <td>  515441</td>\n",
        "      <td>  406631</td>\n",
        "      <td> 0.782366</td>\n",
        "      <td> 0.795339</td>\n",
        "      <td> 0.788899</td>\n",
        "      <td>                                SHRIPAD YESSO NAIK</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 237903</td>\n",
        "      <td> 0.585059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>296</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                        44 - Sangli</td>\n",
        "      <td>  861582</td>\n",
        "      <td> 558707</td>\n",
        "      <td>  787525</td>\n",
        "      <td> 487952</td>\n",
        "      <td> 1649107</td>\n",
        "      <td> 1046659</td>\n",
        "      <td> 0.648466</td>\n",
        "      <td> 0.619602</td>\n",
        "      <td> 0.634682</td>\n",
        "      <td>                                  SANJAYKAKA PATIL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 611563</td>\n",
        "      <td> 0.584300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>293</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                        41 - Latur </td>\n",
        "      <td>  897919</td>\n",
        "      <td> 578053</td>\n",
        "      <td>  784688</td>\n",
        "      <td> 479103</td>\n",
        "      <td> 1682607</td>\n",
        "      <td> 1057156</td>\n",
        "      <td> 0.643770</td>\n",
        "      <td> 0.610565</td>\n",
        "      <td> 0.628285</td>\n",
        "      <td>                         DR. SUNIL BALIRAM GAIKWAD</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 616509</td>\n",
        "      <td> 0.583177</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>244</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                         21 - DEWAS</td>\n",
        "      <td>  843555</td>\n",
        "      <td> 654121</td>\n",
        "      <td>  773660</td>\n",
        "      <td> 489847</td>\n",
        "      <td> 1617215</td>\n",
        "      <td> 1143968</td>\n",
        "      <td> 0.775434</td>\n",
        "      <td> 0.633155</td>\n",
        "      <td> 0.707369</td>\n",
        "      <td>                                    MANOHAR UNTWAL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 665646</td>\n",
        "      <td> 0.581875</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>499</th>\n",
        "      <td>      Uttarakhand</td>\n",
        "      <td>      4 - Nainital-udhamsingh Nagar</td>\n",
        "      <td>  857781</td>\n",
        "      <td> 590700</td>\n",
        "      <td>  753029</td>\n",
        "      <td> 510735</td>\n",
        "      <td> 1610810</td>\n",
        "      <td> 1101435</td>\n",
        "      <td> 0.688637</td>\n",
        "      <td> 0.678241</td>\n",
        "      <td> 0.683777</td>\n",
        "      <td>                             BHAGAT SINGH KOSHYARI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 636769</td>\n",
        "      <td> 0.578127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                       4 - Mahesana</td>\n",
        "      <td>  777821</td>\n",
        "      <td> 544710</td>\n",
        "      <td>  720398</td>\n",
        "      <td> 459548</td>\n",
        "      <td> 1498219</td>\n",
        "      <td> 1004258</td>\n",
        "      <td> 0.700303</td>\n",
        "      <td> 0.637908</td>\n",
        "      <td> 0.670301</td>\n",
        "      <td>                        PATEL JAYSHREEBEN KANUBHAI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 580250</td>\n",
        "      <td> 0.577790</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>150</th>\n",
        "      <td>          Haryana</td>\n",
        "      <td>                     10 - Faridabad</td>\n",
        "      <td>  969407</td>\n",
        "      <td> 651411</td>\n",
        "      <td>  770945</td>\n",
        "      <td> 479315</td>\n",
        "      <td> 1740352</td>\n",
        "      <td> 1130726</td>\n",
        "      <td> 0.671969</td>\n",
        "      <td> 0.621724</td>\n",
        "      <td> 0.649711</td>\n",
        "      <td>                                       KRISHAN PAL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 652516</td>\n",
        "      <td> 0.577077</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>496</th>\n",
        "      <td>      Uttarakhand</td>\n",
        "      <td>                  1 - Tehri Garhwal</td>\n",
        "      <td>  712039</td>\n",
        "      <td> 403088</td>\n",
        "      <td>  640806</td>\n",
        "      <td> 373126</td>\n",
        "      <td> 1352845</td>\n",
        "      <td>  776214</td>\n",
        "      <td> 0.566104</td>\n",
        "      <td> 0.582276</td>\n",
        "      <td> 0.573764</td>\n",
        "      <td>                             MALA RAJYA LAXMI SHAH</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 446733</td>\n",
        "      <td> 0.575528</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>286</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                          34 - Pune</td>\n",
        "      <td>  949567</td>\n",
        "      <td> 533686</td>\n",
        "      <td>  886269</td>\n",
        "      <td> 459588</td>\n",
        "      <td> 1835836</td>\n",
        "      <td>  993274</td>\n",
        "      <td> 0.562031</td>\n",
        "      <td> 0.518565</td>\n",
        "      <td> 0.541047</td>\n",
        "      <td>                                      ANIL SHIROLE</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 569825</td>\n",
        "      <td> 0.573684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>116</th>\n",
        "      <td>          Gujarat</td>\n",
        "      <td>                    2 - Banaskantha</td>\n",
        "      <td>  796124</td>\n",
        "      <td> 513893</td>\n",
        "      <td>  719587</td>\n",
        "      <td> 372741</td>\n",
        "      <td> 1515711</td>\n",
        "      <td>  886634</td>\n",
        "      <td> 0.645494</td>\n",
        "      <td> 0.517993</td>\n",
        "      <td> 0.584962</td>\n",
        "      <td>                     CHAUDHARY HARIBHAI PARTHIBHAI</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 507856</td>\n",
        "      <td> 0.572791</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>290</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                        38 - Shirdi</td>\n",
        "      <td>  765921</td>\n",
        "      <td> 520229</td>\n",
        "      <td>  693791</td>\n",
        "      <td> 412416</td>\n",
        "      <td> 1459712</td>\n",
        "      <td>  932645</td>\n",
        "      <td> 0.679220</td>\n",
        "      <td> 0.594438</td>\n",
        "      <td> 0.638924</td>\n",
        "      <td>                           LOKHANDE SADASHIV KISAN</td>\n",
        "      <td> SHS</td>\n",
        "      <td>                            Shivsena</td>\n",
        "      <td> 532936</td>\n",
        "      <td> 0.571424</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>371</th>\n",
        "      <td>        Rajasthan</td>\n",
        "      <td>                      23 - Bhilwara</td>\n",
        "      <td>  904030</td>\n",
        "      <td> 581524</td>\n",
        "      <td>  850847</td>\n",
        "      <td> 522566</td>\n",
        "      <td> 1754877</td>\n",
        "      <td> 1104090</td>\n",
        "      <td> 0.643257</td>\n",
        "      <td> 0.614172</td>\n",
        "      <td> 0.629155</td>\n",
        "      <td>                                   SUBHASH BAHERIA</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 630317</td>\n",
        "      <td> 0.570893</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>151</th>\n",
        "      <td> Himachal Pradesh</td>\n",
        "      <td>                         1 - Kangra</td>\n",
        "      <td>  645888</td>\n",
        "      <td> 392927</td>\n",
        "      <td>  612713</td>\n",
        "      <td> 406518</td>\n",
        "      <td> 1258601</td>\n",
        "      <td>  799445</td>\n",
        "      <td> 0.608352</td>\n",
        "      <td> 0.663472</td>\n",
        "      <td> 0.635185</td>\n",
        "      <td>                                      SHANTA KUMAR</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 456163</td>\n",
        "      <td> 0.570600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>251</th>\n",
        "      <td>   Madhya Pradesh</td>\n",
        "      <td>                       28 - KHANDWA</td>\n",
        "      <td>  912747</td>\n",
        "      <td> 677574</td>\n",
        "      <td>  846663</td>\n",
        "      <td> 579753</td>\n",
        "      <td> 1759410</td>\n",
        "      <td> 1257327</td>\n",
        "      <td> 0.742346</td>\n",
        "      <td> 0.684751</td>\n",
        "      <td> 0.714630</td>\n",
        "      <td>            NANDKUMAR SINGH CHOUHAN (NANDU BHAIYA)</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 717357</td>\n",
        "      <td> 0.570541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>289</th>\n",
        "      <td>      Maharashtra</td>\n",
        "      <td>                   37 - Ahmadnagar </td>\n",
        "      <td>  898819</td>\n",
        "      <td> 595960</td>\n",
        "      <td>  800589</td>\n",
        "      <td> 466358</td>\n",
        "      <td> 1699408</td>\n",
        "      <td> 1062318</td>\n",
        "      <td> 0.663048</td>\n",
        "      <td> 0.582519</td>\n",
        "      <td> 0.625111</td>\n",
        "      <td>                      GANDHI DILIPKUMAR MANSUKHLAL</td>\n",
        "      <td> BJP</td>\n",
        "      <td>              Bharatiya Janata Party</td>\n",
        "      <td> 605185</td>\n",
        "      <td> 0.569683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>543 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "                State                             PC Name  Male Electors  \\\n",
        "138           Gujarat                          24 - Surat         803829   \n",
        "134           Gujarat                       20 - Vadodara         849077   \n",
        "139           Gujarat                        25 - Navsari         972090   \n",
        "278       Maharashtra                   26 - Mumbai North         972645   \n",
        "120           Gujarat                     6 - Gandhinagar         900744   \n",
        "355         Rajasthan                          7 - Jaipur        1047468   \n",
        "241    Madhya Pradesh                        18 - VIDISHA         872410   \n",
        "364         Rajasthan                        16 - Jodhpur         910466   \n",
        "370         Rajasthan                      22 - Rajsamand         879526   \n",
        "415           Tripura                    2 - Tripura East         581599   \n",
        "255       Maharashtra                         3 - Jalgaon         909027   \n",
        "363         Rajasthan                           15 - Pali         994082   \n",
        "310      NCT OF Delhi  4 - NEW DELHI                              830322   \n",
        "249    Madhya Pradesh                         26 - INDORE        1106461   \n",
        "240    Madhya Pradesh                    17 - HOSHANGABAD         835492   \n",
        "313      NCT OF Delhi  7 - SOUTH DELHI                           1005289   \n",
        "121           Gujarat                  7 - Ahmedabad East         852765   \n",
        "122           Gujarat                  8 - Ahmedabad West         800933   \n",
        "451     Uttar Pradesh                     36 - Rae Bareli         857875   \n",
        "242    Madhya Pradesh                         19 - BHOPAL        1039004   \n",
        "245    Madhya Pradesh                         22 - UJJAIN         790889   \n",
        "350         Rajasthan                         2 - Bikaner         847064   \n",
        "125           Gujarat                      11 - Porbandar         807383   \n",
        "414           Tripura                    1 - Tripura West         635976   \n",
        "354         Rajasthan                    6 - Jaipur Rural         906275   \n",
        "252    Madhya Pradesh                          29 - BETUL         836835   \n",
        "280       Maharashtra              28 - Mumbai North East         923016   \n",
        "307      NCT OF Delhi  1 - CHANDNI CHOWK                          791317   \n",
        "356         Rajasthan                           8 - Alwar         871339   \n",
        "332            Orissa                           19 - Aska         750999   \n",
        "357         Rajasthan                       9 - BHARATPUR         911069   \n",
        "246    Madhya Pradesh                       23 - MANDSOUR         838076   \n",
        "256       Maharashtra                           4 - Raver         842682   \n",
        "369         Rajasthan                    21 - Chittorgarh         928572   \n",
        "129           Gujarat                      15 - Bhavnagar         834571   \n",
        "429     Uttar Pradesh                    14 - Bulandshahr         930831   \n",
        "436     Uttar Pradesh                       21 - Mainpuri         900568   \n",
        "115           Gujarat                         1 - Kachchh         806343   \n",
        "497       Uttarakhand                         2 - Garhwal         652891   \n",
        "131           Gujarat                          17 - Kheda         833232   \n",
        "288       Maharashtra                         36 - Shirur         973236   \n",
        "243    Madhya Pradesh                        20 - RAJGARH         827001   \n",
        "418     Uttar Pradesh                   3 - Muzaffarnagar         875186   \n",
        "373         Rajasthan                 25 - JHALAWAR-BARAN         868977   \n",
        "124           Gujarat                         10 - Rajkot         864760   \n",
        "113               Goa                       1 - North Goa         255870   \n",
        "296       Maharashtra                         44 - Sangli         861582   \n",
        "293       Maharashtra                         41 - Latur          897919   \n",
        "244    Madhya Pradesh                          21 - DEWAS         843555   \n",
        "499       Uttarakhand       4 - Nainital-udhamsingh Nagar         857781   \n",
        "118           Gujarat                        4 - Mahesana         777821   \n",
        "150           Haryana                      10 - Faridabad         969407   \n",
        "496       Uttarakhand                   1 - Tehri Garhwal         712039   \n",
        "286       Maharashtra                           34 - Pune         949567   \n",
        "116           Gujarat                     2 - Banaskantha         796124   \n",
        "290       Maharashtra                         38 - Shirdi         765921   \n",
        "371         Rajasthan                       23 - Bhilwara         904030   \n",
        "151  Himachal Pradesh                          1 - Kangra         645888   \n",
        "251    Madhya Pradesh                        28 - KHANDWA         912747   \n",
        "289       Maharashtra                    37 - Ahmadnagar          898819   \n",
        "                  ...                                 ...            ...   \n",
        "\n",
        "     Male Voters  Female Electors  Female Voters  Total Electors  \\\n",
        "138       539368           680239         408554         1484068   \n",
        "134       625045           789244         536532         1638321   \n",
        "139       649206           792532         511541         1764622   \n",
        "278       527803           811225         418759         1783870   \n",
        "120       620889           833228         514606         1733972   \n",
        "355       716874           910350         579932         1957818   \n",
        "241       621326           761960         452147         1634370   \n",
        "364       600132           816897         478466         1727363   \n",
        "370       508738           819875         473381         1699401   \n",
        "415       489156           558670         461954         1140269   \n",
        "255       594892           798906         395440         1707933   \n",
        "363       581099           898948         514488         1893030   \n",
        "310       546295           659825         423517         1490147   \n",
        "249       736759          1008842         580058         2115303   \n",
        "240       593957           732635         437218         1568127   \n",
        "313       638406           747452         464004         1752741   \n",
        "121       560074           749067         425451         1601832   \n",
        "122       534008           733467         430601         1534400   \n",
        "451       437735           737079         387401         1594954   \n",
        "242       639683           917932         490499         1956936   \n",
        "245       571525           734592         444880         1525481   \n",
        "350       526983           744004         402768         1591068   \n",
        "125       471495           731840         337938         1539223   \n",
        "414       546566           612574         526183         1248550   \n",
        "354       554084           793187         459607         1699462   \n",
        "252       574150           770987         473569         1607822   \n",
        "280       485775           745341         375986         1668357   \n",
        "307       550825           655911         431038         1447228   \n",
        "356       587242           756728         475063         1628067   \n",
        "332       449485           657781         446796         1408780   \n",
        "357       548105           775828         414327         1686897   \n",
        "246       640484           788495         520865         1626571   \n",
        "256       554159           750688         455054         1593370   \n",
        "369       632047           889575         540582         1818147   \n",
        "129       519525           759960         397877         1594531   \n",
        "429       549484           805616         460226         1736447   \n",
        "436       580173           752497         419092         1653065   \n",
        "115       520905           727439         425335         1533782   \n",
        "497       323143           616192         358881         1269083   \n",
        "131       542955           766244         412951         1599476   \n",
        "288       613828           850876         475678         1824112   \n",
        "243       598652           751747         412072         1578748   \n",
        "418       620606           713297         486828         1588483   \n",
        "373       635659           800865         510705         1669842   \n",
        "124       593333           790957         463736         1655717   \n",
        "113       200184           259571         206447          515441   \n",
        "296       558707           787525         487952         1649107   \n",
        "293       578053           784688         479103         1682607   \n",
        "244       654121           773660         489847         1617215   \n",
        "499       590700           753029         510735         1610810   \n",
        "118       544710           720398         459548         1498219   \n",
        "150       651411           770945         479315         1740352   \n",
        "496       403088           640806         373126         1352845   \n",
        "286       533686           886269         459588         1835836   \n",
        "116       513893           719587         372741         1515711   \n",
        "290       520229           693791         412416         1459712   \n",
        "371       581524           850847         522566         1754877   \n",
        "151       392927           612713         406518         1258601   \n",
        "251       677574           846663         579753         1759410   \n",
        "289       595960           800589         466358         1699408   \n",
        "             ...              ...            ...             ...   \n",
        "\n",
        "     Total Voters  Male Turnout  Female Turnout  Total Turnout  \\\n",
        "138        947922      0.670998        0.600604       0.638732   \n",
        "134       1161577      0.736146        0.679805       0.709005   \n",
        "139       1160747      0.667846        0.645452       0.657788   \n",
        "278        946562      0.542647        0.516206       0.530623   \n",
        "120       1135495      0.689307        0.617605       0.654852   \n",
        "355       1296806      0.684387        0.637043       0.662373   \n",
        "241       1073473      0.712195        0.593400       0.656811   \n",
        "364       1078598      0.659148        0.585712       0.624419   \n",
        "370        982119      0.578423        0.577382       0.577921   \n",
        "415        951110      0.841054        0.826882       0.834110   \n",
        "255        990332      0.654427        0.494977       0.579842   \n",
        "363       1095587      0.584558        0.572322       0.578748   \n",
        "310        969812      0.657932        0.641863       0.650816   \n",
        "249       1316817      0.665870        0.574974       0.622519   \n",
        "240       1031175      0.710907        0.596775       0.657584   \n",
        "313       1102410      0.635047        0.620781       0.628963   \n",
        "121        985525      0.656774        0.567975       0.615249   \n",
        "122        964609      0.666732        0.587076       0.628656   \n",
        "451        825136      0.510255        0.525590       0.517342   \n",
        "242       1130182      0.615669        0.534352       0.577526   \n",
        "245       1016405      0.722636        0.605615       0.666285   \n",
        "350        929751      0.622129        0.541352       0.584357   \n",
        "125        809433      0.583979        0.461765       0.525871   \n",
        "414       1072749      0.859413        0.858971       0.859196   \n",
        "354       1013691      0.611386        0.579443       0.596478   \n",
        "252       1047719      0.686097        0.614237       0.651639   \n",
        "280        861761      0.526291        0.504448       0.516533   \n",
        "307        981863      0.696086        0.657159       0.678444   \n",
        "356       1062305      0.673954        0.627786       0.652495   \n",
        "332        896281      0.598516        0.679247       0.636211   \n",
        "357        962432      0.601606        0.534045       0.570534   \n",
        "246       1161349      0.764231        0.660581       0.713986   \n",
        "256       1009213      0.657613        0.606183       0.633383   \n",
        "369       1172629      0.680666        0.607686       0.644958   \n",
        "129        917402      0.622505        0.523550       0.575343   \n",
        "429       1009710      0.590316        0.571272       0.581480   \n",
        "436        999265      0.644230        0.556935       0.604492   \n",
        "115        946240      0.646009        0.584702       0.616933   \n",
        "497        682024      0.494942        0.582417       0.537415   \n",
        "131        955906      0.651625        0.538929       0.597637   \n",
        "288       1089506      0.630708        0.559045       0.597280   \n",
        "243       1010724      0.723883        0.548153       0.640206   \n",
        "418       1107434      0.709113        0.682504       0.697165   \n",
        "373       1146364      0.731503        0.637692       0.686510   \n",
        "124       1057069      0.686124        0.586297       0.638436   \n",
        "113        406631      0.782366        0.795339       0.788899   \n",
        "296       1046659      0.648466        0.619602       0.634682   \n",
        "293       1057156      0.643770        0.610565       0.628285   \n",
        "244       1143968      0.775434        0.633155       0.707369   \n",
        "499       1101435      0.688637        0.678241       0.683777   \n",
        "118       1004258      0.700303        0.637908       0.670301   \n",
        "150       1130726      0.671969        0.621724       0.649711   \n",
        "496        776214      0.566104        0.582276       0.573764   \n",
        "286        993274      0.562031        0.518565       0.541047   \n",
        "116        886634      0.645494        0.517993       0.584962   \n",
        "290        932645      0.679220        0.594438       0.638924   \n",
        "371       1104090      0.643257        0.614172       0.629155   \n",
        "151        799445      0.608352        0.663472       0.635185   \n",
        "251       1257327      0.742346        0.684751       0.714630   \n",
        "289       1062318      0.663048        0.582519       0.625111   \n",
        "              ...           ...             ...            ...   \n",
        "\n",
        "                                        Candidate Name Party Abbreviation  \\\n",
        "138                            DARSHANA VIKRAM JARDOSH                BJP   \n",
        "134                                      NARENDRA MODI                BJP   \n",
        "139                                        C. R. PATIL                BJP   \n",
        "278                              GOPAL CHINAYYA SHETTY                BJP   \n",
        "120                                         L.K.ADVANI                BJP   \n",
        "355                                   RAMCHARAN BOHARA                BJP   \n",
        "241                                      SUSHMA SWARAJ                BJP   \n",
        "364                            GAJENDRASINGH SHEKHAWAT                BJP   \n",
        "370                               HARIOM SINGH RATHORE                BJP   \n",
        "415                                 JITENDRA CHOUDHURY                CPM   \n",
        "255                                    A.T. NANA PATIL                BJP   \n",
        "363                                      P P CHOUDHARY                BJP   \n",
        "310                                           UDIT RAJ                BJP   \n",
        "249                              SUMITRA MAHAJAN (TAI)                BJP   \n",
        "240                                  UDAY PRATAP SINGH                BJP   \n",
        "313                                        NEIPHIU RIO                NPF   \n",
        "121                                       PARESH RAWAL                BJP   \n",
        "122                                DR. KIRIT P SOLANKI                BJP   \n",
        "451                                       SONIA GANDHI                INC   \n",
        "242                                        ALOK SANJAR                BJP   \n",
        "245                           PROF. CHINTAMANI MALVIYA                BJP   \n",
        "350                                  ARJUN RAM MEGHWAL                BJP   \n",
        "125                   RADADIYA VITHALBHAI  HANSRAJBHAI                BJP   \n",
        "414                                SANKAR PRASAD DATTA                CPM   \n",
        "354                         RAJYAVARDHAN SINGH RATHORE                BJP   \n",
        "252                                       JYOTI DHURVE                BJP   \n",
        "280                                     KIRIT  SOMAIYA                BJP   \n",
        "307                                       MANOJ TIWARI                BJP   \n",
        "356                                         CHAND NATH                BJP   \n",
        "332                                 LADU KISHORE SWAIN                BJD   \n",
        "357                                      BAHADUR SINGH                BJP   \n",
        "246                                       SUDHIR GUPTA                BJP   \n",
        "256                              KHADASE RAKSHA NIKHIL                BJP   \n",
        "369                              CHANDRA PRAKASH JOSHI                BJP   \n",
        "129                    DR. BHARATIBEN DHIRUBHAI SHIYAL                BJP   \n",
        "429                                        BHOLA SINGH                BJP   \n",
        "436                                MULAYAM SINGH YADAV                 SP   \n",
        "115                            CHAVDA VINOD LAKHAMASHI                BJP   \n",
        "497  (MAJ GEN (RETD.) ) BHUWAN CHANDRA KHANDURI (AVSM)                BJP   \n",
        "131     CHAUHAN DEVUSINH JESINGBHAI (CHAUHAN DEVUSINH)                BJP   \n",
        "288                         ADHALRAO SHIVAJI DATTATREY                SHS   \n",
        "243                                       RODMAL NAGAR                BJP   \n",
        "418                         (DR.) SANJEEV KUMAR BALYAN                BJP   \n",
        "373                                     DUSHYANT SINGH                BJP   \n",
        "124                   KUNDARIYA MOHANBHAI KALYANJIBHAI                BJP   \n",
        "113                                 SHRIPAD YESSO NAIK                BJP   \n",
        "296                                   SANJAYKAKA PATIL                BJP   \n",
        "293                          DR. SUNIL BALIRAM GAIKWAD                BJP   \n",
        "244                                     MANOHAR UNTWAL                BJP   \n",
        "499                              BHAGAT SINGH KOSHYARI                BJP   \n",
        "118                         PATEL JAYSHREEBEN KANUBHAI                BJP   \n",
        "150                                        KRISHAN PAL                BJP   \n",
        "496                              MALA RAJYA LAXMI SHAH                BJP   \n",
        "286                                       ANIL SHIROLE                BJP   \n",
        "116                      CHAUDHARY HARIBHAI PARTHIBHAI                BJP   \n",
        "290                            LOKHANDE SADASHIV KISAN                SHS   \n",
        "371                                    SUBHASH BAHERIA                BJP   \n",
        "151                                       SHANTA KUMAR                BJP   \n",
        "251             NANDKUMAR SINGH CHOUHAN (NANDU BHAIYA)                BJP   \n",
        "289                       GANDHI DILIPKUMAR MANSUKHLAL                BJP   \n",
        "                                                   ...                ...   \n",
        "\n",
        "                              Party Name  Total Votes Polled  \\\n",
        "138               Bharatiya Janata Party              718412   \n",
        "134               Bharatiya Janata Party              845464   \n",
        "139               Bharatiya Janata Party              820831   \n",
        "278               Bharatiya Janata Party              664004   \n",
        "120               Bharatiya Janata Party              773539   \n",
        "355               Bharatiya Janata Party              863358   \n",
        "241               Bharatiya Janata Party              714348   \n",
        "364               Bharatiya Janata Party              713515   \n",
        "370               Bharatiya Janata Party              644794   \n",
        "415  Communist Party of India  (Marxist)              623771   \n",
        "255               Bharatiya Janata Party              647773   \n",
        "363               Bharatiya Janata Party              711772   \n",
        "310               Bharatiya Janata Party              629860   \n",
        "249               Bharatiya Janata Party              854972   \n",
        "240               Bharatiya Janata Party              669128   \n",
        "313                   Naga Peoples Front              713372   \n",
        "121               Bharatiya Janata Party              633582   \n",
        "122               Bharatiya Janata Party              617104   \n",
        "451             Indian National Congress              526434   \n",
        "242               Bharatiya Janata Party              714178   \n",
        "245               Bharatiya Janata Party              641101   \n",
        "350               Bharatiya Janata Party              584932   \n",
        "125               Bharatiya Janata Party              508437   \n",
        "414  Communist Party of India  (Marxist)              671665   \n",
        "354               Bharatiya Janata Party              632930   \n",
        "252               Bharatiya Janata Party              643651   \n",
        "280               Bharatiya Janata Party              525285   \n",
        "307               Bharatiya Janata Party              596125   \n",
        "356               Bharatiya Janata Party              642278   \n",
        "332                      Biju Janata Dal              541473   \n",
        "357               Bharatiya Janata Party              579825   \n",
        "246               Bharatiya Janata Party              698335   \n",
        "256               Bharatiya Janata Party              605452   \n",
        "369               Bharatiya Janata Party              703236   \n",
        "129               Bharatiya Janata Party              549529   \n",
        "429               Bharatiya Janata Party              604449   \n",
        "436                      Samajwadi Party              595918   \n",
        "115               Bharatiya Janata Party              562855   \n",
        "497               Bharatiya Janata Party              405690   \n",
        "131               Bharatiya Janata Party              568235   \n",
        "288                             Shivsena              643415   \n",
        "243               Bharatiya Janata Party              596727   \n",
        "418               Bharatiya Janata Party              653391   \n",
        "373               Bharatiya Janata Party              676102   \n",
        "124               Bharatiya Janata Party              621524   \n",
        "113               Bharatiya Janata Party              237903   \n",
        "296               Bharatiya Janata Party              611563   \n",
        "293               Bharatiya Janata Party              616509   \n",
        "244               Bharatiya Janata Party              665646   \n",
        "499               Bharatiya Janata Party              636769   \n",
        "118               Bharatiya Janata Party              580250   \n",
        "150               Bharatiya Janata Party              652516   \n",
        "496               Bharatiya Janata Party              446733   \n",
        "286               Bharatiya Janata Party              569825   \n",
        "116               Bharatiya Janata Party              507856   \n",
        "290                             Shivsena              532936   \n",
        "371               Bharatiya Janata Party              630317   \n",
        "151               Bharatiya Janata Party              456163   \n",
        "251               Bharatiya Janata Party              717357   \n",
        "289               Bharatiya Janata Party              605185   \n",
        "                                     ...                 ...   \n",
        "\n",
        "     Winner Percentage  \n",
        "138           0.757881  \n",
        "134           0.727859  \n",
        "139           0.707158  \n",
        "278           0.701490  \n",
        "120           0.681235  \n",
        "355           0.665757  \n",
        "241           0.665455  \n",
        "364           0.661521  \n",
        "370           0.656533  \n",
        "415           0.655835  \n",
        "255           0.654097  \n",
        "363           0.649672  \n",
        "310           0.649466  \n",
        "249           0.649272  \n",
        "240           0.648899  \n",
        "313           0.647102  \n",
        "121           0.642888  \n",
        "122           0.639745  \n",
        "451           0.637997  \n",
        "242           0.631914  \n",
        "245           0.630753  \n",
        "350           0.629128  \n",
        "125           0.628140  \n",
        "414           0.626116  \n",
        "354           0.624382  \n",
        "252           0.614336  \n",
        "280           0.609548  \n",
        "307           0.607137  \n",
        "356           0.604608  \n",
        "332           0.604133  \n",
        "357           0.602458  \n",
        "246           0.601314  \n",
        "256           0.599925  \n",
        "369           0.599709  \n",
        "129           0.599006  \n",
        "429           0.598636  \n",
        "436           0.596356  \n",
        "115           0.594833  \n",
        "497           0.594832  \n",
        "131           0.594447  \n",
        "288           0.590557  \n",
        "243           0.590396  \n",
        "418           0.590004  \n",
        "373           0.589780  \n",
        "124           0.587969  \n",
        "113           0.585059  \n",
        "296           0.584300  \n",
        "293           0.583177  \n",
        "244           0.581875  \n",
        "499           0.578127  \n",
        "118           0.577790  \n",
        "150           0.577077  \n",
        "496           0.575528  \n",
        "286           0.573684  \n",
        "116           0.572791  \n",
        "290           0.571424  \n",
        "371           0.570893  \n",
        "151           0.570600  \n",
        "251           0.570541  \n",
        "289           0.569683  \n",
        "                   ...  \n",
        "\n",
        "[543 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Analyzing Winners By Party"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = pd.DataFrame(csv_data[\"Party Abbreviation\"].value_counts(), columns=[\"Seats\"])\n",
      "results[\"Seats Percentage\"] = results[\"Seats\"] / len(winners)\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Seats</th>\n",
        "      <th>Seats Percentage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BJP</th>\n",
        "      <td> 282</td>\n",
        "      <td> 0.519337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INC</th>\n",
        "      <td>  44</td>\n",
        "      <td> 0.081031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADMK</th>\n",
        "      <td>  37</td>\n",
        "      <td> 0.068140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AITC</th>\n",
        "      <td>  34</td>\n",
        "      <td> 0.062615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BJD</th>\n",
        "      <td>  20</td>\n",
        "      <td> 0.036832</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SHS</th>\n",
        "      <td>  18</td>\n",
        "      <td> 0.033149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TDP</th>\n",
        "      <td>  16</td>\n",
        "      <td> 0.029466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TRS</th>\n",
        "      <td>  11</td>\n",
        "      <td> 0.020258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CPM</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.016575</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YSRCP</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.016575</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LJP</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.011050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NCP</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.011050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SP</th>\n",
        "      <td>   5</td>\n",
        "      <td> 0.009208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAAP</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RJD</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SAD</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AIUDF</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IND</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BLSP</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JKPDP</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INLD</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JMM</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IUML</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JD(U)</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AD</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JD(S)</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RSP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KEC(M)</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CPI</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SDF</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AINRC</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SWP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPEP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AIMIM</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPF</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PMK</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>36 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "        Seats  Seats Percentage\n",
        "BJP       282          0.519337\n",
        "INC        44          0.081031\n",
        "ADMK       37          0.068140\n",
        "AITC       34          0.062615\n",
        "BJD        20          0.036832\n",
        "SHS        18          0.033149\n",
        "TDP        16          0.029466\n",
        "TRS        11          0.020258\n",
        "CPM         9          0.016575\n",
        "YSRCP       9          0.016575\n",
        "LJP         6          0.011050\n",
        "NCP         6          0.011050\n",
        "SP          5          0.009208\n",
        "AAAP        4          0.007366\n",
        "RJD         4          0.007366\n",
        "SAD         4          0.007366\n",
        "AIUDF       3          0.005525\n",
        "IND         3          0.005525\n",
        "BLSP        3          0.005525\n",
        "JKPDP       3          0.005525\n",
        "INLD        2          0.003683\n",
        "JMM         2          0.003683\n",
        "IUML        2          0.003683\n",
        "JD(U)       2          0.003683\n",
        "AD          2          0.003683\n",
        "JD(S)       2          0.003683\n",
        "RSP         1          0.001842\n",
        "KEC(M)      1          0.001842\n",
        "CPI         1          0.001842\n",
        "SDF         1          0.001842\n",
        "AINRC       1          0.001842\n",
        "SWP         1          0.001842\n",
        "NPEP        1          0.001842\n",
        "AIMIM       1          0.001842\n",
        "NPF         1          0.001842\n",
        "PMK         1          0.001842\n",
        "\n",
        "[36 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_votes = pd.pivot_table(results_data, values=\"Total Votes Polled\", rows=\"Party Abbreviation\", aggfunc=\"sum\")\n",
      "all_votes_average = pd.pivot_table(results_data, values=\"Total Votes Polled\", rows=\"Party Abbreviation\", aggfunc=\"mean\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_total_votes = lambda x: all_votes[x]\n",
      "get_average_votes = lambda x: all_votes_average[x]\n",
      "\n",
      "results[\"Total Votes\"] = pd.Series(results.index.values).apply(get_total_votes).values\n",
      "results[\"Average Votes\"] = pd.Series(results.index.values).apply(get_average_votes).values\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Seats</th>\n",
        "      <th>Seats Percentage</th>\n",
        "      <th>Total Votes</th>\n",
        "      <th>Average Votes</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BJP</th>\n",
        "      <td> 282</td>\n",
        "      <td> 0.519337</td>\n",
        "      <td> 171657549</td>\n",
        "      <td> 401069.039720</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INC</th>\n",
        "      <td>  44</td>\n",
        "      <td> 0.081031</td>\n",
        "      <td> 106938242</td>\n",
        "      <td> 230470.349138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADMK</th>\n",
        "      <td>  37</td>\n",
        "      <td> 0.068140</td>\n",
        "      <td>  18115825</td>\n",
        "      <td> 452895.625000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AITC</th>\n",
        "      <td>  34</td>\n",
        "      <td> 0.062615</td>\n",
        "      <td>  21259681</td>\n",
        "      <td> 162287.641221</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BJD</th>\n",
        "      <td>  20</td>\n",
        "      <td> 0.036832</td>\n",
        "      <td>   9491497</td>\n",
        "      <td> 451976.047619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SHS</th>\n",
        "      <td>  18</td>\n",
        "      <td> 0.033149</td>\n",
        "      <td>  10262982</td>\n",
        "      <td> 176947.965517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TDP</th>\n",
        "      <td>  16</td>\n",
        "      <td> 0.029466</td>\n",
        "      <td>  14094545</td>\n",
        "      <td> 469818.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TRS</th>\n",
        "      <td>  11</td>\n",
        "      <td> 0.020258</td>\n",
        "      <td>   6736490</td>\n",
        "      <td> 396264.117647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CPM</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.016575</td>\n",
        "      <td>  17986773</td>\n",
        "      <td> 193406.161290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YSRCP</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.016575</td>\n",
        "      <td>  13991280</td>\n",
        "      <td> 368191.578947</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LJP</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.011050</td>\n",
        "      <td>   2295929</td>\n",
        "      <td> 327989.857143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NCP</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.011050</td>\n",
        "      <td>   8635554</td>\n",
        "      <td> 239876.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SP</th>\n",
        "      <td>   5</td>\n",
        "      <td> 0.009208</td>\n",
        "      <td>  18672916</td>\n",
        "      <td>  94786.375635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAAP</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "      <td>  11325635</td>\n",
        "      <td>  26216.747685</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RJD</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "      <td>   7442313</td>\n",
        "      <td> 248077.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SAD</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "      <td>   3636148</td>\n",
        "      <td> 363614.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AIUDF</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>   2333040</td>\n",
        "      <td> 129613.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IND</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>  16743719</td>\n",
        "      <td>   5175.801855</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BLSP</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>   1078473</td>\n",
        "      <td> 269618.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JKPDP</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>    732644</td>\n",
        "      <td> 146528.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INLD</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   2799899</td>\n",
        "      <td> 279989.900000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JMM</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   1637990</td>\n",
        "      <td>  77999.523810</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IUML</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   1100096</td>\n",
        "      <td>  44003.840000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JD(U)</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   5992196</td>\n",
        "      <td>  64432.215054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AD</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>    821820</td>\n",
        "      <td> 117402.857143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JD(S)</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   3731481</td>\n",
        "      <td> 109749.441176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RSP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   1666380</td>\n",
        "      <td> 277730.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KEC(M)</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    424194</td>\n",
        "      <td> 424194.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CPI</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   4327298</td>\n",
        "      <td>  64586.537313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SDF</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    163698</td>\n",
        "      <td> 163698.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AINRC</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    255826</td>\n",
        "      <td> 255826.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SWP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   1105073</td>\n",
        "      <td> 552536.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPEP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    576444</td>\n",
        "      <td>  82349.142857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AIMIM</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    685729</td>\n",
        "      <td> 137145.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPF</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    994505</td>\n",
        "      <td> 497252.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PMK</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   1827566</td>\n",
        "      <td> 203062.888889</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>36 rows \u00d7 4 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "        Seats  Seats Percentage  Total Votes  Average Votes\n",
        "BJP       282          0.519337    171657549  401069.039720\n",
        "INC        44          0.081031    106938242  230470.349138\n",
        "ADMK       37          0.068140     18115825  452895.625000\n",
        "AITC       34          0.062615     21259681  162287.641221\n",
        "BJD        20          0.036832      9491497  451976.047619\n",
        "SHS        18          0.033149     10262982  176947.965517\n",
        "TDP        16          0.029466     14094545  469818.166667\n",
        "TRS        11          0.020258      6736490  396264.117647\n",
        "CPM         9          0.016575     17986773  193406.161290\n",
        "YSRCP       9          0.016575     13991280  368191.578947\n",
        "LJP         6          0.011050      2295929  327989.857143\n",
        "NCP         6          0.011050      8635554  239876.500000\n",
        "SP          5          0.009208     18672916   94786.375635\n",
        "AAAP        4          0.007366     11325635   26216.747685\n",
        "RJD         4          0.007366      7442313  248077.100000\n",
        "SAD         4          0.007366      3636148  363614.800000\n",
        "AIUDF       3          0.005525      2333040  129613.333333\n",
        "IND         3          0.005525     16743719    5175.801855\n",
        "BLSP        3          0.005525      1078473  269618.250000\n",
        "JKPDP       3          0.005525       732644  146528.800000\n",
        "INLD        2          0.003683      2799899  279989.900000\n",
        "JMM         2          0.003683      1637990   77999.523810\n",
        "IUML        2          0.003683      1100096   44003.840000\n",
        "JD(U)       2          0.003683      5992196   64432.215054\n",
        "AD          2          0.003683       821820  117402.857143\n",
        "JD(S)       2          0.003683      3731481  109749.441176\n",
        "RSP         1          0.001842      1666380  277730.000000\n",
        "KEC(M)      1          0.001842       424194  424194.000000\n",
        "CPI         1          0.001842      4327298   64586.537313\n",
        "SDF         1          0.001842       163698  163698.000000\n",
        "AINRC       1          0.001842       255826  255826.000000\n",
        "SWP         1          0.001842      1105073  552536.500000\n",
        "NPEP        1          0.001842       576444   82349.142857\n",
        "AIMIM       1          0.001842       685729  137145.800000\n",
        "NPF         1          0.001842       994505  497252.500000\n",
        "PMK         1          0.001842      1827566  203062.888889\n",
        "\n",
        "[36 rows x 4 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculating votes that resulted in winning seats"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_winning_votes = pd.pivot_table(csv_data, values=\"Total Votes Polled\", rows=\"Party Abbreviation\", aggfunc=\"sum\")\n",
      "all_winning_votes_average = pd.pivot_table(csv_data, values=\"Total Votes Polled\", rows=\"Party Abbreviation\", aggfunc=\"mean\")\n",
      "all_winning_votes_percentages = pd.pivot_table(csv_data, values=\"Winner Percentage\", rows=\"Party Abbreviation\", aggfunc=\"mean\")\n",
      "average_winning_elecors = pd.pivot_table(csv_data, values=\"Total Electors\", rows=\"Party Abbreviation\", aggfunc=\"mean\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_total_winning_votes = lambda x: all_winning_votes[x]\n",
      "get_average_winning_votes = lambda x: all_winning_votes_average[x]\n",
      "get_average_winning_percentages = lambda x: all_winning_votes_percentages[x]\n",
      "get_average_winning_elecors = lambda x: average_winning_elecors[x]\n",
      "\n",
      "\n",
      "results[\"Total Winning Votes\"] = pd.Series(results.index.values).apply(get_total_winning_votes).values\n",
      "results[\"Average Winning Votes\"] = pd.Series(results.index.values).apply(get_average_winning_votes).values\n",
      "results[\"Average Winning Percentage\"] = pd.Series(results.index.values).apply(get_average_winning_percentages).values\n",
      "results[\"Average Winning Electors\"] = pd.Series(results.index.values).apply(get_average_winning_elecors).values\n",
      "\n",
      "results[\"Loosing Votes\"] = results[\"Total Votes\"] - results[\"Total Winning Votes\"]\n",
      "results[\"Winning Votes Ratio\"] = results[\"Total Winning Votes\"] / results[\"Total Votes\"]\n",
      "\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Seats</th>\n",
        "      <th>Seats Percentage</th>\n",
        "      <th>Total Votes</th>\n",
        "      <th>Average Votes</th>\n",
        "      <th>Total Winning Votes</th>\n",
        "      <th>Average Winning Votes</th>\n",
        "      <th>Average Winning Percentage</th>\n",
        "      <th>Average Winning Electors</th>\n",
        "      <th>Loosing Votes</th>\n",
        "      <th>Winning Votes Ratio</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>BJP</th>\n",
        "      <td> 282</td>\n",
        "      <td> 0.519337</td>\n",
        "      <td> 171657549</td>\n",
        "      <td> 401069.039720</td>\n",
        "      <td> 139553310</td>\n",
        "      <td> 494869</td>\n",
        "      <td> 0.493188</td>\n",
        "      <td> 1606886</td>\n",
        "      <td> 32104239</td>\n",
        "      <td> 0.812975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INC</th>\n",
        "      <td>  44</td>\n",
        "      <td> 0.081031</td>\n",
        "      <td> 106938242</td>\n",
        "      <td> 230470.349138</td>\n",
        "      <td>  18219598</td>\n",
        "      <td> 414081</td>\n",
        "      <td> 0.428075</td>\n",
        "      <td> 1384055</td>\n",
        "      <td> 88718644</td>\n",
        "      <td> 0.170375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADMK</th>\n",
        "      <td>  37</td>\n",
        "      <td> 0.068140</td>\n",
        "      <td>  18115825</td>\n",
        "      <td> 452895.625000</td>\n",
        "      <td>  17415881</td>\n",
        "      <td> 470699</td>\n",
        "      <td> 0.451634</td>\n",
        "      <td> 1413200</td>\n",
        "      <td>   699944</td>\n",
        "      <td> 0.961363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AITC</th>\n",
        "      <td>  34</td>\n",
        "      <td> 0.062615</td>\n",
        "      <td>  21259681</td>\n",
        "      <td> 162287.641221</td>\n",
        "      <td>  18366652</td>\n",
        "      <td> 540195</td>\n",
        "      <td> 0.431486</td>\n",
        "      <td> 1512020</td>\n",
        "      <td>  2893029</td>\n",
        "      <td> 0.863919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BJD</th>\n",
        "      <td>  20</td>\n",
        "      <td> 0.036832</td>\n",
        "      <td>   9491497</td>\n",
        "      <td> 451976.047619</td>\n",
        "      <td>   9169818</td>\n",
        "      <td> 458490</td>\n",
        "      <td> 0.449398</td>\n",
        "      <td> 1389275</td>\n",
        "      <td>   321679</td>\n",
        "      <td> 0.966109</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SHS</th>\n",
        "      <td>  18</td>\n",
        "      <td> 0.033149</td>\n",
        "      <td>  10262982</td>\n",
        "      <td> 176947.965517</td>\n",
        "      <td>   9010919</td>\n",
        "      <td> 500606</td>\n",
        "      <td> 0.510340</td>\n",
        "      <td> 1676883</td>\n",
        "      <td>  1252063</td>\n",
        "      <td> 0.878002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TDP</th>\n",
        "      <td>  16</td>\n",
        "      <td> 0.029466</td>\n",
        "      <td>  14094545</td>\n",
        "      <td> 469818.166667</td>\n",
        "      <td>   9362168</td>\n",
        "      <td> 585135</td>\n",
        "      <td> 0.493163</td>\n",
        "      <td> 1554833</td>\n",
        "      <td>  4732377</td>\n",
        "      <td> 0.664241</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TRS</th>\n",
        "      <td>  11</td>\n",
        "      <td> 0.020258</td>\n",
        "      <td>   6736490</td>\n",
        "      <td> 396264.117647</td>\n",
        "      <td>   5307344</td>\n",
        "      <td> 482485</td>\n",
        "      <td> 0.430386</td>\n",
        "      <td> 1532891</td>\n",
        "      <td>  1429146</td>\n",
        "      <td> 0.787850</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CPM</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.016575</td>\n",
        "      <td>  17986773</td>\n",
        "      <td> 193406.161290</td>\n",
        "      <td>   4069667</td>\n",
        "      <td> 452185</td>\n",
        "      <td> 0.455675</td>\n",
        "      <td> 1264327</td>\n",
        "      <td> 13917106</td>\n",
        "      <td> 0.226259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>YSRCP</th>\n",
        "      <td>   9</td>\n",
        "      <td> 0.016575</td>\n",
        "      <td>  13991280</td>\n",
        "      <td> 368191.578947</td>\n",
        "      <td>   4950808</td>\n",
        "      <td> 550089</td>\n",
        "      <td> 0.478040</td>\n",
        "      <td> 1495688</td>\n",
        "      <td>  9040472</td>\n",
        "      <td> 0.353850</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LJP</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.011050</td>\n",
        "      <td>   2295929</td>\n",
        "      <td> 327989.857143</td>\n",
        "      <td>   1983574</td>\n",
        "      <td> 330595</td>\n",
        "      <td> 0.375127</td>\n",
        "      <td> 1579884</td>\n",
        "      <td>   312355</td>\n",
        "      <td> 0.863953</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NCP</th>\n",
        "      <td>   6</td>\n",
        "      <td> 0.011050</td>\n",
        "      <td>   8635554</td>\n",
        "      <td> 239876.500000</td>\n",
        "      <td>   2594704</td>\n",
        "      <td> 432450</td>\n",
        "      <td> 0.483694</td>\n",
        "      <td> 1419258</td>\n",
        "      <td>  6040850</td>\n",
        "      <td> 0.300468</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SP</th>\n",
        "      <td>   5</td>\n",
        "      <td> 0.009208</td>\n",
        "      <td>  18672916</td>\n",
        "      <td>  94786.375635</td>\n",
        "      <td>   2458349</td>\n",
        "      <td> 491669</td>\n",
        "      <td> 0.471728</td>\n",
        "      <td> 1714211</td>\n",
        "      <td> 16214567</td>\n",
        "      <td> 0.131653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AAAP</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "      <td>  11325635</td>\n",
        "      <td>  26216.747685</td>\n",
        "      <td>   1716952</td>\n",
        "      <td> 429238</td>\n",
        "      <td> 0.401053</td>\n",
        "      <td> 1464262</td>\n",
        "      <td>  9608683</td>\n",
        "      <td> 0.151599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RJD</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "      <td>   7442313</td>\n",
        "      <td> 248077.100000</td>\n",
        "      <td>   1429688</td>\n",
        "      <td> 357422</td>\n",
        "      <td> 0.367278</td>\n",
        "      <td> 1636930</td>\n",
        "      <td>  6012625</td>\n",
        "      <td> 0.192103</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SAD</th>\n",
        "      <td>   4</td>\n",
        "      <td> 0.007366</td>\n",
        "      <td>   3636148</td>\n",
        "      <td> 363614.800000</td>\n",
        "      <td>   1817385</td>\n",
        "      <td> 454346</td>\n",
        "      <td> 0.411916</td>\n",
        "      <td> 1543844</td>\n",
        "      <td>  1818763</td>\n",
        "      <td> 0.499811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AIUDF</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>   2333040</td>\n",
        "      <td> 129613.333333</td>\n",
        "      <td>   1350137</td>\n",
        "      <td> 450045</td>\n",
        "      <td> 0.389700</td>\n",
        "      <td> 1382908</td>\n",
        "      <td>   982903</td>\n",
        "      <td> 0.578703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IND</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>  16743719</td>\n",
        "      <td>   5175.801855</td>\n",
        "      <td>   1374887</td>\n",
        "      <td> 458295</td>\n",
        "      <td> 0.463283</td>\n",
        "      <td> 1271567</td>\n",
        "      <td> 15368832</td>\n",
        "      <td> 0.082114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BLSP</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>   1078473</td>\n",
        "      <td> 269618.250000</td>\n",
        "      <td>   1072804</td>\n",
        "      <td> 357601</td>\n",
        "      <td> 0.427755</td>\n",
        "      <td> 1522716</td>\n",
        "      <td>     5669</td>\n",
        "      <td> 0.994743</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JKPDP</th>\n",
        "      <td>   3</td>\n",
        "      <td> 0.005525</td>\n",
        "      <td>    732644</td>\n",
        "      <td> 146528.800000</td>\n",
        "      <td>    533629</td>\n",
        "      <td> 177876</td>\n",
        "      <td> 0.472012</td>\n",
        "      <td> 1232333</td>\n",
        "      <td>   199015</td>\n",
        "      <td> 0.728361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INLD</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   2799899</td>\n",
        "      <td> 279989.900000</td>\n",
        "      <td>   1000848</td>\n",
        "      <td> 500424</td>\n",
        "      <td> 0.411830</td>\n",
        "      <td> 1589081</td>\n",
        "      <td>  1799051</td>\n",
        "      <td> 0.357459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JMM</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   1637990</td>\n",
        "      <td>  77999.523810</td>\n",
        "      <td>    715322</td>\n",
        "      <td> 357661</td>\n",
        "      <td> 0.385344</td>\n",
        "      <td> 1300163</td>\n",
        "      <td>   922668</td>\n",
        "      <td> 0.436707</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IUML</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   1100096</td>\n",
        "      <td>  44003.840000</td>\n",
        "      <td>    816226</td>\n",
        "      <td> 408113</td>\n",
        "      <td> 0.473571</td>\n",
        "      <td> 1189616</td>\n",
        "      <td>   283870</td>\n",
        "      <td> 0.741959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JD(U)</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   5992196</td>\n",
        "      <td>  64432.215054</td>\n",
        "      <td>    740808</td>\n",
        "      <td> 370404</td>\n",
        "      <td> 0.380420</td>\n",
        "      <td> 1767467</td>\n",
        "      <td>  5251388</td>\n",
        "      <td> 0.123629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AD</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>    821820</td>\n",
        "      <td> 117402.857143</td>\n",
        "      <td>    812325</td>\n",
        "      <td> 406162</td>\n",
        "      <td> 0.426682</td>\n",
        "      <td> 1718643</td>\n",
        "      <td>     9495</td>\n",
        "      <td> 0.988446</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>JD(S)</th>\n",
        "      <td>   2</td>\n",
        "      <td> 0.003683</td>\n",
        "      <td>   3731481</td>\n",
        "      <td> 109749.441176</td>\n",
        "      <td>   1034211</td>\n",
        "      <td> 517105</td>\n",
        "      <td> 0.442053</td>\n",
        "      <td> 1615299</td>\n",
        "      <td>  2697270</td>\n",
        "      <td> 0.277158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RSP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   1666380</td>\n",
        "      <td> 277730.000000</td>\n",
        "      <td>    408528</td>\n",
        "      <td> 408528</td>\n",
        "      <td> 0.464735</td>\n",
        "      <td> 1219415</td>\n",
        "      <td>  1257852</td>\n",
        "      <td> 0.245159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KEC(M)</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    424194</td>\n",
        "      <td> 424194.000000</td>\n",
        "      <td>    424194</td>\n",
        "      <td> 424194</td>\n",
        "      <td> 0.510072</td>\n",
        "      <td> 1161463</td>\n",
        "      <td>        0</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CPI</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   4327298</td>\n",
        "      <td>  64586.537313</td>\n",
        "      <td>    389209</td>\n",
        "      <td> 389209</td>\n",
        "      <td> 0.422821</td>\n",
        "      <td> 1275288</td>\n",
        "      <td>  3938089</td>\n",
        "      <td> 0.089943</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SDF</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    163698</td>\n",
        "      <td> 163698.000000</td>\n",
        "      <td>    163698</td>\n",
        "      <td> 163698</td>\n",
        "      <td> 0.529824</td>\n",
        "      <td>  370611</td>\n",
        "      <td>        0</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AINRC</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    255826</td>\n",
        "      <td> 255826.000000</td>\n",
        "      <td>    255826</td>\n",
        "      <td> 255826</td>\n",
        "      <td> 0.345703</td>\n",
        "      <td>  901357</td>\n",
        "      <td>        0</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SWP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   1105073</td>\n",
        "      <td> 552536.500000</td>\n",
        "      <td>    640428</td>\n",
        "      <td> 640428</td>\n",
        "      <td> 0.538686</td>\n",
        "      <td> 1630598</td>\n",
        "      <td>   464645</td>\n",
        "      <td> 0.579535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPEP</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    576444</td>\n",
        "      <td>  82349.142857</td>\n",
        "      <td>    239301</td>\n",
        "      <td> 239301</td>\n",
        "      <td> 0.522410</td>\n",
        "      <td>  586501</td>\n",
        "      <td>   337143</td>\n",
        "      <td> 0.415133</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AIMIM</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    685729</td>\n",
        "      <td> 137145.800000</td>\n",
        "      <td>    513868</td>\n",
        "      <td> 513868</td>\n",
        "      <td> 0.528986</td>\n",
        "      <td> 1823664</td>\n",
        "      <td>   171861</td>\n",
        "      <td> 0.749375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NPF</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>    994505</td>\n",
        "      <td> 497252.500000</td>\n",
        "      <td>    713372</td>\n",
        "      <td> 713372</td>\n",
        "      <td> 0.647102</td>\n",
        "      <td> 1752741</td>\n",
        "      <td>   281133</td>\n",
        "      <td> 0.717314</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PMK</th>\n",
        "      <td>   1</td>\n",
        "      <td> 0.001842</td>\n",
        "      <td>   1827566</td>\n",
        "      <td> 203062.888889</td>\n",
        "      <td>    468194</td>\n",
        "      <td> 468194</td>\n",
        "      <td> 0.425111</td>\n",
        "      <td> 1358273</td>\n",
        "      <td>  1359372</td>\n",
        "      <td> 0.256184</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>36 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "        Seats  Seats Percentage  Total Votes  Average Votes  \\\n",
        "BJP       282          0.519337    171657549  401069.039720   \n",
        "INC        44          0.081031    106938242  230470.349138   \n",
        "ADMK       37          0.068140     18115825  452895.625000   \n",
        "AITC       34          0.062615     21259681  162287.641221   \n",
        "BJD        20          0.036832      9491497  451976.047619   \n",
        "SHS        18          0.033149     10262982  176947.965517   \n",
        "TDP        16          0.029466     14094545  469818.166667   \n",
        "TRS        11          0.020258      6736490  396264.117647   \n",
        "CPM         9          0.016575     17986773  193406.161290   \n",
        "YSRCP       9          0.016575     13991280  368191.578947   \n",
        "LJP         6          0.011050      2295929  327989.857143   \n",
        "NCP         6          0.011050      8635554  239876.500000   \n",
        "SP          5          0.009208     18672916   94786.375635   \n",
        "AAAP        4          0.007366     11325635   26216.747685   \n",
        "RJD         4          0.007366      7442313  248077.100000   \n",
        "SAD         4          0.007366      3636148  363614.800000   \n",
        "AIUDF       3          0.005525      2333040  129613.333333   \n",
        "IND         3          0.005525     16743719    5175.801855   \n",
        "BLSP        3          0.005525      1078473  269618.250000   \n",
        "JKPDP       3          0.005525       732644  146528.800000   \n",
        "INLD        2          0.003683      2799899  279989.900000   \n",
        "JMM         2          0.003683      1637990   77999.523810   \n",
        "IUML        2          0.003683      1100096   44003.840000   \n",
        "JD(U)       2          0.003683      5992196   64432.215054   \n",
        "AD          2          0.003683       821820  117402.857143   \n",
        "JD(S)       2          0.003683      3731481  109749.441176   \n",
        "RSP         1          0.001842      1666380  277730.000000   \n",
        "KEC(M)      1          0.001842       424194  424194.000000   \n",
        "CPI         1          0.001842      4327298   64586.537313   \n",
        "SDF         1          0.001842       163698  163698.000000   \n",
        "AINRC       1          0.001842       255826  255826.000000   \n",
        "SWP         1          0.001842      1105073  552536.500000   \n",
        "NPEP        1          0.001842       576444   82349.142857   \n",
        "AIMIM       1          0.001842       685729  137145.800000   \n",
        "NPF         1          0.001842       994505  497252.500000   \n",
        "PMK         1          0.001842      1827566  203062.888889   \n",
        "\n",
        "        Total Winning Votes  Average Winning Votes  \\\n",
        "BJP               139553310                 494869   \n",
        "INC                18219598                 414081   \n",
        "ADMK               17415881                 470699   \n",
        "AITC               18366652                 540195   \n",
        "BJD                 9169818                 458490   \n",
        "SHS                 9010919                 500606   \n",
        "TDP                 9362168                 585135   \n",
        "TRS                 5307344                 482485   \n",
        "CPM                 4069667                 452185   \n",
        "YSRCP               4950808                 550089   \n",
        "LJP                 1983574                 330595   \n",
        "NCP                 2594704                 432450   \n",
        "SP                  2458349                 491669   \n",
        "AAAP                1716952                 429238   \n",
        "RJD                 1429688                 357422   \n",
        "SAD                 1817385                 454346   \n",
        "AIUDF               1350137                 450045   \n",
        "IND                 1374887                 458295   \n",
        "BLSP                1072804                 357601   \n",
        "JKPDP                533629                 177876   \n",
        "INLD                1000848                 500424   \n",
        "JMM                  715322                 357661   \n",
        "IUML                 816226                 408113   \n",
        "JD(U)                740808                 370404   \n",
        "AD                   812325                 406162   \n",
        "JD(S)               1034211                 517105   \n",
        "RSP                  408528                 408528   \n",
        "KEC(M)               424194                 424194   \n",
        "CPI                  389209                 389209   \n",
        "SDF                  163698                 163698   \n",
        "AINRC                255826                 255826   \n",
        "SWP                  640428                 640428   \n",
        "NPEP                 239301                 239301   \n",
        "AIMIM                513868                 513868   \n",
        "NPF                  713372                 713372   \n",
        "PMK                  468194                 468194   \n",
        "\n",
        "        Average Winning Percentage  Average Winning Electors  Loosing Votes  \\\n",
        "BJP                       0.493188                   1606886       32104239   \n",
        "INC                       0.428075                   1384055       88718644   \n",
        "ADMK                      0.451634                   1413200         699944   \n",
        "AITC                      0.431486                   1512020        2893029   \n",
        "BJD                       0.449398                   1389275         321679   \n",
        "SHS                       0.510340                   1676883        1252063   \n",
        "TDP                       0.493163                   1554833        4732377   \n",
        "TRS                       0.430386                   1532891        1429146   \n",
        "CPM                       0.455675                   1264327       13917106   \n",
        "YSRCP                     0.478040                   1495688        9040472   \n",
        "LJP                       0.375127                   1579884         312355   \n",
        "NCP                       0.483694                   1419258        6040850   \n",
        "SP                        0.471728                   1714211       16214567   \n",
        "AAAP                      0.401053                   1464262        9608683   \n",
        "RJD                       0.367278                   1636930        6012625   \n",
        "SAD                       0.411916                   1543844        1818763   \n",
        "AIUDF                     0.389700                   1382908         982903   \n",
        "IND                       0.463283                   1271567       15368832   \n",
        "BLSP                      0.427755                   1522716           5669   \n",
        "JKPDP                     0.472012                   1232333         199015   \n",
        "INLD                      0.411830                   1589081        1799051   \n",
        "JMM                       0.385344                   1300163         922668   \n",
        "IUML                      0.473571                   1189616         283870   \n",
        "JD(U)                     0.380420                   1767467        5251388   \n",
        "AD                        0.426682                   1718643           9495   \n",
        "JD(S)                     0.442053                   1615299        2697270   \n",
        "RSP                       0.464735                   1219415        1257852   \n",
        "KEC(M)                    0.510072                   1161463              0   \n",
        "CPI                       0.422821                   1275288        3938089   \n",
        "SDF                       0.529824                    370611              0   \n",
        "AINRC                     0.345703                    901357              0   \n",
        "SWP                       0.538686                   1630598         464645   \n",
        "NPEP                      0.522410                    586501         337143   \n",
        "AIMIM                     0.528986                   1823664         171861   \n",
        "NPF                       0.647102                   1752741         281133   \n",
        "PMK                       0.425111                   1358273        1359372   \n",
        "\n",
        "        Winning Votes Ratio  \n",
        "BJP                0.812975  \n",
        "INC                0.170375  \n",
        "ADMK               0.961363  \n",
        "AITC               0.863919  \n",
        "BJD                0.966109  \n",
        "SHS                0.878002  \n",
        "TDP                0.664241  \n",
        "TRS                0.787850  \n",
        "CPM                0.226259  \n",
        "YSRCP              0.353850  \n",
        "LJP                0.863953  \n",
        "NCP                0.300468  \n",
        "SP                 0.131653  \n",
        "AAAP               0.151599  \n",
        "RJD                0.192103  \n",
        "SAD                0.499811  \n",
        "AIUDF              0.578703  \n",
        "IND                0.082114  \n",
        "BLSP               0.994743  \n",
        "JKPDP              0.728361  \n",
        "INLD               0.357459  \n",
        "JMM                0.436707  \n",
        "IUML               0.741959  \n",
        "JD(U)              0.123629  \n",
        "AD                 0.988446  \n",
        "JD(S)              0.277158  \n",
        "RSP                0.245159  \n",
        "KEC(M)             1.000000  \n",
        "CPI                0.089943  \n",
        "SDF                1.000000  \n",
        "AINRC              1.000000  \n",
        "SWP                0.579535  \n",
        "NPEP               0.415133  \n",
        "AIMIM              0.749375  \n",
        "NPF                0.717314  \n",
        "PMK                0.256184  \n",
        "\n",
        "[36 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Statistical Analysis of Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Seats</th>\n",
        "      <th>Seats Percentage</th>\n",
        "      <th>Total Votes</th>\n",
        "      <th>Average Votes</th>\n",
        "      <th>Total Winning Votes</th>\n",
        "      <th>Average Winning Votes</th>\n",
        "      <th>Average Winning Percentage</th>\n",
        "      <th>Average Winning Electors</th>\n",
        "      <th>Loosing Votes</th>\n",
        "      <th>Winning Votes Ratio</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>  36.000000</td>\n",
        "      <td> 36.000000</td>\n",
        "      <td> 3.600000e+01</td>\n",
        "      <td>     36.000000</td>\n",
        "      <td> 3.600000e+01</td>\n",
        "      <td>     36.000000</td>\n",
        "      <td> 36.000000</td>\n",
        "      <td>      36.000000</td>\n",
        "      <td>       36.000000</td>\n",
        "      <td> 36.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  15.083333</td>\n",
        "      <td>  0.027778</td>\n",
        "      <td> 1.365393e+07</td>\n",
        "      <td> 236299.539018</td>\n",
        "      <td> 7.252629e+06</td>\n",
        "      <td> 435134.944444</td>\n",
        "      <td>  0.453897</td>\n",
        "      <td> 1412726.416667</td>\n",
        "      <td>  6401299.944444</td>\n",
        "      <td>  0.560735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  46.987156</td>\n",
        "      <td>  0.086533</td>\n",
        "      <td> 3.248419e+07</td>\n",
        "      <td> 150275.821702</td>\n",
        "      <td> 2.324585e+07</td>\n",
        "      <td> 113343.272362</td>\n",
        "      <td>  0.059478</td>\n",
        "      <td>  305902.010647</td>\n",
        "      <td> 15550652.495029</td>\n",
        "      <td>  0.323618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   1.000000</td>\n",
        "      <td>  0.001842</td>\n",
        "      <td> 1.636980e+05</td>\n",
        "      <td>   5175.801855</td>\n",
        "      <td> 1.636980e+05</td>\n",
        "      <td> 163698.000000</td>\n",
        "      <td>  0.345703</td>\n",
        "      <td>  370611.000000</td>\n",
        "      <td>        0.000000</td>\n",
        "      <td>  0.082114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>   1.000000</td>\n",
        "      <td>  0.001842</td>\n",
        "      <td> 1.094690e+06</td>\n",
        "      <td> 115489.503151</td>\n",
        "      <td> 6.137282e+05</td>\n",
        "      <td> 384507.750000</td>\n",
        "      <td>  0.420095</td>\n",
        "      <td> 1274357.750000</td>\n",
        "      <td>   305233.750000</td>\n",
        "      <td>  0.253428</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  0.005525</td>\n",
        "      <td> 3.683814e+06</td>\n",
        "      <td> 216766.619013</td>\n",
        "      <td> 1.211470e+06</td>\n",
        "      <td> 451115.000000</td>\n",
        "      <td>  0.450516</td>\n",
        "      <td> 1479975.000000</td>\n",
        "      <td>  1308612.000000</td>\n",
        "      <td>  0.579119</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>   9.000000</td>\n",
        "      <td>  0.016575</td>\n",
        "      <td> 1.199205e+07</td>\n",
        "      <td> 364758.994737</td>\n",
        "      <td> 4.289952e+06</td>\n",
        "      <td> 496257.750000</td>\n",
        "      <td>  0.486061</td>\n",
        "      <td> 1608989.250000</td>\n",
        "      <td>  5441697.250000</td>\n",
        "      <td>  0.863928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 282.000000</td>\n",
        "      <td>  0.519337</td>\n",
        "      <td> 1.716575e+08</td>\n",
        "      <td> 552536.500000</td>\n",
        "      <td> 1.395533e+08</td>\n",
        "      <td> 713372.000000</td>\n",
        "      <td>  0.647102</td>\n",
        "      <td> 1823664.000000</td>\n",
        "      <td> 88718644.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "            Seats  Seats Percentage   Total Votes  Average Votes  \\\n",
        "count   36.000000         36.000000  3.600000e+01      36.000000   \n",
        "mean    15.083333          0.027778  1.365393e+07  236299.539018   \n",
        "std     46.987156          0.086533  3.248419e+07  150275.821702   \n",
        "min      1.000000          0.001842  1.636980e+05    5175.801855   \n",
        "25%      1.000000          0.001842  1.094690e+06  115489.503151   \n",
        "50%      3.000000          0.005525  3.683814e+06  216766.619013   \n",
        "75%      9.000000          0.016575  1.199205e+07  364758.994737   \n",
        "max    282.000000          0.519337  1.716575e+08  552536.500000   \n",
        "\n",
        "       Total Winning Votes  Average Winning Votes  Average Winning Percentage  \\\n",
        "count         3.600000e+01              36.000000                   36.000000   \n",
        "mean          7.252629e+06          435134.944444                    0.453897   \n",
        "std           2.324585e+07          113343.272362                    0.059478   \n",
        "min           1.636980e+05          163698.000000                    0.345703   \n",
        "25%           6.137282e+05          384507.750000                    0.420095   \n",
        "50%           1.211470e+06          451115.000000                    0.450516   \n",
        "75%           4.289952e+06          496257.750000                    0.486061   \n",
        "max           1.395533e+08          713372.000000                    0.647102   \n",
        "\n",
        "       Average Winning Electors    Loosing Votes  Winning Votes Ratio  \n",
        "count                 36.000000        36.000000            36.000000  \n",
        "mean             1412726.416667   6401299.944444             0.560735  \n",
        "std               305902.010647  15550652.495029             0.323618  \n",
        "min               370611.000000         0.000000             0.082114  \n",
        "25%              1274357.750000    305233.750000             0.253428  \n",
        "50%              1479975.000000   1308612.000000             0.579119  \n",
        "75%              1608989.250000   5441697.250000             0.863928  \n",
        "max              1823664.000000  88718644.000000             1.000000  \n",
        "\n",
        "[8 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Visualizing the results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(18,8))\n",
      "cmap = plt.cm.hsv\n",
      "colors = cmap(np.linspace(0., 1., len(results[\"Seats\"])))\n",
      "plt.pie(results[\"Seats\"], labels=results.index, explode=np.array(range(len(results)))/10, autopct='%1.1f%%',\n",
      "        colors = colors)\n",
      "\n",
      "plt.axis(\"equal\")\n",
      "\n",
      "plt.title(\"Indian general election 2014 results of seats per party\")\n",
      "\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LjbQIIFlZWQx9/nmub9qU+9evZ54KCwkQNqAbsMHpJHbKFK6qXp1Rn3yiiTpF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ZOi8ycIVSUkXEzmdu+7uDR1oVu/brx4oX5fmSAIdOrUiei7kfxk1hG54wOkc19Ach6Jr9K6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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f509003d710>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(18,8))\n",
      "\n",
      "plt.pie(results.sort(\"Total Votes\", ascending=False)[\"Total Votes\"], labels=results.sort(\"Total Votes\", ascending=False).index,\n",
      "        explode=np.array(range(len(results)))**1.5/40, autopct='%1.1f%%', colors = colors)\n",
      "\n",
      "plt.axis(\"equal\")\n",
      "\n",
      "plt.title(\"Indian general election 2014 results of votes per party\")\n",
      "\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hIiIiIiJ5kgqMHCg5OZlxn33Ge0OH8pDDwRC7nTJmh5Js9QvwdmAgB61W3hg+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9Pfi5+fH9XV1S7bqqqqXHw1JBIJ999/PyNHjuT111//7RQiIiIil0SbFzBCQkK4a8idfLZ/PS/4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69TAyMqJBgwbcvJmWApecnIyGhgbTp0/PNN7CwoK6detiZGSErq4ujo6Oxd5GWI4cOXLkyPm3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mRkJIGBgdy4cQNnZ2fatm1L7969MRgMbNq0iZdffpnixYuTmJhIixYtGDp06F3XePPNN2nX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\n/g/InUU7Ua2X0gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f5090039890>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(18,10))\n",
      "for counter in range(len(results)):\n",
      "    x = results[\"Total Votes\"][counter]\n",
      "    y = results[\"Seats\"][counter]\n",
      "    label = \"%s\\n%s\" % (results.index[counter], y)\n",
      "    plt.scatter(x,y, c = colors[counter],\n",
      "                s=y/x * 1e9, label =results.index[counter])\n",
      "    plt.annotate(label, \n",
      "        xy = (x, y), xytext = (-20, 20),\n",
      "        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
      "        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
      "        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))\n",
      "\n",
      "plt.xscale(\"log\")\n",
      "plt.yscale(\"log\")\n",
      "plt.xlim(results[\"Total Votes\"].min()*0.95, results[\"Total Votes\"].max()*1.05)\n",
      "plt.ylim(0)\n",
      "\n",
      "plt.xlabel(\"Total Votes (log)\")\n",
      "plt.ylabel(\"Seats (log)\")\n",
      "\n",
      "plt.title(\"Indian General Elections 2014 Seats vs Total Votes on a log scale\")\n",
      "\n",
      "plt.grid()\n",
      "\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qVX0VHh6gf//7OtcdDFCJhYeHq1+/e5WbO1jHjh1T3mV2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uvcT27WkY7dTM/WcQarUa30aNaDzKTO8HtFy5cgVfXxOK4kxWdjYK4OzkBIo9GRmXuZKaiqeH\nBxr1zWBNEV99exRPTzdCQky8/36/UvUeMWIoP/9yiMsp88l3DAJ9A+pMXgJ2cY/TJdiJTRu3sH79\n12XuVlBQHHS9fDmbJk1cWLBgCD/+uBMnJz0vvNCZCxeyuHDhOgUFMsHBh9HpNLi6SkhSCosWXUVR\nYOrU5ixZcpLw8DQefvjOqTGSBGFhPsTFpdO6aWcKzRe5EPU7KtcATIZG4NAI7LxApQPZAoXXITsJ\nUiOwux6JkriNVoHN+es7Mxg5ciQ63b2ZpPd/QUUjGhISMgkJWfzfEWStGDSoecloGkGoDjHPWRAE\noTTRNtafGgcgoHh0wwMPNOGBB5rQtq1XSe/w7S+RVqtMdHQarVtXbQm/+qBFhx8N8VMC6GZ+gPNp\n8ez5/hDbt18kMjIZvV6PohRf38yZpQMQAG+80Yu3395TMgUF7swBMWfONv7zn/189tlDBAd7EhFx\nhbZt6/BlyMZUKonvvx9L377f8P77e3nttV4l28qaOhAc7Elk5BWGDy+dA6GsYyRJYujQQObM2U6n\nTj6lkk3ePKYuhhi/9FJXQkOX8PjjHW6rT/mBlfKmYLi5OZKQkE12tsL69clYrQoqFcycWToA0aCB\nAzqdmt9+u8Bnnz3EgQPJ5WaolySJCRPaMHLkGt58s09NLrHWgoOD2b59I/36DsWct7DCfXt07crn\nS5YQ2uH2+3krB8QdJDAYDTRt0hgHe3tSUlNp3rQpyZcugaLQqFEjcnKKE41eSEzE37d4So8sHQap\nkP37nyEg4M7RNLczGAx88O+32HfgJF8sbkdB83ngPaHiN7vqUmRUSQvQJ77J3H++zuzZs1Cryx/R\nYjQWB10LCswMGvQdmzefLdmmVqto3tyN5s3d0Osv89FHbjz0UAvmzTvEk0+GYDRqmTNnH3q9huHD\n3XnllQR27+7AtWvmW9VRoFUrd956qw/jxq1j69bJDH94MBcuXCD50hWSLu/mesI1LGYT0hkZQ/SP\n+DRpQbfOYfQc15XevV+ldetKVn8Q6kVwsBfr1p0pZOtRLQAAIABJREFUc1uzZm42z58kCIIgCIJQ\n32o8BSM+PoOzZzNK/jsqKhV//+Ie4psvmzeTUPr5Od+RYPBeokJFc1ohn2jL+HZtWDozhBlPm/n3\nWw3w8bEnKelGmccNGNCMrKxCTp68M7fFzWt/++2+rF8fR1LSDebM6c577+0ruV+yrLB4cUTdXpgN\nGAwaNm16lJUro/nqq6gK933++c58880Jjhy5XPK3n346Q1paHv7+zqSm5t6xv6IoGI1aPvigP2+8\nUXZw4+rVvDvmPtuKq6uRceOCWLYsqiQIoChlB1YqcuSIialTG5CY2JULF7qQlNSVJk0MJCWVnX3+\nrbf68MEH/VGpbp6z/PP16uXP66/3YuLEttWqky117tyZnb9vQus4E4Wz5e5nNBppGxREZFRUyYu+\noiilwjb5+fmYzbdenHNyczEaDNjb21NkMpF14waO/11VwNXVlcMREfj7+aGodmFwOIyjoyPOzs5U\nRqfTMezhQeze8Qv+N97B7tQjkLmv9tMyFBnStmB/vA9tdauJitjPnDmzKww+3M5o1DJ//kO8//4+\nFEUhJSWHnJziqRKKoqAo4OJiKPf4J55oyNy5TQgOLnsJzW7dGvPFFw8zdOgqsrJk2rVrx8NDBvPc\n04/z+t9e5Z//+DtPPvEECWdjOXc6khVfL2H69Oki+HAP6ds3gKIiC19+eStJ8cmTV0lOLvvfIUGo\nCTHPWRAEoTTRNtafGo+AyM018cILm8nKKkSjUdGihRuLFw9lzJi1TJr0I3q9hqIiCwMGNGXDhgkl\nx92rSbN+OHWK2T170sfajB705dTFKDxcf2POnA3ArQzjt9f/jTd6MWLEmjvKuflCazBomDWrC++9\nt5dFi4Yyb94gJk78gfx8M5IkMWxYYH1cVo3dvE5XVyNbtkymd++v8fQsnpN+c6rCTTeXUl29ekzJ\nMpzFo2P8GTy4OT16+DF//pE/lF98gvHj25T599TUXNzdjdjb2244+O2f3V/+0p3PPz96x7byRiR8\n+eUxwsJkLJbieN2ECcV1PnjQxKef3jk9ZPRoT/797+RSK2xA8QvinfWp+Mtw+2iMu/W96dSpEzt3\nbmL69L6gzUQxD0Gi+AX49ir17N6dg0dvv58Sf6yy1WrlWnoGWTdkDkdcxc5opFVg8ffA2ckJi8VS\nck883N2Jib2AX7P9uHlJTJ78BAsWLKt23WNPR7Jw4SI+nvcE2QkGchvMgEaTQFN6SlC5TBmoLn+N\nMfULvL2cef2dF5g6dWqVAw+3f3YdOjQkIMCF8+ezyMtz5Oef47FY5JL9Onf2KXXMzf/v46Mvmd7z\nx+0379vQoYGkp+czePBK9u17HFdXY9WvU7gn/PTTeF56aSsffLAfg0FDQIArn346qNw24F79N1UQ\nBEEQBKEsklJJt+/ixW8yfHh2mXP460JCQiYHDjRlypSXyt1HkiRSK1iZoCKb7VbQcsp+2jYsfxj3\nTblks0P7K9ftMnhkzHAaN25c6TG1tXLlJTp3/istWrSo83MBfPDBTF54wQU7u7pdxq1v329YuXJU\nlZ+jJUsiycsz8fLLd06JWLEimW7dXqN58+blHnvo0CGyshYzeLB/rep8U3LyDbZu3VUqB0RlLBaZ\nQ4cK6NnzYZvUoyzR0VeJi+vEmDFP1Nk58vPzmTfvWTp0OEdU1EkwD0FFUPkHSGdp2yGmSqMVyhJz\n5gaHDkLzlnH06dOLbt26VHnlgaNHL5OWNpiHHx59x99lWWbnzp18+MlCdu3YgsG1OVaHjuQbwsAx\nGNQOoNKCbAJLNmSfxN4UiepGBEW5lxg+fDSvzJ5B586dyw0chYcvJCQkptKcJWlpeaxbt4MZM2o3\numfv3iy6dRtaYeLLP1qy5BJDh75Fo0aNanVuoXaOHDnCtWtf8PDDtmmj/ig318QXX+QyZ878Oilf\nEARBEAShIpIklTvau1Y5IP7sHHDiEfN44m7EsObb7wlqF8TAhwZU6wd/XTKZTJw5c4br169TUFBA\nYWEharUao9GI0WikSZMm+Pj4VNrTXh9eeaU7ixZF8OabD1Zp/zVrTt8xcka4uzQaDUOGDKRt29as\nXbueooKTyOZuqPCDUmMdakpBIQNUiTi56pk+/Qk8PGyTgFSlUtG/f3/69+9PUVER0dHRREZGsmd/\nBNGnVv93eogJnU6Pg4MDYT3b0KNbP8LCXiUoKOie+c4LgiAIgiAIwv1M/KqulERLgvE3N2XbyZ9Z\nfmk54yePx9GxfkaE3GQymTh16hSRkZFE7osg4mAEMYlnCDD446nywIAeg6LHipVCqYh8CjhnOg8a\nibB2oYT1DqNjl06EhYXdlaDEkCEtGDKk6qM6duyYWoe1EWqqcePGvPDCs0RGRnJg/0aKijRYTZ1Q\n0xYonUi0akwopCCpUjAYtPj5uePj06VWwYddu3aVm81Yr9fTsWNHOnbsyPTpIqmfIAj/WypqHwVB\nEP5Xibax/ogARBUZMDLMPJZD6Xv48oulTJg8vl6GMcfExPDFvIWsXLkKH403YZb2hOW3YyojaU8w\n9uayE9JBcX/yZa4Qsf84kQdPsNhhARGmKBo1asSMV2fw6KRJdV5/4c9Hq9XStWtXunTpwoULFziw\n/yiJF3egVftiMXkDoGCiePWQPwa6FKAAhVwkKQdJnYMs5+Lh4UHjxsE4Ojpy5Eg26el3f9SOIAiC\nIAiCIAi2Ve8BiCee2MCmTWfx8rInOvq5+j59rUhIdLM+gEe+F999/R2jJ4ymWbNmVT6+qtduNptZ\nu3YtCz9YQGxMLE+bp3DSshtfSi/vWFl9ffHBFx9GyA9DNsjI7Di/mwV/+YrXZr9O116NmDChG/7+\nd39O+P32bGzYEEt8fCb29lpmzOhUJ+e4l++JJEk0bdqUpk2bkpuby6VLl7h8OYX4+LNI6jQUVRyS\npEFCRfGaGDKybOHDD+HQIQVPTw0HDrTCycmpylMcqno/7vcIdk2erXv5WRFsS3zWQm3c7+2jIAhC\nXRBtY/2p8TKcNfX44x3YsuX+7nlvQWseMU/gh9U/cvZs+csT/lFl164oCqdPnaZP1z4sePIznouc\nysWC47xlea3awYfyqFAxgAdZn7eCqPzf0Z1Rs/Kr7/h26TekpaXZ5Bw1db89Gx06NGTy5LpdJvN+\nuScODg60atWKfv0eZODAB2nXrg1du3amY8f2hIQGExbWlk6dQujevSt/+1tbfvstBK1Wh5ubW7Xy\nK9wv96O2avJs/a/cG0F81oIgCIIg3L/qPQDRq5f/n2JpOB/8GGmeyE/fr+f8+fNVOqaia8/MzGT5\nl8tJiEhgWc48duVsZBwj0GG7ZSj/yA9fusphvGB5mtaXW7D8y+Xs3b0HWZbr7JwVud+eDX9/F4zG\nul095H67J7dI6HQ6jEYj9vb22NnZYTAY0Gq19Orlgqtrze5bVe/H/b6Wc02erfv3WRGqS3zWQm3c\n7+2jIAhCXRBtY/2p9wDEn4k3vgw3j2Pd6h+4du1ajcpQFIXDhw6z9IultEppxlDLAFpRP0tw3qRG\nTSclhGfMU7mw/zxLF35510dDCIIgCIIgCIIgCH8uIgllLfnizwPmAYR/G87TM57GaKx6r9T169dZ\nv3Y9crqVJ8yP4oE757lSh7WtmAvOTDGN41jGCZZ/uZxuPbvSs3eve2IZz5rKyckhPT0dk8mExWJB\npVKh0Wiwt7fHy8tLLK/4JyXm8QmCIJRNtI+CIAilibax/oi3LxsIVjpwLf8qa1etZfLjk1GpKh9Y\nkpqaysrlK+laFEY3pROqe2QwioREmNKBZuYAftz3C2mp1xgxZgRqtfpuV61Ssiyzc+dOwletxsnh\nN6KPZ2GxWHHSeKJBj0rRoCAjSxYKlRxyzZm4Orvj49sIHz9vAgMDcXZ2vtuXIQiCIAiCIAiC8Kck\nAhA20ts6gHWp33LowCG69+xe4b5ms5kVX63gYdMAgmhZTzWsHhecmWIex9pzG1jz3RrGPjoWrbZu\n8x3UVHp6Ol8t+5rPP12ENt8F97w2DBjdgo5u7TDiDJayR3DIWMi5fpUb169wMvYyv23diZ+fP117\ndKJp06b39ciP/3ViLWdBEISyifZREAShNNE21p9673afOPEHundfRnx8Bo0bf8rXX0fVdxXqhAoV\nA83D2bt7H5mZmWXuM3HiD3Tp8iXnzl3nkw8UDkeZ6rmW1aNFy3jzSHTJar5fuQar1Vqn56vus3Hx\n4kUeHfsYAb7NWf/macZcDeeFnAh6yM/hhj9GXIDygwgqNDjjgx+dCDaPoLflZfTnW/Dzmu189sn/\nceTIURRFKff4detiWLbsGBkZBXzyyUGiolJqeunl+jN+XyZOjKF79yji4/Np3PgQX3+dWo1j/3z3\noyw1ebb+V+6NID5rQRAEQRDuX/U+AiI8fHR9n7LeuOBGZ2tPfv7xZ6Y+ObVUD/pnn/Xi22UXGF40\nmkCa3aVaVo8aNaMsQ/n+8np+/P5HRo8fXaUpJjVR1WdDURQWLVrCa6+8Qbei53nNkoA97rU+vxod\nvoThawoly3SJI79t52RUNF269ytz/zFjgmp9ToDCwkJSUlK4cuUKKYmXuHbtGmazGatspVuYhj5d\njXg19Mfb35dGjdwxmUzodHW3OkpdCw+v+X2r6jNyv0ewa/Js/ZnbVuFO4rMWauN+bx8FQRDqgmgb\n60+lAQhJUiHL5fcC25osK0hSXQ7MUCFX0KtdW2FyV+LSThEVdZzQ0JCSvxcUFPDd8u8YVPRghcEH\nWVaQKui1tzVJUVXYyw/FozvGmIez8vw6dmzbwYDBA+qpdqVlZ+cxefw00uNNTM/bhTfBZewlUbuV\nRCVcaEyYaRpJqUdYtWI19k7uKIpTtaZlVHRfZVkmISGBo/sPcTE5iQYaZ7wtjrSwOtGDVuhQo0aF\nFZlCLFy9ns2Vs7FEq4+Sbs0hsFlzOvXoitWqrfb3RZZlUlNTKSgoQJZlDAYDnp6eGAyGsu+GJFWr\nDZAkqdJnquL6UePpL8Xtx92ZOiNJ6ipdtyRhkza1Jrf4bt4f4Zbi70jdfQ6KUtf/jgqCIAiCINRM\npQEIo9GJ3Nyr9VEXAHJzTRiNdZcI0GByJs9krrPyVagYZBrO2i0rCAxsgYODAwCbN/5KUFEgbam4\nZzM3T8FI2S+CdcFodSDXZMa+kh51DRrGmIfzReTXtApuRePGjeuphrckJCSwcsV6nE++zHjr26jL\neXx1GMnNqf2PewkV/kpXdPjy67lwjh87QbsObauckNNksqLV6u/4m9Vq5cjhIxzZfxCjWUMnky9j\n6YvWWnGZPjgTagEsUIiZ4/GX2XBhHYmpKiStjlGjHiv3xbKwsJCff/6Zg7v3ErnvEFGxpzGqNNir\n9aiQKJTNZJryaeHrT1iXTnTs3Z2RI0fi7e0NgF6vx2xWYTZb0Worv3aNRoepFrOLcnOtGI36yncs\n81gLRqPzXZnHZzQ6k5tb+YUbjVry8m6+JNbsObVYZBRFVa3ksIqikJsrV2ulHqFu2NnZkZtbd+UX\n/zvqWHcnEO5rYp6zIAhCaaJtrD+VdpE0bRpKfHxefdQFgPh4E82ata+z8v0tLYk/W2fFA+BJQwLl\n1hw9EgFAbGwsl85dop+1d4XHZRUWkpNqR0Ma1G0Fb9MsI4T49Kwq7WuPHUPM/dmwdj1mc90FccoS\nExPDt1//gOulYTxkfbfc4AOAGwFcuACW2g2DKNHQ6INLbnsyknVEHTtR5WvPyCjA1bVRyX+npqay\ndOFiEn4/zpi8Njxj6koIvmip3gojBrR0VZrwvKknblHebP1iAwO69yExMfGO/c6fP89fX34FP09v\nvnzydbwWRvD3E95cKJrI1YIpnM8dx7ncsVzKf5RMyzSWJraj85pLHH5lEcFNAxk/dAS7d+9GkiT8\n/dtx7lzZuU3+yNXVlYyMikeAVCQ+XqJZM7daHNuiRsfWVrNmbYiPt1S6n4ODDkdHZ5KTi2p8royM\nfFxdvasVwEhJyUWvbyRWerkHNGnShMRECbO5bvLqxMdfp1mzTnVStiAIgiAIQm1UGoAIDm7LmTM6\nUlJy6rwyiYlZJCba0bJl3a0M4U9TMs65EZeeXmfnAOhg6cyxI5Hk5uayaf0mHjE/hJbyV5GQFYUd\nMVcJTu+BupovpLXR1hzC0UiZrMLCKu0fREsaFnjx+/addVyzW2JjY9nwwyZMJ/vif21ipUuWGnBE\nkxpCVMJlm5xfkiT8HZpy+mBDVDmOnIg6icVS8YtmYaGFy5cVvLwaYbVa2b1zF98uW07nzIZMMofh\ng0ut6xWfnovunDdHC15mwFE3OrUJYdHChWRlZfH0lGl0Ce6AvGA3B3IfZlvOIP6mhDKAxriXMcLG\ngIZONOBZ2vBNQW8SCyfS+9frzBg6kS5tOmA0NmLPnmwKC6vygu2AJLmQklL9Lt4TJ3Ixm53w9XWq\n9rEnT16lqMibxo0b35UIdmBgIJcvO5KQUHmgpl27Jvz+ez5mc/WDZGazlaQkMw0a+FX5GItFZufO\nNNq2fVBMwbgH2Nvb4+sbxp49V2o1XaksmZkFRERAmzahNi1X+PMQPXyCIAilibax/khKFX79xMbG\n8vPPHxMWZqVVK1ecnfWo1baZX2q1yly/XsiZM1lERekYO/avBAQEVFxpSSKVmv9ou0oKP/r8m7bd\nsmnt44ybnR2aOkisuE73LWoPiQap7gyUHyy1XVEUTFaZpBvZHIvPgyOhPJo7rcJARV04Ih1lX/A3\ndApT0dLTBQedDlUFLyl55LNE8y1jJo2p06kYiqJw9uwFln35E/LpvuQeHULb3GdQV+H+FJHLSbeP\nadHtPK2aOOLh6Ihaparxy5dVltlz7iRar4sEtE/Bu5mJ0LDgO4bAK4qCyWQlI6OAK1cUfH1DaNjQ\nmx9Wr8V8KZvhliCcajm9xiIrZOSbOXOlkOiDOh69NLAkmBFDCsP0S7lOHmOkZnxU2BknapesUkFh\nqXSG1/QRDB83lJCOarp0MdCsmSt2dtpy72dBQQEnThzA1TUPT0899vbactsMs1kmPd3C6dNFxMUZ\nePTR7ri6GlGpJFSq8j+v4jwaF4iJSeTwvots3ZJDeqoaSSVhbzDSqkUgYQ/0oGOXLvTv3x87O7ty\nyyooKCA7OxtFUbC3t8fRsWbD15OSkliz5gPatcsnKMgFV1cjGk3p65ZlhQ0bTpGVdZ6wMC0BAXoM\nhvKfT0VRMJtlMjMLSEmx4u4eREBAcxRFKTcprKIoFBZaSEzMIjKyACenvowePaVa0zaEupOXl8c3\n33yKh8dZ2rVzpFEjR7TamrVRsqyQk1NEfPx1jh6V6d37OTp27My7777LpEmTaNKkie0vQBAEQRAE\noRwV5YSrUgACioePnzwZwdmzh8nLy0KWbTN0VKVS4+joTmBgN9q3D8PDw4P169ezYsUKwsPDy8z2\nX9sABMANsjgtHeO8+35yDNeQpcp7dqsrksOcl2J5VBmNtrx8BbKBhjlNCcruRBtao6n/hUkASOIS\n0booEtyiKNDkoEgV398EEjniEMWEKRPqrEe1qKiIVSu+p0Xa47TOeQJv2lQ49eKPTBSQwkmyXPdT\nZJeEoqrdsqeyIpNtvk6RPoVM++M4eepp0MDrtj0kNBodTk5euLl5YjQa2fjjehyzJPpbW6CyQXJR\nlazC2eRIq/SmtFMa40rxS7WCwt9YzxqOspQ+9Me2gaEkcnjSbh8ZfnbM+ttMrl9PoLAwHyr4HppM\nZjIy0rl+PYWionwUpew2Q5JUaDT2ODo2pGFDDwwGHVFR8UREnGHatIfRau/8zPPy8jhzOobT0dHY\ny2r8cnW0S3PmYTzwQc9nJOODHj8MRKryOehQxHFrNlOnPcZzs16kRYsWZGVlsWrVKvZt2U1kRCRJ\n1y7hoLFDQiLXko+HkzsdQ0Lp0q87k6ZMxtfXt8r3KjMzkxMnjhEXt5/s7PRy20pZVrh27TqZmVcp\nKrqOLFc0tUdCrdbg6OiBm1sDnJ2LR4isWrWK3r17lxsI1OmM+Pi0Jji4K0FBQWRmZvLGG28wb948\nkQviHlBYWEh09EliYvaTnp6E2VyzaTmSpMJodKR58860bdux5Hn4/PPPWbhwIfv27cPNrWbTmoQ/\nHzHPWRAEoTTRNtqWTQIQ9clsNjN27Fj0ej2rVq0q1WNniwBEfXiUQUxlOI8xsUr7H+ME/8eXLOBD\n7Ci/t/ZeoKDQ0aEf7679N4MHD66Tc0yd+AQXfzIwqmghMla28CYP8heM3P057De4wqfGDuzcv5WQ\nkJBS2y0WC6MGD+fI7/sZIbdlIeMrnTpSE8+xmp40YzfxnCSRXxmCWx0lMVVQeFV3hC0+2ew4tBcv\nL6/KD6ohWZaZMmUK2dnZ/Pjjj2i1WqxWK/M//ZR3//UmY2QPniv0pD0OpY79lGQ+JonDdMSH4mSW\nFyhgsTaNpapUGvs34cLFZB5Sd2NQfhhhtKQ1/iXBPxmZ81whkjh26U+whp306f0Ar8z9K927d6+z\na66JvXv3MmrUKDZu3Ei3bt0q3d9qtTJ58mSysrL46aefyl35RPjzeOWVVzhy5Ajbtm0Tn7cAiB/Z\ngiAIZRFto23ddwEIKO4ZGjZsGL6+vixbtuyOYcb3QwAikQSG0oUkTmCkaj2NJkw8yYuc4wIbWYkn\nHnVcy9pZxndseHA7G3f+bPOyf/31Vx4fO5O/5Eej/+9L5g+8SBzbeZqf8aS5zc9ZXUf4lqhmH3M8\n5mipkTpv//NNdn+8ljX5UxnJEgJwZxmT0Ng4v8dJLtODj/HAQDTjcajllIvKKCj8SxvJz01y+P3I\nflxcap/Lojxms5lRo0bh5OTE3LlzeXzcBNRnr/BVXhOaVfKd+g9JfMkVdhFCI26tqHENE9M5RzQK\nK3mbzpWsSgOQQz7fsZV37VYyctJo3v/kg5LVbe4FW7Zs4bHHHmPbtm20b19+Al9FUUhOTub06dO8\n8847WCwWli9fTqtWrUReiD8xWZaZOLE4CB4eHl7ulB1BEARBEARbuS8DEFA81HrQoEGEhIQwf/78\nkh/J90MA4m3mYKCQj3izWscpKPydd/meDWxmDc1pWkc1rL188mlsaE/kmUibzjHOzs6mZUAwozK/\nIZC+d2zbxxdsYS5TCS+1rb4pKCy3G8bwlzvx1jv/Kvn7yZMn6de1N1EFr+CLK/mYGMkSnDHyHY+h\ns+E0m285xLtsQoXCEPz5Dz1sMtWjIgoKM3X7udbfn7WbNtTpuQoKCujVqxdnok/xltmPlxWfKl/f\nB1zkK1LYRQje3Lms5/ekMZMLfMubPETXKpV3nRxeMnzOPpcYNv72C8HBwdW+nrqydu1aZs2axe7d\nu2nR4tYqIIqicPDgQT77+Au2bd+KYlHhoQtGoxi5lH8Ek5KLTqvjgV59mfXKs/Tv37/kBTU3N5dt\n27YRcegQJw8eJjcnB41WQ0DLloT17EHfvn0JDAy8W5csVENhYSEDBw6kc+fOfPTRR3e7OoIgCIIg\n/MndtwEIgBs3btC3b18GDRrEe++9B9z7AYgCCuiIH4fZQjMqTqhZnsUsZy4fsp5v6UJHG9fQdl7W\nvYHxRRfe+8/7Nivzs8/m893r+5ic/32Z2+PZybdM5CHepAfP2uy8NZFBIvPtwricloS9vT1ms5ku\nbUJ5/mx7nlBuDYkvwsw4vsKKzDqewmCDRKNXyKID77ONh/HDkUf4lcY48DX90NfxSiqFWAi138Dc\nrz5j3LhxdXaemJgYHuzWA4fsQsbjxXs0q9Jxu7hOH1x5n4t8Qyq76EDDPwQhDnKDR4glnHfpV43v\n2HfSVl5xXMyW3dvo0KFDta6nLi1btoy3336bvXv30rhxY2JiYpgy4SmSz1+jef4MApSx2OOD9N8A\njhUT2xkNSPgzhASHRRjdzXwy/31+27SJ71Z8RyeNke55ZtrLKpyRMANnsRBpp+VXpZDgNm145c25\nPPTQQ3f12oXKZWZm0rNnT6ZPn86sWbPudnWEu0gMMxYEQShNtI22VVEA4p4fi+ns7MzWrVvZsGED\n779vu5fcurSR7+lIhxoHHwCmM40v+ZRhTGIjm21YO9t61jSNZYu/oqioZsnT/khRFOb/ZyHd8p8v\nd59A+jKL/ezmM37gBazYPoFoVbnThKZSL1auXAXA55/9H16XJB5X7uxV16NlHU/hgJ6hLCKP2t0v\nBYVnWMUMguiAJ24Y2MZwirAyhJ/JpnYJNytjQMPXeT148ennSEtLq5Nz5Ofn88iAQXyQ481hwlhP\nOh9ysVplvIY/U2jAgxzn6h/uSTecWUsgE/g7l6j6NUxWBvF59gsMeXAwiYmJ1apPXXryySd58cUX\nGTBgAHPnvk23jg/gcOoxRuTF0VZ5GQd8S4IPAGp0DGAdCmausJvBuUfQX+zNhEdGoF26ihMF9mzN\n0fIv2Y4RGHgQPQPRMxN7vsrXcbHAkSePxvHimPE8OmIkGRkZd/Hqhcq4ubmxefNmPvzwQ3744Ye7\nXR1BEARBEP5H3fMjIG66cuUKvXv3ZtasWbz44ov39AiISQxmOuMZyyO1Lusox3iEyfyDV3iOJ2xQ\nO9vr5TSU11f/wya9oDt37mTaIy8yOzf6jpelsuSTxbdMQMbKNL7HDtdan78mYtnGnmZ/5WRcBC0a\nNWF12gQ606TMfa3IPM0q4kljE8/hXMX8IH+0mdPMYS3HGIPuttEOVmReZC/7SWEzw/DGvkblV9Ur\n2kPkTerAF18vtXnZLz03k/RvfuG7guJRD5cpoifHeA1/nqFRtcp6m0TCucrvhNDgD3ky3iKJgzjx\nK59V+szd7kP1KjaHnmLHoV331Lz67t16cvRwJI8oh3GnXaX7WyhgK8PIIxFP0liLjvbVGKGTj8Lr\nOhM/uxn47cD+SpdRFu6uqKgoBg0axE8//USPHj3udnUEQRD+v707DYiqbBs4/h/2TRHXZFFMFFHB\nEHDFxCXFnkftVXMPxcpcUtNMTVvMynIrt0RORdC1AAAgAElEQVStXHIlrTSzMrVwR2QUxBW3UcQF\nU0HZGZj3g8qjCTjDbEjX71Nn5pzrvs7McNtccy9CiHLoqR4B8YCrqys7d+4s8/NXNWiIQ0lLA02b\nCKIpe9jKlyxmEh9RQIFB4hpSy8xAYmMOGSTWvFmLaJ4+Qqsvgg5U4nV+oSaN+ZLmXOe0QXLQVX06\ncvtaOvPmzaNypi1B1C72XEss+Ib+PIc7HZjPTdJL1eZXRDEev0eKDw/iL+R5euNFK37gNLdLFV9b\nb+f5Ehn5PXfu3DFo3P379/P9ylXMy6pV+JgbtmynCR9xgfVc1yne+3jSh+p0II6Uf4yEeBd3rnOR\n73QcafR2fh+yT6Sy+KsIna4zpq8WRnAu/hZemoHsZQRqMp94jRX2VOVZ8lHRnDx8dVyjxAEFc3Nt\nGZuSRYeWrbh+Xbf3RpiWv78/q1atomfPnpw+/b8+8/bt28ybN5/w/q/x8n/78fqgYaxevZrs7Gwz\nZiuEEEKI8uapKUAA1K5dm+3btwOwmXvrA/zMBq6SbM60HpFMEpZY4KbjL7QlqUsd9vMbuznAKwwn\n5/7w/SSSUZtx+sEDgernUO5S6h0nMzOTP3b+RiADtb7GEiv+jy9pzwQW0IZT/FH4XK4WX74MwQIL\nAjKHsGT2V4xMb/HE4okFFizgZTrgTQjzuMa9L++7OUsOeU9s7wJ/c5CL9KFekc8rUDCZQKbSjLb8\nxAGuAZCFmkvc1fHuSlYTR16w8GDVd98ZNO5nUz5gWpYrVf7xS7wXDvxOE8Zwhl+5N+R/KVe4/Y/X\nLaqIwsuH1KEn1ehAHDfuFyHWcA0rFMyjNtNZhkaHkVWWWLIsYwLvT3qPjIwMXW/R4M6dO8e7E98n\nJGsTbVhCReryBz3IJ5c7nOc8RQ+7v8RWbrCOk1TmIvkM5w4FaLhMPtt1mCr0ZoEdfW5l88bAV4qt\neIuyoXPnznz22Wd06dIFpVJJr+69qVndlS2To3Fe14y6W/+L7Xe+LBi+CvdqtXhn7ETu3jVs3yHM\nJyoqytwpCCFEmSN9o+k8VQUIoHDV9SmM4g9+IYa9RLLCvEk95ChKAnhOp6Hc2qhKFXbyE1lk0YXe\npJLGh3zOYpYbtJ3SCKAJyjj9CxBxcXG42ftgR0Wdr23Ja4SzkTWEsZsFaNAwn2Auc0TvvLThqvHj\n0rUk+hCg1fkKFHxOd16mKW2Zy2Vu8wV/skGLfL9mH2HUx/4Jv1QPogEr6EB3fuVnLnCcW4SyRacv\n2doYnlGfiNnzDBZPpVJxIDqa/tQo8nlfnNiML4M5yW5S2cEtNvG3VrGn4slLVC0sQnyIijjSCcYZ\nW3LZiW6fYx88aaNowto1a3S6zhheGzSSxtnvUon6KLCgLd9ihT1/MoAcbhPDxMfe+1zucpBBrMYa\nNyzZigvHUDOKu1wjn5Hc0enzMjXPlnMHDhK5fr2hb08YWHh4OF26dKFl85ZkbHHGTu3EO5lreJGh\ntGcA3RjJx+nbmJW+j7iIK7QOeF5GtwghhBBCb2WqAJGRkcGgQYMYOnQoa9euLfHc79jCWIbgijvb\n+NlEGT7ZUWIJ0GLedWnYY88GltMYH9rwH14ghCWsNPgXSl09iyfpmRl6L0aoVCpxzdXuC3xR6vI8\nb3GA/Szhe4bRhF7sYq5eOWmrAA1emmo4/GN9gZIoUPABXRhKa55nLu2ox2qePJVlJ6foruUCp6HU\nZiv/YRhRKEmhAA3774+IMJS2uJJ87arBFqP8JmIxrxRUx6GEnTxa4Mw6GtKLYzTCkV94dAHEkGLW\nA1GgYBp16EpVOhLHi1RmIzdQoGAE1VlM0TuvlGRERjcWzVyo83WGdPLkSY4cjqdhwajCxyywogPr\nySWV43xFPtmkkfjIdWf4jlYU0O7+DiEVsOA3XIglj1VkkYOG4zqMsrJFwRcZlnw25T0ZBVHGJSUl\n8cPan/DJb81NTTIOOKPi2GPnuVGP8Tnf0fjCi3Rp998yMdpH6EdWeRdCiMdJ32g6ZaoA8eOPP9K7\nd2+WLl3Kzz+XXFRYype8wTgWMpNETnKdqybKsmQJKAmkidHi55DD64QRRh/e4UPucJcDWnxpNSYF\nCpra+qFU6jcKInq3Etds/dbOqIwno9jNHa5wgl9JYDNpJvhspHCCYC23iHzYAc7jggPDCGYmOzjA\nhcIpGUXJI59jXKMpVbVuQwNsoguzOEItnFjKcZ3zLIkCBU3taur9/j8Q9es2uuY6l3jOZM7xLVd5\nA1e+Ipk/uEWOluujTOciLljRGmd+5xbruY4GDV2pwi6O6lzQ60ggScmXuXrVfH3QwvlLqJf3GpYP\nFcDuomILIdQkhJvEY0tlLj5UrNWg4RyzefuhOH9TwDDuMAR7dpNLNSz4Ad3WAOiADdkpf7N//359\nb0sY0ag3xnA79TZu1CODNKyw5jDbizxXgYJB6k9wVLmxOGKJiTMVQgghRHlSpgoQycnJeHh4AGBp\nWfyvnwDNacM2NqNGTSbprKRsLAR3hlM0ooHR4p/lAj0ZzEwW4IEbV7nOh3xutPa01Ti7ASdPntQr\nxqGDsbhrOYWhOMf4mWnUJod0NBSgJpttTNMrpjauEU2rUmy7aoMVv3GCT9mGIzZkkMOX/Fns+Se4\nSi0q4qTDSIsIjvECW3DEmiP8zRoSuUGWzrmWJCDDGeWhWL3j5OfnE3/mFE1xKvG88dSiNc5s4xZ5\naEgnn40PbaVZ1BoQD3TEhVNk8j0pZJDPebLZTxqu2GCJhiQdtuSEe2t6NLXxNlgBpjR2/P4XHupH\nd91xojbN+Jy7nOcu50jlFMdZVPh8BpfJ5johD32WqqCgK7b8Qg7nKOA4ahbp+FmxQEHvLPjtl1/0\nuylhNDdv3uSvv/5iPoeohgcpXCKZM2yl+OKCAgU9siaw6IvFFBSUvcWQhfZknrMQQjxO+kbTMXoB\nYsiQIdSoUQNfX99HHv/9999p0KAB9erVY8aMGQC4u7uTlJQE8MT/wQlnBFs5wDYO8RJ9UZSRWkoW\nWVR4wpcnffjRiERiUPInQwkjhNacMNPuDw9zynMkK0u/L7VXb1ymSim+xD/Ml+58SBLtmUA92lEZ\nT46zRa+Y2kjhJI2oqfN1AdRiA69ync+YRy/a482Ff0wneFgCV2hCFZ3aWE4HbjCEhTzPEHyojC17\nuKJzriV5Tl2Zowf0H4mTmJhIDWsHKj1hG8jKWDMSd2IIZD9N6UYVCrQcudAcZ76hAVdpzTd404wK\nXCYXBQoCqISyFH9PARleBinAlEZWVhaqy2eozKN9rAIFrrSlLd8ykCu0ZC7OeBU+/zdKnsPxkfVq\nFCjohz1bcEFFNT6jAs9gofOokMACC5S79+h3Y8JoVixbSQtFV+rQmAG8z3eoeJsVNKRlidc1pCWW\ndx3488/ii6RCCCGEECXRbb+1UggPD2fUqFGEhYUVPpafn8+bb77Jjh07cHNzIygoiG7dutGjRw/e\nfPNNtm7dSrdu3bSKXwcvIih5vQhTyiYLO+yM3k4t3AlnAOEMMHpb2rDT2JGVod+uEzl5WVhjr3cu\nDlSiIV1oSBf+w6d6x9NGDplUuD+PvjTssKYLjehCoxLPu0M2LqVoxwZL2uBKG1yZQavSplmsSthw\nN03/rTivX7+Om6VunwEfHNn8j3VXilsD4mHWWNCFqnR5aDqLG9aklGLr0lr51Ym/YJ7deFQqFS72\nHljeLf5zYYUDjRhBI0YUPpZKIi3Jo7h/BipjwVs48haOOufUECsSz57V+TphGoej42mc1a7w2BJL\nOjKQjk/YgUiBAr+c9sTHx9OxY0djpymMROY5CyHE46RvNB2jFyDatGmDSqV65LGYmBi8vLzw9PQE\noG/fvmzevJlJkyaxbNkyreKOZjAe3Lu+IpVozHO0JgSAfUQBmOU4l1yiOYQttoQQDEAUewHK9XES\nyVTKrn7v+P4Qpgd/yNoe5+XnYokNZ+6/nvXuv75Pw3Emd7G5/+cUdX+hvxDqG/w4l3xSyCSKZEJw\nu/988v3nzXd8ktvk5tz7lby0739ISAi5ubmkq3OJ4nZhEeHBdApTHNsAx7lAFEcIwf/+80fuP1/8\n8Xmukptjrff9l+Z437595Of/b6HIK/c/n673P5/FHReQgwMFRN3fajPkfmHLEMcpFJCTl2+W10OO\nn3ysUl2gDv8FIP7+56HJ/c/Hk45T8/4mIeEWD5SF+5FjOZZjOZZjOZZj8x7PnTuXuLi4wu/3JVFo\nTLBUuUqlomvXriQkJACwceNGtm3bxtdffw3A6tWrOXjwIAsWLNAqnkKh4JqZd34oTn2cuUgclSh5\nEb3yZhqzyJtiyceffFLqGLbWdnyivo2NAUZBmNpMPPmLMOoXs3WkoUSwh3iOs5i2Rm1HV1tR8VWr\nDH7d95decXbt2sX73QewO62+XnEeLmDo4nXOEUQfhqLdCKwHFvETCWF3iVi5VOc29ZWYmEhwwIv8\nX7puIw6OModAPmGBEUZsnUJN1xrWnLl2xeCxhf5e6R1O1Q2t6cJrOl+72GYMbT73ZOzYsUbITJhC\nVFRU4f8wCiGEuEf6RsNSKBTF7ohmYeJcgHsJlVd22JOt46rx5UG2RTb2Dg56xbC1ticP/aZxmIsd\nTqSZ4H13wZ4bZfDz9TfZVKpSWe84bm5uqPLSDZBR6ajIxU2HHUYeOGd9hVrenoZPSAuenp7cyblC\nHrptj1gJHw4baRBcAmoa+hhvMV6hn+AOLVE6btX5ugIKiLXeSsuWJa8VIYQQQghRHLMUINzc3AoX\nm4R7+5G7u7ubIxWDc8SRtBK2USyv0qzv4uio+1zxh9V2e5aU+9MNnjY18CUe468B8BzuHOFvo7ej\nq8M2t3iudTO949StW5c7mjxukKtXnNKMftCgQUkqAXjrfK3S/iwBgfptIVtaNjY2eHk24ub9qSHa\nqkoAR0lHbYTRZDFWGgLaPm/wuMIwBgzoT4JmNykkPfnkhxxmO5VqVqR58+ZGykyYgvzCJ4QQj5O+\n0XTMUoAIDAzkzJkzqFQqcnNziYyM1HrRybLOm0Yc5YS50zC5o7YnaNy4sV4xgloEcBnzbWWoj2do\nyT7OG72d+lTnb7K4VcZGQSjtUg3yBVyhUBDQ0JdY7hogK91cIBsHbHlGx11G8lBzJPsUAQH6bSGr\nj67/15lLNht0usaBGlTGi63313AwFDUa1tmoealHD4PGFYbj5OTEgIEDiLTRfpFeNXlscJzOqAkj\nyvUoRiGEEEIYl9ELEP369aNVq1YkJibi4eHB8uXLsbKyYuHChXTu3JmGDRvSp08ffHx8jJ2KSfgS\nQCxx5k7DpPLJJy4rgaZNm+oVp3lwAFcdns4CRDW8OMAFo7djgQXP4YqSG0ZvS1v5FBCfdVXv9/+B\nji915Ud7/UYRRZViJ4sf+JuO6F5E+Zm9+Pn4UqWKboULQxo+cihnLVaTh27TV55lArMw7JfJn8jB\n08sLPz+/J58szObjzz8i8ZndfG/5+RO3WVWTx1y7V6kR5ER4+GDTJCiM5sHCYUIIIf5H+kbTMXoB\nYt26dVy5coWcnBySkpIIDw8HoEuXLpw+fZqzZ8/y7rvvGjsNk2lCEIeIN3caJnWaM9SoXB0XF92H\nvT8sICCAZMtYA2VlWgWouai4RaoJ1rDoTCM2cM7o7WjrVy7SqJ633u//A0Nef42NmhRSyTNIPG0U\noCGCFEbQW+drF1XYwshJo42QlfZq1apFp06dOGqt27azdenNGSrwPVkGyeMOBYx3UPPBrJkGiSeM\nx8XFhZ37/mB/rTXMtBvA2SKm8GjQcJgdTHF4AZrdYOOWSKysjL55lhBCCCHKMbNMwSjPmhBAHEef\n+ItSeaIknoCm+g8/9/X15UaOivQy9Ou+tpIsD1GvjhcrFTFGb+tVWrGBc6QaeOh8aS1yOsOIieMM\nFq9GjRp06dSZbxXXSh1D1zUgfuUmlalMELqNxDrAMU5aXqRHGZhusGjpXM7bLSMF7T+DltjSkkiG\nkUcS+Xq1r0HDaNtcOvV4iU6dOukVS5iGu7s7B47spd27jfi0SnfertCSZRbvspZP+dpqPG84NmCV\n5ziGzu7Lrzu34OTkZO6UhQHIPGchhHic9I2mIwUIA6tGDexx4AIXzZ2KySht4gloq38BwtbWlh4v\n9eKgxTIDZGU6+eQRa7OMtz+YyCKHA0YvPj1DRULx4TtOGbUdbZwjjVhS6N1b95EDJZky/WM+t7vG\nJROsdZFBPmO4yFSGodBhOkIWOYQ7zGDe0gXY2NgYMUPt1KhRg6+XR7DLvid3dFiPpAYt8OFD2pLN\n5VIWITRomGCTw7E6Nfli0aJSxRDm4ezszHsfTOHitfNMXzMZ32lO1JyQQcDHVVn9+7ccOx/P8OHD\nZOSDEEIIIQziqS1AzGIq+4gydxpF8ieIPRwwdxoms8c2mqBm+u+AADB6/Ahi7BZToOevsaZ0jJ/x\n8q5LWFgYttUq8KcJdvIYRTu+IIG7eu4Woa9P7OJ5bejr2NnZGTRuo0aNeGviO7zmoCpVQUeXNSAm\ncZFggvgvrXVqY4rN1/i1C+Dll1/WNT2j6dmzB5/Ofo9tDm1J4aDW1zVmAs/wLk3J5FcdR9ZcI5//\nc8hhdz13tu3dQ4UKFXRNW5QBVlZWdO3alSlTpvDZjOlMmjSJ4OBgWXCyHJJ5zkII8TjpGw0jKiqK\nqVOnlnjOU1uAeIeptCbE3GkUqSdhLGW1udMwCSVx/G17i+efN8yWe0FBQdTwqMpJfjdIPFM46PQV\nb028tzL8uA8mMNnxN9RGLqC04lna481Eoo3aTkl+RUVUxZtM+ehDo8SfOGUyt2tXYbqV8bY3/Z4U\nNnGHubyt03VzLTfwS7VYFq1YbKTMSm/4iDf4+rt57KrYnVjrSVovTNmQcVSzGEBv0uhhnc5+ckss\n/qSQz3SLLJrYZ9BoxGvsVsaadSFOIYQQQghhXiEhIeW3AFGWdaIrF0kinmPmTsXoIuyWM2zMMCwt\nLQ0W862JI4h2nG+weMZ0hQSuW5woXANg0ODBVPB1ZY7lX0Zv+wt6sYVL/Mllo7f1T6nk8IbDfr5d\nt8po88KtrKzYvGMby6pmMsdStyKENmtA/MgNRnGRrXyBC9r9al9AAR9brWRBtZ/Zvm8nVatW1Skv\nU+nZswcnE+Op0+US39vWIsZmNFfZ81gxQk02KcSgtJrMBrtaPNM6iQNH42jz2VQG17SjgVM2gx1y\nmUsGy8lkKZmMt8qinXMe3nbpnO/7X3YcjObTWbOwtbU1090KIXQh85yFEOJx0jeajkKj0Tx1qyUq\nFAqulfFFHr9gGqlcYAlzzJ2K0dwmlWftAjh98TTVq1c3WNzs7Gwa1/cnOOkj/EuxK4GpFJBPhGMb\nRk8fwJujRxY+rlKpCGrsz66MkTSkplFz+I3jvMEa9vESHlp+idaXmgJetv+Tmn3bsGjZ10Zv79Kl\nS7zQug2dUyz4PLcWDuhX7MpHwxwu8yUpbOULmuKt1XXnSOZVx1nk1rVk428/4erqqlceppKUlETE\nV0v5ZdMfJJ4/RkXbZ7C2cECtySE16zJ1annTKbQdI0e/gbf3/16LgoIC4uLiiI2NJSE2lru3bmNt\na0Odhg0JCAykRYsWODs7m/HOhBBCCCFEWaRQKCiuzCAFCCO5zlXa0hAVR3CmornTMYq5isUc6n6M\nNT+tNXjsgwcP0qVdd8ZlHaUChituGFKU5WxuNN3K7uidWFg8Opho8aJFLJkwi10ZI6mIvVHzmMMO\nvmY3UXTjGRyN2lY+Bbxqt4ergVX5ecfvJvvV++bNm4x6dSix2/9ieaYnralU4vlR3C5yFMQpMujP\nKaypxnqmU4cnFxEucIXF1j/zrdWvvPvhZN4aP9agI35MKS8vD5VKRVZWFra2ttSuXdvg63cIIcq2\nqKgo+aVPCCH+QfpGwyqpACFTMIykBjUJpj0rWGfuVIyigAIiHJYzYvzIJ59cCs2bN2fIG4PY5GCc\n+Pq6zin+sv2c79Z/+1jxAeCN4cNp3qsj3RyWkWXkhSLfpiOv0JJgNnGeNKO1k0M+/e2jSHquEj/+\n/otJh9xXqVKFtZt+YMbqb3m50kU6OyWyiRuoKXjitRo07OI2fRzO0cr+OO5dgrlSOYv+FT7lQ4tl\n/MxekrhOOplkks11brGdQ3yuWE1ohQkEOQ5D/UY1Yo7F8vbE8U9t8QHA2tqaevXq4efnh7e3txQf\nhBBCCCGESckICCP6jCmssoggsSCGKlQ2dzoGFaFYxqrGP7Avfr/RVknPzs7G17spzS5NohlhRmmj\nNPLIZoljCGOmv/LI1It/ys/PJ7zvK1z89QhbMl81+kiICHbzPluYTnNep6FOW0o+ySGuM9hxL43b\ntWDlhnVm/eKanZ3Nxo0bWTRjNhfOnaeVVWUC061oonHEGUssUHCXfI6RjtJRTTR3sK3szIh3xvFK\nWBjOzs6o1Wq2b9/O3qg9KHfHEH8igbSsOxRoNDjZOtDIy4fANs1o3qYlXbt2xd7euO+dEEIIIYQQ\n5YVMwTADDRo6OTWlXsiz2O20YE3WEnOnZDAqLhFk/wK7lbvx8fExalsJCQmEtO5Ir7vLaciLRm1L\nG/nkscqhJ14vOBL545oiRz88rKCggFGvj2DX+l/5LrM/TfEwWm47OMVAuzXYO9lRP9OBJZmt8NRz\n+k8Waj62PsK3dmeYu3ghffv1K1Pb8p09e5ZDhw4Ruz+aY4eU3L17B41Gg4ODIz7P+RHQuiUBAQH4\n+vqWqbyFEEIIIYQor6QAYQZKohn1zEDiz8TR1NufmVc+4CX+Y+609FZAAS849qTz5BeZMHmiSdqM\njo7mxY7d6J2xCh86m6TNoqjJZb39QFyaZ7D1j01YW1trdZ1Go2H1qlW8PfIthmW35D11J2ywMlhe\nd8nmHbst/OqYyNLVy+jQoQMzp3/GnBmzaW/hzoiM+rTDTacREWdJZbHNKVZaJNKuQ3vmf7OYZ555\nxmA5G5vM4xNCiKJJ/yiEEI+TvtGwyuUaELOYyj6izJ1Gsb61n8/wccNxcnJiWeRyRthP4Ca3zJ2W\n3pYoVpDumcW4CW+brM0WLVqwZdtPbHAKI44NJmv3YTlksNK+OzWCc/n5tx+0Lj7AvT/AV8LCiE88\nTtzzBQQ6fkkkSnJR65VTBjl8w358HWai7uFJwrmThIaGYm1tzZQPP+Di9WQ6zhrFGM8TNHD6gTfs\n9rKU4yhJIQs1mvtFPDUFnCON7znDRKto2lb8jVZOW7Ea2Y6YE/F8/8ump6r4IIQQQgghhDC9qKgo\npk6dWuI5MgLCCPbyF2Mqh3HiwnEqVrw3BH7siLe4vuIya7OWmjm70jPl1IuixMXF8Z8XulM3PZT/\nZM/CzkS7i5xjDxscwgn9v3Z8vSICK6vSj17QaDRs3ryZ+Z/O5uTxk7ye14JX1S2oreUaIRo0nOI6\nS2wOsMriEMGtWjP2/QklVmw1Gg2xsbFER0ej3H0AZcwhEpMvkpevxkKhQAO4VqpKwHP+BLRtRUBQ\nIO3bt5cFCoUQQgghhBA6kykYJpRFFu0dfJm3/ku6du1a+HhmZiZBjQIZnNSHd/JHmTHD0rnFbUIc\nuhM+dQhj3xlntjzS0tJ4683xbP3xD3plfoM3LxitrRwy+N1mMsccNvD1igi6d+9u0PgnTpxg8byF\nrF2zDluNJQGWtQnIqIlvQU2csMUGS3JQk0Y28ZZXUDpeRZmrwtbejkGvDWboyOHUrl271O3n5+dT\nUFCg02gOIYQQQgghhCiJFCBM6BObiaR0vkjkz+sfe+7y5cu0CQhm8t9v8XpB2dnV4UnSSecFh160\nCg9m9oI5ZWIxvz/++IPwAa9TO70dz2ePpyaNDRZbTQ5xbGSHw4e0DW3Boq/nU7my8XYx0Wg0qFQq\nlEolyoOHOBF7lMz0DHJzc7G1tcWpYgV8W/gT0CyIwMBAXF1djZZLeSHz+IQQomjSPwohxOOkbzSs\nkgoQhlsJT3CUw0TaruDYNwlFPu/u7s72fTto26wtlqmWDNEMMHGGurvDHbo5DKThS43LTPEBoFOn\nTpw4e5Q5s75k8VedqJLvRbO7I/CjB1bYlCrmTVQctF5CjNUy/Pz8WD51IaGhoQbO/HEKhYI6depQ\np04devXqZfT2/g3i4uLkHxEhhCiC9I9CCPE46RtNRwoQBpJJJmMdw5k5fybVq1cv9jwvLy/+iv6L\nTsGdSL2Vxrj8ESbMUjd/c5MuDn0I7N2Mhd98VWaKDw84Ozsz7ZOpvP/hFDZv3szcGYvYcvwt6lmG\n8Ex6IB4E4E5T7HF+7NoCCrjJOZJQkmyl5IpDDFfUxwgbFMb8Mbvx9vY2/Q0Jg0lNTTV3CkIIUSZJ\n/yiEEI+TvtF0ntpdMMqSffzFOPshPBfqx6BBT55aUb9+ffYo97C05ipG2k4ggwwTZKmbQxymtcOL\nvDC8M4uWRbBnzx5zp1Qsa2trevXqxSezPiDm6F5GLOyC+9BLRDd6j2k2bkx3rM0XFRuxwDmQeRWf\nY3aFBrxvU5lvK3fgZofvCfmgEl9EvktyyiXmL/qy1MWHqKgow96YidsxZFxDxDLV6ymM69/wPj4N\n92juHKV/NGwsc7+fQn//lvfwabhPc+co/aPhYpn7vRTakQKEAXyp+ITLdc7z9aqlWo8S8PDw4MDR\naNK75eLn8Dy72GfkLLWTQw7vWk/jvxUG8NE305g++zMUCsVT8QcdFRWFl5cXgwYN4qsl84k9to87\nGakcOPoXW/dFsm57BD9ELWd7zCZUyWe5evMSv+z4kffen0JoaCiOjo56t28K/4Z/QPSNoVKp9G5f\nGMbT0Hfo62m4R3PnKP2jYWNJ//j0M/ffpKk8Dfdp7hylfzRcLOkbnw5P5SKUISEh7Nq1y9xpCCGE\nEEIIIYQQ4iFt27YttiD0VBYghBBCCCGEEEII8XSRKRhCCCGEEEIIIYQwOilACCGEEEIIIYQQwuik\nACGEEEIIIYQQQgijkwKEEEIIIYQQQlVhAHQAAAkQSURBVAghjM7K3AkIIYQxaTQa3nvvPe7evUtg\nYCBhYWHmTkkIIcqEvXv3smbNGtRqNSdOnGDfvrKxJbgQQpjb5cuXGT16NC4uLtSvX5+JEyeaO6Vy\nQwoQQohybdOmTSQnJ1O1alXc3d3NnY4QQpQZwcHBBAcHs3nzZpo1a2budIQQosxISEigZ8+eDBgw\ngL59+5o7nXJFpmAIIcq1xMREWrduzezZs4mIiDB3OkIIUeasXbuW/v37mzsNIYQoM1q1asXSpUvp\n0KEDoaGh5k6nXJEChBDiqTNkyBBq1KiBr6/vI4///vvvNGjQgHr16jFjxgwA3N3dqVSpEgAWFtLl\nCSHKN136R4BLly7h7OyMo6OjqVMVQgiT0qV/XL58OZ988gk7d+5k69at5ki33FJoNBqNuZMQQghd\n7NmzBycnJ8LCwkhISAAgPz8fb29vduzYgZubG0FBQaxbtw5PT09GjRqFg4MDPj4+DB8+3MzZCyGE\n8ejSP/r4+DB16lRCQ0Np0aKFmTMXQgjj0qV/zMvLY9q0aVSrVo0KFSowc+ZMM2dffsgaEEKIp06b\nNm1QqVSPPBYTE4OXlxeenp4A9O3bl82bNzNp0iS++eYb0ycphBBmoEv/+KAAIYQQ/wa6/v/jxo0b\nTZ/kv4CMRxZClAvJycl4eHgUHru7u5OcnGzGjIQQomyQ/lEIIYom/aPpSQFCCFEuKBQKc6cghBBl\nkvSPQghRNOkfTU8KEEKIcsHNzY2kpKTC46SkJNl2UwghkP5RCCGKI/2j6UkBQghRLgQGBnLmzBlU\nKhW5ublERkbSrVs3c6clhBBmJ/2jEEIUTfpH05MChBDiqdOvXz9atWpFYmIiHh4eLF++HCsrKxYu\nXEjnzp1p2LAhffr0wcfHx9ypCiGESUn/KIQQRZP+sWyQbTiFEEIIIYQQQghhdDICQgghhBBCCCGE\nEEYnBQghhBBCCCGEEEIYnRQghBBCCCGEEEIIYXRSgBBCCCGEEEIIIYTRSQFCCCGEEEIIIYQQRicF\nCCGEEEIIIYQQQhidFCCEEEIIIYQQQghhdFKAEEIIIf4Fbt68ib+/P/7+/tSsWRN3d3f8/f1p2rQp\narX6kXPnzp1LVlbWE2OGhISgVCofeeyjjz5i8uTJjzwWFxdHw4YNi42zcuVKrl69qsPdFC8hIYEh\nQ4YAsGLFCkaNGlWqONevX+fFF180SE5CCCGEuEcKEEIIIcS/QJUqVThy5AhHjhxh2LBhjBs3jiNH\njnD48GGsrKweOXfevHlkZmY+MaZCoUChUDzyWP/+/YmMjHzksfXr19O/f/9i46xYsYIrV67ocDfF\nmzVrFsOHDy/Mr7Rq1KiBi4sLhw8fNkheQgghhJAChBBCCPGvpNFo2LlzJ/7+/vj5+fHqq6+Sm5vL\n/PnzuXLlCu3ataNDhw4ADB8+nKCgIBo3bszUqVNLjFuvXj1cXFyIiYkpfGzDhg3069ePuLg4WrRo\nQZMmTejRowepqals3LiR2NhYBgwYQNOmTcnOzkapVBISEkJgYCChoaFcu3YNgPnz59OoUSOaNGlC\nv379Hms7JyeH6OhogoKCHntOpVLRvn17mjRpQseOHUlKSgLg3LlztGjRAj8/P9577z0qVKhQeE23\nbt1Yt26dzq+tEEIIIYomBQghhBDiXyg7O5vw8HA2bNjA0aNHUavVREREMHr0aFxdXYmKimLnzp0A\nTJ8+nUOHDhEfH8+uXbtISEgoMXa/fv1Yv349ANHR0VSpUoW6desSFhbGrFmziI+Px9fXl48++ohe\nvXoRGBjI2rVrOXz4MJaWlowaNYoffviB2NhYwsPDmTJlCgAzZswgLi6O+Ph4lixZ8li7R44cwdvb\nu8icRo0aRXh4OPHx8QwYMIDRo0cDMGbMGMaOHcvRo0fx8PB45JpmzZqxe/du3V5YIYQQQhRLChBC\nCCHEv1B+fj7PPvssXl5eAAwaNKjYL9uRkZEEBATQtGlTjh8/zsmTJ0uM3adPHzZu3IhGo2H9+vX0\n69ePtLQ00tLSaNOmTZHtaTQaAE6fPs3x48fp2LEj/v7+fPrppyQnJwPg5+dH//79WbNmDZaWlo+1\ne/HiRWrWrFlkTtHR0YXTQAYOHMjevXsLH3/55ZcBHhtVUbNmTVQqVYn3KoQQQgjtWT35FCGEEEKU\nRw++9D/476LWTLhw4QJz5swhNjYWZ2dnwsPDyc7OLjGuu7s7derUISoqih9//JHo6OgS24b/rdeg\n0Who1KgR+/fvf+yarVu3snv3brZs2cKnn35KQkLCI4UIhULxWNyS2nyS4l4TIYQQQpSOjIAQQggh\n/oUsLS1RqVScO3cOgFWrVtG2bVsAKlSowJ07dwC4c+cOjo6OVKxYkevXr/Pbb79pFb9fv36MHTuW\nunXr4urqirOzMy4uLoUjD1atWkVISMhj7Xl7e3Pjxo3CokVeXh4nTpxAo9Fw6dIlQkJC+Pzzz0lL\nSyMjI+ORNmvXrl24XgQ8WnBo1apV4bSQNWvW8PzzzwPQokULNm7cCFD4/ANXr16ldu3aWt2vEEII\nIZ5MRkAIIYQQ/0L29vYsX76cl19+GbVaTbNmzRg2bBgAQ4cOJTQ0FDc3t8KFKhs0aICHhwfBwcFa\nxe/VqxejR49m4cKFhY+tXLmSYcOGkZmZSd26dVm+fDkAgwcPZtiwYTg4OLB//342btzI6NGjSUtL\nQ61WM3bsWOrXr88rr7xCWloaGo2GMWPGULFixUfabNKkCadPny48fniXjgULFhAeHs6sWbOoXr16\nYdtz585l4MCBTJ8+nc6dO+Ps7Fx4fUxMTGGhQgghhBD6U2h0HY8ohBBCCFFGDR48mOHDh9O8eXOt\nzs/KysLe3h64NwIiMjKSn376CYABAwYwfvx4/P39jZavEEII8W8iIyCEEEIIUW6MHz+eOXPmaF2A\nUCqVvPnmm2g0GlxcXFi2bBkAKSkppKamSvFBCCGEMCAZASGEEEIIIYQQQgijk0UohRBCCCGEEEII\nYXRSgBBCCCGEEEIIIYTRSQFCCCGEEEIIIYQQRicFCCGEEEIIIYQQQhidFCCEEEIIIYQQQghhdFKA\nEEIIIYQQQgghhNH9PwqtklthNqK7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f509024a690>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "        Seats  Seats Percentage  Total Votes  Average Votes  \\\n",
        "BJP       282          0.519337    171657549  401069.039720   \n",
        "INC        44          0.081031    106938242  230470.349138   \n",
        "ADMK       37          0.068140     18115825  452895.625000   \n",
        "AITC       34          0.062615     21259681  162287.641221   \n",
        "BJD        20          0.036832      9491497  451976.047619   \n",
        "SHS        18          0.033149     10262982  176947.965517   \n",
        "TDP        16          0.029466     14094545  469818.166667   \n",
        "TRS        11          0.020258      6736490  396264.117647   \n",
        "CPM         9          0.016575     17986773  193406.161290   \n",
        "YSRCP       9          0.016575     13991280  368191.578947   \n",
        "LJP         6          0.011050      2295929  327989.857143   \n",
        "NCP         6          0.011050      8635554  239876.500000   \n",
        "SP          5          0.009208     18672916   94786.375635   \n",
        "AAAP        4          0.007366     11325635   26216.747685   \n",
        "RJD         4          0.007366      7442313  248077.100000   \n",
        "SAD         4          0.007366      3636148  363614.800000   \n",
        "AIUDF       3          0.005525      2333040  129613.333333   \n",
        "IND         3          0.005525     16743719    5175.801855   \n",
        "BLSP        3          0.005525      1078473  269618.250000   \n",
        "JKPDP       3          0.005525       732644  146528.800000   \n",
        "INLD        2          0.003683      2799899  279989.900000   \n",
        "JMM         2          0.003683      1637990   77999.523810   \n",
        "IUML        2          0.003683      1100096   44003.840000   \n",
        "JD(U)       2          0.003683      5992196   64432.215054   \n",
        "AD          2          0.003683       821820  117402.857143   \n",
        "JD(S)       2          0.003683      3731481  109749.441176   \n",
        "RSP         1          0.001842      1666380  277730.000000   \n",
        "KEC(M)      1          0.001842       424194  424194.000000   \n",
        "CPI         1          0.001842      4327298   64586.537313   \n",
        "SDF         1          0.001842       163698  163698.000000   \n",
        "AINRC       1          0.001842       255826  255826.000000   \n",
        "SWP         1          0.001842      1105073  552536.500000   \n",
        "NPEP        1          0.001842       576444   82349.142857   \n",
        "AIMIM       1          0.001842       685729  137145.800000   \n",
        "NPF         1          0.001842       994505  497252.500000   \n",
        "PMK         1          0.001842      1827566  203062.888889   \n",
        "\n",
        "        Total Winning Votes  Average Winning Votes  \\\n",
        "BJP               139553310                 494869   \n",
        "INC                18219598                 414081   \n",
        "ADMK               17415881                 470699   \n",
        "AITC               18366652                 540195   \n",
        "BJD                 9169818                 458490   \n",
        "SHS                 9010919                 500606   \n",
        "TDP                 9362168                 585135   \n",
        "TRS                 5307344                 482485   \n",
        "CPM                 4069667                 452185   \n",
        "YSRCP               4950808                 550089   \n",
        "LJP                 1983574                 330595   \n",
        "NCP                 2594704                 432450   \n",
        "SP                  2458349                 491669   \n",
        "AAAP                1716952                 429238   \n",
        "RJD                 1429688                 357422   \n",
        "SAD                 1817385                 454346   \n",
        "AIUDF               1350137                 450045   \n",
        "IND                 1374887                 458295   \n",
        "BLSP                1072804                 357601   \n",
        "JKPDP                533629                 177876   \n",
        "INLD                1000848                 500424   \n",
        "JMM                  715322                 357661   \n",
        "IUML                 816226                 408113   \n",
        "JD(U)                740808                 370404   \n",
        "AD                   812325                 406162   \n",
        "JD(S)               1034211                 517105   \n",
        "RSP                  408528                 408528   \n",
        "KEC(M)               424194                 424194   \n",
        "CPI                  389209                 389209   \n",
        "SDF                  163698                 163698   \n",
        "AINRC                255826                 255826   \n",
        "SWP                  640428                 640428   \n",
        "NPEP                 239301                 239301   \n",
        "AIMIM                513868                 513868   \n",
        "NPF                  713372                 713372   \n",
        "PMK                  468194                 468194   \n",
        "\n",
        "        Average Winning Percentage  Average Winning Electors  Loosing Votes  \\\n",
        "BJP                       0.493188                   1606886       32104239   \n",
        "INC                       0.428075                   1384055       88718644   \n",
        "ADMK                      0.451634                   1413200         699944   \n",
        "AITC                      0.431486                   1512020        2893029   \n",
        "BJD                       0.449398                   1389275         321679   \n",
        "SHS                       0.510340                   1676883        1252063   \n",
        "TDP                       0.493163                   1554833        4732377   \n",
        "TRS                       0.430386                   1532891        1429146   \n",
        "CPM                       0.455675                   1264327       13917106   \n",
        "YSRCP                     0.478040                   1495688        9040472   \n",
        "LJP                       0.375127                   1579884         312355   \n",
        "NCP                       0.483694                   1419258        6040850   \n",
        "SP                        0.471728                   1714211       16214567   \n",
        "AAAP                      0.401053                   1464262        9608683   \n",
        "RJD                       0.367278                   1636930        6012625   \n",
        "SAD                       0.411916                   1543844        1818763   \n",
        "AIUDF                     0.389700                   1382908         982903   \n",
        "IND                       0.463283                   1271567       15368832   \n",
        "BLSP                      0.427755                   1522716           5669   \n",
        "JKPDP                     0.472012                   1232333         199015   \n",
        "INLD                      0.411830                   1589081        1799051   \n",
        "JMM                       0.385344                   1300163         922668   \n",
        "IUML                      0.473571                   1189616         283870   \n",
        "JD(U)                     0.380420                   1767467        5251388   \n",
        "AD                        0.426682                   1718643           9495   \n",
        "JD(S)                     0.442053                   1615299        2697270   \n",
        "RSP                       0.464735                   1219415        1257852   \n",
        "KEC(M)                    0.510072                   1161463              0   \n",
        "CPI                       0.422821                   1275288        3938089   \n",
        "SDF                       0.529824                    370611              0   \n",
        "AINRC                     0.345703                    901357              0   \n",
        "SWP                       0.538686                   1630598         464645   \n",
        "NPEP                      0.522410                    586501         337143   \n",
        "AIMIM                     0.528986                   1823664         171861   \n",
        "NPF                       0.647102                   1752741         281133   \n",
        "PMK                       0.425111                   1358273        1359372   \n",
        "\n",
        "        Winning Votes Ratio  \n",
        "BJP                0.812975  \n",
        "INC                0.170375  \n",
        "ADMK               0.961363  \n",
        "AITC               0.863919  \n",
        "BJD                0.966109  \n",
        "SHS                0.878002  \n",
        "TDP                0.664241  \n",
        "TRS                0.787850  \n",
        "CPM                0.226259  \n",
        "YSRCP              0.353850  \n",
        "LJP                0.863953  \n",
        "NCP                0.300468  \n",
        "SP                 0.131653  \n",
        "AAAP               0.151599  \n",
        "RJD                0.192103  \n",
        "SAD                0.499811  \n",
        "AIUDF              0.578703  \n",
        "IND                0.082114  \n",
        "BLSP               0.994743  \n",
        "JKPDP              0.728361  \n",
        "INLD               0.357459  \n",
        "JMM                0.436707  \n",
        "IUML               0.741959  \n",
        "JD(U)              0.123629  \n",
        "AD                 0.988446  \n",
        "JD(S)              0.277158  \n",
        "RSP                0.245159  \n",
        "KEC(M)             1.000000  \n",
        "CPI                0.089943  \n",
        "SDF                1.000000  \n",
        "AINRC              1.000000  \n",
        "SWP                0.579535  \n",
        "NPEP               0.415133  \n",
        "AIMIM              0.749375  \n",
        "NPF                0.717314  \n",
        "PMK                0.256184  \n",
        "\n",
        "[36 rows x 10 columns]\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/IPython-Tutorial/data/2014_indian_election_turnout.csv
================================================
State,PC Name,Male Electors,Male Voters,Female Electors,Female Voters,Total Electors,Total Voters
Andaman & Nicobar Islands,1 - Andaman & Nicobar Islands,142782,101178,126578,89150,269360,190328
Andhra Pradesh,1 - Adilabad ,687389,519364,698844,526475,1386233,1045839
Andhra Pradesh,2 - Peddapalle ,725767,520598,699594,501586,1425361,1022184
Andhra Pradesh,3 - Karimnagar ,777458,552489,773376,573202,1550834,1125691
Andhra Pradesh,4 - Nizamabad,724504,469712,771689,564212,1496193,1033924
Andhra Pradesh,5 - Zahirabad,717811,546746,727435,548060,1445246,1094806
Andhra Pradesh,6 - Medak,775903,606863,760812,584233,1536715,1191096
Andhra Pradesh,7 - Malkajgiri,1723413,878093,1459912,742304,3183325,1620397
Andhra Pradesh,8 - Secundrabad,1012378,545749,881269,458020,1893647,1003769
Andhra Pradesh,9 - Hyderabad,961290,526510,862374,444911,1823664,971421
Andhra Pradesh,10 - CHELVELLA,1153049,696749,1032130,619113,2185179,1315862
Andhra Pradesh,11 - Mahbubnagar,709711,511764,708961,503036,1418672,1014800
Andhra Pradesh,12 - Nagarkurnool,745038,570342,732300,538626,1477338,1108968
Andhra Pradesh,13 - Nalgonda,747281,602193,748299,587206,1495580,1189399
Andhra Pradesh,14 - Bhongir ,756963,622333,735288,589610,1492251,1211943
Andhra Pradesh,15 - Warangal,771756,592484,766025,582147,1537781,1174631
Andhra Pradesh,16 - Mahabubabad  ,688398,562073,698945,562297,1387343,1124370
Andhra Pradesh,17 - Khammam ,712329,588728,727960,594169,1440289,1182897
Andhra Pradesh,18 - Aruku ,622416,451350,650308,458264,1272724,909614
Andhra Pradesh,19 - Srikakulam,706828,505010,707161,546436,1413989,1051446
Andhra Pradesh,20 - Vizianagaram,700837,555005,703290,565311,1404127,1120316
Andhra Pradesh,21 - Visakhapatnam,875187,585270,847850,578288,1723037,1163558
Andhra Pradesh,22 - Anakapalli,689132,563818,712342,584254,1401474,1148072
Andhra Pradesh,23 - Kakinada,709101,558689,709189,541310,1418290,1099999
Andhra Pradesh,24 - Amalapuram ,682607,568534,675259,552393,1357866,1120927
Andhra Pradesh,25 - Rajahmundry,701707,577999,719581,576382,1421288,1154381
Andhra Pradesh,26 - Narsapuram,652668,539357,672475,549594,1325143,1088951
Andhra Pradesh,27 - Eluru ,706916,597603,720844,604093,1427760,1201696
Andhra Pradesh,28 - Machilipatnam ,675774,567908,693537,573157,1369311,1141065
Andhra Pradesh,29 - Vijayawada,781156,602198,783357,592877,1564513,1195075
Andhra Pradesh,30 - Guntur,773027,617483,798989,627443,1572016,1244926
Andhra Pradesh,31 - Narasaraopet,748465,638080,766396,643978,1514861,1282058
Andhra Pradesh,32 - Bapatla ,686482,587223,706483,597411,1392965,1184634
Andhra Pradesh,33 - Ongole ,736245,603025,734238,605200,1470483,1208225
Andhra Pradesh,34 - Nandyal,783266,603562,793862,601394,1577128,1204956
Andhra Pradesh,35 - Kurnool,738791,544802,743016,520930,1481807,1065732
Andhra Pradesh,36 - Anantapur,775509,612890,761403,592164,1536912,1205054
Andhra Pradesh,37 - Hindupur,734638,601932,711865,575325,1446503,1177257
Andhra Pradesh,38 - Kadapa,765036,588805,785543,611857,1550579,1200662
Andhra Pradesh,39 - Nellore,796583,593468,809557,594180,1606140,1187648
Andhra Pradesh,40 - Tirupati ,778778,604834,795766,608230,1574544,1213064
Andhra Pradesh,41 - Rajampet,735347,573019,752151,585298,1487498,1158317
Andhra Pradesh,42 - Chittoor ,723996,598259,728145,600656,1452141,1198915
Arunachal Pradesh,1 - ARUNACHAL WEST,219334,159978,227181,175687,446515,335665
Arunachal Pradesh,2 - ARUNACHAL EAST,160293,129313,152579,131978,312872,261291
Assam,1 - Karimganj ,615198,482484,550799,404436,1165997,886920
Assam,2 - Silchar,554540,428949,505635,370881,1060175,799830
Assam,3 - Autonomous District,358880,279409,343010,263871,701890,543280
Assam,4 - Dhubri,798124,709191,754430,660433,1552554,1369624
Assam,5 - Kokrajhar,776071,636657,729405,587212,1505476,1223869
Assam,6 - Barpeta,755559,634405,674616,571458,1430175,1205863
Assam,7 - Gauhati,988067,785494,934203,726235,1922270,1511729
Assam,8 - Mangaldoi,791539,651272,724137,581965,1515676,1233237
Assam,9 - Tezpur,654866,505643,604702,475045,1259568,980688
Assam,10 - Nowgong,792424,639392,731457,590682,1523881,1230074
Assam,11 - Kaliabor,752785,605292,705080,561198,1457865,1166490
Assam,12 - Jorhat,634236,483829,600212,447507,1234448,931336
Assam,13 - Dibrugarh,579657,462412,544648,428556,1124305,890968
Assam,14 - Lakhimpur,735263,572334,695731,539641,1430994,1111975
Bihar,1 - Valmiki Nagar,786297,484621,670279,415493,1456576,900114
Bihar,2 - Paschim Champaran,769352,469114,643721,385686,1413073,854800
Bihar,3 - Purvi Champaran,788561,442103,664402,380568,1452963,822671
Bihar,4 - Sheohar,794679,433416,695045,409478,1489724,842894
Bihar,5 - Sitamarhi,832370,449303,742544,451285,1574914,900588
Bihar,6 - Madhubani,877284,413878,750685,446575,1627969,860453
Bihar,7 - Jhanjharpur,884884,452082,765869,489167,1650753,941249
Bihar,8 - Supaul,796979,468183,728613,502345,1525592,970528
Bihar,9 - Araria,842884,492492,744464,483319,1587348,975811
Bihar,10 - Kishanganj,762877,471209,676113,457281,1438990,928490
Bihar,11 - Katihar,769044,495564,677434,482266,1446478,977830
Bihar,12 - Purnia,833610,511512,749357,506220,1582967,1017732
Bihar,13 - Madhepura,899271,508914,826307,525885,1725578,1034799
Bihar,14 - Darbhanga,797470,407047,697976,421295,1495446,828342
Bihar,15 - Muzaffarpur,830943,490954,720420,457933,1551363,948887
Bihar,16 - Vaishali,854781,482598,732421,443339,1587202,925937
Bihar,17 - Gopalganj (SC),861019,446731,791748,456852,1652767,903583
Bihar,18 - Siwan,841735,443867,722125,440154,1563860,884021
Bihar,19 - Maharajganj,882264,420364,762260,426290,1644524,846654
Bihar,20 - Saran,833361,450964,705383,412290,1538744,863254
Bihar,21 - Hajipur (SC),895047,478252,754500,426501,1649547,904753
Bihar,22 - Ujiarpur,768123,433926,660322,424994,1428445,858920
Bihar,23 - Samastipur (SC),805601,430736,697404,432463,1503005,863199
Bihar,24 - Begusarai,949825,546041,828934,531814,1778759,1077855
Bihar,25 - Khagaria,793168,445965,698900,450266,1492068,896231
Bihar,26 - Bhagalpur,895185,522657,790154,451359,1685339,974016
Bihar,27 - Banka,825010,461098,724446,438255,1549456,899353
Bihar,28 - Munger,921436,506184,775110,408166,1696546,914350
Bihar,29 - Nalanda,1036575,503097,915392,418664,1951967,921761
Bihar,30 - Patna Sahib,1052278,511447,893971,370815,1946249,882262
Bihar,31 - Pataliputra,934086,553915,801988,424734,1736074,978649
Bihar,32 - Arrah,1010040,516366,822292,376847,1832332,893213
Bihar,33 - Buxar,878312,493930,762255,394274,1640567,888204
Bihar,34 - Sasaram (SC),859397,474117,748350,372671,1607747,846788
Bihar,35 - Karakat,845707,453576,724282,336351,1569989,789927
Bihar,36 - Jahanabad,751585,443737,671661,367779,1423246,811516
Bihar,37 - Aurangabad,825574,430800,710579,355474,1536153,786274
Bihar,38 - Gaya (SC),790868,444510,709883,364868,1500751,809378
Bihar,39 - Nawada,887050,478217,788739,406224,1675789,884441
Bihar,40 - Jamui (SC),828406,415743,722530,359896,1550936,775639
Chandigarh,1 - CHANDIGARH,333621,244956,281593,208499,615214,453455
Chattisgarh,1 - SARGUJA,771303,610877,751769,576444,1523072,1187321
Chattisgarh,2 - RAIGARH,807353,630570,798471,615616,1605824,1246186
Chattisgarh,3 - JANJGIR-CHAMPA,890439,558677,847093,514670,1737532,1073347
Chattisgarh,4 - KORBA,725821,546046,693789,506674,1419610,1052720
Chattisgarh,5 - BILASPUR,889222,573253,838103,517204,1727325,1090457
Chattisgarh,6 - RAJNANDGAON,794427,601874,793668,576422,1588095,1178296
Chattisgarh,7 - DURG,945656,660504,910125,597838,1855781,1258342
Chattisgarh,8 - RAIPUR,979133,659070,925097,591775,1904230,1250845
Chattisgarh,9 - MAHASAMUND,757276,576917,758471,554292,1515747,1131209
Chattisgarh,10 - BASTAR,632814,387112,665245,382801,1298059,769913
Chattisgarh,11 - KANKER,722339,515793,725435,501150,1447774,1016943
Dadra & Nagar Haveli,1 - Dadar & Nagar Haveli,106215,87800,90402,77486,196617,165286
Daman & Diu,1 - Daman & diu,57011,42378,54816,44855,111827,87233
Goa,1 - North Goa,255870,200184,259571,206447,515441,406631
Goa,2 - South Goa,272438,195582,272898,214787,545336,410369
Gujarat,1 - Kachchh,806343,520905,727439,425335,1533782,946240
Gujarat,2 - Banaskantha,796124,513893,719587,372741,1515711,886634
Gujarat,3 - Patan,846195,529513,782446,426286,1628641,955799
Gujarat,4 - Mahesana,777821,544710,720398,459548,1498219,1004258
Gujarat,5 - Sabarkantha,833521,587807,782319,506195,1615840,1094002
Gujarat,6 - Gandhinagar,900744,620889,833228,514606,1733972,1135495
Gujarat,7 - Ahmedabad East,852765,560074,749067,425451,1601832,985525
Gujarat,8 - Ahmedabad West,800933,534008,733467,430601,1534400,964609
Gujarat,9 - Surendranagar,877745,542339,778912,402338,1656657,944677
Gujarat,10 - Rajkot,864760,593333,790957,463736,1655717,1057069
Gujarat,11 - Porbandar,807383,471495,731840,337938,1539223,809433
Gujarat,12 - Jamnagar,771004,486177,699948,366466,1470952,852643
Gujarat,13 - Junagadh,772017,516158,713526,425220,1485543,941378
Gujarat,14 - Amreli,777662,457547,708624,351269,1486286,808816
Gujarat,15 - Bhavnagar,834571,519525,759960,397877,1594531,917402
Gujarat,16 - Anand,781120,541176,715739,429718,1496859,970894
Gujarat,17 - Kheda,833232,542955,766244,412951,1599476,955906
Gujarat,18 - Panchmahal,820230,516017,756437,417444,1576667,933461
Gujarat,19 - Dahod,712184,469459,699581,430922,1411765,900381
Gujarat,20 - Vadodara,849077,625045,789244,536532,1638321,1161577
Gujarat,21 - Chhota Udaipur,798160,593192,738145,507350,1536305,1100542
Gujarat,22 - Bharuch,734861,564668,682687,495543,1417548,1060211
Gujarat,23 - Bardoli,829648,637345,784458,568834,1614106,1206179
Gujarat,24 - Surat,803829,539368,680239,408554,1484068,947922
Gujarat,25 - Navsari,972090,649206,792532,511541,1764622,1160747
Gujarat,26 - Valsad,775073,582311,736988,539892,1512061,1122203
Haryana,1 - Ambala,909708,667400,782519,551595,1692227,1218995
Haryana,2 - Kurukshetra,807700,618607,690759,517285,1498459,1135892
Haryana,3 - Sirsa,886303,692891,774254,586214,1660557,1279105
Haryana,4 - Hisar,825688,637718,691918,518196,1517606,1155914
Haryana,5 - Karnal,913321,666808,771000,526692,1684321,1193500
Haryana,6 - Sonipat,777824,551168,639364,434469,1417188,985637
Haryana,7 - Rohtak,850125,578351,717379,465980,1567504,1044331
Haryana,8 - Bhiwani-Mahendragarh,792166,559833,681746,470598,1473912,1030431
Haryana,9 - Gurgaon,984370,727397,861253,593222,1845623,1320619
Haryana,10 - Faridabad,969407,651411,770945,479315,1740352,1130726
Himachal Pradesh,1 - Kangra,645888,392927,612713,406518,1258601,799445
Himachal Pradesh,2 - Mandi,587833,377569,562575,348525,1150408,726094
Himachal Pradesh,3 - Hamirpur,633572,395804,614127,439401,1247699,835205
Himachal Pradesh,4 - Shimla,607137,403332,546226,334425,1153363,737757
Jammu & Kashmir,1 - Baramulla,624008,256249,566737,209743,1190745,465992
Jammu & Kashmir,2 - Srinagar,630716,178554,574517,133658,1205233,312212
Jammu & Kashmir,3 - Anantnag,684820,211582,616203,163697,1301023,375279
Jammu & Kashmir,4 - Ladakh,86259,58035,80504,59994,166763,118029
Jammu & Kashmir,5 - Udhampur,798910,557757,691334,484001,1490244,1041758
Jammu & Kashmir,6 - Jammu,977308,665407,870847,588186,1848155,1253593
Jharkhand,1 - Rajmahal,691254,491007,661918,460556,1353172,951563
Jharkhand,2 - Dumka,646885,466521,600270,436539,1247155,903060
Jharkhand,3 - Godda,825706,550327,764922,499115,1590628,1049442
Jharkhand,4 - Chatra,696514,383079,616031,329892,1312545,712971
Jharkhand,5 - Kodarma,870016,535513,769613,489393,1639629,1024906
Jharkhand,6 - Giridih,811986,531267,703151,438730,1515137,969997
Jharkhand,7 - Dhanbad,1032005,642644,857989,501258,1889994,1143902
Jharkhand,8 - Ranchi,868783,562894,779681,486889,1648464,1049783
Jharkhand,9 - Jamshedpur,811632,548176,770033,500964,1581665,1049140
Jharkhand,10 - Singhbhum,583444,403528,569126,391758,1152570,795286
Jharkhand,11 - Khunti,565544,373120,546308,363835,1111852,736955
Jharkhand,12 - Lohardaga,578883,333938,540261,317522,1119144,651460
Jharkhand,13 - Palamau,890010,523454,755947,453869,1645957,977323
Jharkhand,14 - Hazaribagh,812487,515008,706344,452144,1518831,967152
Karnataka,1 - Chikkodi,746113,564257,696183,506846,1442296,1071103
Karnataka,2 - Belgaum,807598,570120,773419,508427,1581017,1078547
Karnataka,3 - Bagalkot,793338,561781,775282,517529,1568620,1079310
Karnataka,4 - Bijapur,847815,521565,774820,445192,1622635,966757
Karnataka,5 - Gulbarga,878311,523062,843679,474576,1721990,997638
Karnataka,6 - Raichur,835969,508764,825637,460125,1661606,968889
Karnataka,7 - Bidar,839154,506363,761808,453021,1600962,959384
Karnataka,8 - Koppal,770316,526001,764789,480507,1535105,1006508
Karnataka,9 - Bellary,750789,542128,737156,503644,1487945,1045772
Karnataka,10 - Haveri,807076,595257,751673,520711,1558749,1115968
Karnataka,11 - Dharwad,809486,562991,769538,478235,1579024,1041226
Karnataka,12 - Uttara Kannada,742865,521541,707734,479497,1450599,1001038
Karnataka,13 - Davanagere,772126,581292,750586,533576,1522712,1114868
Karnataka,14 - Shimoga,778767,572407,783474,556601,1562241,1129008
Karnataka,15 - Udupi Chikmagalur,679286,511470,708009,522638,1387295,1034108
Karnataka,16 - Hassan,789668,586090,771668,561082,1561336,1147172
Karnataka,17 - Dakshina Kannada,774500,595647,790781,611827,1565281,1207474
Karnataka,18 - Chitradurga,844864,573250,816408,523249,1661272,1096499
Karnataka,19 - Tumkur,764561,565612,753957,535872,1518518,1101484
Karnataka,20 - Mandya,839052,604906,830210,587732,1669262,1192638
Karnataka,21 - Mysore,867893,598003,855241,561591,1723134,1159594
Karnataka,22 - Chamarajanagar,789383,585209,766396,547820,1555779,1133029
Karnataka,23 - Bangalore Rural,1135845,758619,1054552,696625,2190397,1455244
Karnataka,24 - Bangalore North,1260356,721638,1141116,635080,2401472,1356718
Karnataka,25 - Bangalore central,1010586,574475,921077,500114,1931663,1074589
Karnataka,26 - Bangalore South,1051316,597291,948566,516435,1999882,1113726
Karnataka,27 - Chikkballapur,843740,653492,814602,609782,1658342,1263274
Karnataka,28 - Kolar,755450,582601,737525,544722,1492975,1127323
Kerala,1 - Kasaragod,595047,459537,648683,514678,1243730,974215
Kerala,2 - Kannur,548454,439557,621812,507560,1170266,947117
Kerala,3 - Vadakara,561168,437556,621336,521786,1182504,959342
Kerala,4 - Wayanad,614822,454300,634598,460706,1249420,915006
Kerala,5 - Kozhikode,571709,457548,610775,485461,1182484,943009
Kerala,6 - Malappuram,598207,415980,600237,437487,1198444,853467
Kerala,7 - Ponnani,574106,405116,606683,466476,1180789,871592
Kerala,8 - Palakkad,587379,444510,621379,465812,1208758,910322
Kerala,9 - Alathur ,591950,452858,624401,474370,1216351,927228
Kerala,10 - Thrissur,606518,434202,668770,486303,1275288,920505
Kerala,11 - Chalakudy,565081,436712,585410,447321,1150491,884033
Kerala,12 - Ernakulam,565809,428565,590658,422269,1156467,850834
Kerala,13 - Idukki,578849,425049,579886,394717,1158735,819766
Kerala,14 - Kottayam,573571,426879,587892,404757,1161463,831636
Kerala,15 - Alappuzha,611877,477885,659447,519579,1271324,997464
Kerala,16 - Mavelikkara ,592702,416969,659966,472091,1252668,889060
Kerala,17 - Pathanamthitta,631495,425771,692411,443681,1323906,869452
Kerala,18 - Kollam,575296,408322,644119,470734,1219415,879056
Kerala,19 - Attingal,575780,396246,675618,463104,1251398,859350
Kerala,20 - Thiruvananthapuram,614438,434623,658310,438816,1272748,873439
Lakshadweep,1 - Lakshadweep,25433,21585,24489,21654,49922,43239
Madhya Pradesh,1 - MORENA,938466,538708,764026,315571,1702492,854279
Madhya Pradesh,2 - BHIND,890851,469603,707318,259481,1598169,729084
Madhya Pradesh,3 - GWALIOR,1024155,595934,852848,394978,1877003,990912
Madhya Pradesh,4 - GUNA,857328,581485,748291,395144,1605619,976629
Madhya Pradesh,5 - SAGAR,815357,536674,704827,355255,1520184,891929
Madhya Pradesh,6 - TIKAMGARH,820908,466689,708095,299632,1529003,766321
Madhya Pradesh,7 - DAMOH,882506,557919,768600,354161,1651106,912080
Madhya Pradesh,8 - KHAJURAHO,907312,517341,795482,357136,1702794,874477
Madhya Pradesh,9 - SATNA,772026,499928,686058,413516,1458084,913444
Madhya Pradesh,10 - REWA,821800,457663,722919,372339,1544719,830002
Madhya Pradesh,11 - SIDHI,915700,548026,820350,441370,1736050,989396
Madhya Pradesh,12 - SHAHDOL,806945,527939,754376,440579,1561321,968518
Madhya Pradesh,13 - JABALPUR,897949,558617,813734,443567,1711683,1002184
Madhya Pradesh,14 - MANDLA,925971,641915,898453,576603,1824424,1218518
Madhya Pradesh,15 - BALAGHAT,822667,571601,807102,541763,1629769,1113364
Madhya Pradesh,16 - CHHINDWARA,721482,584759,679795,522739,1401277,1107498
Madhya Pradesh,17 - HOSHANGABAD,835492,593957,732635,437218,1568127,1031175
Madhya Pradesh,18 - VIDISHA,872410,621326,761960,452147,1634370,1073473
Madhya Pradesh,19 - BHOPAL,1039004,639683,917932,490499,1956936,1130182
Madhya Pradesh,20 - RAJGARH,827001,598652,751747,412072,1578748,1010724
Madhya Pradesh,21 - DEWAS,843555,654121,773660,489847,1617215,1143968
Madhya Pradesh,22 - UJJAIN,790889,571525,734592,444880,1525481,1016405
Madhya Pradesh,23 - MANDSOUR,838076,640484,788495,520865,1626571,1161349
Madhya Pradesh,24 - RATLAM,860947,570248,841701,512433,1702648,1082681
Madhya Pradesh,25 - DHAR,858093,586865,810348,489951,1668441,1076816
Madhya Pradesh,26 - INDORE,1106461,736759,1008842,580058,2115303,1316817
Madhya Pradesh,27 - KHARGONE,866897,614411,836374,538114,1703271,1152525
Madhya Pradesh,28 - KHANDWA,912747,677574,846663,579753,1759410,1257327
Madhya Pradesh,29 - BETUL,836835,574150,770987,473569,1607822,1047719
Maharashtra,1 - Nandurbar ,852379,584580,820336,532096,1672715,1116676
Maharashtra,2 - Dhule,878610,543252,795859,439831,1674469,983083
Maharashtra,3 - Jalgaon,909027,594892,798906,395440,1707933,990332
Maharashtra,4 - Raver,842682,554159,750688,455054,1593370,1009213
Maharashtra,5 - Buldhana,845547,535993,750687,442633,1596234,978626
Maharashtra,6 - Akola,869922,544432,776541,434059,1646463,978491
Maharashtra,7 - Amravati ,848050,556250,763315,447811,1611365,1004061
Maharashtra,8 - Wardha,817514,554658,747039,458787,1564553,1013445
Maharashtra,9 - Ramtek ,887157,576939,790088,473377,1677245,1050316
Maharashtra,10 - Nagpur ,979995,579529,920792,505529,1900787,1085058
Maharashtra,11 - Bhandara - gondiya,841442,615957,814842,581239,1656284,1197196
Maharashtra,12 - Gadchiroli-Chimur,753927,537086,715840,490043,1469767,1027129
Maharashtra,13 - Chandrapur,921099,602923,832591,506820,1753690,1109743
Maharashtra,14 - Yavatmal-Washim,916049,585785,827449,447164,1743498,1032949
Maharashtra,15 - Hingoli ,839540,576774,746654,474390,1586194,1051164
Maharashtra,16 - Nanded,878553,549945,808504,463405,1687057,1013350
Maharashtra,17 - Parbhani,945899,637950,857893,524283,1803792,1162233
Maharashtra,18 - Jalna,866726,596359,745328,469900,1612054,1066259
Maharashtra,19 - Aurangabad,846023,554669,743370,428264,1589393,982933
Maharashtra,20 - Dindori ,808018,546989,722121,423193,1530139,970182
Maharashtra,21 - Nashik,850457,524572,742780,412833,1593237,937405
Maharashtra,22 - Palghar ,830432,532639,747645,459979,1578077,992618
Maharashtra,23 - Bhiwandi,945405,503035,751184,372570,1696589,875605
Maharashtra,24 - Kalyan,1045495,470781,876551,353415,1922046,824196
Maharashtra,25 - Thane,1142158,591189,931284,463000,2073442,1054189
Maharashtra,26 - Mumbai North,972645,527803,811225,418759,1783870,946562
Maharashtra,27 - Mumbai North West,986908,503191,788520,392326,1775428,895517
Maharashtra,28 - Mumbai North East,923016,485775,745341,375986,1668357,861761
Maharashtra,29 - Mumbai North central,967474,472265,771420,373027,1738894,845292
Maharashtra,30 - Mumbai South central,793801,424521,654084,344539,1447885,769060
Maharashtra,31 - Mumbai   South,828964,437435,656882,343280,1485846,780715
Maharashtra,32 - Raigad,752491,485586,780290,502180,1532781,987766
Maharashtra,33 - Maval,1035960,647176,917781,527159,1953741,1174335
Maharashtra,34 - Pune,949567,533686,886269,459588,1835836,993274
Maharashtra,35 - Baramati,960387,602604,853156,463952,1813543,1066556
Maharashtra,36 - Shirur,973236,613828,850876,475678,1824112,1089506
Maharashtra,37 - Ahmadnagar ,898819,595960,800589,466358,1699408,1062318
Maharashtra,38 - Shirdi,765921,520229,693791,412416,1459712,932645
Maharashtra,39 - Beed,963462,681038,829188,551164,1792650,1232202
Maharashtra,40 - Osmanabad,932838,612411,793955,505740,1726793,1118151
Maharashtra,41 - Latur ,897919,578053,784688,479103,1682607,1057156
Maharashtra,42 - Solapur ,893736,527346,809019,423855,1702755,951201
Maharashtra,43 - Madha,912736,595626,814572,483993,1727308,1079619
Maharashtra,44 - Sangli,861582,558707,787525,487952,1649107,1046659
Maharashtra,45 - Satara,884020,519692,835978,456989,1719998,976681
Maharashtra,46 - Ratnagiri - sindhudurg,665668,444364,701693,451892,1367361,896256
Maharashtra,47 - Kolhapur,909326,664298,848974,595991,1758300,1260289
Maharashtra,48 - Hatkanangle,850574,631647,780024,557224,1630598,1188871
Manipur,1 - Inner manipur,417122,306721,438237,334150,855359,640871
Manipur,2 - Outer manipur,454309,378706,464657,393060,918966,771766
Meghalaya,1 - Shillong,480409,292944,500331,327043,980740,619987
Meghalaya,2 - Tura ,297230,231830,289271,226241,586501,458071
Mizoram,1 - MIZORAM,346219,216167,355951,217034,702170,433201
Nagaland,1 - Nagaland,600490,529325,582458,509585,1182948,1038910
NCT OF Delhi,1 - CHANDNI CHOWK                 ,791317,550825,655911,431038,1447228,981863
NCT OF Delhi,2 - NORTH EAST DELHI              ,1079406,741003,878301,576335,1957707,1317338
NCT OF Delhi,3 - EAST DELHI                    ,1023325,676648,806253,519688,1829578,1196336
NCT OF Delhi,4 - NEW DELHI                     ,830322,546295,659825,423517,1490147,969812
NCT OF Delhi,5 - NORTH WEST DELHI              ,1212553,756939,981872,599097,2194425,1356036
NCT OF Delhi,6 - WEST DELHI                    ,1108886,743406,930524,604565,2039410,1347971
NCT OF Delhi,7 - SOUTH DELHI                   ,1005289,638406,747452,464004,1752741,1102410
Orissa,1 - Bargarh,751140,595911,679577,527390,1430717,1123301
Orissa,2 - Sundargarh ,718689,515665,691843,495046,1410532,1010711
Orissa,3 - Sambalpur,669036,505640,628062,478669,1297098,984309
Orissa,4 - Keonjhar ,691369,550822,655314,533047,1346683,1083869
Orissa,5 - Mayurbhanj ,670100,523329,657455,530070,1327555,1053399
Orissa,6 - Balasore,709342,531966,656876,517484,1366218,1049450
Orissa,7 - Bhadrak ,774566,537966,694932,543373,1469498,1081339
Orissa,8 - Jajpur ,696385,511297,607348,469138,1303733,980435
Orissa,9 - Dhenkanal,722328,551897,641139,490204,1363467,1042101
Orissa,10 - Bolangir,814968,605802,748057,564603,1563025,1170405
Orissa,11 - Kalahandi,750694,568938,723441,548598,1474135,1117536
Orissa,12 - Nabarangpur ,645875,512721,651335,509451,1297210,1022172
Orissa,13 - Kandhamal,582340,417777,561262,421638,1143602,839415
Orissa,14 - Cuttack,731918,522770,639699,455834,1371617,978604
Orissa,15 - Kendrapara ,828491,580768,726953,560231,1555444,1140999
Orissa,16 - Jagatsinghpur ,797923,588605,701750,543327,1499673,1131932
Orissa,17 - Puri,742939,540898,661642,498491,1404581,1039389
Orissa,18 - Bhubaneswar,835850,483992,691918,407830,1527768,891822
Orissa,19 - Aska,750999,449485,657781,446796,1408780,896281
Orissa,20 - Berhampur,680089,451051,654179,454311,1334268,905362
Orissa,21 - Koraput ,629268,485223,671169,504221,1300437,989444
Puducherry,1 - Puducherry,432048,351360,469309,388657,901357,740017
Punjab,1 - Gurdaspur,784477,532819,715860,509880,1500337,1042699
Punjab,2 - Amritsar,779164,537641,698098,469555,1477262,1007196
Punjab,3 - Khadoor Sahib,817062,536595,746194,503923,1563256,1040518
Punjab,4 - Jalandhar,808530,532246,742967,508516,1551497,1040762
Punjab,5 - Hoshiarpur,762065,473343,723221,487954,1485286,961297
Punjab,6 - Anandpur Sahib,817186,554256,747535,532307,1564721,1086563
Punjab,7 - Ludhiana,835632,600960,725569,499497,1561201,1100457
Punjab,8 - Fatehgarh Sahib,740390,551251,656567,479703,1396957,1030954
Punjab,9 - Faridkot,768206,548771,686869,483336,1455075,1032107
Punjab,10 - Firozpur,808857,590511,713254,514901,1522111,1105412
Punjab,11 - Bathinda,813119,627099,712170,549668,1525289,1176767
Punjab,12 - Sangrur,758197,583487,666546,515980,1424743,1099467
Punjab,13 - Patiala,834231,593646,746042,527287,1580273,1120933
Rajasthan,1 - Ganganagar,909749,681063,808672,575743,1718421,1256806
Rajasthan,2 - Bikaner,847064,526983,744004,402768,1591068,929751
Rajasthan,3 - Churu,928040,600164,825785,530940,1753825,1131104
Rajasthan,4 - Jhunjhunu,893354,524368,803434,482097,1696788,1006465
Rajasthan,5 - Sikar,944231,555682,826193,510068,1770424,1065750
Rajasthan,6 - Jaipur Rural,906275,554084,793187,459607,1699462,1013691
Rajasthan,7 - Jaipur,1047468,716874,910350,579932,1957818,1296806
Rajasthan,8 - Alwar,871339,587242,756728,475063,1628067,1062305
Rajasthan,9 - BHARATPUR,911069,548105,775828,414327,1686897,962432
Rajasthan,10 - KARAULI-DHOLPUR,845665,491661,703997,354280,1549662,845941
Rajasthan,11 - Dausa,814648,519866,709447,410626,1524095,930492
Rajasthan,12 - TONK-SAWAI MADHOPUR,910730,583685,800045,459355,1710775,1043040
Rajasthan,13 - Ajmer,869354,614697,813938,541617,1683292,1156314
Rajasthan,14 - Nagaur,886731,532331,791931,471688,1678662,1004019
Rajasthan,15 - Pali,994082,581099,898948,514488,1893030,1095587
Rajasthan,16 - Jodhpur,910466,600132,816897,478466,1727363,1078598
Rajasthan,17 - Barmer,895593,655642,781989,563477,1677582,1219119
Rajasthan,18 - Jalore,964047,581038,860921,506234,1824968,1087272
Rajasthan,19 - Udaipur,930007,601374,887933,591828,1817940,1193202
Rajasthan,20 - Banswara,863920,579152,828582,592036,1692502,1171188
Rajasthan,21 - Chittorgarh,928572,632047,889575,540582,1818147,1172629
Rajasthan,22 - Rajsamand,879526,508738,819875,473381,1699401,982119
Rajasthan,23 - Bhilwara,904030,581524,850847,522566,1754877,1104090
Rajasthan,24 - Kota,913386,634766,831153,520194,1744539,1154960
Rajasthan,25 - JHALAWAR-BARAN,868977,635659,800865,510705,1669842,1146364
Sikkim,1 - Sikkim,190886,158222,179725,150745,370611,308967
Tamil Nadu,1 - Thiruvallur ,852794,645535,850039,608905,1702833,1254440
Tamil Nadu,2 - Chennai North,707696,464795,714690,445319,1422386,910114
Tamil Nadu,3 - Chennai South,901207,552474,894573,528689,1795780,1081163
Tamil Nadu,4 - Chennai central,665278,422278,662749,392616,1328027,814894
Tamil Nadu,5 - Sriperumbudur,982862,666226,963641,620421,1946503,1286647
Tamil Nadu,6 - Kancheepuram ,736993,575483,743130,552916,1480123,1128399
Tamil Nadu,7 - Arakkonam,692883,543546,708662,545506,1401545,1089052
Tamil Nadu,8 - Vellore,652064,483973,660187,490797,1312251,974770
Tamil Nadu,9 - Krishnagiri,705468,545948,674489,522443,1379957,1068391
Tamil Nadu,10 - Dharmapuri,696496,567633,661777,533712,1358273,1101345
Tamil Nadu,11 - Tiruvannamalai,675321,529027,677645,538420,1352966,1067447
Tamil Nadu,12 - Arani,683921,548023,685747,548023,1369668,1096046
Tamil Nadu,13 - Viluppuram,694932,537023,692075,530618,1387007,1067641
Tamil Nadu,14 - Kallakurichi,707219,542991,705280,564250,1412499,1107241
Tamil Nadu,15 - Salem,759981,593783,738369,556513,1498350,1150296
Tamil Nadu,16 - Namakkal,661113,526519,668439,532236,1329552,1058755
Tamil Nadu,17 - Erode,656659,511553,664736,497029,1321395,1008582
Tamil Nadu,18 - Tiruppur,697730,543998,677859,506192,1375589,1050190
Tamil Nadu,19 - Nilgiris ,629731,473163,639432,459913,1269163,933076
Tamil Nadu,20 - Coimbatore,869034,608666,851177,567954,1720211,1176620
Tamil Nadu,21 - Pollachi,686943,514739,694562,497928,1381505,1012667
Tamil Nadu,22 - Dindigul,696160,538540,704371,544824,1400531,1083364
Tamil Nadu,23 - Karur,641693,511860,656629,534674,1298322,1046534
Tamil Nadu,24 - Tiruchirappalli,689228,492050,697912,496200,1387140,988250
Tamil Nadu,25 - Perambalur,634285,500157,651149,530669,1285434,1030826
Tamil Nadu,26 - Cuddalore ,625652,486263,622256,498275,1247908,984538
Tamil Nadu,27 - Chidambaram ,686864,531368,679325,557055,1366189,1088423
Tamil Nadu,28 - Mayiladuthurai,679940,503949,670378,521589,1350318,1025538
Tamil Nadu,29 - Nagapattinam ,606339,454047,604287,488155,1210626,942202
Tamil Nadu,30 - Thanjavur,662576,482240,677474,530018,1340050,1012258
Tamil Nadu,31 - Sivaganga,704489,475004,707884,552032,1412373,1027036
Tamil Nadu,32 - Madurai,717175,486336,724259,492010,1441434,978346
Tamil Nadu,33 - Theni ,720709,529187,720593,546396,1441302,1075583
Tamil Nadu,34 - Virudhunagar,669589,497074,680906,513856,1350495,1010930
Tamil Nadu,35 - Ramanathapuram,731467,465888,724424,535160,1455891,1001048
Tamil Nadu,36 - Thoothukkudi,651366,449405,659231,467373,1310597,916778
Tamil Nadu,37 - Tenkasi ,688958,490748,693123,527190,1382081,1017938
Tamil Nadu,38 - Tirunelveli,705592,469328,714843,493002,1420435,962330
Tamil Nadu,39 - Kanniyakumari,743378,489129,724418,501613,1467796,990742
Tripura,1 - Tripura West,635976,546566,612574,526183,1248550,1072749
Tripura,2 - Tripura East,581599,489156,558670,461954,1140269,951110
Uttar Pradesh,1 - Saharanpur,873318,653286,735515,541022,1608833,1194308
Uttar Pradesh,2 - Kairana,840623,618538,691132,500786,1531755,1119324
Uttar Pradesh,3 - Muzaffarnagar,875186,620606,713297,486828,1588483,1107434
Uttar Pradesh,4 - Bijnor,848606,577146,713459,483200,1562065,1060346
Uttar Pradesh,5 - Nagina,795554,484006,697857,458190,1493411,942196
Uttar Pradesh,6 - Moradabad,961962,627713,810084,500252,1772046,1127965
Uttar Pradesh,7 - Rampur,872084,526970,744900,429419,1616984,956389
Uttar Pradesh,8 - Sambhal,931603,600271,761642,456731,1693245,1057002
Uttar Pradesh,9 - Amroha,829446,589901,714796,505959,1544242,1095860
Uttar Pradesh,10 - Meerut,971956,632847,792432,480537,1764388,1113384
Uttar Pradesh,11 - Baghpat,850681,582774,654494,421489,1505175,1004263
Uttar Pradesh,12 - Ghaziabad,1333051,782170,1024495,560150,2357546,1342320
Uttar Pradesh,13 - Gautam Buddha Nagar,1105855,690761,880262,508477,1986117,1199238
Uttar Pradesh,14 - Bulandshahr,930831,549484,805616,460226,1736447,1009710
Uttar Pradesh,15 - Aligarh,965995,596785,827131,467572,1793126,1064357
Uttar Pradesh,16 - Hathras,969908,596931,789019,452342,1758927,1049273
Uttar Pradesh,17 - Mathura,931615,619091,750645,457777,1682260,1076868
Uttar Pradesh,18 - Agra,1003777,622009,810962,448391,1814739,1070400
Uttar Pradesh,19 - Fatehpur Sikri,877271,565971,703311,401997,1580582,967968
Uttar Pradesh,20 - Firozabad,902514,643433,734224,461173,1636738,1104606
Uttar Pradesh,21 - Mainpuri,900568,580173,752497,419092,1653065,999265
Uttar Pradesh,22 - Etah,856974,520292,720483,405989,1577457,926281
Uttar Pradesh,23 - Badaun,978040,595491,791105,432103,1769145,1027594
Uttar Pradesh,24 - Aonla,917060,572792,736517,422849,1653577,995641
Uttar Pradesh,25 - Bareilly,911410,575414,752671,442477,1664081,1017891
Uttar Pradesh,26 - Pilibhit,903420,586205,767731,464342,1671151,1050547
Uttar Pradesh,27 - Shahjahanpur,1100020,664459,879274,465382,1979294,1129841
Uttar Pradesh,28 - Kheri,904863,606301,774603,471567,1679466,1077868
Uttar Pradesh,29 - Dhaurahra,846798,605309,711241,454962,1558039,1060271
Uttar Pradesh,30 - Sitapur,837981,582088,712282,444884,1550263,1026972
Uttar Pradesh,31 - Hardoi,939831,565543,774556,407351,1714387,972894
Uttar Pradesh,32 - Misrikh,948290,590503,777295,407811,1725585,998314
Uttar Pradesh,33 - Unnao,1194396,674580,969996,527023,2164392,1201603
Uttar Pradesh,34 - Mohanlalganj,995313,638688,842881,477900,1838194,1116588
Uttar Pradesh,35 - Lucknow,1052171,582227,897785,451556,1949956,1033783
Uttar Pradesh,36 - Rae Bareli,857875,437735,737079,387401,1594954,825136
Uttar Pradesh,37 - Amethi,890595,461365,779248,413260,1669843,874625
Uttar Pradesh,38 - Sultanpur,910349,499378,793349,465603,1703698,964981
Uttar Pradesh,39 - Pratapgarh,923668,457693,792957,436763,1716625,894456
Uttar Pradesh,40 - Farrukhabad,891934,560566,721847,410111,1613781,970677
Uttar Pradesh,41 - Etawah,938271,530688,768966,408916,1707237,939604
Uttar Pradesh,42 - Kannauj,1000007,629781,808882,484679,1808889,1114460
Uttar Pradesh,43 - Kanpur,889345,485765,721903,349314,1611248,835079
Uttar Pradesh,44 - Akbarpur,977662,559901,790961,411474,1768623,971375
Uttar Pradesh,45 - Jalaun,1045684,579206,857648,529960,1903332,1109166
Uttar Pradesh,46 - Jhansi,1034085,734103,897930,586701,1932015,1320804
Uttar Pradesh,47 - Hamirpur,949328,548768,788665,426472,1737993,975240
Uttar Pradesh,48 - Banda,884813,474053,717104,384273,1601917,858326
Uttar Pradesh,49 - Fatehpur,985922,575050,818855,481638,1804777,1056688
Uttar Pradesh,50 - Kaushambi,955324,491057,783670,418981,1738994,910038
Uttar Pradesh,51 - Phulpur,1063897,541728,849377,418613,1913274,960341
Uttar Pradesh,52 - Allahabad,917403,507043,749166,384539,1666569,891582
Uttar Pradesh,53 - Barabanki,925946,611206,795336,456962,1721282,1068168
Uttar Pradesh,54 - Faizabad,936036,558096,802665,464620,1738701,1022716
Uttar Pradesh,55 - Ambedkar Nagar,923553,541200,795221,493204,1718774,1034404
Uttar Pradesh,56 - Bahraich,882776,522627,755869,411636,1638645,934263
Uttar Pradesh,57 - Kaiserganj,928269,527337,783698,416020,1711967,943357
Uttar Pradesh,58 - Shrawasti,976415,559177,811665,420461,1788080,979638
Uttar Pradesh,59 - Gonda,935168,488241,775659,385491,1710827,873732
Uttar Pradesh,60 - Domariyaganj,961957,490141,799458,444886,1761415,935027
Uttar Pradesh,61 - Basti,1022508,549746,825105,498788,1847613,1048534
Uttar Pradesh,62 - Sant Kabir Nagar,1045430,520999,858897,490650,1904327,1011649
Uttar Pradesh,63 - Maharajganj,946234,547576,796897,508502,1743131,1056078
Uttar Pradesh,64 - Gorakhpur,1055476,569717,849022,470482,1904498,1040199
Uttar Pradesh,65 - Kushi Nagar,930637,505827,750355,444618,1680992,950445
Uttar Pradesh,66 - Deoria,997314,508610,809612,462947,1806926,971557
Uttar Pradesh,67 - Bansgaon,979644,459744,780466,418133,1760110,877877
Uttar Pradesh,68 - Lalganj,906751,456696,754732,441959,1661483,898655
Uttar Pradesh,69 - Azamgarh,941548,504787,761674,455431,1703222,960218
Uttar Pradesh,70 - Ghosi,1040034,547483,851079,492173,1891113,1039656
Uttar Pradesh,71 - Salempur,904632,450367,757105,404382,1661737,854749
Uttar Pradesh,72 - Ballia,973384,573053,794887,369158,1768271,942211
Uttar Pradesh,73 - Jaunpur,1002938,527542,845904,479601,1848842,1007143
Uttar Pradesh,74 - Machhlishahr,1026789,513121,865180,484781,1891969,997902
Uttar Pradesh,75 - Ghazipur,983352,529153,818167,457520,1801519,986673
Uttar Pradesh,76 - Chandauli,927629,546179,741511,434393,1669140,980572
Uttar Pradesh,77 - Varanasi,986224,594293,781262,436392,1767486,1030685
Uttar Pradesh,78 - Bhadohi,1016000,527290,818598,454262,1834598,981552
Uttar Pradesh,79 - Mirzapur,938504,557621,782157,450006,1720661,1007627
Uttar Pradesh,80 - Robertsganj,901163,497152,738398,388721,1639561,885873
Uttarakhand,1 - Tehri Garhwal,712039,403088,640806,373126,1352845,776214
Uttarakhand,2 - Garhwal,652891,323143,616192,358881,1269083,682024
Uttarakhand,3 - Almora,640059,312965,614269,343560,1254328,656525
Uttarakhand,4 - Nainital-udhamsingh Nagar,857781,590700,753029,510735,1610810,1101435
Uttarakhand,5 - Hardwar,888328,638871,754545,536821,1642873,1175692
West Bengal,1 - Cooch behar,848649,688855,764768,643554,1613417,1332409
West Bengal,2 - Alipurduars,755765,619981,715146,603585,1470911,1223566
West Bengal,3 - Jalpaiguri,795704,676450,735765,625667,1531469,1302117
West Bengal,4 - Darjeeling,737184,587678,699942,554331,1437126,1142009
West Bengal,5 - Raiganj,724014,564181,663512,544012,1387526,1108193
West Bengal,6 - Balurghat,653848,549173,600649,513880,1254497,1063053
West Bengal,7 - Maldaha Uttar,740749,579515,684679,583470,1425428,1162985
West Bengal,8 - Maldaha Dakshin,692386,546640,654757,545767,1347143,1092407
West Bengal,9 - Jangipur,714890,559542,676766,559542,1391656,1119084
West Bengal,10 - Baharampur,752943,579487,700840,575100,1453783,1154587
West Bengal,11 - Murshidabad,782286,652896,729812,634867,1512098,1287763
West Bengal,12 - Krishnanagar,769981,639431,706802,608483,1476783,1247914
West Bengal,13 - Ranaghat,831318,693242,771531,659954,1602849,1353196
West Bengal,14 - Bangaon,796650,661484,744063,622204,1540713,1283688
West Bengal,15 - Barrackpore,682366,567820,604856,483310,1287222,1051130
West Bengal,16 - Dum dum,715569,591353,690412,541291,1405981,1132644
West Bengal,17 - Barasat,774305,665637,738487,603694,1512792,1269331
West Bengal,18 - Basirhat,777768,677519,712828,596252,1490596,1273771
West Bengal,19 - Joynagar,757902,631335,700822,554717,1458724,1186052
West Bengal,20 - Mathurapur,772279,659848,716505,605683,1488784,1265531
West Bengal,21 - Diamond harbour,816259,683496,739655,577800,1555914,1261296
West Bengal,22 - Jadavpur,811441,666972,784305,605390,1595746,1272362
West Bengal,23 - Kolkata Dakshin,890251,627205,795045,540774,1685296,1167979
West Bengal,24 - Kolkata Uttar,797437,535236,636548,420542,1433985,955778
West Bengal,25 - Howrah,802653,621783,702446,503616,1505099,1125399
West Bengal,26 - Uluberia,761951,628832,686681,557195,1448632,1186027
West Bengal,27 - Srerampur,847931,681607,776107,608826,1624038,1290433
West Bengal,28 - Hooghly,836584,697366,793458,651504,1630042,1348870
West Bengal,29 - Arambagh,833629,707525,766664,654409,1600293,1361934
West Bengal,30 - Tamluk,796779,685563,730494,652121,1527273,1337684
West Bengal,31 - Kanthi,777345,664251,713064,626560,1490409,1290811
West Bengal,32 - Ghatal,835803,691145,774686,675564,1610489,1366709
West Bengal,33 - Jhargram,753840,646539,721272,611074,1475112,1257613
West Bengal,34 - Medinipur,770363,647026,729310,613006,1499673,1260032
West Bengal,35 - Purulia,764758,627033,707175,577642,1471933,1204675
West Bengal,36 - Bankura,775893,645606,727919,590713,1503812,1236319
West Bengal,37 - Bishnupur,759317,667837,707604,604233,1466921,1272070
West Bengal,38 - Bardhaman Purba,795545,683049,736699,637873,1532244,1320922
West Bengal,39 - Burdwan - durgapur,821377,700899,762121,630343,1583498,1331242
West Bengal,40 - Asansol,791896,633230,677788,509165,1469684,1142395
West Bengal,41 - Bolpur,798384,681996,740045,622760,1538429,1304756
West Bengal,42 - Birbhum,773457,670443,721651,605376,1495108,1275819


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FILE: notebooks/IPython-Tutorial/data/2014_indian_elections_results.csv
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Name of State/ UT	Parliamentary Constituency	Candidate Name	Total Votes Polled	Winner or Not?	Party Abbreviation	Party Name
Andhra Pradesh	Adilabad	GODAM NAGESH	430847	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Adilabad	NETHAWATH RAMDAS	41032	no	IND	Independent
Andhra Pradesh	Adilabad	RAMESH RATHOD	184198	no	TDP	Telugu Desam
Andhra Pradesh	Adilabad	RATHOD SADASHIV	94420	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Adilabad	MOSALI CHINNAIAH	8859	no	IND	Independent
Andhra Pradesh	Adilabad	NARESH	259557	no	INC	Indian National Congress
Andhra Pradesh	Adilabad	PAWAR KRISHNA	5055	no	IND	Independent
Andhra Pradesh	Adilabad	BANKA SAHADEV	4787	no	IND	Independent
Andhra Pradesh	Adilabad	None of the Above	17084	no	NOTA	None of the Above
Andhra Pradesh	Peddapalle	BALKA SUMAN	565496	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Peddapalle	None of the Above	5361	no	NOTA	None of the Above
Andhra Pradesh	Peddapalle	G. VIVEKANAND	274338	no	INC	Indian National Congress
Andhra Pradesh	Peddapalle	KALVALA SHANKAR	9290	no	PPOI	Pyramid Party of India
Andhra Pradesh	Peddapalle	MOUTAM RAVINDER	2674	no	IND	Independent
Andhra Pradesh	Peddapalle	VELTHURU MALLAIAH	45977	no	RP(K)	Republican Paksha (Khoripa)
Andhra Pradesh	Peddapalle	DR.JANAPATI SARAT BABU	63334	no	TDP	Telugu Desam
Andhra Pradesh	Peddapalle	KAMILLA JAYA RAO	2742	no	IND	Independent
Andhra Pradesh	Peddapalle	THALLAPALLI SRINIVAS	3699	no	IND	Independent
Andhra Pradesh	Peddapalle	VENKATA SWAMY INJAM	3866	no	MaSP	Mahajana Socialist Party
Andhra Pradesh	Peddapalle	GORRE RAMESH	2361	no	IND	Independent
Andhra Pradesh	Peddapalle	BUPELLI NARAYANA	7258	no	IND	Independent
Andhra Pradesh	Peddapalle	BOTHA VENKATA MALLAIAH	2245	no	BCUF	B. C. United Front
Andhra Pradesh	Peddapalle	JINNA RAMADEVI	9199	no	IND	Independent
Andhra Pradesh	Peddapalle	J.V. RAJU	3020	no	NIP	New India Party
Andhra Pradesh	Peddapalle	GADDALA VINAY KUMAR	3266	no	IND	Independent
Andhra Pradesh	Peddapalle	KALVALA SANJEEV	8609	no	RPI	Republican Party of India
Andhra Pradesh	Peddapalle	TAGARAM SHANKAR LAL	9449	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Karimnagar	VINOD KUMAR BOINAPALLY	505783	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Karimnagar	SRINIVAS REDDY LINGAMPALLY	3865	no	IND	Independent
Andhra Pradesh	Karimnagar	RANAVENI LAKSHMAN	2062	no	NIP	New India Party
Andhra Pradesh	Karimnagar	MALLESH YADAV BARLA	2760	no	IND	Independent
Andhra Pradesh	Karimnagar	REDDI MALLA SRINIVAS	8040	no	RPI(A)	Republican Party of India (A)
Andhra Pradesh	Karimnagar	BURLA. RAVI	4769	no	NCP	Nationalist Congress Party
Andhra Pradesh	Karimnagar	KANDEM PRABHAKAR	4731	no	IND	Independent
Andhra Pradesh	Karimnagar	B. LAXMAN	11333	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Karimnagar	SHAIK MAHMOOD	39325	no	WPOI	Welfare Party Of India
Andhra Pradesh	Karimnagar	PONNAM PRABHAKAR	300706	no	INC	Indian National Congress
Andhra Pradesh	Karimnagar	CHENNAMANENI VIDYA SAGAR RAO	214828	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Karimnagar	MEESALA RAJI REDDY	3080	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Karimnagar	MOHAMMED AMJAD MOHIUDDIN	6720	no	IND	Independent
Andhra Pradesh	Karimnagar	UPPULETI.LAXMAN	2448	no	IND	Independent
Andhra Pradesh	Karimnagar	L. PRABHAKAR REDDY	3944	no	PPOI	Pyramid Party of India
Andhra Pradesh	Karimnagar	PULIKOTA RAMESH	1777	no	IND	Independent
Andhra Pradesh	Karimnagar	DR.DHARMAIAH VENDAVETLA	3773	no	BMUP	Bahujan Mukti Party
Andhra Pradesh	Karimnagar	None of the Above	5747	no	NOTA	None of the Above
Andhra Pradesh	Nizamabad	KALVAKUNTLA KAVITHA	439307	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Nizamabad	BANJA VEERAPPA	3651	no	PPOI	Pyramid Party of India
Andhra Pradesh	Nizamabad	CHIRULINGADRULA VENKATESHAM	3091	no	IND	Independent
Andhra Pradesh	Nizamabad	KANDEM PRABHAKAR	3990	no	IND	Independent
Andhra Pradesh	Nizamabad	MALIK MOHTASIM KHAN	43814	no	WPOI	Welfare Party Of India
Andhra Pradesh	Nizamabad	S. CHANDRAIAH	2817	no	IND	Independent
Andhra Pradesh	Nizamabad	BAGWAN B	8129	no	IND	Independent
Andhra Pradesh	Nizamabad	ABDUL GANI	4791	no	BCUF	B. C. United Front
Andhra Pradesh	Nizamabad	ENDALA LAKSHMINARAYANA	225333	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Nizamabad	ABDUL KAREEM KHAN (BABU)	1601	no	IND	Independent
Andhra Pradesh	Nizamabad	TALARI RAMULU	7421	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Nizamabad	SAGAR SUDDALA	1978	no	BMUP	Bahujan Mukti Party
Andhra Pradesh	Nizamabad	MADHU YASKHI GOUD	272123	no	INC	Indian National Congress
Andhra Pradesh	Nizamabad	RAPELLY SRINIVAS	2621	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Nizamabad	KOTAPATI NARSIMHAM NAIDU	2462	no	IND	Independent
Andhra Pradesh	Nizamabad	None of the Above	7266	no	NOTA	None of the Above
Andhra Pradesh	Nizamabad	SINGAREDDY RAVINDRA REDDY	3529	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Zahirabad	B.B. PATIL	508661	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Zahirabad	MARRI DURGESH	18027	no	RPI(A)	Republican Party of India (A)
Andhra Pradesh	Zahirabad	SURESH KUMAR SHETKAR	364030	no	INC	Indian National Congress
Andhra Pradesh	Zahirabad	TANDALE AMBAJI RAO	2323	no	IND	Independent
Andhra Pradesh	Zahirabad	SYED FEROZUDDIN	8180	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Zahirabad	DR. SUNEEL KUMAR CHOWDHARY	3285	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Zahirabad	RAMA RAO	4426	no	IND	Independent
Andhra Pradesh	Zahirabad	K.MADAN MOHAN RAO	157497	no	TDP	Telugu Desam
Andhra Pradesh	Zahirabad	BENJAMIN RAJU	4804	no	IND	Independent
Andhra Pradesh	Zahirabad	MAHMOOD MOHIUDDIN	12383	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Zahirabad	None of the Above	11190	no	NOTA	None of the Above
Andhra Pradesh	Medak	KALVAKUNTLA CHANDRASEKHAR RAO	657492	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Medak	IQBAL ALI ZAFAR	5010	no	MBT	Majlis Bachao Tahreek
Andhra Pradesh	Medak	None of the Above	10781	no	NOTA	None of the Above
Andhra Pradesh	Medak	BOMMA BALESHAM	3297	no	PPOI	Pyramid Party of India
Andhra Pradesh	Medak	DR.P. SHRAVAN KUMAR REDDY	260463	no	INC	Indian National Congress
Andhra Pradesh	Medak	MEKA. SUBBA RAO	4441	no	IND	Independent
Andhra Pradesh	Medak	BALARAJU MITTAPALLY	2973	no	IND	Independent
Andhra Pradesh	Medak	GURJADA BHEERAIAH YADAV	2053	no	IND	Independent
Andhra Pradesh	Medak	PRABHUGOUD, PULLAIAHGARI	11752	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Medak	KOMPELLI PRABHUDAS	7599	no	RPI(A)	Republican Party of India (A)
Andhra Pradesh	Medak	CHAGANLA NARENDRA NATH	181804	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Medak	GAJABINKAR BANSILAL	3281	no	IND	Independent
Andhra Pradesh	Medak	Y. SHANKAR GOUD	6643	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Medak	KUNDETI. RAVI.	33507	no	IND	Independent
Andhra Pradesh	Malkajgiri	CH.MALLA REDDY	523336	yes	TDP	Telugu Desam
Andhra Pradesh	Malkajgiri	V.DINESH REDDY	115170	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Malkajgiri	SATHYANARAYANA YAADAV PEDDIBOYINA	597	no	BCBDP	B.C.Bharata Desam Party
Andhra Pradesh	Malkajgiri	B.VEERA HANUMANTHA REDDY	775	no	IND	Independent
Andhra Pradesh	Malkajgiri	HANUMANTH RAO MYNAMPALLY	494965	no	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Malkajgiri	SARVEY SATHYANARAYANA	233711	no	INC	Indian National Congress
Andhra Pradesh	Malkajgiri	POLISETTY KRISHNA RAO	496	no	IND	Independent
Andhra Pradesh	Malkajgiri	MURARI.T.N	1390	no	SHS	Shivsena
Andhra Pradesh	Malkajgiri	M.NAGESWARA RAO	1301	no	IND	Independent
Andhra Pradesh	Malkajgiri	BANDI SUDHAKAR	4094	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Malkajgiri	DR.N.V.SUDHA KIRAN	8291	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Malkajgiri	JAJULA BHASKAR	8003	no	SHP	Shramajeevi Party
Andhra Pradesh	Malkajgiri	KANTE SAYANNA	2262	no	IND	Independent
Andhra Pradesh	Malkajgiri	PADURI SRINIVAS REDDY	803	no	TLP	Telangana Loksatta Party
Andhra Pradesh	Malkajgiri	PEESARI SATISH REDDY	1325	no	IND	Independent
Andhra Pradesh	Malkajgiri	RAHUL PANDIT	899	no	IND	Independent
Andhra Pradesh	Malkajgiri	ANJANEYULU.E	2677	no	IND	Independent
Andhra Pradesh	Malkajgiri	DR.JAYAPRAKASH  NARAYAN .N	158243	no	LSP	Lok Satta Party
Andhra Pradesh	Malkajgiri	POTTURI TULASI DAS	411	no	IND	Independent
Andhra Pradesh	Malkajgiri	KARUNAKAR.R	6133	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Malkajgiri	VATTIKUTI RAMARAO GOUD	1032	no	IND	Independent
Andhra Pradesh	Malkajgiri	D.BIKSHAPATHI	5717	no	IND	Independent
Andhra Pradesh	Malkajgiri	SUTHARAPU PADMAIAH	538	no	APRSSP	Andhra Pradesh Rashtra Samaikya Samithi Party
Andhra Pradesh	Malkajgiri	M.T.VARUGHESE	1026	no	IND	Independent
Andhra Pradesh	Malkajgiri	GAJABINKAR BANSILAL	1416	no	IND	Independent
Andhra Pradesh	Malkajgiri	None of the Above	8716	no	NOTA	None of the Above
Andhra Pradesh	Malkajgiri	SURAKANTI VENKAT REDDY	2049	no	PPOI	Pyramid Party of India
Andhra Pradesh	Malkajgiri	DASARI BHARATH REDDY	1696	no	IND	Independent
Andhra Pradesh	Malkajgiri	DIVAKAR DHARANIKOTA SUDHAKAR	18543	no	AIMIM	All India Majlis-E-Ittehadul Muslimeen
Andhra Pradesh	Malkajgiri	PROF. K.NAGESHWAR	13236	no	IND	Independent
Andhra Pradesh	Malkajgiri	KOYALKAR BHOJ RAJ	1546	no	IND	Independent
Andhra Pradesh	Secundrabad	BANDARU DATTATREYA	438271	yes	BJP	Bharatiya Janata Party
Andhra Pradesh	Secundrabad	SYED MADANI QUADRI	376	no	IND	Independent
Andhra Pradesh	Secundrabad	DR.N.S.UDAY KUMAR	928	no	pjdl	Prem Janata Dal
Andhra Pradesh	Secundrabad	KHAJA KHALID ANWAR	2863	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Secundrabad	K. SRINIVASULU	3691	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Secundrabad	AVULA RANGANAYAKULU	1144	no	IND	Independent
Andhra Pradesh	Secundrabad	SONU SARSAL VALMIKI	2311	no	TECPI	Telangana Communist Party of India
Andhra Pradesh	Secundrabad	S. LAKSHMAN RAO	886	no	IND	Independent
Andhra Pradesh	Secundrabad	SYED SAJID ALI	45187	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Secundrabad	MOHAMMED RIZWAN HUSSAIN	370	no	IND	Independent
Andhra Pradesh	Secundrabad	K.SAI KRISHNA MOHAN RAO	2603	no	ICSP	Indian Christian Secular Party
Andhra Pradesh	Secundrabad	NARLA MOHAN RAO	145120	no	AIMIM	All India Majlis-E-Ittehadul Muslimeen
Andhra Pradesh	Secundrabad	DR.MALATHI LATHA EPPA	873	no	JAP	Jantantra Party
Andhra Pradesh	Secundrabad	None of the Above	6572	no	NOTA	None of the Above
Andhra Pradesh	Secundrabad	CH. MURAHARI	554	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Andhra Pradesh	Secundrabad	SHAKTHI SATYAVATHI	638	no	PPOI	Pyramid Party of India
Andhra Pradesh	Secundrabad	KARANKOTE NARENDER	474	no	AIFB	All India Forward Bloc
Andhra Pradesh	Secundrabad	B.DEEPAK KUMAR	1813	no	RPI(KH)	Republican Party of India (Khobragade)
Andhra Pradesh	Secundrabad	MOHD ABUBAKAR PARWAIZ SIDDIQUI	322	no	IND	Independent
Andhra Pradesh	Secundrabad	ABDUS SATTAR MUJAHED	809	no	ABML(S)	Akhil Bharatiya Muslim League (Secular)
Andhra Pradesh	Secundrabad	MICHAEL DANIEL OVELZ	291	no	IND	Independent
Andhra Pradesh	Secundrabad	KUMAR CHOWDARY YADAV.G	547	no	sps	Samaikyandhra Parirakshana Samithi
Andhra Pradesh	Secundrabad	ANANTH RAO GUNDEPALLY	779	no	IND	Independent
Andhra Pradesh	Secundrabad	CHAYA RATAN MUSINIPALLY	11184	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Secundrabad	J.RAMULU	2058	no	RSP	Revolutionary Socialist Party
Andhra Pradesh	Secundrabad	RAGHU DESHABOINA	1496	no	BMUP	Bahujan Mukti Party
Andhra Pradesh	Secundrabad	T.BHEEMSEN	143847	no	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Secundrabad	M.ANJAN KUMAR YADAV	183536	no	INC	Indian National Congress
Andhra Pradesh	Secundrabad	KLHAJA SIRAJUDDIN AZAZ	959	no	mmsp	Majlis Markaz-e-Siyasee Party
Andhra Pradesh	Secundrabad	B.SUBRAMANYAM	2481	no	IND	Independent
Andhra Pradesh	Secundrabad	MOHD IMTIAZ HUSSAIN	786	no	IUML	Indian Union Muslim League
Andhra Pradesh	Hyderabad	ASADUDDIN OWAISI	513868	yes	AIMIM	All India Majlis-E-Ittehadul Muslimeen
Andhra Pradesh	Hyderabad	None of the Above	5013	no	NOTA	None of the Above
Andhra Pradesh	Hyderabad	LUBNA SARWATH	6118	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Hyderabad	N.MAHENDER SINGH	485	no	NBNP	Nava Bharat National Party
Andhra Pradesh	Hyderabad	M A HABEEB	5992	no	IND	Independent
Andhra Pradesh	Hyderabad	BODDU SAINATH REDDY	10743	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Hyderabad	S KRISHNA REDDY	49310	no	INC	Indian National Congress
Andhra Pradesh	Hyderabad	SYED MUSTAFA MAHMOOD	2102	no	MBT	Majlis Bachao Tahreek
Andhra Pradesh	Hyderabad	VENU GOPAL.P	3100	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Hyderabad	RASHID SHAREEF	37195	no	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Hyderabad	S.KHAJA MOINUDDIN	1593	no	AIACP	All India Azaad Congress Party
Andhra Pradesh	Hyderabad	SHAIK MOIN	626	no	IND	Independent
Andhra Pradesh	Hyderabad	DR.BHAGAVANTH RAO	311414	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Hyderabad	MD OSMAN	728	no	IND	Independent
Andhra Pradesh	Hyderabad	JAYA VINDHYALA	598	no	SP(I)	Socialist Party (India)
Andhra Pradesh	Hyderabad	SYED ABDUL GAFFER	740	no	IND	Independent
Andhra Pradesh	Hyderabad	BINGHI RAJASHEKAR	21796	no	PPOI	Pyramid Party of India
Andhra Pradesh	CHELVELLA	KONDA VISHWESHWAR REDDY	435077	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	CHELVELLA	CHAN PASHA	56835	no	ABML(S)	Akhil Bharatiya Muslim League (Secular)
Andhra Pradesh	CHELVELLA	G.MALLESHAM GOUD	24660	no	IND	Independent
Andhra Pradesh	CHELVELLA	KUMMARI GIRI	5789	no	PPOI	Pyramid Party of India
Andhra Pradesh	CHELVELLA	REGATTE VENKAT REDDY	7462	no	AAAP	Aam Aadmi Party
Andhra Pradesh	CHELVELLA	SYED HASHMATHULLAH QUADRI	1411	no	IUML	Indian Union Muslim League
Andhra Pradesh	CHELVELLA	MD YUNUS MIYA	2904	no	MCPI	Marxist Communist Party of India (United)
Andhra Pradesh	CHELVELLA	SANIKE RAM GOPAL YADAV	2058	no	IND	Independent
Andhra Pradesh	CHELVELLA	SUTHARAPU PADMAIAH	2214	no	APRSSP	Andhra Pradesh Rashtra Samaikya Samithi Party
Andhra Pradesh	CHELVELLA	DR.GOLLA RAMAIAH	1765	no	TLP	Telangana Loksatta Party
Andhra Pradesh	CHELVELLA	TULLA VEERENDER	353203	no	TDP	Telugu Desam
Andhra Pradesh	CHELVELLA	PATLOLLA KARTIK REDDY	362054	no	INC	Indian National Congress
Andhra Pradesh	CHELVELLA	SAMALA RAVINDAR	7732	no	BSP	Bahujan Samaj Party
Andhra Pradesh	CHELVELLA	KONDA RAGHAVA REDDY	40135	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	CHELVELLA	DR. P. SADHU SATYANADHAN	2545	no	ICSP	Indian Christian Secular Party
Andhra Pradesh	CHELVELLA	None of the Above	10018	no	NOTA	None of the Above
Andhra Pradesh	Mahbubnagar	AP JITHENDER REDDY	334228	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Mahbubnagar	None of the Above	9037	no	NOTA	None of the Above
Andhra Pradesh	Mahbubnagar	S. SHIVA SHANKAR	3252	no	IND	Independent
Andhra Pradesh	Mahbubnagar	DR. CHANDRA SEKHAR. A	4033	no	IND	Independent
Andhra Pradesh	Mahbubnagar	KADIRE KRISHNA	15779	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Mahbubnagar	DR. JANARDHAN REDDY NAGAM	272791	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Mahbubnagar	H.A. RAHMAN	9105	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Mahbubnagar	IBRAHIM SYED	4549	no	IND	Independent
Andhra Pradesh	Mahbubnagar	JAIPAL REDDY SUDINI	331638	no	INC	Indian National Congress
Andhra Pradesh	Mahbubnagar	S.R.PREMAIAH	30388	no	PPOI	Pyramid Party of India
Andhra Pradesh	Nagarkurnool	YELLAIAH NANDI	420075	yes	INC	Indian National Congress
Andhra Pradesh	Nagarkurnool	BAHADHUR SRINIVAS	12089	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Nagarkurnool	BUDDULA SRINIVAS	54680	no	IND	Independent
Andhra Pradesh	Nagarkurnool	BAKKANI NARSIMLU	183352	no	TDP	Telugu Desam
Andhra Pradesh	Nagarkurnool	MAREDU GOPAL	22985	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Nagarkurnool	DR. MANDA JAGANNATH	403399	no	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Nagarkurnool	None of the Above	12388	no	NOTA	None of the Above
Andhra Pradesh	Nalgonda	GUTHA SUKHENDER REDDY	472093	yes	INC	Indian National Congress
Andhra Pradesh	Nalgonda	MANUPURI DAYA SAGAR	4495	no	PPOI	Pyramid Party of India
Andhra Pradesh	Nalgonda	MARRI NEHEMIAH	56259	no	IND	Independent
Andhra Pradesh	Nalgonda	NANDYALA NARSIMHA REDDY	54423	no	CPM	Communist Party of India  (Marxist)
Andhra Pradesh	Nalgonda	DR. PALLA RAJESHWAR REDDY	260677	no	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Nalgonda	KUNDARAPU RAMESH	11517	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Nalgonda	GUNNAM NAGI REDDY	39385	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Nalgonda	TERA CHINNAPA REDDY	278937	no	TDP	Telugu Desam
Andhra Pradesh	Nalgonda	TEJAVATH BELLAIAH NAIK	2308	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Nalgonda	None of the Above	9305	no	NOTA	None of the Above
Andhra Pradesh	Bhongir	DR. BOORA NARSAIAH GOUD	448245	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Bhongir	None of the Above	10921	no	NOTA	None of the Above
Andhra Pradesh	Bhongir	CHERUPALLY SEETHARAMULU	54040	no	CPM	Communist Party of India  (Marxist)
Andhra Pradesh	Bhongir	NAVULLA MALLA REDDY	26805	no	IND	Independent
Andhra Pradesh	Bhongir	VARIKUPPALA KRISHNA VADDERA	35011	no	IND	Independent
Andhra Pradesh	Bhongir	GUDURU JANARDHAN REDDY	2341	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Bhongir	INDRASENA REDDY NALLU	183217	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Bhongir	GUAHATI MOTHILAL	2600	no	AIMIM	All India Majlis-E-Ittehadul Muslimeen
Andhra Pradesh	Bhongir	RAVULAPALLY KONDAL RAO	7722	no	IND	Independent
Andhra Pradesh	Bhongir	NAGULA SRINIVAS YADAV	1859	no	MaSP	Mahajana Socialist Party
Andhra Pradesh	Bhongir	BOILLA NARENDER	6850	no	RPI	Republican Party of India
Andhra Pradesh	Bhongir	KAPILAVAI DILEEP KUMAR	5552	no	RLD	Rashtriya Lok Dal
Andhra Pradesh	Bhongir	ARVAPALLY AMBATI	9029	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Bhongir	KOMATIREDDY RAJGOPAL REDDY	417751	no	INC	Indian National Congress
Andhra Pradesh	Warangal	KADIYAM SRIHARI	661639	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Warangal	RAJAIAH SIRICILLA	269065	no	INC	Indian National Congress
Andhra Pradesh	Warangal	VENU ESAMPELLY	1541	no	AIFB	All India Forward Bloc
Andhra Pradesh	Warangal	VENKANNA NEERUDU	2098	no	BMUP	Bahujan Mukti Party
Andhra Pradesh	Warangal	JANNU MAHENDAR	3211	no	IND	Independent
Andhra Pradesh	Warangal	KANAKAM RAMESH	2013	no	IND	Independent
Andhra Pradesh	Warangal	VENKANNA KANNAM	2258	no	MCPI	Marxist Communist Party of India (United)
Andhra Pradesh	Warangal	PARAMESHWAR RAMAGALLA	187139	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Warangal	CHILUVERU PRATHAP	9178	no	IND	Independent
Andhra Pradesh	Warangal	GANNARAPU SURENDER	9536	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Warangal	THUPPARI NARASIMHA SWAMY	7350	no	PPOI	Pyramid Party of India
Andhra Pradesh	Warangal	CHINTHA SWAMY	5569	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Warangal	None of the Above	14034	no	NOTA	None of the Above
Andhra Pradesh	Mahabubabad	PROF. AZMEERA SEETARAM NAIK	320569	yes	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Mahabubabad	None of the Above	6705	no	NOTA	None of the Above
Andhra Pradesh	Mahabubabad	PADIGA YARRAIAH	3492	no	IND	Independent
Andhra Pradesh	Mahabubabad	LAXMAN BUKYA	14026	no	IND	Independent
Andhra Pradesh	Mahabubabad	LAXMINARAYANA BORRA	5888	no	IND	Independent
Andhra Pradesh	Mahabubabad	KAVITHA RANI LUNAVATH	6627	no	PPOI	Pyramid Party of India
Andhra Pradesh	Mahabubabad	PONAKA SAMMAKKA	3475	no	IND	Independent
Andhra Pradesh	Mahabubabad	BODA RAASI	6224	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Mahabubabad	PAYAM CHANDHAR RAO (CHINNA CHANDHRANNA)	94014	no	IND	Independent
Andhra Pradesh	Mahabubabad	TELLAM VENKATA RAO	128465	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Mahabubabad	BANOTH MOHANLAL	215904	no	TDP	Telugu Desam
Andhra Pradesh	Mahabubabad	BALUNAYAK BHUKYA	3094	no	IND	Independent
Andhra Pradesh	Mahabubabad	DATLA NAGESHWAR RAO	6854	no	IND	Independent
Andhra Pradesh	Mahabubabad	METLA PAPAIAH	3922	no	IND	Independent
Andhra Pradesh	Mahabubabad	GUGULOTU JAGAN	3671	no	MCPI	Marxist Communist Party of India (United)
Andhra Pradesh	Mahabubabad	EESAM SUDHAKAR	2199	no	IND	Independent
Andhra Pradesh	Mahabubabad	V. GOURI NAGAMANI	13664	no	IND	Independent
Andhra Pradesh	Mahabubabad	P. BALRAM	285577	no	INC	Indian National Congress
Andhra Pradesh	Khammam	PONGULETI  SRINIVASA REDDY	421957	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Khammam	LINGALA RAVI KUMAR	9454	no	IND	Independent
Andhra Pradesh	Khammam	NAGIREDDY RAJI REDDY	4480	no	IND	Independent
Andhra Pradesh	Khammam	BANOTH LAXMA NAIK	1338	no	IND	Independent
Andhra Pradesh	Khammam	BUSIREDDY SHANKAR REDDY	963	no	IND	Independent
Andhra Pradesh	Khammam	MEDARAMETLA  RAMA RAO	4135	no	PPOI	Pyramid Party of India
Andhra Pradesh	Khammam	BANOTH KISHAN NAYAK	1611	no	IND	Independent
Andhra Pradesh	Khammam	RAMESH KOMMU	1267	no	IND	Independent
Andhra Pradesh	Khammam	KAKI BHASKAR	5354	no	IND	Independent
Andhra Pradesh	Khammam	NAMA NAGESWARA RAO	409983	no	TDP	Telugu Desam
Andhra Pradesh	Khammam	KANKANALA NARAYANA	187653	no	CPI	Communist Party of India
Andhra Pradesh	Khammam	SANAPA POMMAIAH	886	no	IND	Independent
Andhra Pradesh	Khammam	BUDAN BAIG SHAIK	89063	no	TRS	Telangana Rashtra Samithi
Andhra Pradesh	Khammam	GUGULOTHU SARVAN	1488	no	IND	Independent
Andhra Pradesh	Khammam	GUGULOTHU RANJITH KUMAR	1432	no	IND	Independent
Andhra Pradesh	Khammam	MUDIGE NAGESWARA RAO	1194	no	IND	Independent
Andhra Pradesh	Khammam	MARIKANTI VASUDASU	877	no	IND	Independent
Andhra Pradesh	Khammam	MOKALLA RAMESH	5386	no	IND	Independent
Andhra Pradesh	Khammam	KALTHI VENKATESWARLU	1059	no	IND	Independent
Andhra Pradesh	Khammam	JERRIPOTHULA SRINIVAS	1116	no	IND	Independent
Andhra Pradesh	Khammam	NAGARJUNA CHERUKURU	4999	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Khammam	KALTHI RAMULAMMA	980	no	IND	Independent
Andhra Pradesh	Khammam	BACHALA LAXMAIAH	6494	no	IND	Independent
Andhra Pradesh	Khammam	GOKINEPALLI VENKATESWARA RAO	8053	no	IND	Independent
Andhra Pradesh	Khammam	DODDA RAM BABU	4757	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Khammam	KANAKAM TIRUMALA RAO	916	no	IND	Independent
Andhra Pradesh	Khammam	RAMU KANDULA	1019	no	IND	Independent
Andhra Pradesh	Khammam	None of the Above	4983	no	NOTA	None of the Above
Andhra Pradesh	Aruku	KOTHAPALLI GEETHA	413191	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Aruku	None of the Above	16532	no	NOTA	None of the Above
Andhra Pradesh	Aruku	GUMMIDI SANDHYARANI	321793	no	TDP	Telugu Desam
Andhra Pradesh	Aruku	MIDIYAM BABURAO	38898	no	CPM	Communist Party of India  (Marxist)
Andhra Pradesh	Aruku	IILLA RAMI REDDY	5692	no	IND	Independent
Andhra Pradesh	Aruku	KANGALA BALUDORA	23215	no	IND	Independent
Andhra Pradesh	Aruku	KISHORE CHANDRA DEO	52884	no	INC	Indian National Congress
Andhra Pradesh	Aruku	VANUGU SANKARARAO	4614	no	IND	Independent
Andhra Pradesh	Aruku	BURJABARIKI DHANARAJU	8569	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Aruku	SALLANGI RATNAM	7688	no	IND	Independent
Andhra Pradesh	Aruku	BIDDIKA RAMAIAH	7587	no	IND	Independent
Andhra Pradesh	Aruku	CHETTI SANKARARAO	8951	no	IND	Independent
Andhra Pradesh	Srikakulam	RAMMOHAN NAIDU KINJARAPU	556163	yes	TDP	Telugu Desam
Andhra Pradesh	Srikakulam	KADIYAM JAYALAKSHMI	5021	no	PPOI	Pyramid Party of India
Andhra Pradesh	Srikakulam	JAIDEV INJARAPU	2557	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Srikakulam	KILLI KRUPARANI	24163	no	INC	Indian National Congress
Andhra Pradesh	Srikakulam	BODDEPALLI RAJARAO	8047	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Srikakulam	KINJARAPU TEJESWARA RAO	2074	no	IND	Independent
Andhra Pradesh	Srikakulam	TOTA TEJESWARA RAO	2144	no	IND	Independent
Andhra Pradesh	Srikakulam	VASUDEVA RAO BODDU	5131	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Andhra Pradesh	Srikakulam	REDDY SHANTHI	428591	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Srikakulam	PAIDI RAJA RAO	11422	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Srikakulam	None of the Above	6133	no	NOTA	None of the Above
Andhra Pradesh	Vizianagaram	ASHOK GAJAPATHI RAJU PUSAPATI	536549	yes	TDP	Telugu Desam
Andhra Pradesh	Vizianagaram	None of the Above	6528	no	NOTA	None of the Above
Andhra Pradesh	Vizianagaram	JHANSI LAKSHMI BOTCHA	122487	no	INC	Indian National Congress
Andhra Pradesh	Vizianagaram	HARIKISHAN KUPPILI	3603	no	IND	Independent
Andhra Pradesh	Vizianagaram	YELLA RAO SIYYADULA	5125	no	IND	Independent
Andhra Pradesh	Vizianagaram	VENKATA SWETHA CHALAPATHI KUMARA KRISHNA RANGARAO RAVU	429638	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Vizianagaram	PRASADA RAO NANDA	6401	no	PPOI	Pyramid Party of India
Andhra Pradesh	Vizianagaram	TADIVAKA RAMESH NAIDU	3388	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Vizianagaram	BONU KRISHNA	4092	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Vizianagaram	NARU SIMHADRI NAIDU	2505	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Visakhapatnam	KAMBHAMPATI HARI BABU	566832	yes	BJP	Bharatiya Janata Party
Andhra Pradesh	Visakhapatnam	CHALAPATHIRAO RONANKI	1594	no	IND	Independent
Andhra Pradesh	Visakhapatnam	DR|| BHAVANI SANKAR SASTRY.A	1910	no	ICSP	Indian Christian Secular Party
Andhra Pradesh	Visakhapatnam	SREENADH REDDY	1379	no	IND	Independent
Andhra Pradesh	Visakhapatnam	NAGENDRA SAIRAM	2377	no	IND	Independent
Andhra Pradesh	Visakhapatnam	KURADA SATYANARAYANA PATNAIK	1927	no	IND	Independent
Andhra Pradesh	Visakhapatnam	SABBAM HARI	6644	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Visakhapatnam	KUTIKUPPALA SURYA RAO	1889	no	IND	Independent
Andhra Pradesh	Visakhapatnam	IMANDI VENKATA KURMARAO	14947	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Visakhapatnam	J. PADMAJA	1540	no	IND	Independent
Andhra Pradesh	Visakhapatnam	SEEMAKURTHI SRINUVASU (BHAGHAVAN SRINU)	1759	no	IND	Independent
Andhra Pradesh	Visakhapatnam	BOLISETTI SATYANARAYANA (SATYA)	50632	no	INC	Indian National Congress
Andhra Pradesh	Visakhapatnam	DURGAPRASAD GUNTU	2611	no	IND	Independent
Andhra Pradesh	Visakhapatnam	KUNA RAMAM	6175	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Visakhapatnam	UMAMAHESWARARAO ARETI	1751	no	IND	Independent
Andhra Pradesh	Visakhapatnam	JT RAMARAO	4323	no	sps	Samaikyandhra Parirakshana Samithi
Andhra Pradesh	Visakhapatnam	Y.S VIJAYAMMA	476344	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Visakhapatnam	TANUKU DIVAKAR	5418	no	IND	Independent
Andhra Pradesh	Visakhapatnam	GEDELA KRISHNARAO	1118	no	IND	Independent
Andhra Pradesh	Visakhapatnam	CHANDRASEKHAR BUDDA	1348	no	IND	Independent
Andhra Pradesh	Visakhapatnam	DADI JYOTHI BHAVANI	2717	no	PPOI	Pyramid Party of India
Andhra Pradesh	Visakhapatnam	BRAHMA SWAROOP KASIBHATTA	994	no	IND	Independent
Andhra Pradesh	Visakhapatnam	None of the Above	7329	no	NOTA	None of the Above
Andhra Pradesh	Anakapalli	MUTTAMSETTI SRINIVASA RAO (AVANTHI)	568463	yes	TDP	Telugu Desam
Andhra Pradesh	Anakapalli	None of the Above	8604	no	NOTA	None of the Above
Andhra Pradesh	Anakapalli	VIJAYA LAKSHMI THOTA	17770	no	INC	Indian National Congress
Andhra Pradesh	Anakapalli	GUDIVADA AMARNADH	520531	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Anakapalli	SARISA LEELA PRASANNA KUMARI	2875	no	PPOI	Pyramid Party of India
Andhra Pradesh	Anakapalli	VADLAMURI KRISHNA SWAROOP	5506	no	DABAP	Dalita Bahujana Party
Andhra Pradesh	Anakapalli	APPALARAJU SADARAM	3228	no	IND	Independent
Andhra Pradesh	Anakapalli	KARANAM TIRUPATHI RAO	14834	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Anakapalli	KONDARAJU JAMPANA	6261	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Kakinada	THOTA NARASIMHAM	514402	yes	TDP	Telugu Desam
Andhra Pradesh	Kakinada	PEPAKAYALA SUBHA RAJENDRA	1161	no	IND	Independent
Andhra Pradesh	Kakinada	KOLA VENKATA PRASAD VARMA	3473	no	RJD	Rashtriya Janata Dal
Andhra Pradesh	Kakinada	DONAM NEELAKANTAM	1697	no	IND	Independent
Andhra Pradesh	Kakinada	MOTHA SARADA	6836	no	RPI(KH)	Republican Party of India (Khobragade)
Andhra Pradesh	Kakinada	ROKKAM DAYAKAR	1837	no	RP(K)	Republican Paksha (Khoripa)
Andhra Pradesh	Kakinada	TUMMALAPALLI SATYA RAMAKRISHNA	6442	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Kakinada	MUTHABATHULA RATNA KUMAR	2511	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Kakinada	TEKUMUDI VENKATA RAO	3826	no	PPOI	Pyramid Party of India
Andhra Pradesh	Kakinada	VINJAMALLA CHITTI BABU	1400	no	IND	Independent
Andhra Pradesh	Kakinada	GUTTULA ANNAPURNA SESHAGIRI RAO (BATHPARTY-SESHU)	1417	no	IND	Independent
Andhra Pradesh	Kakinada	MERLA BHAVANI SANKARA PRASAD	910	no	IND	Independent
Andhra Pradesh	Kakinada	DONKA LEELAVATHI	3756	no	IND	Independent
Andhra Pradesh	Kakinada	CHALAMALASETTY SUNIL	510971	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Kakinada	CHAGANTI SURYANARAYANA MURTY	1236	no	IND	Independent
Andhra Pradesh	Kakinada	ARJUNARAO YEGUPATI	1495	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Andhra Pradesh	Kakinada	PRABHAKARA RAO MADIKI	496	no	IND	Independent
Andhra Pradesh	Kakinada	JALLURI VENKATESWARLU (J.V)	6636	no	IND	Independent
Andhra Pradesh	Kakinada	KARRA VENKATA APPALA NARASIMHA RAO	1907	no	IND	Independent
Andhra Pradesh	Kakinada	SRINIVAS DANGETI	2356	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Kakinada	KUNCHE RAMADAS	1122	no	IND	Independent
Andhra Pradesh	Kakinada	MALLIPUDI MANGAPATI PALLAM RAJU	19754	no	INC	Indian National Congress
Andhra Pradesh	Kakinada	None of the Above	4358	no	NOTA	None of the Above
Andhra Pradesh	Amalapuram	DR PANDULA RAVINDRA BABU	594547	yes	TDP	Telugu Desam
Andhra Pradesh	Amalapuram	None of the Above	6141	no	NOTA	None of the Above
Andhra Pradesh	Amalapuram	AITHABATHULA JOGESWARA VENKATA BUTCHI MAHESWARA RAO	12182	no	INC	Indian National Congress
Andhra Pradesh	Amalapuram	G.V.HARSHA KUMAR	9931	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Amalapuram	PINIPE VISWARUPU	473971	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Amalapuram	KOMMU SATYAVATHI	625	no	IND	Independent
Andhra Pradesh	Amalapuram	YALANGI RAMESH	1171	no	IND	Independent
Andhra Pradesh	Amalapuram	UNDRU GANI RAJU	862	no	IND	Independent
Andhra Pradesh	Amalapuram	VADDI WINSTON CHURCHILL	1684	no	IND	Independent
Andhra Pradesh	Amalapuram	KUNCHE PEDASATYANARAYANA	4560	no	PPOI	Pyramid Party of India
Andhra Pradesh	Amalapuram	KONKI VENKATA RAO	3233	no	RP(K)	Republican Paksha (Khoripa)
Andhra Pradesh	Amalapuram	KASI CHANDRAMOULI	1110	no	IND	Independent
Andhra Pradesh	Amalapuram	GEDDAM SAMPADA RAO	7219	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Amalapuram	BHASKARA RAO BUNGA	824	no	IND	Independent
Andhra Pradesh	Amalapuram	VIJAYA CHAKRAVARTHY PANTHAGADA	2867	no	RPI(KH)	Republican Party of India (Khobragade)
Andhra Pradesh	Rajahmundry	MURALI MOHAN MAGANTI	630573	yes	TDP	Telugu Desam
Andhra Pradesh	Rajahmundry	ANNAMDEVULA DHARMA RAO	590	no	IND	Independent
Andhra Pradesh	Rajahmundry	POLU PEDA VENGALA REDDY	765	no	IND	Independent
Andhra Pradesh	Rajahmundry	MUTYALA RAMBABU	1282	no	IND	Independent
Andhra Pradesh	Rajahmundry	CHEEKATLA VENKATESWARA RAO	1356	no	IND	Independent
Andhra Pradesh	Rajahmundry	MULLAPUDI SATYANARAYANA	11718	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Rajahmundry	KOLLABATHULA RICHMOND CAREY	5481	no	GaAP	Gareeb Aadmi Party
Andhra Pradesh	Rajahmundry	MARRE BABJI	6079	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Rajahmundry	PALLA VENKATA NAIDU(PRADEEP)	989	no	IND	Independent
Andhra Pradesh	Rajahmundry	ILLA GOVINDA RAJAN	1390	no	IND	Independent
Andhra Pradesh	Rajahmundry	SANGISETTI SRINIVASA RAO	1329	no	PPOI	Pyramid Party of India
Andhra Pradesh	Rajahmundry	BODDU VENKATARAMANA CHOWDARY	463139	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Rajahmundry	DURGESH KANDULA	21243	no	INC	Indian National Congress
Andhra Pradesh	Rajahmundry	MEDA SRINIVAS	991	no	RPC(S)	Rashtriya Praja Congress  (Secular)
Andhra Pradesh	Rajahmundry	None of the Above	7456	no	NOTA	None of the Above
Andhra Pradesh	Narsapuram	GOKARAJU GANGA RAJU	540306	yes	BJP	Bharatiya Janata Party
Andhra Pradesh	Narsapuram	None of the Above	8004	no	NOTA	None of the Above
Andhra Pradesh	Narsapuram	GITADAS DAS	23270	no	pjdl	Prem Janata Dal
Andhra Pradesh	Narsapuram	VANKA RAVINDRANATH	454955	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Narsapuram	MEDAPATI  VARAHALA REDDY	1250	no	IND	Independent
Andhra Pradesh	Narsapuram	VOLETI VENKATA LAKSHMANA RAO	1298	no	IND	Independent
Andhra Pradesh	Narsapuram	VANGALAPUDI YASHYAA	1281	no	IND	Independent
Andhra Pradesh	Narsapuram	BADAMPUDI SIVA	1605	no	IND	Independent
Andhra Pradesh	Narsapuram	NALLI RAJESH	6708	no	RP(K)	Republican Paksha (Khoripa)
Andhra Pradesh	Narsapuram	ADDANKI (SEKHAR BABU) DORABABU	1344	no	IND	Independent
Andhra Pradesh	Narsapuram	CHINTAPALLI KANAKA RAO	8489	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Narsapuram	SAROJA  POLASAPALLI	6840	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Narsapuram	SHIVAJI GOTTUMUKKALA	2286	no	IND	Independent
Andhra Pradesh	Narsapuram	S.V.P.K.H.G.KRISHNAM RAJU	4232	no	PPOI	Pyramid Party of India
Andhra Pradesh	Narsapuram	KANUMURU BAPI RAJU	27083	no	INC	Indian National Congress
Andhra Pradesh	Eluru	MAGANTI VENKATESWARA RAO (BABU)	623471	yes	TDP	Telugu Desam
Andhra Pradesh	Eluru	JULURI BHASKARA VEERA VENKATA ANIL KUMAR GUPTA	751	no	IND	Independent
Andhra Pradesh	Eluru	MENDEM SANTOSH KUMAR ( PEDABABULU)	3423	no	ILP(AP)	Indian Labour Party (Ambedkar Phule)
Andhra Pradesh	Eluru	K.V.V.SATYANARAYANA	2873	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Eluru	ASHOK YANTRAPATI	1212	no	MaSP	Mahajana Socialist Party
Andhra Pradesh	Eluru	GOVADA VIJAYA KUMARI	4483	no	pjdl	Prem Janata Dal
Andhra Pradesh	Eluru	MUSUNURI NAGESWARA RAO	11770	no	INC	Indian National Congress
Andhra Pradesh	Eluru	U VENKATESWARARAO (U.V)	3971	no	IND	Independent
Andhra Pradesh	Eluru	THOTA CHANDRA SEKHAR	521545	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Eluru	SAVANAPUDI NAGARAJU	2318	no	MCPI	Marxist Communist Party of India (United)
Andhra Pradesh	Eluru	K.VIJAYA LAKSHMI PANDIT	1320	no	IND	Independent
Andhra Pradesh	Eluru	GANGADHARA RAO NANNAPANENI	1302	no	IND	Independent
Andhra Pradesh	Eluru	NETALA RAMESH BABU	11670	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Eluru	C.V.SARADA DEVI	2720	no	IND	Independent
Andhra Pradesh	Eluru	U .V. RAMA RAO	1323	no	PPOI	Pyramid Party of India
Andhra Pradesh	Eluru	None of the Above	7544	no	NOTA	None of the Above
Andhra Pradesh	Machilipatnam	KONAKALLA NARAYANA RAO	587280	yes	TDP	Telugu Desam
Andhra Pradesh	Machilipatnam	None of the Above	8171	no	NOTA	None of the Above
Andhra Pradesh	Machilipatnam	KOLUSU PARTHA SARATHY	506223	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Machilipatnam	YUNDURI SUBRAMANYASWARA RAO (MANI)	1597	no	IND	Independent
Andhra Pradesh	Machilipatnam	JAMPANA SRINIVASA GOUD	931	no	IND	Independent
Andhra Pradesh	Machilipatnam	PRASADA RAO PEGGEM	4085	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Machilipatnam	SISTLA RAMESH	14111	no	INC	Indian National Congress
Andhra Pradesh	Machilipatnam	KAMMILI SRINIVAS	7692	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Machilipatnam	MOHAMMAD ISHAQ	1770	no	IND	Independent
Andhra Pradesh	Machilipatnam	PARASA SRINIVASA RAO	3399	no	IND	Independent
Andhra Pradesh	Machilipatnam	YARLAGADDA RAMA MOHAN RAO	1405	no	IND	Independent
Andhra Pradesh	Machilipatnam	VADAPALLI RAGHUNADH	4401	no	PPOI	Pyramid Party of India
Andhra Pradesh	Vijayawada	KESINENI SRINIVAS	592696	yes	TDP	Telugu Desam
Andhra Pradesh	Vijayawada	RAJA KUMARI GUNDAPANENI	2570	no	PPOI	Pyramid Party of India
Andhra Pradesh	Vijayawada	M.PHANI PRAKASH	2809	no	IND	Independent
Andhra Pradesh	Vijayawada	KAMBALA. SUBRAHMANYAM (SASTRY)	7538	no	WPOI	Welfare Party Of India
Andhra Pradesh	Vijayawada	CHIGURUPATI RAMALINGESWARA RAO	3579	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Vijayawada	TATINENI SRINIVASA RAO	2456	no	IND	Independent
Andhra Pradesh	Vijayawada	HAR MOHINDER SINGH SAHANI	3088	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Vijayawada	ISHWARYA LAKSHMI CHINNAM	5296	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Vijayawada	SUNKARA KRISHNA MURTHY	803	no	ARPS	Andhra Rastra Praja Samithi
Andhra Pradesh	Vijayawada	V.R.K.MURTHY.	748	no	IND	Independent
Andhra Pradesh	Vijayawada	YARAM KATAM RAJU	1402	no	IND	Independent
Andhra Pradesh	Vijayawada	P.V. KRISHNAIAH	423	no	IND	Independent
Andhra Pradesh	Vijayawada	KONERU RAMACHANDRA PRASAD	1096	no	IND	Independent
Andhra Pradesh	Vijayawada	CHATRAGADDA PRATAP	950	no	IND	Independent
Andhra Pradesh	Vijayawada	AHMED ANSARI SYED	504	no	IND	Independent
Andhra Pradesh	Vijayawada	ALTAF ALI MOHAMED	1167	no	IND	Independent
Andhra Pradesh	Vijayawada	KONERU RAJENDRA PRASAD	1014	no	IND	Independent
Andhra Pradesh	Vijayawada	DASARI VENKATA NAGESH	1873	no	IND	Independent
Andhra Pradesh	Vijayawada	SURAPANENI SARATBABU	1521	no	IND	Independent
Andhra Pradesh	Vijayawada	AVINASH DEVINENI	39751	no	INC	Indian National Congress
Andhra Pradesh	Vijayawada	KONERU RAJENDRA PRASAD	517834	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Vijayawada	BOLISETTY HARIBABU	667	no	IND	Independent
Andhra Pradesh	Vijayawada	None of the Above	5290	no	NOTA	None of the Above
Andhra Pradesh	Guntur	JAYADEV GALLA	618417	yes	TDP	Telugu Desam
Andhra Pradesh	Guntur	None of the Above	7596	no	NOTA	None of the Above
Andhra Pradesh	Guntur	ABDUL WAHEED SHAIK	46818	no	INC	Indian National Congress
Andhra Pradesh	Guntur	EMANI CHANDRA SEKHAR RAO	1455	no	NPT	Navodyam Party
Andhra Pradesh	Guntur	MALLELA VENKATA RAO	5608	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Guntur	ARUDALA VENKATESWARA RAO	908	no	IND	Independent
Andhra Pradesh	Guntur	MALLELA BABU RAO	4223	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Guntur	UDAIA KUMAR MUSUKU	2852	no	ICSP	Indian Christian Secular Party
Andhra Pradesh	Guntur	BALASHOWRY VALLABHANENI	549306	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Guntur	KATA VEERA VARA PRASAD	2344	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Guntur	BITRA VENKATA SIVANNARAYANA	1169	no	IND	Independent
Andhra Pradesh	Guntur	DR. KOUTHARAPU RAVINDRA	2594	no	GRIP	Great India Party
Andhra Pradesh	Guntur	ABBURI KOTESWARA RAO	1636	no	PPOI	Pyramid Party of India
Andhra Pradesh	Narasaraopet	SAMBASIVA RAO RAYAPATI	632464	yes	TDP	Telugu Desam
Andhra Pradesh	Narasaraopet	RAVU SUBRAHMANYAM	1025	no	IND	Independent
Andhra Pradesh	Narasaraopet	AYODHYA RAMIREDDY ALLA	597184	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Narasaraopet	JANGALA SINGARAIAH YADAV	973	no	IND	Independent
Andhra Pradesh	Narasaraopet	MEKALA HANUMANTHA RAO YADAV	4911	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Narasaraopet	YELURI SRILATHA	5142	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Narasaraopet	ROSAIAH SIVAPARAPU	1877	no	MCPI	Marxist Communist Party of India (United)
Andhra Pradesh	Narasaraopet	KANUMURI KOTESWARA RAO	1404	no	IND	Independent
Andhra Pradesh	Narasaraopet	PARUVURI RAMESH	1118	no	IND	Independent
Andhra Pradesh	Narasaraopet	AKKI REDDY BHIMANADULA	7032	no	PPOI	Pyramid Party of India
Andhra Pradesh	Narasaraopet	KONDAPALLI VENKATESWARLU	22943	no	INC	Indian National Congress
Andhra Pradesh	Narasaraopet	None of the Above	5985	no	NOTA	None of the Above
Andhra Pradesh	Bapatla	MALYADRI SRIRAM	578145	yes	TDP	Telugu Desam
Andhra Pradesh	Bapatla	None of the Above	6146	no	NOTA	None of the Above
Andhra Pradesh	Bapatla	GADDE THYAGARAJU	749	no	IND	Independent
Andhra Pradesh	Bapatla	RATNABABU MAMIDI	1206	no	IND	Independent
Andhra Pradesh	Bapatla	R.D.WILSON	3657	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Bapatla	BABURAO GOLLA	823	no	IND	Independent
Andhra Pradesh	Bapatla	GOGULAMUDI NARASIMHA RAO	720	no	IND	Independent
Andhra Pradesh	Bapatla	BANDELA NAGESWARA RAO	668	no	IND	Independent
Andhra Pradesh	Bapatla	DWARAPALLI PHILIP	8113	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Bapatla	YALLAMATI RAMESH	8414	no	PPOI	Pyramid Party of India
Andhra Pradesh	Bapatla	VARIKUTI AMRUTHAPANI	545391	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Bapatla	PANABAKA LAKSHMI	23072	no	INC	Indian National Congress
Andhra Pradesh	Bapatla	NAKKALA VEERANJANEYULU	615	no	IND	Independent
Andhra Pradesh	Bapatla	PERLI PRASANNA DEEVENA KUMAR (PRASANNA)	2967	no	ILP(AP)	Indian Labour Party (Ambedkar Phule)
Andhra Pradesh	Bapatla	EDA CHENNAIAH	3948	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Ongole	Y.V.SUBBA REDDY	589960	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Ongole	KRISHNA RAO VEMULA	5863	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Ongole	BELLAMKONDA VENKATRAO	1001	no	IND	Independent
Andhra Pradesh	Ongole	DARISI PAVAN KUMAR	13357	no	INC	Indian National Congress
Andhra Pradesh	Ongole	MAGUNTA SREENIVASULU REDDY	574302	no	TDP	Telugu Desam
Andhra Pradesh	Ongole	DAMA MOHAN RAO	492	no	IND	Independent
Andhra Pradesh	Ongole	KAVURI VENUBABU NAIDU	3207	no	IND	Independent
Andhra Pradesh	Ongole	S.S.N.RAJA YADAV	4299	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Ongole	PIDAPARTHI MADHUSUDHANA REDDY	624	no	IND	Independent
Andhra Pradesh	Ongole	CHAPALA VENKATA RAO	880	no	IND	Independent
Andhra Pradesh	Ongole	B. BALAJEE RAO	657	no	IND	Independent
Andhra Pradesh	Ongole	RADHAKRISHNA SOMISETTY	1936	no	IND	Independent
Andhra Pradesh	Ongole	PAMMI VENKA REDDY	1387	no	PPOI	Pyramid Party of India
Andhra Pradesh	Ongole	GOLLAPALLI KISHORE KUMAR REDDY	3724	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Ongole	KIRAN DASARANNA MATTEGUNTA	755	no	IND	Independent
Andhra Pradesh	Ongole	None of the Above	5781	no	NOTA	None of the Above
Andhra Pradesh	Nandyal	S.P.Y REDDY	622411	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Nandyal	None of the Above	7115	no	NOTA	None of the Above
Andhra Pradesh	Nandyal	P.NARENDRA DEV	936	no	IND	Independent
Andhra Pradesh	Nandyal	NOSSAM MALLIKARJUNA REDDY	7189	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Nandyal	M RAMANA REDDY	5400	no	IND	Independent
Andhra Pradesh	Nandyal	VANGALA SREENIVASA REDDY	2437	no	IND	Independent
Andhra Pradesh	Nandyal	OBULESU THUDUM	11784	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Nandyal	K.P.KAMBAGIRI SWAMI	2708	no	IND	Independent
Andhra Pradesh	Nandyal	I.V PAKKIRA REDDY	1036	no	IND	Independent
Andhra Pradesh	Nandyal	ULLAGI DAVID JAYA KUMAR	2499	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Nandyal	P.V.N. REDDY	5598	no	AIMIM	All India Majlis-E-Ittehadul Muslimeen
Andhra Pradesh	Nandyal	B.Y.RAMAIAH	16378	no	INC	Indian National Congress
Andhra Pradesh	Nandyal	PIDISELA VIJAY KUMAR	1325	no	IND	Independent
Andhra Pradesh	Nandyal	GADDALA BALASWAMY	1495	no	IND	Independent
Andhra Pradesh	Nandyal	N.MD.FAROOK	516645	no	TDP	Telugu Desam
Andhra Pradesh	Kurnool	BUTTA RENUKA	472782	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Kurnool	R.J.MALLIKAL	5746	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Kurnool	VISSA KIRAN KUMAR	6522	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Kurnool	B.T. NAIDU	428651	no	TDP	Telugu Desam
Andhra Pradesh	Kurnool	KOTLA JAYASURYAPRAKASH REDDY	116603	no	INC	Indian National Congress
Andhra Pradesh	Kurnool	NAGELLA ELIA	1990	no	WPOI	Welfare Party Of India
Andhra Pradesh	Kurnool	G.JAKEER HUSSAIN	3600	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Kurnool	KONDIPARTHI .VENKAT  PRASAD	1614	no	RPI	Republican Party of India
Andhra Pradesh	Kurnool	D YUGANDHAR	2820	no	IND	Independent
Andhra Pradesh	Kurnool	KOTHURU.SATYANARAYANA GUPTA	2021	no	IND	Independent
Andhra Pradesh	Kurnool	D CHAND BASHA	2605	no	IND	Independent
Andhra Pradesh	Kurnool	None of the Above	9385	no	NOTA	None of the Above
Andhra Pradesh	Kurnool	NOOR AHMED MADANI	11393	no	SDP	Socialistic Democratic Party
Andhra Pradesh	Anantapur	J.C. DIVAKAR REDDI	606509	yes	TDP	Telugu Desam
Andhra Pradesh	Anantapur	None of the Above	8798	no	NOTA	None of the Above
Andhra Pradesh	Anantapur	KASANI NAGARAJU	6201	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Anantapur	SHAIK. MOHAMMAD SHAFI.	3018	no	IND	Independent
Andhra Pradesh	Anantapur	T.KULDEEP CHOWDARY	6517	no	STR	Samaikya Telugu Rajyam
Andhra Pradesh	Anantapur	ANIL CHOWDARY.  P	16659	no	INC	Indian National Congress
Andhra Pradesh	Anantapur	PAVAN KUMAR	2070	no	PPOI	Pyramid Party of India
Andhra Pradesh	Anantapur	B. SRINIVASULU	1589	no	IND	Independent
Andhra Pradesh	Anantapur	G. NAGARAJU	1086	no	IND	Independent
Andhra Pradesh	Anantapur	BOYA ANIL KUMAR	2779	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Anantapur	ANANTHA VENKATARAMIREDDY	545240	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Anantapur	D. SOMANATH	2263	no	IND	Independent
Andhra Pradesh	Anantapur	BUZIREDDY VENUGOPAL REDDY	1381	no	ICSP	Indian Christian Secular Party
Andhra Pradesh	Anantapur	G. LALITHA	944	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Andhra Pradesh	Hindupur	KRISTAPPA NIMMALA	604291	yes	TDP	Telugu Desam
Andhra Pradesh	Hindupur	D. SOMANATH	2032	no	IND	Independent
Andhra Pradesh	Hindupur	ASADULLAH K.M	1099	no	IND	Independent
Andhra Pradesh	Hindupur	MADDIPATLA SUDHAKAR NAIDU	2332	no	IND	Independent
Andhra Pradesh	Hindupur	DUDDUKUNTA SREEDHAR REDDY	506966	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Hindupur	G.C VENKATARAMUDU	36452	no	INC	Indian National Congress
Andhra Pradesh	Hindupur	K.PEDDA REDDI	1152	no	IND	Independent
Andhra Pradesh	Hindupur	ANAND	3101	no	PPOI	Pyramid Party of India
Andhra Pradesh	Hindupur	APPIREDDYGARI KONDA REDDY	1257	no	IND	Independent
Andhra Pradesh	Hindupur	CHINTA SARATH KUMAR REDDY	1275	no	IND	Independent
Andhra Pradesh	Hindupur	K.MOHAMMAD SHAMEER BASHA	4590	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Hindupur	AMUDALA SUBRAMANYAM	4521	no	RPS	Rayalaseema Parirakshana Samithi
Andhra Pradesh	Hindupur	None of the Above	8189	no	NOTA	None of the Above
Andhra Pradesh	Kadapa	Y.S. AVINASH REDDY	671983	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Kadapa	None of the Above	6058	no	NOTA	None of the Above
Andhra Pradesh	Kadapa	DR. SHAIK GHOUSE PEER	3219	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Kadapa	YELLIPALEM RAMESH.REDDY	3809	no	JD(U)	Janata Dal  (United)
Andhra Pradesh	Kadapa	POREDDY PRABHAKARA REDDY	1217	no	IND	Independent
Andhra Pradesh	Kadapa	G.V.A.K. REDDY	888	no	JD(S)	Janata Dal  (Secular)
Andhra Pradesh	Kadapa	RACHINENI. VENUGOPAL	1034	no	RPC(S)	Rashtriya Praja Congress  (Secular)
Andhra Pradesh	Kadapa	MALIKIREDDY. HANUMANTHA REDDY	5515	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Kadapa	VARADI SOBHARANI	2240	no	DABAP	Dalita Bahujana Party
Andhra Pradesh	Kadapa	B. PULLAIAH	2565	no	ANC	Ambedkar National Congress
Andhra Pradesh	Kadapa	PUTHA. LAKSHMI REDDY	933	no	AIFB	All India Forward Bloc
Andhra Pradesh	Kadapa	SAJID HUSSAIN	3401	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Kadapa	GAJJALA.RAMA SUBBA REDDY	1821	no	PPOI	Pyramid Party of India
Andhra Pradesh	Kadapa	AJAYA KUMAR VEENA	14319	no	INC	Indian National Congress
Andhra Pradesh	Kadapa	SRINIVASA REDDY REDDEPPAGARI	481660	no	TDP	Telugu Desam
Andhra Pradesh	Nellore	MEKAPATI RAJAMOHAN REDDY	576396	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Nellore	PATTAPU RAVI	3299	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Nellore	ADALA PRABHAKARA REDDY	562918	no	TDP	Telugu Desam
Andhra Pradesh	Nellore	KOLATI GOPINATH	999	no	PPOI	Pyramid Party of India
Andhra Pradesh	Nellore	MEDA MALLA REDDY	820	no	IND	Independent
Andhra Pradesh	Nellore	CHEMIKALA TIRUPATI	1424	no	ARPS	Andhra Rastra Praja Samithi
Andhra Pradesh	Nellore	MALLI VENKATESWARLU	686	no	IND	Independent
Andhra Pradesh	Nellore	PANDHITI SUBBAIAH	736	no	IND	Independent
Andhra Pradesh	Nellore	NAVEEN SUKPALLI	626	no	IND	Independent
Andhra Pradesh	Nellore	SYED HANEEF	5578	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Nellore	MALYADRI RAVULAKOLLU	875	no	IND	Independent
Andhra Pradesh	Nellore	ANANDA RAO SOMPALLI	760	no	IND	Independent
Andhra Pradesh	Nellore	NARAYANA REDDY VAKATI	22870	no	INC	Indian National Congress
Andhra Pradesh	Nellore	None of the Above	5549	no	NOTA	None of the Above
Andhra Pradesh	Nellore	CHANDRA SEKHAR YADAV	4112	no	RDHP	Rajyadhikara Party
Andhra Pradesh	Tirupati	VARAPRASAD RAO VELAGAPALLI	580376	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Tirupati	None of the Above	8690	no	NOTA	None of the Above
Andhra Pradesh	Tirupati	DR. U RAMACHANDRA, M.B.B.S	6036	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Tirupati	K.S. MUNIRATHNAM	1696	no	IND	Independent
Andhra Pradesh	Tirupati	KALAVAPALLI SAHADEVAIAH	2510	no	IND	Independent
Andhra Pradesh	Tirupati	KARUMANCHI JAYARAM	542951	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Tirupati	THYDULA SUBBARAMAIAH	1403	no	IND	Independent
Andhra Pradesh	Tirupati	NUNE VENGAIAH	1829	no	IND	Independent
Andhra Pradesh	Tirupati	NEERUGUTTA NAGESWARA	7280	no	AAAP	Aam Aadmi Party
Andhra Pradesh	Tirupati	SONGA RATNAMMA	1645	no	IND	Independent
Andhra Pradesh	Tirupati	M SOLMON	9816	no	ARPS	Andhra Rastra Praja Samithi
Andhra Pradesh	Tirupati	KATTAMANCHI PRABHAKAR	1455	no	IND	Independent
Andhra Pradesh	Tirupati	DEGALA SURYA NARAYANA	2876	no	PPOI	Pyramid Party of India
Andhra Pradesh	Tirupati	CHINTA MOHAN	33333	no	INC	Indian National Congress
Andhra Pradesh	Tirupati	KOTHAPALLI SUBRAMANYAM	11168	no	CPM	Communist Party of India  (Marxist)
Andhra Pradesh	Rajampet	P.V.MIDHUN REDDY	601752	yes	YSRCP	Yuvajana Sramika Rythu Congress Party
Andhra Pradesh	Rajampet	G.MUJEEB HUSSAIN	59777	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Rajampet	ANNAYYAGARI SAI PRATHAP	29332	no	INC	Indian National Congress
Andhra Pradesh	Rajampet	S NARENDRA BABU	15086	no	MaSP	Mahajana Socialist Party
Andhra Pradesh	Rajampet	N.  DEVA	3896	no	HJP	Hindustan Janta Party
Andhra Pradesh	Rajampet	V PATTABHI	3549	no	IND	Independent
Andhra Pradesh	Rajampet	VENKATA SUBBAIAH NALLUGARI	8189	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Rajampet	SHAIK JEELANI SAHEB	2630	no	IND	Independent
Andhra Pradesh	Rajampet	D.  PURANDESWARI	426990	no	BJP	Bharatiya Janata Party
Andhra Pradesh	Rajampet	None of the Above	7116	no	NOTA	None of the Above
Andhra Pradesh	Chittoor	NARAMALLI SIVAPRASAD	594862	yes	TDP	Telugu Desam
Andhra Pradesh	Chittoor	None of the Above	6996	no	NOTA	None of the Above
Andhra Pradesh	Chittoor	JITHENDRA KUMAR.DHANDU	15897	no	BSP	Bahujan Samaj Party
Andhra Pradesh	Chittoor	UDAYA C.S	3803	no	PPOI	Pyramid Party of India
Andhra Pradesh	Chittoor	DR.B.RAJAGOPAL	16572	no	INC	Indian National Congress
Andhra Pradesh	Chittoor	S. ANJAPPA	4321	no	IND	Independent
Andhra Pradesh	Chittoor	P. PUSHPA RAJ	5740	no	JaSPa	Jai Samaikyandhra Party
Andhra Pradesh	Chittoor	G.SAMANYAKIRAN	550724	no	YSRCP	Yuvajana Sramika Rythu Congress Party
Arunachal Pradesh	ARUNACHAL WEST	KIREN RIJIJU	169367	yes	BJP	Bharatiya Janata Party
Arunachal Pradesh	ARUNACHAL WEST	None of the Above	1816	no	NOTA	None of the Above
Arunachal Pradesh	ARUNACHAL WEST	TAKAM SANJOY	127629	no	INC	Indian National Congress
Arunachal Pradesh	ARUNACHAL WEST	JALLEY SONAM	14664	no	PPA	People's Party of Arunachal
Arunachal Pradesh	ARUNACHAL WEST	GICHO KABAK	6065	no	NCP	Nationalist Congress Party
Arunachal Pradesh	ARUNACHAL WEST	GUMJUM HAIDER	9135	no	AITC	All India Trinamool Congress
Arunachal Pradesh	ARUNACHAL WEST	HABUNG PAYENG	3647	no	AAAP	Aam Aadmi Party
Arunachal Pradesh	ARUNACHAL WEST	TABA TAKU	980	no	LB	Lok Bharati
Arunachal Pradesh	ARUNACHAL WEST	SUBU KECHI	2362	no	IND	Independent
Arunachal Pradesh	ARUNACHAL EAST	NINONG ERING	118455	yes	INC	Indian National Congress
Arunachal Pradesh	ARUNACHAL EAST	TAPIR GAO	105977	no	BJP	Bharatiya Janata Party
Arunachal Pradesh	ARUNACHAL EAST	WANGMAN LOWANGCHA	32354	no	PPA	People's Party of Arunachal
Arunachal Pradesh	ARUNACHAL EAST	None of the Above	4505	no	NOTA	None of the Above
Assam	Karimganj	RADHESHYAM BISWAS	362866	yes	AIUDF	All India United Democratic Front
Assam	Karimganj	KRISHNA DAS	260772	no	BJP	Bharatiya Janata Party
Assam	Karimganj	PROBASH CH. SARKAR	3114	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Assam	Karimganj	RINKU MALAKAR	2275	no	SP	Samajwadi Party
Assam	Karimganj	SABUJ DAS	1974	no	IND	Independent
Assam	Karimganj	SAJALENDU MALAKAR	2465	no	IND	Independent
Assam	Karimganj	PINTU MALAKAR	1664	no	IND	Independent
Assam	Karimganj	LALIT MOHON SUKLABAIDYA	226562	no	INC	Indian National Congress
Assam	Karimganj	SUJIT MALLICK	3437	no	IND	Independent
Assam	Karimganj	TARUN KUMAR DAS	3658	no	AAAP	Aam Aadmi Party
Assam	Karimganj	SANJIT KUMAR DAS	1880	no	IND	Independent
Assam	Karimganj	GOUTAM MALAKAR	2447	no	IND	Independent
Assam	Karimganj	MAHANANDA DAS	1592	no	IND	Independent
Assam	Karimganj	RABINDRA NATH CHOUDHURY	5556	no	AGP	Asom Gana Parishad
Assam	Karimganj	PRADIP SUKLABAIDYA	2392	no	IND	Independent
Assam	Karimganj	None of the Above	4266	no	NOTA	None of the Above
Assam	Silchar	SUSHMITA DEV	336451	yes	INC	Indian National Congress
Assam	Silchar	None of the Above	4310	no	NOTA	None of the Above
Assam	Silchar	JAYANTA MALLICK	1605	no	IND	Independent
Assam	Silchar	NIRUPAM MANDAL	2462	no	IND	Independent
Assam	Silchar	KUTUB AHMED MAZUMDER	85530	no	AIUDF	All India United Democratic Front
Assam	Silchar	KABINDRA PURKAYASTHA	301210	no	BJP	Bharatiya Janata Party
Assam	Silchar	SUKAMAL DAS	6221	no	IND	Independent
Assam	Silchar	BIJOY KRISHNA NATH	7679	no	AGP	Asom Gana Parishad
Assam	Silchar	MADHU SUDHAN DAS	3151	no	IND	Independent
Assam	Silchar	ABDUL MANNAN BARBHUIYA	3118	no	AAAP	Aam Aadmi Party
Assam	Silchar	RASHID AHMED LASKAR	3347	no	SP	Samajwadi Party
Assam	Silchar	KAMAL DAS	2170	no	IND	Independent
Assam	Silchar	NILU MANDOL	1922	no	SHS	Shivsena
Assam	Silchar	SHUVADIP DATTA	8577	no	IND	Independent
Assam	Silchar	WAZID REJA OSMANI	8282	no	AITC	All India Trinamool Congress
Assam	Silchar	UPENDRA CHANDRA DAS	2740	no	IND	Independent
Assam	Silchar	REJAMOND ALI BARBHUIYA	12460	no	CPM	Communist Party of India  (Marxist)
Assam	Silchar	ARUNANGSHU BHATTACHARJEE	8595	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Assam	Autonomous District	BIREN SINGH ENGTI	213152	yes	INC	Indian National Congress
Assam	Autonomous District	CHOMANG KRO	108299	no	IND	Independent
Assam	Autonomous District	PRATIMA ENGHEEPI	11762	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Assam	Autonomous District	JOY RAM ENGLENG	189057	no	BJP	Bharatiya Janata Party
Assam	Autonomous District	ASHWENG J. RENGMA	9263	no	JMM	Jharkhand Mukti Morcha
Assam	Autonomous District	None of the Above	11747	no	NOTA	None of the Above
Assam	Dhubri	BADRUDDIN AJMAL	592569	yes	AIUDF	All India United Democratic Front
Assam	Dhubri	SURAT JAMAN MONDAL	5935	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Assam	Dhubri	AZMAL HUSSAIN	4468	no	AAAP	Aam Aadmi Party
Assam	Dhubri	ANOWAR HUSSAIN	9897	no	AGP	Asom Gana Parishad
Assam	Dhubri	DR. DEBAMOY SANYAL	298985	no	BJP	Bharatiya Janata Party
Assam	Dhubri	WAZED ALI CHOUDHURY	362839	no	INC	Indian National Congress
Assam	Dhubri	None of the Above	5811	no	NOTA	None of the Above
Assam	Dhubri	ALI HUSSAIN	23816	no	IND	Independent
Assam	Dhubri	MOHAR UDDIN MONDAL	40208	no	IND	Independent
Assam	Dhubri	MOHENDRA CHANDRA ROY	4607	no	IND	Independent
Assam	Dhubri	YOUSUB ALI AHMED	3277	no	SP	Samajwadi Party
Assam	Dhubri	KASIR UDDIN SK.	1455	no	IUML	Indian Union Muslim League
Assam	Dhubri	ZESMINA KHATUN	7874	no	AITC	All India Trinamool Congress
Assam	Dhubri	KASHEM ALI AKONDA	2785	no	IND	Independent
Assam	Dhubri	RAM EKBAL SHAHANI	2375	no	IND	Independent
Assam	Dhubri	MONIRUL HUSSAIN	2723	no	IND	Independent
Assam	Kokrajhar	NABA KUMAR SARANIA (HIRA)	634428	yes	IND	Independent
Assam	Kokrajhar	None of the Above	18183	no	NOTA	None of the Above
Assam	Kokrajhar	SANSUMA KHUNGGUR BWISWMUTHIARY	11239	no	IND	Independent
Assam	Kokrajhar	SABDA RAM RABHA	20074	no	IND	Independent
Assam	Kokrajhar	CHANDAN BRAHMA	243759	no	BOPF	Bodoland Peoples Front
Assam	Kokrajhar	URKHAO GWRA BRAHMA	278649	no	IND	Independent
Assam	Kokrajhar	RANJIT SHEKHAR MOOSHAHARY	17537	no	AITC	All India Trinamool Congress
Assam	Barpeta	SIRAJ UDDIN AJMAL	394702	yes	AIUDF	All India United Democratic Front
Assam	Barpeta	KHURSHIDA ANOWARA BEGUM	5710	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Assam	Barpeta	ABUL AWAL	3099	no	SP	Samajwadi Party
Assam	Barpeta	UDDHAB BARMAN	27575	no	CPM	Communist Party of India  (Marxist)
Assam	Barpeta	DR. PERVEZ ALI AHMED	10944	no	AITC	All India Trinamool Congress
Assam	Barpeta	None of the Above	4785	no	NOTA	None of the Above
Assam	Barpeta	PHANI BHUSAN CHOUDHURY	73733	no	AGP	Asom Gana Parishad
Assam	Barpeta	SAMSUL HOQUE	24444	no	IND	Independent
Assam	Barpeta	KAMAL UDDIN	3433	no	IND	Independent
Assam	Barpeta	CHANDRA MOHAN PATOWARY	352361	no	BJP	Bharatiya Janata Party
Assam	Barpeta	BHADRESWAR BARMAN	1695	no	IND	Independent
Assam	Barpeta	ISMAIL HUSSAIN	277802	no	INC	Indian National Congress
Assam	Barpeta	GAUTAM KUMAR SARMA	1859	no	IND	Independent
Assam	Barpeta	ENTAZ ALI	1937	no	LB	Lok Bharati
Assam	Barpeta	BARUN KARMAKAR	1649	no	IND	Independent
Assam	Barpeta	DILIR KHAN	20135	no	IND	Independent
Assam	Gauhati	BIJOYA CHAKRAVARTY	764985	yes	BJP	Bharatiya Janata Party
Assam	Gauhati	ANIL BHAGAWATI	5782	no	IND	Independent
Assam	Gauhati	DR. FARUK AAHAMMED BHUYAN	3540	no	IND	Independent
Assam	Gauhati	GOPI BARUAH	2411	no	LB	Lok Bharati
Assam	Gauhati	PRANJAL BORDOLOY	4809	no	AAAP	Aam Aadmi Party
Assam	Gauhati	KHARGESWAR DAS	2532	no	IND	Independent
Assam	Gauhati	None of the Above	6720	no	NOTA	None of the Above
Assam	Gauhati	GOPI NATH DAS	137254	no	AIUDF	All India United Democratic Front
Assam	Gauhati	MD. ZAKIR HUSSAIN	2275	no	IND	Independent
Assam	Gauhati	DHIRAJ MEDHI	2845	no	IND	Independent
Assam	Gauhati	PADMESHWAR PHUKAN	2629	no	IND	Independent
Assam	Gauhati	AJAD ALI	2385	no	IND	Independent
Assam	Gauhati	BIRENDRA PRASAD BAISHYA	86546	no	AGP	Asom Gana Parishad
Assam	Gauhati	MANASH BORAH	449201	no	INC	Indian National Congress
Assam	Gauhati	PRADIP KALITA	7345	no	IND	Independent
Assam	Gauhati	KAZI NEKIB AHMED	6636	no	rdsc	Regional Democratic Secular Congress
Assam	Gauhati	BANDANA BARMAN BARUAH	3240	no	SP	Samajwadi Party
Assam	Gauhati	BIJU PHUKAN	8162	no	AITC	All India Trinamool Congress
Assam	Gauhati	BENEDICT ALOK ARENG	12432	no	IND	Independent
Assam	Mangaldoi	RAMEN DEKA	486357	yes	BJP	Bharatiya Janata Party
Assam	Mangaldoi	AROON BAROOA	4164	no	rdsc	Regional Democratic Secular Congress
Assam	Mangaldoi	SAHNUR ALI	5531	no	IND	Independent
Assam	Mangaldoi	SAHADEV DAS	86347	no	BOPF	Bodoland Peoples Front
Assam	Mangaldoi	RATUL KUMAR CHOUDHURY	3658	no	SP	Samajwadi Party
Assam	Mangaldoi	NAMRATA SARMA	3424	no	AAAP	Aam Aadmi Party
Assam	Mangaldoi	DEBA KUMAR BARMAN	8341	no	AITC	All India Trinamool Congress
Assam	Mangaldoi	PARESH BAISHYA	74710	no	AIUDF	All India United Democratic Front
Assam	Mangaldoi	SHACHINDRA NATH BRAHMA	12443	no	IND	Independent
Assam	Mangaldoi	SWARNALATA CHALIHA	7600	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Assam	Mangaldoi	KIRIP CHALIHA	463473	no	INC	Indian National Congress
Assam	Mangaldoi	MADHAB RAJBANGSHI	66467	no	AGP	Asom Gana Parishad
Assam	Mangaldoi	None of the Above	10722	no	NOTA	None of the Above
Assam	Tezpur	RAM PRASAD SARMAH	446511	yes	BJP	Bharatiya Janata Party
Assam	Tezpur	None of the Above	16667	no	NOTA	None of the Above
Assam	Tezpur	ELIAS KUJUR	5207	no	IND	Independent
Assam	Tezpur	MONI KUMAR SUBBA	62730	no	IND	Independent
Assam	Tezpur	RAJEN SAIKIA	4246	no	AIFB	All India Forward Bloc
Assam	Tezpur	GOPI CHAND SHAHABADI	8712	no	AITC	All India Trinamool Congress
Assam	Tezpur	JOSEPH TOPPO	40489	no	AGP	Asom Gana Parishad
Assam	Tezpur	KHEMRAJ CHETRY	24910	no	CPM	Communist Party of India  (Marxist)
Assam	Tezpur	LAKSHIKANTA KURMI	10725	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Assam	Tezpur	BHUPEN KUMAR BORAH	360491	no	INC	Indian National Congress
Assam	Nowgong	RAJEN GOHAIN	494146	yes	BJP	Bharatiya Janata Party
Assam	Nowgong	DR. ADITYA LANGTHASA	314012	no	AIUDF	All India United Democratic Front
Assam	Nowgong	JONJONALI BARUAH	350587	no	INC	Indian National Congress
Assam	Nowgong	MRIDULA BARKAKOTY	35142	no	AGP	Asom Gana Parishad
Assam	Nowgong	SELIMA SULTANA	6462	no	IND	Independent
Assam	Nowgong	DIPAK KUMAR BORAH	6835	no	AITC	All India Trinamool Congress
Assam	Nowgong	RAFIQUL ISLAM	2783	no	AIFB	All India Forward Bloc
Assam	Nowgong	None of the Above	8782	no	NOTA	None of the Above
Assam	Nowgong	FARUK HAZARIKA	11325	no	IND	Independent
Assam	Kaliabor	GOURAV GOGOI	443315	yes	INC	Indian National Congress
Assam	Kaliabor	BINOD GOGOI	9583	no	IND	Independent
Assam	Kaliabor	JITEN GOGOI	8271	no	IND	Independent
Assam	Kaliabor	ARUP KUMAR MAHANTA	5433	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Assam	Kaliabor	MRINAL KUMAR SAIKIA	349441	no	BJP	Bharatiya Janata Party
Assam	Kaliabor	JINTI GOGOI	2584	no	AAAP	Aam Aadmi Party
Assam	Kaliabor	SAHABA AHMED	2411	no	IND	Independent
Assam	Kaliabor	MITHUAL KUMAR	2966	no	AIFB	All India Forward Bloc
Assam	Kaliabor	MONTU SAIKIA	9133	no	IND	Independent
Assam	Kaliabor	SAMSUL ALAM	6538	no	AITC	All India Trinamool Congress
Assam	Kaliabor	RAHMAT ULLAH	4985	no	IND	Independent
Assam	Kaliabor	BIJOY KUMAR TIRU	231295	no	AIUDF	All India United Democratic Front
Assam	Kaliabor	DR. ARUN KUMAR SARMA	78132	no	AGP	Asom Gana Parishad
Assam	Kaliabor	None of the Above	12403	no	NOTA	None of the Above
Assam	Jorhat	KAMAKHYA PRASAD TASA	456420	yes	BJP	Bharatiya Janata Party
Assam	Jorhat	None of the Above	14648	no	NOTA	None of the Above
Assam	Jorhat	NASIR AHMED	7331	no	AIUDF	All India United Democratic Front
Assam	Jorhat	RAJ KUMAR DOWARAH	4737	no	IND	Independent
Assam	Jorhat	GUNIN BASUMATARI	5754	no	SP	Samajwadi Party
Assam	Jorhat	DRUPAD BORGOHAIN	28930	no	CPI	Communist Party of India
Assam	Jorhat	PRODIP HAZARIKA	46626	no	AGP	Asom Gana Parishad
Assam	Jorhat	BIJOY KRISHNA HANDIQUE	354000	no	INC	Indian National Congress
Assam	Jorhat	MANOROM GOGOI	3659	no	AAAP	Aam Aadmi Party
Assam	Jorhat	HOREN BORGOHAIN	3472	no	AIFB	All India Forward Bloc
Assam	Jorhat	RIBULAYA GOGOI	5759	no	AITC	All India Trinamool Congress
Assam	Dibrugarh	RAMESWAR TELI	494364	yes	BJP	Bharatiya Janata Party
Assam	Dibrugarh	CHENI RAM MORAN	7112	no	AIFB	All India Forward Bloc
Assam	Dibrugarh	NAVAJYOTI KALITA	8582	no	AITC	All India Trinamool Congress
Assam	Dibrugarh	ANUP PHUKON	45710	no	AGP	Asom Gana Parishad
Assam	Dibrugarh	SUBHAS SEN	9374	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Assam	Dibrugarh	PABAN SINGH GHATOWAR	309017	no	INC	Indian National Congress
Assam	Dibrugarh	None of the Above	16809	no	NOTA	None of the Above
Assam	Lakhimpur	SARBANANDA SONOWAL	612543	yes	BJP	Bharatiya Janata Party
Assam	Lakhimpur	None of the Above	11204	no	NOTA	None of the Above
Assam	Lakhimpur	KHAIRUL ISLAM	4119	no	IND	Independent
Assam	Lakhimpur	HARI PRASAD DIHINGIA	81753	no	AGP	Asom Gana Parishad
Assam	Lakhimpur	DR. HIRAMONI DEKA SONOWAL	2893	no	AAAP	Aam Aadmi Party
Assam	Lakhimpur	KESHAB GOGOI	2752	no	IND	Independent
Assam	Lakhimpur	HRISHIKESH BARUAH	6013	no	IND	Independent
Assam	Lakhimpur	DHEBOR GOHAIN BORUAH	9095	no	AITC	All India Trinamool Congress
Assam	Lakhimpur	RANEE NARAH	320405	no	INC	Indian National Congress
Assam	Lakhimpur	TANISH ORANG	4721	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Assam	Lakhimpur	RANJIT SINGH GORH	6335	no	IND	Independent
Assam	Lakhimpur	DAVID HORO	5903	no	IND	Independent
Assam	Lakhimpur	HEM KANTA MIRI	6896	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Assam	Lakhimpur	MAMUN IMDADUL HAQUE CHAWDHURY	37343	no	AIUDF	All India United Democratic Front
Bihar	Valmiki Nagar	SATISH CHANDRA DUBEY	364011	yes	BJP	Bharatiya Janata Party
Bihar	Valmiki Nagar	None of the Above	15515	no	NOTA	None of the Above
Bihar	Valmiki Nagar	PROF. AMARESH PRASAD	27603	no	JMM	Jharkhand Mukti Morcha
Bihar	Valmiki Nagar	NANDLAL RAM	10781	no	IND	Independent
Bihar	Valmiki Nagar	DHARMESH PRASAD VARMA	6008	no	AAAP	Aam Aadmi Party
Bihar	Valmiki Nagar	AMAR SAHANI	35888	no	BED	Bharatiya Ekta Dal
Bihar	Valmiki Nagar	MANOHAR MANOJ	11509	no	IND	Independent
Bihar	Valmiki Nagar	DILIP VARMA	58817	no	IND	Independent
Bihar	Valmiki Nagar	VIRENDRA PRASAD GUPTA	12581	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Valmiki Nagar	DACTAR MAHTO	5519	no	JPS	Janvadi Party(Socialist)
Bihar	Valmiki Nagar	SHAILESH KUMAR DIWAKAR	11668	no	BSP	Bahujan Samaj Party
Bihar	Valmiki Nagar	DRAVYAN URAON	8013	no	IND	Independent
Bihar	Valmiki Nagar	OM SHANTI BABA	4371	no	IND	Independent
Bihar	Valmiki Nagar	BAIDYA NATH PRASAD MAHATO	81612	no	JD(U)	Janata Dal  (United)
Bihar	Valmiki Nagar	PURNMASI RAM	246218	no	INC	Indian National Congress
Bihar	Paschim Champaran	DR. SANJAY JAISWAL	371232	yes	BJP	Bharatiya Janata Party
Bihar	Paschim Champaran	ANAND PRASAD	5818	no	IND	Independent
Bihar	Paschim Champaran	PRABHU RAJ NARAIN RAO	17157	no	CPM	Communist Party of India  (Marxist)
Bihar	Paschim Champaran	ANAND KAUSHAL SINGH	4297	no	AAAP	Aam Aadmi Party
Bihar	Paschim Champaran	PRAKASH JHA	260978	no	JD(U)	Janata Dal  (United)
Bihar	Paschim Champaran	SUNIL KUMAR RAO	4940	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Paschim Champaran	RAGHUNATH JHA	121800	no	RJD	Rashtriya Janata Dal
Bihar	Paschim Champaran	SYED SHAMIM AKHTAR	12415	no	BSP	Bahujan Samaj Party
Bihar	Paschim Champaran	RATAN KUMAR SARKAR	6474	no	IND	Independent
Bihar	Paschim Champaran	MOHAN KUMAR	11260	no	IND	Independent
Bihar	Paschim Champaran	SYED IRSHAD AKHTAR	9123	no	IND	Independent
Bihar	Paschim Champaran	NAFISH AHAMAD	10502	no	IND	Independent
Bihar	Paschim Champaran	None of the Above	18804	no	NOTA	None of the Above
Bihar	Purvi Champaran	RADHA MOHAN SINGH	400452	yes	BJP	Bharatiya Janata Party
Bihar	Purvi Champaran	AMIT KUMAR CHOUBEY	7512	no	AAAP	Aam Aadmi Party
Bihar	Purvi Champaran	SHAMBHU DAS	11371	no	IND	Independent
Bihar	Purvi Champaran	None of the Above	13261	no	NOTA	None of the Above
Bihar	Purvi Champaran	SURESH PASWAN	10356	no	BSP	Bahujan Samaj Party
Bihar	Purvi Champaran	PRAMOD KUMAR PANDEY	10505	no	IND	Independent
Bihar	Purvi Champaran	RAGHUNATH GUPTA	2459	no	SP	Samajwadi Party
Bihar	Purvi Champaran	ANIL KUMAR SINGH	2819	no	JMBP	Jai Maha Bharath Party
Bihar	Purvi Champaran	SHATRUGHAN TIWARI	4893	no	BJKVP	Bajjikanchal Vikas Party
Bihar	Purvi Champaran	MOQIM ANSARI	2811	no	JKNPP	Jammu & Kashmir National Panthers Party
Bihar	Purvi Champaran	PARAS NATH PANDEY	3027	no	JPS	Janvadi Party(Socialist)
Bihar	Purvi Champaran	BINOD KUMAR SRIVASTAVA	208289	no	RJD	Rashtriya Janata Dal
Bihar	Purvi Champaran	AVANEESH KUMAR SINGH	128604	no	JD(U)	Janata Dal  (United)
Bihar	Purvi Champaran	DILIP SINGH	10759	no	IND	Independent
Bihar	Purvi Champaran	MD. NASIRUDDIN	2607	no	JDR	Janta Dal Rashtravadi
Bihar	Purvi Champaran	PAWAN KUMAR	2946	no	KS	Kalinga Sena
Bihar	Sheohar	RAMA DEVI	372506	yes	BJP	Bharatiya Janata Party
Bihar	Sheohar	MD NASRUDDIN ANSARI	3072	no	NaLP	National Lokmat Party
Bihar	Sheohar	None of the Above	11670	no	NOTA	None of the Above
Bihar	Sheohar	SHAHID ALI KHAN	79108	no	JD(U)	Janata Dal  (United)
Bihar	Sheohar	MD ANWARUL HAQUE	236267	no	RJD	Rashtriya Janata Dal
Bihar	Sheohar	SHATRUGHNA SAHU	8256	no	AAAP	Aam Aadmi Party
Bihar	Sheohar	SANJAY PRASAD	8245	no	IND	Independent
Bihar	Sheohar	ANGESH KUMAR	26446	no	BSP	Bahujan Samaj Party
Bihar	Sheohar	LAXMAN PASWAN	18681	no	JMM	Jharkhand Mukti Morcha
Bihar	Sheohar	LOVELY ANAND	46008	no	SP	Samajwadi Party
Bihar	Sheohar	RAMASHISH MAHTO	5941	no	IND	Independent
Bihar	Sheohar	JAGRANATH SAHANI	4399	no	SKLP	Sarvajan Kalyan Loktantrik Party
Bihar	Sheohar	JATA SHANKAR AATREY	4641	no	ravp	Rajnaitik Vikalp Party
Bihar	Sheohar	RAM LOBHIT PASWAN	2648	no	BMUP	Bahujan Mukti Party
Bihar	Sheohar	RAMASHANKAR SINGH	2845	no	AIFB	All India Forward Bloc
Bihar	Sheohar	SHIVANANDAN PRASAD	12161	no	IND	Independent
Bihar	Sitamarhi	RAM KUMAR SHARMA	411265	yes	BLSP	Rashtriya Lok Samta Party
Bihar	Sitamarhi	None of the Above	5949	no	NOTA	None of the Above
Bihar	Sitamarhi	JAY KUMAR CHAUDHARY	3880	no	LD	Lok Dal
Bihar	Sitamarhi	CHANDRIKA PRASAD	8846	no	IND	Independent
Bihar	Sitamarhi	SYED ABU DOJANA	4692	no	SP	Samajwadi Party
Bihar	Sitamarhi	RAJEEV RANJAN	11250	no	AITC	All India Trinamool Congress
Bihar	Sitamarhi	AJAY KUMAR	5454	no	IND	Independent
Bihar	Sitamarhi	SATYENDRA BHANDARI	7853	no	BMUP	Bahujan Mukti Party
Bihar	Sitamarhi	SONE LAL SAH	6115	no	IND	Independent
Bihar	Sitamarhi	SURENDRA KUMAR	4501	no	BJKVP	Bajjikanchal Vikas Party
Bihar	Sitamarhi	SITARAM YADAV	263300	no	RJD	Rashtriya Janata Dal
Bihar	Sitamarhi	MAHESH KUMAR	8521	no	BSP	Bahujan Samaj Party
Bihar	Sitamarhi	MAHESH NANDAN SINGH	20613	no	IND	Independent
Bihar	Sitamarhi	ARJUN RAI	97188	no	JD(U)	Janata Dal  (United)
Bihar	Sitamarhi	MOHAMMAD FAIYAZ AHMAD	7586	no	IND	Independent
Bihar	Sitamarhi	NARENDRA MISHRA	6078	no	IND	Independent
Bihar	Sitamarhi	KISHORI DAS	18043	no	AAAP	Aam Aadmi Party
Bihar	Sitamarhi	MAHENDRA PRASAD	3695	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Sitamarhi	VINOD SAH	2996	no	IND	Independent
Bihar	Sitamarhi	BHARAT BHUSHAN SAHNI	2763	no	SKLP	Sarvajan Kalyan Loktantrik Party
Bihar	Madhubani	HUKM DEO NARAYAN YADAV	358040	yes	BJP	Bharatiya Janata Party
Bihar	Madhubani	DHRUB NARAYAN KARN	6750	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Madhubani	HARI NARAYAN YADAV	10115	no	BSP	Bahujan Samaj Party
Bihar	Madhubani	PROF. GHULAM GHOUS	56392	no	JD(U)	Janata Dal  (United)
Bihar	Madhubani	ABDUL BARI SIDDIQUI	337505	no	RJD	Rashtriya Janata Dal
Bihar	Madhubani	NEERAJ PATHAK	9718	no	AAAP	Aam Aadmi Party
Bihar	Madhubani	KUMARI RITA	30942	no	SHS	Shivsena
Bihar	Madhubani	ANIRUD SAHU	9474	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Madhubani	FAIZ AHMAD	8464	no	JPJD	Jai Prakash Janata Dal
Bihar	Madhubani	None of the Above	18937	no	NOTA	None of the Above
Bihar	Madhubani	VIJAY KUMAR	7453	no	IND	Independent
Bihar	Madhubani	RATNESHWAR JHA	6663	no	AKBMP	Akhil Bhartiya Mithila Party
Bihar	Jhanjharpur	BIRENDRA KUMAR CHAUDHARY	335481	yes	BJP	Bharatiya Janata Party
Bihar	Jhanjharpur	HAIDAR	5653	no	VIP	VANCHITSAMAJ INSAAF PARTY
Bihar	Jhanjharpur	BARUN KUMAR JHA	13833	no	IND	Independent
Bihar	Jhanjharpur	JANAK ROY	8689	no	IND	Independent
Bihar	Jhanjharpur	BIPIN KUMAR JHA	7358	no	BVM	Bharat Vikas Morcha
Bihar	Jhanjharpur	RAM CHANDRA SAFI	8083	no	IND	Independent
Bihar	Jhanjharpur	PAWAN KUMAR	17790	no	IND	Independent
Bihar	Jhanjharpur	VIGHNESH BHAGAT	3727	no	BMUP	Bahujan Mukti Party
Bihar	Jhanjharpur	TILIYA DEVI	9441	no	AAAP	Aam Aadmi Party
Bihar	Jhanjharpur	MD. AKHTAR ALI	9388	no	JPJD	Jai Prakash Janata Dal
Bihar	Jhanjharpur	None of the Above	8043	no	NOTA	None of the Above
Bihar	Jhanjharpur	CHHEDI RAM	7632	no	IND	Independent
Bihar	Jhanjharpur	DR. BINAY KUMAR JHA	3956	no	IND	Independent
Bihar	Jhanjharpur	CHANDRA MOHAN JHA	9823	no	IND	Independent
Bihar	Jhanjharpur	IBRAHIM ABBASI	4811	no	IND	Independent
Bihar	Jhanjharpur	MANGANI LAL MANDAL	280073	no	RJD	Rashtriya Janata Dal
Bihar	Jhanjharpur	MANILAL SAHU	14190	no	BSP	Bahujan Samaj Party
Bihar	Jhanjharpur	MD. FAIYAZ KHAN	5439	no	JDR	Janta Dal Rashtravadi
Bihar	Jhanjharpur	DEVENDRA PRASAD YADAV	183591	no	JD(U)	Janata Dal  (United)
Bihar	Jhanjharpur	AWDHESH KUMAR SHINGH	4248	no	IND	Independent
Bihar	Supaul	RANJEET RANJAN	332927	yes	INC	Indian National Congress
Bihar	Supaul	DILESHWAR KAMAIT	273255	no	JD(U)	Janata Dal  (United)
Bihar	Supaul	KAMESHWAR CHAUPAL	249693	no	BJP	Bharatiya Janata Party
Bihar	Supaul	JAY NARAYAN YADAV	11566	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Supaul	AMAN KUMAR SAMAJSEVI	21233	no	BSP	Bahujan Samaj Party
Bihar	Supaul	PARMESHWAR JHA	9604	no	AAAP	Aam Aadmi Party
Bihar	Supaul	SHAKALDEV MANDAL	5964	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Supaul	RAMDEV SHARMA	10687	no	IND	Independent
Bihar	Supaul	BINOD KUMAR SAHU	8908	no	IND	Independent
Bihar	Supaul	DEV NARAYAN YADAV	4825	no	BMUP	Bahujan Mukti Party
Bihar	Supaul	MOHAMMAD AMANULLAH	5116	no	IUML	Indian Union Muslim League
Bihar	Supaul	SURESH KUMAR AZAD	14754	no	JHP	Jai Hind Party
Bihar	Supaul	None of the Above	21996	no	NOTA	None of the Above
Bihar	Araria	TASLEEM UDDIN	407978	yes	RJD	Rashtriya Janata Dal
Bihar	Araria	ABDUL RAHMAN	17724	no	BSP	Bahujan Samaj Party
Bihar	Araria	RAJESH KUMAR	4646	no	BVM	Bharat Vikas Morcha
Bihar	Araria	RAMANAND RISHIDEO	3411	no	LD	Lok Dal
Bihar	Araria	MD. ASLAM BEG	5149	no	JDR	Janta Dal Rashtravadi
Bihar	Araria	None of the Above	16608	no	NOTA	None of the Above
Bihar	Araria	BIDAYANAND PASWAN	3350	no	BMUP	Bahujan Mukti Party
Bihar	Araria	CHANDRA BHUSHAN	5685	no	AAAP	Aam Aadmi Party
Bihar	Araria	VIJAY KUMAR MANDAL	221769	no	JD(U)	Janata Dal  (United)
Bihar	Araria	PRADEEP KUMAR SINGH	261474	no	BJP	Bharatiya Janata Party
Bihar	Araria	PANKAJ KISHOR MANDAL	10704	no	IND	Independent
Bihar	Araria	ALAMDAR HUSSAIN	5645	no	IND	Independent
Bihar	Araria	SANJAY KUMAR RISHIDEV	5292	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Araria	SAR WAT JAHRE ANSARI	6376	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Kishanganj	MOHAMMAD ASRARUL HAQUE	493461	yes	INC	Indian National Congress
Bihar	Kishanganj	ALIMUDDIN ANSARI	15010	no	AAAP	Aam Aadmi Party
Bihar	Kishanganj	MD. GULAB SIDDIKI	5559	no	LKJP	Loktantrik Janata Party (Secular)
Bihar	Kishanganj	DR DILIP  KUMAR JAISWAL	298849	no	BJP	Bharatiya Janata Party
Bihar	Kishanganj	MOHAMMAD WASIQUR RAHMAN	5422	no	IUML	Indian Union Muslim League
Bihar	Kishanganj	AKHTARUL IMAN	55822	no	JD(U)	Janata Dal  (United)
Bihar	Kishanganj	CHHOTE LAL MAHTO	11392	no	IND	Independent
Bihar	Kishanganj	MD.NAUSHAD	14903	no	BSP	Bahujan Samaj Party
Bihar	Kishanganj	CHITRANJAN SINGH	5152	no	IND	Independent
Bihar	Kishanganj	MANUWER ALAM	5714	no	JDR	Janta Dal Rashtravadi
Bihar	Kishanganj	None of the Above	17206	no	NOTA	None of the Above
Bihar	Katihar	TARIQ ANWAR	431292	yes	NCP	Nationalist Congress Party
Bihar	Katihar	None of the Above	3287	no	NOTA	None of the Above
Bihar	Katihar	SURESH RAY	4450	no	RVNP	Rashtravadi Janata Party
Bihar	Katihar	NALINI MANDAL	7969	no	IND	Independent
Bihar	Katihar	BALESHWAR MARANDI	33593	no	JMM	Jharkhand Mukti Morcha
Bihar	Katihar	SATYANARAYAN BHAGAT	2550	no	PSS	Proutist Sarva Samaj
Bihar	Katihar	MD. TARIQ ANWAR	4514	no	IND	Independent
Bihar	Katihar	MD. IQBAL AHMAD	1553	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Katihar	KANHAI MANDAL	10516	no	IND	Independent
Bihar	Katihar	MAHBUB ALAM	9461	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Katihar	SHIVENDRA KUMAR	2685	no	BSP	Bahujan Samaj Party
Bihar	Katihar	RAJ KUMAR MANDAL	2141	no	IND	Independent
Bihar	Katihar	SANJAY SINGH	1675	no	IND	Independent
Bihar	Katihar	ASHOK KUMAR	9000	no	IND	Independent
Bihar	Katihar	DR. RAM PRAKASH MAHTO	100765	no	JD(U)	Janata Dal  (United)
Bihar	Katihar	FULMANI HEMBRAM	3096	no	BVM	Bharat Vikas Morcha
Bihar	Katihar	BIBHAKER JHA @ VICTOR JHA	4323	no	AAAP	Aam Aadmi Party
Bihar	Katihar	MAHAMMAD HAMID MOBARAK	8392	no	AITC	All India Trinamool Congress
Bihar	Katihar	MAHESH MANDAL	1980	no	BMUP	Bahujan Mukti Party
Bihar	Katihar	ABDUR RAHMAN	2489	no	BMF	Bharatiya Momin Front
Bihar	Katihar	ASHOK KU BHAGAT	15547	no	IND	Independent
Bihar	Katihar	NIKHIL KUMAR CHOUDHARY	316552	no	BJP	Bharatiya Janata Party
Bihar	Purnia	SANTOSH KUMAR	418826	yes	JD(U)	Janata Dal  (United)
Bihar	Purnia	MD SHAHID AKHTAR	5137	no	BMUP	Bahujan Mukti Party
Bihar	Purnia	PANKAJ KUMAR SINGH	7912	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Purnia	NAVLESH PATHAK	18776	no	IND	Independent
Bihar	Purnia	MANSUR ALAM	3370	no	SP	Samajwadi Party
Bihar	Purnia	UDAY SINGH ALIAS PAPPU SINGH	302157	no	BJP	Bharatiya Janata Party
Bihar	Purnia	AMARNATH TIWARI	124344	no	INC	Indian National Congress
Bihar	Purnia	FAGU MARANDI	3440	no	JDP	Jharkhand Disom Party
Bihar	Purnia	PRAMOD NARAYAN PODDAR	4664	no	BJJD	Bhartiya Jantantrik Janata Dal
Bihar	Purnia	RAJ KUMAR ORAON	11196	no	BSP	Bahujan Samaj Party
Bihar	Purnia	ANIL KUMAR MAHTO	9162	no	JPS	Janvadi Party(Socialist)
Bihar	Purnia	MD AKHTAR ALI	5849	no	IND	Independent
Bihar	Purnia	SYED SHAH EQBAL ALAM	6324	no	BEMP	Bhartiya Ekta Manch Party
Bihar	Purnia	SANJAY SINGH	9196	no	IND	Independent
Bihar	Purnia	MD SHAMSHER ALAM	50446	no	JMM	Jharkhand Mukti Morcha
Bihar	Purnia	KUNJ BIHARI PASWAN	8321	no	IND	Independent
Bihar	Purnia	SUDEEP ROY	16630	no	AAAP	Aam Aadmi Party
Bihar	Purnia	None of the Above	11982	no	NOTA	None of the Above
Bihar	Madhepura	RAJESH RANJAN @ PAPPU YADAV	368937	yes	RJD	Rashtriya Janata Dal
Bihar	Madhepura	None of the Above	21924	no	NOTA	None of the Above
Bihar	Madhepura	MINA DEVI	9039	no	IND	Independent
Bihar	Madhepura	SAJAN KUMAR JHA	4396	no	JMBP	Jai Maha Bharath Party
Bihar	Madhepura	PRASANNA KUMAR	5370	no	IND	Independent
Bihar	Madhepura	MOHAMMAD ARSHAD HUSSAIN	9585	no	PECP	Peace Party
Bihar	Madhepura	BHIKHA PASWAN	6649	no	BVM	Bharat Vikas Morcha
Bihar	Madhepura	RAJO SAH	12247	no	IND	Independent
Bihar	Madhepura	VIJAY KUMAR SINGH	252534	no	BJP	Bharatiya Janata Party
Bihar	Madhepura	ANWAR ALAM	9538	no	AAAP	Aam Aadmi Party
Bihar	Madhepura	CHANDRASHEKHAR YADAV	3768	no	BMUP	Bahujan Mukti Party
Bihar	Madhepura	SHARAD YADAV	312728	no	JD(U)	Janata Dal  (United)
Bihar	Madhepura	GULJAR KUMAR	18084	no	BSP	Bahujan Samaj Party
Bihar	Darbhanga	KIRTI AZAD	314949	yes	BJP	Bharatiya Janata Party
Bihar	Darbhanga	MD. ALI ASHRAF FATMI	279906	no	RJD	Rashtriya Janata Dal
Bihar	Darbhanga	BRAHMADEO PRASAD KARYEE	2929	no	BJKVP	Bajjikanchal Vikas Party
Bihar	Darbhanga	KRISHNA KUMAR PASWAN	10080	no	IND	Independent
Bihar	Darbhanga	SANJAY KUMAR JHA	104494	no	JD(U)	Janata Dal  (United)
Bihar	Darbhanga	COLONEL LAKSHMESHWAR MISHRA	9111	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Darbhanga	DR. PRABHAT RANJAN DAS	7550	no	AAAP	Aam Aadmi Party
Bihar	Darbhanga	KEDAR KU. KESHAV	25277	no	SHS	Shivsena
Bihar	Darbhanga	SADRE ALAM KHAN	4558	no	AKBMP	Akhil Bhartiya Mithila Party
Bihar	Darbhanga	FAISAL AHMAD	2900	no	JDR	Janta Dal Rashtravadi
Bihar	Darbhanga	DURGA NAND MAHAVIR NAYAK	8835	no	BSP	Bahujan Samaj Party
Bihar	Darbhanga	PRAVIN KUMAR JHA	16125	no	IND	Independent
Bihar	Darbhanga	PRADEEP PASWAN	5963	no	IND	Independent
Bihar	Darbhanga	JAINATH	2956	no	BMUP	Bahujan Mukti Party
Bihar	Darbhanga	HADAY NA. YADAV	11606	no	CPM	Communist Party of India  (Marxist)
Bihar	Darbhanga	None of the Above	21103	no	NOTA	None of the Above
Bihar	Muzaffarpur	AJAY NISHAD	469295	yes	BJP	Bharatiya Janata Party
Bihar	Muzaffarpur	DEVENDRA RAKESH	2299	no	BJKVP	Bajjikanchal Vikas Party
Bihar	Muzaffarpur	None of the Above	9690	no	NOTA	None of the Above
Bihar	Muzaffarpur	SHATRUDHAN SAHNI	8223	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Muzaffarpur	AKSHAY VERMA	7569	no	SKLP	Sarvajan Kalyan Loktantrik Party
Bihar	Muzaffarpur	KAMLESH CHOUDHARY	3147	no	IND	Independent
Bihar	Muzaffarpur	MOHMMAD TAIYAB	2269	no	IND	Independent
Bihar	Muzaffarpur	AJITANSH GAUR	2755	no	IND	Independent
Bihar	Muzaffarpur	PANKAJ KUMAR SRIVASTAVA	3365	no	JPJD	Jai Prakash Janata Dal
Bihar	Muzaffarpur	RAVI SHANKAR PASWAN	3334	no	IND	Independent
Bihar	Muzaffarpur	NAVAL KISHORE JHA	2364	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Muzaffarpur	SURESH KUMAR	5211	no	IND	Independent
Bihar	Muzaffarpur	PRABHAT SUMAN	6945	no	AAAP	Aam Aadmi Party
Bihar	Muzaffarpur	NILU SINGH	2271	no	PBI	Proutist Bloc, India
Bihar	Muzaffarpur	ASHOK KUMAR SINGH	3734	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Bihar	Muzaffarpur	AKHILESH PD SINGH	246873	no	INC	Indian National Congress
Bihar	Muzaffarpur	VIJENDRA CHAUDHARY	85140	no	JD(U)	Janata Dal  (United)
Bihar	Muzaffarpur	MOHAMMAD ANWARUL HAQUE	2354	no	IND	Independent
Bihar	Muzaffarpur	ASHOK KUMAR JHA	19945	no	SHS	Shivsena
Bihar	Muzaffarpur	DEEPAK KUMAR	1809	no	IND	Independent
Bihar	Muzaffarpur	SHRAVAN KUMAR JHA	3746	no	IND	Independent
Bihar	Muzaffarpur	MADHUR SHEEL	4309	no	JMBP	Jai Maha Bharath Party
Bihar	Muzaffarpur	RUPESH KUMAR KUAR	2370	no	IND	Independent
Bihar	Muzaffarpur	NAGESHWAR PRASAD SINGH	9202	no	IND	Independent
Bihar	Muzaffarpur	RAGHVENDRA PRATAP SINGH	13283	no	ravp	Rajnaitik Vikalp Party
Bihar	Muzaffarpur	MAHESH PASWAN	1749	no	IND	Independent
Bihar	Muzaffarpur	KUNAL PRIYADARSHI	2471	no	RKSP	Rashtriya Krantikari Samajwadi Party
Bihar	Muzaffarpur	RAJESH KUMAR	6118	no	AIFB	All India Forward Bloc
Bihar	Muzaffarpur	RAJEEV RANJAN	9069	no	BSP	Bahujan Samaj Party
Bihar	Muzaffarpur	MAHESH RAI	7978	no	BMUP	Bahujan Mukti Party
Bihar	Vaishali	RAMA KISHORE SINGH	305450	yes	LJP	Lok Jan Shakti Party
Bihar	Vaishali	JITENDRA PRASAD	12369	no	IND	Independent
Bihar	Vaishali	RAAJ MANGAL PRASAD	7768	no	AAAP	Aam Aadmi Party
Bihar	Vaishali	SHANKAR MAHTO	23677	no	BSP	Bahujan Samaj Party
Bihar	Vaishali	PARMESHWAR RAM	4000	no	BMUP	Bahujan Mukti Party
Bihar	Vaishali	DR. MOHAMMAD NABI HASSAN	22455	no	IND	Independent
Bihar	Vaishali	MOHAMMAD UMAR ANSARI	8254	no	IND	Independent
Bihar	Vaishali	RAGHUVANSH PRASAD SINGH	206183	no	RJD	Rashtriya Janata Dal
Bihar	Vaishali	None of the Above	6060	no	NOTA	None of the Above
Bihar	Vaishali	BALENDRA SINGH	15178	no	IND	Independent
Bihar	Vaishali	VINOD PANDIT	6430	no	LPSP	Lokpriya Samaj Party
Bihar	Vaishali	JAYNARAYAN SAH	3191	no	BJKVP	Bajjikanchal Vikas Party
Bihar	Vaishali	LALJI KUMAR RAKESH	2667	no	IND	Independent
Bihar	Vaishali	MD.HASIB	11075	no	RSMD	Rashtriya Samanta Dal
Bihar	Vaishali	RAMESHWAR SAH	5483	no	IND	Independent
Bihar	Vaishali	RAM PUKAR RAI	7182	no	IND	Independent
Bihar	Vaishali	RANJEET KUMAR JHA	11540	no	IND	Independent
Bihar	Vaishali	ANU SHUKLA	104229	no	IND	Independent
Bihar	Vaishali	INDRADEO ROY	5218	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Bihar	Vaishali	MUKESH RAM	5497	no	RKSP	Rashtriya Krantikari Samajwadi Party
Bihar	Vaishali	VIJAY KUMAR SAHNI	145182	no	JD(U)	Janata Dal  (United)
Bihar	Vaishali	SANDHYA DEVI	2983	no	IND	Independent
Bihar	Vaishali	MD. NAUSHAD	3866	no	VIP	VANCHITSAMAJ INSAAF PARTY
Bihar	Gopalganj (SC)	JANAK RAM	478773	yes	BJP	Bharatiya Janata Party
Bihar	Gopalganj (SC)	RAMASHANKAR RAM	7147	no	RJJM	Rashtriya Jan-Jagram Morcha
Bihar	Gopalganj (SC)	JALESHWAR NATH RAM	6264	no	BMUP	Bahujan Mukti Party
Bihar	Gopalganj (SC)	SURENDRA PASWAN	4717	no	RBCP	Rashtriya Bahujan Congress Party
Bihar	Gopalganj (SC)	JITENDRA MANJHI	12423	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Gopalganj (SC)	MUDRIKA BAITHA	8491	no	IND	Independent
Bihar	Gopalganj (SC)	SATEYNDRA BAITHA	9509	no	IND	Independent
Bihar	Gopalganj (SC)	DINANATH MANJHI	13983	no	IND	Independent
Bihar	Gopalganj (SC)	None of the Above	17841	no	NOTA	None of the Above
Bihar	Gopalganj (SC)	SUNITA KUMAR	4586	no	SP	Samajwadi Party
Bihar	Gopalganj (SC)	CHANDRADIP RAM	16751	no	BSP	Bahujan Samaj Party
Bihar	Gopalganj (SC)	DR. JYOTI BHARTI	191837	no	INC	Indian National Congress
Bihar	Gopalganj (SC)	SURENDRA RAM	16680	no	BED	Bharatiya Ekta Dal
Bihar	Gopalganj (SC)	ANIL KUMAR	100419	no	JD(U)	Janata Dal  (United)
Bihar	Gopalganj (SC)	RAM KUMAR MANJHI	14162	no	IND	Independent
Bihar	Siwan	OM PRAKASH YADAV	372670	yes	BJP	Bharatiya Janata Party
Bihar	Siwan	MANOJ KUMAR SINGH	79239	no	JD(U)	Janata Dal  (United)
Bihar	Siwan	GANESH RAM	14584	no	BSP	Bahujan Samaj Party
Bihar	Siwan	KHALIL MIYA	9580	no	GGP	Gondvana Gantantra Party
Bihar	Siwan	PARMESHWAR  MANJHI	5683	no	BMUP	Bahujan Mukti Party
Bihar	Siwan	RAM BINAY RAY	11757	no	BED	Bharatiya Ekta Dal
Bihar	Siwan	RANJAN KUMAR	8851	no	BVM	Bharat Vikas Morcha
Bihar	Siwan	None of the Above	21409	no	NOTA	None of the Above
Bihar	Siwan	SATYA PRAKASH PANDEY	10542	no	AAAP	Aam Aadmi Party
Bihar	Siwan	HENA SHAHAB	258823	no	RJD	Rashtriya Janata Dal
Bihar	Siwan	PARAMANAND GOND	4016	no	SWSP	Swatantra Samaj Party
Bihar	Siwan	AMAR NATH YADAV	81006	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Siwan	NARAD PNDIT	3250	no	JPJD	Jai Prakash Janata Dal
Bihar	Siwan	NAGENDRA TIWARI	2611	no	LKJP	Loktantrik Janata Party (Secular)
Bihar	Maharajganj	"JANARDAN SINGH ""SIGRIWAL"""	320753	yes	BJP	Bharatiya Janata Party
Bihar	Maharajganj	None of the Above	23404	no	NOTA	None of the Above
Bihar	Maharajganj	UMASHANKAR TIWARI	11599	no	IND	Independent
Bihar	Maharajganj	BYAS MANJHI	4796	no	BMUP	Bahujan Mukti Party
Bihar	Maharajganj	NAGENDRA PRASAD	11189	no	BSP	Bahujan Samaj Party
Bihar	Maharajganj	BRINDA PATHAK	17186	no	IND	Independent
Bihar	Maharajganj	RINA RANI	6430	no	AAAP	Aam Aadmi Party
Bihar	Maharajganj	DR. GOPAL PRASAD	10602	no	IND	Independent
Bihar	Maharajganj	SHIV NATH MAHTO	8874	no	IND	Independent
Bihar	Maharajganj	MANORANJAN SINGH	149483	no	JD(U)	Janata Dal  (United)
Bihar	Maharajganj	PRABHU NATH SINGH	282338	no	RJD	Rashtriya Janata Dal
Bihar	Saran	RAJIV PRATAP RUDY	355120	yes	BJP	Bharatiya Janata Party
Bihar	Saran	SALEEM PERWEZ	107008	no	JD(U)	Janata Dal  (United)
Bihar	Saran	PARMATMA SINGH	5099	no	AAAP	Aam Aadmi Party
Bihar	Saran	LALU RAI	9957	no	IND	Independent
Bihar	Saran	BAL MUKUND CHAUHAN	15500	no	BSP	Bahujan Samaj Party
Bihar	Saran	RANJAN KUMAR	3217	no	JMBP	Jai Maha Bharath Party
Bihar	Saran	LALAN PRASAD	14687	no	IND	Independent
Bihar	Saran	NIRAJ RAM	2818	no	BMUP	Bahujan Mukti Party
Bihar	Saran	RAJESH KUMAR	9748	no	BED	Bharatiya Ekta Dal
Bihar	Saran	None of the Above	19163	no	NOTA	None of the Above
Bihar	Saran	RABRI DEVI	314172	no	RJD	Rashtriya Janata Dal
Bihar	Saran	CHANDAN KUMAR ALIAS CHANDAN GUPTA	6765	no	BJPARTY	Bihar Janta Party
Bihar	Hajipur (SC)	RAMVILAS PASWAN	455652	yes	LJP	Lok Jan Shakti Party
Bihar	Hajipur (SC)	DINA NATH RAM	21045	no	IND	Independent
Bihar	Hajipur (SC)	SANJEEV PRASAD TONI	230152	no	INC	Indian National Congress
Bihar	Hajipur (SC)	None of the Above	15094	no	NOTA	None of the Above
Bihar	Hajipur (SC)	SURESH PASWAN	7544	no	IND	Independent
Bihar	Hajipur (SC)	SATYA NARAYAN PASWAN	3216	no	AIFB	All India Forward Bloc
Bihar	Hajipur (SC)	VIPHIYA DEVI	24399	no	IND	Independent
Bihar	Hajipur (SC)	RAJ KR. PASWAN	6344	no	AAAP	Aam Aadmi Party
Bihar	Hajipur (SC)	UPENDER RAM	8896	no	IND	Independent
Bihar	Hajipur (SC)	VIRCHANDRA PASWAN	4185	no	SP	Samajwadi Party
Bihar	Hajipur (SC)	UJJAWAL KANT HUNKAR	8957	no	BMF	Bharatiya Momin Front
Bihar	Hajipur (SC)	RINA RAGNI	3882	no	BMUP	Bahujan Mukti Party
Bihar	Hajipur (SC)	DHANESHWAR RAM	12688	no	BSP	Bahujan Samaj Party
Bihar	Hajipur (SC)	RAM SUNDAR DAS	95790	no	JD(U)	Janata Dal  (United)
Bihar	Hajipur (SC)	RANDHIR KUMAR RAM	3711	no	BJKVP	Bajjikanchal Vikas Party
Bihar	Hajipur (SC)	VIDHAN CHANDRA RANA	3198	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Ujiarpur	NITYANAND RAI	317352	yes	BJP	Bharatiya Janata Party
Bihar	Ujiarpur	PHOOL BABU SINGH	6967	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Ujiarpur	ASHWAMEDH DEVI	119669	no	JD(U)	Janata Dal  (United)
Bihar	Ujiarpur	MADHURI	7661	no	BMUP	Bahujan Mukti Party
Bihar	Ujiarpur	VINDESHWARI SAHNI	7969	no	PSS	Proutist Sarva Samaj
Bihar	Ujiarpur	SURESH CHAUDHARI	8917	no	AAAP	Aam Aadmi Party
Bihar	Ujiarpur	ALOK KUMAR MEHTA	256883	no	RJD	Rashtriya Janata Dal
Bihar	Ujiarpur	MD. TAHIR ARAMAN	2536	no	NADP	Naya Daur Party
Bihar	Ujiarpur	PRITHIVINATH PRASAD	2482	no	MHD	Mahamukti Dal
Bihar	Ujiarpur	SHIVANI UMANI	12520	no	JDR	Janta Dal Rashtravadi
Bihar	Ujiarpur	DHARMENDRA SAHANI	15198	no	BSP	Bahujan Samaj Party
Bihar	Ujiarpur	RAM DEO VERMA	53044	no	CPM	Communist Party of India  (Marxist)
Bihar	Ujiarpur	PREM LATA DEVI	3152	no	JPJD	Jai Prakash Janata Dal
Bihar	Ujiarpur	MITHILESH KUMAR JHA	7810	no	JMM	Jharkhand Mukti Morcha
Bihar	Ujiarpur	ARJUN KUMAR SAH	4201	no	IND	Independent
Bihar	Ujiarpur	AMRENDRA KUMAR YADAV	5631	no	RKSP	Rashtriya Krantikari Samajwadi Party
Bihar	Ujiarpur	MINTU PASWAN	3098	no	IND	Independent
Bihar	Ujiarpur	KISHOR KUMAR	5669	no	BLSP	Rashtriya Lok Samta Party
Bihar	Ujiarpur	JITENDRA KUMAR RAI	11990	no	SHS	Shivsena
Bihar	Ujiarpur	None of the Above	6171	no	NOTA	None of the Above
Bihar	Samastipur (SC)	RAM CHANDRA PASWAN	270401	yes	LJP	Lok Jan Shakti Party
Bihar	Samastipur (SC)	None of the Above	29211	no	NOTA	None of the Above
Bihar	Samastipur (SC)	AMIT KUMAR	7423	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Samastipur (SC)	SHATRUDHAN KUMAR PASWAN	12322	no	RKSP	Rashtriya Krantikari Samajwadi Party
Bihar	Samastipur (SC)	ASHOK PASWAN	6603	no	BMUP	Bahujan Mukti Party
Bihar	Samastipur (SC)	MITHILESH KUMAR RAJAK	13504	no	IND	Independent
Bihar	Samastipur (SC)	GANESH KUMAR	9954	no	AAAP	Aam Aadmi Party
Bihar	Samastipur (SC)	BALESHWAR PASWAN	15837	no	JDR	Janta Dal Rashtravadi
Bihar	Samastipur (SC)	VIJAY KUMAR RAM	11813	no	ravp	Rajnaitik Vikalp Party
Bihar	Samastipur (SC)	LAL BAHADUR SADA	9665	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Samastipur (SC)	DR. ASHOK KUMAR	263529	no	INC	Indian National Congress
Bihar	Samastipur (SC)	MAHESHWAR HAZARI	200124	no	JD(U)	Janata Dal  (United)
Bihar	Samastipur (SC)	RAM CHANDRA RAM	12813	no	BSP	Bahujan Samaj Party
Bihar	Begusarai	BHOLA SINGH	428227	yes	BJP	Bharatiya Janata Party
Bihar	Begusarai	RAM PADARATH YADAV	9014	no	BSP	Bahujan Samaj Party
Bihar	Begusarai	RAJENDRA YADAV	4605	no	IND	Independent
Bihar	Begusarai	MOHAMMAD JAVED	3405	no	IND	Independent
Bihar	Begusarai	RAJENDRA PRASAD SINGH	192639	no	CPI	Communist Party of India
Bihar	Begusarai	VAIJ NATH PASWAN	12143	no	IND	Independent
Bihar	Begusarai	MD. TANVEER HASSAN	369892	no	RJD	Rashtriya Janata Dal
Bihar	Begusarai	PANKAJ PASWAN	3323	no	IND	Independent
Bihar	Begusarai	None of the Above	26622	no	NOTA	None of the Above
Bihar	Begusarai	SUNIL KUMAR	8639	no	AAAP	Aam Aadmi Party
Bihar	Begusarai	SUSHIL KUMAR	11144	no	IND	Independent
Bihar	Begusarai	PINKI DEVI	4858	no	BMUP	Bahujan Mukti Party
Bihar	Begusarai	HARI KANT PRASAD	3344	no	BBC	Bharatiya Bahujan Congress
Bihar	Khagaria	CHOUDHARY MAHBOOB ALI KAISER	313806	yes	LJP	Lok Jan Shakti Party
Bihar	Khagaria	UMESH CHANDRA BHARTI	14926	no	IND	Independent
Bihar	Khagaria	MAKKHAN SAH	6651	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Khagaria	TEJ BAHADUR SINGH	4053	no	PSS	Proutist Sarva Samaj
Bihar	Khagaria	GANESH MUKHIYA NISHAD	4522	no	SKLP	Sarvajan Kalyan Loktantrik Party
Bihar	Khagaria	JAGDISH CHANDRA VASU	24490	no	CPM	Communist Party of India  (Marxist)
Bihar	Khagaria	RAJEEV RANJAN	5722	no	LD	Lok Dal
Bihar	Khagaria	None of the Above	23868	no	NOTA	None of the Above
Bihar	Khagaria	RAJESH KUMAR	6480	no	BSP	Bahujan Samaj Party
Bihar	Khagaria	KRISHNA KUMARI YADAV	237803	no	RJD	Rashtriya Janata Dal
Bihar	Khagaria	BRAHAMDEO MUKHIA	5215	no	RND	Rashtriya Naujawan Dal
Bihar	Khagaria	DINESH CHANDRA YADAV	220316	no	JD(U)	Janata Dal  (United)
Bihar	Khagaria	DR. SWAMI VIVEKANAND	14491	no	AAAP	Aam Aadmi Party
Bihar	Khagaria	SATISH PRASAD SINGH	13888	no	IND	Independent
Bihar	Bhagalpur	SHAILESH KUMAR URPH BULO MANDAL	367623	yes	RJD	Rashtriya Janata Dal
Bihar	Bhagalpur	NAUSHABA KHANAM	11265	no	BSP	Bahujan Samaj Party
Bihar	Bhagalpur	NISHI KANT MANDAL	3147	no	BMUP	Bahujan Mukti Party
Bihar	Bhagalpur	ABU QAISER	132256	no	JD(U)	Janata Dal  (United)
Bihar	Bhagalpur	SYED SHAHNAWAZ HUSSAIN	358138	no	BJP	Bharatiya Janata Party
Bihar	Bhagalpur	RINKI KUMARI	5778	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Bhagalpur	PROF. YOGENDRA MAHTO	5830	no	AAAP	Aam Aadmi Party
Bihar	Bhagalpur	INDRADEO KUMAR SINGH	12710	no	IND	Independent
Bihar	Bhagalpur	GAURAV KUMAR GHOSH	3251	no	NTP	National Tiger Party
Bihar	Bhagalpur	RAMJEE MANDAL	18937	no	IND	Independent
Bihar	Bhagalpur	PUROSOTTAM CHOUBEY	2416	no	SP	Samajwadi Party
Bihar	Bhagalpur	KUMKUM SINGH	8025	no	BVM	Bharat Vikas Morcha
Bihar	Bhagalpur	SUMAN KUMAR	3444	no	IND	Independent
Bihar	Bhagalpur	ANAND KUMAR JAIN	4734	no	IND	Independent
Bihar	Bhagalpur	RANJAN KUMAR	5165	no	IND	Independent
Bihar	Bhagalpur	SIKANDAR TANTI	7122	no	IND	Independent
Bihar	Bhagalpur	VIREN DAS	3005	no	SaBP	Sankhyanupati Bhagidari Party
Bihar	Bhagalpur	MAHADEO KUMAR	9295	no	IND	Independent
Bihar	Bhagalpur	None of the Above	11875	no	NOTA	None of the Above
Bihar	Banka	JAI PRAKASH NARAYAN YADAV	285150	yes	RJD	Rashtriya Janata Dal
Bihar	Banka	AKHIL BHARTIYA SHARMA	4721	no	IND	Independent
Bihar	Banka	ALIMUDDIN ANSARI	12871	no	IND	Independent
Bihar	Banka	MITHLESH KUMAR SINGH	2977	no	NLP	National Loktantrik Party
Bihar	Banka	None of the Above	9753	no	NOTA	None of the Above
Bihar	Banka	PUTUL KUMARI	275006	no	BJP	Bharatiya Janata Party
Bihar	Banka	SANJAY KUMAR -I	220708	no	CPI	Communist Party of India
Bihar	Banka	VIJAY BHARTI	4317	no	ravp	Rajnaitik Vikalp Party
Bihar	Banka	SANJAY KUMAR-II	10558	no	BSP	Bahujan Samaj Party
Bihar	Banka	NIRAJ KUMAR	6058	no	AAAP	Aam Aadmi Party
Bihar	Banka	SUBODH NARAYAN JHA	4412	no	AITC	All India Trinamool Congress
Bihar	Banka	NARESH KUMAR PRIYADARSHI	30814	no	IND	Independent
Bihar	Banka	MUKESH KUMAR SINHA	2405	no	BEMP	Bhartiya Ekta Manch Party
Bihar	Banka	DEEPAK KUMAR	2481	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Bihar	Banka	UMAKANT YADAV	2266	no	BMUP	Bahujan Mukti Party
Bihar	Banka	MAHBOOB ALAM ANSARI	2280	no	BMF	Bharatiya Momin Front
Bihar	Banka	SARGUN SAH	11056	no	IND	Independent
Bihar	Banka	INDRARAJ SINGH SAINI	11520	no	IND	Independent
Bihar	Munger	VEENA DEVI	352911	yes	LJP	Lok Jan Shakti Party
Bihar	Munger	None of the Above	15420	no	NOTA	None of the Above
Bihar	Munger	ASHOK KUMAR SINGH	50469	no	SHS	Shivsena
Bihar	Munger	SURYODAY PASWAN	5113	no	SaBP	Sankhyanupati Bhagidari Party
Bihar	Munger	PRAGATI MEHTA	182971	no	RJD	Rashtriya Janata Dal
Bihar	Munger	SANDEEP	7481	no	AAAP	Aam Aadmi Party
Bihar	Munger	SHARWAN KUMAR ANAND	8112	no	IND	Independent
Bihar	Munger	MUZAFFAR FAKHRUDDIN	10264	no	IND	Independent
Bihar	Munger	RAJIV RANJAN SINGH ALIAS LALAN SINGH	243827	no	JD(U)	Janata Dal  (United)
Bihar	Munger	KAMLESHWARI MANDAL	11225	no	BSP	Bahujan Samaj Party
Bihar	Munger	UCHIT KUMAR	11122	no	IND	Independent
Bihar	Munger	RAM BADAN RAI	6769	no	SP	Samajwadi Party
Bihar	Munger	SHANKAR PRASAD	3956	no	BMUP	Bahujan Mukti Party
Bihar	Munger	PRAMOD KUMAR	4710	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Bihar	Nalanda	KAUSHLENDRA KUMAR	321982	yes	JD(U)	Janata Dal  (United)
Bihar	Nalanda	YOGENDRA PRASAD	4600	no	BIP	Bharatiya Inqalab Party
Bihar	Nalanda	SATYA NAND SHARMA	312355	no	LJP	Lok Jan Shakti Party
Bihar	Nalanda	RAKESH RANJAN SINHA	13381	no	RPI(A)	Republican Party of India (A)
Bihar	Nalanda	NAYEB ALI	4613	no	AITC	All India Trinamool Congress
Bihar	Nalanda	LATA SINHA	2237	no	IND	Independent
Bihar	Nalanda	NIRAJ SHARMA	2342	no	ravp	Rajnaitik Vikalp Party
Bihar	Nalanda	SHASHI YADAV	19477	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Nalanda	PRAMOD KUMAR NIRALA	2779	no	SaBP	Sankhyanupati Bhagidari Party
Bihar	Nalanda	VISHWAJIT KUMAR	2604	no	IND	Independent
Bihar	Nalanda	NARESH PRASAD SINGH	3620	no	IND	Independent
Bihar	Nalanda	SHAILESH KUMAR	15370	no	APCP	Ati Picchara party
Bihar	Nalanda	PARNAV PRAKASH	18224	no	AAAP	Aam Aadmi Party
Bihar	Nalanda	SANJAY KUMAR	23595	no	BSP	Bahujan Samaj Party
Bihar	Nalanda	ASHISH RANJAN SINHA	127270	no	INC	Indian National Congress
Bihar	Nalanda	BHOLA SAO	2213	no	KVD	Krantikari Vikas Dal
Bihar	Nalanda	MUNNA PRASAD	6048	no	IND	Independent
Bihar	Nalanda	MAHESH PASWAN	4236	no	BBMP	Bharat Bhrashtachar Mitao Party
Bihar	Nalanda	AWADH KUMAR	6346	no	IND	Independent
Bihar	Nalanda	LALLU RAM	6788	no	BMUP	Bahujan Mukti Party
Bihar	Nalanda	DHIRENDRA KUMAR	8757	no	JPS	Janvadi Party(Socialist)
Bihar	Nalanda	KAUSHAL KUMAR KAUSHALENDRA SINHA ALIAS BHARAT MANAS	7472	no	IND	Independent
Bihar	Nalanda	None of the Above	5452	no	NOTA	None of the Above
Bihar	Patna Sahib	SHATRUGHANA SINHA	485905	yes	BJP	Bharatiya Janata Party
Bihar	Patna Sahib	None of the Above	7727	no	NOTA	None of the Above
Bihar	Patna Sahib	KUMAR RAJIV	2914	no	IND	Independent
Bihar	Patna Sahib	GAJENDRA KUMAR	2166	no	SaBP	Sankhyanupati Bhagidari Party
Bihar	Patna Sahib	SHREEKANT LABH	3178	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Patna Sahib	DR.GOPAL PRASAD SINHA	91024	no	JD(U)	Janata Dal  (United)
Bihar	Patna Sahib	PARWEZ AHMAD	1856	no	LKJP	Loktantrik Janata Party (Secular)
Bihar	Patna Sahib	SHYAM NANDAN PRASAD	1834	no	IND	Independent
Bihar	Patna Sahib	GANESH SAW	9691	no	BSP	Bahujan Samaj Party
Bihar	Patna Sahib	LAL BAHADUR SINGH PATEL	2502	no	KVD	Krantikari Vikas Dal
Bihar	Patna Sahib	JAUHAR AZAD	1834	no	JDR	Janta Dal Rashtravadi
Bihar	Patna Sahib	NAND KISHORE SINGH	6057	no	IND	Independent
Bihar	Patna Sahib	KUNAL SINGH	220100	no	INC	Indian National Congress
Bihar	Patna Sahib	VIJAY KUMAR SAHNI	1243	no	IND	Independent
Bihar	Patna Sahib	DR. RAKESH DUTTA MISHRA	2687	no	BJKD	Bharatiya Jan Kranti Dal (Democratic)
Bihar	Patna Sahib	GANGA VISHNU PRASAD	8834	no	IND	Independent
Bihar	Patna Sahib	UMESH KUMAR	10836	no	SP	Samajwadi Party
Bihar	Patna Sahib	ANIL KUMAR	2478	no	IND	Independent
Bihar	Patna Sahib	PARVEEN AMANULLAH	16381	no	AAAP	Aam Aadmi Party
Bihar	Patna Sahib	CHANDRA BHUSHAN PRASAD CHANDRAVANSHI	3015	no	BMUP	Bahujan Mukti Party
Bihar	Pataliputra	RAM KRIPAL YADAV	383262	yes	BJP	Bharatiya Janata Party
Bihar	Pataliputra	PANCHAM LAL	2520	no	IND	Independent
Bihar	Pataliputra	DURGESH NANDAN SINGH ALIAS DURGESH NANDAN	16645	no	IND	Independent
Bihar	Pataliputra	RAMESHWAR PRASAD	51623	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Pataliputra	ABINASH KUMAR	5854	no	BJKD	Bharatiya Jan Kranti Dal (Democratic)
Bihar	Pataliputra	DEO KUMAR VERMA	2484	no	RPI(A)	Republican Party of India (A)
Bihar	Pataliputra	NIRAJ KUMAR	8084	no	IND	Independent
Bihar	Pataliputra	MISHA BHARTI	342940	no	RJD	Rashtriya Janata Dal
Bihar	Pataliputra	MINENDRA KUMAR SINGH	1697	no	IND	Independent
Bihar	Pataliputra	JAI PRAKASH YADAV	4804	no	IND	Independent
Bihar	Pataliputra	RAJ KUMAR RAM	17188	no	BSP	Bahujan Samaj Party
Bihar	Pataliputra	RANJAN PRASAD YADAV	97228	no	JD(U)	Janata Dal  (United)
Bihar	Pataliputra	KUMAR INDRA BHUSHAN	5412	no	IND	Independent
Bihar	Pataliputra	SUNIL KUMAR SINGH	4555	no	IND	Independent
Bihar	Pataliputra	MUKUL KUMAR SHARMA	3015	no	SaBP	Sankhyanupati Bhagidari Party
Bihar	Pataliputra	NAGENDRA KUMAR	4302	no	BVM	Bharat Vikas Morcha
Bihar	Pataliputra	KUNDAN PRASAD SINGH	5637	no	AAAP	Aam Aadmi Party
Bihar	Pataliputra	SUNIL KUMAR	6269	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Bihar	Pataliputra	CHANDRA DEV PRASAD	2268	no	BMUP	Bahujan Mukti Party
Bihar	Pataliputra	SHATRUDHAN SAW	8184	no	BED	Bharatiya Ekta Dal
Bihar	Pataliputra	None of the Above	4678	no	NOTA	None of the Above
Bihar	Arrah	RAJ KUMAR SINGH	391074	yes	BJP	Bharatiya Janata Party
Bihar	Arrah	SRIBHAGWAN SINGH KUSHWAHA	255204	no	RJD	Rashtriya Janata Dal
Bihar	Arrah	SHANKAR DAYAL PASWAN	5610	no	IND	Independent
Bihar	Arrah	ASHOK RAM	7305	no	IND	Independent
Bihar	Arrah	MEENA SINGH	75962	no	JD(U)	Janata Dal  (United)
Bihar	Arrah	GOPAL SINGH	5879	no	IND	Independent
Bihar	Arrah	BAIJNATH YADAV	6886	no	BMUP	Bahujan Mukti Party
Bihar	Arrah	DR. SURENDRA KUMAR PRASAD	7518	no	AAAP	Aam Aadmi Party
Bihar	Arrah	RAJU YADAV	98805	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Arrah	BHARAT BHUSHAN PANDEY	10950	no	ABJS	Akhil Bharatiya Jan Sangh
Bihar	Arrah	SHAMBHU PRASAD SHARMA	5680	no	HVD	Hindustan Vikas Dal
Bihar	Arrah	RAMESH CHANDRA BADAL	7637	no	BSP	Bahujan Samaj Party
Bihar	Arrah	None of the Above	14703	no	NOTA	None of the Above
Bihar	Buxar	ASHWINI KUMAR CHOUBEY	319012	yes	BJP	Bharatiya Janata Party
Bihar	Buxar	None of the Above	9179	no	NOTA	None of the Above
Bihar	Buxar	SWETA PATHAK	7879	no	AAAP	Aam Aadmi Party
Bihar	Buxar	SHYAM LAL SINGH KUSHWAHA	117012	no	JD(U)	Janata Dal  (United)
Bihar	Buxar	PRADEEP KUMAR PRASAD	6901	no	PMSP	Pragatisheel Manav Samaj Party
Bihar	Buxar	JAGADANAND SINGH	186674	no	RJD	Rashtriya Janata Dal
Bihar	Buxar	INDU SINGH	13051	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Buxar	LALLAN RUPNARAIN PATHAK	5294	no	IND	Independent
Bihar	Buxar	MOKARRAM HUSSAIN	7514	no	SBSP	Suheldev Bhartiya Samaj Party
Bihar	Buxar	DADAN YADAV	184788	no	BSP	Bahujan Samaj Party
Bihar	Buxar	SHEO GOPAL CHOUDHARY	12422	no	IND	Independent
Bihar	Buxar	TAFIR HUSSIAN	2241	no	HVD	Hindustan Vikas Dal
Bihar	Buxar	PARSHURAM PRASAD	2066	no	BMUP	Bahujan Mukti Party
Bihar	Buxar	NARENDRA PRATAP SINGH	6130	no	IND	Independent
Bihar	Buxar	RAJENDRA KUMAR SINGH CHOUHAN	2740	no	BVM	Bharat Vikas Morcha
Bihar	Buxar	MONIBUR RAHMAN	1864	no	JDR	Janta Dal Rashtravadi
Bihar	Buxar	SHIO KUMAR SHARMA	3437	no	JPS	Janvadi Party(Socialist)
Bihar	Sasaram (SC)	CHHEDI PASWAN	366087	yes	BJP	Bharatiya Janata Party
Bihar	Sasaram (SC)	None of the Above	13379	no	NOTA	None of the Above
Bihar	Sasaram (SC)	BALESHWAR BHARTI	31528	no	BSP	Bahujan Samaj Party
Bihar	Sasaram (SC)	RAJNARAIN RAO	4395	no	IND	Independent
Bihar	Sasaram (SC)	NAND LAL RAM	2808	no	IND	Independent
Bihar	Sasaram (SC)	GEETA ARYA	11005	no	AAAP	Aam Aadmi Party
Bihar	Sasaram (SC)	MEIRA KUMAR	302760	no	INC	Indian National Congress
Bihar	Sasaram (SC)	RAVIKANT CHAUDHARY	3980	no	IND	Independent
Bihar	Sasaram (SC)	TETRI DEVI	7486	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Sasaram (SC)	SAROJ RAM	3906	no	BMUP	Bahujan Mukti Party
Bihar	Sasaram (SC)	KARRA PARASU RAMAIAH	93310	no	JD(U)	Janata Dal  (United)
Bihar	Sasaram (SC)	SURENDRA RAM	6144	no	IND	Independent
Bihar	Karakat	UPENDRA KUSHWAHA	338892	yes	BLSP	Rashtriya Lok Samta Party
Bihar	Karakat	KANTI SINGH	233651	no	RJD	Rashtriya Janata Dal
Bihar	Karakat	MAHABALI SINGH	76709	no	JD(U)	Janata Dal  (United)
Bihar	Karakat	SANJAY KEWAT	45503	no	BSP	Bahujan Samaj Party
Bihar	Karakat	GHULAM KUNDANAM	6938	no	AAAP	Aam Aadmi Party
Bihar	Karakat	DINESH KUMAR	2359	no	BMUP	Bahujan Mukti Party
Bihar	Karakat	BHAIRAW DAYAL SINGH	6974	no	IND	Independent
Bihar	Karakat	RAM DAYAL SINGH	5459	no	IND	Independent
Bihar	Karakat	SATYA NARAYAN SINGH	9919	no	IND	Independent
Bihar	Karakat	MD. DILBAS ANSARI	2813	no	IND	Independent
Bihar	Karakat	IMRAN ALI	2048	no	IND	Independent
Bihar	Karakat	VEENA BHARTI	4002	no	JPS	Janvadi Party(Socialist)
Bihar	Karakat	PRADEEP KUMAR JOSHI	6994	no	RSWD	Rashtra Sewa Dal
Bihar	Karakat	RAJANI DUBEY	4795	no	ABJS	Akhil Bharatiya Jan Sangh
Bihar	Karakat	RAJA RAM SINGH	32686	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Karakat	None of the Above	10185	no	NOTA	None of the Above
Bihar	Jahanabad	DR. ARUN KUMAR	322647	yes	BLSP	Rashtriya Lok Samta Party
Bihar	Jahanabad	RAVINDRA SINGH	10580	no	BSP	Bahujan Samaj Party
Bihar	Jahanabad	ANIL KUMAR SHARMA	100851	no	JD(U)	Janata Dal  (United)
Bihar	Jahanabad	RAMADHAR SINGH	34365	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Jahanabad	SURENDRA PRASAD YADAV	280307	no	RJD	Rashtriya Janata Dal
Bihar	Jahanabad	RAHUL RANJAN	3521	no	JMM	Jharkhand Mukti Morcha
Bihar	Jahanabad	None of the Above	10352	no	NOTA	None of the Above
Bihar	Jahanabad	MANJAY KUMAR	8874	no	IND	Independent
Bihar	Jahanabad	NITYANAND SINGH	10625	no	IND	Independent
Bihar	Jahanabad	ARJUN THAKUR	7757	no	IND	Independent
Bihar	Jahanabad	SUDAY YADAV	4202	no	IND	Independent
Bihar	Jahanabad	CHANDRA BHUSHAN SHARMA	3711	no	AAAP	Aam Aadmi Party
Bihar	Jahanabad	MD. ISTIYAK	6041	no	AITC	All India Trinamool Congress
Bihar	Jahanabad	PARSHURAM VIND	2721	no	BMUP	Bahujan Mukti Party
Bihar	Jahanabad	MANJU DEVI	2826	no	KVD	Krantikari Vikas Dal
Bihar	Jahanabad	NARENDRA KUMAR	2136	no	SHS	Shivsena
Bihar	Aurangabad	SUSHIL KUMAR SINGH	307941	yes	BJP	Bharatiya Janata Party
Bihar	Aurangabad	DURGA THAKUR	3696	no	IND	Independent
Bihar	Aurangabad	SOMARU PASWAN	8678	no	IND	Independent
Bihar	Aurangabad	BIJEET KUMAR	6121	no	IND	Independent
Bihar	Aurangabad	RAMJI SINGH	7195	no	IND	Independent
Bihar	Aurangabad	UPENDRA NATH	9255	no	BMUP	Bahujan Mukti Party
Bihar	Aurangabad	RAKESH SHARMA	5726	no	IND	Independent
Bihar	Aurangabad	BAGI KUMAR VERMA	136137	no	JD(U)	Janata Dal  (United)
Bihar	Aurangabad	USHA SHARAN	4608	no	SSD	Shoshit Samaj Dal
Bihar	Aurangabad	SANTOSH KUMAR	27833	no	BSP	Bahujan Samaj Party
Bihar	Aurangabad	RAGINI LATA SINGH	6703	no	AAAP	Aam Aadmi Party
Bihar	Aurangabad	RITA DEVI	3333	no	SP	Samajwadi Party
Bihar	Aurangabad	NIKHIL KUMAR	241594	no	INC	Indian National Congress
Bihar	Aurangabad	None of the Above	17454	no	NOTA	None of the Above
Bihar	Gaya (SC)	HARI MANJHI	326230	yes	BJP	Bharatiya Janata Party
Bihar	Gaya (SC)	JITAN RAM MANJHI	131828	no	JD(U)	Janata Dal  (United)
Bihar	Gaya (SC)	DR. DEV KUMAR CHOUDHARY	19651	no	IND	Independent
Bihar	Gaya (SC)	NIRANJAN KUMAR	7624	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Gaya (SC)	RAMJI MANJHI	210726	no	RJD	Rashtriya Janata Dal
Bihar	Gaya (SC)	SHIV SHANKAR KUMAR	5924	no	BBMP	Bharat Bhrashtachar Mitao Party
Bihar	Gaya (SC)	BHUNESWAR DAS	7549	no	RAHM	Rashtriya Ahinsa Manch
Bihar	Gaya (SC)	KAPIL CHOUDHARY	9004	no	AITC	All India Trinamool Congress
Bihar	Gaya (SC)	MAMTA KUMARI	6065	no	SP	Samajwadi Party
Bihar	Gaya (SC)	SHIV KUMAR KANT	16028	no	BSP	Bahujan Samaj Party
Bihar	Gaya (SC)	DHANANJAY KISHORE	7312	no	AAAP	Aam Aadmi Party
Bihar	Gaya (SC)	INDRA DEO CHOUDHARY	5544	no	BMUP	Bahujan Mukti Party
Bihar	Gaya (SC)	ASHOK KUMAR	36863	no	JMM	Jharkhand Mukti Morcha
Bihar	Gaya (SC)	None of the Above	19030	no	NOTA	None of the Above
Bihar	Nawada	GIRIRAJ SINGH	390248	yes	BJP	Bharatiya Janata Party
Bihar	Nawada	VIJAY RAM	3291	no	MOSP	Moolniwasi Samaj Party
Bihar	Nawada	MANISH KUMAR	6443	no	IND	Independent
Bihar	Nawada	SURENDRA RAJBANSHI	3612	no	SP	Samajwadi Party
Bihar	Nawada	UMAKANT RAHI	2169	no	SSD	Shoshit Samaj Dal
Bihar	Nawada	KAUSHAL YADAV	168217	no	JD(U)	Janata Dal  (United)
Bihar	Nawada	RAJ BALLABH PRASAD	250091	no	RJD	Rashtriya Janata Dal
Bihar	Nawada	PRAMENDRA KUMAR YADAV	1650	no	BED	Bharatiya Ekta Dal
Bihar	Nawada	SATISH KUMAR	6736	no	IND	Independent
Bihar	Nawada	SOHAIL ANWER	7997	no	BSP	Bahujan Samaj Party
Bihar	Nawada	ASHOK KUMAR	2596	no	ravp	Rajnaitik Vikalp Party
Bihar	Nawada	RAJENDRA SINGH	4057	no	AAAP	Aam Aadmi Party
Bihar	Nawada	ADITYA NARAYAN	3743	no	IND	Independent
Bihar	Nawada	SAFDAR ISMAIL	12072	no	IND	Independent
Bihar	Nawada	MOHAMMAD JAHID HUSAIN	3138	no	AITC	All India Trinamool Congress
Bihar	Nawada	AZAD GEETA PRASAD SHARMA	3568	no	IND	Independent
Bihar	Nawada	SHULOCHANA DEVI	7324	no	IND	Independent
Bihar	Nawada	None of the Above	7489	no	NOTA	None of the Above
Bihar	Jamui (SC)	CHIRAG KUMAR PASWAN	285354	yes	LJP	Lok Jan Shakti Party
Bihar	Jamui (SC)	UDAY NARAIN CHOUDHARY	198599	no	JD(U)	Janata Dal  (United)
Bihar	Jamui (SC)	SUDHANSU SHEKHAR BHASKAR	199407	no	RJD	Rashtriya Janata Dal
Bihar	Jamui (SC)	BRAHMDEV ANAND PASWAN	15008	no	BSP	Bahujan Samaj Party
Bihar	Jamui (SC)	JAY PRAKASH DAS	6253	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Bihar	Jamui (SC)	VISHUN CHOUDHARY	7147	no	SHS	Shivsena
Bihar	Jamui (SC)	VINOD KUMAR DAS	7159	no	AAAP	Aam Aadmi Party
Bihar	Jamui (SC)	DILIP KUMAR	7635	no	IND	Independent
Bihar	Jamui (SC)	UPENDRA RAVIDAS	10748	no	JMM	Jharkhand Mukti Morcha
Bihar	Jamui (SC)	SUBHASH PASWAN	10452	no	IND	Independent
Bihar	Jamui (SC)	MANOJ KUMAR MANJHI	8360	no	IND	Independent
Bihar	Jamui (SC)	None of the Above	19517	no	NOTA	None of the Above
Goa	North Goa	SHRIPAD YESSO NAIK	237903	yes	BJP	Bharatiya Janata Party
Goa	North Goa	ADV. SUHAS NAIK	5640	no	CPI	Communist Party of India
Goa	North Goa	DAYANAND G. NARVEKAR	4128	no	IND	Independent
Goa	North Goa	MITTU MUJAVAR	1813	no	IND	Independent
Goa	North Goa	RAVI NAIK	132304	no	INC	Indian National Congress
Goa	North Goa	None of the Above	5770	no	NOTA	None of the Above
Goa	North Goa	RAJU DSOUZA	3216	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
Goa	North Goa	DR.DATTARAM DESAI	15857	no	AAAP	Aam Aadmi Party
Goa	South Goa	ADV. NARENDRA KESHAV SAWAIKAR	198776	yes	BJP	Bharatiya Janata Party
Goa	South Goa	ADV.JAWAHAR LEONARDO DIAS	530	no	GOA	Goemcarancho Otrec Astro
Goa	South Goa	SAVIO OLIVER RODRIGUES	783	no	GSRP	Goa Su-Raj Party
Goa	South Goa	SHALOM MATHEW FRANCISCO SARDINHA	2586	no	IND	Independent
Goa	South Goa	BALKRISHNA SALGAONKAR	683	no	IND	Independent
Goa	South Goa	CLIFFTON DE SOUZA	666	no	BMUP	Bahujan Mukti Party
Goa	South Goa	None of the Above	4333	no	NOTA	None of the Above
Goa	South Goa	GOVIND SHEPU GAUDE	7152	no	IND	Independent
Goa	South Goa	SWATI S. KERKAR	11246	no	AAAP	Aam Aadmi Party
Goa	South Goa	CHURCHILL ALEMAO	11941	no	AITC	All India Trinamool Congress
Goa	South Goa	ALEIXO REGINALDO LOURENCO	166446	no	INC	Indian National Congress
Goa	South Goa	ADV. RAJU MANGESHKAR	4437	no	CPI	Communist Party of India
Goa	South Goa	VENUS HABIB	790	no	IND	Independent
Gujarat	Kachchh	CHAVDA VINOD LAKHAMASHI	562855	yes	BJP	Bharatiya Janata Party
Gujarat	Kachchh	HIRJI PUNJABHAI SIJU	21106	no	BMUP	Bahujan Mukti Party
Gujarat	Kachchh	DR.DINESH PARMAR	308373	no	INC	Indian National Congress
Gujarat	Kachchh	None of the Above	16879	no	NOTA	None of the Above
Gujarat	Kachchh	DANICHA GOVINDBHAI PUNAMCHAND	15797	no	AAAP	Aam Aadmi Party
Gujarat	Kachchh	KAMALBHAI MATANG	21230	no	BSP	Bahujan Samaj Party
Gujarat	Banaskantha	CHAUDHARY HARIBHAI PARTHIBHAI	507856	yes	BJP	Bharatiya Janata Party
Gujarat	Banaskantha	MAHENDRABHAI KESARABHAI BUMBADIA	3302	no	IND	Independent
Gujarat	Banaskantha	SHRIMALI ASHOKBHAI BALCHNDBHAI	2535	no	IND	Independent
Gujarat	Banaskantha	SANJAYKUMAR SOMNATHBHAI RAVAL	6571	no	AAAP	Aam Aadmi Party
Gujarat	Banaskantha	BABAJI THAKOR	10897	no	IND	Independent
Gujarat	Banaskantha	None of the Above	17397	no	NOTA	None of the Above
Gujarat	Banaskantha	THAKOR BHUPATAJI RAVAJI	1770	no	IND	Independent
Gujarat	Banaskantha	DABHI NAVAJIBHAI MADHABHAI	2118	no	IND	Independent
Gujarat	Banaskantha	MADHU NIRUPABEN NATVARLAL	2064	no	IND	Independent
Gujarat	Banaskantha	CHOUDHARY ADAMBHAI NASIRBHAI	2007	no	SP	Samajwadi Party
Gujarat	Banaskantha	GAMAR VADHABHAI RADHABHAI	825	no	IND	Independent
Gujarat	Banaskantha	SOLANKI SAYBABHAI NANABHAI	7207	no	IND	Independent
Gujarat	Banaskantha	SOLANKI DINESHKUMAR ALJIBHAI	5388	no	IND	Independent
Gujarat	Banaskantha	PATEL JOITABHAI KASNABHAI	305522	no	INC	Indian National Congress
Gujarat	Banaskantha	MAHANT PARSOTAMGIRI TURANTGIRI	11175	no	BSP	Bahujan Samaj Party
Gujarat	Patan	LILADHARBHAI KHODAJI VAGHELA	518538	yes	BJP	Bharatiya Janata Party
Gujarat	Patan	RATHOD BHAVSINHBHAI DAHYABHAI	379819	no	INC	Indian National Congress
Gujarat	Patan	VAGHDA JIVABHAI DEVABHAI	7492	no	IND	Independent
Gujarat	Patan	PARMAR MAGANBHAI AMARABHAI	9900	no	BSP	Bahujan Samaj Party
Gujarat	Patan	JAGRALA  IMRAN MAHEMUD	1978	no	IND	Independent
Gujarat	Patan	DESAI ISHWARBHAI MAHADEVBHAI	1752	no	IND	Independent
Gujarat	Patan	None of the Above	12061	no	NOTA	None of the Above
Gujarat	Patan	PARMAR BHAICHANDBHAI SOMABHAI	910	no	RPIE	Republican Party of India Ektavadi
Gujarat	Patan	CHHAGANBHAI NARANBHAI PRAJAPATI	2539	no	IND	Independent
Gujarat	Patan	BHORANIYA SOYABBHAI HASAMBHAI	2550	no	IND	Independent
Gujarat	Patan	MOLAPIYA ABDULKUDUS ABDULMAJID	5629	no	IND	Independent
Gujarat	Patan	BABUBHAI KARSHANBHAI RABARI	1843	no	SP	Samajwadi Party
Gujarat	Patan	IMRANKHAN MAHMADKHAN NAGORI	1840	no	IND	Independent
Gujarat	Patan	GOVINDJI OKHAJI THAKOR	6188	no	JD(U)	Janata Dal  (United)
Gujarat	Patan	JAKASIJI JOGAJI THAKOR	2760	no	BMUP	Bahujan Mukti Party
Gujarat	Mahesana	PATEL JAYSHREEBEN KANUBHAI	580250	yes	BJP	Bharatiya Janata Party
Gujarat	Mahesana	PATEL DILIPBHAI SOMABHAI	1032	no	HJP	Hindustan Janta Party
Gujarat	Mahesana	THAKOR SAMARATJI BALVANTSINH	2427	no	IND	Independent
Gujarat	Mahesana	PRAJAPATI VITTHALBHAI VIRDAS	5982	no	IND	Independent
Gujarat	Mahesana	THAKOR KEVALJI NATHAJI	9766	no	BSP	Bahujan Samaj Party
Gujarat	Mahesana	None of the Above	11615	no	NOTA	None of the Above
Gujarat	Mahesana	GIRISHJI JENAJI DABHI	4825	no	BNJD	Bharatiya National Janta Dal
Gujarat	Mahesana	THAKOR MANSANGJI PUNJAJI	1251	no	BMUP	Bahujan Mukti Party
Gujarat	Mahesana	THAKOR BABUJI DHANAJI	1804	no	IND	Independent
Gujarat	Mahesana	THAKOR BALDEVJI MAGANJI	1653	no	IND	Independent
Gujarat	Mahesana	ZALA INDRAJITSINH MAHENDRASINH	1340	no	SP	Samajwadi Party
Gujarat	Mahesana	MAHMAD AZAM HAIDERKHAN PATHAN	6139	no	IND	Independent
Gujarat	Mahesana	SMT VANDANABEN DINESHKUMAR PATEL	3731	no	AAAP	Aam Aadmi Party
Gujarat	Mahesana	CHAUDHARY DINESHBHAI SALUBHAI	1084	no	VHS	Vishva Hindustani Sangathan
Gujarat	Mahesana	PATEL JIVABHAI AMBALAL	371359	no	INC	Indian National Congress
Gujarat	Sabarkantha	RATHOD DIPSINH SHANKARSINH	552205	yes	BJP	Bharatiya Janata Party
Gujarat	Sabarkantha	BALUSINGH SOMANSINGH NARVE	3527	no	IND	Independent
Gujarat	Sabarkantha	PATEL PURUSOTTAMBHAI AMBALAL	1876	no	IND	Independent
Gujarat	Sabarkantha	PATEL BHOGILAL HARIBHAI	9046	no	BNJD	Bharatiya National Janta Dal
Gujarat	Sabarkantha	SHEKH ARIF KHAN	2314	no	SP	Samajwadi Party
Gujarat	Sabarkantha	None of the Above	22334	no	NOTA	None of the Above
Gujarat	Sabarkantha	VELANI SHANTILAL KARAMSHI	6816	no	IND	Independent
Gujarat	Sabarkantha	PATEL ISHWARBHAI SAKRAJI	1674	no	IND	Independent
Gujarat	Sabarkantha	SOLANKI CHHAGANBHAI KEVALABHAI	9795	no	IND	Independent
Gujarat	Sabarkantha	SHANKERSINH VAGHELA BAPU	467750	no	INC	Indian National Congress
Gujarat	Sabarkantha	BHAMBHI CHANDUBHAI MULCHANDBHAI	16665	no	BSP	Bahujan Samaj Party
Gujarat	Gandhinagar	L.K.ADVANI	773539	yes	BJP	Bharatiya Janata Party
Gujarat	Gandhinagar	NIRANJAN GHOSH	6068	no	BSP	Bahujan Samaj Party
Gujarat	Gandhinagar	PAGI GANPATBHAI MAVJIBHAI	924	no	IND	Independent
Gujarat	Gandhinagar	THAKOR CHANDRASINH JAVANSINH	668	no	IND	Independent
Gujarat	Gandhinagar	THAKOR RAVAJI JUHAJI	809	no	VHS	Vishva Hindustani Sangathan
Gujarat	Gandhinagar	BRAHMBHATT SANJAYBHAI AMARKUMAR	1474	no	IND	Independent
Gujarat	Gandhinagar	PATEL PIYUSHKUMAR DASHRATHLAL	653	no	NYP	National Youth Party
Gujarat	Gandhinagar	MANOJ J. NAYAK	683	no	LoRP	Loktantrik Rashrtavadi Party
Gujarat	Gandhinagar	RITURAJ MEHTA	19966	no	AAAP	Aam Aadmi Party
Gujarat	Gandhinagar	RAHUL CHIMANBHAI MEHTA	9767	no	IND	Independent
Gujarat	Gandhinagar	KIRITBHAI ISHVARBHAI PATEL	290418	no	INC	Indian National Congress
Gujarat	Gandhinagar	DIPIKABEN SUTARIYA	684	no	IND	Independent
Gujarat	Gandhinagar	DESAI KHODABHAI LALJIBHAI	793	no	IND	Independent
Gujarat	Gandhinagar	M. K. SHAH	3767	no	IND	Independent
Gujarat	Gandhinagar	AJMALBHAI RAMABHAI VANIYA	1639	no	BMUP	Bahujan Mukti Party
Gujarat	Gandhinagar	VAGHELA KISHORSINH MAHOBATSINH	6705	no	IND	Independent
Gujarat	Gandhinagar	BIRJUSINH MOTISINH KSHATRIYA	984	no	IND	Independent
Gujarat	Gandhinagar	None of the Above	12777	no	NOTA	None of the Above
Gujarat	Gandhinagar	RAJ PRAJAPATI	3177	no	IND	Independent
Gujarat	Ahmedabad East	PARESH RAWAL	633582	yes	BJP	Bharatiya Janata Party
Gujarat	Ahmedabad East	ROSHAN PRIYAVADAN SHAH	4046	no	IND	Independent
Gujarat	Ahmedabad East	PATEL HIMMATSINGH PRAHLADSINGH	306949	no	INC	Indian National Congress
Gujarat	Ahmedabad East	ROHIT RAJUBHAI VIRJIBHAI ALIAS MANOJBHAI SONTARIYA	6023	no	BSP	Bahujan Samaj Party
Gujarat	Ahmedabad East	None of the Above	14358	no	NOTA	None of the Above
Gujarat	Ahmedabad East	VIJAYKUMAR M. VADHEL	704	no	HND	Hindusthan Nirman Dal
Gujarat	Ahmedabad East	NARANBHAI T. SENGAL (DR.N.T.SENGAL)	782	no	BSDL	Bahujan Suraksha Dal
Gujarat	Ahmedabad East	BUDDHPRIYA JASVANT SOMABHAI	698	no	PAP	Prajatantra Aadhar Party
Gujarat	Ahmedabad East	ATIKBHAI MEV	1094	no	IND	Independent
Gujarat	Ahmedabad East	DASHRATHBHAI M. DEVDA	2299	no	IND	Independent
Gujarat	Ahmedabad East	ADITYA RAVAL	987	no	VHS	Vishva Hindustani Sangathan
Gujarat	Ahmedabad East	DINESH VAGHELA	11349	no	AAAP	Aam Aadmi Party
Gujarat	Ahmedabad East	KHALIFA SAMSUDDIN NASIRUDDIN (JUGNU)	895	no	ABCD(A)	Akhil Bharatiya Congress Dal (Ambedkar)
Gujarat	Ahmedabad East	ANILKUMAR SHARMA	1201	no	IND	Independent
Gujarat	Ahmedabad East	DUTT AAKASH - ADVOCATE	558	no	JD(U)	Janata Dal  (United)
Gujarat	Ahmedabad West	DR. KIRIT P SOLANKI	617104	yes	BJP	Bharatiya Janata Party
Gujarat	Ahmedabad West	ISHWARBAHI DHANABHAI MAKWANA	296793	no	INC	Indian National Congress
Gujarat	Ahmedabad West	J J MEVADA	17332	no	AAAP	Aam Aadmi Party
Gujarat	Ahmedabad West	CHAVDA MANSUKHBHAI NAGARBHAI	6205	no	BSP	Bahujan Samaj Party
Gujarat	Ahmedabad West	DR J G PARMAR	2564	no	BMUP	Bahujan Mukti Party
Gujarat	Ahmedabad West	SOLANKI RAMESHBHAI DANABHAI	1256	no	IND	Independent
Gujarat	Ahmedabad West	AMRUT SONARA	941	no	BSDL	Bahujan Suraksha Dal
Gujarat	Ahmedabad West	SOLANKI VITTHALBHAI MAGANBHAI	2837	no	IND	Independent
Gujarat	Ahmedabad West	PARMAR HARJIVANBHAI KALABHAI	807	no	IND	Independent
Gujarat	Ahmedabad West	AAYAR MULJIBHAI KHANABHAI	808	no	IND	Independent
Gujarat	Ahmedabad West	None of the Above	16571	no	NOTA	None of the Above
Gujarat	Ahmedabad West	NARENDRA SANKHALIYA	1391	no	LSWP	Loktantrik Samajwadi Party
Gujarat	Surendranagar	FATEPARA DEVAJIBHAI GOVINDBHAI	529003	yes	BJP	Bharatiya Janata Party
Gujarat	Surendranagar	BAR AJAMALBHAI KARMANBHAI	2251	no	BMUP	Bahujan Mukti Party
Gujarat	Surendranagar	PARMAR VASHARAMBHAI BAVALBHAI	1751	no	IND	Independent
Gujarat	Surendranagar	MANSINH SHIVUBHA ZALA	2154	no	IND	Independent
Gujarat	Surendranagar	VADALIYA KALUBHAI MALUBHAI	3109	no	IND	Independent
Gujarat	Surendranagar	MAJETHIYA SAMARATBHAI JERAMBHAI	1341	no	HJP	Hindustan Janta Party
Gujarat	Surendranagar	MAKWANA VASHARAMBHAI KARSHANBHAI	1413	no	IND	Independent
Gujarat	Surendranagar	SANKALIYA GANGARAMBHAI TAPUBHAI	11208	no	IND	Independent
Gujarat	Surendranagar	VORA BHAVANBHAI DEVAJIBHAI	4821	no	IND	Independent
Gujarat	Surendranagar	VAGHELA PRAKASHBHAI BACHUBHAI	6433	no	IND	Independent
Gujarat	Surendranagar	CHAVDA PALABHAI NANJIBHAI	1227	no	IND	Independent
Gujarat	Surendranagar	SAPARA VIPULBHAI RAMESHBHAI	14524	no	IND	Independent
Gujarat	Surendranagar	None of the Above	11024	no	NOTA	None of the Above
Gujarat	Surendranagar	KOLI PATEL SOMABHAI GANDALAL	326096	no	INC	Indian National Congress
Gujarat	Surendranagar	PARMAR PRABHUBHAI GOKALBHAI	2968	no	IND	Independent
Gujarat	Surendranagar	JETHABHAI MANJIBHAI PATEL	13375	no	AAAP	Aam Aadmi Party
Gujarat	Surendranagar	PARMAR MINAXIBEN VIJAYBHAI	10753	no	BSP	Bahujan Samaj Party
Gujarat	Surendranagar	MAKWANA UKABHAI AMRABHAI	1226	no	IND	Independent
Gujarat	Rajkot	KUNDARIYA MOHANBHAI KALYANJIBHAI	621524	yes	BJP	Bharatiya Janata Party
Gujarat	Rajkot	PRAVINCHANDRA CHUNILAL PAREKH	1562	no	IND	Independent
Gujarat	Rajkot	ANIRUDDHSINH JUVANSINH ZALA	1636	no	IND	Independent
Gujarat	Rajkot	KUNVARJIBHAI MOHANBHAI BAVALIYA	375096	no	INC	Indian National Congress
Gujarat	Rajkot	VINODBHAI PRAGJIBHAI NAGANI	5752	no	IND	Independent
Gujarat	Rajkot	None of the Above	18249	no	NOTA	None of the Above
Gujarat	Rajkot	ROHITBHAI MOHANBHAI SAGATHIYA	1344	no	BMUP	Bahujan Mukti Party
Gujarat	Rajkot	RAJESHBHAI LAVJIBHAI PIPALVA	808	no	LoRP	Loktantrik Rashrtavadi Party
Gujarat	Rajkot	POPATBHAI DEVAJIBHAI CHAVDA (BHUVA)	1408	no	IND	Independent
Gujarat	Rajkot	JITENDRA BHURABHAI CHAUHAN	990	no	RPI(KH)	Republican Party of India (Khobragade)
Gujarat	Rajkot	BASIRBHAI SULEMANBHAI SAMA	2076	no	IND	Independent
Gujarat	Rajkot	ANKUR AMRUTKUMAR DHAMELIYA(PATEL)	10785	no	AAAP	Aam Aadmi Party
Gujarat	Rajkot	PATHAN BISMILLAKHAN ABDULKHAN (BABAKHAN PATHAN)	1080	no	IND	Independent
Gujarat	Rajkot	NARENDRABHAI MAHAJAN	1161	no	IND	Independent
Gujarat	Rajkot	DENGADA PRAVINBHAI MEGHAJIBHAI	945	no	RPI(A)	Republican Party of India (A)
Gujarat	Rajkot	JIVANBHAI DEVAJIBHAI PARMAR	12653	no	BSP	Bahujan Samaj Party
Gujarat	Porbandar	RADADIYA VITHALBHAI  HANSRAJBHAI	508437	yes	BJP	Bharatiya Janata Party
Gujarat	Porbandar	SADIYA VRAJALAL PABABHAI	12180	no	BSP	Bahujan Samaj Party
Gujarat	Porbandar	JADEJA KANDHALBHAI SARMANBHAI	240466	no	NCP	Nationalist Congress Party
Gujarat	Porbandar	None of the Above	16443	no	NOTA	None of the Above
Gujarat	Porbandar	PATEL BUTANI RAJESHBHAI MAGANBHAI	2868	no	IND	Independent
Gujarat	Porbandar	KHUNTI BHARATBHAI MALDEBHAI	2205	no	IND	Independent
Gujarat	Porbandar	IRFANSHAH HABIBSHAH SUHARAVARDI	3825	no	SP	Samajwadi Party
Gujarat	Porbandar	KHOKHANI LALITABEN MANSUKHBHAI	1569	no	IND	Independent
Gujarat	Porbandar	JOSHI HARISH LILADHAR	1024	no	IND	Independent
Gujarat	Porbandar	TUKADIA G.R.	1704	no	IND	Independent
Gujarat	Porbandar	RATHOD CHANDULAL MOHANLAL	3009	no	IND	Independent
Gujarat	Porbandar	AGHERA DHIRAJLAL BHURABHAI	1671	no	IND	Independent
Gujarat	Porbandar	MANSUKH SUNDARAJI DHOKAI	7939	no	AAAP	Aam Aadmi Party
Gujarat	Porbandar	VAKIL VINZUDA RANJITBHAI NARANBHAI	4475	no	IND	Independent
Gujarat	Porbandar	UNADAKAT PRAKASH VALLABHADAS	1618	no	IND	Independent
Gujarat	Jamnagar	POONAMBEN HEMATBHAI MAADAM	484412	yes	BJP	Bharatiya Janata Party
Gujarat	Jamnagar	SAIYAD ABUBAKAR IBRAHIM	940	no	SP	Samajwadi Party
Gujarat	Jamnagar	VANIYA GANGAJIBHAI	795	no	IND	Independent
Gujarat	Jamnagar	SAMA YUSUF	8234	no	BSP	Bahujan Samaj Party
Gujarat	Jamnagar	KASAMBHAI	1254	no	RKEP	Rashtriya Komi Ekta Party
Gujarat	Jamnagar	PANDYA CHIRAGBHAI HARIOMBHAI	1541	no	IND	Independent
Gujarat	Jamnagar	DALIT JITESH BABUBHAI RATHORE	706	no	IND	Independent
Gujarat	Jamnagar	MEMAN RAFIK ABUBAKAR POPATPUTRA	5811	no	IND	Independent
Gujarat	Jamnagar	VAGHER ALI ISHAK PALANI	3946	no	IND	Independent
Gujarat	Jamnagar	AHIR VIKRAMBHAI ARJANBHAI MADAM	309123	no	INC	Indian National Congress
Gujarat	Jamnagar	DALIT ASHOK NATHABHAI CHAVDA	744	no	IND	Independent
Gujarat	Jamnagar	JHALA RAJENDRASINH	4911	no	AAAP	Aam Aadmi Party
Gujarat	Jamnagar	DHARAVIYA VALLABHBHAI	1043	no	IND	Independent
Gujarat	Jamnagar	SACHADA HABIB ISHABHAI	819	no	IND	Independent
Gujarat	Jamnagar	SODHA SALIMBHAI NURMAMADBHAI	885	no	IND	Independent
Gujarat	Jamnagar	SUTHAR HANSABEN HARSUKHBHAI GORECHA	592	no	IND	Independent
Gujarat	Jamnagar	DHANJIBHAI LALJIBHAI RANEVADIA	777	no	IND	Independent
Gujarat	Jamnagar	VAGHER JAVIDBHAI OSMANBHAI NOLE	1247	no	IND	Independent
Gujarat	Jamnagar	CHANDRAVIJAYSINH TAKHUBHA RANA	876	no	IND	Independent
Gujarat	Jamnagar	SUMARA AMANDBHAI NOORMAMADBHAI SUMARA	760	no	IND	Independent
Gujarat	Jamnagar	SANDHI MAMADBHAI HAJIBHAI SAFIA	1377	no	IND	Independent
Gujarat	Jamnagar	MAMAD HAJI BOLIM	8596	no	IND	Independent
Gujarat	Jamnagar	None of the Above	6588	no	NOTA	None of the Above
Gujarat	Jamnagar	NARIYA PRAVINBHAI VALLABHBHAI	1811	no	IND	Independent
Gujarat	Jamnagar	PADHIYAR LALJIBHAI KARABHAI	1188	no	IND	Independent
Gujarat	Jamnagar	BATHWAR NANJIBHAI	3667	no	IND	Independent
Gujarat	Junagadh	CHUDASAMA RAJESHBHAI NARANBHAI	513179	yes	BJP	Bharatiya Janata Party
Gujarat	Junagadh	None of the Above	17022	no	NOTA	None of the Above
Gujarat	Junagadh	SAIYED ALTAF HUSAIN ABDULLAH MIYAN	2717	no	IND	Independent
Gujarat	Junagadh	KADRI IBRAHIM SAIYED HUSEN	2409	no	SP	Samajwadi Party
Gujarat	Junagadh	HARILAL RANCHHODBHAI CHAUHAN	7574	no	IND	Independent
Gujarat	Junagadh	PUNJABHAI BHIMABHAI VANSH	377347	no	INC	Indian National Congress
Gujarat	Junagadh	SOLANKI HARIBHAI BOGHABHAI	2565	no	BMUP	Bahujan Mukti Party
Gujarat	Junagadh	GADHIYA SOYEB HUSHENBHAI	1891	no	IND	Independent
Gujarat	Junagadh	ATUL GOVINDBHAI SHEKHADA	16674	no	AAAP	Aam Aadmi Party
Gujarat	Amreli	KACHHADIYA NARANBHAI BHIKHABHAI	436715	yes	BJP	Bharatiya Janata Party
Gujarat	Amreli	SANGANI VIJAYBHAI DINESHBHAI	1938	no	YuS	Yuva Sarkar
Gujarat	Amreli	CHAVADA MANUBHAI PARSHOTTMBHAI	13803	no	JD(U)	Janata Dal  (United)
Gujarat	Amreli	SUKHADIYA NATHALAL VALLABHBHAI	15520	no	AAAP	Aam Aadmi Party
Gujarat	Amreli	MEHULBHAI HIMATBHAI SUKHADIA	3439	no	IND	Independent
Gujarat	Amreli	DAFADA RAMJIBHAI NANJIBHAI	7822	no	BSP	Bahujan Samaj Party
Gujarat	Amreli	DHIMECHA NARESHBHAI NANJIBHAI	2062	no	IND	Independent
Gujarat	Amreli	JADAV MUSTAKBHAI	1553	no	IND	Independent
Gujarat	Amreli	RAMANI SURESHBHAI DHIRUBHAI	10114	no	IND	Independent
Gujarat	Amreli	RATHOD JIVANBHAI R.	1809	no	RPI(A)	Republican Party of India (A)
Gujarat	Amreli	NATHABHAI DAYABHAI TOTA	4032	no	IND	Independent
Gujarat	Amreli	VALODARA VRAJLAL JIVABHAI	8167	no	IND	Independent
Gujarat	Amreli	USMANBHAI P. MAGHARA	2216	no	IND	Independent
Gujarat	Amreli	THUMMAR VIRJIBHAI KESHAVBHAI (VIRJIBHAI THUMMAR)	280483	no	INC	Indian National Congress
Gujarat	Amreli	None of the Above	19143	no	NOTA	None of the Above
Gujarat	Bhavnagar	DR. BHARATIBEN DHIRUBHAI SHIYAL	549529	yes	BJP	Bharatiya Janata Party
Gujarat	Bhavnagar	None of the Above	9590	no	NOTA	None of the Above
Gujarat	Bhavnagar	VAGHELA NARENDRABHAI SHAVJIBHAI	9113	no	IND	Independent
Gujarat	Bhavnagar	BHAVES GHANSHYAMBHAI RAJYAGURU	5912	no	IND	Independent
Gujarat	Bhavnagar	RATHOD PRAVINSINH CHANDRASINH	5530	no	IND	Independent
Gujarat	Bhavnagar	MARU MANHAR VALAJIBHAI	2257	no	IND	Independent
Gujarat	Bhavnagar	KHADRANI ASIMBHAI PIRBHAI	1359	no	IND	Independent
Gujarat	Bhavnagar	RASIDKHAN HASANKHAN PATHAN	2707	no	IND	Independent
Gujarat	Bhavnagar	KAGADA RAMESHBHAI PUNABHAI	3542	no	IND	Independent
Gujarat	Bhavnagar	GOHIL PRAVINSINH DHIRUBHA	2276	no	IND	Independent
Gujarat	Bhavnagar	GOHEL BHARATBHAI BHIMABHAI	1734	no	IND	Independent
Gujarat	Bhavnagar	JAGADISHBHAI AMARABHAI VEGAD	1525	no	IND	Independent
Gujarat	Bhavnagar	MEHTA YASHVANTRAY ODHAVJIBHAI	1852	no	IND	Independent
Gujarat	Bhavnagar	VEGAD NATHABHAI (VEGADBHAI PRAGNACHAKSHU CANDIDATE)	7550	no	IND	Independent
Gujarat	Bhavnagar	GITA CHETAN PAUNDA (ADVOCATE GITABA JADEJA)	9345	no	BSP	Bahujan Samaj Party
Gujarat	Bhavnagar	DR. KANUBHAI V. KALSARIA	49540	no	AAAP	Aam Aadmi Party
Gujarat	Bhavnagar	RATHOD PRAVINBHAI JINABHAI	254041	no	INC	Indian National Congress
Gujarat	Anand	DILIP PATEL	490829	yes	BJP	Bharatiya Janata Party
Gujarat	Anand	SOLANKI BHARATBHAI MADHAVSINH	427403	no	INC	Indian National Congress
Gujarat	Anand	PURSOTTAMBHAI ALIAS KANUBHAI MATHURBHAI CHAUHAN	6480	no	BSP	Bahujan Samaj Party
Gujarat	Anand	MAHIDA MAHENDRASINH SAHEBSINH	1790	no	SP	Samajwadi Party
Gujarat	Anand	PATEL JAYESHBHAI ARVINDBHAI	1459	no	IND	Independent
Gujarat	Anand	PADHIYAR VIKRAMSINH (VAKIL)	876	no	RSPS	Rashtriya Samaj Paksha
Gujarat	Anand	RABARI HITESHKUMAR LALJIBHAI	2975	no	IND	Independent
Gujarat	Anand	PATEL DILIPBHAI MANILAL	1466	no	IND	Independent
Gujarat	Anand	HITENDRASINH MOHANSINH PARMAR	1260	no	BNJD	Bharatiya National Janta Dal
Gujarat	Anand	RAVJIBHAI S PARMAR	2442	no	AAAP	Aam Aadmi Party
Gujarat	Anand	PATEL NAINESHKUMAR UMEDBHAI	1063	no	IND	Independent
Gujarat	Anand	GIRISHBHAI DAS (ADVOCATE)	4003	no	BMUP	Bahujan Mukti Party
Gujarat	Anand	PARMAR AMARSINH DAHYABHAI	1265	no	IND	Independent
Gujarat	Anand	VAHORA FIROJBHAI WALIMAHAMADBHAI (KASORWALA)	6689	no	IND	Independent
Gujarat	Anand	VAGHELA BHARAT P	4022	no	IND	Independent
Gujarat	Anand	None of the Above	16872	no	NOTA	None of the Above
Gujarat	Kheda	CHAUHAN DEVUSINH JESINGBHAI (CHAUHAN DEVUSINH)	568235	yes	BJP	Bharatiya Janata Party
Gujarat	Kheda	RANVEER PRANAYRAJ GOVINDBHAI	944	no	BMUP	Bahujan Mukti Party
Gujarat	Kheda	PARIKH VIRAL HASMUKHBHAI	846	no	IND	Independent
Gujarat	Kheda	MALEK YAKUBMIYA NABIMIYA	955	no	IND	Independent
Gujarat	Kheda	RATANSINH UDESINH CHAUHAN	3495	no	IND	Independent
Gujarat	Kheda	ROSHAN PRIYAVADAN SHAH	7442	no	IND	Independent
Gujarat	Kheda	PANDAV BHAILALBHAI KALUBHAI	5791	no	BSP	Bahujan Samaj Party
Gujarat	Kheda	CHAUHAN DEVUSING MOTISHING	1860	no	IND	Independent
Gujarat	Kheda	BADHIWALA LABHUBHAI JIVRAJBHAI	3742	no	AAAP	Aam Aadmi Party
Gujarat	Kheda	ABDUL RAZAKKHAN PATHAN	1024	no	ADPT	Apna Desh Party
Gujarat	Kheda	MALEK SADIK HUSHEN MAHAMMD HUSHEN	1444	no	IND	Independent
Gujarat	Kheda	None of the Above	20333	no	NOTA	None of the Above
Gujarat	Kheda	MALEK SABIRHUSEN ISMAELBHAI	2720	no	IND	Independent
Gujarat	Kheda	PATHAN AMANULLAKHA SITABKHA	803	no	IND	Independent
Gujarat	Kheda	KHRISTI ADWARD KHUSHALBHAI	938	no	IND	Independent
Gujarat	Kheda	DINSHA PATEL	335334	no	INC	Indian National Congress
Gujarat	Panchmahal	CHAUHAN PRABHATSINH PRATAPSINH	508274	yes	BJP	Bharatiya Janata Party
Gujarat	Panchmahal	None of the Above	25981	no	NOTA	None of the Above
Gujarat	Panchmahal	VANKAR MANILAL BHANABHAI	11375	no	IND	Independent
Gujarat	Panchmahal	RAMSINH PARMAR	337678	no	INC	Indian National Congress
Gujarat	Panchmahal	PIYUSHKUMAR DILIPBHAI PARMAR	6935	no	AAAP	Aam Aadmi Party
Gujarat	Panchmahal	PATEL PANKAJBHAI RAVJIBHAI	5426	no	IND	Independent
Gujarat	Panchmahal	MANSURI MUKHATYAR MOHAMMAD (PANTER M. LALA)	10857	no	IND	Independent
Gujarat	Panchmahal	GORA SHOEB MOHMADHANIF	1497	no	IND	Independent
Gujarat	Panchmahal	GIRI RAMCHANDRA VAIJNATH	15956	no	BSP	Bahujan Samaj Party
Gujarat	Panchmahal	SHAIKH KALIM ABDULLATIF	1993	no	SP	Samajwadi Party
Gujarat	Panchmahal	SHAIKH MAJITMIYA JIVAMIYA	1337	no	JD(U)	Janata Dal  (United)
Gujarat	Panchmahal	CHAVADA HARISHCHANDRASINH PRABHATSINH	3751	no	IND	Independent
Gujarat	Panchmahal	S. N. CHAVADA (CHAVADA VAKIL)	2401	no	IND	Independent
Gujarat	Dahod	JASVANTSINH SUMANBHAI BHABHOR	511111	yes	BJP	Bharatiya Janata Party
Gujarat	Dahod	BHABHOR MAVJIBHAI TITUBHAI	4167	no	SP	Samajwadi Party
Gujarat	Dahod	KUMARI INDUBEN NATHUBHAI SANGADA	6838	no	IND	Independent
Gujarat	Dahod	BAMANIYA JYOTISHKUMAR BAPUBHAI	11233	no	BSP	Bahujan Samaj Party
Gujarat	Dahod	MEDA JAGDISHBHAI MANILAL	5078	no	BNJD	Bharatiya National Janta Dal
Gujarat	Dahod	BHURA NAVALABHAI MANABHAI	11244	no	IND	Independent
Gujarat	Dahod	K.C.MUNIA ADVOCATE	4849	no	AAAP	Aam Aadmi Party
Gujarat	Dahod	None of the Above	32305	no	NOTA	None of the Above
Gujarat	Dahod	TAVIYAD DR. PRABHABEN KISHORSINH	280757	no	INC	Indian National Congress
Gujarat	Dahod	RAMSINGBHAI NANJIBHAI KALARA	3841	no	JD(U)	Janata Dal  (United)
Gujarat	Dahod	KATARA SINGAJIBHAI JALJIBHAI	28958	no	CPM	Communist Party of India  (Marxist)
Gujarat	Vadodara	NARENDRA MODI	845464	yes	BJP	Bharatiya Janata Party
Gujarat	Vadodara	MISTRI MADHUSUDAN DEVRAM	275336	no	INC	Indian National Congress
Gujarat	Vadodara	ROHIT MADHUSUDAN MOHANBHAI	5782	no	BSP	Bahujan Samaj Party
Gujarat	Vadodara	SUNIL DIGAMBAR KULKARNI	10101	no	AAAP	Aam Aadmi Party
Gujarat	Vadodara	JADAV AMBALAL KANABHAI	1382	no	JD(U)	Janata Dal  (United)
Gujarat	Vadodara	PATHAN MAHEMUDKHAN RAZAKKHAN	1109	no	ADPT	Apna Desh Party
Gujarat	Vadodara	PATHAN SAHEBKHAN ASIFKHAN	2101	no	SP	Samajwadi Party
Gujarat	Vadodara	None of the Above	18053	no	NOTA	None of the Above
Gujarat	Vadodara	TAPAN DASGUPTA	2249	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Gujarat	Chhota Udaipur	RAMSINH RATHWA	607916	yes	BJP	Bharatiya Janata Party
Gujarat	Chhota Udaipur	None of the Above	28815	no	NOTA	None of the Above
Gujarat	Chhota Udaipur	NARANBHAI JEMALABHAI RATHVA	428187	no	INC	Indian National Congress
Gujarat	Chhota Udaipur	PROF. ARJUNBHAI VERSINGBHAI RATHVA	23116	no	AAAP	Aam Aadmi Party
Gujarat	Chhota Udaipur	VASAVA PRAFULBHAI DEVJIBHAI	12508	no	JD(U)	Janata Dal  (United)
Gujarat	Bharuch	VASAVA MANSUKHBHAI DHANJIBHAI	548902	yes	BJP	Bharatiya Janata Party
Gujarat	Bharuch	ANILKUMAR CHHITUBHAI BHAGAT	49289	no	JD(U)	Janata Dal  (United)
Gujarat	Bharuch	JAYENDRASINH RANA	4818	no	AAAP	Aam Aadmi Party
Gujarat	Bharuch	SAIYAD MOHSIN BAPU NANUMIYAWALA	1406	no	BMUP	Bahujan Mukti Party
Gujarat	Bharuch	BHURA SHABBIRBHAI VALIBHAI	2694	no	IND	Independent
Gujarat	Bharuch	SHAILESHKUMAR MAGANBHAI PARMAR	5125	no	IND	Independent
Gujarat	Bharuch	VIRSANGBHAI PARBATBHAI GOHIL	3353	no	IND	Independent
Gujarat	Bharuch	RAFIKBHAI SULEMAN SAPA	2644	no	IND	Independent
Gujarat	Bharuch	SUKHRAMSINGH	7275	no	BSP	Bahujan Samaj Party
Gujarat	Bharuch	None of the Above	23615	no	NOTA	None of the Above
Gujarat	Bharuch	NITIN ISHWARLAL VAKIL (ADVOCATE)	2661	no	IND	Independent
Gujarat	Bharuch	SINDHI MAYYUDEEN UMARBHAI	9862	no	IND	Independent
Gujarat	Bharuch	SAYYED ASIF ZAFAR ALI	1083	no	ADPT	Apna Desh Party
Gujarat	Bharuch	ANANDKUMAR SARVARSINH VASAVA	1855	no	IND	Independent
Gujarat	Bharuch	PATEL JAYESHBHAI AMBALALBHAI (JAYESH KAKA)	395629	no	INC	Indian National Congress
Gujarat	Bardoli	VASAVA PARBHUBHAI NAGARBHAI	622769	yes	BJP	Bharatiya Janata Party
Gujarat	Bardoli	GAMIT SURENDRABHAI SIMABHAI	5351	no	IND	Independent
Gujarat	Bardoli	CHAUDHARI CHANDUBHAI MACHALABHAI	10842	no	AAAP	Aam Aadmi Party
Gujarat	Bardoli	GAMIT MOVALIYABHAI NOPARIYABHAI	11625	no	BSP	Bahujan Samaj Party
Gujarat	Bardoli	RATHOD RAMESHBHAI BHIKHABHAI	8607	no	IND	Independent
Gujarat	Bardoli	BHAILALBHAI CHHANABHAI RATHOD	5334	no	ADSP	Aadivasi Sena Party
Gujarat	Bardoli	None of the Above	19991	no	NOTA	None of the Above
Gujarat	Bardoli	CHAUDHARI RENIYABHAI SHANKARBHAI	2184	no	HND	Hindusthan Nirman Dal
Gujarat	Bardoli	CHAUDHARI REVABEN SHANKARBHAI	13270	no	CPI	Communist Party of India
Gujarat	Bardoli	CHAUDHARI TUSHARBHAI AMARSINHBHAI	498885	no	INC	Indian National Congress
Gujarat	Bardoli	JAGATSINH LALJIBHAI VASAVA	7321	no	JD(U)	Janata Dal  (United)
Gujarat	Surat	DARSHANA VIKRAM JARDOSH	718412	yes	BJP	Bharatiya Janata Party
Gujarat	Surat	MUKESHBHAI LAVJIBHAI AMBALIYA	2695	no	IND	Independent
Gujarat	Surat	OMPRAKASH SHRIVASTAV	6346	no	BSP	Bahujan Samaj Party
Gujarat	Surat	KIRITBHAI HARJIBHAI VASANI	2640	no	YuS	Yuva Sarkar
Gujarat	Surat	MOHANBHAI B. PATEL	18877	no	AAAP	Aam Aadmi Party
Gujarat	Surat	DESAI NAISHADHBHAI BHUPATBHAI	185222	no	INC	Indian National Congress
Gujarat	Surat	VASAVA KISHORBHAI CHHOTUBHAI	1187	no	JD(U)	Janata Dal  (United)
Gujarat	Surat	None of the Above	10936	no	NOTA	None of the Above
Gujarat	Surat	MAVJIBHAI LAXMANBHAI SANDIS	1607	no	IND	Independent
Gujarat	Navsari	C. R. PATIL	820831	yes	BJP	Bharatiya Janata Party
Gujarat	Navsari	SONAL KELLOGG	1089	no	VoP	Voters Party
Gujarat	Navsari	LATABEN ASHOKKUMAR DWIVEDI	7560	no	IND	Independent
Gujarat	Navsari	RAMJAN MANSURI(JOURNALIST)	1787	no	IND	Independent
Gujarat	Navsari	PERCY MUNSHI	2235	no	IND	Independent
Gujarat	Navsari	ASLAM MISTRY	3853	no	BBC	Bharatiya Bahujan Congress
Gujarat	Navsari	RAVSHAHEB BHIMRAV PATIL (BANDHU)	2888	no	IND	Independent
Gujarat	Navsari	PYARELAL BHARTI	834	no	HND	Hindusthan Nirman Dal
Gujarat	Navsari	ROHIT GANDHI	4267	no	IND	Independent
Gujarat	Navsari	MAKSUD MIRZA	262715	no	INC	Indian National Congress
Gujarat	Navsari	VIMAL PATEL (ENDHAL)	2739	no	IND	Independent
Gujarat	Navsari	None of the Above	9322	no	NOTA	None of the Above
Gujarat	Navsari	KESHAVJI L. SARADVA	1059	no	IND	Independent
Gujarat	Navsari	WARDE RAJUBHAI BHIMRAO	2156	no	BMUP	Bahujan Mukti Party
Gujarat	Navsari	PATEL BHUPENDRAKUMAR DHIRUBHAI	1264	no	JD(U)	Janata Dal  (United)
Gujarat	Navsari	CHAUHAN KESAVBHAI MALABHAI (MASTER)	11240	no	BSP	Bahujan Samaj Party
Gujarat	Navsari	ARUN S. PATHAK(JOURNALIST)	1030	no	IND	Independent
Gujarat	Navsari	MEHUL PATEL	14299	no	AAAP	Aam Aadmi Party
Gujarat	Navsari	SAIYED MEHMUD AHMED	6069	no	IND	Independent
Gujarat	Navsari	HASAN SHAIKH	3510	no	IND	Independent
Gujarat	Valsad	DR. K.C.PATEL	617772	yes	BJP	Bharatiya Janata Party
Gujarat	Valsad	PATEL SHAILESHBHAI GANDABHAI	2982	no	JD(U)	Janata Dal  (United)
Gujarat	Valsad	PATEL GOVINDBHAI RANCHHODBHAI	8047	no	AAAP	Aam Aadmi Party
Gujarat	Valsad	DR.PANKAJKUMAR PARBHUBHAI PATEL	6028	no	ADSP	Aadivasi Sena Party
Gujarat	Valsad	TALAVIYA BABUBHAI CHHAGANBHAI	2967	no	BMUP	Bahujan Mukti Party
Gujarat	Valsad	PATEL BUDHABHAI RANCHHODBHAI	12757	no	IND	Independent
Gujarat	Valsad	KISHANBHAI  VESTABHAI PATEL	409768	no	INC	Indian National Congress
Gujarat	Valsad	GAURANGBHAI RAMESHBHAI PATEL	11372	no	HJP	Hindustan Janta Party
Gujarat	Valsad	VADIA LAXMANBHAI CHHAGANBHAI	9702	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Gujarat	Valsad	THAKRIYA RATILAL VAJIRBHAI	14202	no	BSP	Bahujan Samaj Party
Gujarat	Valsad	None of the Above	26606	no	NOTA	None of the Above
Haryana	Ambala	RATTAN LAL KATARIA	612121	yes	BJP	Bharatiya Janata Party
Haryana	Ambala	None of the Above	7816	no	NOTA	None of the Above
Haryana	Ambala	RAJ KUMAR BALMIKI	272047	no	INC	Indian National Congress
Haryana	Ambala	RAMESH CHAND	2715	no	IND	Independent
Haryana	Ambala	RAM JAWARI	1290	no	RAKP	Rashtriya Karmyog Party
Haryana	Ambala	ISHWAR SINGH	1446	no	IND	Independent
Haryana	Ambala	BHURA RAM	3388	no	IND	Independent
Haryana	Ambala	RAJENDER KUMAR BHATAULI	3432	no	BSCP	Bhartiya Shakti Chetna Party
Haryana	Ambala	SURINDER PAL SINGH	63626	no	AAAP	Aam Aadmi Party
Haryana	Ambala	ARUN KUMAR	11123	no	CPI	Communist Party of India
Haryana	Ambala	PHOOL CHAND RATTUWALA	3095	no	IND	Independent
Haryana	Ambala	DR. KUSUM SHERWAL	129571	no	INLD	Indian National Lok Dal
Haryana	Ambala	DR. KAPOOR SINGH	102627	no	BSP	Bahujan Samaj Party
Haryana	Ambala	GURNAM SINGH	933	no	IND	Independent
Haryana	Ambala	BHUPINDER KUMAR	3765	no	IND	Independent
Haryana	Kurukshetra	RAJ KUMAR SAINI	418112	yes	BJP	Bharatiya Janata Party
Haryana	Kurukshetra	BALBIR SINGH SAINI	288376	no	INLD	Indian National Lok Dal
Haryana	Kurukshetra	CHUNI MANOJ KUMAR	2770	no	IND	Independent
Haryana	Kurukshetra	CHATTAR SINGH	68926	no	BSP	Bahujan Samaj Party
Haryana	Kurukshetra	RANBIR SINGH SHARMA	2765	no	IND	Independent
Haryana	Kurukshetra	VISHNU BHAGWAN AGGARWAL	2083	no	IND	Independent
Haryana	Kurukshetra	BALWINDER KAUR	32554	no	AAAP	Aam Aadmi Party
Haryana	Kurukshetra	JASVINDER JASHT BHUKHRI	3887	no	IND	Independent
Haryana	Kurukshetra	NAVEEN JINDAL	287722	no	INC	Indian National Congress
Haryana	Kurukshetra	MASTER DHARAMPAL DHIMAN BRAHAMAN	1994	no	IND	Independent
Haryana	Kurukshetra	RAM KISHAN	1635	no	LPR	Lok Parivartan Party(DC)
Haryana	Kurukshetra	RAMESHWAR KUMAR SAINI	2344	no	BSCP	Bhartiya Shakti Chetna Party
Haryana	Kurukshetra	CA. SATISH SINGAL	760	no	IND	Independent
Haryana	Kurukshetra	KANTA ALLARIA	1534	no	IBSPK	Indian Bahujan Sandesh Party (Kanshiram)
Haryana	Kurukshetra	ONKAR SINGH	1483	no	NAJC	National Janhit Congress (AB)
Haryana	Kurukshetra	RAJ KUMAR SAINI	6061	no	IND	Independent
Haryana	Kurukshetra	KULDEEP SINGH MALLHI	1665	no	RPI	Republican Party of India
Haryana	Kurukshetra	GURMEET SINGH	1092	no	LD	Lok Dal
Haryana	Kurukshetra	DAVINDER KUMAR	2041	no	IND	Independent
Haryana	Kurukshetra	ANIL KUMAR	1593	no	IND	Independent
Haryana	Kurukshetra	ASHWANI MALHOTRA	3251	no	AITC	All India Trinamool Congress
Haryana	Kurukshetra	KIRAN PAL	762	no	BIP	Bharatiya Inqalab Party
Haryana	Kurukshetra	None of the Above	2482	no	NOTA	None of the Above
Haryana	Sirsa	CHARANJEET SINGH RORI	506370	yes	INLD	Indian National Lok Dal
Haryana	Sirsa	None of the Above	4033	no	NOTA	None of the Above
Haryana	Sirsa	SANJIV BHOJRAJ	2350	no	VIP	VANCHITSAMAJ INSAAF PARTY
Haryana	Sirsa	BALWANT SINGH	2201	no	BhSMP	Bharatiya Sant Mat Party
Haryana	Sirsa	MANJEET SINGH	1334	no	IND	Independent
Haryana	Sirsa	RAJ KUMAR NAGAR	4208	no	SP	Samajwadi Party
Haryana	Sirsa	HANSRAJ HANSA	2901	no	JM	Jan Morcha
Haryana	Sirsa	RANVIR KHOBRA	7308	no	IND	Independent
Haryana	Sirsa	JAGDISH	2145	no	IND	Independent
Haryana	Sirsa	FAKIR CHAND	1197	no	IND	Independent
Haryana	Sirsa	JAIPAL BHATTI	1365	no	IND	Independent
Haryana	Sirsa	ASHOK TANWAR	390634	no	INC	Indian National Congress
Haryana	Sirsa	MANGERAM	20750	no	BSP	Bahujan Samaj Party
Haryana	Sirsa	SURENDER KUMAR (URF) MENPAL PHOOLA	3432	no	IND	Independent
Haryana	Sirsa	COMRADE RAM KUMAR	11194	no	CPM	Communist Party of India  (Marxist)
Haryana	Sirsa	DR. SUSHIL INDORA	241067	no	HJCBL	Haryana Janhit Congress (BL)
Haryana	Sirsa	RAJBIR PALNA	5636	no	IND	Independent
Haryana	Sirsa	POONAM CHAND RATTI	66844	no	AAAP	Aam Aadmi Party
Haryana	Sirsa	MEWA SINGH	4136	no	IND	Independent
Haryana	Hisar	DUSHYANT CHAUTALA	494478	yes	INLD	Indian National Lok Dal
Haryana	Hisar	NARESH BATRA	587	no	IND	Independent
Haryana	Hisar	SATBIR	363	no	IND	Independent
Haryana	Hisar	VIKAS	684	no	IND	Independent
Haryana	Hisar	KULDEEP BISHNOI	462631	no	HJCBL	Haryana Janhit Congress (BL)
Haryana	Hisar	RAM PHAL	463	no	IND	Independent
Haryana	Hisar	HANSRAJ	245	no	IND	Independent
Haryana	Hisar	KULDEEP S/O MADAN LAL	959	no	IND	Independent
Haryana	Hisar	KULDEEP S/O SAJJAN	395	no	IND	Independent
Haryana	Hisar	SUSHIL KAUSHIK	700	no	IND	Independent
Haryana	Hisar	VISHNU KUMAR	1048	no	IND	Independent
Haryana	Hisar	PHUL SINGH	6533	no	CPM	Communist Party of India  (Marxist)
Haryana	Hisar	DR. RAJESH MAHENDIA	1723	no	IND	Independent
Haryana	Hisar	SUSHIL NIDHI	273	no	IND	Independent
Haryana	Hisar	OM PARKASH	1471	no	BhSMP	Bharatiya Sant Mat Party
Haryana	Hisar	SUNDER	134	no	IND	Independent
Haryana	Hisar	SHIV KUMAR	447	no	IND	Independent
Haryana	Hisar	SITA RAM	128	no	IND	Independent
Haryana	Hisar	UMED SINGH	342	no	IND	Independent
Haryana	Hisar	ARUN KUMAR	307	no	IND	Independent
Haryana	Hisar	BALWAN SINGH	213	no	IND	Independent
Haryana	Hisar	RAKESH KUMAR (RINKU BHAI)	176	no	IND	Independent
Haryana	Hisar	DR. YUDHBIR SINGH KHYALIA	28490	no	AAAP	Aam Aadmi Party
Haryana	Hisar	MAMTA RANI	1195	no	RPP(LB)	Rashtrawadi Parivartan Party (L.B,)
Haryana	Hisar	SHRAVAN KUMAR ASIJA	371	no	IND	Independent
Haryana	Hisar	KULDEEP S/O ISHWAR SINGH	1242	no	IND	Independent
Haryana	Hisar	MANGE RAM	30446	no	BSP	Bahujan Samaj Party
Haryana	Hisar	KULDEEP S/O NARAIN SINGH	5251	no	IND	Independent
Haryana	Hisar	ATAM PARKASH	300	no	IND	Independent
Haryana	Hisar	KULDEEP S/O PARDUMN	2587	no	IND	Independent
Haryana	Hisar	RAJ SINGH	600	no	IND	Independent
Haryana	Hisar	SAMPAT SINGH	102509	no	INC	Indian National Congress
Haryana	Hisar	NARENDER	408	no	IND	Independent
Haryana	Hisar	VIJAY KUMAR SHARMA	1788	no	IND	Independent
Haryana	Hisar	KARTAR SINGH	1143	no	IND	Independent
Haryana	Hisar	YUDHVIR	197	no	IND	Independent
Haryana	Hisar	KULDEEP SINGH S/O SHISHPAL	705	no	BMUP	Bahujan Mukti Party
Haryana	Hisar	BALJEET	208	no	IND	Independent
Haryana	Hisar	KULDEEP S/O KARAN SINGH	1886	no	IND	Independent
Haryana	Hisar	NARINDER KUMAR	388	no	IND	Independent
Haryana	Hisar	RAJ KUMAR	255	no	IND	Independent
Haryana	Hisar	None of the Above	1645	no	NOTA	None of the Above
Haryana	Karnal	ASHWINI KUMAR	594817	yes	BJP	Bharatiya Janata Party
Haryana	Karnal	None of the Above	2929	no	NOTA	None of the Above
Haryana	Karnal	SAVITRI	1468	no	IND	Independent
Haryana	Karnal	RAJESH KUMAR	1937	no	IND	Independent
Haryana	Karnal	ARVIND TIWARI	1260	no	IND	Independent
Haryana	Karnal	ASHISH	1314	no	AIFB	All India Forward Bloc
Haryana	Karnal	MAHMOOD HASSAN	2723	no	IND	Independent
Haryana	Karnal	DAVINDER SHARMA	806	no	RAKP	Rashtriya Karmyog Party
Haryana	Karnal	SHASHI SAINI	1544	no	IND	Independent
Haryana	Karnal	JAGDISH	3442	no	IND	Independent
Haryana	Karnal	RAMPHAL SHARMA	940	no	IND	Independent
Haryana	Karnal	PALE RAM KASHYAP	2654	no	RJP(E)	Rashtriya Janshakti Party(Eklavya)
Haryana	Karnal	PURAN SINGH PANWAR	865	no	RVNP	Rashtravadi Janata Party
Haryana	Karnal	PARAMJIT SINGH	32060	no	AAAP	Aam Aadmi Party
Haryana	Karnal	JAGBIR SINGH RANA	2775	no	IND	Independent
Haryana	Karnal	DEVENDER JAGLAN	759	no	RPI(A)	Republican Party of India (A)
Haryana	Karnal	KHATAB SINGH MOR	885	no	BSCP	Bhartiya Shakti Chetna Party
Haryana	Karnal	JASWINDER SINGH SANDHU	187902	no	INLD	Indian National Lok Dal
Haryana	Karnal	KISMAT SHARMA	1757	no	IND	Independent
Haryana	Karnal	MAM CHAND	10945	no	CPI	Communist Party of India
Haryana	Karnal	MAHINDER PAL	1276	no	IND	Independent
Haryana	Karnal	MARATHA VIRENDER VERMA	102628	no	BSP	Bahujan Samaj Party
Haryana	Karnal	ARVIND KUMAR SHARMA	234670	no	INC	Indian National Congress
Haryana	Karnal	DULI CHAND	1144	no	LPR	Lok Parivartan Party(DC)
Haryana	Sonipat	RAMESH CHANDER	347203	yes	BJP	Bharatiya Janata Party
Haryana	Sonipat	SANT DHARAMVIR CHOTIWALA	943	no	IND	Independent
Haryana	Sonipat	DALBIR SINGH CHAHAL	671	no	RVNP	Rashtravadi Janata Party
Haryana	Sonipat	HARI PARKASH COMRADE	1757	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Haryana	Sonipat	SUKHBIR SINGH	5533	no	IND	Independent
Haryana	Sonipat	PADAM SINGH	264404	no	INLD	Indian National Lok Dal
Haryana	Sonipat	JAGBIR SINGH MALIK	269789	no	INC	Indian National Congress
Haryana	Sonipat	JAI SINGH	48597	no	AAAP	Aam Aadmi Party
Haryana	Sonipat	SUMAN SINGH	24103	no	BSP	Bahujan Samaj Party
Haryana	Sonipat	BHUPESHWAR DAYAL	2525	no	IND	Independent
Haryana	Sonipat	BIJENDER KUMAR	965	no	IND	Independent
Haryana	Sonipat	ANUP SING DAHIYA	259	no	RJAVP	Rashtriya Jatigat Aarakshan Virodhi Party
Haryana	Sonipat	ASHOK KUMAR VASHISHTH	492	no	RPI(A)	Republican Party of India (A)
Haryana	Sonipat	ANIL KUMAR	468	no	RBHP	Rashtriya Bahujan Hitay Party
Haryana	Sonipat	SANJAY	3950	no	IND	Independent
Haryana	Sonipat	PADAM SINGH	1375	no	IND	Independent
Haryana	Sonipat	JAGBIR	357	no	IND	Independent
Haryana	Sonipat	SITENDER SINGH	899	no	IND	Independent
Haryana	Sonipat	SATYA NARAYAN	550	no	IND	Independent
Haryana	Sonipat	GULAB SINGH	916	no	PHRC	Poorvanchal Rashtriya Congress
Haryana	Sonipat	RAMESH	4405	no	IND	Independent
Haryana	Sonipat	JASWANT	656	no	IND	Independent
Haryana	Sonipat	MANOJ KUMAR	2417	no	IND	Independent
Haryana	Sonipat	None of the Above	2403	no	NOTA	None of the Above
Haryana	Rohtak	DEEPENDER SINGH HOODA	490063	yes	INC	Indian National Congress
Haryana	Rohtak	None of the Above	4932	no	NOTA	None of the Above
Haryana	Rohtak	ANIL	829	no	RBHP	Rashtriya Bahujan Hitay Party
Haryana	Rohtak	MANOJ KUMAR	18690	no	BSP	Bahujan Samaj Party
Haryana	Rohtak	OM PARKASH DHANKAR	319436	no	BJP	Bharatiya Janata Party
Haryana	Rohtak	JAI SINGH	614	no	IND	Independent
Haryana	Rohtak	RAVINDER KUMAR	418	no	IBSPK	Indian Bahujan Sandesh Party (Kanshiram)
Haryana	Rohtak	SUNIL KUMAR	947	no	AITC	All India Trinamool Congress
Haryana	Rohtak	SATPAL	2449	no	IND	Independent
Haryana	Rohtak	SAJJAN SINGH	2032	no	IND	Independent
Haryana	Rohtak	VISHNU DUTT	3151	no	IND	Independent
Haryana	Rohtak	NAVEEN JAIHIND	46759	no	AAAP	Aam Aadmi Party
Haryana	Rohtak	KARAN SINGH	394	no	IND	Independent
Haryana	Rohtak	SHAMSHER SINGH KHARKARA	151120	no	INLD	Indian National Lok Dal
Haryana	Rohtak	JAI KARAN MANDAUTHI	2497	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Haryana	Bhiwani-Mahendragarh	DHARAMBIR S/O BHALE RAM	404542	yes	BJP	Bharatiya Janata Party
Haryana	Bhiwani-Mahendragarh	BAHADUR SINGH	275148	no	INLD	Indian National Lok Dal
Haryana	Bhiwani-Mahendragarh	PURAN MAL SHARMA	3195	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	SHRUTI CHOUDHRY	268115	no	INC	Indian National Congress
Haryana	Bhiwani-Mahendragarh	OMBIR	1617	no	LPR	Lok Parivartan Party(DC)
Haryana	Bhiwani-Mahendragarh	VED PAL TANWAR	27834	no	BSP	Bahujan Samaj Party
Haryana	Bhiwani-Mahendragarh	LALIT AGGARWAL	22200	no	AAAP	Aam Aadmi Party
Haryana	Bhiwani-Mahendragarh	LAL CHAND	6265	no	RBC	Republican Backward Congress
Haryana	Bhiwani-Mahendragarh	SAMSER	391	no	NLP	National Loktantrik Party
Haryana	Bhiwani-Mahendragarh	PARDEEP SINGH TANWAR	1334	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	NARESH KUMAR	1493	no	SP	Samajwadi Party
Haryana	Bhiwani-Mahendragarh	DHARM BIR S/O HARI SINGH	995	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	RAKESH KUMAR	293	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	CHITTER SINGH BALI	1060	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	SUDHIR CHANDWAS	523	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	NARENDER PICHOPA-KHURD	3151	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	DHARAMBIR S/O BANWARI	708	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	VEDPARKASH BHARTIYA	1020	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	SATPAL S/O SUBE	826	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	MASTER SHER SINGH	3441	no	CPM	Communist Party of India  (Marxist)
Haryana	Bhiwani-Mahendragarh	SHER SINGH	361	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	RAJIV	603	no	BMUP	Bahujan Mukti Party
Haryana	Bhiwani-Mahendragarh	SATPAL S/O LOKRAM	297	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	SUKHBIR SINGH	275	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	MAHAVIR SINGH	393	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	DHARAMBIR SINGH	2357	no	IND	Independent
Haryana	Bhiwani-Mahendragarh	None of the Above	1994	no	NOTA	None of the Above
Haryana	Gurgaon	INDERJIT SINGH RAO	644780	yes	BJP	Bharatiya Janata Party
Haryana	Gurgaon	None of the Above	2657	no	NOTA	None of the Above
Haryana	Gurgaon	AKBAR KASMI	1429	no	RUC	Rashtriya Ulama Council
Haryana	Gurgaon	MANOJ YADAV	3043	no	IND	Independent
Haryana	Gurgaon	HEMANT KUMAR SAINI	1153	no	BMUP	Bahujan Mukti Party
Haryana	Gurgaon	ASHOK BHARDWAJ	713	no	NAJC	National Janhit Congress (AB)
Haryana	Gurgaon	AHSHAN ALI	455	no	IND	Independent
Haryana	Gurgaon	MAHENDER JAT	1021	no	IND	Independent
Haryana	Gurgaon	RAJEEV YADAV	493	no	IND	Independent
Haryana	Gurgaon	USHA RANI VERMA	489	no	IND	Independent
Haryana	Gurgaon	RAJESH	566	no	IND	Independent
Haryana	Gurgaon	OMBIR SHARMA	566	no	IND	Independent
Haryana	Gurgaon	SURINDER KUMAR KHULLAR	686	no	IND	Independent
Haryana	Gurgaon	KUSHESHWAR BHAGAT	7821	no	IND	Independent
Haryana	Gurgaon	UMED SINGH	927	no	RMOP	RASHTRIYA MORCHA PARTY
Haryana	Gurgaon	DHARAMPAL	65009	no	BSP	Bahujan Samaj Party
Haryana	Gurgaon	YOGENDRA YADAV	79452	no	AAAP	Aam Aadmi Party
Haryana	Gurgaon	RAO DHARAM PAL	133713	no	INC	Indian National Congress
Haryana	Gurgaon	ABDUL LATIF	699	no	IND	Independent
Haryana	Gurgaon	ZAKIR HUSSAIN	370058	no	INLD	Indian National Lok Dal
Haryana	Gurgaon	KARAN SINGH JOURNALIST	938	no	IND	Independent
Haryana	Gurgaon	FAUJI JAI KAWAR TYAGI DIXIT	3147	no	IND	Independent
Haryana	Gurgaon	MUL CHAND	804	no	IND	Independent
Haryana	Faridabad	KRISHAN PAL	652516	yes	BJP	Bharatiya Janata Party
Haryana	Faridabad	DR. KSHETRA PAL SINGH	1943	no	IND	Independent
Haryana	Faridabad	PT. RAJENDER SHARMA	66000	no	BSP	Bahujan Samaj Party
Haryana	Faridabad	HAMID KHAN	2762	no	hkd	Hindustan Kranti Dal
Haryana	Faridabad	DEEPAK GAUR	479	no	AVIRP	AARAKSHAN VIRODHI PARTY
Haryana	Faridabad	SANJAY MAURYA	1098	no	IND	Independent
Haryana	Faridabad	NANAK CHAND	2551	no	IND	Independent
Haryana	Faridabad	SUKHVEER	1082	no	RBCP	Rashtriya Bahujan Congress Party
Haryana	Faridabad	PURSHOTAM DAGAR	67437	no	AAAP	Aam Aadmi Party
Haryana	Faridabad	ADVOCATE KAMAL CHAND ARYA	967	no	BMUP	Bahujan Mukti Party
Haryana	Faridabad	RAVINDER BHATI	1689	no	ABHM	Akhil Bharat Hindu Mahasabha
Haryana	Faridabad	MOHD. MUKIM	1735	no	NLP	National Loktantrik Party
Haryana	Faridabad	AVTAR SINGH BHADANA	185643	no	INC	Indian National Congress
Haryana	Faridabad	MANDHEER SINGH MAAN	644	no	IND	Independent
Haryana	Faridabad	SHUSHILA	525	no	IND	Independent
Haryana	Faridabad	VIJAY RAJ	407	no	IND	Independent
Haryana	Faridabad	KUSUM KUMARI	620	no	AIPF	All India Peoples' Front (Radical)
Haryana	Faridabad	MUKESH KUMAR SINGH	442	no	IND	Independent
Haryana	Faridabad	CHAUDHARY DAYA CHAND	711	no	BSCP	Bhartiya Shakti Chetna Party
Haryana	Faridabad	LALIT MITTAL	420	no	IND	Independent
Haryana	Faridabad	R K ANAND	132472	no	INLD	Indian National Lok Dal
Haryana	Faridabad	NIRMALA	894	no	JD(U)	Janata Dal  (United)
Haryana	Faridabad	KHUSH DIL SEHGAL (KAPOOR)	685	no	ABHKP	Akhil Bharatiya Hind Kranti Party
Haryana	Faridabad	AHERWAN DHARMENDER RAWAT	664	no	IND	Independent
Haryana	Faridabad	LAXMAN	657	no	IND	Independent
Haryana	Faridabad	RAJENDER	409	no	IND	Independent
Haryana	Faridabad	KHEME THAKUR	1940	no	SP	Samajwadi Party
Haryana	Faridabad	None of the Above	3334	no	NOTA	None of the Above
Himachal Pradesh	Kangra	SHANTA KUMAR	456163	yes	BJP	Bharatiya Janata Party
Himachal Pradesh	Kangra	PREM CHAND VISHVAKARMA	4106	no	IND	Independent
Himachal Pradesh	Kangra	DHANI RAM	1085	no	IND	Independent
Himachal Pradesh	Kangra	CHANDER KUMAR	286091	no	INC	Indian National Congress
Himachal Pradesh	Kangra	BALDEV RAJ	997	no	HiSP	Himachal Swabhiman Party
Himachal Pradesh	Kangra	DR. RAJAN SUSHANT	24430	no	AAAP	Aam Aadmi Party
Himachal Pradesh	Kangra	DR. ATUL THAKUR BHARMORI	1133	no	IND	Independent
Himachal Pradesh	Kangra	BHUPINDER MEHRA	5585	no	IND	Independent
Himachal Pradesh	Kangra	RAMAN KUMAR	637	no	BMUP	Bahujan Mukti Party
Himachal Pradesh	Kangra	LAL HUSSAIN	5949	no	BSP	Bahujan Samaj Party
Himachal Pradesh	Kangra	PARVESH YADAV	808	no	SP	Samajwadi Party
Himachal Pradesh	Kangra	ARTI SONI	3757	no	SHS	Shivsena
Himachal Pradesh	Kangra	None of the Above	8704	no	NOTA	None of the Above
Himachal Pradesh	Mandi	RAM SWAROOP SHARMA	362824	yes	BJP	Bharatiya Janata Party
Himachal Pradesh	Mandi	None of the Above	6191	no	NOTA	None of the Above
Himachal Pradesh	Mandi	PUNI CHAND	923	no	SP	Samajwadi Party
Himachal Pradesh	Mandi	BHAG CHAND RANA	1868	no	IND	Independent
Himachal Pradesh	Mandi	SUBHASH MOHAN SNEHI	1928	no	IND	Independent
Himachal Pradesh	Mandi	JAI CHAND THAKUR	9359	no	AAAP	Aam Aadmi Party
Himachal Pradesh	Mandi	LALA RAM	5167	no	BSP	Bahujan Samaj Party
Himachal Pradesh	Mandi	DEVENDER DEV	901	no	BMUP	Bahujan Mukti Party
Himachal Pradesh	Mandi	PRATIBHA SINGH	322968	no	INC	Indian National Congress
Himachal Pradesh	Mandi	KUSHAL BHARDWAJ	13965	no	CPM	Communist Party of India  (Marxist)
Himachal Pradesh	Hamirpur	ANURAG SINGH THAKUR	448035	yes	BJP	Bharatiya Janata Party
Himachal Pradesh	Hamirpur	RAKESH CHOUDHARY	5827	no	BSP	Bahujan Samaj Party
Himachal Pradesh	Hamirpur	AMIN CHAND	647	no	IND	Independent
Himachal Pradesh	Hamirpur	ASHISH KUMAR	603	no	IND	Independent
Himachal Pradesh	Hamirpur	KAMAL KANTA BATRA	15329	no	AAAP	Aam Aadmi Party
Himachal Pradesh	Hamirpur	SHIV DUTT VASISHT	952	no	SHS	Shivsena
Himachal Pradesh	Hamirpur	RAJINDER SINGH RANA	349632	no	INC	Indian National Congress
Himachal Pradesh	Hamirpur	DEV RAJ BHARDWAJ	1692	no	IND	Independent
Himachal Pradesh	Hamirpur	URMILA SHARMA	2328	no	SP	Samajwadi Party
Himachal Pradesh	Hamirpur	RAJINDER SINGH RANA	3687	no	IND	Independent
Himachal Pradesh	Hamirpur	None of the Above	6473	no	NOTA	None of the Above
Himachal Pradesh	Shimla	VIRENDER KASHYAP	385973	yes	BJP	Bharatiya Janata Party
Himachal Pradesh	Shimla	None of the Above	7788	no	NOTA	None of the Above
Himachal Pradesh	Shimla	SUBHASH CHANDER	14233	no	AAAP	Aam Aadmi Party
Himachal Pradesh	Shimla	VIRENDER KUMAR KASHYAP	6173	no	IND	Independent
Himachal Pradesh	Shimla	SHURVEER SINGH	4385	no	SP	Samajwadi Party
Himachal Pradesh	Shimla	JAGAT RAM	11434	no	CPM	Communist Party of India  (Marxist)
Himachal Pradesh	Shimla	MOHAN LAL BRAKTA	301786	no	INC	Indian National Congress
Himachal Pradesh	Shimla	GURNAM SINGH KOLI	5985	no	BSP	Bahujan Samaj Party
Jammu & Kashmir	Baramulla	MUZAFFAR HUSSAIN BAIG	175277	yes	JKPDP	Jammu & Kashmir Peoples Democratic Party
Jammu & Kashmir	Baramulla	SALAMUDDIN BAJAD	71154	no	JPC	Jammu & Kashmir People Conference
Jammu & Kashmir	Baramulla	TANVEER HUSSAIN	5788	no	IND	Independent
Jammu & Kashmir	Baramulla	SYED MOHD RAFIQ SHAH	4141	no	JKNPP	Jammu & Kashmir National Panthers Party
Jammu & Kashmir	Baramulla	GHULAM MOHAMMAD MIR	6558	no	BJP	Bharatiya Janata Party
Jammu & Kashmir	Baramulla	SHARIEF UD-DIN SHARIQ	146058	no	JKN	Jammu & Kashmir National Conference
Jammu & Kashmir	Baramulla	MOHD SHAFI BHAT	3143	no	BSP	Bahujan Samaj Party
Jammu & Kashmir	Baramulla	AB. HAMID MALIK	3655	no	IND	Independent
Jammu & Kashmir	Baramulla	BASHIR AHMAD JAGAL	2694	no	PRISM	Prism
Jammu & Kashmir	Baramulla	ASHIQ HUSSAIN GANIE	1943	no	IND	Independent
Jammu & Kashmir	Baramulla	ER. ABDUL RASHID SHEIKH	22090	no	IND	Independent
Jammu & Kashmir	Baramulla	NISAR AHMAD AHANGER	2050	no	IND	Independent
Jammu & Kashmir	Baramulla	ABDUL HUSSAIN	7301	no	IND	Independent
Jammu & Kashmir	Baramulla	AYAZ AHMAD SOFI	5566	no	GaAP	Gareeb Aadmi Party
Jammu & Kashmir	Baramulla	MOHD ABDULLAH CHATWAL	4006	no	IND	Independent
Jammu & Kashmir	Baramulla	None of the Above	4568	no	NOTA	None of the Above
Jammu & Kashmir	Srinagar	TARIQ HAMEED KARRA	157923	yes	JKPDP	Jammu & Kashmir Peoples Democratic Party
Jammu & Kashmir	Srinagar	None of the Above	4979	no	NOTA	None of the Above
Jammu & Kashmir	Srinagar	AGA SYED MOHSIN	16050	no	IND	Independent
Jammu & Kashmir	Srinagar	MOHAMMAD SHAFI GUROO	1157	no	IND	Independent
Jammu & Kashmir	Srinagar	FAYAZ AHMAD BHAT	4467	no	BJP	Bharatiya Janata Party
Jammu & Kashmir	Srinagar	CHETAN SHARMA	608	no	IND	Independent
Jammu & Kashmir	Srinagar	MUSHTAQ SHAMIM	521	no	ABML(S)	Akhil Bharatiya Muslim League (Secular)
Jammu & Kashmir	Srinagar	FAROOQ ABDULLAH	115643	no	JKN	Jammu & Kashmir National Conference
Jammu & Kashmir	Srinagar	MIRZA SAJAD HUSSAIN BEIGH	2802	no	IND	Independent
Jammu & Kashmir	Srinagar	DR RAJA MUZAFFAR BHAT	3271	no	AAAP	Aam Aadmi Party
Jammu & Kashmir	Srinagar	BASHIR MOHD RESHI	1215	no	IND	Independent
Jammu & Kashmir	Srinagar	MOHAMMAD MAQBOOL MALIK	583	no	JKNPP	Jammu & Kashmir National Panthers Party
Jammu & Kashmir	Srinagar	RIYAZ AHMAD WANI	461	no	IND	Independent
Jammu & Kashmir	Srinagar	ABDUL RASHID TANTRAY	1668	no	IND	Independent
Jammu & Kashmir	Srinagar	RABIA ALTAF	864	no	IND	Independent
Jammu & Kashmir	Anantnag	MEHBOOBA MUFTI	200429	yes	JKPDP	Jammu & Kashmir Peoples Democratic Party
Jammu & Kashmir	Anantnag	MOHAMMAD YAQOOB RATHER	1568	no	AKAKRP	All Jammu and Kashmir Republican Party
Jammu & Kashmir	Anantnag	SYED ABID AHMAD SHAH	4345	no	JKNPP	Jammu & Kashmir National Panthers Party
Jammu & Kashmir	Anantnag	ASIF JEELANI	803	no	AIFB	All India Forward Bloc
Jammu & Kashmir	Anantnag	MOHAMMAD SHARIF	3312	no	BSP	Bahujan Samaj Party
Jammu & Kashmir	Anantnag	MOHD YOUSIF  GANIE	3286	no	ASP	Ambedkar Samaj Party
Jammu & Kashmir	Anantnag	ABDUL AHAD MIR	3439	no	IND	Independent
Jammu & Kashmir	Anantnag	None of the Above	5936	no	NOTA	None of the Above
Jammu & Kashmir	Anantnag	TANVEER HUSSAIN KHAN	7340	no	IND	Independent
Jammu & Kashmir	Anantnag	GH. NABI SHAH	1837	no	SP	Samajwadi Party
Jammu & Kashmir	Anantnag	MUSHTAQ AHMAD MALIK	4720	no	BJP	Bharatiya Janata Party
Jammu & Kashmir	Anantnag	DR. TANVIR MAQBOOL DAR	3252	no	AAAP	Aam Aadmi Party
Jammu & Kashmir	Anantnag	MIRZA MEHBOOB BEG	135012	no	JKN	Jammu & Kashmir National Conference
Jammu & Kashmir	Ladakh	THUPSTAN CHHEWANG	31111	yes	BJP	Bharatiya Janata Party
Jammu & Kashmir	Ladakh	None of the Above	1207	no	NOTA	None of the Above
Jammu & Kashmir	Ladakh	TSERING SAMPHEL	26402	no	INC	Indian National Congress
Jammu & Kashmir	Ladakh	SYED MOHD KAZIM	28234	no	IND	Independent
Jammu & Kashmir	Ladakh	GHULAM RAZA	31075	no	IND	Independent
Jammu & Kashmir	Udhampur	DR. JITENDRA SINGH	487369	yes	BJP	Bharatiya Janata Party
Jammu & Kashmir	Udhampur	None of the Above	10478	no	NOTA	None of the Above
Jammu & Kashmir	Udhampur	NAZAKAT HUSSAIN	9217	no	IND	Independent
Jammu & Kashmir	Udhampur	GIRDHARI LAL	1971	no	BHBP	Bharatiya Bahujan Party
Jammu & Kashmir	Udhampur	JAGDISH KUMAR	1809	no	JMBP	Jai Maha Bharath Party
Jammu & Kashmir	Udhampur	SMT. AMRIT BARSHA	1842	no	SP	Samajwadi Party
Jammu & Kashmir	Udhampur	DHARAM PAL BALGOTRA	16437	no	BSP	Bahujan Samaj Party
Jammu & Kashmir	Udhampur	ANIL KUMAR GUPTA	5755	no	IND	Independent
Jammu & Kashmir	Udhampur	PROF. BHIM SINGH	25312	no	JKNPP	Jammu & Kashmir National Panthers Party
Jammu & Kashmir	Udhampur	SHAM LAL	2187	no	AJKMP	All J & K Kisan Majdoor Party
Jammu & Kashmir	Udhampur	BANSI LAL	7339	no	IND	Independent
Jammu & Kashmir	Udhampur	MOHD ARSHAD MALIK	30461	no	JKPDP	Jammu & Kashmir Peoples Democratic Party
Jammu & Kashmir	Udhampur	GHULAM NABI AZAD	426393	no	INC	Indian National Congress
Jammu & Kashmir	Udhampur	ANIL KHAJURIA	15188	no	IND	Independent
Jammu & Kashmir	Jammu	JUGAL KISHORE	619995	yes	BJP	Bharatiya Janata Party
Jammu & Kashmir	Jammu	ASHOK KUMAR SHARMA	2582	no	IND	Independent
Jammu & Kashmir	Jammu	SATISH POONCHI	1981	no	IND	Independent
Jammu & Kashmir	Jammu	MOHD. ABBAS KHAN	2469	no	IND	Independent
Jammu & Kashmir	Jammu	PERSEEN SINGH	7173	no	IND	Independent
Jammu & Kashmir	Jammu	MOHD. IMTIAZ	7423	no	IND	Independent
Jammu & Kashmir	Jammu	GURSAGAR SINGH	6042	no	SDP	Socialistic Democratic Party
Jammu & Kashmir	Jammu	MEHAR MANAV BHAGAT	2013	no	BHBP	Bharatiya Bahujan Party
Jammu & Kashmir	Jammu	ABDUL MAJID CHOUHAN	4177	no	IND	Independent
Jammu & Kashmir	Jammu	HARI CHAND JALMERIA	9071	no	JKNPP	Jammu & Kashmir National Panthers Party
Jammu & Kashmir	Jammu	VIJAY KUMAR	5125	no	IND	Independent
Jammu & Kashmir	Jammu	ASHOK KUMAR	31199	no	BSP	Bahujan Samaj Party
Jammu & Kashmir	Jammu	LABHA RAM	5046	no	IND	Independent
Jammu & Kashmir	Jammu	SUBASH CHANDER	2205	no	AJKMP	All J & K Kisan Majdoor Party
Jammu & Kashmir	Jammu	YASHPAL SHARMA	168554	no	JKPDP	Jammu & Kashmir Peoples Democratic Party
Jammu & Kashmir	Jammu	NIRMAL SINGH	5286	no	IND	Independent
Jammu & Kashmir	Jammu	MADAN LAL SHARMA	362715	no	INC	Indian National Congress
Jammu & Kashmir	Jammu	BAHADUR SINGH CHOWDHARY	3164	no	SP	Samajwadi Party
Jammu & Kashmir	Jammu	RAJ KUMAR	2991	no	IND	Independent
Jammu & Kashmir	Jammu	None of the Above	4382	no	NOTA	None of the Above
Karnataka	Chikkodi	PRAKASH BABANNA HUKKERI	474373	yes	INC	Indian National Congress
Karnataka	Chikkodi	PRATAPARAO  PATIL	42738	no	NCP	Nationalist Congress Party
Karnataka	Chikkodi	SHRINIK ANNASAHEB JANGATE	3622	no	IND	Independent
Karnataka	Chikkodi	KATTI RAMESH VISHWANATH	471370	no	BJP	Bharatiya Janata Party
Karnataka	Chikkodi	KADAPURE MACHINDRA DAVALU	14493	no	BSP	Bahujan Samaj Party
Karnataka	Chikkodi	MOHAN GURAPPA MOTANNAVAR	3458	no	IND	Independent
Karnataka	Chikkodi	SHRIMANT BALASAHEB PATIL	39992	no	JD(S)	Janata Dal  (Secular)
Karnataka	Chikkodi	ASHFAQ AHMED MADAKI	3529	no	AAAP	Aam Aadmi Party
Karnataka	Chikkodi	MAGADUM GOUSMOHIUDDIN ISMAIL MAGDUM	2624	no	IND	Independent
Karnataka	Chikkodi	JITENDRA SUBHASH NERLE	1414	no	IND	Independent
Karnataka	Chikkodi	APPASAHEB SRIPATHI KURANE	1457	no	SJPA	Sarva Janata Party
Karnataka	Chikkodi	BASAPPA KALLAPPA HANCHANAL	1744	no	IND	Independent
Karnataka	Chikkodi	None of the Above	10289	no	NOTA	None of the Above
Karnataka	Belgaum	ANGADI SURESH CHANNABASAPPA	554417	yes	BJP	Bharatiya Janata Party
Karnataka	Belgaum	DAYANAND GURUPUTRAYYA CHIKKAMATH	1261	no	SP	Samajwadi Party
Karnataka	Belgaum	RAMAPPA (RAMCHANDRA) MAREPPA CHALAWADI	3504	no	IND	Independent
Karnataka	Belgaum	RAMESH G KADALASKAR	3535	no	BSP	Bahujan Samaj Party
Karnataka	Belgaum	ANTAKKANAVAR PARAPPA SHANKAREPPA	773	no	IND	Independent
Karnataka	Belgaum	MOHAN YALLAPPA MORE	1457	no	IND	Independent
Karnataka	Belgaum	LAXMI R. HEBBALKAR	478557	no	INC	Indian National Congress
Karnataka	Belgaum	S. P. RATHOD	1753	no	IND	Independent
Karnataka	Belgaum	KHANAGOUDAR S B	920	no	IND	Independent
Karnataka	Belgaum	SANTOSH M. SHET @ RAIKAR	3245	no	IND	Independent
Karnataka	Belgaum	MAJUKAR HANMANT KRISHANA	1187	no	IND	Independent
Karnataka	Belgaum	BAGWAN NASIR PAPULSAB	5477	no	JD(S)	Janata Dal  (Secular)
Karnataka	Belgaum	ANGADI MUTTAPPA CHANNASANGAPPA	8524	no	AAAP	Aam Aadmi Party
Karnataka	Belgaum	SANTOSH MANOHAR BAVADEKAR	1656	no	IND	Independent
Karnataka	Belgaum	DR. GAONKAR RAJMAHENDRA SAKHARAM	781	no	IND	Independent
Karnataka	Belgaum	None of the Above	11500	no	NOTA	None of the Above
Karnataka	Bagalkot	GADDIGOUDAR PARVTAGOUDA CHANDANAGOUDA	571548	yes	BJP	Bharatiya Janata Party
Karnataka	Bagalkot	SHANKAR BIDARI	10959	no	IND	Independent
Karnataka	Bagalkot	PARASHURAM MANASING RATHOD	3897	no	IND	Independent
Karnataka	Bagalkot	Y.C KAMBLE	7370	no	BSP	Bahujan Samaj Party
Karnataka	Bagalkot	NAGAPPA PAKKIRAPPA SHIRAGUPPI	1194	no	IND	Independent
Karnataka	Bagalkot	MUTTAPPA MUDAKAPPA HIREKUMBI	2037	no	IND	Independent
Karnataka	Bagalkot	VASANAGOUDA NINGANAGOUDA BHANDI	4325	no	IND	Independent
Karnataka	Bagalkot	None of the Above	10764	no	NOTA	None of the Above
Karnataka	Bagalkot	AJAY KUMAR SARNAIK	454988	no	INC	Indian National Congress
Karnataka	Bagalkot	KANABUR SADASHIV SIDAMALLAPPA	1180	no	JD(U)	Janata Dal  (United)
Karnataka	Bagalkot	HUNASHYAL RAVI	7237	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bagalkot	MAHESH BASAPPA NANDIHAL	813	no	KAP	Karunaadu Party
Karnataka	Bagalkot	KAZI KUTUBUDDIN	2004	no	AAAP	Aam Aadmi Party
Karnataka	Bagalkot	AJJODI BHEEMAPPA DHOOLAPPA	994	no	IND	Independent
Karnataka	Bijapur	RAMESH JIGAJINAGI	471757	yes	BJP	Bharatiya Janata Party
Karnataka	Bijapur	BHAGWAN DHARMANNA KAMBALE	1340	no	RPI(A)	Republican Party of India (A)
Karnataka	Bijapur	SUDHAKAR KANAMADI	2818	no	BSP	Bahujan Samaj Party
Karnataka	Bijapur	HANMANT MOTHER-BASALINGAVVA HOLER	2775	no	IND	Independent
Karnataka	Bijapur	RAJU SHRIMANT PADAGANUR	1422	no	IND	Independent
Karnataka	Bijapur	K SHIVRAM	57551	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bijapur	RUDRESH DAYAPPA DODAMANI	2259	no	IND	Independent
Karnataka	Bijapur	SHRIKANT DEVENDRAPPA ARAKERI	1930	no	KAP	Karunaadu Party
Karnataka	Bijapur	SATISH ASANGIHAL	2194	no	IND	Independent
Karnataka	Bijapur	SHRIDHAR NARAYANKAR	4717	no	AAAP	Aam Aadmi Party
Karnataka	Bijapur	RAVI (RAJESH) Y VALLYAPUR	1283	no	IND	Independent
Karnataka	Bijapur	SIDDAPPA B GANJI	2691	no	IND	Independent
Karnataka	Bijapur	HUNNUR MARAGANNA MALAPPA	3795	no	IND	Independent
Karnataka	Bijapur	PRAKASH RATHOD	401938	no	INC	Indian National Congress
Karnataka	Bijapur	None of the Above	8287	no	NOTA	None of the Above
Karnataka	Gulbarga	MALLIKARJUN KHARGE	507193	yes	INC	Indian National Congress
Karnataka	Gulbarga	RAMU	4085	no	IND	Independent
Karnataka	Gulbarga	SHANKAR JADHAV	2877	no	BHPP	Bharatiya Peoples Party
Karnataka	Gulbarga	B.T.LALITHA NAIK	9074	no	AAAP	Aam Aadmi Party
Karnataka	Gulbarga	S.M. SHARMA	4943	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Karnataka	Gulbarga	D.G. SAGAR	15690	no	JD(S)	Janata Dal  (Secular)
Karnataka	Gulbarga	REVUNAIK BELAMAGI	432460	no	BJP	Bharatiya Janata Party
Karnataka	Gulbarga	DANNI MAHADEV B.	11428	no	BSP	Bahujan Samaj Party
Karnataka	Gulbarga	None of the Above	9888	no	NOTA	None of the Above
Karnataka	Raichur	B.V.NAYAK	443659	yes	INC	Indian National Congress
Karnataka	Raichur	D.B.NAYAK	21706	no	JD(S)	Janata Dal  (Secular)
Karnataka	Raichur	MARUTI	3618	no	NDEP	National Development Party
Karnataka	Raichur	RAJA THIMAPPA NAYAK	12254	no	BSP	Bahujan Samaj Party
Karnataka	Raichur	BHIMARAYA JARADABANDI	4234	no	AAAP	Aam Aadmi Party
Karnataka	Raichur	M NAGARAJ	6938	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Karnataka	Raichur	CHINNAYYA NAYAK	6540	no	IND	Independent
Karnataka	Raichur	K SOMASHEKAR YADGIRI	6788	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Karnataka	Raichur	ARAKERA SHIVANAGOUDA NAYAK	442160	no	BJP	Bharatiya Janata Party
Karnataka	Raichur	RANGANAYAK	4935	no	PPOI	Pyramid Party of India
Karnataka	Raichur	BHAGAVANTAPPA	2881	no	LSP	Lok Satta Party
Karnataka	Raichur	None of the Above	13176	no	NOTA	None of the Above
Karnataka	Bidar	BHAGWANTH KHUBA	459290	yes	BJP	Bharatiya Janata Party
Karnataka	Bidar	GOVIND	1427	no	SP	Samajwadi Party
Karnataka	Bidar	MOHAMMED RAFIQ	2095	no	IND	Independent
Karnataka	Bidar	HATTARKI YASHWANTHRAO SHIVARAYA	1414	no	BHPP	Bharatiya Peoples Party
Karnataka	Bidar	RAMANNA	1378	no	HJP	Hindustan Janta Party
Karnataka	Bidar	SHRAVANA S.BHANDE MUNGANAL	2862	no	RSPS	Rashtriya Samaj Paksha
Karnataka	Bidar	SIDDARAMAPPA NAGAPPA KARABASTI CHINCHANSOOR	1742	no	IND	Independent
Karnataka	Bidar	NAGANATH PATIL	2204	no	IND	Independent
Karnataka	Bidar	None of the Above	2817	no	NOTA	None of the Above
Karnataka	Bidar	BABU PASHA	2869	no	IND	Independent
Karnataka	Bidar	N. DHARAM SINGH	367068	no	INC	Indian National Congress
Karnataka	Bidar	ISMAIL ZABI ULLAH	5818	no	IND	Independent
Karnataka	Bidar	BALBHEEM VEERAPPA UNNE	7118	no	IND	Independent
Karnataka	Bidar	SYED WAHEED	1302	no	BMUP	Bahujan Mukti Party
Karnataka	Bidar	ABDUL HAMEED FARAAN	3747	no	WPOI	Welfare Party Of India
Karnataka	Bidar	PRABHU	5097	no	IND	Independent
Karnataka	Bidar	MIRZA SHAFEE BAIG	1462	no	IND	Independent
Karnataka	Bidar	SUBHASH BHIMSHA SINDANKERA	1406	no	IND	Independent
Karnataka	Bidar	KALLALING HUGAR NELOGI	2307	no	IND	Independent
Karnataka	Bidar	SHAMALA.M.UDANOOR	1259	no	IND	Independent
Karnataka	Bidar	MACHENDAR	4379	no	IND	Independent
Karnataka	Bidar	BANDEPPA KHASHEMPUR	58728	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bidar	RAMESH GAYKAWAD	1976	no	ANC	Ambedkar National Congress
Karnataka	Bidar	SHANKAR BHAYYA	15079	no	BSP	Bahujan Samaj Party
Karnataka	Bidar	CHANDRAKANT	4540	no	AAAP	Aam Aadmi Party
Karnataka	Koppal	KARADI SANGANNA AMARAPPA	486383	yes	BJP	Bharatiya Janata Party
Karnataka	Koppal	COM I:- D H PUJAR	2636	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Karnataka	Koppal	NAGAPPA. G.  KARATAGI	2894	no	IND	Independent
Karnataka	Koppal	THIMMAPPA UPPAR	2459	no	BDBRAJP	Bharatiya Dr. B.R.Ambedkar Janta Party
Karnataka	Koppal	SYED  ARIF	9529	no	BSP	Bahujan Samaj Party
Karnataka	Koppal	BASAVARAJ HITNAL	453969	no	INC	Indian National Congress
Karnataka	Koppal	BHARADWHAJA	2089	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Karnataka	Koppal	SHIVAKUMAR NAVALISIDDAPPA TONTAPUR	3425	no	AAAP	Aam Aadmi Party
Karnataka	Koppal	RAMESH KOTI	1867	no	RPI(A)	Republican Party of India (A)
Karnataka	Koppal	K. M. RANGANATHREDDY	2305	no	SP	Samajwadi Party
Karnataka	Koppal	ANOJIRAO G	2012	no	IND	Independent
Karnataka	Koppal	NAZEER HUSSAIN	2129	no	RKSP	Rashtriya Krantikari Samajwadi Party
Karnataka	Koppal	B.MANOHARA HANUMANTAPPA	4433	no	IND	Independent
Karnataka	Koppal	GOVINDHAREDDY PACHARALLI	2839	no	IND	Independent
Karnataka	Koppal	V. GOVINDA	6300	no	IND	Independent
Karnataka	Koppal	SURESH	8292	no	IND	Independent
Karnataka	Koppal	None of the Above	12947	no	NOTA	None of the Above
Karnataka	Bellary	B. SREERAMULU	534406	yes	BJP	Bharatiya Janata Party
Karnataka	Bellary	G. NATARAJA	1997	no	KAP	Karunaadu Party
Karnataka	Bellary	None of the Above	11320	no	NOTA	None of the Above
Karnataka	Bellary	MALAGI SHIVAKUMAR GIRIYAPPA	6038	no	AAAP	Aam Aadmi Party
Karnataka	Bellary	N.Y. HANUMANTAPPA	449262	no	INC	Indian National Congress
Karnataka	Bellary	B. RAMUDU (ADDIGERI RAMANNA)	4557	no	BSP	Bahujan Samaj Party
Karnataka	Bellary	COMRADE S. BHEEMAPPA	2042	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Karnataka	Bellary	R. RAVINAYAKA	12613	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bellary	K. RAGHAVENDRA	3501	no	PPOI	Pyramid Party of India
Karnataka	Bellary	DODDAMANI PRASAD	2456	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Karnataka	Bellary	N.B. KRISHNAPPA NAYAKA	3888	no	IND	Independent
Karnataka	Bellary	N.R. MALLAYYA SWAMY	5206	no	IND	Independent
Karnataka	Bellary	A. DEVDAS	8486	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Karnataka	Haveri	UDASI SHIVAKUMAR CHANNABASAPPA	566790	yes	BJP	Bharatiya Janata Party
Karnataka	Haveri	HANUMANTAPPA  D. KABBAR	5997	no	IND	Independent
Karnataka	Haveri	SHIVAKUMAR  TALAWAR	3780	no	IND	Independent
Karnataka	Haveri	B.M.JAYADEV MATHAD	2296	no	IND	Independent
Karnataka	Haveri	RAVI  MENASINAKAI	9814	no	JD(S)	Janata Dal  (Secular)
Karnataka	Haveri	KABBINAD VERABHADRAPPA VEERAPPA	1224	no	IND	Independent
Karnataka	Haveri	CHANDRASHEKHARAYYA GURULINGAYYA KULKARNI	1314	no	IND	Independent
Karnataka	Haveri	MEHABOOBKHAN  PATHAN	2329	no	IND	Independent
Karnataka	Haveri	KUBER VASANNA	883	no	IND	Independent
Karnataka	Haveri	H M SIRQUAZI	1602	no	AAAP	Aam Aadmi Party
Karnataka	Haveri	BASAVANTEPPA HONNAPPA HULLATTI	974	no	SJPA	Sarva Janata Party
Karnataka	Haveri	BIDARAGADDI  V.B	3528	no	IND	Independent
Karnataka	Haveri	SURESH M.MUNDARGI	771	no	RCMP	Rani Chennamma Party
Karnataka	Haveri	SHIDDAPPA KALLAPPA POOJAR	15656	no	IND	Independent
Karnataka	Haveri	SALEEM  AHMED	479219	no	INC	Indian National Congress
Karnataka	Haveri	HALAPPA  TIMMENAHALLI	6229	no	BSP	Bahujan Samaj Party
Karnataka	Haveri	GIRISHGOUDA C.BISTANAGOUDAR	946	no	IND	Independent
Karnataka	Haveri	D PRASAD	1490	no	RPI(A)	Republican Party of India (A)
Karnataka	Haveri	RUDRESH RAMANNA HADAGALI	7320	no	IND	Independent
Karnataka	Haveri	None of the Above	3806	no	NOTA	None of the Above
Karnataka	Dharwad	PRALHAD JOSHI	545395	yes	BJP	Bharatiya Janata Party
Karnataka	Dharwad	MARUTI RAMAPPA HANASI	1587	no	IND	Independent
Karnataka	Dharwad	SHAKEEL ABDULSATTAR DODWAD	1368	no	IND	Independent
Karnataka	Dharwad	BANKAPUR. HANUMANTHAPPA MALLAPPA	8836	no	JD(S)	Janata Dal  (Secular)
Karnataka	Dharwad	VIKAS B. SOPPIN	2174	no	IND	Independent
Karnataka	Dharwad	PRAMOD HANUMANTHARAO MUTHALIK	5465	no	IND	Independent
Karnataka	Dharwad	IRAPPA BHARAMAPPA MADAR	6858	no	BSP	Bahujan Samaj Party
Karnataka	Dharwad	IMAMSAB MEHBOOBSAB TORGAL.	995	no	IND	Independent
Karnataka	Dharwad	VINAY KULKARNI	431738	no	INC	Indian National Congress
Karnataka	Dharwad	IRSHAD BEGAM ADHONI.	1053	no	IND	Independent
Karnataka	Dharwad	GURUSHANTAPPA H. CHALAVADI.	4128	no	RPI(A)	Republican Party of India (A)
Karnataka	Dharwad	SANJAY KOTHARI	2359	no	IND	Independent
Karnataka	Dharwad	SHREEKANT KABADI	3018	no	IND	Independent
Karnataka	Dharwad	DOKKA ANANDARAJ	985	no	IND	Independent
Karnataka	Dharwad	GANGADHAR S. BADIGER	1785	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Karnataka	Dharwad	SHAILENDRA PATIL	6196	no	IND	Independent
Karnataka	Dharwad	HEMANT KUMAR	4349	no	AAAP	Aam Aadmi Party
Karnataka	Dharwad	None of the Above	12937	no	NOTA	None of the Above
Karnataka	Uttara Kannada	ANANTKUMAR HEGDE	546939	yes	BJP	Bharatiya Janata Party
Karnataka	Uttara Kannada	BASAVARAJ DUNDAPPA HADAPAD	2032	no	RCMP	Rani Chennamma Party
Karnataka	Uttara Kannada	NAIK SANTHOSH	7404	no	BSP	Bahujan Samaj Party
Karnataka	Uttara Kannada	ELIYAS KATI	6281	no	SJPA	Sarva Janata Party
Karnataka	Uttara Kannada	RAGHAVENDRA K. THANE	6215	no	AAAP	Aam Aadmi Party
Karnataka	Uttara Kannada	SADANAND DESHBHANDARI ANNAPPA	3565	no	IND	Independent
Karnataka	Uttara Kannada	ASHOK SAHADEVAPPA CHALAVADI	4246	no	IND	Independent
Karnataka	Uttara Kannada	OLWIN MARIAN LOPIS	1840	no	BMUP	Bahujan Mukti Party
Karnataka	Uttara Kannada	PRASHANT R DESHPANDE	406239	no	INC	Indian National Congress
Karnataka	Uttara Kannada	None of the Above	16277	no	NOTA	None of the Above
Karnataka	Davanagere	G M SIDDESHWARA	518894	yes	BJP	Bharatiya Janata Party
Karnataka	Davanagere	K SYED ZABIULLA	1582	no	IND	Independent
Karnataka	Davanagere	H.K. KENCHVEERAPPA	3972	no	VJCP	Vichara Jagruthi Congress Paksha
Karnataka	Davanagere	A.K.PARASHURAMA KOTEMALLUR	5581	no	BSP	Bahujan Samaj Party
Karnataka	Davanagere	SIDDESH	1216	no	IND	Independent
Karnataka	Davanagere	PARASURAM KURUBAR	1332	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Karnataka	Davanagere	G.M.SIDDESHI	2732	no	IND	Independent
Karnataka	Davanagere	S.S. SUBHAN KHAN	3312	no	IND	Independent
Karnataka	Davanagere	None of the Above	4536	no	NOTA	None of the Above
Karnataka	Davanagere	ALUR M.G. SWAMY	1122	no	IND	Independent
Karnataka	Davanagere	COM. H.K. RAMACHANDRAPPA	8064	no	CPI	Communist Party of India
Karnataka	Davanagere	MAHIMA J PATEL	46911	no	JD(S)	Janata Dal  (Secular)
Karnataka	Davanagere	SRI NAGARAJAPPA	1226	no	IND	Independent
Karnataka	Davanagere	K.J. BASAVARAJ	2582	no	AAAP	Aam Aadmi Party
Karnataka	Davanagere	A.T. DADAKHALANDAR	1331	no	IND	Independent
Karnataka	Davanagere	G.C.PATEL	1145	no	SJPA	Sarva Janata Party
Karnataka	Davanagere	DR. SRIDHARA UDUPA	946	no	IND	Independent
Karnataka	Davanagere	B. GNANA PRAKASH	7097	no	IND	Independent
Karnataka	Davanagere	S.S. MALLIKARJUN	501287	no	INC	Indian National Congress
Karnataka	Shimoga	B. S. YEDDYURAPPA	606216	yes	BJP	Bharatiya Janata Party
Karnataka	Shimoga	ANIL.M.R	1517	no	IND	Independent
Karnataka	Shimoga	K.G. SREEDHAR	7542	no	AAAP	Aam Aadmi Party
Karnataka	Shimoga	MANJUNATH BHANDARY	242911	no	INC	Indian National Congress
Karnataka	Shimoga	GEETHA  SHIVARAJKUMAR	240636	no	JD(S)	Janata Dal  (Secular)
Karnataka	Shimoga	B. DHARMAPPA	4537	no	JD(U)	Janata Dal  (United)
Karnataka	Shimoga	None of the Above	7077	no	NOTA	None of the Above
Karnataka	Shimoga	KUNAJE  MANJUNATHA  GOWDA	5053	no	BSP	Bahujan Samaj Party
Karnataka	Shimoga	FAYAZ AHAMED   ALIAS   MOHAMAD FAYAZ AHAMED	1339	no	SJPA	Sarva Janata Party
Karnataka	Shimoga	MAINUDDIN M.S.	1295	no	IND	Independent
Karnataka	Shimoga	H.  SURESHA POOJARI	2709	no	IND	Independent
Karnataka	Shimoga	SHRILATHA SHETTY	2073	no	IND	Independent
Karnataka	Shimoga	B.K. SHASHI KUMAR	3869	no	KAP	Karunaadu Party
Karnataka	Shimoga	S. SATISH SALIAN	2234	no	IND	Independent
Karnataka	Udupi Chikmagalur	SHOBHA KARANDLAJE	581168	yes	BJP	Bharatiya Janata Party
Karnataka	Udupi Chikmagalur	SRINIVASA POOJARY	1899	no	IND	Independent
Karnataka	Udupi Chikmagalur	MANJUNATHA G	1089	no	IND	Independent
Karnataka	Udupi Chikmagalur	SUDHEER KANCHAN	1689	no	IND	Independent
Karnataka	Udupi Chikmagalur	M D MAINUDDIN KHAN ALIAS ASFIYA	1214	no	IND	Independent
Karnataka	Udupi Chikmagalur	K JAYAPRAKASH HEGDE	399525	no	INC	Indian National Congress
Karnataka	Udupi Chikmagalur	None of the Above	7828	no	NOTA	None of the Above
Karnataka	Udupi Chikmagalur	GURUDEVA S  H	6049	no	AAAP	Aam Aadmi Party
Karnataka	Udupi Chikmagalur	COMRADE C J JAGANNATH	1612	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Karnataka	Udupi Chikmagalur	ZAKIR HUSSAIN	7449	no	BSP	Bahujan Samaj Party
Karnataka	Udupi Chikmagalur	S VIJAYA KUMAR	9691	no	CPI	Communist Party of India
Karnataka	Udupi Chikmagalur	V. DHANANJAYA KUMAR	14895	no	JD(S)	Janata Dal  (Secular)
Karnataka	Hassan	H.D. DEVEGOWDA	509841	yes	JD(S)	Janata Dal  (Secular)
Karnataka	Hassan	DR. BANUPRAKASH. K	1456	no	IND	Independent
Karnataka	Hassan	M.MAHESH (HARSHA)	3062	no	IND	Independent
Karnataka	Hassan	C.H. VIJAYASHANKAR	165688	no	BJP	Bharatiya Janata Party
Karnataka	Hassan	SANTHOSH MOHAN	8018	no	AAAP	Aam Aadmi Party
Karnataka	Hassan	T.D. JAGADEESHA	2083	no	IND	Independent
Karnataka	Hassan	JAVARAIAH	1581	no	IND	Independent
Karnataka	Hassan	S.N. MALLAPPA	2388	no	IND	Independent
Karnataka	Hassan	S.B. RAJEGOWDA	3841	no	IND	Independent
Karnataka	Hassan	MANJUNATHA	3536	no	BDBRAJP	Bharatiya Dr. B.R.Ambedkar Janta Party
Karnataka	Hassan	H.D. REVANNA	6798	no	KAP	Karunaadu Party
Karnataka	Hassan	A.P. AHAMAD	18905	no	BSP	Bahujan Samaj Party
Karnataka	Hassan	None of the Above	7334	no	NOTA	None of the Above
Karnataka	Hassan	MANJU. A.	409379	no	INC	Indian National Congress
Karnataka	Hassan	RUDREGOWDA	3262	no	JVBP	Jai Vijaya Bharathi Party
Karnataka	Dakshina Kannada	NALIN KUMAR KATEEL	642739	yes	BJP	Bharatiya Janata Party
Karnataka	Dakshina Kannada	SUBRAMANYA GOWDA K.	2450	no	IND	Independent
Karnataka	Dakshina Kannada	K.N. SOMASHEKAR	3421	no	IND	Independent
Karnataka	Dakshina Kannada	ADVOCATE ANANDA GATTY	1116	no	IND	Independent
Karnataka	Dakshina Kannada	DEEPAK RAJESH COELHO	905	no	IND	Independent
Karnataka	Dakshina Kannada	None of the Above	7109	no	NOTA	None of the Above
Karnataka	Dakshina Kannada	SUPREETH KUMAR POOJARY	657	no	HJP	Hindustan Janta Party
Karnataka	Dakshina Kannada	MAXIM PINTO	1072	no	IND	Independent
Karnataka	Dakshina Kannada	K. KUSHALA BELLARE	801	no	IND	Independent
Karnataka	Dakshina Kannada	JANARDHANA POOJARY	499030	no	INC	Indian National Congress
Karnataka	Dakshina Kannada	MOOSE KUNHI ALIAS ABDUL KAREEM	4471	no	BSP	Bahujan Samaj Party
Karnataka	Dakshina Kannada	HANEEF KHAN KODAJE	27254	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Karnataka	Dakshina Kannada	SUDATTA JAIN SHIRTHADY	1613	no	IND	Independent
Karnataka	Dakshina Kannada	K YADAVA SHETTY	9394	no	CPM	Communist Party of India  (Marxist)
Karnataka	Dakshina Kannada	M. R. VASUDEVA	5442	no	AAAP	Aam Aadmi Party
Karnataka	Chitradurga	B.N.CHANDRAPPA	467511	yes	INC	Indian National Congress
Karnataka	Chitradurga	T.SOMESH SHILPI	4511	no	IND	Independent
Karnataka	Chitradurga	GULLIHATTY.D.SHEKHAR	202108	no	JD(S)	Janata Dal  (Secular)
Karnataka	Chitradurga	JANARDHANA SWAMY	366220	no	BJP	Bharatiya Janata Party
Karnataka	Chitradurga	T.D.RAJAGIRI	9441	no	BSP	Bahujan Samaj Party
Karnataka	Chitradurga	T.DAYANANDA	1818	no	IND	Independent
Karnataka	Chitradurga	None of the Above	8895	no	NOTA	None of the Above
Karnataka	Chitradurga	MOHANA DASARI	14226	no	AAAP	Aam Aadmi Party
Karnataka	Chitradurga	S. MEETYA NAIK	3558	no	SP	Samajwadi Party
Karnataka	Chitradurga	M.KUMBAIAH	3970	no	JD(U)	Janata Dal  (United)
Karnataka	Chitradurga	H.SIDDAGANGAPPA	2951	no	ANC	Ambedkar National Congress
Karnataka	Chitradurga	BASAVANAGIDEVA SHARANARU	2171	no	IND	Independent
Karnataka	Chitradurga	T.BHAVANA	3601	no	IND	Independent
Karnataka	Chitradurga	N.S.MANJUNATHA NERENAHAL	2702	no	IND	Independent
Karnataka	Chitradurga	GANESHA	2816	no	IND	Independent
Karnataka	Tumkur	MUDDAHANUMEGOWDA.S.P.	429868	yes	INC	Indian National Congress
Karnataka	Tumkur	None of the Above	12934	no	NOTA	None of the Above
Karnataka	Tumkur	L.P.YADUNANDANA	1508	no	IND	Independent
Karnataka	Tumkur	S.B.HANUMANTHARAYAPPA	6301	no	IND	Independent
Karnataka	Tumkur	SHIVAKUMAR	1674	no	IND	Independent
Karnataka	Tumkur	GOVINDARJU.G	1063	no	IND	Independent
Karnataka	Tumkur	A.S.D'SILVA	8322	no	AAAP	Aam Aadmi Party
Karnataka	Tumkur	CHINNAPPA.V	10448	no	CPI	Communist Party of India
Karnataka	Tumkur	BRAHMANANDA REDDY	1672	no	IND	Independent
Karnataka	Tumkur	G.NAGENDRA	1094	no	IND	Independent
Karnataka	Tumkur	ANWAR	1245	no	IND	Independent
Karnataka	Tumkur	NILUFAR JAMANI AHMED	4732	no	BSP	Bahujan Samaj Party
Karnataka	Tumkur	K.V.SRINIVAS KALKERE	2195	no	IND	Independent
Karnataka	Tumkur	H.L.MOHAN KUMAR	1528	no	HJP	Hindustan Janta Party
Karnataka	Tumkur	G.S.BASAVARAJ	355827	no	BJP	Bharatiya Janata Party
Karnataka	Tumkur	A.KRISHNAPPA	258683	no	JD(S)	Janata Dal  (Secular)
Karnataka	Tumkur	B.VENKATESHA	2390	no	KAP	Karunaadu Party
Karnataka	Mandya	C.S.PUTTARAJU	524370	yes	JD(S)	Janata Dal  (Secular)
Karnataka	Mandya	KRISHNAMURTHY KOMMERAHALLI	1643	no	RPI(A)	Republican Party of India (A)
Karnataka	Mandya	ARUNAKUMARA	1179	no	IND	Independent
Karnataka	Mandya	ASHRAF	2163	no	IND	Independent
Karnataka	Mandya	N.C.PUTTARAJU	4021	no	IND	Independent
Karnataka	Mandya	JAYACHANDRA	1119	no	BMUP	Bahujan Mukti Party
Karnataka	Mandya	M.KRISHNAMURTHY	22391	no	BSP	Bahujan Samaj Party
Karnataka	Mandya	PROF. B.SHIVALINGAIAH	86993	no	BJP	Bharatiya Janata Party
Karnataka	Mandya	K.UDAYAKUMARA	3070	no	IND	Independent
Karnataka	Mandya	DR. C.S.HANUMANTHAPPA	4098	no	AAAP	Aam Aadmi Party
Karnataka	Mandya	H.K.KRISHNA	8604	no	KAP	Karunaadu Party
Karnataka	Mandya	BHANUPRAKASH.K.L	4354	no	IND	Independent
Karnataka	Mandya	K.SHIVANANDA	1165	no	HJP	Hindustan Janta Party
Karnataka	Mandya	K.MAHADEVAPPA	1423	no	BDBRAJP	Bharatiya Dr. B.R.Ambedkar Janta Party
Karnataka	Mandya	ARAVIND PREMANAND	1172	no	IND	Independent
Karnataka	Mandya	RAMYA	518852	no	INC	Indian National Congress
Karnataka	Mandya	None of the Above	6021	no	NOTA	None of the Above
Karnataka	Mysore	PRATHAP SIMHA	503908	yes	BJP	Bharatiya Janata Party
Karnataka	Mysore	PADMAMMA M V	5650	no	AAAP	Aam Aadmi Party
Karnataka	Mysore	C MOHAN KUMAR	13637	no	BSP	Bahujan Samaj Party
Karnataka	Mysore	CHANDRASHEKARAIAH	138587	no	JD(S)	Janata Dal  (Secular)
Karnataka	Mysore	D ESWAR THOREMAVU	1246	no	IND	Independent
Karnataka	Mysore	D S NIRVANAPPA	978	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Karnataka	Mysore	None of the Above	8924	no	NOTA	None of the Above
Karnataka	Mysore	BOJANNA N SOMAIAH	2032	no	IND	Independent
Karnataka	Mysore	HUNSUR K CHANDRASHEKAR	1342	no	DPPS	Democratic Prajakranthi Party Secularist
Karnataka	Mysore	THIMME GOWDA	1516	no	SJPA	Sarva Janata Party
Karnataka	Mysore	SAMPATHU N	2078	no	IND	Independent
Karnataka	Mysore	RATHI POOVAIAH	3726	no	KAP	Karunaadu Party
Karnataka	Mysore	BANNUR KUMAR	1215	no	IND	Independent
Karnataka	Mysore	PRAVEEN M S	1439	no	IND	Independent
Karnataka	Mysore	ADAGOORU H VISHWANATH	472300	no	INC	Indian National Congress
Karnataka	Mysore	S P MAHADEVAPPA	1016	no	JD(U)	Janata Dal  (United)
Karnataka	Chamarajanagar	R. DHRUVANARAYANA	567782	yes	INC	Indian National Congress
Karnataka	Chamarajanagar	M SHIVANNA (KOTE)	58760	no	JD(S)	Janata Dal  (Secular)
Karnataka	Chamarajanagar	PRADEEP KUMAR M	2492	no	KAP	Karunaadu Party
Karnataka	Chamarajanagar	KATHAVARAY	1949	no	asdp	Dr. Ambedkar Samajvadi Democratic Party
Karnataka	Chamarajanagar	SAMPATH KUMAR	3357	no	AAAP	Aam Aadmi Party
Karnataka	Chamarajanagar	S. GOVINDA NAIK	5398	no	IND	Independent
Karnataka	Chamarajanagar	NIRMALA KUMARI	6604	no	IND	Independent
Karnataka	Chamarajanagar	CHAUDAHALLY JAVARAIAH	2008	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Karnataka	Chamarajanagar	B GURUSWAMY	2043	no	SP	Samajwadi Party
Karnataka	Chamarajanagar	SHIVAMALLU	34846	no	BSP	Bahujan Samaj Party
Karnataka	Chamarajanagar	None of the Above	12697	no	NOTA	None of the Above
Karnataka	Chamarajanagar	KANDEGALA SRINIVASAMURTHY	1825	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Karnataka	Chamarajanagar	BANNUR SURENDRA	2762	no	SJPA	Sarva Janata Party
Karnataka	Chamarajanagar	A. R. KRISHNA MURTHY	426600	no	BJP	Bharatiya Janata Party
Karnataka	Chamarajanagar	ANANDA G	3906	no	IND	Independent
Karnataka	Bangalore Rural	D K SURESH	652723	yes	INC	Indian National Congress
Karnataka	Bangalore Rural	S.SIDDARAMAIAH (HEGGADE)	2142	no	IND	Independent
Karnataka	Bangalore Rural	SHANKARAPPA	1008	no	JVBP	Jai Vijaya Bharathi Party
Karnataka	Bangalore Rural	BASAVEGOWDA	6154	no	RPI(A)	Republican Party of India (A)
Karnataka	Bangalore Rural	C THOPAIAH	11594	no	BSP	Bahujan Samaj Party
Karnataka	Bangalore Rural	SURESH	2782	no	IND	Independent
Karnataka	Bangalore Rural	M.P.MUNAWER SHARIFF	2577	no	IND	Independent
Karnataka	Bangalore Rural	KENCHAIAH	859	no	IND	Independent
Karnataka	Bangalore Rural	J.T.PRAKASH	1503	no	IND	Independent
Karnataka	Bangalore Rural	R. PRABHAKARA REDDY	317870	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bangalore Rural	RAVI KRISHNA REDDY	17195	no	AAAP	Aam Aadmi Party
Karnataka	Bangalore Rural	K A MOHAN	1345	no	KDC	Kamarajar Deseeya Congress
Karnataka	Bangalore Rural	J NATARAJU	958	no	IND	Independent
Karnataka	Bangalore Rural	MALLESHA	5420	no	IND	Independent
Karnataka	Bangalore Rural	MUNIRAJU GOWDA . P	421243	no	BJP	Bharatiya Janata Party
Karnataka	Bangalore Rural	None of the Above	9871	no	NOTA	None of the Above
Karnataka	Bangalore North	D.V SADANANDA GOWDA	718326	yes	BJP	Bharatiya Janata Party
Karnataka	Bangalore North	None of the Above	11996	no	NOTA	None of the Above
Karnataka	Bangalore North	MU.VENKATESH SIMHA BHOVI	1033	no	IND	Independent
Karnataka	Bangalore North	DR.MEERLAYAQ HUSSAIN	612	no	IND	Independent
Karnataka	Bangalore North	MANOHAR H.A.	919	no	IND	Independent
Karnataka	Bangalore North	SUNIL.V.J	1561	no	IND	Independent
Karnataka	Bangalore North	ABDUL AZEEM	92681	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bangalore North	BABU  MATHEW	28107	no	AAAP	Aam Aadmi Party
Karnataka	Bangalore North	MUTHURAJU. M	829	no	RPI(A)	Republican Party of India (A)
Karnataka	Bangalore North	C.NARAYANA SWAMY	488562	no	INC	Indian National Congress
Karnataka	Bangalore North	T.S.CHAITHANYA (S.J.R)	911	no	IND	Independent
Karnataka	Bangalore North	S. RAMU	1131	no	IND	Independent
Karnataka	Bangalore North	MOHAMMED QUTBUDDIN	680	no	IND	Independent
Karnataka	Bangalore North	SUN STAR D.JAYARAM	3784	no	GaAP	Gareeb Aadmi Party
Karnataka	Bangalore North	V VELU	5586	no	BSP	Bahujan Samaj Party
Karnataka	Bangalore central	P.C. MOHAN	557130	yes	BJP	Bharatiya Janata Party
Karnataka	Bangalore central	None of the Above	8449	no	NOTA	None of the Above
Karnataka	Bangalore central	NANDINI ALVA	20387	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bangalore central	G. NARAYANASWAMY (KAPALI)	1228	no	IND	Independent
Karnataka	Bangalore central	BHOVI RAMA C.B.K	942	no	IND	Independent
Karnataka	Bangalore central	B.K. NARAYANA-SWAMY	700	no	IND	Independent
Karnataka	Bangalore central	MODI SAIFULLA	3204	no	IND	Independent
Karnataka	Bangalore central	SURAIYA ZAITHOON	519	no	IND	Independent
Karnataka	Bangalore central	ISMAIL KHAN SAIF ALI	2276	no	AITC	All India Trinamool Congress
Karnataka	Bangalore central	RAMANUJ SINGH	1836	no	IND	Independent
Karnataka	Bangalore central	AMBROSE D'MELLO	366	no	IND	Independent
Karnataka	Bangalore central	KADUGUDI SONNAPPA	543	no	RPI(A)	Republican Party of India (A)
Karnataka	Bangalore central	H.L. MANJUNATHA	707	no	KCVP	Kannada Chalavali Vatal Paksha
Karnataka	Bangalore central	E.SALAM	365	no	IND	Independent
Karnataka	Bangalore central	B.V.VENKATESHAPPA	668	no	IND	Independent
Karnataka	Bangalore central	K.MURTHY	1507	no	IND	Independent
Karnataka	Bangalore central	PRASHANTH.S.M	903	no	IND	Independent
Karnataka	Bangalore central	SHAFI AHMED	549	no	IND	Independent
Karnataka	Bangalore central	DHANANJAYA. H.H	422	no	IND	Independent
Karnataka	Bangalore central	DR. MEER LAYAQ HUSSAIN	1878	no	IND	Independent
Karnataka	Bangalore central	V. BALAKRISHNAN	39869	no	AAAP	Aam Aadmi Party
Karnataka	Bangalore central	R. MOHAN RAJU	5806	no	BSP	Bahujan Samaj Party
Karnataka	Bangalore central	SEETHA RAMAN.E	2543	no	IND	Independent
Karnataka	Bangalore central	SYED ASIF BUKHAARI	411	no	IND	Independent
Karnataka	Bangalore central	RIZWAN ARSHAD	419630	no	INC	Indian National Congress
Karnataka	Bangalore central	BUDAYYA .B. PUJERI	553	no	KAP	Karunaadu Party
Karnataka	Bangalore central	ZAHEEDA  SHEREEN	1198	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Karnataka	Bangalore South	ANANTH KUMAR	633816	yes	BJP	Bharatiya Janata Party
Karnataka	Bangalore South	M. UMADEVI	918	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Karnataka	Bangalore South	PRABHU	2325	no	IND	Independent
Karnataka	Bangalore South	G. VENKATESH BHOVI	736	no	IND	Independent
Karnataka	Bangalore South	NANDAN NILEKANI	405241	no	INC	Indian National Congress
Karnataka	Bangalore South	DR. PRADEEP GADUGESH	549	no	IND	Independent
Karnataka	Bangalore South	DR. KODUR VENKATESH	502	no	IND	Independent
Karnataka	Bangalore South	YOGESH	2061	no	IND	Independent
Karnataka	Bangalore South	SHAMBULINGEGOWDA (GANDIVADI)	581	no	IND	Independent
Karnataka	Bangalore South	NINA P NAYAK	21403	no	AAAP	Aam Aadmi Party
Karnataka	Bangalore South	GOVINDAIAH B.R.	672	no	IND	Independent
Karnataka	Bangalore South	T. E. MOSES	604	no	IND	Independent
Karnataka	Bangalore South	T.K. DASAR	929	no	IND	Independent
Karnataka	Bangalore South	RUTH MANORAMA	25677	no	JD(S)	Janata Dal  (Secular)
Karnataka	Bangalore South	SYED MEHABOOB	365	no	JD(U)	Janata Dal  (United)
Karnataka	Bangalore South	N. HANUMEGOWDA	414	no	IND	Independent
Karnataka	Bangalore South	RAVIKUMARA T	781	no	SP	Samajwadi Party
Karnataka	Bangalore South	GAYATHRI	484	no	PPOI	Pyramid Party of India
Karnataka	Bangalore South	MAHADEVA SWAMY. B. M	428	no	BDBRAJP	Bharatiya Dr. B.R.Ambedkar Janta Party
Karnataka	Bangalore South	KHAN ABDUL R	2747	no	BSP	Bahujan Samaj Party
Karnataka	Bangalore South	SYED RAFIQ AHMED	246	no	IND	Independent
Karnataka	Bangalore South	PRAMOD  MUTALIK	4247	no	IND	Independent
Karnataka	Bangalore South	G.R. SHIVASHANKAR	586	no	AIFB	All India Forward Bloc
Karnataka	Bangalore South	None of the Above	7414	no	NOTA	None of the Above
Karnataka	Chikkballapur	M VEERAPPA MOILY	424800	yes	INC	Indian National Congress
Karnataka	Chikkballapur	None of the Above	7682	no	NOTA	None of the Above
Karnataka	Chikkballapur	JAYAPRAKASHA YADAV	1586	no	IND	Independent
Karnataka	Chikkballapur	G C VENKATARAMANAPPA	892	no	RPI(A)	Republican Party of India (A)
Karnataka	Chikkballapur	YAKUB SHARIFF	931	no	BMUP	Bahujan Mukti Party
Karnataka	Chikkballapur	VENKATESH D T	3410	no	IND	Independent
Karnataka	Chikkballapur	B N BACHE GOWDA	415280	no	BJP	Bharatiya Janata Party
Karnataka	Chikkballapur	H D KUMARA SWAMY	346339	no	JD(S)	Janata Dal  (Secular)
Karnataka	Chikkballapur	H N RAMAKRISHNA REDDY	686	no	PPOI	Pyramid Party of India
Karnataka	Chikkballapur	K S SWAMY	4689	no	IND	Independent
Karnataka	Chikkballapur	D N NARASIMHAMURTHY	1897	no	IND	Independent
Karnataka	Chikkballapur	B N GOPALAGOWDA	1465	no	IND	Independent
Karnataka	Chikkballapur	NANJAPPA	1215	no	IND	Independent
Karnataka	Chikkballapur	CHITRA PRASAD	6279	no	BSP	Bahujan Samaj Party
Karnataka	Chikkballapur	G N RAVI	7127	no	IND	Independent
Karnataka	Chikkballapur	T V MANJUNATHA GOWDA	3895	no	IND	Independent
Karnataka	Chikkballapur	REDDY G K C	799	no	JD(U)	Janata Dal  (United)
Karnataka	Chikkballapur	G V SREERAMA REDDY	26071	no	CPM	Communist Party of India  (Marxist)
Karnataka	Chikkballapur	ROBIN MATHEWS NAREKATTU	1404	no	SP	Samajwadi Party
Karnataka	Chikkballapur	K ARKESH	6827	no	AAAP	Aam Aadmi Party
Karnataka	Kolar	K.H.MUNIYAPPA	418926	yes	INC	Indian National Congress
Karnataka	Kolar	K.NARENDRABABU	1089	no	IND	Independent
Karnataka	Kolar	DR.C.MOHAN	2903	no	IND	Independent
Karnataka	Kolar	BEEDA SRINIVAS N	1404	no	IND	Independent
Karnataka	Kolar	A.PRASAD	1275	no	IND	Independent
Karnataka	Kolar	KOTIGANAHALLI RAMAIAH	17563	no	AAAP	Aam Aadmi Party
Karnataka	Kolar	V.MUNISWAMY	4803	no	SP	Samajwadi Party
Karnataka	Kolar	KOLAR KESAVA	371076	no	JD(S)	Janata Dal  (Secular)
Karnataka	Kolar	M.S.NARAYANASWAMY	1291	no	IND	Independent
Karnataka	Kolar	EM.NARAYANASWAMY	267322	no	BJP	Bharatiya Janata Party
Karnataka	Kolar	N.VENKATESH	3602	no	IND	Independent
Karnataka	Kolar	H.N.NARAYANASWAMY	1583	no	IND	Independent
Karnataka	Kolar	VINAYAKA PRASAD.H.N	2074	no	IND	Independent
Karnataka	Kolar	NARAYANASWAMY	1517	no	IND	Independent
Karnataka	Kolar	G.N.VENKATESH	6233	no	IND	Independent
Karnataka	Kolar	H.V.ANJAPPA	920	no	IND	Independent
Karnataka	Kolar	R.RAVINDRA KUMAR	1144	no	BDBRAJP	Bharatiya Dr. B.R.Ambedkar Janta Party
Karnataka	Kolar	B.V.VENKATAGIRIYAPPA	6716	no	IND	Independent
Karnataka	Kolar	V.SRINIVASA	1424	no	IND	Independent
Karnataka	Kolar	D.M.AMBAREESHA	4934	no	BSP	Bahujan Samaj Party
Karnataka	Kolar	M.MUNIYAPPA	2667	no	IND	Independent
Karnataka	Kolar	None of the Above	5098	no	NOTA	None of the Above
Karnataka	Kolar	K.H.MADHUSOODAN	1759	no	IND	Independent
Kerala	Kasaragod	P KARUNAKARAN	384964	yes	CPM	Communist Party of India  (Marxist)
Kerala	Kasaragod	None of the Above	6103	no	NOTA	None of the Above
Kerala	Kasaragod	ABBAS MOTHALAPPARA	632	no	AITC	All India Trinamool Congress
Kerala	Kasaragod	K SURENDRAN	172826	no	BJP	Bharatiya Janata Party
Kerala	Kasaragod	ADV. T SIDDIQUE	378043	no	INC	Indian National Congress
Kerala	Kasaragod	ABOOBACKER SIDDIQ	880	no	IND	Independent
Kerala	Kasaragod	GOTHRA MOOPAN NELLIKADAN KANNAN	2655	no	IND	Independent
Kerala	Kasaragod	AMBALATHARA KUNHIKRISHNAN	4996	no	AAAP	Aam Aadmi Party
Kerala	Kasaragod	ADV. BASHEER ALADY	3104	no	BSP	Bahujan Samaj Party
Kerala	Kasaragod	P K RAMAN	1222	no	IND	Independent
Kerala	Kasaragod	KARUNAKARAN PAINGAPPADAN	1002	no	IND	Independent
Kerala	Kasaragod	KARUNAKARAN KALIPPURAYIL	824	no	IND	Independent
Kerala	Kasaragod	K K ASHOKAN	3057	no	IND	Independent
Kerala	Kasaragod	MANOHARAN K	4194	no	IND	Independent
Kerala	Kasaragod	ABDUL SALAM N U	9713	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Kannur	P K SREEMATHI TEACHER	427622	yes	CPM	Communist Party of India  (Marxist)
Kerala	Kannur	P P MOHANAN	2371	no	IND	Independent
Kerala	Kannur	SASIDHARAN K V	6106	no	AAAP	Aam Aadmi Party
Kerala	Kannur	SREEMATHI PUTHALATH	1500	no	IND	Independent
Kerala	Kannur	K SUDHAKARAN (SREESHYLAM)	2745	no	IND	Independent
Kerala	Kannur	N ABDULLA DAVUD	2109	no	BSP	Bahujan Samaj Party
Kerala	Kannur	K SUDHAKARAN (KOLLON HOUSE)	4240	no	IND	Independent
Kerala	Kannur	P P ABOOBACKAR	1536	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Kannur	K SUDHAKARAN	421056	no	INC	Indian National Congress
Kerala	Kannur	K K ABDUL JABBAR	19170	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Kannur	P C MOHANAN MASTER	51636	no	BJP	Bharatiya Janata Party
Kerala	Kannur	None of the Above	7026	no	NOTA	None of the Above
Kerala	Vadakara	MULLAPPALLY RAMACHANDRAN	416479	yes	INC	Indian National Congress
Kerala	Vadakara	None of the Above	6107	no	NOTA	None of the Above
Kerala	Vadakara	ADV.A.N.SHAMSEER	413173	no	CPM	Communist Party of India  (Marxist)
Kerala	Vadakara	P.SHARAFUDEEN	1679	no	IND	Independent
Kerala	Vadakara	P.ABDUL HAMEED  MASTER	15058	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Vadakara	V.K.SAJEEVAN	76313	no	BJP	Bharatiya Janata Party
Kerala	Vadakara	SASEENDRAN	2150	no	BSP	Bahujan Samaj Party
Kerala	Vadakara	ADV.P.KUMARANKUTTY	17229	no	IND	Independent
Kerala	Vadakara	ATTUVEPPIL KUNHIKKANNAN	731	no	IND	Independent
Kerala	Vadakara	A.P.SHAMSEER	3485	no	IND	Independent
Kerala	Vadakara	A.M.SMITHA	693	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Vadakara	ALI AKBAR	6245	no	AAAP	Aam Aadmi Party
Kerala	Wayanad	M I SHANAVAS	377035	yes	INC	Indian National Congress
Kerala	Wayanad	ADV. P P A SAGEER	10684	no	AAAP	Aam Aadmi Party
Kerala	Wayanad	SATHYAN PUTHANVEETTIL	2855	no	IND	Independent
Kerala	Wayanad	SATHYAN THAZHEMANGAD	5476	no	IND	Independent
Kerala	Wayanad	ABRAHAM BENHUR	1308	no	IND	Independent
Kerala	Wayanad	CLEATUS	1517	no	IND	Independent
Kerala	Wayanad	SATHYAN MOKERI	356165	no	CPI	Communist Party of India
Kerala	Wayanad	P R RASMILNATH	80752	no	BJP	Bharatiya Janata Party
Kerala	Wayanad	JALEEL NEELAMBRA	14327	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Wayanad	SAM P MATHEW	1222	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Wayanad	SINOJ A C	1104	no	IND	Independent
Kerala	Wayanad	RAMLA MAMPAD	12645	no	WPOI	Welfare Party Of India
Kerala	Wayanad	VAPPAN	1317	no	BSP	Bahujan Samaj Party
Kerala	Wayanad	P V ANVAR	37123	no	IND	Independent
Kerala	Wayanad	SATHEESH CHANDRAN	741	no	AITC	All India Trinamool Congress
Kerala	Wayanad	None of the Above	10735	no	NOTA	None of the Above
Kerala	Kozhikode	M .K RAGHAVAN	397615	yes	INC	Indian National Congress
Kerala	Kozhikode	None of the Above	6381	no	NOTA	None of the Above
Kerala	Kozhikode	ADV.N.P.PRATHAP KUMAR	6993	no	IND	Independent
Kerala	Kozhikode	MUSTHAFA KOMMERI	10596	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Kozhikode	A.VIJAYARAGHAVAN	380732	no	CPM	Communist Party of India  (Marxist)
Kerala	Kozhikode	C K.PADMANABHAN	115760	no	BJP	Bharatiya Janata Party
Kerala	Kozhikode	THRISSUR NAZEER	665	no	IND	Independent
Kerala	Kozhikode	VELAYUDHAN K.P.	1909	no	BSP	Bahujan Samaj Party
Kerala	Kozhikode	MUHAMMED RIYAS	473	no	IND	Independent
Kerala	Kozhikode	K P RATHEESH	13934	no	AAAP	Aam Aadmi Party
Kerala	Kozhikode	V M RAGHAVAN	964	no	IND	Independent
Kerala	Kozhikode	VIJAYARAGHAVAN.K.	1991	no	IND	Independent
Kerala	Kozhikode	M RAGHAVAN	2331	no	IND	Independent
Kerala	Kozhikode	M.VIJAYARAGHAVAN	2665	no	IND	Independent
Kerala	Malappuram	E. AHAMED	437723	yes	IUML	Indian Union Muslim League
Kerala	Malappuram	SREEDHARAN	1330	no	IND	Independent
Kerala	Malappuram	ANWAR SHAKEEL OMAR	1215	no	IND	Independent
Kerala	Malappuram	ADV. N.SREEPRAKASH	64705	no	BJP	Bharatiya Janata Party
Kerala	Malappuram	P.K.SAINABA	242984	no	CPM	Communist Party of India  (Marxist)
Kerala	Malappuram	GOPINATHAN.N	2491	no	IND	Independent
Kerala	Malappuram	PROF. P.ISMAIL	29216	no	WPOI	Welfare Party Of India
Kerala	Malappuram	DR. M.V. IBRAHIM	1376	no	IND	Independent
Kerala	Malappuram	ILYAS	2745	no	BSP	Bahujan Samaj Party
Kerala	Malappuram	NASARUDHEEN	47853	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Malappuram	None of the Above	21829	no	NOTA	None of the Above
Kerala	Ponnani	E. T. MOHAMMED BASHEER	378503	yes	IUML	Indian Union Muslim League
Kerala	Ponnani	None of the Above	7494	no	NOTA	None of the Above
Kerala	Ponnani	V.T.IKRAMUL HAQUE	26640	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Ponnani	V.ABDURAHMAN	353093	no	IND	Independent
Kerala	Ponnani	K.NARAYANAN MASTER	75212	no	BJP	Bharatiya Janata Party
Kerala	Ponnani	ABULLAIS.T.P	11034	no	IND	Independent
Kerala	Ponnani	T.AYYAPPAN	2153	no	BSP	Bahujan Samaj Party
Kerala	Ponnani	BINDU M.K.	2261	no	IND	Independent
Kerala	Ponnani	ABDURAHIMAN VARIKKOTTIL	2434	no	IND	Independent
Kerala	Ponnani	ABDURAHIMAN VAYARAKATH	1220	no	IND	Independent
Kerala	Ponnani	ABDURAHMAN VADAKKATHINAKATH	2044	no	IND	Independent
Kerala	Ponnani	SHYLOCK P.V.	9504	no	AAAP	Aam Aadmi Party
Kerala	Palakkad	M B RAJESH	412897	yes	CPM	Communist Party of India  (Marxist)
Kerala	Palakkad	SOBHASURENDRAN	136541	no	BJP	Bharatiya Janata Party
Kerala	Palakkad	VEERENDRAKUMAR O P	3946	no	IND	Independent
Kerala	Palakkad	B PADMANABHAN	4933	no	AAAP	Aam Aadmi Party
Kerala	Palakkad	PRADEEP	770	no	IND	Independent
Kerala	Palakkad	ABOOBAKKER SIDHIKH	828	no	AITC	All India Trinamool Congress
Kerala	Palakkad	C.K. RAMAKRISHNAN	1092	no	IND	Independent
Kerala	Palakkad	M P VEERENDRAKUMAR	307597	no	SJD	Socialist Janata (Democratic)
Kerala	Palakkad	ABHIMOD	1231	no	IND	Independent
Kerala	Palakkad	SREEJITH	1416	no	IND	Independent
Kerala	Palakkad	HARI ARUMBIL	2009	no	BSP	Bahujan Samaj Party
Kerala	Palakkad	THENNILAPURAM RADHAKRISHNAN	8667	no	WPOI	Welfare Party Of India
Kerala	Palakkad	V S  SHANAVAS	1946	no	IND	Independent
Kerala	Palakkad	E S KHAJA HUSSAIN	12504	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Palakkad	RAJESH S	2654	no	SHS	Shivsena
Kerala	Palakkad	None of the Above	11291	no	NOTA	None of the Above
Kerala	Alathur	P.K.BIJU	411808	yes	CPM	Communist Party of India  (Marxist)
Kerala	Alathur	None of the Above	21417	no	NOTA	None of the Above
Kerala	Alathur	PREMAKUMARI	4436	no	BSP	Bahujan Samaj Party
Kerala	Alathur	KRISHNAN ERANHIKKAL	7820	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Alathur	SHEEBA	374496	no	INC	Indian National Congress
Kerala	Alathur	SHAJU MON VATTEKKAD	87803	no	BJP	Bharatiya Janata Party
Kerala	Alathur	ALBIN M U	5260	no	IND	Independent
Kerala	Alathur	K S VELAYUDHAN	4392	no	IND	Independent
Kerala	Alathur	KRISHNANKUTTY V	932	no	IND	Independent
Kerala	Alathur	K BIJU	2911	no	IND	Independent
Kerala	Alathur	A BIJU	1692	no	IND	Independent
Kerala	Alathur	R BIJU	2159	no	IND	Independent
Kerala	Alathur	VIJAYAN AMBALAKKAD	2102	no	IND	Independent
Kerala	Thrissur	C. N. JAYADEVAN	389209	yes	CPI	Communist Party of India
Kerala	Thrissur	JIJU CHEENIKKAL	450	no	IND	Independent
Kerala	Thrissur	MANIYANGATTIL SUKUMARAN	949	no	IND	Independent
Kerala	Thrissur	SUFEERA K. P.	6894	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Thrissur	SARAH JOSEPH	44638	no	AAAP	Aam Aadmi Party
Kerala	Thrissur	ADV. A. JAYARAM	2110	no	BSP	Bahujan Samaj Party
Kerala	Thrissur	HAJI MOIDEEN SHAH	659	no	JD(U)	Janata Dal  (United)
Kerala	Thrissur	JASMINSHA	3959	no	IND	Independent
Kerala	Thrissur	M. NARAYANANKUTTY (M. N. KUTTY)	616	no	IND	Independent
Kerala	Thrissur	K. P. DHANAPALAN	350982	no	INC	Indian National Congress
Kerala	Thrissur	T. L. SANTHOSH	6044	no	IND	Independent
Kerala	Thrissur	SOMAN PILLAI	546	no	igp	Indian Gandhiyan Party
Kerala	Thrissur	K.SIVARAMAN	718	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Thrissur	K.P. SREESAN	102681	no	BJP	Bharatiya Janata Party
Kerala	Thrissur	None of the Above	10050	no	NOTA	None of the Above
Kerala	Chalakudy	INNOCENT	358440	yes	IND	Independent
Kerala	Chalakudy	None of the Above	10552	no	NOTA	None of the Above
Kerala	Chalakudy	K M NOORDEEN	35189	no	AAAP	Aam Aadmi Party
Kerala	Chalakudy	K. AMBUJAKSHAN	12942	no	WPOI	Welfare Party Of India
Kerala	Chalakudy	ADV. B. GOPALAKRISHNAN	92848	no	BJP	Bharatiya Janata Party
Kerala	Chalakudy	P C CHACKO	344556	no	INC	Indian National Congress
Kerala	Chalakudy	JAYAN KONIKARA	932	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Chalakudy	M G PURUSHOTHAMAN	1942	no	BSP	Bahujan Samaj Party
Kerala	Chalakudy	SHAFEER MUHAMMED	14386	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Chalakudy	EDAPPALLY BASHEER	1346	no	IND	Independent
Kerala	Chalakudy	ANNAMMA JOY	862	no	AITC	All India Trinamool Congress
Kerala	Chalakudy	SAJI THURUTHIKUNNEL	1114	no	SHS	Shivsena
Kerala	Chalakudy	VINCENT	4596	no	IND	Independent
Kerala	Chalakudy	JAISON PANIKULANGARA	2052	no	IND	Independent
Kerala	Chalakudy	T D BABURAJAN	900	no	IND	Independent
Kerala	Chalakudy	ABDUL KAREEM	1376	no	RJD	Rashtriya Janata Dal
Kerala	Ernakulam	PROF. K.V. THOMAS	353841	yes	INC	Indian National Congress
Kerala	Ernakulam	ANIL KUMAR	1180	no	IND	Independent
Kerala	Ernakulam	K C KARTHIKEYAN	2685	no	BSP	Bahujan Samaj Party
Kerala	Ernakulam	DENSIL MENDEZ	2630	no	JD(U)	Janata Dal  (United)
Kerala	Ernakulam	KISHORE	5011	no	IND	Independent
Kerala	Ernakulam	ADV. RADHAKRISHNAN P T	2104	no	IND	Independent
Kerala	Ernakulam	RAJANEESH BABU	8246	no	IND	Independent
Kerala	Ernakulam	JUNO JOHN BABY	1077	no	IND	Independent
Kerala	Ernakulam	K.V. BHASKARAN	22733	no	IND	Independent
Kerala	Ernakulam	ANITHA PRATAP	51517	no	AAAP	Aam Aadmi Party
Kerala	Ernakulam	ZULFIKAL ALI	14825	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Ernakulam	P.D. CHANDRABHANU	6156	no	SRP	Socialist Republican Party
Kerala	Ernakulam	A.N. RADHAKRISHNAN	99003	no	BJP	Bharatiya Janata Party
Kerala	Ernakulam	CHRISTY FERNANDEZ	266794	no	IND	Independent
Kerala	Ernakulam	N J PIOUS	2347	no	IND	Independent
Kerala	Ernakulam	KRISHNANKUTTY M.K.	950	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Ernakulam	None of the Above	9735	no	NOTA	None of the Above
Kerala	Idukki	ADV.JOICE GEORGE	382019	yes	IND	Independent
Kerala	Idukki	None of the Above	12338	no	NOTA	None of the Above
Kerala	Idukki	P.C.JOLLY	550	no	IND	Independent
Kerala	Idukki	ADV.SABU VARGHESE	50438	no	BJP	Bharatiya Janata Party
Kerala	Idukki	ADV.DEAN KURIAKOSE	331477	no	INC	Indian National Congress
Kerala	Idukki	ADV.CHITTOOR RAJAMANNAR	415	no	IND	Independent
Kerala	Idukki	MUHAMMED SHARAFUDHEEN	10401	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Idukki	SILVI SUNIL	11215	no	AAAP	Aam Aadmi Party
Kerala	Idukki	APPANCHIRA PONNAPPAN	2477	no	BSP	Bahujan Samaj Party
Kerala	Idukki	T.K.TOMY	3971	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Idukki	SOMINI PRABHAKARAN	1925	no	IND	Independent
Kerala	Idukki	JAMES JOSEPH	1158	no	IND	Independent
Kerala	Idukki	SHOBY JOSEPH	1179	no	IND	Independent
Kerala	Idukki	ANISH MARIYIL	629	no	IND	Independent
Kerala	Idukki	JOYCE GEORGE	6024	no	IND	Independent
Kerala	Idukki	JOICE GEORGE	3057	no	IND	Independent
Kerala	Idukki	BIJU JOSEPH	493	no	IND	Independent
Kerala	Kottayam	JOSE K. MANI	424194	yes	KEC(M)	Kerala Congress  (M)
Kerala	Kottayam	PRAVEEN K. MOHAN	925	no	IND	Independent
Kerala	Kottayam	RATHEESH PERUMAL	2157	no	IND	Independent
Kerala	Kottayam	JAMES JOSEPH	1831	no	IND	Independent
Kerala	Kottayam	SASIKKUTTAN VAKATHANAM	1048	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Kerala	Kottayam	N.K.BIJU	2879	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Kerala	Kottayam	ADV.ANIL AIKKARA	26381	no	AAAP	Aam Aadmi Party
Kerala	Kottayam	ADV.NOBLE MATHEW	44357	no	IND	Independent
Kerala	Kottayam	ADV.MATHEW T. THOMAS	303595	no	JD(S)	Janata Dal  (Secular)
Kerala	Kottayam	ROY ARACKAL	3513	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Kottayam	SREENI K. JACOB	6732	no	BSP	Bahujan Samaj Party
Kerala	Kottayam	None of the Above	14024	no	NOTA	None of the Above
Kerala	Alappuzha	K C VENUGOPAL	462525	yes	INC	Indian National Congress
Kerala	Alappuzha	None of the Above	11338	no	NOTA	None of the Above
Kerala	Alappuzha	C B CHANDRABABU	443118	no	CPM	Communist Party of India  (Marxist)
Kerala	Alappuzha	P V NATESAN	3385	no	BSP	Bahujan Samaj Party
Kerala	Alappuzha	ADV.M.A.BINDU	5921	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Kerala	Alappuzha	JAYACHANDRAN SARASAMMA	481	no	IND	Independent
Kerala	Alappuzha	M M PAULOSE	402	no	IND	Independent
Kerala	Alappuzha	PROF.A.V.THAMARAKSHAN	43051	no	RSPK(B)	Revolutionary Socialist Party of Kerala  (Bolshevik) RSP.B
Kerala	Alappuzha	VENUGOPAL P C	3149	no	IND	Independent
Kerala	Alappuzha	D MOHANAN	9414	no	AAAP	Aam Aadmi Party
Kerala	Alappuzha	CHANDRABABU G	1615	no	IND	Independent
Kerala	Alappuzha	T S BALAKRISHNAN	1363	no	IND	Independent
Kerala	Alappuzha	S.B.BASHEER	709	no	IND	Independent
Kerala	Alappuzha	THULASEEDHARAN PALLICKAL	10993	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Mavelikkara	KODIKUNNIL SURESH	402432	yes	INC	Indian National Congress
Kerala	Mavelikkara	PALLICKAL SURENDRAN	1486	no	IND	Independent
Kerala	Mavelikkara	PIRAVATHOOR SREEDHARAN	1207	no	IND	Independent
Kerala	Mavelikkara	JYOTHISH PERUMPULICKAL	8946	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Mavelikkara	ADV.P.K.JAYAKRISHNAN	3603	no	BSP	Bahujan Samaj Party
Kerala	Mavelikkara	ADV.P.SUDHEER	79743	no	BJP	Bharatiya Janata Party
Kerala	Mavelikkara	N SADANANDAN	7753	no	AAAP	Aam Aadmi Party
Kerala	Mavelikkara	CHENGARA SURENDRAN	369695	no	CPI	Communist Party of India
Kerala	Mavelikkara	SASIKALA K S	4736	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Kerala	Mavelikkara	None of the Above	9459	no	NOTA	None of the Above
Kerala	Pathanamthitta	ANTO ANTONY	358842	yes	INC	Indian National Congress
Kerala	Pathanamthitta	None of the Above	16538	no	NOTA	None of the Above
Kerala	Pathanamthitta	S RADHAMANI	2901	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Kerala	Pathanamthitta	SABUKUTTY JOY (SABU PUTHUCHIRA)	682	no	SLAP	Social Action Party
Kerala	Pathanamthitta	ADV. PEELIPOSE THOMAS	302651	no	IND	Independent
Kerala	Pathanamthitta	MANSOOR THENGANA	11353	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Pathanamthitta	ALEYAMMA VARGHESE	1236	no	AITC	All India Trinamool Congress
Kerala	Pathanamthitta	MATHEW PAREY	384	no	IND	Independent
Kerala	Pathanamthitta	AJITH KUMAR K. K	748	no	IND	Independent
Kerala	Pathanamthitta	PUSHPANGATHAN	953	no	IND	Independent
Kerala	Pathanamthitta	M T REMESH	138954	no	BJP	Bharatiya Janata Party
Kerala	Pathanamthitta	SELEENA PRAKKANAM	10384	no	BSP	Bahujan Samaj Party
Kerala	Pathanamthitta	PEELIPOSE	16493	no	IND	Independent
Kerala	Pathanamthitta	SANTHOSH KUMAR	4847	no	IND	Independent
Kerala	Pathanamthitta	LALITHA MALAYALAPPUZHA	460	no	IND	Independent
Kerala	Pathanamthitta	SHILVIN KOTTACKAKATHU	1005	no	IND	Independent
Kerala	Pathanamthitta	S RADHAKRISHNAN	1021	no	IND	Independent
Kerala	Kollam	N.K.PREMACHANDRAN	408528	yes	RSP	Revolutionary Socialist Party
Kerala	Kollam	C. INDULAL EX-SERVICE MAN	1709	no	IND	Independent
Kerala	Kollam	M. BABY	3364	no	IND	Independent
Kerala	Kollam	V.S. PREMACHANDRAN	3333	no	IND	Independent
Kerala	Kollam	R. PREMACHANDRAN	2808	no	IND	Independent
Kerala	Kollam	ADV.S.PRAHLADAN	4266	no	BSP	Bahujan Samaj Party
Kerala	Kollam	A.K.SALAHUDEEN	12812	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Kollam	P.M. VELAYUDHAN	58671	no	BJP	Bharatiya Janata Party
Kerala	Kollam	A. JOSEKUTTY	1702	no	IND	Independent
Kerala	Kollam	K. BHASKARAN	3108	no	IND	Independent
Kerala	Kollam	M.A. BABY	370879	no	CPM	Communist Party of India  (Marxist)
Kerala	Kollam	None of the Above	7876	no	NOTA	None of the Above
Kerala	Attingal	DR.A .SAMPATH	392478	yes	CPM	Communist Party of India  (Marxist)
Kerala	Attingal	None of the Above	6924	no	NOTA	None of the Above
Kerala	Attingal	GIRIJA KUMARI.S	90528	no	BJP	Bharatiya Janata Party
Kerala	Attingal	SUNILKRISHNAN	3850	no	IND	Independent
Kerala	Attingal	SRI.M.K.MANOJ KUMAR	11225	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Attingal	SMT. PRIYA SUNIL	4862	no	WPOI	Welfare Party Of India
Kerala	Attingal	ANIL KUMAR N S	8586	no	BSP	Bahujan Samaj Party
Kerala	Attingal	DAS K VARKALA	2375	no	IND	Independent
Kerala	Attingal	ADV.BINDHU KRISHNA	323100	no	INC	Indian National Congress
Kerala	Attingal	SRI. VIVEKANANDAN	615	no	IND	Independent
Kerala	Attingal	SRI.IRINCHAYAM SURESH	1052	no	IND	Independent
Kerala	Attingal	SRI .ADV.M. R.SARIN	576	no	JD(U)	Janata Dal  (United)
Kerala	Attingal	SRI.SURESHKUMAR THONNAKKAL	647	no	IND	Independent
Kerala	Attingal	SRI. VAKKOM G AJITH	5511	no	SHS	Shivsena
Kerala	Attingal	SRI. HARIHARAN K S	4064	no	IND	Independent
Kerala	Attingal	SRI. ADV. CHIRAYINKEEZHU GOPINATHAN	736	no	RPI(A)	Republican Party of India (A)
Kerala	Attingal	SRI.SAMBATH ANILKUMAR	2221	no	IND	Independent
Kerala	Thiruvananthapuram	DR. SHASHI THAROOR	297806	yes	INC	Indian National Congress
Kerala	Thiruvananthapuram	SRI. JAIN WILSON	2105	no	IND	Independent
Kerala	Thiruvananthapuram	SRI. ADV. PERURKADA HARIKUMAR	902	no	SHS	Shivsena
Kerala	Thiruvananthapuram	SMT. SYAMALAKUMARI	1851	no	IND	Independent
Kerala	Thiruvananthapuram	SRI. THOMAS JOSEPH	261	no	RPI(A)	Republican Party of India (A)
Kerala	Thiruvananthapuram	SRI.BENET BABU BENJAMIN	4231	no	IND	Independent
Kerala	Thiruvananthapuram	SMT. R CHANDRIKA KATTAKADA	292	no	RPI	Republican Party of India
Kerala	Thiruvananthapuram	SRI.ANIL KUMAR Y	371	no	IND	Independent
Kerala	Thiruvananthapuram	SRI. AJIT JOY	14153	no	AAAP	Aam Aadmi Party
Kerala	Thiruvananthapuram	SRI.GEORGE MANKIDIYIL	843	no	IND	Independent
Kerala	Thiruvananthapuram	SRI. J SUDHAKARAN	3260	no	BSP	Bahujan Samaj Party
Kerala	Thiruvananthapuram	SRI. V S JAYAKUMAR	445	no	IND	Independent
Kerala	Thiruvananthapuram	SRI. O V SREEDATH	356	no	SRP	Socialist Republican Party
Kerala	Thiruvananthapuram	SRI. DOCTOR BENNET ABRAHAM	248941	no	CPI	Communist Party of India
Kerala	Thiruvananthapuram	SRI. O M JYOTHEENDRA NATH	2445	no	IND	Independent
Kerala	Thiruvananthapuram	SRI.KUNNIL SHAJAHAN	4818	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Kerala	Thiruvananthapuram	SRI. KUNNUKUZHI S MANI	1717	no	IND	Independent
Kerala	Thiruvananthapuram	SRI. O RAJAGOPAL	282336	no	BJP	Bharatiya Janata Party
Kerala	Thiruvananthapuram	FR.STEPHEN	1271	no	IND	Independent
Kerala	Thiruvananthapuram	SRI.SHAJAR KHAN	1691	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Kerala	Thiruvananthapuram	None of the Above	3344	no	NOTA	None of the Above
Madhya Pradesh	MORENA	ANOOP MISHRA	375567	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	MORENA	None of the Above	4792	no	NOTA	None of the Above
Madhya Pradesh	MORENA	RAM NARESH	3546	no	IND	Independent
Madhya Pradesh	MORENA	ANOOP NAGAR	852	no	IND	Independent
Madhya Pradesh	MORENA	MANESH KUMAR RAWAT	975	no	JMBP	Jai Maha Bharath Party
Madhya Pradesh	MORENA	KULDEEP SINGH	1058	no	IND	Independent
Madhya Pradesh	MORENA	VISNU SHARMA VIRTHARE	2114	no	AITC	All India Trinamool Congress
Madhya Pradesh	MORENA	AKASH GUPTA	809	no	IND	Independent
Madhya Pradesh	MORENA	SHAKTI SINGH TOMAR	1145	no	RAHM	Rashtriya Ahinsa Manch
Madhya Pradesh	MORENA	BRINDAWAN SINGH SIKARWAR	242586	no	BSP	Bahujan Samaj Party
Madhya Pradesh	MORENA	DR GOVIND SINGH	184253	no	INC	Indian National Congress
Madhya Pradesh	MORENA	SIYARAM SINGH CHOUDRI	6471	no	IND	Independent
Madhya Pradesh	MORENA	RAJESH KUSHWAH	5554	no	SP	Samajwadi Party
Madhya Pradesh	MORENA	DEVENDRA KUMAR AGRVAL	13806	no	AAAP	Aam Aadmi Party
Madhya Pradesh	MORENA	JAYANT SINGH TOMAR	1429	no	LSWP	Loktantrik Samajwadi Party
Madhya Pradesh	MORENA	SATANDRA SINGH (TONY TOMAR)	4347	no	IND	Independent
Madhya Pradesh	MORENA	ARJUN SINGH (PAPPU KUSHWAH)	3006	no	RSMD	Rashtriya Samanta Dal
Madhya Pradesh	MORENA	CHHAVENDRA	1969	no	IND	Independent
Madhya Pradesh	BHIND	DR. BHAGIRATH PRASAD	404474	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	BHIND	VIJAYSINGH KORI	2699	no	AIFB	All India Forward Bloc
Madhya Pradesh	BHIND	KRISHNA DEVI	7730	no	AAAP	Aam Aadmi Party
Madhya Pradesh	BHIND	HAKIM SINGH	2899	no	IND	Independent
Madhya Pradesh	BHIND	IMARTI DEVI	244513	no	INC	Indian National Congress
Madhya Pradesh	BHIND	PHOOLSINGH BARAIYA	20403	no	BA S D	Bahujan Sangharshh Dal
Madhya Pradesh	BHIND	MANISH KATROLIYA	33803	no	BSP	Bahujan Samaj Party
Madhya Pradesh	BHIND	B.S. THANESWARIYA	1793	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	BHIND	ANITA HITENDRA CHAUDHARY	5198	no	SP	Samajwadi Party
Madhya Pradesh	BHIND	None of the Above	5572	no	NOTA	None of the Above
Madhya Pradesh	GWALIOR	NARENDRA SINGH TOMAR	442796	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	GWALIOR	NEELAM AGRWAL	11510	no	AAAP	Aam Aadmi Party
Madhya Pradesh	GWALIOR	ASIF KHAN	4969	no	IND	Independent
Madhya Pradesh	GWALIOR	K. P. KAMTE	1607	no	BRMD	Bharatiya Rashtriya Mazdoor Dal
Madhya Pradesh	GWALIOR	PRAJESH CHOUDHARY	1981	no	IND	Independent
Madhya Pradesh	GWALIOR	BALWANT SINGH KUSHWAH ADVOCATE	5327	no	SP	Samajwadi Party
Madhya Pradesh	GWALIOR	NARAYAN SINGH KUSHWAH	3963	no	IND	Independent
Madhya Pradesh	GWALIOR	ALOK SHARMA	68196	no	BSP	Bahujan Samaj Party
Madhya Pradesh	GWALIOR	HIRADAY NARAYAN SINGH TOMAR (GUDDU)	2244	no	ABHM	Akhil Bharat Hindu Mahasabha
Madhya Pradesh	GWALIOR	MADAN MOHAN URF MOHAN SINGH TOMAR	1477	no	bjyp	Bhartiya Jan Yug Party
Madhya Pradesh	GWALIOR	AKHILESH YADAV	10297	no	CPM	Communist Party of India  (Marxist)
Madhya Pradesh	GWALIOR	ASHOK SINGH	413097	no	INC	Indian National Congress
Madhya Pradesh	GWALIOR	C.L. KARODIYA (PRIJAPATI)	4719	no	JND	Jan-Nyay Dal
Madhya Pradesh	GWALIOR	RAM PATHWAR KORI (RAMSEBAK)	1687	no	IND	Independent
Madhya Pradesh	GWALIOR	VIKAS SHARMA	1408	no	BhNaP	Bharatiya Navyuvak Party
Madhya Pradesh	GWALIOR	KISHAN LAL KUSHWAH	1412	no	RSMD	Rashtriya Samanta Dal
Madhya Pradesh	GWALIOR	SHIV SINGH KUSHAWAH	2101	no	MAHP	Mahanwadi Party
Madhya Pradesh	GWALIOR	DEEPAK KUMAR BANSAL RANGWALE (PRERAK)	2122	no	IND	Independent
Madhya Pradesh	GWALIOR	COMRADE SUNIL GOPAL	2054	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Madhya Pradesh	GWALIOR	GEETA RAJPUT	1067	no	ALHP	Al-Hind Party
Madhya Pradesh	GWALIOR	CAPTAN BABU LAL GOSWAMI	2659	no	IND	Independent
Madhya Pradesh	GWALIOR	None of the Above	4219	no	NOTA	None of the Above
Madhya Pradesh	GUNA	JYOTIRADITYA M SCINDIA	517036	yes	INC	Indian National Congress
Madhya Pradesh	GUNA	None of the Above	12481	no	NOTA	None of the Above
Madhya Pradesh	GUNA	SHAILENDRA SINGH KUSHWAH	4073	no	AAAP	Aam Aadmi Party
Madhya Pradesh	GUNA	LAKHAN SINGH BAGHEL	27412	no	BSP	Bahujan Samaj Party
Madhya Pradesh	GUNA	CHANDRAPAL SINGH TOMAR	2197	no	IND	Independent
Madhya Pradesh	GUNA	NAND KISHORE TYAGI	2072	no	IND	Independent
Madhya Pradesh	GUNA	ANWAR KHAN	1321	no	IND	Independent
Madhya Pradesh	GUNA	LALLI RAM RAI	5575	no	IND	Independent
Madhya Pradesh	GUNA	GIRRAJ YADAV	3294	no	IND	Independent
Madhya Pradesh	GUNA	MAHENDRA KUMAR	2845	no	IND	Independent
Madhya Pradesh	GUNA	JAIBHANSINGH PAWAIYA	396244	no	BJP	Bharatiya Janata Party
Madhya Pradesh	GUNA	NARAYANRAO GUPCHUP	2079	no	IND	Independent
Madhya Pradesh	SAGAR	LAXMI NARAYAN YADAV	482580	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	SAGAR	MAHENDRA KUMAR CHOUDHARY	1632	no	IND	Independent
Madhya Pradesh	SAGAR	KASHIRAM PATEL	1128	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	SAGAR	M. HANIF QURASHI	3856	no	IND	Independent
Madhya Pradesh	SAGAR	SHITIN KESHRWANI	2020	no	IND	Independent
Madhya Pradesh	SAGAR	GOVIND SINGH RAJPUT	361843	no	INC	Indian National Congress
Madhya Pradesh	SAGAR	SAROJ KATIYAR KURMI	19917	no	BSP	Bahujan Samaj Party
Madhya Pradesh	SAGAR	ATUL MISHRA	6328	no	AAAP	Aam Aadmi Party
Madhya Pradesh	SAGAR	VINAY KUMAR	1565	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	SAGAR	BHARAT SEN ADVOCATE	1556	no	IND	Independent
Madhya Pradesh	SAGAR	None of the Above	9504	no	NOTA	None of the Above
Madhya Pradesh	TIKAMGARH	DR. VIRENDRA KUMAR	422979	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	TIKAMGARH	AHIRWAR DR. KAMLESH VERMA	214248	no	INC	Indian National Congress
Madhya Pradesh	TIKAMGARH	KHANGAR NEERAJ	15194	no	CPI	Communist Party of India
Madhya Pradesh	TIKAMGARH	AHIRWAR SEWAK RAM	23975	no	BSP	Bahujan Samaj Party
Madhya Pradesh	TIKAMGARH	RAMESH CHAND VERMA	4992	no	AAAP	Aam Aadmi Party
Madhya Pradesh	TIKAMGARH	AHIRWAR RAMCHARAN	3094	no	PHRC	Poorvanchal Rashtriya Congress
Madhya Pradesh	TIKAMGARH	GIRDHARI LAL KORI	5219	no	IND	Independent
Madhya Pradesh	TIKAMGARH	CHATURBHUJ SNEHI	4035	no	IND	Independent
Madhya Pradesh	TIKAMGARH	DR. AMBESH KUMARI AHIRWAR	47497	no	SP	Samajwadi Party
Madhya Pradesh	TIKAMGARH	LAKHAN LAL CHADHAR	4548	no	IND	Independent
Madhya Pradesh	TIKAMGARH	GANESH KUMHAR	2230	no	IND	Independent
Madhya Pradesh	TIKAMGARH	SHEEL CHANDRA AHIRWAR	3855	no	IND	Independent
Madhya Pradesh	TIKAMGARH	BHAGWAN DAS BANSHKAR	4400	no	IND	Independent
Madhya Pradesh	TIKAMGARH	None of the Above	10055	no	NOTA	None of the Above
Madhya Pradesh	DAMOH	PRAHALAD SINGH PATEL	513079	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	DAMOH	None of the Above	12600	no	NOTA	None of the Above
Madhya Pradesh	DAMOH	IRFAN KHAN	2580	no	IND	Independent
Madhya Pradesh	DAMOH	PRAHLAD SINGH PATEL	9266	no	IND	Independent
Madhya Pradesh	DAMOH	PRAHLAD SINGH LODI	9343	no	IND	Independent
Madhya Pradesh	DAMOH	DIVAN UPENDR PRATAP SINGH (RAJA SAB)	16350	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	DAMOH	ANANT LAL BASOR	2836	no	IND	Independent
Madhya Pradesh	DAMOH	SANTOSH BHARTI	11298	no	AAAP	Aam Aadmi Party
Madhya Pradesh	DAMOH	DR.GULAB KHAN MASTER	3429	no	IND	Independent
Madhya Pradesh	DAMOH	CHOUDHARY MAHENDRA PRATAP SINGH	299780	no	INC	Indian National Congress
Madhya Pradesh	DAMOH	DEVENDRA CHAOURASIA	31519	no	BSP	Bahujan Samaj Party
Madhya Pradesh	KHAJURAHO	NAGENDRA SINGH	474966	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	KHAJURAHO	SMT. DURGA SHARMA / ALIAS MADHU KAPIL	1936	no	BC	Bundelkhand Congress
Madhya Pradesh	KHAJURAHO	SANJEEV KUMAR MISHRA	3187	no	AIFB	All India Forward Bloc
Madhya Pradesh	KHAJURAHO	SHRIDHAR KHARE	10997	no	IND	Independent
Madhya Pradesh	KHAJURAHO	AMIT BHATNAGAR	8930	no	AAAP	Aam Aadmi Party
Madhya Pradesh	KHAJURAHO	AVNISH TIWARI	2625	no	SHS	Shivsena
Madhya Pradesh	KHAJURAHO	AYENDRA  KUMAR SHARMA	4084	no	IND	Independent
Madhya Pradesh	KHAJURAHO	ANWAR KHAN	3505	no	IND	Independent
Madhya Pradesh	KHAJURAHO	KRISHAN SHARAN BHAIYA RAJA BUNDELA	1679	no	BBPSP	Brihattar Bharat Prajatantra Sewa Party
Madhya Pradesh	KHAJURAHO	RAJA PATERIA	227476	no	INC	Indian National Congress
Madhya Pradesh	KHAJURAHO	MAHESH CHANDRA	3012	no	RSMD	Rashtriya Samanta Dal
Madhya Pradesh	KHAJURAHO	SIDDHARTH SUKHLAL KUSHWAHA	40069	no	SP	Samajwadi Party
Madhya Pradesh	KHAJURAHO	RAM NATH LODHEE	13826	no	IND	Independent
Madhya Pradesh	KHAJURAHO	JAGDEESH KUMAR PATEL	2549	no	AD	Apna Dal
Madhya Pradesh	KHAJURAHO	RAM LAKHAN SINGH	60368	no	BSP	Bahujan Samaj Party
Madhya Pradesh	KHAJURAHO	IMRAN	1484	no	PECP	Peace Party
Madhya Pradesh	KHAJURAHO	JAYANT PRATAP SINGH / VIJAY SINGH	5906	no	IND	Independent
Madhya Pradesh	KHAJURAHO	None of the Above	7878	no	NOTA	None of the Above
Madhya Pradesh	SATNA	GANESH SINGH	375288	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	SATNA	None of the Above	13036	no	NOTA	None of the Above
Madhya Pradesh	SATNA	SHYAM LAL KUSHWAHA	2155	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	SATNA	AJAY PRATAP SINGH	2021	no	IND	Independent
Madhya Pradesh	SATNA	SHRI RAM SHUKLA RAJJAN	7986	no	IND	Independent
Madhya Pradesh	SATNA	MAHGOO SINGH MUTHEL	950	no	DaVP	Dalit Vikas Party(Bharat)
Madhya Pradesh	SATNA	RAJ KUMAR GOTAM	1202	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	SATNA	VINOD KUMAR RAVI	924	no	BSDS	Bhartiya Shramik Dal Socialist
Madhya Pradesh	SATNA	DHARMENDRA SINGH TIWARI	124602	no	BSP	Bahujan Samaj Party
Madhya Pradesh	SATNA	DADHIBAL PRASAD PATEL	1503	no	AD	Apna Dal
Madhya Pradesh	SATNA	AJAY  SINGH RAHUL BHAIYYA	366600	no	INC	Indian National Congress
Madhya Pradesh	SATNA	ASHOK KUMAR SAKET	1694	no	IND	Independent
Madhya Pradesh	SATNA	KESHAV PRASAD TRIPATHI AD. K.P.	2253	no	IND	Independent
Madhya Pradesh	SATNA	BIRENDRA BHAI PATEL	5305	no	IND	Independent
Madhya Pradesh	SATNA	JAGDISH SINGH	7925	no	AAAP	Aam Aadmi Party
Madhya Pradesh	REWA	JANARDAN MISHRA	383320	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	REWA	PURNANAND TIWARI	5835	no	AAAP	Aam Aadmi Party
Madhya Pradesh	REWA	PRABHUNATH PRASAD SAKET	4389	no	IND	Independent
Madhya Pradesh	REWA	DEORAJ SINGH PATEL	175567	no	BSP	Bahujan Samaj Party
Madhya Pradesh	REWA	SUNDERLAL TIWARI	214594	no	INC	Indian National Congress
Madhya Pradesh	REWA	PRAMOD TIWARI	4794	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	REWA	R.D. VISHWAKARMA	3164	no	AD	Apna Dal
Madhya Pradesh	REWA	BRAMHADATTA MISHRA URF CHHOTE	4718	no	IND	Independent
Madhya Pradesh	REWA	JANARDAN MISHRA	3608	no	IND	Independent
Madhya Pradesh	REWA	ABDUL WAFATI ANSARI URF YUVRAJ TAILORS	7527	no	SAP	Samata Party
Madhya Pradesh	REWA	VISHWA NATH DWIVEDI	5057	no	IND	Independent
Madhya Pradesh	REWA	GIRISH KUMAR GAUTAM	3260	no	IND	Independent
Madhya Pradesh	REWA	RAMKISHAN NIRAT (SAKET)	2055	no	RPI(A)	Republican Party of India (A)
Madhya Pradesh	REWA	MANGLESHWAR BHAIYA	1456	no	NADP	Naya Daur Party
Madhya Pradesh	REWA	None of the Above	10658	no	NOTA	None of the Above
Madhya Pradesh	SIDHI	RITI PATHAK	475678	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	SIDHI	SMT.  ASHA SINGH	10037	no	RsAD	Rashtriya Apna Dal
Madhya Pradesh	SIDHI	MOHD. JAHANGEER ANSARI	3763	no	IND	Independent
Madhya Pradesh	SIDHI	"SUNIL SONI ""KHADDI-42"""	6627	no	BA S D	Bahujan Sangharshh Dal
Madhya Pradesh	SIDHI	PUSPENDRA SINGH	23298	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	SIDHI	BHAGAWATI CHARAN PANDEY(NEELU)	10725	no	IND	Independent
Madhya Pradesh	SIDHI	LAL BAHADUR SINGH	1979	no	SamSP	Samata Samadhan Party
Madhya Pradesh	SIDHI	URMILA	4348	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Madhya Pradesh	SIDHI	BRAHMANAND PRATAP SINGH	5472	no	IND	Independent
Madhya Pradesh	SIDHI	RAMAKANT PANDEY MALAIHNA	7579	no	IND	Independent
Madhya Pradesh	SIDHI	PANKAJ SINGH PATEL	9691	no	AAAP	Aam Aadmi Party
Madhya Pradesh	SIDHI	RAMA SHANKAR SHAHWAL	39387	no	BSP	Bahujan Samaj Party
Madhya Pradesh	SIDHI	SANAT KUMAR TRIPATHI	5830	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	SIDHI	INDRAJEET KUMAR	367632	no	INC	Indian National Congress
Madhya Pradesh	SIDHI	None of the Above	17350	no	NOTA	None of the Above
Madhya Pradesh	SHAHDOL	DALPAT SINGH PARASTE	525419	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	SHAHDOL	None of the Above	21376	no	NOTA	None of the Above
Madhya Pradesh	SHAHDOL	PHOOL SINGH PARASTE	19409	no	BSP	Bahujan Samaj Party
Madhya Pradesh	SHAHDOL	ER. VIJAY KUMAR KOL	20312	no	AAAP	Aam Aadmi Party
Madhya Pradesh	SHAHDOL	RAMRATAN SINGH PAWLEY	20834	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	SHAHDOL	PARMESHWAR SINGH	27619	no	CPI	Communist Party of India
Madhya Pradesh	SHAHDOL	MAHAVEER PARSHAD MANJHI	7507	no	IND	Independent
Madhya Pradesh	SHAHDOL	RAM GOPAL SINGH	5320	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	SHAHDOL	RAJES NANDINI SINGH	284118	no	INC	Indian National Congress
Madhya Pradesh	SHAHDOL	SAHDEV SINGH PARASTE	5089	no	NPEP	National Peoples Party
Madhya Pradesh	SHAHDOL	JOGENDRA SINGH	7151	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	SHAHDOL	RAM SINGH	3849	no	CSM	Chhattisgarh Swabhiman Manch
Madhya Pradesh	SHAHDOL	YOGESH KUMAR SINGH AD.	5580	no	SP	Samajwadi Party
Madhya Pradesh	SHAHDOL	LALA KOL	8288	no	IND	Independent
Madhya Pradesh	SHAHDOL	RAJU BAIGA	6647	no	IND	Independent
Madhya Pradesh	JABALPUR	RAKESH SINGH	564609	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	JABALPUR	"ER. PRAVIN GAJBHIYE ""DADA"""	1412	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	JABALPUR	DR. DHAI AKSHAR (RAKESH SONKAR)	2007	no	IND	Independent
Madhya Pradesh	JABALPUR	VINAY KUMAR JAIN	6400	no	IND	Independent
Madhya Pradesh	JABALPUR	LAL DASH	2234	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	JABALPUR	VIKRANT	2797	no	IND	Independent
Madhya Pradesh	JABALPUR	VIRENDRA KUMAR SHARMA	7164	no	IND	Independent
Madhya Pradesh	JABALPUR	MANAK LAL GONTIYA	4261	no	APoI	Ambedkarite Party of India
Madhya Pradesh	JABALPUR	DIGVIJAY SINGH (SENIOR JOURNLIST)	1852	no	BSCP	Bhartiya Shakti Chetna Party
Madhya Pradesh	JABALPUR	NAVEEN JAIN (YOGACHARYA)	2990	no	IND	Independent
Madhya Pradesh	JABALPUR	MALKHAN PATEL	1562	no	IND	Independent
Madhya Pradesh	JABALPUR	VIVEK KRISHNA TANKHA	355970	no	INC	Indian National Congress
Madhya Pradesh	JABALPUR	AFTAB ALAM	16008	no	BSP	Bahujan Samaj Party
Madhya Pradesh	JABALPUR	CAPTAIN HANFEE, VEER CHAKRA	15977	no	AAAP	Aam Aadmi Party
Madhya Pradesh	JABALPUR	MOHD. ADIL	9053	no	IUML	Indian Union Muslim League
Madhya Pradesh	JABALPUR	None of the Above	7888	no	NOTA	None of the Above
Madhya Pradesh	MANDLA	FAGGAN SINGH KULASTE	585720	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	MANDLA	KHUMAN SINGH ARMO	11548	no	AAAP	Aam Aadmi Party
Madhya Pradesh	MANDLA	ANUJ GANGA SINGH PATTA	56572	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	MANDLA	SHAMBHU SINGH MARAVI	21266	no	BSP	Bahujan Samaj Party
Madhya Pradesh	MANDLA	OMKAR SINGH MARKAM	475251	no	INC	Indian National Congress
Madhya Pradesh	MANDLA	BAISHAKHOO MARAVI	4956	no	BHSSP	Bhartiya Satya Sangharsh Party
Madhya Pradesh	MANDLA	INDER SINGH UIKEY	11930	no	APoI	Ambedkarite Party of India
Madhya Pradesh	MANDLA	PHOOL SINGH DHURWEY	4980	no	CSM	Chhattisgarh Swabhiman Manch
Madhya Pradesh	MANDLA	GHANSHYAM KUDAPE	11265	no	IND	Independent
Madhya Pradesh	MANDLA	MAHATALAL BARKADE	6724	no	NPEP	National Peoples Party
Madhya Pradesh	MANDLA	None of the Above	28306	no	NOTA	None of the Above
Madhya Pradesh	BALAGHAT	BODHSINGH BHAGAT	480594	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	BALAGHAT	None of the Above	6922	no	NOTA	None of the Above
Madhya Pradesh	BALAGHAT	GAUTAM RAMTEKE	3250	no	RPI(KH)	Republican Party of India (Khobragade)
Madhya Pradesh	BALAGHAT	DR. ASRA ANJUM FAROOQUI	3243	no	RPI(A)	Republican Party of India (A)
Madhya Pradesh	BALAGHAT	SHANKAR KANSRE LODHI (SAMAJ SEWAK)	6374	no	IND	Independent
Madhya Pradesh	BALAGHAT	ASIF KHAN	2652	no	MNDP	Minorities Democratic Party
Madhya Pradesh	BALAGHAT	LAXMINARAYAN DEHARIYA	4427	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	BALAGHAT	VIVEK MISHRA (LALU BHAIYA)	4397	no	JMBP	Jai Maha Bharath Party
Madhya Pradesh	BALAGHAT	SANDEEP SONI	10444	no	IND	Independent
Madhya Pradesh	BALAGHAT	NANDLAL UIKEY	3008	no	CSM	Chhattisgarh Swabhiman Manch
Madhya Pradesh	BALAGHAT	JABALPUR HIGH COURT ADVOCATE AND WRITER SUKHARAM ASTKAR LANJI	11767	no	IND	Independent
Madhya Pradesh	BALAGHAT	ANUBHA MUNJARE	99392	no	SP	Samajwadi Party
Madhya Pradesh	BALAGHAT	HINA LIKHIRAM KAWRE	384553	no	INC	Indian National Congress
Madhya Pradesh	BALAGHAT	ASHOK KUMAR MASIH	20297	no	CPI	Communist Party of India
Madhya Pradesh	BALAGHAT	U. K. CHOUDHARY (MUNNA BHAIYA)	9898	no	AAAP	Aam Aadmi Party
Madhya Pradesh	BALAGHAT	YOGESH NANAJI SAMRITE	46345	no	BSP	Bahujan Samaj Party
Madhya Pradesh	BALAGHAT	SMT. HIRASAN UIKEY	15801	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	CHHINDWARA	KAMAL NATH	559755	yes	INC	Indian National Congress
Madhya Pradesh	CHHINDWARA	DHANIRAM YADUVANSHI	6601	no	IND	Independent
Madhya Pradesh	CHHINDWARA	PARDESHI HARTAPSHAH TIRGAM	25628	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	CHHINDWARA	CHOUDHARY CHANDRABHAN KUBER SINGH	443218	no	BJP	Bharatiya Janata Party
Madhya Pradesh	CHHINDWARA	ER. KRIPA SHANKAR YADAV	2140	no	IND	Independent
Madhya Pradesh	CHHINDWARA	NITINKUMAR SAHU	12241	no	BSP	Bahujan Samaj Party
Madhya Pradesh	CHHINDWARA	SUBHASH SHUKLA	2035	no	SP	Samajwadi Party
Madhya Pradesh	CHHINDWARA	MAHESH MADHAVLAL DUBEY	5696	no	AAAP	Aam Aadmi Party
Madhya Pradesh	CHHINDWARA	ARVIND DONGRE	2484	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	CHHINDWARA	SITARAM SAREYAM	12762	no	IND	Independent
Madhya Pradesh	CHHINDWARA	JALASRAM NAGWANSHI	3117	no	IND	Independent
Madhya Pradesh	CHHINDWARA	RAMESH PATIL	2872	no	APoI	Ambedkarite Party of India
Madhya Pradesh	CHHINDWARA	DWARKA PRASAD PAHADE	3450	no	IND	Independent
Madhya Pradesh	CHHINDWARA	None of the Above	25499	no	NOTA	None of the Above
Madhya Pradesh	HOSHANGABAD	UDAY PRATAP SINGH	669128	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	HOSHANGABAD	None of the Above	18741	no	NOTA	None of the Above
Madhya Pradesh	HOSHANGABAD	JAY PRAKASH PATEL	5225	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	HOSHANGABAD	"DEVENDRA PATEL ""GUDDU BHAIYA"""	279168	no	INC	Indian National Congress
Madhya Pradesh	HOSHANGABAD	UDAY PRATAP  SINGH	6378	no	IND	Independent
Madhya Pradesh	HOSHANGABAD	Devendra Patel urf Gudda Bhaiyya	13412	no	IND	Independent
Madhya Pradesh	HOSHANGABAD	MAYA VISHWAKARMA	17837	no	AAAP	Aam Aadmi Party
Madhya Pradesh	HOSHANGABAD	PRADEEP AHIRWAR	14154	no	BSP	Bahujan Samaj Party
Madhya Pradesh	HOSHANGABAD	MUKESH BABU AHIRWAR	7132	no	IND	Independent
Madhya Pradesh	VIDISHA	SUSHMA SWARAJ	714348	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	VIDISHA	ENGINEEAR BHAGWAT SINGH RAJPUT	5204	no	AAAP	Aam Aadmi Party
Madhya Pradesh	VIDISHA	AMAR SINGH PATEL	9699	no	BSP	Bahujan Samaj Party
Madhya Pradesh	VIDISHA	NARMADA PRASHAD AHIRWAR	1270	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	VIDISHA	MANOJ KUMAR YADAV	3293	no	SP	Samajwadi Party
Madhya Pradesh	VIDISHA	SHOIAB KHAN	2200	no	PRSP	Prajatantrik Samadhan Party
Madhya Pradesh	VIDISHA	LAKSHMAN SINGH	303650	no	INC	Indian National Congress
Madhya Pradesh	VIDISHA	None of the Above	10618	no	NOTA	None of the Above
Madhya Pradesh	VIDISHA	BABULAL	1088	no	SamSP	Samata Samadhan Party
Madhya Pradesh	VIDISHA	KAMLESH SALLAM	10824	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	VIDISHA	RAMDAYAL KORI	4159	no	IND	Independent
Madhya Pradesh	VIDISHA	CHANDRESH SINGH	5359	no	IND	Independent
Madhya Pradesh	VIDISHA	GANESH RAJAK	1761	no	SAP	Samata Party
Madhya Pradesh	BHOPAL	ALOK SANJAR	714178	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	BHOPAL	None of the Above	5181	no	NOTA	None of the Above
Madhya Pradesh	BHOPAL	GAUTAM SHINDE	614	no	AD	Apna Dal
Madhya Pradesh	BHOPAL	MEHBOOB KHAN	569	no	IND	Independent
Madhya Pradesh	BHOPAL	MAHANT GOPAL DAS BABA SHAHAB	570	no	IND	Independent
Madhya Pradesh	BHOPAL	RAJARAM MEHAR	796	no	PSS	Proutist Sarva Samaj
Madhya Pradesh	BHOPAL	MD. ATEEK	625	no	PRSP	Prajatantrik Samadhan Party
Madhya Pradesh	BHOPAL	SATISH BABU NAIDU	832	no	IND	Independent
Madhya Pradesh	BHOPAL	ASHOK PAWAR	551	no	SamSP	Samata Samadhan Party
Madhya Pradesh	BHOPAL	ABDUL TAHIR (BABLU - ADVOCATE)	1878	no	IND	Independent
Madhya Pradesh	BHOPAL	GUNDESH KHAMBRA	681	no	IND	Independent
Madhya Pradesh	BHOPAL	RAJESH KEER	1348	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	BHOPAL	BRAJENDRA GAPPU CHATURVEDI	684	no	IND	Independent
Madhya Pradesh	BHOPAL	COLONEL KESARI SINGH	1840	no	IND	Independent
Madhya Pradesh	BHOPAL	PEEYUSH SONI	421	no	IND	Independent
Madhya Pradesh	BHOPAL	DANIEL JOHN	465	no	IND	Independent
Madhya Pradesh	BHOPAL	BALBHADRA MISHRA	523	no	bjyp	Bhartiya Jan Yug Party
Madhya Pradesh	BHOPAL	BADSHAH KHAN	622	no	NADP	Naya Daur Party
Madhya Pradesh	BHOPAL	M C MITTAL JAI GURUDEO TATDHARI	620	no	IND	Independent
Madhya Pradesh	BHOPAL	PRABHAWATI SHAKYA KHOT	425	no	IND	Independent
Madhya Pradesh	BHOPAL	MASTER SAJID SIDDIQUI	5557	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Madhya Pradesh	BHOPAL	SHAILENDRA KUMAR SHAILI	7643	no	CPI	Communist Party of India
Madhya Pradesh	BHOPAL	J C BARAI	628	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Madhya Pradesh	BHOPAL	RACHNA DHINGRA	21298	no	AAAP	Aam Aadmi Party
Madhya Pradesh	BHOPAL	P C SHARMA (PRAKASH MANGILAL SHARMA )	343482	no	INC	Indian National Congress
Madhya Pradesh	BHOPAL	VIJAY KUMAR	1494	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Madhya Pradesh	BHOPAL	SHEEBA MALIK	5742	no	SP	Samajwadi Party
Madhya Pradesh	BHOPAL	SUNIL BORSE	10152	no	BSP	Bahujan Samaj Party
Madhya Pradesh	BHOPAL	MUKESH KUMAR BANSAL	763	no	SaVP	Samta Vikas Party
Madhya Pradesh	RAJGARH	RODMAL NAGAR	596727	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	RAJGARH	DR.SHIVNARAYAN VERMA	13864	no	BSP	Bahujan Samaj Party
Madhya Pradesh	RAJGARH	ROD JI	8818	no	IND	Independent
Madhya Pradesh	RAJGARH	IMAMUDDIN KHAN	4334	no	IND	Independent
Madhya Pradesh	RAJGARH	DR.PRASHANT TRIPATHI	8699	no	AAAP	Aam Aadmi Party
Madhya Pradesh	RAJGARH	AMLABE NARAYAN SINGH	367990	no	INC	Indian National Congress
Madhya Pradesh	RAJGARH	None of the Above	10292	no	NOTA	None of the Above
Madhya Pradesh	DEWAS	MANOHAR UNTWAL	665646	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	DEWAS	SAJJAN SINGH VERMA	405333	no	INC	Indian National Congress
Madhya Pradesh	DEWAS	HEMRAJ SINGH BAMNIYA	4657	no	BA S D	Bahujan Sangharshh Dal
Madhya Pradesh	DEWAS	SUJEET SANGTE	4216	no	AAAP	Aam Aadmi Party
Madhya Pradesh	DEWAS	SOUDAN SINGH DALMIYA	1509	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	DEWAS	KAMAL SINGH CHOUHAN	16376	no	PRSP	Prajatantrik Samadhan Party
Madhya Pradesh	DEWAS	RADHESHYAM MALVIYA	4862	no	IND	Independent
Madhya Pradesh	DEWAS	GOKUL PRASAD DONGARE	17238	no	BSP	Bahujan Samaj Party
Madhya Pradesh	DEWAS	None of the Above	10253	no	NOTA	None of the Above
Madhya Pradesh	DEWAS	SOBHAL SINGH SISODIYA	1002	no	SamSP	Samata Samadhan Party
Madhya Pradesh	DEWAS	SAJAN SINGH	10113	no	IND	Independent
Madhya Pradesh	DEWAS	MANOHAR AHIRIYA	2763	no	IND	Independent
Madhya Pradesh	UJJAIN	PROF. CHINTAMANI MALVIYA	641101	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	UJJAIN	RADHESHAYAM PANAVAR	1257	no	AIFB	All India Forward Bloc
Madhya Pradesh	UJJAIN	SURENDRA MEHAR	3636	no	IND	Independent
Madhya Pradesh	UJJAIN	None of the Above	12287	no	NOTA	None of the Above
Madhya Pradesh	UJJAIN	ER. BRAJMOHAN MALVIYA	848	no	PRSP	Prajatantrik Samadhan Party
Madhya Pradesh	UJJAIN	NANDLAL CHAUHAN	927	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	UJJAIN	ANEETA HINDOLIYA	5917	no	AAAP	Aam Aadmi Party
Madhya Pradesh	UJJAIN	RAMPRASAD JATWA	9969	no	BSP	Bahujan Samaj Party
Madhya Pradesh	UJJAIN	LALCHAND GOMAY BERWA	2015	no	IND	Independent
Madhya Pradesh	UJJAIN	ASHOK	1669	no	IND	Independent
Madhya Pradesh	UJJAIN	BHAWARLAL TUFAN	902	no	JMBP	Jai Maha Bharath Party
Madhya Pradesh	UJJAIN	PREMCHAND GUDDU	331438	no	INC	Indian National Congress
Madhya Pradesh	UJJAIN	ANIL PALE	4439	no	BA S D	Bahujan Sangharshh Dal
Madhya Pradesh	MANDSOUR	SUDHIR GUPTA	698335	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	MANDSOUR	MEENAKSHI NATARAJAN	394686	no	INC	Indian National Congress
Madhya Pradesh	MANDSOUR	BANSHI LAL PATIDAR	3501	no	BA S D	Bahujan Sangharshh Dal
Madhya Pradesh	MANDSOUR	BANO BI	3441	no	BMSM	Bharatiya Minorities Suraksha Mahasangh
Madhya Pradesh	MANDSOUR	PARAS SAKLECHA DADA	10183	no	AAAP	Aam Aadmi Party
Madhya Pradesh	MANDSOUR	RAJU MALVIYA	1513	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	MANDSOUR	KRANTIPARIVARTAN	8104	no	BSP	Bahujan Samaj Party
Madhya Pradesh	MANDSOUR	MINAKSHI CHOUHAN	3151	no	IND	Independent
Madhya Pradesh	MANDSOUR	PROF. PURUSHOTTAM MEGHWAL	2626	no	IND	Independent
Madhya Pradesh	MANDSOUR	SUDHIR GAUTAM	7615	no	IND	Independent
Madhya Pradesh	MANDSOUR	SHAIKH AZIZUDDIN QURAISHI	7992	no	AIFB	All India Forward Bloc
Madhya Pradesh	MANDSOUR	MINAKSHI PANDEY	8333	no	IND	Independent
Madhya Pradesh	MANDSOUR	ISMAILE MEW	1616	no	IND	Independent
Madhya Pradesh	MANDSOUR	NILESH TIWARI	1685	no	IND	Independent
Madhya Pradesh	MANDSOUR	None of the Above	8568	no	NOTA	None of the Above
Madhya Pradesh	RATLAM	DILEEPSINGH BHURIA	545970	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	RATLAM	None of the Above	30364	no	NOTA	None of the Above
Madhya Pradesh	RATLAM	ILIYAS BHAI MACHAR	3581	no	MNDP	Minorities Democratic Party
Madhya Pradesh	RATLAM	BHADIYA DAWAR	6638	no	IND	Independent
Madhya Pradesh	RATLAM	MEENA ATUL DAVID	10980	no	IND	Independent
Madhya Pradesh	RATLAM	KANTILAL BHURIA	437523	no	INC	Indian National Congress
Madhya Pradesh	RATLAM	BABUSINGH	18485	no	BSP	Bahujan Samaj Party
Madhya Pradesh	RATLAM	BHERUSINGH DAMOR	13116	no	JD(U)	Janata Dal  (United)
Madhya Pradesh	RATLAM	COMRADE LATA BHABOR	6078	no	CPM	Communist Party of India  (Marxist)
Madhya Pradesh	RATLAM	ENARI MACHAR	6085	no	AAAP	Aam Aadmi Party
Madhya Pradesh	RATLAM	KAILASH VASUNIYA	3861	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	DHAR	SAVITRI THAKUR	558387	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	DHAR	DR. HEMLATA JAMUNA DEVI	10683	no	AAAP	Aam Aadmi Party
Madhya Pradesh	DHAR	UMANG SINGHAR	454059	no	INC	Indian National Congress
Madhya Pradesh	DHAR	RAMA SINGH PATEL	8303	no	IND	Independent
Madhya Pradesh	DHAR	AJAY RAWAT	14666	no	BSP	Bahujan Samaj Party
Madhya Pradesh	DHAR	BHERULAL IMLIYAR	3800	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	DHAR	KARAN BURMAN GAREEB	11481	no	SamSP	Samata Samadhan Party
Madhya Pradesh	DHAR	None of the Above	15437	no	NOTA	None of the Above
Madhya Pradesh	INDORE	SUMITRA MAHAJAN (TAI)	854972	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	INDORE	JAMEELA BI	981	no	IND	Independent
Madhya Pradesh	INDORE	MOMD.TANVEER	1576	no	IND	Independent
Madhya Pradesh	INDORE	None of the Above	5944	no	NOTA	None of the Above
Madhya Pradesh	INDORE	DHARAMDAS AHIRWAR	7422	no	BSP	Bahujan Samaj Party
Madhya Pradesh	INDORE	PARMANAND TOLANI	1768	no	IND	Independent
Madhya Pradesh	INDORE	SANTOSH KUMAR DEEN BANDHU	517	no	IND	Independent
Madhya Pradesh	INDORE	RAMCHANDR PARMAR	477	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	INDORE	ANIL TRIVEDI	35169	no	AAAP	Aam Aadmi Party
Madhya Pradesh	INDORE	SYED NIZAM AHMED	740	no	IND	Independent
Madhya Pradesh	INDORE	MOHAMMAD IMTIYAZ KHAN	3862	no	IND	Independent
Madhya Pradesh	INDORE	SATYANARAYAN PATEL	388071	no	INC	Indian National Congress
Madhya Pradesh	INDORE	MAMTA BHATI	579	no	RAHM	Rashtriya Ahinsa Manch
Madhya Pradesh	INDORE	HARISH SAHU	400	no	SamSP	Samata Samadhan Party
Madhya Pradesh	INDORE	PRAVEEN KUMAR AJMERA JAIN	1867	no	IND	Independent
Madhya Pradesh	INDORE	SHAKTI SUDHA SHARMA	740	no	IND	Independent
Madhya Pradesh	INDORE	DR. NEHA SHARMA	1619	no	SP	Samajwadi Party
Madhya Pradesh	INDORE	GHANSHYAM CHANDEL	598	no	IND	Independent
Madhya Pradesh	INDORE	RAVI SIRWAIYA	1356	no	IND	Independent
Madhya Pradesh	INDORE	P.T. JAGDISH CHANDRA BHATT	1352	no	JMBP	Jai Maha Bharath Party
Madhya Pradesh	INDORE	RAMCHANDRA VASAINI (RAMA SETH)	3725	no	IND	Independent
Madhya Pradesh	INDORE	RAKESH KUMAR PATEL	2528	no	IND	Independent
Madhya Pradesh	INDORE	AJEET KUMAR JAIN PATWA (DHARTI PAKAD)	554	no	IND	Independent
Madhya Pradesh	KHARGONE	SUBHASH PATEL	649354	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	KHARGONE	RAMESH PATEL	391475	no	INC	Indian National Congress
Madhya Pradesh	KHARGONE	NAHARSINGH RICHHUJI BARDE	8266	no	BSP	Bahujan Samaj Party
Madhya Pradesh	KHARGONE	KAILASH AWASYA	31222	no	AAAP	Aam Aadmi Party
Madhya Pradesh	KHARGONE	None of the Above	22141	no	NOTA	None of the Above
Madhya Pradesh	KHARGONE	GANGARAM PRATAP GAWLE	8544	no	AMS	Aadijan Mukti Sena
Madhya Pradesh	KHARGONE	KAILASH PATEL	7040	no	IND	Independent
Madhya Pradesh	KHARGONE	COMRADE JYOTI GORE	25930	no	CPI	Communist Party of India
Madhya Pradesh	KHARGONE	GAFFAR NOOR MO.	8553	no	IND	Independent
Madhya Pradesh	KHANDWA	NANDKUMAR SINGH CHOUHAN (NANDU BHAIYA)	717357	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	KHANDWA	NANDLAL SINGH CHOUHAN (NANDU BHAIYA)	5307	no	IND	Independent
Madhya Pradesh	KHANDWA	NANDLAL SINGH CHOUHAN	6226	no	IND	Independent
Madhya Pradesh	KHANDWA	SHARIF RAJGEER	2255	no	ABVCP	Akhil Bhartiya Vikas Congress Party
Madhya Pradesh	KHANDWA	ZIAUDDIN INAMDAR	1075	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	KHANDWA	BABA ABDUL HAMID	12036	no	IND	Independent
Madhya Pradesh	KHANDWA	None of the Above	17149	no	NOTA	None of the Above
Madhya Pradesh	KHANDWA	KASHINATH UPADHYAY	2429	no	IND	Independent
Madhya Pradesh	KHANDWA	ALOK AGARWAL	16799	no	AAAP	Aam Aadmi Party
Madhya Pradesh	KHANDWA	SANJAY S/O LAXMAN SOLANKI	13691	no	BSP	Bahujan Samaj Party
Madhya Pradesh	KHANDWA	ISHAMUDDIN (LEDAR) SAYYADH	1134	no	SAP	Samata Party
Madhya Pradesh	KHANDWA	BHAIYALAL UIKEY	1208	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	KHANDWA	VINOD KUMAR BHAGWAN BIRADE	1783	no	RPI(A)	Republican Party of India (A)
Madhya Pradesh	KHANDWA	DR. RAMESHSINGH - SOLANKI	1235	no	PRSP	Prajatantrik Samadhan Party
Madhya Pradesh	KHANDWA	ARUN SUBHASH CHANDRA YADAV	457643	no	INC	Indian National Congress
Madhya Pradesh	BETUL	JYOTI DHURVE	643651	yes	BJP	Bharatiya Janata Party
Madhya Pradesh	BETUL	KALLUSHING KUMRE	4546	no	GGP	Gondvana Gantantra Party
Madhya Pradesh	BETUL	FAGRAM	4668	no	SWJP	Samajwadi Jan Parishad
Madhya Pradesh	BETUL	RAJESH SARIYAM	20627	no	AAAP	Aam Aadmi Party
Madhya Pradesh	BETUL	None of the Above	26726	no	NOTA	None of the Above
Madhya Pradesh	BETUL	SOHAN LAL UIKEY	16461	no	BSP	Bahujan Samaj Party
Madhya Pradesh	BETUL	BHUPENDRA SINGH UIKEY	12933	no	IND	Independent
Madhya Pradesh	BETUL	GABBAR SINH WARKADE	3070	no	BMUP	Bahujan Mukti Party
Madhya Pradesh	BETUL	"AJAY SHAH ""MAKRAI"""	315037	no	INC	Indian National Congress
Maharashtra	Nandurbar	DR.GAVIT HEENA VAIJAYKUMAR	579486	yes	BJP	Bharatiya Janata Party
Maharashtra	Nandurbar	GAVIT MANIKRAO HODLYA	472581	no	INC	Indian National Congress
Maharashtra	Nandurbar	VASAVE AMIT SHEKLAL	12133	no	BSP	Bahujan Samaj Party
Maharashtra	Nandurbar	ARJUNSING DIVANSING VASAVE	4712	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Nandurbar	RANJIT JUGLA PADVI	3498	no	BMUP	Bahujan Mukti Party
Maharashtra	Nandurbar	VIRENDRA RAVAJI VALVI	3511	no	AAAP	Aam Aadmi Party
Maharashtra	Nandurbar	MAHESH JAYSING PAWARA	4389	no	IND	Independent
Maharashtra	Nandurbar	ADV.SOBJI DEVALYA GAVIT	9184	no	IND	Independent
Maharashtra	Nandurbar	C.S.VALVI	6004	no	IND	Independent
Maharashtra	Nandurbar	None of the Above	21178	no	NOTA	None of the Above
Maharashtra	Dhule	DR. BHAMRE SUBHASH RAMRAO	529450	yes	BJP	Bharatiya Janata Party
Maharashtra	Dhule	None of the Above	2503	no	NOTA	None of the Above
Maharashtra	Dhule	NIHAL AH.AB.RAHEMAN DANEWALA	1148	no	SP	Samajwadi Party
Maharashtra	Dhule	MO. ISMAIL JUMMAN	740	no	IND	Independent
Maharashtra	Dhule	ABDUL HAMID SHAIKH HABIB	914	no	IND	Independent
Maharashtra	Dhule	SHAIKH MUKHATAR AH. MO. QASIM	765	no	WPOI	Welfare Party Of India
Maharashtra	Dhule	JAGDANE RAMESH SOMA	828	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Dhule	MAHEMOOD BHAIJAAN	792	no	AwVP	Awami Vikas Party
Maharashtra	Dhule	SAMBHAJI DEVIDAS PATIL	1149	no	BMUP	Bahujan Mukti Party
Maharashtra	Dhule	SAYYAD MOHAMMAD QAYAM	1600	no	IND	Independent
Maharashtra	Dhule	AMRISHBHAI RASIKLAL PATEL	398727	no	INC	Indian National Congress
Maharashtra	Dhule	RAMESH RAGHO MORE	8057	no	IND	Independent
Maharashtra	Dhule	JAFAR BAPUJI PATHAN	1355	no	IND	Independent
Maharashtra	Dhule	PINJARI JAYNUDDIN HUSEN	1545	no	IND	Independent
Maharashtra	Dhule	ISHI YOGESH YASHWANT	9897	no	BSP	Bahujan Samaj Party
Maharashtra	Dhule	DR BHUPESH PRAKASH PATIL	6117	no	IND	Independent
Maharashtra	Dhule	ANSARI NIHAL AH. MOHD. HAROON	9339	no	AAAP	Aam Aadmi Party
Maharashtra	Dhule	DEORE MAHENDRA TRYAMBAK	2091	no	IND	Independent
Maharashtra	Dhule	SAYYAD SALIM SAYYAD ALIM	5278	no	IND	Independent
Maharashtra	Dhule	GAZI ATEZAD MUBEEN KHAN	788	no	IND	Independent
Maharashtra	Jalgaon	A.T. NANA PATIL	647773	yes	BJP	Bharatiya Janata Party
Maharashtra	Jalgaon	DR.MUBIN A. KHALIL A.	1676	no	IND	Independent
Maharashtra	Jalgaon	PATIL SANDIP YUVRAJ	2879	no	IND	Independent
Maharashtra	Jalgaon	DR. SANGRAM GOKULSINGH  PATIL	7390	no	AAAP	Aam Aadmi Party
Maharashtra	Jalgaon	MORE ISHWAR DAYARAM (EX-SERVICEMAN)	2718	no	BMUP	Bahujan Mukti Party
Maharashtra	Jalgaon	BAGUL  V. T.(JIBHAU)	10838	no	BSP	Bahujan Samaj Party
Maharashtra	Jalgaon	ADV. ASHOK DAGADU SHINDE	1708	no	PRCP	Prabuddha Republican Party
Maharashtra	Jalgaon	LALIT GAURISHANKAR SHARMA	8140	no	IND	Independent
Maharashtra	Jalgaon	NANASO M.P. PAGARE	1917	no	RVNP	Rashtravadi Janata Party
Maharashtra	Jalgaon	ANNASAHEB DR.SATISH BHASKARRAO PATIL	264248	no	NCP	Nationalist Congress Party
Maharashtra	Jalgaon	MAMASHRI SURESH PATIL (PATRAKAR)	958	no	HJP	Hindustan Janta Party
Maharashtra	Jalgaon	RAGIB AHMED ABDUL NABI BAHADUR	2675	no	SP	Samajwadi Party
Maharashtra	Jalgaon	ABRAR BEG SARVAR BEG MIRZA	3212	no	RPI	Republican Party of India
Maharashtra	Jalgaon	ASHA SAHEBRAO PATIL	1579	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Jalgaon	ASHOK TARACHAND PATIL	5801	no	IND	Independent
Maharashtra	Jalgaon	IQBAL RAHEMAN DESHMUKH	2246	no	IND	Independent
Maharashtra	Jalgaon	SATISH BHASKAR PATIL (NANDGAON BK)	6157	no	IND	Independent
Maharashtra	Jalgaon	SATISH BHASKAR PATIL (VARKHEDI)	1537	no	IND	Independent
Maharashtra	Jalgaon	SATISH BHASKAR PATIL (MUDI PRA DANGRI)	3998	no	IND	Independent
Maharashtra	Jalgaon	VIJAY BHIMRAO NIKAM	9614	no	IND	Independent
Maharashtra	Jalgaon	None of the Above	3268	no	NOTA	None of the Above
Maharashtra	Raver	KHADASE RAKSHA NIKHIL	605452	yes	BJP	Bharatiya Janata Party
Maharashtra	Raver	None of the Above	4212	no	NOTA	None of the Above
Maharashtra	Raver	ZAGADYA PURMYA PAWARA	3707	no	IND	Independent
Maharashtra	Raver	MANISHDADA SATISH JAIN	14599	no	IND	Independent
Maharashtra	Raver	SURESH HIRAMAN INGALE	1258	no	IND	Independent
Maharashtra	Raver	DNYANESHWAR AMALE-PATIL	1220	no	AIFB	All India Forward Bloc
Maharashtra	Raver	KHATIK MOHAMMAD HAROON	4376	no	SP	Samajwadi Party
Maharashtra	Raver	SONAWANE VANDANA SHIVAJI	978	no	IND	Independent
Maharashtra	Raver	SHAIKH SHAHERA BI. JAINODDIN SHAIKH	1375	no	IND	Independent
Maharashtra	Raver	D.D.WANI (PHOTOGRAPHER) (DNYANESHWAR DIVAKAR WANI)	3349	no	IND	Independent
Maharashtra	Raver	ASHOK TRYAMBAK INGALE	1131	no	IND	Independent
Maharashtra	Raver	J.G.SURWADE	1634	no	IND	Independent
Maharashtra	Raver	WAMANRAO MADHAV SASANE	1757	no	BMUP	Bahujan Mukti Party
Maharashtra	Raver	MANYAR SHE. SAJID SHE. KADAR	1606	no	MNDP	Minorities Democratic Party
Maharashtra	Raver	SHAIKH SHAKIR	4009	no	IND	Independent
Maharashtra	Raver	MANISHDADA JAIN	287384	no	NCP	Nationalist Congress Party
Maharashtra	Raver	GOVINDA NARAYAN GUNJAL	2844	no	IND	Independent
Maharashtra	Raver	DR. ULHAS VASUDEO PATIL	21332	no	IND	Independent
Maharashtra	Raver	RAJEEV SATYENDRA SHARMA	3756	no	AAAP	Aam Aadmi Party
Maharashtra	Raver	MOHAN SHAMRAO CHAVHAN (MANOJSIR VARDIKAR)	8797	no	IND	Independent
Maharashtra	Raver	BHANDE DASHRATH MOTIRAM	29752	no	BSP	Bahujan Samaj Party
Maharashtra	Raver	KADRI SANIYA SAIYED ALI	2658	no	IND	Independent
Maharashtra	Raver	GAIKWAD BHAGWAN MADHAV	1206	no	PRCP	Prabuddha Republican Party
Maharashtra	Raver	SHAIKH MO. ASGAR SAKHAWAT	821	no	RPI	Republican Party of India
Maharashtra	Buldhana	JADHAV PRATAPRAO GANPATRAO	509145	yes	SHS	Shivsena
Maharashtra	Buldhana	None of the Above	10546	no	NOTA	None of the Above
Maharashtra	Buldhana	ABDUL  HAFEEZ ABDUL AZIZ	33783	no	BSP	Bahujan Samaj Party
Maharashtra	Buldhana	INGLE KRUSHANARAO GANPATRAO	349566	no	NCP	Nationalist Congress Party
Maharashtra	Buldhana	PARMESHWAR HIRAMAN GAWAI	6983	no	ARP	Ambedkarist Republican Party
Maharashtra	Buldhana	NAMDEO PUNDLIK DONGARDIVE	6959	no	IND	Independent
Maharashtra	Buldhana	BALASAHEB SHANKAR DARADE	29793	no	IND	Independent
Maharashtra	Buldhana	SANJAY MAHADEV WANKHADE	4520	no	IND	Independent
Maharashtra	Buldhana	SAMBARE DINKAR TUKARAM	2006	no	IND	Independent
Maharashtra	Buldhana	SANDESH ASHOK AMBEDKAR	7927	no	BMUP	Bahujan Mukti Party
Maharashtra	Buldhana	COMMANDER (RTD) ASHOK  RAUT	2077	no	IND	Independent
Maharashtra	Buldhana	KRUSHNARAO INGLE	1787	no	IND	Independent
Maharashtra	Buldhana	CHINCHOLE GANGARAM KALUJI	1917	no	IND	Independent
Maharashtra	Buldhana	SMT. PRABHATAI MADHUKAR PARLEWAR	1725	no	NRSN	Narayani Sena
Maharashtra	Buldhana	ADV. RAVINDRA DNYANDEO BHOJANE	2448	no	APoI	Ambedkarite Party of India
Maharashtra	Buldhana	RAVINDRASING RAMSING PAWAR	1265	no	LB	Lok Bharati
Maharashtra	Buldhana	VASANTRAO SUKHDEV DANDAGE	2750	no	SP	Samajwadi Party
Maharashtra	Buldhana	SUDHIR BABAN SURVE	3429	no	AAAP	Aam Aadmi Party
Maharashtra	Akola	DHOTRE SANJAY SHAMRAO	456472	yes	BJP	Bharatiya Janata Party
Maharashtra	Akola	None of the Above	6206	no	NOTA	None of the Above
Maharashtra	Akola	AJAY PANJABRAO HINGANKAR	8076	no	AAAP	Aam Aadmi Party
Maharashtra	Akola	PATEL HIDAYAT ULLA BARKAT ULLA	253356	no	INC	Indian National Congress
Maharashtra	Akola	BHAI B.C. KAMBLE	7858	no	BSP	Bahujan Samaj Party
Maharashtra	Akola	AMBEDKAR PRAKASH YASHWANT	238776	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Akola	SANDIP KISANRAO WANKHEDE	3756	no	IND	Independent
Maharashtra	Akola	SHAIKH HAMEED IMAM	3991	no	BMUP	Bahujan Mukti Party
Maharashtra	Amravati	ADSUL ANANDRAO VITHOBA	467212	yes	SHS	Shivsena
Maharashtra	Amravati	GUNWANT SUDAMRAO DEVPARE	98200	no	BSP	Bahujan Samaj Party
Maharashtra	Amravati	JYOTI GAJANAN MAKODE	3724	no	APoI	Ambedkarite Party of India
Maharashtra	Amravati	BHAVNA BHAVESH WASNIK	6424	no	AAAP	Aam Aadmi Party
Maharashtra	Amravati	RAMTEKE RAMESH DEVIDASRAO	1442	no	RPI(KH)	Republican Party of India (Khobragade)
Maharashtra	Amravati	DR.RAJENDRA RAMKRISHNA GAWAI	54278	no	RPI	Republican Party of India
Maharashtra	Amravati	DONGRE PRIYA ANIL	1734	no	IND	Independent
Maharashtra	Amravati	ADV. BANDYA SANE ALIAS BANDU SAMPATRAO SANE	3732	no	IND	Independent
Maharashtra	Amravati	KIRANTAI DADARAO ALIAS KOKUBHAU KOKATE	970	no	IND	Independent
Maharashtra	Amravati	JYOTI SANJAY DEWKAR	1312	no	IND	Independent
Maharashtra	Amravati	MANOHAR BALIRAM SONONE	2489	no	IND	Independent
Maharashtra	Amravati	SUNIL DAVID AMRAVATI	1463	no	BMUP	Bahujan Mukti Party
Maharashtra	Amravati	NAVNEET RAVI RANA	329280	no	NCP	Nationalist Congress Party
Maharashtra	Amravati	ATHAWALEY SANJAY HIRAMANJI	1649	no	IND	Independent
Maharashtra	Amravati	ASHATAI ABHYANKAR AMRAVATI	809	no	IND	Independent
Maharashtra	Amravati	VISHWANATH GOTUJI JAMNEKAR	2011	no	IND	Independent
Maharashtra	Amravati	SIRSAT HARIDAS ANKUSH	5268	no	IND	Independent
Maharashtra	Amravati	RAJU MAHADEORAO SONONE	10052	no	IND	Independent
Maharashtra	Amravati	RAJU S. MANKAR	7873	no	IND	Independent
Maharashtra	Amravati	None of the Above	4139	no	NOTA	None of the Above
Maharashtra	Wardha	RAMDAS CHANDRABHANJI TADAS	537518	yes	BJP	Bharatiya Janata Party
Maharashtra	Wardha	DESHMUKH JAIDEEP RAJENDRA	1431	no	IND	Independent
Maharashtra	Wardha	SHAMBHARKAR RAJENDRA GULABRAO	4436	no	IND	Independent
Maharashtra	Wardha	MANISH VINAYAKRAO TELRANDHE	5127	no	IND	Independent
Maharashtra	Wardha	DR. ANKUSH VIJAYRAO NAWALE	1858	no	SP	Samajwadi Party
Maharashtra	Wardha	TADAS VILAS KRUSHNAJI	2124	no	IND	Independent
Maharashtra	Wardha	NARENDRA CHINDUJI MESHRAM	2627	no	GGP	Gondvana Gantantra Party
Maharashtra	Wardha	ARVIND SHAMRAO LILLORE	614	no	IND	Independent
Maharashtra	Wardha	None of the Above	3343	no	NOTA	None of the Above
Maharashtra	Wardha	BHASKAR MAROTRAO NEWARE	3156	no	IND	Independent
Maharashtra	Wardha	CHETAN BHIMRAJ PENDAM	90866	no	BSP	Bahujan Samaj Party
Maharashtra	Wardha	JAGDISH BHAGWAN KADU	853	no	IND	Independent
Maharashtra	Wardha	DEEPAKSINGH TRYAMBAKRAO DESHMUKH	1122	no	IND	Independent
Maharashtra	Wardha	UBALE SHRIKRUSHNA CHAMPATRAO	6405	no	ARP	Ambedkarist Republican Party
Maharashtra	Wardha	RAUT JAGANATH NILKANTH	2638	no	IND	Independent
Maharashtra	Wardha	KISHOR RAMBHAU KINKAR	2518	no	BMUP	Bahujan Mukti Party
Maharashtra	Wardha	RAVI ALIAS SHAILESH GANESHRAO YELNE	3881	no	IND	Independent
Maharashtra	Wardha	SANJAYBHAU NARAYANRAO CHIDAM	770	no	IND	Independent
Maharashtra	Wardha	MEGHE SAGAR DATTATRAYA	321735	no	INC	Indian National Congress
Maharashtra	Wardha	MOHD. ALIM PATEL MOHD. WAHID	15738	no	AAAP	Aam Aadmi Party
Maharashtra	Wardha	SHAMBABA ALIAS SHAMSUNDAR PANJABRAO NICHIT	3440	no	IND	Independent
Maharashtra	Wardha	SACHIN PANDURANGJI RAUT ALIAS GURUBHAU	1245	no	IND	Independent
Maharashtra	Ramtek	KRUPAL BALAJI TUMANE	519892	yes	SHS	Shivsena
Maharashtra	Ramtek	RAHULKANT ALIAS RAMESH SINHA	1542	no	IND	Independent
Maharashtra	Ramtek	None of the Above	4816	no	NOTA	None of the Above
Maharashtra	Ramtek	RAHUL SHESHRAOJI MESHRAM	1189	no	IND	Independent
Maharashtra	Ramtek	KIRAN PREM KUMAR RODGE (PATANKAR)	95051	no	BSP	Bahujan Samaj Party
Maharashtra	Ramtek	PANKAJ SEWAKRAM MASURKAR	1453	no	HJP	Hindustan Janta Party
Maharashtra	Ramtek	KHOBRAGADE CHANDRABHAN JAIRAM	2925	no	IND	Independent
Maharashtra	Ramtek	GOUTAM SHRIRAM WASNIK	6353	no	IND	Independent
Maharashtra	Ramtek	TUMANE GOPAL AJABRAO	6304	no	IND	Independent
Maharashtra	Ramtek	DONGRE ASHOK NAMAJI	3999	no	IND	Independent
Maharashtra	Ramtek	GURUDAS UDDHAV BAWANE	3419	no	IND	Independent
Maharashtra	Ramtek	NARESH MAHADEO PATIL	2841	no	BMUP	Bahujan Mukti Party
Maharashtra	Ramtek	COM. BANDU RAMCHANDRA MESHRAM	1602	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Maharashtra	Ramtek	PROF. DR. NATTHU MADHAO LOKHANDE	4756	no	IND	Independent
Maharashtra	Ramtek	NILESH MAHADEO DHOKE	2537	no	IND	Independent
Maharashtra	Ramtek	LOKHANDE GANESH HAIBATRAO	1735	no	IND	Independent
Maharashtra	Ramtek	GANESH BAPURAO PATIL	6222	no	GGP	Gondvana Gantantra Party
Maharashtra	Ramtek	CHAWARE MAYA NAMDEO	2682	no	SP	Samajwadi Party
Maharashtra	Ramtek	MUKUL WASNIK	344101	no	INC	Indian National Congress
Maharashtra	Ramtek	PRATAP GOSWAMI	25889	no	AAAP	Aam Aadmi Party
Maharashtra	Ramtek	DHIMATI VIDYA KISHOR BHIMTE	3634	no	APoI	Ambedkarite Party of India
Maharashtra	Ramtek	DR. SANDEEP NATTHUJI NANDESHWAR	2640	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Ramtek	DR. SUNILBHAU VISHWANATHJI NARNAVRE	2310	no	RPI(KH)	Republican Party of India (Khobragade)
Maharashtra	Ramtek	SANDESH BHIVRAM BHALEKAR	2424	no	AIFB	All India Forward Bloc
Maharashtra	Nagpur	GADKARI NITIN JAIRAM	587767	yes	BJP	Bharatiya Janata Party
Maharashtra	Nagpur	ADV. SURESH  SHINDE	677	no	IND	Independent
Maharashtra	Nagpur	DR.  MOHAN RAMRAO GAIKWAD	96433	no	BSP	Bahujan Samaj Party
Maharashtra	Nagpur	SUNIL DATTU PENDOR	836	no	BMUP	Bahujan Mukti Party
Maharashtra	Nagpur	RAJESH SURESH SADHANKAR	537	no	IND	Independent
Maharashtra	Nagpur	KANPHADE RAJENDRAKUMAR  EX. ADDITIONAL COLLECTOR	2269	no	IND	Independent
Maharashtra	Nagpur	BABURAO DAMADUJI MESHRAM	539	no	IND	Independent
Maharashtra	Nagpur	DILIP NARAYAN BHORKAR	415	no	IND	Independent
Maharashtra	Nagpur	DR. DHARMENDRA  MANDLIK (PARATE)	426	no	HJP	Hindustan Janta Party
Maharashtra	Nagpur	PHUSEY GHANSHYAM RAMAJI	655	no	RPI	Republican Party of India
Maharashtra	Nagpur	DHIRAJ JAIDEV GAJBHIYE	390	no	IND	Independent
Maharashtra	Nagpur	BASHIR KHAN	466	no	MNDP	Minorities Democratic Party
Maharashtra	Nagpur	BABULAL SUNDARLAL BANJARE	419	no	IND	Independent
Maharashtra	Nagpur	AJAZ KHAN	542	no	IND	Independent
Maharashtra	Nagpur	SHAHID SHARIF	452	no	JD(U)	Janata Dal  (United)
Maharashtra	Nagpur	PANKAJ PRAFULKUMAR BHANSALI	439	no	IND	Independent
Maharashtra	Nagpur	KAVITA VINODKUMAR TIBDIWAL	2408	no	IND	Independent
Maharashtra	Nagpur	DR. PRADEEP RAMBHAUJI  NAGRALE	1366	no	APoI	Ambedkarite Party of India
Maharashtra	Nagpur	CHANDA  SUDHIR MANWATKAR	1880	no	IND	Independent
Maharashtra	Nagpur	RAVINDRA MANIK BORKAR	760	no	IND	Independent
Maharashtra	Nagpur	MOHAN MAROTRAO KAREMORE	665	no	IND	Independent
Maharashtra	Nagpur	GAUS MOHAMMED SHEIKH AYYUB SHEIKH	784	no	IND	Independent
Maharashtra	Nagpur	SOMKUVAR VIJAY SITARAM	1077	no	IND	Independent
Maharashtra	Nagpur	SHRIDHAR NARAYAN SALVE	1187	no	IND	Independent
Maharashtra	Nagpur	SENAPATI UTTAM SAPAN	2017	no	IND	Independent
Maharashtra	Nagpur	JIVAN RAMTEKE	807	no	IND	Independent
Maharashtra	Nagpur	ANJALI ANISH DAMANIA	69081	no	AAAP	Aam Aadmi Party
Maharashtra	Nagpur	None of the Above	3460	no	NOTA	None of the Above
Maharashtra	Nagpur	SHAKIL WASI AHAMAD	542	no	IND	Independent
Maharashtra	Nagpur	ANITA NAGDIGAMBAR TEKAM	660	no	IND	Independent
Maharashtra	Nagpur	ATEEQUE AHMAD MALIK	373	no	IND	Independent
Maharashtra	Nagpur	CHIMOTE RAMMURTI KESHVRAO	528	no	GGP	Gondvana Gantantra Party
Maharashtra	Nagpur	VILAS MUTTEMWAR	302939	no	INC	Indian National Congress
Maharashtra	Nagpur	ANSARI JAVED AHEMAD	1262	no	DESEP	Democratic Secular Party
Maharashtra	Bhandara - gondiya	NANABHAU FALGUNRAO PATOLE	606129	yes	BJP	Bharatiya Janata Party
Maharashtra	Bhandara - gondiya	None of the Above	4032	no	NOTA	None of the Above
Maharashtra	Bhandara - gondiya	ENG. SANJAY RAGHUNATH NASARE	50958	no	BSP	Bahujan Samaj Party
Maharashtra	Bhandara - gondiya	ARUN NAGORAO GAJBHIYE	1876	no	IND	Independent
Maharashtra	Bhandara - gondiya	ISHWAR LAHANU NANDAGAWALI	5941	no	IND	Independent
Maharashtra	Bhandara - gondiya	CHOLE OMPRAKASH BAKARAM	4417	no	IND	Independent
Maharashtra	Bhandara - gondiya	DHANANJAY SHAMLALJI RAJABHOJ	6128	no	IND	Independent
Maharashtra	Bhandara - gondiya	ADV.DADASAHEB ALIAS DHANU BHIKAJI WALTHARE	10133	no	IND	Independent
Maharashtra	Bhandara - gondiya	JITENDRA AADKUJI RAUT	1077	no	ABMP	Akhil Bharatiya Manavata Paksha
Maharashtra	Bhandara - gondiya	DR.KOHAPARE HARSHAWARDHAN MUNESHWAR	1544	no	BMUP	Bahujan Mukti Party
Maharashtra	Bhandara - gondiya	PATEL PRAFUL MANOHARBHAI	456875	no	NCP	Nationalist Congress Party
Maharashtra	Bhandara - gondiya	SAYYAD AFJALALI ALIAS CHHANUBHAI	1578	no	MNDP	Minorities Democratic Party
Maharashtra	Bhandara - gondiya	THAKARE RAMESHWAR (RAMESHWAR) SUNDARLAL	1260	no	SP	Samajwadi Party
Maharashtra	Bhandara - gondiya	PRASHANT SHYAMSUNDAR MISHRA	8647	no	AAAP	Aam Aadmi Party
Maharashtra	Bhandara - gondiya	RAMAN MOTIRAM BANSOD	1429	no	APoI	Ambedkarite Party of India
Maharashtra	Bhandara - gondiya	RANJEET AMARDAS SUKHDEVE	987	no	ASP	Ambedkar Samaj Party
Maharashtra	Bhandara - gondiya	DAHARWAL GANESHDEO RAGHUJI	1542	no	GGP	Gondvana Gantantra Party
Maharashtra	Bhandara - gondiya	PATLE ANJALI AGASTIKUMAR	1955	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Bhandara - gondiya	BANTE JITENDRA RAMAJI	1729	no	IND	Independent
Maharashtra	Bhandara - gondiya	MIRZA VAHED BEG AHMADBEG	899	no	IND	Independent
Maharashtra	Bhandara - gondiya	MORESHWAR RAMAJI MESHRAM	10127	no	IND	Independent
Maharashtra	Bhandara - gondiya	MATE MANOHAR ALIAS MAMA	1380	no	IND	Independent
Maharashtra	Bhandara - gondiya	VARSHA GOVIND TIDKE	594	no	IND	Independent
Maharashtra	Bhandara - gondiya	WADHAVE DAMODHAR NATTHU	577	no	IND	Independent
Maharashtra	Bhandara - gondiya	SUSHMA VITTHAL NAGPURE	1477	no	IND	Independent
Maharashtra	Bhandara - gondiya	VISHAL ARVIND BHOYAR	4169	no	IND	Independent
Maharashtra	Bhandara - gondiya	ZANZAD SHALIKRAM SHANKARRAO	9736	no	IND	Independent
Maharashtra	Gadchiroli-Chimur	ASHOK MAHADEORAO NETE	535982	yes	BJP	Bharatiya Janata Party
Maharashtra	Gadchiroli-Chimur	None of the Above	24488	no	NOTA	None of the Above
Maharashtra	Gadchiroli-Chimur	DR. NAMDEO DALLUJI USENDI	299112	no	INC	Indian National Congress
Maharashtra	Gadchiroli-Chimur	SATISH GOKULDAS PENDAM	8156	no	AITC	All India Trinamool Congress
Maharashtra	Gadchiroli-Chimur	DR. GAJBE RAMESHKUMAR BABURAOJI	45458	no	AAAP	Aam Aadmi Party
Maharashtra	Gadchiroli-Chimur	RAMRAO GOVINDA NANNAWARE	66906	no	BSP	Bahujan Samaj Party
Maharashtra	Gadchiroli-Chimur	ADV. PRABHAKAR MAHAGUJI DADMAL	3422	no	RPI	Republican Party of India
Maharashtra	Gadchiroli-Chimur	DIWAKAR PENDAM (EX. SERVICEMAN)	3730	no	BMUP	Bahujan Mukti Party
Maharashtra	Gadchiroli-Chimur	VINOD ANKUSH NANNAWARE	4287	no	SP	Samajwadi Party
Maharashtra	Gadchiroli-Chimur	DANDEKAR BABURAO LAXMAN	6470	no	IND	Independent
Maharashtra	Gadchiroli-Chimur	NAMDEO ANANDRAO KANNAKE	22512	no	CPI	Communist Party of India
Maharashtra	Gadchiroli-Chimur	DEORAO MONBA NANNAWARE	6606	no	APoI	Ambedkarite Party of India
Maharashtra	Chandrapur	AHIR HANSRAJ GANGARAM	508049	yes	BJP	Bharatiya Janata Party
Maharashtra	Chandrapur	None of the Above	8257	no	NOTA	None of the Above
Maharashtra	Chandrapur	KUMBHARE HANSRAJ GULAB	49229	no	BSP	Bahujan Samaj Party
Maharashtra	Chandrapur	ASHOK VITHOBA KHANDALE	8302	no	RPI	Republican Party of India
Maharashtra	Chandrapur	DEOTALE SANJAY WAMANRAO	271780	no	INC	Indian National Congress
Maharashtra	Chandrapur	NITIN VASANT POHANE	3772	no	LB	Lok Bharati
Maharashtra	Chandrapur	NANDKISHOR GANGARAM RANGARI	4850	no	APoI	Ambedkarite Party of India
Maharashtra	Chandrapur	PANKAJKUMAR  SHARMA	3216	no	AITC	All India Trinamool Congress
Maharashtra	Chandrapur	FIRAJ  PATHAN	4341	no	BMUP	Bahujan Mukti Party
Maharashtra	Chandrapur	SIDDHARTH  RAUT	2188	no	PRCP	Prabuddha Republican Party
Maharashtra	Chandrapur	QURESHI M. EKHALAK M. YUSUF	4176	no	SP(I)	Socialist Party (India)
Maharashtra	Chandrapur	GHAYWAN ROSHAN JAMBUWANTRAO	2736	no	RP(K)	Republican Paksha (Khoripa)
Maharashtra	Chandrapur	CHATAP WAMANRAO SADASHIV	204413	no	AAAP	Aam Aadmi Party
Maharashtra	Chandrapur	VINOD DINANATH MESHRAM	8936	no	IND	Independent
Maharashtra	Chandrapur	SANJAY NILKANTH GAWANDE	2167	no	IND	Independent
Maharashtra	Chandrapur	ATUL ASHOK MUNGINWAR	2693	no	IND	Independent
Maharashtra	Chandrapur	KARTIK GAJANAN KODAPE	3879	no	IND	Independent
Maharashtra	Chandrapur	NAMDEO  SHEDMAKE	5829	no	IND	Independent
Maharashtra	Chandrapur	PRAMOD MANGARUJI SORTE	10930	no	IND	Independent
Maharashtra	Yavatmal-Washim	GAWALI BHAVANA PUNDLIKRAO	477905	yes	SHS	Shivsena
Maharashtra	Yavatmal-Washim	None of the Above	5583	no	NOTA	None of the Above
Maharashtra	Yavatmal-Washim	RATHOD NARESH FULSING	6423	no	AAAP	Aam Aadmi Party
Maharashtra	Yavatmal-Washim	RATHOD MOHAN MALLU	22143	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Yavatmal-Washim	GAIKAWAD RAJENDRA VITTHAL	7482	no	IND	Independent
Maharashtra	Yavatmal-Washim	GURNULE RAMESH VITHOBA	2484	no	IND	Independent
Maharashtra	Yavatmal-Washim	SAWAKE RAMKRUSHNA PUNDLIKRAO	2193	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Maharashtra	Yavatmal-Washim	SUDHAKAR NAMDEORAO GHAYAWAN	3602	no	RPI(KH)	Republican Party of India (Khobragade)
Maharashtra	Yavatmal-Washim	ULHAS VASRAMJI JADHAO	7166	no	IND	Independent
Maharashtra	Yavatmal-Washim	NARESH MAHADEORAO GUGHANE	3422	no	IND	Independent
Maharashtra	Yavatmal-Washim	CHAVAN VINOD NARAYAN	2177	no	IND	Independent
Maharashtra	Yavatmal-Washim	PATIL UPENDRA BABARAO	895	no	IND	Independent
Maharashtra	Yavatmal-Washim	PRAKASH RAGHUNATH RAUT	2514	no	IND	Independent
Maharashtra	Yavatmal-Washim	PRASHANT PANDHARINATH SURVE	1558	no	IND	Independent
Maharashtra	Yavatmal-Washim	KAMBLE UTTAM BHAGAJI	1402	no	PRCP	Prabuddha Republican Party
Maharashtra	Yavatmal-Washim	RAGHUNATH SHANKARRAO SHELKE	1911	no	ARP	Ambedkarist Republican Party
Maharashtra	Yavatmal-Washim	RATHOD INDUWAR KASHIRAM	4627	no	BMUP	Bahujan Mukti Party
Maharashtra	Yavatmal-Washim	AHMAD PARVEZ IQBAL AHMAD NUR MOHAMMAD	3262	no	SP	Samajwadi Party
Maharashtra	Yavatmal-Washim	DHOTE JAMBUWANTRAO BAPURAO	4708	no	AIFB	All India Forward Bloc
Maharashtra	Yavatmal-Washim	ABRAR AHMAD MAHMUD KHAN	5672	no	WPOI	Welfare Party Of India
Maharashtra	Yavatmal-Washim	RAJU OR BHARATRAJE UDDHAORAO PATIL	26194	no	MNS	Maharashtra Navnirman sena
Maharashtra	Yavatmal-Washim	RATHOD BALIRAM PARASHRAM	48981	no	BSP	Bahujan Samaj Party
Maharashtra	Yavatmal-Washim	ADV.SHIVAJIRAO SHIVARAMJI MOGHE	384089	no	INC	Indian National Congress
Maharashtra	Yavatmal-Washim	SURESH RAJARAM MUKHMALE	2130	no	MVA	Maharashtra Vikas Aghadi
Maharashtra	Yavatmal-Washim	MESHRAM DNYANESHWAR TUKARAM	1036	no	IND	Independent
Maharashtra	Yavatmal-Washim	SHYAMKUMAR BAPURAO LOKHANDE	1218	no	IND	Independent
Maharashtra	Yavatmal-Washim	SHAIKH JABBAR SHAIKH USUF	2172	no	IND	Independent
Maharashtra	Hingoli	RAJEEV SHANKARRAO SATAV	467397	yes	INC	Indian National Congress
Maharashtra	Hingoli	JADHAV CHUNNILAL MOHAN	25145	no	BSP	Bahujan Samaj Party
Maharashtra	Hingoli	D.B.NAIK	14986	no	CPM	Communist Party of India  (Marxist)
Maharashtra	Hingoli	WANKHEDE SUBHASH BAPURAO	465765	no	SHS	Shivsena
Maharashtra	Hingoli	UTTAMRAO PANDURANG RATHOD	9770	no	BMUP	Bahujan Mukti Party
Maharashtra	Hingoli	DESAI PANDURANG DEVRAO	1675	no	JD(S)	Janata Dal  (Secular)
Maharashtra	Hingoli	RAMRAO HARISING RATHOD	9577	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Hingoli	PRINCIPAL DR.VITHAL NAMDEORAO KADAM	3729	no	AAAP	Aam Aadmi Party
Maharashtra	Hingoli	SHAIKH NAYEEM SHAIKH LAL	2514	no	SP	Samajwadi Party
Maharashtra	Hingoli	SANTOSH NAMDEORAO SAGNE	1526	no	AwVP	Awami Vikas Party
Maharashtra	Hingoli	UTTAM RATHOD	2147	no	IND	Independent
Maharashtra	Hingoli	DESHMUKH BALASAHEB DESAI	2154	no	IND	Independent
Maharashtra	Hingoli	DHABE UTTAM MAROTI	7878	no	IND	Independent
Maharashtra	Hingoli	DEVJI GANGARAM ASOLE	3165	no	IND	Independent
Maharashtra	Hingoli	NAIKWADE DIGAMBAR GUNAJI	2049	no	IND	Independent
Maharashtra	Hingoli	BHAROSE ANANT RAMRAO	2506	no	IND	Independent
Maharashtra	Hingoli	WANKHEDE SUBHASH	6157	no	IND	Independent
Maharashtra	Hingoli	SATAV ATMARAM RAGHOJI	5000	no	IND	Independent
Maharashtra	Hingoli	SATAV RAJU SHANKAR	3522	no	IND	Independent
Maharashtra	Hingoli	SURYAWANSHI ASHOK BHAGWANRAO	1292	no	IND	Independent
Maharashtra	Hingoli	SURAJ VISHWANATH KONDARWAD	1322	no	IND	Independent
Maharashtra	Hingoli	WANKHEDE SUBHASH NAGORAO	6284	no	JD(U)	Janata Dal  (United)
Maharashtra	Hingoli	GHUNNAR PRAKASH VITTHALRAO	2497	no	IND	Independent
Maharashtra	Hingoli	None of the Above	3107	no	NOTA	None of the Above
Maharashtra	Nanded	ASHOK SHANKARRAO CHAVAN	493075	yes	INC	Indian National Congress
Maharashtra	Nanded	PATHAN ZAFAR ALIKHAN MEHMOOD ALIKHAN	1993	no	IND	Independent
Maharashtra	Nanded	DR. HANSRAJ DADARAOJI VAIDYA	22809	no	BSP	Bahujan Samaj Party
Maharashtra	Nanded	ALTAF AHMED IQBAL AHMED	1547	no	IUML	Indian Union Muslim League
Maharashtra	Nanded	VENUGOPAL SHASTRI TUMULURI S/O RAMKRISHNA	1040	no	IND	Independent
Maharashtra	Nanded	SHINDE BALAJI VISHWANATHRAO	1724	no	SP	Samajwadi Party
Maharashtra	Nanded	GHUNNAR PRAKASH VITHALRAO	1753	no	IND	Independent
Maharashtra	Nanded	RAMCHANDRA GANGARAM  BHARANDE	3067	no	IND	Independent
Maharashtra	Nanded	HANMANTE VIJAY CHANDRAO	1479	no	IND	Independent
Maharashtra	Nanded	RAMCHANDRA GYANOBA SAWANT	4206	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Nanded	SAYYED TANVEER SAYYADHAMAJA	1288	no	MBT	Majlis Bachao Tahreek
Maharashtra	Nanded	SHAIKH AFZALUDDIN AZEEMUDDIN	1065	no	LB	Lok Bharati
Maharashtra	Nanded	None of the Above	3155	no	NOTA	None of the Above
Maharashtra	Nanded	RAJRATNA AMBEDKAR	28447	no	BMUP	Bahujan Mukti Party
Maharashtra	Nanded	MOHD. MAROOF AB. RAUF	4771	no	IND	Independent
Maharashtra	Nanded	D. B. PATIL	411620	no	BJP	Bharatiya Janata Party
Maharashtra	Nanded	SONTAKKE LAXMAN KISHAN	1667	no	IND	Independent
Maharashtra	Nanded	ADV. DEELIP PRABHAKARRAO KULKARNI (PHULWALKAR)	1285	no	IND	Independent
Maharashtra	Nanded	BOCHARE SHANKAR MAHADJI	5694	no	IND	Independent
Maharashtra	Nanded	GRANTHI NARENDRASINGH ANMOLSINGH	3397	no	AAAP	Aam Aadmi Party
Maharashtra	Nanded	DATTARAM BALAJI SURWANSHI	2882	no	IND	Independent
Maharashtra	Nanded	FEROZ KHAN GAZI	8088	no	IND	Independent
Maharashtra	Nanded	PANJABRAO SHRIHARI WADJE PATIL	5825	no	IND	Independent
Maharashtra	Nanded	ADV. DHONDOPANT NAGORAO HANUMANTE	1473	no	IND	Independent
Maharashtra	Parbhani	JADHAV SANJAY (BANDU) HARIBHAU	578455	yes	SHS	Shivsena
Maharashtra	Parbhani	RAMRAO DHANSING RATHOD SIR NIRANKARI	3120	no	IND	Independent
Maharashtra	Parbhani	SK. SALEEM	2947	no	MBT	Majlis Bachao Tahreek
Maharashtra	Parbhani	COMRED BHISE GANPAT DEVRAO	7251	no	IND	Independent
Maharashtra	Parbhani	BHAMBALE VIJAY MANIKRAO	451300	no	NCP	Nationalist Congress Party
Maharashtra	Parbhani	SALMA SHRINIWAS KULKARNI	4449	no	AAAP	Aam Aadmi Party
Maharashtra	Parbhani	BABAN (ANNA) MULEY	5836	no	BMUP	Bahujan Mukti Party
Maharashtra	Parbhani	GULMIR KHAN	33716	no	BSP	Bahujan Samaj Party
Maharashtra	Parbhani	COMRADE RAJAN KSHIRSAGAR	12404	no	CPI	Communist Party of India
Maharashtra	Parbhani	None of the Above	17502	no	NOTA	None of the Above
Maharashtra	Parbhani	KAJALE PRADIP MAHADEV	4781	no	IND	Independent
Maharashtra	Parbhani	NISAR SUBHAN KHAN PATHAN	12341	no	IND	Independent
Maharashtra	Parbhani	PRAMOD MAROTI PANDITKAR	4103	no	IND	Independent
Maharashtra	Parbhani	ADV. AJAY SITARAM KARANDE PATIL	5507	no	SP	Samajwadi Party
Maharashtra	Parbhani	MD. ILIYAS MD. AMIR MANIYAR	8006	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Parbhani	ASHOK BABARAO (AMBHORE) DUDHGAONKAR	1869	no	ANC	Ambedkar National Congress
Maharashtra	Parbhani	SAYED ABDUL RAHIM	2492	no	WPOI	Welfare Party Of India
Maharashtra	Parbhani	UDHAV RANGRAO PAWAR (PATIL)	6154	no	IND	Independent
Maharashtra	Jalna	DANVE RAOSAHEB DADARAO	591428	yes	BJP	Bharatiya Janata Party
Maharashtra	Jalna	UNGE RAMBHAU KUNDLIK	2240	no	IND	Independent
Maharashtra	Jalna	SANJAY NIVRUTTI HIVRALE	1821	no	ANC	Ambedkar National Congress
Maharashtra	Jalna	RAMBHAU HARIBHAU UGLE	3283	no	RSSDP	Rashtriya Sant Sandesh Party
Maharashtra	Jalna	DR. WANKHEDE SHARADCHANDRA GANAPATRAO	23719	no	BSP	Bahujan Samaj Party
Maharashtra	Jalna	RATHOD RAMESH LALSING	2462	no	BMUP	Bahujan Mukti Party
Maharashtra	Jalna	M. JAVED A. WAHAB	7704	no	IND	Independent
Maharashtra	Jalna	KARANDE GANESH NAVNATHRAO	2874	no	IND	Independent
Maharashtra	Jalna	AGRAWAL KUNJBIHARI JUGALKISHOR	3469	no	SP	Samajwadi Party
Maharashtra	Jalna	SAYYAD HUSAIN AHMAD	1655	no	IND	Independent
Maharashtra	Jalna	BABASAHEB PATIL SHINDE	6547	no	IND	Independent
Maharashtra	Jalna	KAKARWAL PRATAPSING MAHAJAN	4237	no	IND	Independent
Maharashtra	Jalna	AUTADE VILAS KESHAVRAO	384630	no	INC	Indian National Congress
Maharashtra	Jalna	SAPKAL LILABAI DHARMA	1110	no	IND	Independent
Maharashtra	Jalna	VITTHAL SAHEBRAO SHELKE	4587	no	APoI	Ambedkarite Party of India
Maharashtra	Jalna	ASHOK VITTHAL SONWANE	1894	no	IND	Independent
Maharashtra	Jalna	MOHAMMAD ASAD SHAIKH AHEMAD	1342	no	MNDP	Minorities Democratic Party
Maharashtra	Jalna	AD. MAHESH VITTHALRAO KHARAT	1967	no	IND	Independent
Maharashtra	Jalna	NADE DNYANESHWAR DAGDUJI	6953	no	IND	Independent
Maharashtra	Jalna	RAMBHAU VITTHAL CHINCHORE	1120	no	IND	Independent
Maharashtra	Jalna	MIRZA AFSAR BEG	2467	no	WPOI	Welfare Party Of India
Maharashtra	Jalna	DILIP DATTA MHASKE	4622	no	AAAP	Aam Aadmi Party
Maharashtra	Jalna	None of the Above	4128	no	NOTA	None of the Above
Maharashtra	Aurangabad	CHANDRAKANT BHAURAO KHAIRE	520902	yes	SHS	Shivsena
Maharashtra	Aurangabad	BANSWAL MANNALAL PREMCHAND	893	no	PRCP	Prabuddha Republican Party
Maharashtra	Aurangabad	GAYAKE SADASHIV AMBADAS	2646	no	SP	Samajwadi Party
Maharashtra	Aurangabad	JAGDIP VISHWANATH SHINDE	1530	no	IND	Independent
Maharashtra	Aurangabad	AHAMAD AZIZ AHAMAD MATEEN	1572	no	IND	Independent
Maharashtra	Aurangabad	SHAIK SAYYAD SHAIK MAHMOOD	1581	no	IND	Independent
Maharashtra	Aurangabad	SUBHASH KALYANRAO LOMTE	11974	no	AAAP	Aam Aadmi Party
Maharashtra	Aurangabad	None of the Above	6372	no	NOTA	None of the Above
Maharashtra	Aurangabad	JEVRIKAR INDRAKUMAR DNYANOBA	37419	no	BSP	Bahujan Samaj Party
Maharashtra	Aurangabad	KAILAS CHANDRABHAN THENGDE	2123	no	IND	Independent
Maharashtra	Aurangabad	TRIBHUVAN MADHUKAR PADMAKAR	6135	no	IND	Independent
Maharashtra	Aurangabad	DR. FEROZ KHAN MURTUZA KHAN	978	no	IND	Independent
Maharashtra	Aurangabad	PATIL NITIN SURESH	358902	no	INC	Indian National Congress
Maharashtra	Aurangabad	BALASAHEB ASARAM SARATE	880	no	IND	Independent
Maharashtra	Aurangabad	RAJU BABURAO DOLE	1238	no	IND	Independent
Maharashtra	Aurangabad	NADEEM RANA	2922	no	IND	Independent
Maharashtra	Aurangabad	ABDUL KHUDUS ABDUL SAMAD SHAIKH	3775	no	BMUP	Bahujan Mukti Party
Maharashtra	Aurangabad	BALASAHEB VITTHAL AWARE	614	no	IND	Independent
Maharashtra	Aurangabad	DANDGE NANASAHEB DAMODHAR	5901	no	IND	Independent
Maharashtra	Aurangabad	BANSODE UDHAV GOVARDHAN	1795	no	IND	Independent
Maharashtra	Aurangabad	BHANUDAS RAMDAS SARODE	934	no	IND	Independent
Maharashtra	Aurangabad	SAYYAD SHAFIYODDIN VAHIYODDIN	2885	no	WPOI	Welfare Party Of India
Maharashtra	Aurangabad	JAWAHARLAL LAXMAN BHAGURE	878	no	IND	Independent
Maharashtra	Aurangabad	NANDARKAR VISHAL UDDHAV	1306	no	IND	Independent
Maharashtra	Aurangabad	ANKUSH MUNJAJI TUPSAMUNDRE	2019	no	IND	Independent
Maharashtra	Aurangabad	PHULARE SURESH ASARAM	2520	no	IND	Independent
Maharashtra	Aurangabad	JADHAV PUSHPA SHANTILAL	1038	no	ANC	Ambedkar National Congress
Maharashtra	Aurangabad	MOHMMAD KISMATWALA KASIM KISMATWALA	1201	no	GaAP	Gareeb Aadmi Party
Maharashtra	Dindori	CHAVAN HARISHCHANDRA DEORAM	542784	yes	BJP	Bharatiya Janata Party
Maharashtra	Dindori	DR. BHARATI PRAVIN PAWAR	295165	no	NCP	Nationalist Congress Party
Maharashtra	Dindori	COM. HEMANT MOTIRAM WAGHERE	72599	no	CPM	Communist Party of India  (Marxist)
Maharashtra	Dindori	PROF. DNYANESHWAR DAMU MALI	4067	no	AAAP	Aam Aadmi Party
Maharashtra	Dindori	MALI SHARAD SAHEBRAO	17724	no	BSP	Bahujan Samaj Party
Maharashtra	Dindori	PRABHAT CHINDHU SONAWANE	5578	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Dindori	None of the Above	10897	no	NOTA	None of the Above
Maharashtra	Dindori	BHARAT KISAN MATE	2024	no	BMUP	Bahujan Mukti Party
Maharashtra	Dindori	KAILAS SAKHARAM MORE	3900	no	IND	Independent
Maharashtra	Dindori	AJITDADA BHIKA PAWAR	5177	no	IND	Independent
Maharashtra	Dindori	GAIKWAD ABHIJEET KALYANRAO	10267	no	IND	Independent
Maharashtra	Nashik	GODSE HEMANT TUKARAM	494735	yes	SHS	Shivsena
Maharashtra	Nashik	PATHAN AYUB MADDEKHAN	697	no	IUML	Indian Union Muslim League
Maharashtra	Nashik	FASALE BABAN NAMDEO	3792	no	IND	Independent
Maharashtra	Nashik	None of the Above	8916	no	NOTA	None of the Above
Maharashtra	Nashik	PRAKASH GIRDHAR KANOJE	1121	no	IND	Independent
Maharashtra	Nashik	DR. PRADEEP PAWAR	63050	no	MNS	Maharashtra Navnirman sena
Maharashtra	Nashik	VISHNU RAMDAS JADHAV	1666	no	IND	Independent
Maharashtra	Nashik	PATHAN SAKHAWATKHA YASINKHA	845	no	BMUP	Bahujan Mukti Party
Maharashtra	Nashik	MAKSUD ILIYAS KHAN	1253	no	IND	Independent
Maharashtra	Nashik	PRAMOD GYANIRAM NATHEKAR	1580	no	IND	Independent
Maharashtra	Nashik	CHHAGAN BHUJBAL	307399	no	NCP	Nationalist Congress Party
Maharashtra	Nashik	VIJAY BALIRAM PANDHARE	9672	no	AAAP	Aam Aadmi Party
Maharashtra	Nashik	ADV. TANAJI SUKDEO JAIBHAVE	17154	no	CPM	Communist Party of India  (Marxist)
Maharashtra	Nashik	DINKAR DHARMA PATIL	20896	no	BSP	Bahujan Samaj Party
Maharashtra	Nashik	AVHAD MAHESH ZUNJAR	1204	no	AIFB	All India Forward Bloc
Maharashtra	Nashik	SARKATE DEVIDAS PIRAJI	3425	no	IND	Independent
Maharashtra	Palghar	ADV. CHINTAMAN NAVASHA WANGA	533201	yes	BJP	Bharatiya Janata Party
Maharashtra	Palghar	BALIRAM SUKUR JADHAV	293681	no	BVA	Bahujan Vikas Aaghadi
Maharashtra	Palghar	KHARAPADE LADAKYA RUPA	76890	no	CPM	Communist Party of India  (Marxist)
Maharashtra	Palghar	DILIP ATMARAM DUMADA	10742	no	BMUP	Bahujan Mukti Party
Maharashtra	Palghar	HARIBHAU SOMA VARTHA	12974	no	IND	Independent
Maharashtra	Palghar	GAVARI SHYAM ANANT	8176	no	BSP	Bahujan Samaj Party
Maharashtra	Palghar	PANDURANG JETHYA PARDHI	16182	no	AAAP	Aam Aadmi Party
Maharashtra	Palghar	MOHAN BARKU GUHE	6691	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Palghar	KASHINATH LAXMAN KOKERA	4327	no	IND	Independent
Maharashtra	Palghar	SACHIN DAMODAR SHINGDA	7957	no	IND	Independent
Maharashtra	Palghar	None of the Above	21797	no	NOTA	None of the Above
Maharashtra	Bhiwandi	KAPIL MORESHWAR PATIL	411070	yes	BJP	Bharatiya Janata Party
Maharashtra	Bhiwandi	None of the Above	9311	no	NOTA	None of the Above
Maharashtra	Bhiwandi	ENGINEER NAVID BETAB	2435	no	IND	Independent
Maharashtra	Bhiwandi	YOGESH MOTIRAM KATHORE	3058	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Bhiwandi	ADV. DR. ANITA BALASAHEB KOLEKAR	1376	no	PRCP	Prabuddha Republican Party
Maharashtra	Bhiwandi	HARISHCHANDRA DATTU PATIL	4058	no	IND	Independent
Maharashtra	Bhiwandi	SHAH ABRAR AHMED NAZEER	2120	no	PECP	Peace Party
Maharashtra	Bhiwandi	SURESH KALURAM JADHAV	5941	no	IND	Independent
Maharashtra	Bhiwandi	ANSARI JAHID MUKHTAR	2126	no	BMUP	Bahujan Mukti Party
Maharashtra	Bhiwandi	ANSARI JALAL ENGINEER	11055	no	AAAP	Aam Aadmi Party
Maharashtra	Bhiwandi	ANSARI MUMTAZ ABDUL SATAR	14068	no	BSP	Bahujan Samaj Party
Maharashtra	Bhiwandi	SURESH (BALYA MAMA) GOPINATH MHATRE	93647	no	MNS	Maharashtra Navnirman sena
Maharashtra	Bhiwandi	PATIL VISHWANATH RAMCHANDRA	301620	no	INC	Indian National Congress
Maharashtra	Bhiwandi	MADHUKAR VITTHAL PATIL	13720	no	CPI	Communist Party of India
Maharashtra	Kalyan	DR.SHRIKANT EKNATH SHINDE	440892	yes	SHS	Shivsena
Maharashtra	Kalyan	SHASHIKANT DADA RASAL	2096	no	IND	Independent
Maharashtra	Kalyan	SHIVA KRISHNAMURTHY IYAR	788	no	IND	Independent
Maharashtra	Kalyan	PRAMOD (RAJU) RATAN PATIL	122349	no	MNS	Maharashtra Navnirman sena
Maharashtra	Kalyan	SULOCHANA DHARMENDRA SONKAMBLE	1229	no	RBS	Republican Bahujan Sena
Maharashtra	Kalyan	KIRATKAR DAYANAND TULSHIRAM	19643	no	BSP	Bahujan Samaj Party
Maharashtra	Kalyan	SUDHAKAR NARAYAN SHINDE	726	no	APoI	Ambedkarite Party of India
Maharashtra	Kalyan	ANAND PRAKASH PARANJPE	190143	no	NCP	Nationalist Congress Party
Maharashtra	Kalyan	MOHAMMED AHMED MUKHTAR KHAN ALIAS AHMED NETA	2467	no	IND	Independent
Maharashtra	Kalyan	NARESH ARVIND THAKUR	20347	no	AAAP	Aam Aadmi Party
Maharashtra	Kalyan	BELAMKAR MILIND CHANDRAKANT	540	no	LB	Lok Bharati
Maharashtra	Kalyan	TELGOTE PRAKASH SHAMRAO	1600	no	IND	Independent
Maharashtra	Kalyan	MOHAMMAD HAMEED SAYYED	3047	no	SP	Samajwadi Party
Maharashtra	Kalyan	BHOSALE SHAMU RAMU	1869	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Kalyan	DILIP PRABHAKAR ALONI (JOSHI)	1222	no	IND	Independent
Maharashtra	Kalyan	MOTE CHANDRAKANT RAMBHAJI	1279	no	IND	Independent
Maharashtra	Kalyan	ANIL MORE	3531	no	BMUP	Bahujan Mukti Party
Maharashtra	Kalyan	ASMITA PUSHKAR PURANIK	1243	no	IND	Independent
Maharashtra	Kalyan	None of the Above	9185	no	NOTA	None of the Above
Maharashtra	Thane	VICHARE RAJAN BABURAO	595364	yes	SHS	Shivsena
Maharashtra	Thane	None of the Above	13174	no	NOTA	None of the Above
Maharashtra	Thane	SANJAY DILIP KUMAR DAS	918	no	IND	Independent
Maharashtra	Thane	RAJU SHRAVAN CHAVAN	1681	no	RBS	Republican Bahujan Sena
Maharashtra	Thane	NAJAKATALI KABIR ANSARI	1549	no	IND	Independent
Maharashtra	Thane	SOHANLAL PUROHIT	2479	no	IND	Independent
Maharashtra	Thane	TRILOKI GHURAHU YADAV	1066	no	IND	Independent
Maharashtra	Thane	SHAFIK AHAMAD SADAT KHAN	1865	no	IND	Independent
Maharashtra	Thane	NARENDRA PRABHASHANKAR PANDEY	2141	no	IND	Independent
Maharashtra	Thane	KIRTAWADE VIDHYADHAR BHIMRAO	10982	no	BSP	Bahujan Samaj Party
Maharashtra	Thane	SALMAN AJIMULLAH HASMI	1402	no	RAPa	Rashtriya Aam Party
Maharashtra	Thane	QAMRUZZAMAN KUTUBALI MINAI	1196	no	PECP	Peace Party
Maharashtra	Thane	JAIN SURENDRAKUMAR K.	641	no	ABJS	Akhil Bharatiya Jan Sangh
Maharashtra	Thane	ABHAY SUBRAO KULKARNI	1891	no	IND	Independent
Maharashtra	Thane	RAMCHANDRA BHAGOJI GAWADE	493	no	IND	Independent
Maharashtra	Thane	RATHOD BABUSINGH VASRAM	636	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Thane	VISHAL DINKAR SALVE	705	no	IND	Independent
Maharashtra	Thane	NITIN SHANKAR DESHPANDE	2175	no	DaP	Dharmarajya Paksha
Maharashtra	Thane	BHANUDAS DHOTARE	488	no	IND	Independent
Maharashtra	Thane	SANJEEV VISHNU SANE	41535	no	AAAP	Aam Aadmi Party
Maharashtra	Thane	SANJEEV GANESH NAIK	314065	no	NCP	Nationalist Congress Party
Maharashtra	Thane	ABHIJIT RAMESH PANSE	48863	no	MNS	Maharashtra Navnirman sena
Maharashtra	Thane	GOPINATH SAKHARAM MHASKE	2412	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Thane	SHRINATH MITHANTHAYA	891	no	HJP	Hindustan Janta Party
Maharashtra	Thane	RAJENDRA GAJBHIYE	905	no	APoI	Ambedkarite Party of India
Maharashtra	Thane	ASHOK SHANKAR JADHAV	4022	no	BMUP	Bahujan Mukti Party
Maharashtra	Thane	VINOD DEEPCHAND GANGWAL	650	no	IND	Independent
Maharashtra	Mumbai North	GOPAL CHINAYYA SHETTY	664004	yes	BJP	Bharatiya Janata Party
Maharashtra	Mumbai North	SINGH ASHOK CHANDRAPAL	5438	no	BSP	Bahujan Samaj Party
Maharashtra	Mumbai North	SANJAY BRIJKISHORLAL NIRUPAM	217422	no	INC	Indian National Congress
Maharashtra	Mumbai North	GONDANE DIVAKAR LAXMAN	1539	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Mumbai North	THORAT SUNIL UTTAMRAO	653	no	AIFB	All India Forward Bloc
Maharashtra	Mumbai North	HAJI AKHTAR PAPERWALA	626	no	IND	Independent
Maharashtra	Mumbai North	YADAV KAMLESH SHOBHNATH	5506	no	SP	Samajwadi Party
Maharashtra	Mumbai North	SATISH PARASMAL JAIN	32364	no	AAAP	Aam Aadmi Party
Maharashtra	Mumbai North	MOHAMMAD AKHTAR MEHBOOB SHAIKH	462	no	PRCP	Prabuddha Republican Party
Maharashtra	Mumbai North	PRABHAKARAN G. TITUS	331	no	PPIS	People's Party of India(secular)
Maharashtra	Mumbai North	SAKHARAM PANDURANG DUNGHAV	699	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Mumbai North	ARJUN BALU ARDE	1650	no	APoI	Ambedkarite Party of India
Maharashtra	Mumbai North	PRASHANT NARSINH KHARATMAL	1612	no	IND	Independent
Maharashtra	Mumbai North	NITIN RAVINDRA RAJVARDHAN	796	no	BMUP	Bahujan Mukti Party
Maharashtra	Mumbai North	DINESH SHAH	394	no	SVPP	Sardar Vallabhbhai Patel Party
Maharashtra	Mumbai North	KAPIL KANTILAL SONI	2078	no	IND	Independent
Maharashtra	Mumbai North	None of the Above	8758	no	NOTA	None of the Above
Maharashtra	Mumbai North	SHAIKH FATEH MOHAMMAD	768	no	IND	Independent
Maharashtra	Mumbai North	SUBODH G. RANJAN	278	no	IND	Independent
Maharashtra	Mumbai North	SATYAVIJAY VITHOBA CHAVAN	327	no	IND	Independent
Maharashtra	Mumbai North	ARJUN CHAUDHARY	556	no	IND	Independent
Maharashtra	Mumbai North	MILIND SHANKAR REPE	301	no	IND	Independent
Maharashtra	Mumbai North West	GAJANAN CHANDRAKANT KIRTIKAR	464820	yes	SHS	Shivsena
Maharashtra	Mumbai North West	MOHAN  GALVANKAR	694	no	lpi	The Lok Party of India
Maharashtra	Mumbai North West	PRAVIN  CHANDRAKANT  KAURPURIYA	888	no	HSCP	Hindustan Swaraj Congress Party
Maharashtra	Mumbai North West	BHOLE PUSHPA MILIND	10275	no	BSP	Bahujan Samaj Party
Maharashtra	Mumbai North West	MANGESH MADHUKAR HUMNE	1082	no	BMUP	Bahujan Mukti Party
Maharashtra	Mumbai North West	VIJAYANAND  SHANKARRAO  SHINDE	514	no	HND	Hindusthan Nirman Dal
Maharashtra	Mumbai North West	NITIN H.  KHARE	1078	no	IND	Independent
Maharashtra	Mumbai North West	PRATIK  PRABHAKAR  TORASKAR	515	no	IND	Independent
Maharashtra	Mumbai North West	RINA  MAHENDRAKUMAR  ZAVERI	1530	no	IND	Independent
Maharashtra	Mumbai North West	SONKAMBLE  GAJANAN  TUKARAM	1366	no	IND	Independent
Maharashtra	Mumbai North West	None of the Above	11009	no	NOTA	None of the Above
Maharashtra	Mumbai North West	MAYANK RAMESH GANDHI	51860	no	AAAP	Aam Aadmi Party
Maharashtra	Mumbai North West	RAKHI SAWANT	2006	no	RAPa	Rashtriya Aam Party
Maharashtra	Mumbai North West	KAMAT GURUDAS VASANT	281792	no	INC	Indian National Congress
Maharashtra	Mumbai North West	MAHESH WAMAN MANJREKAR	66088	no	MNS	Maharashtra Navnirman sena
Maharashtra	Mumbai North East	KIRIT  SOMAIYA	525285	yes	BJP	Bharatiya Janata Party
Maharashtra	Mumbai North East	None of the Above	7114	no	NOTA	None of the Above
Maharashtra	Mumbai North East	DEEPAK DIGAMBAR SHINDE	1359	no	IND	Independent
Maharashtra	Mumbai North East	KHAN SHAMIM BANU	902	no	IND	Independent
Maharashtra	Mumbai North East	TUPSOUNDARYA SUNITA MOHAN	881	no	IND	Independent
Maharashtra	Mumbai North East	FIROZ AHMED SIDDIQUI	2243	no	IND	Independent
Maharashtra	Mumbai North East	BANSODE DILIP BAPU	2784	no	IND	Independent
Maharashtra	Mumbai North East	SABIR MEHANDI HASAN	626	no	IND	Independent
Maharashtra	Mumbai North East	SANJAY SAOJI DESHPANDE	2752	no	IND	Independent
Maharashtra	Mumbai North East	MATCHINDRA  HANUMANTRAO CHATE	17427	no	BSP	Bahujan Samaj Party
Maharashtra	Mumbai North East	SANJAY DINA PATIL	208163	no	NCP	Nationalist Congress Party
Maharashtra	Mumbai North East	PROF. AVINASH DOLAS	8833	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Mumbai North East	TUKARAM BABURAO MANE	1838	no	BMUP	Bahujan Mukti Party
Maharashtra	Mumbai North East	PRAKASH SADASHIV PANCHARAS	1325	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Mumbai North East	PRASHANT DNYANESHWAR GANGAWANE	1308	no	PRCP	Prabuddha Republican Party
Maharashtra	Mumbai North East	MEDHA PATKAR	76451	no	AAAP	Aam Aadmi Party
Maharashtra	Mumbai North East	SAYED GULAMHUSAIN  AMIRHAMZA	1012	no	RPI	Republican Party of India
Maharashtra	Mumbai North East	HAYAT ABDULLAH SHAIKH	621	no	IUML	Indian Union Muslim League
Maharashtra	Mumbai North East	ADV. AMARSEN BABURAO BABAR	468	no	IND	Independent
Maharashtra	Mumbai North East	ANANDA   SAKHARAM  BHALERAO	369	no	IND	Independent
Maharashtra	Mumbai North central	POONAM MAHAJAN ALIAS POONAM VAJENDLA RAO	478535	yes	BJP	Bharatiya Janata Party
Maharashtra	Mumbai North central	None of the Above	6937	no	NOTA	None of the Above
Maharashtra	Mumbai North central	NIYAZ AHMED ALLAUDDIN SHAIKH	512	no	AwVP	Awami Vikas Party
Maharashtra	Mumbai North central	PHIROZE PALKHIVALA	34824	no	AAAP	Aam Aadmi Party
Maharashtra	Mumbai North central	BIRJE HEMANT ANANT	693	no	AIFB	All India Forward Bloc
Maharashtra	Mumbai North central	MEHER A. MALLICK	588	no	BMUP	Bahujan Mukti Party
Maharashtra	Mumbai North central	DINESH PHULSING RATHOD	1261	no	IND	Independent
Maharashtra	Mumbai North central	FRANCIS LAWRENCE FERNANDES	653	no	IND	Independent
Maharashtra	Mumbai North central	MINAKSHI MUKUND JADHAV	1022	no	IND	Independent
Maharashtra	Mumbai North central	CHAWAN SURYAKANT GANGARAM	787	no	IND	Independent
Maharashtra	Mumbai North central	MOHAMMED RAFIK HUSSAIN SHAIKH	308	no	NLP	National Loktantrik Party
Maharashtra	Mumbai North central	UPESH M. BARIA	334	no	IND	Independent
Maharashtra	Mumbai North central	GUPTA KANHAIYALAL RAJARAM	858	no	IND	Independent
Maharashtra	Mumbai North central	SANJU VERMA	626	no	IND	Independent
Maharashtra	Mumbai North central	SHITAL KUMAR JAIN	1380	no	IND	Independent
Maharashtra	Mumbai North central	SURESH GIGA DABHI	872	no	IND	Independent
Maharashtra	Mumbai North central	ABU FARHAN AZMI	9873	no	SP	Samajwadi Party
Maharashtra	Mumbai North central	AZIMUDDIN KHAN	2099	no	lpi	The Lok Party of India
Maharashtra	Mumbai North central	ASHOK KISAN LOKHANDE	581	no	RPI	Republican Party of India
Maharashtra	Mumbai North central	UMESH SONBA AVHAD	657	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Mumbai North central	SHINDE ANAND VYANKATRAO	10128	no	BSP	Bahujan Samaj Party
Maharashtra	Mumbai North central	DUTT PRIYA SUNIL	291764	no	INC	Indian National Congress
Maharashtra	Mumbai South central	RAHUL RAMESH SHEWALE	381275	yes	SHS	Shivsena
Maharashtra	Mumbai South central	ADV.AIYYAR GANESH	14762	no	BSP	Bahujan Samaj Party
Maharashtra	Mumbai South central	GANESH BHIMRAO BUDHE	514	no	IND	Independent
Maharashtra	Mumbai South central	DILIP GAIKWAD	1338	no	IND	Independent
Maharashtra	Mumbai South central	CLIFFORD MARTIS	800	no	IND	Independent
Maharashtra	Mumbai South central	MOIN SOHAIL SAYYED	1212	no	IND	Independent
Maharashtra	Mumbai South central	EKNATH M. GAIKWAD	242933	no	INC	Indian National Congress
Maharashtra	Mumbai South central	SANGHAPAL HIRACHAND GADEKAR	919	no	APoI	Ambedkarite Party of India
Maharashtra	Mumbai South central	SUNDAR BALAKRISHNAN	27715	no	AAAP	Aam Aadmi Party
Maharashtra	Mumbai South central	SHINDE SARJERAO MAHADEV	418	no	IND	Independent
Maharashtra	Mumbai South central	HUSSAIN AKBAR SAYYED	1430	no	BMUP	Bahujan Mukti Party
Maharashtra	Mumbai South central	ADITYA RAJAN SHIRODKAR	73113	no	MNS	Maharashtra Navnirman sena
Maharashtra	Mumbai South central	MAHENDRA TULSHIRAM BHINGARDIVE	919	no	IND	Independent
Maharashtra	Mumbai South central	PAL JITENDRA KUMAR	453	no	IND	Independent
Maharashtra	Mumbai South central	NOOR MOHAMMAD SAHEB RAJA KHAN	1588	no	IND	Independent
Maharashtra	Mumbai South central	ABBAS HUSSAIN SHAIKH	5575	no	SP	Samajwadi Party
Maharashtra	Mumbai South central	KHAN SIRAJ AHMED	545	no	IND	Independent
Maharashtra	Mumbai South central	NAMDEV SAHEBRAO SABLE	1184	no	IND	Independent
Maharashtra	Mumbai South central	MR. NADAR SELVAKUMAR PERIMBRAJ	686	no	IND	Independent
Maharashtra	Mumbai South central	ASLAM HANIF KHOT	1018	no	PECP	Peace Party
Maharashtra	Mumbai South central	ADV.VAISHALI SURENDRA BORDE	1083	no	IND	Independent
Maharashtra	Mumbai South central	None of the Above	9580	no	NOTA	None of the Above
Maharashtra	Mumbai   South	ARVIND SAWANT	374780	yes	SHS	Shivsena
Maharashtra	Mumbai   South	None of the Above	9573	no	NOTA	None of the Above
Maharashtra	Mumbai   South	GAURAV SHARMA	561	no	IND	Independent
Maharashtra	Mumbai   South	SHAHZAD SHAMSHER KHAN PATHAN	920	no	AwVP	Awami Vikas Party
Maharashtra	Mumbai   South	SHEHBAJ RATHOD	415	no	JMBP	Jai Maha Bharath Party
Maharashtra	Mumbai   South	JAHEDA MOHAMMED HANIF SHAIKH	703	no	IND	Independent
Maharashtra	Mumbai   South	SACHIN PAWAR	1266	no	BMUP	Bahujan Mukti Party
Maharashtra	Mumbai   South	BAIG MIRZA KALIM	677	no	lpi	The Lok Party of India
Maharashtra	Mumbai   South	FARNANDES SANTAN	1078	no	IND	Independent
Maharashtra	Mumbai   South	BASIT FAITH	765	no	IND	Independent
Maharashtra	Mumbai   South	KATARIA JITENDRA KAMABHAI	595	no	IND	Independent
Maharashtra	Mumbai   South	RAJENDRA DAULAT DESAI	1059	no	IND	Independent
Maharashtra	Mumbai   South	PRAKASH REDDY	6903	no	CPI	Communist Party of India
Maharashtra	Mumbai   South	MEERA SANYAL	40388	no	AAAP	Aam Aadmi Party
Maharashtra	Mumbai   South	SHANKAR  SONAWANE	459	no	IND	Independent
Maharashtra	Mumbai   South	RATAN DEVRAM BODKE	1325	no	IND	Independent
Maharashtra	Mumbai   South	DR. OMRAJ L. RATHOD	675	no	IND	Independent
Maharashtra	Mumbai   South	BALA NANDGAONKAR	84864	no	MNS	Maharashtra Navnirman sena
Maharashtra	Mumbai   South	ABDUL LATIF ABDUL KADAR SHAIKH	400	no	IND	Independent
Maharashtra	Mumbai   South	ABDUS SALAM KHAN  QASMI	6677	no	BSP	Bahujan Samaj Party
Maharashtra	Mumbai   South	DEORA MILIND MURLI	246632	no	INC	Indian National Congress
Maharashtra	Raigad	ANANT GEETE	396178	yes	SHS	Shivsena
Maharashtra	Raigad	YASHWANT JAYRAM GAIKWAD	10510	no	BSP	Bahujan Samaj Party
Maharashtra	Raigad	AZIZ ABDUL KADIR MUKADAM	2763	no	SP	Samajwadi Party
Maharashtra	Raigad	RAMESHBHAI KADAM	129730	no	PWPI	Peasants And Workers Party of India
Maharashtra	Raigad	TATKARE SUNIL DATTATREY	394068	no	NCP	Nationalist Congress Party
Maharashtra	Raigad	SANDEEP PANDURANG PARTE	4287	no	HJP	Hindustan Janta Party
Maharashtra	Raigad	DR. APARANTI SANJAY YASHWANT	6759	no	AAAP	Aam Aadmi Party
Maharashtra	Raigad	DR. GHONE ADESH YASHAWANT	3308	no	BMUP	Bahujan Mukti Party
Maharashtra	Raigad	SUNIL TATKARE	9849	no	IND	Independent
Maharashtra	Raigad	MUZZAFAR JAINUDDIN CHAUDHARI  ALIAS MODI	9952	no	IND	Independent
Maharashtra	Raigad	None of the Above	20362	no	NOTA	None of the Above
Maharashtra	Maval	APPA ALIAS SHRIRANG CHANDU BARNE	512226	yes	SHS	Shivsena
Maharashtra	Maval	None of the Above	11186	no	NOTA	None of the Above
Maharashtra	Maval	JAGTAP LAXMAN MURLIDHAR	1600	no	IND	Independent
Maharashtra	Maval	JAGTAP LAXMAN SITARAM	8765	no	IND	Independent
Maharashtra	Maval	SIMA DHARMANNA MANIKADI	1765	no	PRCP	Prabuddha Republican Party
Maharashtra	Maval	ADV.VAISHALI SURENDRA BORDE	2536	no	IND	Independent
Maharashtra	Maval	SIR MANUEL SABINO D'SOUZA	4311	no	IND	Independent
Maharashtra	Maval	SUBHASH GOPALRAO BODHE	3429	no	IND	Independent
Maharashtra	Maval	NANAWARE PRAMOD MAHADEO	4276	no	IND	Independent
Maharashtra	Maval	BHAPKAR MARUTI SAHEBRAO	30566	no	AAAP	Aam Aadmi Party
Maharashtra	Maval	H.B.P.ADV.R.K.PATIL	2336	no	IND	Independent
Maharashtra	Maval	RAJENDRA MARUTI KATE	2016	no	IND	Independent
Maharashtra	Maval	BARNE SHRIRANG CHIMAJI	11259	no	JD(U)	Janata Dal  (United)
Maharashtra	Maval	NARWEKAR RAHUL SURESH	182293	no	NCP	Nationalist Congress Party
Maharashtra	Maval	ABHIJIT ANIL APTE	1085	no	IND	Independent
Maharashtra	Maval	BHIMAPUTRA TEXAS GAIKWAD	25982	no	BSP	Bahujan Samaj Party
Maharashtra	Maval	JAGTAP LAXMANBHAU PANDURANG	354829	no	PWPI	Peasants And Workers Party of India
Maharashtra	Maval	GHARAT SANTOSH ROHIDAS	8940	no	BMUP	Bahujan Mukti Party
Maharashtra	Maval	NURJAHAN YASIN SHAIKH	2024	no	IND	Independent
Maharashtra	Maval	BHIMRAO ANNA KADALE	2911	no	IND	Independent
Maharashtra	Pune	ANIL SHIROLE	569825	yes	BJP	Bharatiya Janata Party
Maharashtra	Pune	DR.VISHWAJEET PATANGRAO KADAM	254056	no	INC	Indian National Congress
Maharashtra	Pune	COLONEL JAYANTRAO CHITALE	1884	no	IND	Independent
Maharashtra	Pune	None of the Above	6438	no	NOTA	None of the Above
Maharashtra	Pune	NANA KSHIRSAGAR	1797	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Pune	SANJAY LAXMAN PADWAL	477	no	bns	Bhartiya Navjawan Sena (Paksha)
Maharashtra	Pune	DAMBALE RAHUL DHARMA	2259	no	IND	Independent
Maharashtra	Pune	SHASHIKANT BHIKAJI OWHAL	359	no	IND	Independent
Maharashtra	Pune	AZAM VAKIL MANIYAR	353	no	IND	Independent
Maharashtra	Pune	SUSHAMA PANDURANG GAIKWAD	590	no	IND	Independent
Maharashtra	Pune	SAIYYAD AFSER  IBRAHIM	1131	no	SP	Samajwadi Party
Maharashtra	Pune	NILESH DHANAVE	840	no	IND	Independent
Maharashtra	Pune	DINESH PANDURANG PAWAR	692	no	IND	Independent
Maharashtra	Pune	MANCHAK TRIMBAKRAO KARALE	1028	no	IND	Independent
Maharashtra	Pune	UMESH BHUJANG MASANKHAMB	494	no	IND	Independent
Maharashtra	Pune	BANSODE CHAYYA TUKARAM	512	no	IND	Independent
Maharashtra	Pune	DEVKULE BIBHISHAN JAGANNATH	1230	no	IND	Independent
Maharashtra	Pune	SHIVAJI  PANDURANG ADHAV	868	no	IND	Independent
Maharashtra	Pune	DEEPAK NATHARAM PAIGUDE	93502	no	MNS	Maharashtra Navnirman sena
Maharashtra	Pune	KHIRID SIMKUMAR BHAGWAN	380	no	IND	Independent
Maharashtra	Pune	YADAV GANGADHAR NATHU	455	no	IND	Independent
Maharashtra	Pune	COL SURESH PATIL (RTD)	1324	no	BMUP	Bahujan Mukti Party
Maharashtra	Pune	IMTIYAZ PEERZADE	14727	no	BSP	Bahujan Samaj Party
Maharashtra	Pune	WARE SUBHASH SHANKARRAO	28657	no	AAAP	Aam Aadmi Party
Maharashtra	Pune	ASHAPAK SHAIKH	585	no	IND	Independent
Maharashtra	Pune	SANTOSH PAWAR	565	no	IND	Independent
Maharashtra	Pune	VIJAY LAXMAN SARODE	303	no	IND	Independent
Maharashtra	Pune	ARUN BHATIA	7222	no	PG	Peoples Guardian
Maharashtra	Pune	RUPALI TAMBOLI	378	no	IND	Independent
Maharashtra	Pune	AJAY VASANT PAITHANKAR	343	no	IND	Independent
Maharashtra	Baramati	SUPRIYA SULE	521562	yes	NCP	Nationalist Congress Party
Maharashtra	Baramati	None of the Above	14216	no	NOTA	None of the Above
Maharashtra	Baramati	MAHADIK PRALHAD DAGADU	2891	no	IND	Independent
Maharashtra	Baramati	RAVINDRA TAKALE	7845	no	BMUP	Bahujan Mukti Party
Maharashtra	Baramati	TATYASAHEB SITARAM TELE	8811	no	JD(S)	Janata Dal  (Secular)
Maharashtra	Baramati	CHAUDHARI KALURAM VINAYAK	24908	no	BSP	Bahujan Samaj Party
Maharashtra	Baramati	KOKARE SHIVAJI JAYSING	4739	no	IND	Independent
Maharashtra	Baramati	SURESHDADA BABURAO VEER	3345	no	IND	Independent
Maharashtra	Baramati	SURESH KHOPADE	26396	no	AAAP	Aam Aadmi Party
Maharashtra	Baramati	MAHADEV JAGANNATH JANKAR	451843	no	RSPS	Rashtriya Samaj Paksha
Maharashtra	Shirur	ADHALRAO SHIVAJI DATTATREY	643415	yes	SHS	Shivsena
Maharashtra	Shirur	WAGHMARE SARJERAO BHIKA	19783	no	BSP	Bahujan Samaj Party
Maharashtra	Shirur	NIKAM SOPANRAO PANDHARINATH	16663	no	AAAP	Aam Aadmi Party
Maharashtra	Shirur	GEETARAM RAMCHANDRA KADAM	1906	no	SP(I)	Socialist Party (India)
Maharashtra	Shirur	UMESH ARJUN KANCHAN	1137	no	IND	Independent
Maharashtra	Shirur	ASHOK SHIRPATI KHANDEBHARAD	36448	no	MNS	Maharashtra Navnirman sena
Maharashtra	Shirur	None of the Above	11995	no	NOTA	None of the Above
Maharashtra	Shirur	NIKAM DEVDATTA JAYWANT	341601	no	NCP	Nationalist Congress Party
Maharashtra	Shirur	BAJARE SITARAM NAMDEO (BABA)	1933	no	BMUP	Bahujan Mukti Party
Maharashtra	Shirur	VIKAS SUDAM KUSEKAR	1174	no	AIFB	All India Forward Bloc
Maharashtra	Shirur	KATORE MAHADEV NIWRITTI	1792	no	IND	Independent
Maharashtra	Shirur	DAMBALE RAM  DHARMA	2433	no	IND	Independent
Maharashtra	Shirur	NILESH YASHWANT MHASKULE	2118	no	IND	Independent
Maharashtra	Shirur	MILINDRAJE DIGAMBAR BHOSALE	3258	no	IND	Independent
Maharashtra	Shirur	SONALI CHANDRASHEKHAR THORAT	3850	no	IND	Independent
Maharashtra	Ahmadnagar	GANDHI DILIPKUMAR MANSUKHLAL	605185	yes	BJP	Bharatiya Janata Party
Maharashtra	Ahmadnagar	None of the Above	7473	no	NOTA	None of the Above
Maharashtra	Ahmadnagar	KAKADE KISAN YASHWANT	8386	no	BSP	Bahujan Samaj Party
Maharashtra	Ahmadnagar	DEEPALI SAYYED	7120	no	AAAP	Aam Aadmi Party
Maharashtra	Ahmadnagar	PETRAS KISAN GAWARE	2525	no	IND	Independent
Maharashtra	Ahmadnagar	DESHMUKH VIKAS NANASAHEB	2202	no	IND	Independent
Maharashtra	Ahmadnagar	H.B.P. AJAY SAHEBRAO BARASKAR MAHARAJ	6003	no	BMUP	Bahujan Mukti Party
Maharashtra	Ahmadnagar	KOLSEPATIL BABAN GANGADHAR	12683	no	IND	Independent
Maharashtra	Ahmadnagar	POPAT MANIK FULE	1061	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Ahmadnagar	LAXMAN BHANUDAS SONALE	3497	no	IND	Independent
Maharashtra	Ahmadnagar	DAMALE SHIVAJIRAO WAMAN	1385	no	bns	Bhartiya Navjawan Sena (Paksha)
Maharashtra	Ahmadnagar	ANIL JAYSING GHANVAT	3086	no	IND	Independent
Maharashtra	Ahmadnagar	SHRIDHAR JAKHUJI DAREKAR	5649	no	IND	Independent
Maharashtra	Ahmadnagar	RAJEEV APPASAHEB RAJALE	396063	no	NCP	Nationalist Congress Party
Maharashtra	Shirdi	LOKHANDE SADASHIV KISAN	532936	yes	SHS	Shivsena
Maharashtra	Shirdi	BHAUSAHEB RAJARAM WAKCHAURE	333014	no	INC	Indian National Congress
Maharashtra	Shirdi	SANTOSH MADHUKAR ROHAM	9296	no	BMUP	Bahujan Mukti Party
Maharashtra	Shirdi	SHENDE RAVINDRA VITTHAL	4728	no	IND	Independent
Maharashtra	Shirdi	ADV. MAHENDRA DADASAHEB SHINDE	10381	no	BSP	Bahujan Samaj Party
Maharashtra	Shirdi	NITIN NAVNATH UDMALE	11580	no	AAAP	Aam Aadmi Party
Maharashtra	Shirdi	BALASAHEB DASHRATH BAGUL	4319	no	IND	Independent
Maharashtra	Shirdi	WAGH GANGADHAR RADHAJI	2874	no	IND	Independent
Maharashtra	Shirdi	RAGHUNATH RAMJI MAKASARE	3193	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Shirdi	UDDHAVRAO BALWANT GAIKWAD	1499	no	IND	Independent
Maharashtra	Shirdi	GHOLAP SANDEEP BHASKAR	2229	no	IND	Independent
Maharashtra	Shirdi	SARODE POPAT RAMBHAU	1429	no	LB	Lok Bharati
Maharashtra	Shirdi	PAWAR VIJAY BANDERAO	2334	no	lpi	The Lok Party of India
Maharashtra	Shirdi	None of the Above	9879	no	NOTA	None of the Above
Maharashtra	Shirdi	SAYAJI SHANKAR KHARAT	2954	no	IND	Independent
Maharashtra	Beed	MUNDE GOPINATHRAO PANDURANG	635995	yes	BJP	Bharatiya Janata Party
Maharashtra	Beed	DHAS SURESH RAMCHANDRA	499541	no	NCP	Nationalist Congress Party
Maharashtra	Beed	TEJAS ANKUSH GHUMARE	5357	no	IND	Independent
Maharashtra	Beed	ARJUN HANMANTA NIMBALKAR	3758	no	IND	Independent
Maharashtra	Beed	SAYYED MUBIN RAHIMTULLA	1122	no	IND	Independent
Maharashtra	Beed	DR. OWHAL JITENDRA MAHADEV	1075	no	IND	Independent
Maharashtra	Beed	SHRIKANT VISHNU GADALE	931	no	IND	Independent
Maharashtra	Beed	ROTHOD DIGAMBAR RAMRAO	14166	no	BSP	Bahujan Samaj Party
Maharashtra	Beed	AGHAV JAYPRAKASH SITARAM	3247	no	IND	Independent
Maharashtra	Beed	MANIKRAO MADHAVRAO BAVNE	1584	no	SP	Samajwadi Party
Maharashtra	Beed	SAYYED MINHAJ ALI SAYYAD WAJED ALI (PENDKHAJURWALE)	3617	no	IND	Independent
Maharashtra	Beed	RAHUL PRABHAKAR KAMBLE	1733	no	APoI	Ambedkarite Party of India
Maharashtra	Beed	KALE KRISHNA BHANUDAS	3163	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Beed	BHAGWAT HARIHAR TAWARE	2698	no	IND	Independent
Maharashtra	Beed	DONGARE MILIND KASHINATH	1930	no	PRCP	Prabuddha Republican Party
Maharashtra	Beed	SHAIKH SHAFIQ SHABBIR	846	no	IND	Independent
Maharashtra	Beed	SAYYED SALIM FATTU	452	no	IND	Independent
Maharashtra	Beed	GANGADHAR SITARAM KALKUTE	848	no	IND	Independent
Maharashtra	Beed	NADU MADHAV	4283	no	AAAP	Aam Aadmi Party
Maharashtra	Beed	JETHLIA SANTOSHBAPU  MITTHULAL	1270	no	IND	Independent
Maharashtra	Beed	INAMDAR JABBERMIYA DAGERASA	3040	no	IND	Independent
Maharashtra	Beed	HOKE (PATIL)RAJEDRA ACHYUTRAO	1264	no	IND	Independent
Maharashtra	Beed	SOLANKE PARMESHWAR KALYANRAO	520	no	IND	Independent
Maharashtra	Beed	SOLANKE PRKASH BHAGWANRAO	8634	no	IND	Independent
Maharashtra	Beed	SHAIKH IPTEKHAR ZAKEEBABA	1628	no	WPOI	Welfare Party Of India
Maharashtra	Beed	TATE ASHOK SANTRAM	1162	no	ARP	Ambedkarist Republican Party
Maharashtra	Beed	KALE BAJIRAO SHANKAR	1018	no	IND	Independent
Maharashtra	Beed	AVINASH NAMDEV WANKHEDE	2797	no	IND	Independent
Maharashtra	Beed	SHINGARE GOVIND BHARAT	944	no	SVRP	Shivrajya Party
Maharashtra	Beed	THORAT DHANRAJ VITTHAL	1065	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Beed	ASHOK DHONDIBA SONWANE	5383	no	IND	Independent
Maharashtra	Beed	SAYYED NAVIDUJJAMMA	3732	no	IND	Independent
Maharashtra	Beed	PRASHANT VISHNU SASANE	1036	no	IND	Independent
Maharashtra	Beed	SAKSAMUDRE BHAGWAN KACHRUBA	1533	no	BMUP	Bahujan Mukti Party
Maharashtra	Beed	ASHOK BHAGOJI THORAT	3264	no	IND	Independent
Maharashtra	Beed	SUMITRABAI DASHRATH PAWAR	314	no	IND	Independent
Maharashtra	Beed	SHAIKH CHAND SHIAKH MIYA	1734	no	IND	Independent
Maharashtra	Beed	SADULLA ASDULLA INAMDAR	2482	no	IND	Independent
Maharashtra	Beed	VEER SHESHERAO CHOKHOBA	713	no	IND	Independent
Maharashtra	Beed	None of the Above	2323	no	NOTA	None of the Above
Maharashtra	Osmanabad	GAIKWAD RAVINDRA VISHWANATH	607699	yes	SHS	Shivsena
Maharashtra	Osmanabad	None of the Above	4613	no	NOTA	None of the Above
Maharashtra	Osmanabad	TARKASE PUSHPA MURLIDHAR	4126	no	HND	Hindusthan Nirman Dal
Maharashtra	Osmanabad	SHINDE RAJENDRA BHAIRAVNATH	1313	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Osmanabad	PADMSINH VIJAYSINH MUNDHE	3361	no	IND	Independent
Maharashtra	Osmanabad	SAYYAD MOHAMD SAYYAD IBRAHIM	1528	no	BMUP	Bahujan Mukti Party
Maharashtra	Osmanabad	ALTAF HUSAIN YENEGURE	1640	no	IND	Independent
Maharashtra	Osmanabad	ADV. SHAILENDRA RAMESHWAR YAVALKAR	2257	no	SP	Samajwadi Party
Maharashtra	Osmanabad	TUPSUNDARE BALAJI BAPURAO	7038	no	IND	Independent
Maharashtra	Osmanabad	KSHIRSAGAR VIJAY MARUTI	1482	no	IND	Independent
Maharashtra	Osmanabad	SHAIKH MUBARAK MAINODDIN	1418	no	IND	Independent
Maharashtra	Osmanabad	KAKASAHEB BABURAO RATHOD	7245	no	IND	Independent
Maharashtra	Osmanabad	TUKARAM DASRAO GANGAWANE	10210	no	IND	Independent
Maharashtra	Osmanabad	GAIKWAD RAVINDRA VISHWANATH	7169	no	IND	Independent
Maharashtra	Osmanabad	SIDDIK IBRAHIM BOUDIWALI	1429	no	IND	Independent
Maharashtra	Osmanabad	DHALE PADMASHEEL RAMCHANDRA	28322	no	BSP	Bahujan Samaj Party
Maharashtra	Osmanabad	PATIL MANOHAR ANANDRAO	2251	no	IND	Independent
Maharashtra	Osmanabad	PATIL PADMASINHA BAJIRAO	373374	no	NCP	Nationalist Congress Party
Maharashtra	Osmanabad	DR. RAMESH SUBRAV BANSODE	1701	no	RPI	Republican Party of India
Maharashtra	Osmanabad	KSHIRSAGAR SHIVAJI JAGANNATH	1978	no	IND	Independent
Maharashtra	Osmanabad	VIKRAM ASHOK SAWALE	4240	no	AAAP	Aam Aadmi Party
Maharashtra	Osmanabad	SUSHILKUMAR VINAYAK PADULE	1963	no	IND	Independent
Maharashtra	Osmanabad	ROKDE MILIND SOMNATH	3725	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Osmanabad	GAIKWAD UMAJI PANDURANG	2355	no	IND	Independent
Maharashtra	Osmanabad	BHOSALE REVAN VISHWANATH	1288	no	JD(S)	Janata Dal  (Secular)
Maharashtra	Osmanabad	NAVNATH DASHRATH UPLEKAR	4092	no	IND	Independent
Maharashtra	Osmanabad	SMT. JADHAV UJVALA EKNATH	3466	no	IND	Independent
Maharashtra	Osmanabad	ROHAN SUBHASH DESHMUKH	26868	no	IND	Independent
Maharashtra	Latur	DR. SUNIL BALIRAM GAIKWAD	616509	yes	BJP	Bharatiya Janata Party
Maharashtra	Latur	DIPRATNA PRABHAKARRAO NILANGEKAR	9829	no	AAAP	Aam Aadmi Party
Maharashtra	Latur	BANSODE DATTATRAY GUNDERAO	363114	no	INC	Indian National Congress
Maharashtra	Latur	KAMBLE DEEPAK ARVIND	20029	no	BSP	Bahujan Samaj Party
Maharashtra	Latur	GAJANAN PANDURANG MANE	3401	no	IND	Independent
Maharashtra	Latur	BHARAT DAMODAR LONDHE	2035	no	IND	Independent
Maharashtra	Latur	JARIPATKE DNYANOBA SOPANRAO	2052	no	IND	Independent
Maharashtra	Latur	PADMAKAR KONDIBA OVHAL	1941	no	IND	Independent
Maharashtra	Latur	KAMBLE MILIND HARIBHAU	942	no	IND	Independent
Maharashtra	Latur	SHIVKHANDESHWAR NARAYAN DAHGE	1337	no	BJSP	Bharatiya Jawala Shakti Paksha
Maharashtra	Latur	JOGDAND FAKIRA VAIJNATH	1306	no	IND	Independent
Maharashtra	Latur	SURYAWANSI SIDHARTH TUKARAM	2830	no	BMUP	Bahujan Mukti Party
Maharashtra	Latur	BHARAT MAROTI KADAM	2724	no	APoI	Ambedkarite Party of India
Maharashtra	Latur	GAJENDRA LIMBAJI AVGHADE	1287	no	IND	Independent
Maharashtra	Latur	RAJKUMAR BHIMRAO CHANDANSHIVE	1765	no	IND	Independent
Maharashtra	Latur	BALAJI DNYANOBA KAMBLE	1840	no	SP	Samajwadi Party
Maharashtra	Latur	SHINDE SUDHIR SHRIPATRAO	8678	no	IND	Independent
Maharashtra	Latur	UDARE MINAKSHI MAHADEV	1541	no	IND	Independent
Maharashtra	Latur	None of the Above	13996	no	NOTA	None of the Above
Maharashtra	Solapur	SHARAD BANSODE	517879	yes	BJP	Bharatiya Janata Party
Maharashtra	Solapur	WAGHMARE PUNDALIK ONKARI	1399	no	BMUP	Bahujan Mukti Party
Maharashtra	Solapur	None of the Above	13778	no	NOTA	None of the Above
Maharashtra	Solapur	SUNITA VIJAYKUMAR UGHADE	2706	no	IND	Independent
Maharashtra	Solapur	SHINDE RAJKUMAR LAXMANRAO	1286	no	IND	Independent
Maharashtra	Solapur	SONKAMBLE GAUTAM VIJAYKUMAR	2458	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Solapur	RAJENDRA DNYANDEO RANDIVE	1460	no	IND	Independent
Maharashtra	Solapur	PAWAR YUVRAJ SIDRAM	1243	no	IND	Independent
Maharashtra	Solapur	SIDRAM KRISHNA DHOBALE	1683	no	IND	Independent
Maharashtra	Solapur	KEWALE CHAYA NAGNATH	1654	no	IND	Independent
Maharashtra	Solapur	SONAWANE SHIVAJI CHANDRAKANT	5487	no	IND	Independent
Maharashtra	Solapur	DATTATRAYA VITTHAL THORAT	824	no	IND	Independent
Maharashtra	Solapur	BHISE KRISHNA NAGNATH	1504	no	IND	Independent
Maharashtra	Solapur	SHINDE SUSHILKUMAR SAMBHAJIRAO	368205	no	INC	Indian National Congress
Maharashtra	Solapur	ADV. SANJEEV SIDRAM SADAFULE	19041	no	BSP	Bahujan Samaj Party
Maharashtra	Solapur	VIKRAM UTTAM KASABE	1333	no	IND	Independent
Maharashtra	Solapur	LALIT BABAR	9261	no	AAAP	Aam Aadmi Party
Maharashtra	Madha	MOHITE PATIL VIJAYSINH SHANKARRAO	489989	yes	NCP	Nationalist Congress Party
Maharashtra	Madha	SUNITA MOHAN TUPSOUNDRYA	2522	no	IND	Independent
Maharashtra	Madha	ADV. SAVITA BALKRISHNA SHINDE	7160	no	AAAP	Aam Aadmi Party
Maharashtra	Madha	BANSODE KUNDAN FULCHAND	15790	no	BSP	Bahujan Samaj Party
Maharashtra	Madha	SADABHAU RAMCHANDRA KHOT	464645	no	SWP	Swabhimani Paksha
Maharashtra	Madha	WALKE VIKRAM VASUDEO	1969	no	IND	Independent
Maharashtra	Madha	JAGTAP NITIN MARUTI	2189	no	IND	Independent
Maharashtra	Madha	NAVANATH BHIMRAO PATIL	8853	no	HiPPa	Hindusthan Praja Paksha
Maharashtra	Madha	BALIRAM SUKHDEV MORE	4139	no	IND	Independent
Maharashtra	Madha	ADV.SUBHASH BALASAHEB PATIL (ANNA)	3293	no	BMUP	Bahujan Mukti Party
Maharashtra	Madha	PRAFULL SHIVRAO KADAM	2301	no	IND	Independent
Maharashtra	Madha	DR.PRAMOD RAMCHANDRA GAWADE	1926	no	IND	Independent
Maharashtra	Madha	NAGAMANI KISAN JAKKAN	8829	no	IND	Independent
Maharashtra	Madha	SURESH SHAMRAO GHADGE	1989	no	AITC	All India Trinamool Congress
Maharashtra	Madha	BHAJANLAL YASHVANT NIMGAONKAR	4625	no	IND	Independent
Maharashtra	Madha	JADHAV SUNIL GUNDA	7115	no	IND	Independent
Maharashtra	Madha	ROHIT RAMRUSHNA MORE	6886	no	IND	Independent
Maharashtra	Madha	DAGADU MANIK PAWAR	2117	no	IND	Independent
Maharashtra	Madha	SATHI BASHIR AHMED BASHAMIYAN SHAIKH	3367	no	IND	Independent
Maharashtra	Madha	POL SUDHIR ARJUN	2814	no	IND	Independent
Maharashtra	Madha	DR.BHISE INDRAKUMAR DEVRAO	3094	no	IND	Independent
Maharashtra	Madha	SHAIKH SHAHAJAN PAIGAMBAR	2336	no	IND	Independent
Maharashtra	Madha	DEVKATE SOMANATH ALIAS SOMESHWAR CHANDRAKANT	2275	no	IND	Independent
Maharashtra	Madha	MOHITE-PATIL PRATAPSINH SHANKARRAO	25187	no	IND	Independent
Maharashtra	Madha	None of the Above	4209	no	NOTA	None of the Above
Maharashtra	Sangli	SANJAYKAKA PATIL	611563	yes	BJP	Bharatiya Janata Party
Maharashtra	Sangli	SUREKHA ALIAS SURAIYYA SHAHIN SHAIKH	849	no	MVA	Maharashtra Vikas Aghadi
Maharashtra	Sangli	TANAJIRAO JAGANNATH  JADHAV(CHINCHANIKAR)	1774	no	IND	Independent
Maharashtra	Sangli	ASHOK DNANU MANE(BHAU)	1494	no	IND	Independent
Maharashtra	Sangli	CHOPADE SHRIPRASAD JANARDAN	4309	no	IND	Independent
Maharashtra	Sangli	DHATTURE HAFIZBHAI HUSAIN	997	no	IND	Independent
Maharashtra	Sangli	MADHUKAR BABURAO KAMBALE	1844	no	IND	Independent
Maharashtra	Sangli	SHARMILA BABA WAKSHE	2481	no	IND	Independent
Maharashtra	Sangli	None of the Above	6110	no	NOTA	None of the Above
Maharashtra	Sangli	SHRIRANG BABURAO PAKHARE	1661	no	IND	Independent
Maharashtra	Sangli	SURESH PARTH MANE (NANA)	8353	no	IND	Independent
Maharashtra	Sangli	SAMINA ABDULMAJID KHAN	4699	no	AAAP	Aam Aadmi Party
Maharashtra	Sangli	BANDGAR NANASO BALASO	11378	no	BSP	Bahujan Samaj Party
Maharashtra	Sangli	ADV. K. D. SHINDE	2460	no	JD(S)	Janata Dal  (Secular)
Maharashtra	Sangli	SIDDHA SHIVAPPA BHOSALE	4309	no	IND	Independent
Maharashtra	Sangli	PATIL PRATIK PRAKASHBAPU	372271	no	INC	Indian National Congress
Maharashtra	Sangli	POURNIMA VILAS KORPALE	1702	no	IND	Independent
Maharashtra	Sangli	NITIN GANPATRAO SAVGAVE	8405	no	BMUP	Bahujan Mukti Party
Maharashtra	Satara	SHRIMANT CHH.  UDAYANRAJE PRATAPSINHA BHONSALE	522531	yes	NCP	Nationalist Congress Party
Maharashtra	Satara	BICHUKALE ABHIJIT VAMANRAO	3644	no	IND	Independent
Maharashtra	Satara	CHANDRAKANT TATU KHANDAIT	12270	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Satara	SANDIP AMRUTRAO MOZAR	18215	no	IND	Independent
Maharashtra	Satara	KADAM SAGAR UTTAMRAO	10047	no	IND	Independent
Maharashtra	Satara	RAJENDRA MADHUKAR CHORAGE	82489	no	AAAP	Aam Aadmi Party
Maharashtra	Satara	PRASHANT VASANT CHAVAN	14523	no	BSP	Bahujan Samaj Party
Maharashtra	Satara	ALTAF ABDULGANI SHIKALGAR	13760	no	BMUP	Bahujan Mukti Party
Maharashtra	Satara	ASHOK WAMAN GAIKWAD	71808	no	RPI(A)	Republican Party of India (A)
Maharashtra	Satara	GAWDE SUKHDEV BHANUDAS	2435	no	IND	Independent
Maharashtra	Satara	PURUSHOTTAM JADHAV	155937	no	IND	Independent
Maharashtra	Satara	None of the Above	10589	no	NOTA	None of the Above
Maharashtra	Satara	ADV. VARSHA  MADGULKAR	9405	no	IND	Independent
Maharashtra	Satara	PANDURANG RAMCHANDRA SHINDE	11304	no	IND	Independent
Maharashtra	Satara	DR. PRAKASH SHAMRAO PAWAR	2164	no	IND	Independent
Maharashtra	Satara	SUBHASH NIVRUTTI SHILEWANT	15073	no	IND	Independent
Maharashtra	Satara	UMESH MUKUND WAGHMARE	4711	no	IND	Independent
Maharashtra	Satara	SUHAS VISHWASRAO DESHMUKH	12122	no	IND	Independent
Maharashtra	Satara	DR. VIJAYSINHA  DILIPRAO PATIL	3654	no	IND	Independent
Maharashtra	Ratnagiri - sindhudurg	VINAYAK BHAURAO RAUT	493088	yes	SHS	Shivsena
Maharashtra	Ratnagiri - sindhudurg	ADV. DIPAK DATTARAM NEVGI	2904	no	SP(I)	Socialist Party (India)
Maharashtra	Ratnagiri - sindhudurg	AYARE RAJENDRA LAHU	13088	no	BSP	Bahujan Samaj Party
Maharashtra	Ratnagiri - sindhudurg	VINOD HANUMANTRAO SAWANT	1395	no	ABHM	Akhil Bharat Hindu Mahasabha
Maharashtra	Ratnagiri - sindhudurg	AJINKYA DHONDU GAWADE	2332	no	IND	Independent
Maharashtra	Ratnagiri - sindhudurg	ARUN HARISHCHANDRA MANJREKAR	5999	no	IND	Independent
Maharashtra	Ratnagiri - sindhudurg	SUNIL ALIAS YASHWANT VASANT PEDNEKAR	7328	no	IND	Independent
Maharashtra	Ratnagiri - sindhudurg	NILESH NARAYAN RANE	343037	no	INC	Indian National Congress
Maharashtra	Ratnagiri - sindhudurg	ABHIJIT SHRIRAM HEGSHETYE	12700	no	AAAP	Aam Aadmi Party
Maharashtra	Ratnagiri - sindhudurg	BIRJE YASHWANT ANANT	1992	no	BMUP	Bahujan Mukti Party
Maharashtra	Ratnagiri - sindhudurg	None of the Above	12393	no	NOTA	None of the Above
Maharashtra	Kolhapur	DHANANJAY BHIMRAO MAHADIK	607665	yes	NCP	Nationalist Congress Party
Maharashtra	Kolhapur	None of the Above	7015	no	NOTA	None of the Above
Maharashtra	Kolhapur	NARAYAN GUNDU POWAR	5495	no	AAAP	Aam Aadmi Party
Maharashtra	Kolhapur	KURANE AJAY PRAKASH	9291	no	BSP	Bahujan Samaj Party
Maharashtra	Kolhapur	KAMBLE SUKUMAR DATTU	915	no	IND	Independent
Maharashtra	Kolhapur	PATIL BAJRANG KRISHNA	5676	no	IND	Independent
Maharashtra	Kolhapur	KAMBLE DAYANAND VASANT	8635	no	BMUP	Bahujan Mukti Party
Maharashtra	Kolhapur	JEEVAN ALIAS C.A.PATIL	898	no	IND	Independent
Maharashtra	Kolhapur	MHAMANE ANIL SUBHASH	736	no	IND	Independent
Maharashtra	Kolhapur	ATUL PRASANNAKUMAR DIGHE	7067	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Kolhapur	YASHWANT BALKRUSHNA JOSHI	1415	no	IND	Independent
Maharashtra	Kolhapur	SAMPATRAO SHAMRAO PAWAR PATIL	13162	no	PWPI	Peasants And Workers Party of India
Maharashtra	Kolhapur	SANJAY SADASHIV MANDALIK	574406	no	SHS	Shivsena
Maharashtra	Kolhapur	PANDURANG TUKARAM CHOUGALE	836	no	IND	Independent
Maharashtra	Kolhapur	SANDEEP GUNDOPANT SANKPAL	10963	no	IND	Independent
Maharashtra	Kolhapur	SHEREGAR CHETAN VENKATESH	6114	no	IND	Independent
Maharashtra	Hatkanangle	RAJU SHETTY	640428	yes	SWP	Swabhimani Paksha
Maharashtra	Hatkanangle	SUSHANT SANTOSH DIGE	2999	no	IND	Independent
Maharashtra	Hatkanangle	AWADE KALLAPPA BABURAO	462618	no	INC	Indian National Congress
Maharashtra	Hatkanangle	KAMBLE SUKUMAR DATTU	2342	no	IND	Independent
Maharashtra	Hatkanangle	RAGHUNATHDADA PATIL	9015	no	AAAP	Aam Aadmi Party
Maharashtra	Hatkanangle	YUVRAJ SHIVAJI KAMBLE	6585	no	IND	Independent
Maharashtra	Hatkanangle	THORAT ANANDRAO TUKARAM	2637	no	IND	Independent
Maharashtra	Hatkanangle	PATIL S R TATYA	3318	no	IND	Independent
Maharashtra	Hatkanangle	SURESHDADA PATIL	25648	no	IND	Independent
Maharashtra	Hatkanangle	DIGAMBAR ALIAS GABBAR SUDAM SAKAT	5186	no	BBM	Bharipa Bahujan Mahasangh
Maharashtra	Hatkanangle	KAMBLE CHANDRAKANT TUKARAM	11499	no	BSP	Bahujan Samaj Party
Maharashtra	Hatkanangle	MANE ARVIND BHIVA	2361	no	IND	Independent
Maharashtra	Hatkanangle	SHAIKH SHABBIR NABI	4176	no	MPS(T)	Maharashtra Parivartan Sena (T)
Maharashtra	Hatkanangle	None of the Above	10059	no	NOTA	None of the Above
Manipur	Inner manipur	DR. THOKCHOM MEINYA	292102	yes	INC	Indian National Congress
Manipur	Inner manipur	DR. IBOMCHA SINGH	3275	no	AAAP	Aam Aadmi Party
Manipur	Inner manipur	OINAM JOTIN SINGH	1403	no	JMBP	Jai Maha Bharath Party
Manipur	Inner manipur	SARANGTHEM MANAOBI	39903	no	AITC	All India Trinamool Congress
Manipur	Inner manipur	DR. GURUMAYUM TONSANA SHARMA	2005	no	MDPF	Manipur Democratic Peoples's Front
Manipur	Inner manipur	R.K. RANJAN SINGH	92443	no	BJP	Bharatiya Janata Party
Manipur	Inner manipur	INDIRA OINAM	7014	no	IND	Independent
Manipur	Inner manipur	MOIRANGTHEM NARA	197428	no	CPI	Communist Party of India
Manipur	Inner manipur	None of the Above	5298	no	NOTA	None of the Above
Manipur	Outer manipur	THANGSO BAITE	296770	yes	INC	Indian National Congress
Manipur	Outer manipur	None of the Above	2206	no	NOTA	None of the Above
Manipur	Outer manipur	LAMLALMOI @ MOMOI GANGTE	603	no	IND	Independent
Manipur	Outer manipur	M. KHAMCHINPAU @ K. ZOU	4331	no	AAAP	Aam Aadmi Party
Manipur	Outer manipur	AMARSON SANKHIL	401	no	IND	Independent
Manipur	Outer manipur	SOSO LORHO	281133	no	NPF	Naga Peoples Front
Manipur	Outer manipur	LAMMINLIEN @ LIEN GANGTE	1085	no	JD(U)	Janata Dal  (United)
Manipur	Outer manipur	MANI CHARENAMEI	34995	no	IND	Independent
Manipur	Outer manipur	KIM GANGTE	12752	no	AITC	All India Trinamool Congress
Manipur	Outer manipur	CHUNGKHOKAI DOUNGEL	61662	no	NCP	Nationalist Congress Party
Manipur	Outer manipur	GANGMUMEI KAMEI	75828	no	BJP	Bharatiya Janata Party
Meghalaya	Shillong	VINCENT H. PALA	209340	yes	INC	Indian National Congress
Meghalaya	Shillong	ARMERINGTON KHARSHIING	8815	no	AAAP	Aam Aadmi Party
Meghalaya	Shillong	PAUL LYNGDOH	106817	no	UDP	United Democratic Party
Meghalaya	Shillong	RICHARD D. SHABONG	7418	no	CPI	Communist Party of India
Meghalaya	Shillong	SHIBUN LYNGDOH	95979	no	BJP	Bharatiya Janata Party
Meghalaya	Shillong	DENIS SIANGSHAI	5092	no	IND	Independent
Meghalaya	Shillong	IVORYNA SHYLLA	6605	no	IND	Independent
Meghalaya	Shillong	PRECHARD B. M. BASAIAWMOIT	168961	no	IND	Independent
Meghalaya	Shillong	None of the Above	10960	no	NOTA	None of the Above
Meghalaya	Tura	PURNO AGITOK SANGMA	239301	yes	NPEP	National Peoples Party
Meghalaya	Tura	None of the Above	19185	no	NOTA	None of the Above
Meghalaya	Tura	DARYL WILLIAM CH MOMIN	199585	no	INC	Indian National Congress
Mizoram	MIZORAM	C. L. RUALA	210485	yes	INC	Indian National Congress
Mizoram	MIZORAM	M.LALMANZUALA	11890	no	AAAP	Aam Aadmi Party
Mizoram	MIZORAM	None of the Above	6495	no	NOTA	None of the Above
Mizoram	MIZORAM	ROBERT ROMAWIA ROYTE	204331	no	IND	Independent
Nagaland	Nagaland	NEIPHIU RIO	713372	yes	NPF	Naga Peoples Front
Nagaland	Nagaland	AKHEI ACHUMI	9695	no	SP(I)	Socialist Party (India)
Nagaland	Nagaland	None of the Above	2696	no	NOTA	None of the Above
Nagaland	Nagaland	K. V. PUSA	313147	no	INC	Indian National Congress
Odisha	Bargarh	PRABHAS KUMAR SINGH	383230	yes	BJD	Biju Janata Dal
Odisha	Bargarh	LINGARAJ PRADHAN	15672	no	AAAP	Aam Aadmi Party
Odisha	Bargarh	SUBASH CHOUHAN	372052	no	BJP	Bharatiya Janata Party
Odisha	Bargarh	ASHOK BISI	21100	no	CPI	Communist Party of India
Odisha	Bargarh	KULAMANI URMA	26216	no	PCHVP	Paschimanchal Vikas Party
Odisha	Bargarh	ASHOK MITTAL	7912	no	AITC	All India Trinamool Congress
Odisha	Bargarh	GAYAN CHAND	8009	no	BSP	Bahujan Samaj Party
Odisha	Bargarh	SANJAY BHOI	274610	no	INC	Indian National Congress
Odisha	Bargarh	None of the Above	14500	no	NOTA	None of the Above
Odisha	Sundargarh	JUAL @ JUEL ORAM	340508	yes	BJP	Bharatiya Janata Party
Odisha	Sundargarh	None of the Above	15835	no	NOTA	None of the Above
Odisha	Sundargarh	DILIP KUMAR TIRKEY	321679	no	BJD	Biju Janata Dal
Odisha	Sundargarh	SAGAR SINGH MANAKI	8448	no	KOKD	Kosal Kranti Dal
Odisha	Sundargarh	JUSTIN LUGUN	9472	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Odisha	Sundargarh	MANSID EKKA	7282	no	IND	Independent
Odisha	Sundargarh	BASIL EKKA	7429	no	AAAP	Aam Aadmi Party
Odisha	Sundargarh	LAWRENCIA LAKRA	9001	no	IND	Independent
Odisha	Sundargarh	LILY PREMA KUJUR	3903	no	BMUP	Bahujan Mukti Party
Odisha	Sundargarh	SATISH KUMAR MINZ	3951	no	IND	Independent
Odisha	Sundargarh	HEMANAND BISWAL	269335	no	INC	Indian National Congress
Odisha	Sundargarh	DAS CHARAN MAJHI	4408	no	JDP	Jharkhand Disom Party
Odisha	Sundargarh	BAGI LAKRA	9460	no	BSP	Bahujan Samaj Party
Odisha	Sambalpur	NAGENDRA KUMAR PRADHAN	358618	yes	BJD	Biju Janata Dal
Odisha	Sambalpur	SHANKARLAL AGRAWALA	3110	no	PBI	Proutist Bloc, India
Odisha	Sambalpur	TARANI BHOI	2559	no	PCHVP	Paschimanchal Vikas Party
Odisha	Sambalpur	NATHU RAM	12211	no	BSP	Bahujan Samaj Party
Odisha	Sambalpur	MD. ALI HUSSAIN	4763	no	IND	Independent
Odisha	Sambalpur	ASHOK PANDA	4018	no	IND	Independent
Odisha	Sambalpur	SURESH PUJARI	328042	no	BJP	Bharatiya Janata Party
Odisha	Sambalpur	AMARNATH PRADHAN	242131	no	INC	Indian National Congress
Odisha	Sambalpur	ANANDA KUMAR SAMAL	7029	no	AAAP	Aam Aadmi Party
Odisha	Sambalpur	DUSMANTA KAR	4012	no	IND	Independent
Odisha	Sambalpur	PRASADI PRADHAN	4502	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Odisha	Sambalpur	None of the Above	13314	no	NOTA	None of the Above
Odisha	Keonjhar	SAKUNTALA LAGURI	434471	yes	BJD	Biju Janata Dal
Odisha	Keonjhar	SUDARSHAN LOHAR	16299	no	RAIM	Rashtriya Indepndent Morcha
Odisha	Keonjhar	SUJIT KUMAR LUGUN	16420	no	IND	Independent
Odisha	Keonjhar	BISHNUPRIYA NAIK	12236	no	ojm	Odisha Jan Morcha
Odisha	Keonjhar	JAGAT MOHAN NAIK	8694	no	BSP	Bahujan Samaj Party
Odisha	Keonjhar	BASUDEB NAIK	101999	no	IND	Independent
Odisha	Keonjhar	ANANTA NAYAK	277154	no	BJP	Bharatiya Janata Party
Odisha	Keonjhar	MADHABA SARDAR	190531	no	INC	Indian National Congress
Odisha	Keonjhar	None of the Above	26065	no	NOTA	None of the Above
Odisha	Mayurbhanj	RAMA CHANDRA HANSDAH	393779	yes	BJD	Biju Janata Dal
Odisha	Mayurbhanj	None of the Above	23517	no	NOTA	None of the Above
Odisha	Mayurbhanj	DEVI PRASANNA BESRA	8133	no	IND	Independent
Odisha	Mayurbhanj	NEPOLE RAGHU MURMU	270913	no	BJP	Bharatiya Janata Party
Odisha	Mayurbhanj	ANTA ALOK BASKEY	7052	no	AJSUP	AJSU Party
Odisha	Mayurbhanj	DASHMAT SOREN	12041	no	BSP	Bahujan Samaj Party
Odisha	Mayurbhanj	DEBASHIS MARNDI	172984	no	JMM	Jharkhand Mukti Morcha
Odisha	Mayurbhanj	CHHABI MAHALI	8183	no	AAAP	Aam Aadmi Party
Odisha	Mayurbhanj	PRABHUDAN MARANDI	14632	no	AOP	Aama Odisha Party
Odisha	Mayurbhanj	SHYAM SUNDAR HANSDAH	142165	no	INC	Indian National Congress
Odisha	Balasore	RABINDRA KUMAR JENA	433768	yes	BJD	Biju Janata Dal
Odisha	Balasore	BICHITRA NANDA DAS	3240	no	BSP	Bahujan Samaj Party
Odisha	Balasore	SHRIKANTA KUMAR JENA	277517	no	INC	Indian National Congress
Odisha	Balasore	PRATAP CHANDRA SARANGI	291943	no	BJP	Bharatiya Janata Party
Odisha	Balasore	KARTIKESWAR PATRA	9856	no	AITC	All India Trinamool Congress
Odisha	Balasore	BISWAJIT RAY	1823	no	IND	Independent
Odisha	Balasore	JAGANNATH DAS	1856	no	IND	Independent
Odisha	Balasore	None of the Above	7716	no	NOTA	None of the Above
Odisha	Balasore	PRASANTA KUMAR MISHRA	14538	no	CPI	Communist Party of India
Odisha	Balasore	MAMATA KUNDU	2664	no	AAAP	Aam Aadmi Party
Odisha	Balasore	SAROJ KUMAR PANDA	4529	no	IND	Independent
Odisha	Bhadrak	ARJUN CHARAN SETHI	502338	yes	BJD	Biju Janata Dal
Odisha	Bhadrak	None of the Above	6750	no	NOTA	None of the Above
Odisha	Bhadrak	SUSANTA KUMAR JENA	2639	no	IND	Independent
Odisha	Bhadrak	ARJUN CHARAN MALLIK	7607	no	BSP	Bahujan Samaj Party
Odisha	Bhadrak	SARAT DAS	216617	no	BJP	Bharatiya Janata Party
Odisha	Bhadrak	NARAYAN CHANDRA JENA	5961	no	AAAP	Aam Aadmi Party
Odisha	Bhadrak	SANGRAM KESHARI JENA	322979	no	INC	Indian National Congress
Odisha	Bhadrak	RAMESH JENA	11930	no	CPI	Communist Party of India
Odisha	Bhadrak	NETRANANDA MALLICK	4518	no	AOP	Aama Odisha Party
Odisha	Jajpur	RITA TARAI	541349	yes	BJD	Biju Janata Dal
Odisha	Jajpur	RANJIT MALIK	3409	no	BMUP	Bahujan Mukti Party
Odisha	Jajpur	NABIN KUMAR DALAI	29142	no	IND	Independent
Odisha	Jajpur	GAYADHAR MALLICK	2753	no	skd	Samata Kranti Dal
Odisha	Jajpur	DEBENDRA KUMAR MALLIK	6926	no	BSP	Bahujan Samaj Party
Odisha	Jajpur	AMIYA KANTA MALLICK	150789	no	BJP	Bharatiya Janata Party
Odisha	Jajpur	ASOK DAS	221078	no	INC	Indian National Congress
Odisha	Jajpur	None of the Above	8655	no	NOTA	None of the Above
Odisha	Jajpur	KALANDI MALIK	8269	no	AAAP	Aam Aadmi Party
Odisha	Jajpur	SUBAS CHANDRA MALLICK	8065	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Odisha	Dhenkanal	TATHAGATA SATPATHY	453277	yes	BJD	Biju Janata Dal
Odisha	Dhenkanal	JIMUTA PRASAD MISHRA	5120	no	AAAP	Aam Aadmi Party
Odisha	Dhenkanal	PRIYABRATA GARNAIK	2039	no	skd	Samata Kranti Dal
Odisha	Dhenkanal	KALAKAR NAIK	8712	no	APoI	Ambedkarite Party of India
Odisha	Dhenkanal	PREMANANDA SAHOO	6574	no	BSP	Bahujan Samaj Party
Odisha	Dhenkanal	NIRMAL SAMAL	7505	no	IND	Independent
Odisha	Dhenkanal	PRADYUMNA KUMAR NAIK	2298	no	BMUP	Bahujan Mukti Party
Odisha	Dhenkanal	SUDHIR KUMAR SAMAL	213794	no	INC	Indian National Congress
Odisha	Dhenkanal	SUPARNO SATPATHY	5447	no	AOP	Aama Odisha Party
Odisha	Dhenkanal	ER. GURU PRASAD PRADHAN	3918	no	IND	Independent
Odisha	Dhenkanal	RUDRA NARAYAN PANY	315937	no	BJP	Bharatiya Janata Party
Odisha	Dhenkanal	MANASI SWAIN	4275	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Odisha	Dhenkanal	None of the Above	13205	no	NOTA	None of the Above
Odisha	Bolangir	KALIKESH NARAYAN SINGH DEO	453519	yes	BJD	Biju Janata Dal
Odisha	Bolangir	None of the Above	21043	no	NOTA	None of the Above
Odisha	Bolangir	ACHUTANANDA NAG	14626	no	APoI	Ambedkarite Party of India
Odisha	Bolangir	NABA KRUSHNA DANTA	9915	no	IND	Independent
Odisha	Bolangir	SHANKAR KUMAR PATRA	8438	no	AOP	Aama Odisha Party
Odisha	Bolangir	SARASWATI NANDA	24515	no	AAAP	Aam Aadmi Party
Odisha	Bolangir	SARAT PATTNAYAK	277616	no	INC	Indian National Congress
Odisha	Bolangir	RANA NAG	11513	no	BSP	Bahujan Samaj Party
Odisha	Bolangir	SANGEETA KUMARI SINGH DEO	349220	no	BJP	Bharatiya Janata Party
Odisha	Kalahandi	ARKA KESHARI DEO	370871	yes	BJD	Biju Janata Dal
Odisha	Kalahandi	BIGHNA RAJ PANDA	8600	no	SAMO	Samruddha Odisha
Odisha	Kalahandi	PARAMESWAR KAND	4435	no	skd	Samata Kranti Dal
Odisha	Kalahandi	SATYA NARAYAN MAHAR	6360	no	AAAP	Aam Aadmi Party
Odisha	Kalahandi	SUNIL CHANDRA NAYAK	7329	no	ojm	Odisha Jan Morcha
Odisha	Kalahandi	PRADIPTA KUMAR NAIK	314524	no	BJP	Bharatiya Janata Party
Odisha	Kalahandi	BHAKTA CHARAN DAS	307967	no	INC	Indian National Congress
Odisha	Kalahandi	BAIKUNTHA DHANGADA MAJHI	5007	no	KS	Kalinga Sena
Odisha	Kalahandi	SIBA HATI	18869	no	SP	Samajwadi Party
Odisha	Kalahandi	SAHADEV JAL	5858	no	BMUP	Bahujan Mukti Party
Odisha	Kalahandi	SAROJ KUMAR NAIK	14115	no	BSP	Bahujan Samaj Party
Odisha	Kalahandi	RAJENDRA KUMAR MUND	10839	no	IND	Independent
Odisha	Kalahandi	DAITARY PRADHAN	8698	no	AOP	Aama Odisha Party
Odisha	Kalahandi	BALARAM HOTA	4885	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Odisha	Kalahandi	CHHABILAL NIAL	8299	no	APoI	Ambedkarite Party of India
Odisha	Kalahandi	None of the Above	20880	no	NOTA	None of the Above
Odisha	Nabarangpur	BALABHADRA MAJHI	373887	yes	BJD	Biju Janata Dal
Odisha	Nabarangpur	GOPAL PUJARI	18675	no	AOP	Aama Odisha Party
Odisha	Nabarangpur	ARJUNA BHATARA	24832	no	ojm	Odisha Jan Morcha
Odisha	Nabarangpur	PRADEEP KUMAR MAJHI	371845	no	INC	Indian National Congress
Odisha	Nabarangpur	CHANDRADHWAJA MAJHI	18388	no	IND	Independent
Odisha	Nabarangpur	PARSURAM MAJHI	138430	no	BJP	Bharatiya Janata Party
Odisha	Nabarangpur	GOPINATH JANI	31707	no	BSP	Bahujan Samaj Party
Odisha	Nabarangpur	None of the Above	44408	no	NOTA	None of the Above
Odisha	Kandhamal	HEMENDRA CHANDRA SINGH	421458	yes	BJD	Biju Janata Dal
Odisha	Kandhamal	NARENDRA MOHANTY	9522	no	AAAP	Aam Aadmi Party
Odisha	Kandhamal	HARIHAR KARAN	240441	no	INC	Indian National Congress
Odisha	Kandhamal	SUKANTA KUMAR PANIGRAHI	108744	no	BJP	Bharatiya Janata Party
Odisha	Kandhamal	LAMBODAR KANHAR	20472	no	IND	Independent
Odisha	Kandhamal	RAM NAYAK	11080	no	BSP	Bahujan Samaj Party
Odisha	Kandhamal	BILASINI NAYAK	7741	no	AOP	Aama Odisha Party
Odisha	Kandhamal	BASANTA KUMAR PRADHAN	5798	no	IND	Independent
Odisha	Kandhamal	None of the Above	14159	no	NOTA	None of the Above
Odisha	Cuttack	BHARTRUHARI MAHATAB	526085	yes	BJD	Biju Janata Dal
Odisha	Cuttack	SANJAY KUMAR SAHOO	5733	no	IND	Independent
Odisha	Cuttack	PRAMOD KUMAR MALLICK	3639	no	BSP	Bahujan Samaj Party
Odisha	Cuttack	JIBESH KISHORE SUNDARAY	2082	no	AITC	All India Trinamool Congress
Odisha	Cuttack	MAHAMEGHABAHAN AIRA KHARABELA SWAIN	58246	no	AOP	Aama Odisha Party
Odisha	Cuttack	BIKASH DAS	4531	no	AAAP	Aam Aadmi Party
Odisha	Cuttack	SAMIR DEY	146093	no	BJP	Bharatiya Janata Party
Odisha	Cuttack	SABYASACHI MAHAPATRA	1812	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Odisha	Cuttack	APARAJITA  MOHANTY	219323	no	INC	Indian National Congress
Odisha	Cuttack	BINAPANI DAS	2171	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Odisha	Cuttack	None of the Above	8889	no	NOTA	None of the Above
Odisha	Kendrapara	BAIJAYANT PANDA	601574	yes	BJD	Biju Janata Dal
Odisha	Kendrapara	SUKUMAR MALLICK	2221	no	IND	Independent
Odisha	Kendrapara	SK. FARUK	6199	no	BSP	Bahujan Samaj Party
Odisha	Kendrapara	DHARANIDHAR NAYAK	392466	no	INC	Indian National Congress
Odisha	Kendrapara	MD.SAMIM AKTAR	6029	no	AAAP	Aam Aadmi Party
Odisha	Kendrapara	BISHNU PRASAD DAS	118707	no	BJP	Bharatiya Janata Party
Odisha	Kendrapara	PRAMOD SAHU	6193	no	IND	Independent
Odisha	Kendrapara	None of the Above	7610	no	NOTA	None of the Above
Odisha	Jagatsinghpur	KULAMANI SAMAL	624492	yes	BJD	Biju Janata Dal
Odisha	Jagatsinghpur	None of the Above	7624	no	NOTA	None of the Above
Odisha	Jagatsinghpur	PRADIP KUMAR MALLICK	6895	no	BSP	Bahujan Samaj Party
Odisha	Jagatsinghpur	BIBHU PRASAD TARAI	348098	no	INC	Indian National Congress
Odisha	Jagatsinghpur	BAIDHAR MALLICK	117448	no	BJP	Bharatiya Janata Party
Odisha	Jagatsinghpur	AJAY KUMAR BEHERA	18099	no	CPI	Communist Party of India
Odisha	Jagatsinghpur	MANAS KUMAR BEHERA	3347	no	IND	Independent
Odisha	Jagatsinghpur	ANUPAMA SETHY	5929	no	AAAP	Aam Aadmi Party
Odisha	Puri	PINAKI MISRA	523161	yes	BJD	Biju Janata Dal
Odisha	Puri	ASHOK PRADHAN	6969	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Odisha	Puri	NIRAD BARAN KHUNTIA	4262	no	AAAP	Aam Aadmi Party
Odisha	Puri	BHASKAR CHANDRA MOHANTY	7094	no	BSP	Bahujan Samaj Party
Odisha	Puri	SUCHARITA MOHANTY	259800	no	INC	Indian National Congress
Odisha	Puri	SABYASACHI MOHAPATRA	13190	no	KS	Kalinga Sena
Odisha	Puri	ASHOK SAHU	215763	no	BJP	Bharatiya Janata Party
Odisha	Puri	None of the Above	9150	no	NOTA	None of the Above
Odisha	Bhubaneswar	PRASANNA KUMAR PATASANI	439252	yes	BJD	Biju Janata Dal
Odisha	Bhubaneswar	None of the Above	8140	no	NOTA	None of the Above
Odisha	Bhubaneswar	BIJAY MOHANTY	145783	no	INC	Indian National Congress
Odisha	Bhubaneswar	MANAS RANJAN MISHRA	3783	no	IND	Independent
Odisha	Bhubaneswar	PRITHIVIRAJ HARICHANDAN	249775	no	BJP	Bharatiya Janata Party
Odisha	Bhubaneswar	KULAMANI NAYAK	1128	no	PBI	Proutist Bloc, India
Odisha	Bhubaneswar	RABINDRA NATH BEHERA	1745	no	SP	Samajwadi Party
Odisha	Bhubaneswar	BISMAYA MAHAPATRA	7112	no	AAAP	Aam Aadmi Party
Odisha	Bhubaneswar	MADHU SUDAN YADAV	3457	no	BSP	Bahujan Samaj Party
Odisha	Bhubaneswar	SUNJOY HANS	29505	no	AOP	Aama Odisha Party
Odisha	Bhubaneswar	PRANA KRUSHNA DALABEHERA	1194	no	SAMO	Samruddha Odisha
Odisha	Bhubaneswar	PRAMILA BEHERA	948	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Odisha	Aska	LADU KISHORE SWAIN	541473	yes	BJD	Biju Janata Dal
Odisha	Aska	SRILOKANATH RATHA	229476	no	INC	Indian National Congress
Odisha	Aska	KALI CHARANA NAYAK	5192	no	IND	Independent
Odisha	Aska	BIJAYA KUMAR MAHAPATRO	9127	no	BSP	Bahujan Samaj Party
Odisha	Aska	BANSIDHAR TRIPATHY	7553	no	AITC	All India Trinamool Congress
Odisha	Aska	MAHESH CHANDRA MOHANTY	67361	no	BJP	Bharatiya Janata Party
Odisha	Aska	PRABHAT KUMAR MAHANTY	11063	no	AAAP	Aam Aadmi Party
Odisha	Aska	K. SHYAMBABU SUBUDHI	5293	no	IND	Independent
Odisha	Aska	SANKAR SAHU	4361	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Odisha	Aska	None of the Above	15382	no	NOTA	None of the Above
Odisha	Berhampur	SIDHANT MOHAPATRA	398107	yes	BJD	Biju Janata Dal
Odisha	Berhampur	DHANADA KANTA MISHRA	8248	no	AAAP	Aam Aadmi Party
Odisha	Berhampur	K. SHYAMBABU SUBUDHI	3383	no	IND	Independent
Odisha	Berhampur	ALI KISHOR PATNAIK	35968	no	CPM	Communist Party of India  (Marxist)
Odisha	Berhampur	SRIHARI PATANAIK	4081	no	IND	Independent
Odisha	Berhampur	A. RAGHUNATH  VARMA	3719	no	IND	Independent
Odisha	Berhampur	RAMA CHANDRA PANDA	158811	no	BJP	Bharatiya Janata Party
Odisha	Berhampur	MANOJ CHOUDHURY	3314	no	AITC	All India Trinamool Congress
Odisha	Berhampur	CHANDRA SEKHAR SAHU	270387	no	INC	Indian National Congress
Odisha	Berhampur	BIDHAN CHANDRA PANDA	2773	no	skd	Samata Kranti Dal
Odisha	Berhampur	ASWINI KUMAR PADHY	3865	no	BSP	Bahujan Samaj Party
Odisha	Berhampur	None of the Above	12706	no	NOTA	None of the Above
Odisha	Koraput	JHINA HIKAKA	395109	yes	BJD	Biju Janata Dal
Odisha	Koraput	PRAKASH HIKAKA	9609	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Odisha	Koraput	GIRIDHAR GAMANG	375781	no	INC	Indian National Congress
Odisha	Koraput	SIBASANKAR ULAKA	89788	no	BJP	Bharatiya Janata Party
Odisha	Koraput	LINGARAJ GOMANGO	13861	no	IND	Independent
Odisha	Koraput	MEGHANADA SABAR	13343	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Odisha	Koraput	KAILASH SABARA	8910	no	skd	Samata Kranti Dal
Odisha	Koraput	PRASKA RENGA	34957	no	BSP	Bahujan Samaj Party
Odisha	Koraput	BANAMALI MAJHI	14854	no	APoI	Ambedkarite Party of India
Odisha	Koraput	None of the Above	33232	no	NOTA	None of the Above
Punjab	Gurdaspur	VINOD KHANNA	482255	yes	BJP	Bharatiya Janata Party
Punjab	Gurdaspur	None of the Above	4625	no	NOTA	None of the Above
Punjab	Gurdaspur	ROHIT MAINGI	4057	no	IND	Independent
Punjab	Gurdaspur	SIKANDER SINGH	2124	no	IND	Independent
Punjab	Gurdaspur	GURPREET SINGH KHANNA	1785	no	BGTD	Bharatiya Gaon Taj Dal
Punjab	Gurdaspur	MUKESH	1649	no	IND	Independent
Punjab	Gurdaspur	SANTOSH KUMARI	3645	no	MEDP	Megh Desham Party
Punjab	Gurdaspur	AMIT AGGARWAL	1195	no	IND	Independent
Punjab	Gurdaspur	VARINDER SINGH	11839	no	CPI	Communist Party of India
Punjab	Gurdaspur	SUCHA SINGH CHHOTEPUR	173376	no	AAAP	Aam Aadmi Party
Punjab	Gurdaspur	GURMEET SINGH BAKHATPURA	2875	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Punjab	Gurdaspur	SUKHWINDER SINGH	5621	no	BSP	Bahujan Samaj Party
Punjab	Gurdaspur	PETER MASIH	1463	no	SSPD	SARVAJAN SAMAJ PARTY (D)
Punjab	Gurdaspur	PARTAP SINGH BAJWA	346190	no	INC	Indian National Congress
Punjab	Amritsar	CAPTAIN AMARINDER SINGH	482876	yes	INC	Indian National Congress
Punjab	Amritsar	BHAGWANT SINGH	2303	no	IND	Independent
Punjab	Amritsar	PARDEEP SINGH WALIA	5870	no	BSP	Bahujan Samaj Party
Punjab	Amritsar	DR. DALJIT SINGH	82633	no	AAAP	Aam Aadmi Party
Punjab	Amritsar	SHAM LAL GANDHIWADI	2691	no	IND	Independent
Punjab	Amritsar	ARUN JAITLEY	380106	no	BJP	Bharatiya Janata Party
Punjab	Amritsar	YUSAF MOHAMMAD	922	no	SP	Samajwadi Party
Punjab	Amritsar	REHMAT MASIH	1214	no	IND	Independent
Punjab	Amritsar	RATTAN SINGH RANDHAWA	4350	no	IND	Independent
Punjab	Amritsar	BAL KRISHAN SHARMA	2878	no	IND	Independent
Punjab	Amritsar	BUTA SINGH	941	no	BSP(A)	Bahujan Samaj Party  (Ambedkar)
Punjab	Amritsar	KANWALJIT SINGH SAHOTA	2123	no	IND	Independent
Punjab	Amritsar	BALBIR SINGH	827	no	BGTD	Bharatiya Gaon Taj Dal
Punjab	Amritsar	AMARJIT SINGH ASAL	12902	no	CPI	Communist Party of India
Punjab	Amritsar	GAGANDEEP KUMAR	3294	no	IND	Independent
Punjab	Amritsar	SURINDER KUMAR KHOSLA	2396	no	IND	Independent
Punjab	Amritsar	MOHINDER SINGH	1140	no	IND	Independent
Punjab	Amritsar	KRISHAN SHARMA	503	no	NBDP	Nav Bharat Democratic Party
Punjab	Amritsar	DR. INDERPAL	3083	no	IND	Independent
Punjab	Amritsar	AMARINDER SINGH	1151	no	IND	Independent
Punjab	Amritsar	GURDIAL SINGH GILL	604	no	DBSP	Democratic Bharatiya Samaj Party
Punjab	Amritsar	ARUN KUMAR JOSHI	9023	no	IND	Independent
Punjab	Amritsar	SURINDER SINGH	833	no	DCP	Democratic Congress Party
Punjab	Amritsar	None of the Above	2533	no	NOTA	None of the Above
Punjab	Khadoor Sahib	RANJIT SINGH BRAHMPURA	467332	yes	SAD	Shiromani Akali Dal
Punjab	Khadoor Sahib	None of the Above	5624	no	NOTA	None of the Above
Punjab	Khadoor Sahib	GURPAL SINGH	1182	no	aimpr	All India Mazdoor Party (Rangreta)
Punjab	Khadoor Sahib	KAWALJIT SINGH	962	no	IND	Independent
Punjab	Khadoor Sahib	BALDEEP SINGH	144521	no	AAAP	Aam Aadmi Party
Punjab	Khadoor Sahib	HARMINDER SINGH GILL	366763	no	INC	Indian National Congress
Punjab	Khadoor Sahib	SUKHWANT SINGH	1053	no	IND	Independent
Punjab	Khadoor Sahib	STEPHEN MASIH	4329	no	SSPD	SARVAJAN SAMAJ PARTY (D)
Punjab	Khadoor Sahib	SUKHDEV SINGH CHOHAN	801	no	IND	Independent
Punjab	Khadoor Sahib	BHUPINDER SINGH	3282	no	IND	Independent
Punjab	Khadoor Sahib	BALWANT SINGH SULTANPURI	2930	no	BSP(A)	Bahujan Samaj Party  (Ambedkar)
Punjab	Khadoor Sahib	BALWINDER SINGH	3804	no	IND	Independent
Punjab	Khadoor Sahib	GURNAM SINGH	9307	no	IND	Independent
Punjab	Khadoor Sahib	SIMRANJIT SINGH MANN	13990	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Khadoor Sahib	MAHINDER SINGH HAMIRA	1720	no	IND	Independent
Punjab	Khadoor Sahib	SUCHA SINGH MANN	8491	no	BSP	Bahujan Samaj Party
Punjab	Khadoor Sahib	SUKHCHAIN CHAND	1313	no	DBSP	Democratic Bharatiya Samaj Party
Punjab	Khadoor Sahib	JASBIR SINGH	3114	no	IND	Independent
Punjab	Jalandhar	SANTOKH SINGH CHAUDHARY	380479	yes	INC	Indian National Congress
Punjab	Jalandhar	JAGAN NATH BAJWA	1083	no	IND	Independent
Punjab	Jalandhar	PAWAN KUMAR TINU	309498	no	SAD	Shiromani Akali Dal
Punjab	Jalandhar	MANJIT KAUR W/O SUKHWINDER SINGH	1044	no	IND	Independent
Punjab	Jalandhar	VIJAY HANS	1192	no	DBSP	Democratic Bharatiya Samaj Party
Punjab	Jalandhar	MANJEET KAUR W/O SUKHDEV SINGH	1952	no	IND	Independent
Punjab	Jalandhar	LOVE KISHOR	3265	no	IND	Independent
Punjab	Jalandhar	TARSEM PETER	6249	no	IND	Independent
Punjab	Jalandhar	SUKHWINDER SINGH KOTLI	46914	no	BSP	Bahujan Samaj Party
Punjab	Jalandhar	SUBHASH GORIA	3497	no	SHS	Shivsena
Punjab	Jalandhar	PARAMJIT KUMAR	717	no	BMUP	Bahujan Mukti Party
Punjab	Jalandhar	DARSHAN NAHAR	10074	no	IND	Independent
Punjab	Jalandhar	VIJAY KUMAR	1528	no	IND	Independent
Punjab	Jalandhar	JYOTI MANN	254121	no	AAAP	Aam Aadmi Party
Punjab	Jalandhar	TARA SINGH GILL	2268	no	BSP(A)	Bahujan Samaj Party  (Ambedkar)
Punjab	Jalandhar	KAMAL THAPAR	933	no	IND	Independent
Punjab	Jalandhar	NIRMAL SINGH BOLINA	2256	no	IND	Independent
Punjab	Jalandhar	KULDIP KUMAR	953	no	IND	Independent
Punjab	Jalandhar	DHARMINDER	2459	no	IND	Independent
Punjab	Jalandhar	VIVEK	741	no	IND	Independent
Punjab	Jalandhar	AMIT JASSI	779	no	IND	Independent
Punjab	Jalandhar	K.K. SABHARWAL	1454	no	IND	Independent
Punjab	Jalandhar	AJAY KUMAR BHAGAT	692	no	IND	Independent
Punjab	Jalandhar	JARNAIL SINGH	1178	no	BhJaSP	Bharti Jan Suraksha Party
Punjab	Jalandhar	None of the Above	5436	no	NOTA	None of the Above
Punjab	Hoshiarpur	VIJAY SAMPLA	346643	yes	BJP	Bharatiya Janata Party
Punjab	Hoshiarpur	None of the Above	5976	no	NOTA	None of the Above
Punjab	Hoshiarpur	DEEPAK KUMAR	1446	no	BMUP	Bahujan Mukti Party
Punjab	Hoshiarpur	JASWINDER SINGH	1230	no	SHS	Shivsena
Punjab	Hoshiarpur	RAVI DUTT	2564	no	IND	Independent
Punjab	Hoshiarpur	BISHAN DASS	1853	no	IND	Independent
Punjab	Hoshiarpur	RAM KISHAN	1689	no	IND	Independent
Punjab	Hoshiarpur	VIJAY KUMAR	889	no	SSPD	SARVAJAN SAMAJ PARTY (D)
Punjab	Hoshiarpur	PAWAN KUMAR	3216	no	IND	Independent
Punjab	Hoshiarpur	MOHINDER SINGH KAYPEE	333061	no	INC	Indian National Congress
Punjab	Hoshiarpur	BHAGWAN SINGH CHAUHAN	40497	no	BSP	Bahujan Samaj Party
Punjab	Hoshiarpur	ANU KUMAR	911	no	IND	Independent
Punjab	Hoshiarpur	DAVINDER SINGH	1488	no	IND	Independent
Punjab	Hoshiarpur	ANOOP SINGH	2158	no	BhJaSP	Bharti Jan Suraksha Party
Punjab	Hoshiarpur	YAMINI GOMAR	213388	no	AAAP	Aam Aadmi Party
Punjab	Hoshiarpur	SHAMSHER SINGH	2527	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Hoshiarpur	LAKHVIR SINGH	1062	no	DBSP	Democratic Bharatiya Samaj Party
Punjab	Hoshiarpur	OM PARKASH JAKHU	699	no	IND	Independent
Punjab	Anandpur Sahib	PREM SINGH CHANDUMAJRA	347394	yes	SAD	Shiromani Akali Dal
Punjab	Anandpur Sahib	DAVINDER KUMAR VERMA	1995	no	IND	Independent
Punjab	Anandpur Sahib	HIMMAT SINGH SHERGILL	306008	no	AAAP	Aam Aadmi Party
Punjab	Anandpur Sahib	K.S.MAKHAN	69124	no	BSP	Bahujan Samaj Party
Punjab	Anandpur Sahib	BALBIR SINGH JADLA	10483	no	CPM	Communist Party of India  (Marxist)
Punjab	Anandpur Sahib	FAQIR CHAND KARIHA	3869	no	IND	Independent
Punjab	Anandpur Sahib	JASWANT SINGH MANN	1706	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Anandpur Sahib	AMBIKA SONI	323697	no	INC	Indian National Congress
Punjab	Anandpur Sahib	DALJIT SINGH	2713	no	IND	Independent
Punjab	Anandpur Sahib	HARCHARAN SINGH	1423	no	IND	Independent
Punjab	Anandpur Sahib	BALJIT KAUR	4250	no	IND	Independent
Punjab	Anandpur Sahib	GURSHARN SINGH PANESAR	1943	no	IND	Independent
Punjab	Anandpur Sahib	NAVNEET CHAUTALA	1307	no	IND	Independent
Punjab	Anandpur Sahib	GURMEET SINGH	1010	no	IND	Independent
Punjab	Anandpur Sahib	GURNAM SINGH	722	no	IND	Independent
Punjab	Anandpur Sahib	RESHAM KAHLON	837	no	BA S D	Bahujan Sangharshh Dal
Punjab	Anandpur Sahib	MANDEEP SINGH BITTU	1427	no	IND	Independent
Punjab	Anandpur Sahib	RAMESH RANI PARJAPATI	718	no	BMUP	Bahujan Mukti Party
Punjab	Anandpur Sahib	None of the Above	5937	no	NOTA	None of the Above
Punjab	Ludhiana	RAVNEET SINGH BITTU	300459	yes	INC	Indian National Congress
Punjab	Ludhiana	None of the Above	3220	no	NOTA	None of the Above
Punjab	Ludhiana	MANPREET SINGH AYALI	256590	no	SAD	Shiromani Akali Dal
Punjab	Ludhiana	SIMARJEET SINGH BAINS	210917	no	IND	Independent
Punjab	Ludhiana	NAVPREET SINGH BEDI	3144	no	IND	Independent
Punjab	Ludhiana	HARVINDER SINGH PHOOLKA	280750	no	AAAP	Aam Aadmi Party
Punjab	Ludhiana	AKSHAYBAR NATH MISHRA	2925	no	JD(U)	Janata Dal  (United)
Punjab	Ludhiana	MOHAMMAD MAKSOOD ANSARI	1111	no	BMUP	Bahujan Mukti Party
Punjab	Ludhiana	KISHAN KUMAR SHARMA	1483	no	JKNPP	Jammu & Kashmir National Panthers Party
Punjab	Ludhiana	DARSHAN SINGH	1028	no	SHS	Shivsena
Punjab	Ludhiana	HARI RAM SAHNI	1168	no	AIFB	All India Forward Bloc
Punjab	Ludhiana	TARSEM SINGH BANGA	1934	no	IND	Independent
Punjab	Ludhiana	BITTU	1286	no	RPI(A)	Republican Party of India (A)
Punjab	Ludhiana	RAJIV KUMAR KALRA	4653	no	IND	Independent
Punjab	Ludhiana	GURDEEP SINGH KAHLON	4509	no	IND	Independent
Punjab	Ludhiana	JASWINDER SINGH BAINS	3238	no	IND	Independent
Punjab	Ludhiana	SIMERJIT SINGH	3980	no	IND	Independent
Punjab	Ludhiana	RAJ KUMAR ATWAL	1089	no	IND	Independent
Punjab	Ludhiana	MOHD. NASEEM ANSARI	1984	no	aicp	New All India Congress Party
Punjab	Ludhiana	SUKHWINDER SINGH SEKHON	4167	no	CPM	Communist Party of India  (Marxist)
Punjab	Ludhiana	KULWINDER PAL SINGH	986	no	IND	Independent
Punjab	Ludhiana	NAVJOT SINGH MANDAIR	8317	no	BSP	Bahujan Samaj Party
Punjab	Ludhiana	JAI PARKASH JAIN	1519	no	IND	Independent
Punjab	Fatehgarh Sahib	HARINDER SINGH KHALSA	367293	yes	AAAP	Aam Aadmi Party
Punjab	Fatehgarh Sahib	PARAMJIT SINGH	1266	no	IND	Independent
Punjab	Fatehgarh Sahib	SARABJEET SINGH	12683	no	BSP	Bahujan Samaj Party
Punjab	Fatehgarh Sahib	GURDEEP SINGH	1930	no	IVD	Inqalab Vikas Dal
Punjab	Fatehgarh Sahib	AVTAR SINGH	848	no	IND	Independent
Punjab	Fatehgarh Sahib	RANJIT SINGH	1679	no	IND	Independent
Punjab	Fatehgarh Sahib	LACHHMAN SINGH S/O S.GULZAR SINGH	2141	no	IND	Independent
Punjab	Fatehgarh Sahib	NIRMAL SINGH	2149	no	NCP	Nationalist Congress Party
Punjab	Fatehgarh Sahib	DHARAM SINGH	3173	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Fatehgarh Sahib	SADHU SINGH	313149	no	INC	Indian National Congress
Punjab	Fatehgarh Sahib	KULWANT SINGH	312815	no	SAD	Shiromani Akali Dal
Punjab	Fatehgarh Sahib	LAKHVIR SINGH	1733	no	JKNPP	Jammu & Kashmir National Panthers Party
Punjab	Fatehgarh Sahib	MANPREET SINGH	886	no	IND	Independent
Punjab	Fatehgarh Sahib	LACHHMAN SINGH S/O S.SAROOP SINGH	2852	no	IND	Independent
Punjab	Fatehgarh Sahib	HARPREET SINGH	2352	no	BMUP	Bahujan Mukti Party
Punjab	Fatehgarh Sahib	None of the Above	4005	no	NOTA	None of the Above
Punjab	Faridkot	PROF. SADHU SINGH	450751	yes	AAAP	Aam Aadmi Party
Punjab	Faridkot	None of the Above	3816	no	NOTA	None of the Above
Punjab	Faridkot	PARAMJEET KAUR W/O GURPREET SINGH	1623	no	IND	Independent
Punjab	Faridkot	GOBIND SINGH	865	no	RPI(A)	Republican Party of India (A)
Punjab	Faridkot	JASVEER SINGH	1693	no	IND	Independent
Punjab	Faridkot	JOGINDER SINGH	251222	no	INC	Indian National Congress
Punjab	Faridkot	REENA KUMARI	2473	no	IND	Independent
Punjab	Faridkot	PARAMJIT KAUR GULSHAN	278235	no	SAD	Shiromani Akali Dal
Punjab	Faridkot	DARSHAN SINGH	1605	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Faridkot	JOGINDER SINGH S/O KESAR SINGH	2667	no	IND	Independent
Punjab	Faridkot	SEEMA RANI	1726	no	IND	Independent
Punjab	Faridkot	SATPAL SINGH	3680	no	IKL	Indian Krantikari Lehar
Punjab	Faridkot	PARAMJIT KAUR W/O AMRIK SINGH	1159	no	BMUP	Bahujan Mukti Party
Punjab	Faridkot	MOTI LAL	1512	no	BhSMP	Bharatiya Sant Mat Party
Punjab	Faridkot	MANGAT RAM MANGA	2213	no	SHS	Shivsena
Punjab	Faridkot	JARNAIL SINGH	1358	no	NBDP	Nav Bharat Democratic Party
Punjab	Faridkot	SANT RAM	8282	no	BSP	Bahujan Samaj Party
Punjab	Faridkot	GURCHARAN SINGH	1531	no	RPI	Republican Party of India
Punjab	Faridkot	KASHMIR SINGH	14573	no	CPI	Communist Party of India
Punjab	Faridkot	BADAL SINGH	1123	no	IND	Independent
Punjab	Firozpur	SHER SINGH GHUBAYA	487932	yes	SAD	Shiromani Akali Dal
Punjab	Firozpur	RAJINDER BHATHEJA	1546	no	IND	Independent
Punjab	Firozpur	SUNIL JAKHAR	456512	no	INC	Indian National Congress
Punjab	Firozpur	SWARAN SINGH	766	no	IND	Independent
Punjab	Firozpur	BALJINDER SINGH	681	no	BGTD	Bharatiya Gaon Taj Dal
Punjab	Firozpur	MANOJ KUMAR MONGA	1896	no	IND	Independent
Punjab	Firozpur	RAJ GILL	3311	no	IND	Independent
Punjab	Firozpur	CHANAN SINGH WATTU	1237	no	IND	Independent
Punjab	Firozpur	SATNAM PAUL KAMBOJ	113412	no	AAAP	Aam Aadmi Party
Punjab	Firozpur	PARAMJEET SINGH DESAL	976	no	IND	Independent
Punjab	Firozpur	DHIAN SINGH MAND	3655	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Firozpur	GURJANT SINGH	1088	no	IND	Independent
Punjab	Firozpur	PANJAB SINGH KATWAL	1759	no	IND	Independent
Punjab	Firozpur	SAMPURAN SINGH	682	no	IND	Independent
Punjab	Firozpur	RAM KUMAR PARJAPAT	22274	no	BSP	Bahujan Samaj Party
Punjab	Firozpur	None of the Above	7685	no	NOTA	None of the Above
Punjab	Bathinda	HARSIMRAT KAUR BADAL	514727	yes	SAD	Shiromani Akali Dal
Punjab	Bathinda	BHUPESH KUMAR	1487	no	IND	Independent
Punjab	Bathinda	SHAMINDER SINGH	4610	no	JKNPP	Jammu & Kashmir National Panthers Party
Punjab	Bathinda	NAVNEET	1893	no	SHS	Shivsena
Punjab	Bathinda	GURDEEP SINGH	1404	no	IKL	Indian Krantikari Lehar
Punjab	Bathinda	KARTAR SINGH	916	no	IND	Independent
Punjab	Bathinda	GEETA RANI	4380	no	ABSR	Akhil Bharatiya Shivsena Rashtrawadi
Punjab	Bathinda	GURCHARAN SINGH	820	no	IND	Independent
Punjab	Bathinda	RAJINDER SINGH	1960	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Bathinda	JAGDISH RAI SHARMA	1248	no	NBDP	Nav Bharat Democratic Party
Punjab	Bathinda	SUSHIL KUMAR JINDAL	1330	no	IND	Independent
Punjab	Bathinda	VIJAY KUMAR	2258	no	IND	Independent
Punjab	Bathinda	MAKHAN LAL	2587	no	BSP(A)	Bahujan Samaj Party  (Ambedkar)
Punjab	Bathinda	SURESH KUMAR GOYAL	944	no	JanSP	Janral Samaj Party
Punjab	Bathinda	DYAL CHAND	955	no	IND	Independent
Punjab	Bathinda	JASPAL SINGH	703	no	IND	Independent
Punjab	Bathinda	GURMEET SINGH RANGHRETA	1386	no	PLP	Punjab Labour Party
Punjab	Bathinda	SANJEEV KUMAR THAPAR	2833	no	IND	Independent
Punjab	Bathinda	JAGDEEP SINGH GEHRI	1665	no	IND	Independent
Punjab	Bathinda	SATISH ARORA	5936	no	IND	Independent
Punjab	Bathinda	SUKHWINDER SINGH	1086	no	BMUP	Bahujan Mukti Party
Punjab	Bathinda	BHAGWANT SINGH SAMAON	5984	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Punjab	Bathinda	JASRAJ SINGH LONGIA	87901	no	AAAP	Aam Aadmi Party
Punjab	Bathinda	ASHISH	6626	no	IND	Independent
Punjab	Bathinda	JAGDEEP SINGH	668	no	IND	Independent
Punjab	Bathinda	None of the Above	4701	no	NOTA	None of the Above
Punjab	Bathinda	MANPREET SINGH BADAL S/O GURDEV SINGH	4618	no	IND	Independent
Punjab	Bathinda	MANPREET SINGH BADAL S/O GURDAS SINGH	495332	no	INC	Indian National Congress
Punjab	Bathinda	SWARAN SINGH	2077	no	IND	Independent
Punjab	Bathinda	KULDEEP SINGH	13732	no	BSP	Bahujan Samaj Party
Punjab	Sangrur	BHAGWANT MANN	533237	yes	AAAP	Aam Aadmi Party
Punjab	Sangrur	VIJAY INDER SINGLA	181410	no	INC	Indian National Congress
Punjab	Sangrur	GURPREET SINGH	2746	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Punjab	Sangrur	KARNAIL SINGH	4127	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Sangrur	PARVEEN KAUR	798	no	IND	Independent
Punjab	Sangrur	KARAM SINGH	1599	no	IND	Independent
Punjab	Sangrur	DHARAM SINGH	3292	no	IND	Independent
Punjab	Sangrur	SUKHDEV SINGH DHINDSA	321516	no	SAD	Shiromani Akali Dal
Punjab	Sangrur	MANOJ KUMAR	1854	no	IND	Independent
Punjab	Sangrur	None of the Above	2188	no	NOTA	None of the Above
Punjab	Sangrur	GURDEEP SINGH	1069	no	DBSP	Democratic Bharatiya Samaj Party
Punjab	Sangrur	BHAGWANT SINGH	2334	no	IND	Independent
Punjab	Sangrur	SUKHDEV SINGH	1233	no	IND	Independent
Punjab	Sangrur	SUKHDEV RAM SHARMA	6934	no	CPI	Communist Party of India
Punjab	Sangrur	JIT SINGH	5879	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Punjab	Sangrur	JASWANT SINGH	1556	no	BMUP	Bahujan Mukti Party
Punjab	Sangrur	JOTI DEVAJI ANSUIA	6831	no	IND	Independent
Punjab	Sangrur	BIJENDER	5395	no	IND	Independent
Punjab	Sangrur	MAHMOOD AHMED THIND	2261	no	IUML	Indian Union Muslim League
Punjab	Sangrur	JOGINDER SINGH AULAKH	3315	no	CPM	Communist Party of India  (Marxist)
Punjab	Sangrur	MADAN BHATTI	8408	no	BSP	Bahujan Samaj Party
Punjab	Sangrur	HARINDER KUMAR	1485	no	IND	Independent
Punjab	Patiala	DR. DHARAM VIRA GANDHI	365671	yes	AAAP	Aam Aadmi Party
Punjab	Patiala	PRENEET KAUR	344729	no	INC	Indian National Congress
Punjab	Patiala	MOHINDER PAL SINGH	2773	no	SAD(M)	Shiromani Akali Dal (Amritsar)(Simranjit Singh Mann)
Punjab	Patiala	DEEPINDER SINGH DHILLON	340109	no	SAD	Shiromani Akali Dal
Punjab	Patiala	RANJIT KAUR	2178	no	BGTD	Bharatiya Gaon Taj Dal
Punjab	Patiala	RAMISHER SINGH BILLA	2625	no	IND	Independent
Punjab	Patiala	PARVEEN KUMAR	4089	no	IND	Independent
Punjab	Patiala	GURPREET SINGH	2378	no	BSP(A)	Bahujan Samaj Party  (Ambedkar)
Punjab	Patiala	NIRMAL SINGH DHALIWAL	8537	no	CPI	Communist Party of India
Punjab	Patiala	PARVESH BHATEJA	4915	no	IND	Independent
Punjab	Patiala	GURBHEJ SINGH	2217	no	IND	Independent
Punjab	Patiala	DHARAM PAL	1271	no	IND	Independent
Punjab	Patiala	RANDHIR SINGH	1234	no	BMUP	Bahujan Mukti Party
Punjab	Patiala	LAKHWINDER SINGH MALHI	809	no	IND	Independent
Punjab	Patiala	HARISH KUMAR	2325	no	SHS	Shivsena
Punjab	Patiala	None of the Above	3008	no	NOTA	None of the Above
Punjab	Patiala	RAJINDER SINGH PAWAR	1625	no	IND	Independent
Punjab	Patiala	AMRIK SINGH	936	no	aicp	New All India Congress Party
Punjab	Patiala	BIBI KAMALDEEP KAUR RAJOANA	15313	no	IND	Independent
Punjab	Patiala	RAM SINGH DHIMAN	13014	no	BSP	Bahujan Samaj Party
Punjab	Patiala	RAVINDER SINGH	1177	no	BhSMP	Bharatiya Sant Mat Party
Rajasthan	Ganganagar	NIHALCHAND	658130	yes	BJP	Bharatiya Janata Party
Rajasthan	Ganganagar	ADVOCATE KARTAR SINGH BAINS	5025	no	AITC	All India Trinamool Congress
Rajasthan	Ganganagar	SHIMLA DEVI NAYAK (ADVOCATE)	106585	no	nuzp	National Unionist Zamindara Party
Rajasthan	Ganganagar	OM PARKASH	3750	no	IND	Independent
Rajasthan	Ganganagar	AMAR SINGH	6686	no	BMUP	Bahujan Mukti Party
Rajasthan	Ganganagar	HETRAM	2943	no	RJVP	Rajasthan Vikas Party
Rajasthan	Ganganagar	DR. BALKRISHAN BAWARI	40016	no	AAAP	Aam Aadmi Party
Rajasthan	Ganganagar	PALARAM	14944	no	CPM	Communist Party of India  (Marxist)
Rajasthan	Ganganagar	SUNIL BHADRAWAL	3729	no	LD	Lok Dal
Rajasthan	Ganganagar	MASTER BHANWARLAL MEGHWAL	366389	no	INC	Indian National Congress
Rajasthan	Ganganagar	JASVINDER SINGH	3395	no	JMBP	Jai Maha Bharath Party
Rajasthan	Ganganagar	BALRAJ BAWARI	8788	no	IND	Independent
Rajasthan	Ganganagar	BALWANT SINGH HARIYALA	5364	no	BhSMP	Bharatiya Sant Mat Party
Rajasthan	Ganganagar	DR. PALUSKAR K. T.	5232	no	PRCP	Prabuddha Republican Party
Rajasthan	Ganganagar	TITAR SINGH	3924	no	IND	Independent
Rajasthan	Ganganagar	DULICHAND MEGHWAL	11981	no	IND	Independent
Rajasthan	Ganganagar	KUMBHA RAM	3007	no	IND	Independent
Rajasthan	Ganganagar	None of the Above	6918	no	NOTA	None of the Above
Rajasthan	Bikaner	ARJUN RAM MEGHWAL	584932	yes	BJP	Bharatiya Janata Party
Rajasthan	Bikaner	None of the Above	13492	no	NOTA	None of the Above
Rajasthan	Bikaner	MANGILAL NAYAK	16839	no	nuzp	National Unionist Zamindara Party
Rajasthan	Bikaner	JAGDISH KUMAR NAYAK	5164	no	IND	Independent
Rajasthan	Bikaner	DR. GAURI SHANKER DAABI	14148	no	AAAP	Aam Aadmi Party
Rajasthan	Bikaner	GOPAL BALMIKI	4398	no	IND	Independent
Rajasthan	Bikaner	RAM RATAN MEGHWAL	2538	no	BMUP	Bahujan Mukti Party
Rajasthan	Bikaner	ER. SHANKAR PANNU	276853	no	INC	Indian National Congress
Rajasthan	Bikaner	BHANWAR LAL	11387	no	BSP	Bahujan Samaj Party
Rajasthan	Churu	RAHUL KASWAN	595756	yes	BJP	Bharatiya Janata Party
Rajasthan	Churu	INDRASINGH JAT	10778	no	CPM	Communist Party of India  (Marxist)
Rajasthan	Churu	ABHINESH MAHARSHI	301017	no	BSP	Bahujan Samaj Party
Rajasthan	Churu	SYOPAT RAM	2330	no	BhSMP	Bharatiya Sant Mat Party
Rajasthan	Churu	TORURAM MEGHWAL	2054	no	BMUP	Bahujan Mukti Party
Rajasthan	Churu	MOHAMMAD ALI KHAN	2313	no	BA S D	Bahujan Sangharshh Dal
Rajasthan	Churu	AJAY KUMAR	2938	no	IND	Independent
Rajasthan	Churu	RAJPAL	3727	no	IND	Independent
Rajasthan	Churu	SALIM GUJAR	4018	no	IND	Independent
Rajasthan	Churu	GORDHAN SARSWAT	3991	no	AITC	All India Trinamool Congress
Rajasthan	Churu	PRATAP SINGH	176912	no	INC	Indian National Congress
Rajasthan	Churu	KRISHAN KUMAR SARAN	13977	no	AAAP	Aam Aadmi Party
Rajasthan	Churu	None of the Above	11293	no	NOTA	None of the Above
Rajasthan	Jhunjhunu	SANTOSH AHLAWAT	488182	yes	BJP	Bharatiya Janata Party
Rajasthan	Jhunjhunu	None of the Above	8038	no	NOTA	None of the Above
Rajasthan	Jhunjhunu	URMILA	5581	no	MEDP	Megh Desham Party
Rajasthan	Jhunjhunu	RAJ BALA OLA	254347	no	INC	Indian National Congress
Rajasthan	Jhunjhunu	DR. RAJKUMAR SHARMA	206288	no	IND	Independent
Rajasthan	Jhunjhunu	OM PRAKASH JHARODA	2506	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Rajasthan	Jhunjhunu	GENERAL R S KADYAN	10386	no	AAAP	Aam Aadmi Party
Rajasthan	Jhunjhunu	RAJBALA	11740	no	IND	Independent
Rajasthan	Jhunjhunu	BHAN SINGH	1885	no	BhSMP	Bharatiya Sant Mat Party
Rajasthan	Jhunjhunu	RAHISA BANO	10423	no	BSP	Bahujan Samaj Party
Rajasthan	Jhunjhunu	BHUDAR MAL	2979	no	AITC	All India Trinamool Congress
Rajasthan	Jhunjhunu	PRAVEEN KUMAR	1178	no	IND	Independent
Rajasthan	Jhunjhunu	MAHABEER PRASAD KUMAWAT	2932	no	IND	Independent
Rajasthan	Sikar	SUMEDHANAND SARASWATI	499428	yes	BJP	Bharatiya Janata Party
Rajasthan	Sikar	VIMAL INDORIA	968	no	JMBP	Jai Maha Bharath Party
Rajasthan	Sikar	AMARA RAM	53134	no	CPM	Communist Party of India  (Marxist)
Rajasthan	Sikar	MAJOR SURENDRA KUMAR PUNIA	15666	no	AAAP	Aam Aadmi Party
Rajasthan	Sikar	AJAYPAL	7036	no	IND	Independent
Rajasthan	Sikar	SWAMI BHAGIRATH SINGH KHARRANTE BHADHADAR	5387	no	ABHM	Akhil Bharat Hindu Mahasabha
Rajasthan	Sikar	DULI CHAND KUMAWAT	4375	no	IND	Independent
Rajasthan	Sikar	PRATAP SINGH	10666	no	IND	Independent
Rajasthan	Sikar	KANHAIYA LAL BALAI	2169	no	IND	Independent
Rajasthan	Sikar	SUBHASH MAHARIA	188841	no	IND	Independent
Rajasthan	Sikar	GULAM NABI AZAD	4112	no	BSP	Bahujan Samaj Party
Rajasthan	Sikar	RAMESH SHARMA	1635	no	bhp	Bhartiya Party
Rajasthan	Sikar	SURENDRA KUMAR PHULWARIYA	3633	no	IND	Independent
Rajasthan	Sikar	PRATAP SINGH JAT	260232	no	INC	Indian National Congress
Rajasthan	Sikar	SATISH KUMAR SHARMA	1058	no	JGP	Jago Party
Rajasthan	Sikar	None of the Above	7410	no	NOTA	None of the Above
Rajasthan	Jaipur Rural	RAJYAVARDHAN SINGH RATHORE	632930	yes	BJP	Bharatiya Janata Party
Rajasthan	Jaipur Rural	None of the Above	11533	no	NOTA	None of the Above
Rajasthan	Jaipur Rural	JAGDISH PRASAD	1378	no	LD	Lok Dal
Rajasthan	Jaipur Rural	SHAYAM SINGH RATHORE	3722	no	LS	Lok Shakti
Rajasthan	Jaipur Rural	AJAY KUMAR SHARMA	5774	no	JGP	Jago Party
Rajasthan	Jaipur Rural	KAMAL  KUMAR  GAUR	9480	no	IND	Independent
Rajasthan	Jaipur Rural	NARAYAN LAL PARIHAR	1327	no	BNNP	Bharat Nav Nirman Party
Rajasthan	Jaipur Rural	DR. OM SINGH MEENA	4520	no	IND	Independent
Rajasthan	Jaipur Rural	NAVIN PILANIA	31617	no	NPEP	National Peoples Party
Rajasthan	Jaipur Rural	TEK CHAND SONWAL	3047	no	APoI	Ambedkarite Party of India
Rajasthan	Jaipur Rural	DHIRAJ GURJAR	1412	no	SP	Samajwadi Party
Rajasthan	Jaipur Rural	DR. C.P. JOSHI	300034	no	INC	Indian National Congress
Rajasthan	Jaipur Rural	ANIL KUMAR	6917	no	AAAP	Aam Aadmi Party
Rajasthan	Jaipur	RAMCHARAN BOHARA	863358	yes	BJP	Bharatiya Janata Party
Rajasthan	Jaipur	HAFIZ KHAN WARASI	5020	no	IND	Independent
Rajasthan	Jaipur	ABDUL AZIZ BHARTIYA	1103	no	RUC	Rashtriya Ulama Council
Rajasthan	Jaipur	IFTEKAR QURISHI	621	no	AIFB	All India Forward Bloc
Rajasthan	Jaipur	KAILASH CHAND SAINI	566	no	HJP	Hindustan Janta Party
Rajasthan	Jaipur	RAM SINGH RAJ	2072	no	IND	Independent
Rajasthan	Jaipur	MUNNA DASS DABODIA	4856	no	IND	Independent
Rajasthan	Jaipur	NEERU AGARWAL	1566	no	nuzp	National Unionist Zamindara Party
Rajasthan	Jaipur	PAWAN SINGH SHEKHAWAT	1987	no	IND	Independent
Rajasthan	Jaipur	ANIL KUMAR SHARMA	4599	no	JGP	Jago Party
Rajasthan	Jaipur	DR. VIRENDRA SINGH	55118	no	AAAP	Aam Aadmi Party
Rajasthan	Jaipur	DR. MAHESH JOSHI	324013	no	INC	Indian National Congress
Rajasthan	Jaipur	RAJU LAL BAIRWA	1899	no	APoI	Ambedkarite Party of India
Rajasthan	Jaipur	FAROOQ SHAH	5193	no	BSP	Bahujan Samaj Party
Rajasthan	Jaipur	DR. TANMAY	893	no	IPGP	Indian Peoples Green Party
Rajasthan	Jaipur	D.K. (DEV KRISHAN) CHHANGANI	15597	no	CPI	Communist Party of India
Rajasthan	Jaipur	None of the Above	8345	no	NOTA	None of the Above
Rajasthan	Alwar	CHAND NATH	642278	yes	BJP	Bharatiya Janata Party
Rajasthan	Alwar	None of the Above	2512	no	NOTA	None of the Above
Rajasthan	Alwar	HARIPRASAD	3580	no	IND	Independent
Rajasthan	Alwar	JAGDISH PRASAD BAIRWA	3390	no	IND	Independent
Rajasthan	Alwar	CHAUDHARY NASRUDEEN TANWAR	662	no	SP(I)	Socialist Party (India)
Rajasthan	Alwar	DR. DASHRATH KUMAR HINUNIA	3612	no	APoI	Ambedkarite Party of India
Rajasthan	Alwar	RAMESH PANVAL	1227	no	IND	Independent
Rajasthan	Alwar	KISHAN LAL	2572	no	IND	Independent
Rajasthan	Alwar	HARI SINGH SHARMA	3672	no	IND	Independent
Rajasthan	Alwar	KHOOBRAJ NEERAJ	702	no	BMUP	Bahujan Mukti Party
Rajasthan	Alwar	NISHAR AHMED	441	no	JMBP	Jai Maha Bharath Party
Rajasthan	Alwar	AZAD HUSAIN	19483	no	BSP	Bahujan Samaj Party
Rajasthan	Alwar	ARUN KUMAR ROOPAK	3412	no	NADP	Naya Daur Party
Rajasthan	Alwar	MANGURAM SAIN	566	no	ABCD(A)	Akhil Bharatiya Congress Dal (Ambedkar)
Rajasthan	Alwar	KHAIM CHAND	1187	no	BYS	Bharatiya Yuva Shakti
Rajasthan	Alwar	DR VIRENDRA VIDROHI	8517	no	AAAP	Aam Aadmi Party
Rajasthan	Alwar	VIRJANAND	1845	no	SP	Samajwadi Party
Rajasthan	Alwar	MURSHID AHMED	2160	no	AITC	All India Trinamool Congress
Rajasthan	Alwar	BHANWAR JITENDRA SINGH	358383	no	INC	Indian National Congress
Rajasthan	Alwar	SURESH SHARMA	2104	no	JGP	Jago Party
Rajasthan	BHARATPUR	BAHADUR SINGH	579825	yes	BJP	Bharatiya Janata Party
Rajasthan	BHARATPUR	MAHENDAR KUMAR JATAV	22090	no	BSP	Bahujan Samaj Party
Rajasthan	BHARATPUR	AMAR CHAND POHIYA	3969	no	RJVP	Rajasthan Vikas Party
Rajasthan	BHARATPUR	GOPAL PAHARIA	7013	no	AAAP	Aam Aadmi Party
Rajasthan	BHARATPUR	LAKHAN SINGH	960	no	IPGP	Indian Peoples Green Party
Rajasthan	BHARATPUR	MAMRAJ	1787	no	IND	Independent
Rajasthan	BHARATPUR	VIMLA UMAR	1792	no	AITC	All India Trinamool Congress
Rajasthan	BHARATPUR	SHEETAL JATAV	2071	no	IND	Independent
Rajasthan	BHARATPUR	PREM CHAND DHAMANI	835	no	IND	Independent
Rajasthan	BHARATPUR	DR.SURESH JATAV	334357	no	INC	Indian National Congress
Rajasthan	BHARATPUR	GHISU LAL	611	no	IND	Independent
Rajasthan	BHARATPUR	MADHURI PUSHKAR	1187	no	IND	Independent
Rajasthan	BHARATPUR	None of the Above	5935	no	NOTA	None of the Above
Rajasthan	KARAULI-DHOLPUR	MANOJ RAJORIA	402407	yes	BJP	Bharatiya Janata Party
Rajasthan	KARAULI-DHOLPUR	None of the Above	5934	no	NOTA	None of the Above
Rajasthan	KARAULI-DHOLPUR	KISHOR KUMAR	3376	no	IND	Independent
Rajasthan	KARAULI-DHOLPUR	KANCHAN BAI	3014	no	IND	Independent
Rajasthan	KARAULI-DHOLPUR	ASHOK KUMAR	1385	no	IND	Independent
Rajasthan	KARAULI-DHOLPUR	RAM SINGH	9458	no	NPEP	National Peoples Party
Rajasthan	KARAULI-DHOLPUR	GHANSHYAM	3726	no	AAAP	Aam Aadmi Party
Rajasthan	KARAULI-DHOLPUR	G.R.BARUA	25150	no	BSP	Bahujan Samaj Party
Rajasthan	KARAULI-DHOLPUR	LAXMIKANT	4767	no	IND	Independent
Rajasthan	KARAULI-DHOLPUR	DHANRAJ BAIRWA	745	no	SP(I)	Socialist Party (India)
Rajasthan	KARAULI-DHOLPUR	RAMESH	1629	no	BYS	Bharatiya Yuva Shakti
Rajasthan	KARAULI-DHOLPUR	LAKKHIRAM	375191	no	INC	Indian National Congress
Rajasthan	KARAULI-DHOLPUR	SHRELAL KHARE	922	no	JMBP	Jai Maha Bharath Party
Rajasthan	KARAULI-DHOLPUR	KAMAL SINGH	7286	no	AITC	All India Trinamool Congress
Rajasthan	KARAULI-DHOLPUR	PHOOLSINGH	951	no	JGP	Jago Party
Rajasthan	Dausa	HARISH CHANDRA MEENA	315059	yes	BJP	Bharatiya Janata Party
Rajasthan	Dausa	BIMLA DEVI MEENA	20037	no	IND	Independent
Rajasthan	Dausa	RUKMANI MEENA JHAKHIWAL	917	no	SP(I)	Socialist Party (India)
Rajasthan	Dausa	NAMONARAIN MEENA	181272	no	INC	Indian National Congress
Rajasthan	Dausa	DR. KIRODI LAL	269655	no	NPEP	National Peoples Party
Rajasthan	Dausa	DR. NASEEM GURJAR	5792	no	IND	Independent
Rajasthan	Dausa	SURAJ BHAN DHANKA	2357	no	SP	Samajwadi Party
Rajasthan	Dausa	DHARMI GHUNAWAT	1635	no	IPGP	Indian Peoples Green Party
Rajasthan	Dausa	SHIV PAL GURJAR	82549	no	IND	Independent
Rajasthan	Dausa	CHANDU LAL MEENA (GEROTA)	15303	no	BSP	Bahujan Samaj Party
Rajasthan	Dausa	ANJU DEVI DHANKA	16935	no	IND	Independent
Rajasthan	Dausa	ANJALI BHADANA	3072	no	IND	Independent
Rajasthan	Dausa	DR. SANJEET KUMAR DHANKA	3202	no	AAAP	Aam Aadmi Party
Rajasthan	Dausa	KHEMRAJ MEENA	4297	no	BYS	Bharatiya Yuva Shakti
Rajasthan	Dausa	None of the Above	8410	no	NOTA	None of the Above
Rajasthan	TONK-SAWAI MADHOPUR	SUKHBIR SINGH JAUNAPURIA	548179	yes	BJP	Bharatiya Janata Party
Rajasthan	TONK-SAWAI MADHOPUR	None of the Above	3664	no	NOTA	None of the Above
Rajasthan	TONK-SAWAI MADHOPUR	RAM HANS	867	no	NADP	Naya Daur Party
Rajasthan	TONK-SAWAI MADHOPUR	KORI LAL	1924	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	JAGMOHAN MEENA	14600	no	NPEP	National Peoples Party
Rajasthan	TONK-SAWAI MADHOPUR	VARIS ALI	2361	no	AAP	awami aamjan party
Rajasthan	TONK-SAWAI MADHOPUR	CHANDAN LAL MEENA	6522	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	RAMESH CHAND	751	no	LD	Lok Dal
Rajasthan	TONK-SAWAI MADHOPUR	SAMPATTI BAI ALIAS SUNITA VERMA	1611	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	GOVIND PRASAD	3125	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	RAM PARKASH	6177	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	DINESH SAINI	2205	no	JMBP	Jai Maha Bharath Party
Rajasthan	TONK-SAWAI MADHOPUR	MAKKHAN	10515	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	SIYA RAM	1104	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	HARI RAM	1045	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	MUKESH	2665	no	AAAP	Aam Aadmi Party
Rajasthan	TONK-SAWAI MADHOPUR	BAJARANG LAL	699	no	RAHM	Rashtriya Ahinsa Manch
Rajasthan	TONK-SAWAI MADHOPUR	SURESH	10392	no	BSP	Bahujan Samaj Party
Rajasthan	TONK-SAWAI MADHOPUR	BHAR SINGH	701	no	SP(I)	Socialist Party (India)
Rajasthan	TONK-SAWAI MADHOPUR	DINESH	2147	no	AITC	All India Trinamool Congress
Rajasthan	TONK-SAWAI MADHOPUR	KAMAL KUMAR DHOBI	1723	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	BABULAL JANGID	7195	no	IND	Independent
Rajasthan	TONK-SAWAI MADHOPUR	MOHAMMED AZHARUDDIN	412868	no	INC	Indian National Congress
Rajasthan	Ajmer	SANWAR LAL JAT	637874	yes	BJP	Bharatiya Janata Party
Rajasthan	Ajmer	JAGDISH SINGH RAWAT	1374	no	IND	Independent
Rajasthan	Ajmer	SANWAR LAL	12149	no	NCP	Nationalist Congress Party
Rajasthan	Ajmer	MUKUL MISHRA	1869	no	ABHM	Akhil Bharat Hindu Mahasabha
Rajasthan	Ajmer	BHANWAR LAL SONI	4970	no	IND	Independent
Rajasthan	Ajmer	ANITA JAIN	910	no	IND	Independent
Rajasthan	Ajmer	RAMLAL	1040	no	AKBAP	Akhil Bhartiya Aamjan Party
Rajasthan	Ajmer	SURENDRA KUMAR JAIN	5184	no	IND	Independent
Rajasthan	Ajmer	KRISHNA KUMAR DADHICH	1015	no	IND	Independent
Rajasthan	Ajmer	SACHIN PILOT	465891	no	INC	Indian National Congress
Rajasthan	Ajmer	JAGDISH	7974	no	BSP	Bahujan Samaj Party
Rajasthan	Ajmer	NARAIN DAS SINDHI	3518	no	IND	Independent
Rajasthan	Ajmer	None of the Above	12546	no	NOTA	None of the Above
Rajasthan	Nagaur	C R CHOUDHARY	414791	yes	BJP	Bharatiya Janata Party
Rajasthan	Nagaur	None of the Above	12185	no	NOTA	None of the Above
Rajasthan	Nagaur	HARIRAM	8658	no	IND	Independent
Rajasthan	Nagaur	AYUB KHAN	7300	no	SP	Samajwadi Party
Rajasthan	Nagaur	HANUMAN BENIWAL	159980	no	IND	Independent
Rajasthan	Nagaur	ABDUL AZIZ	11239	no	BSP	Bahujan Samaj Party
Rajasthan	Nagaur	SUNIL	5251	no	IND	Independent
Rajasthan	Nagaur	CHHOTURAM	15234	no	IND	Independent
Rajasthan	Nagaur	HANUMANRAM	3976	no	AAAP	Aam Aadmi Party
Rajasthan	Nagaur	BHANWAR SINGH RATHORE	5776	no	AITC	All India Trinamool Congress
Rajasthan	Nagaur	MULARAM	4332	no	IND	Independent
Rajasthan	Nagaur	DR JYOTI MIRDHA	339573	no	INC	Indian National Congress
Rajasthan	Nagaur	NEMICHAND	4372	no	AVIRP	AARAKSHAN VIRODHI PARTY
Rajasthan	Nagaur	HARIRAM MEHRADA	11352	no	BA S D	Bahujan Sangharshh Dal
Rajasthan	Pali	P P CHOUDHARY	711772	yes	BJP	Bharatiya Janata Party
Rajasthan	Pali	DEV KISHAN DEHIYA	1264	no	MEDP	Megh Desham Party
Rajasthan	Pali	UDARAM	2058	no	IND	Independent
Rajasthan	Pali	KANHAIYALAL PAREEK	1890	no	IND	Independent
Rajasthan	Pali	MAHESH BHANDARI	5031	no	AAAP	Aam Aadmi Party
Rajasthan	Pali	SHOBHARAM MALI	2825	no	JMBP	Jai Maha Bharath Party
Rajasthan	Pali	SHER SINGH SAFA	5198	no	IND	Independent
Rajasthan	Pali	DR. RAMLAL MOHBARSHA	5794	no	IND	Independent
Rajasthan	Pali	BHOJRAJ	16469	no	BSP	Bahujan Samaj Party
Rajasthan	Pali	MUNNI DEVI GODARA	312733	no	INC	Indian National Congress
Rajasthan	Pali	OM PRAKASH MALI	3306	no	IND	Independent
Rajasthan	Pali	DINESH MEV	2358	no	IND	Independent
Rajasthan	Pali	JAGDISH	7186	no	BMUP	Bahujan Mukti Party
Rajasthan	Pali	None of the Above	17703	no	NOTA	None of the Above
Rajasthan	Jodhpur	GAJENDRASINGH SHEKHAWAT	713515	yes	BJP	Bharatiya Janata Party
Rajasthan	Jodhpur	None of the Above	15085	no	NOTA	None of the Above
Rajasthan	Jodhpur	SOHAN LAL GOYAL	3764	no	IND	Independent
Rajasthan	Jodhpur	GOPARAM MEGHWAL	13511	no	BSP	Bahujan Samaj Party
Rajasthan	Jodhpur	VIGYAN MODI	8552	no	AAAP	Aam Aadmi Party
Rajasthan	Jodhpur	CHANDRESH KUMARI	303464	no	INC	Indian National Congress
Rajasthan	Jodhpur	JETHARAM MEGHWAL	831	no	IND	Independent
Rajasthan	Jodhpur	RAVI GARG	1206	no	BNNP	Bharat Nav Nirman Party
Rajasthan	Jodhpur	RAM DAYAL	2321	no	IND	Independent
Rajasthan	Jodhpur	DHANRAJ GEHLOT	7104	no	JMBP	Jai Maha Bharath Party
Rajasthan	Jodhpur	MEHMOODA BEGAM ABBASI	2631	no	IND	Independent
Rajasthan	Jodhpur	RUGHA RAM CHOUDHARY ADVOCATE	4855	no	IND	Independent
Rajasthan	Jodhpur	DUNGAR MAL PUNIYA	1041	no	IND	Independent
Rajasthan	Jodhpur	JAYDA	718	no	IND	Independent
Rajasthan	Barmer	COL. SONA RAM	488747	yes	BJP	Bharatiya Janata Party
Rajasthan	Barmer	JASWANT SINGH	401286	no	IND	Independent
Rajasthan	Barmer	MASTER BHAIRARAM MEGHWAL	13924	no	IND	Independent
Rajasthan	Barmer	HARISH CHAUDHARY	220881	no	INC	Indian National Congress
Rajasthan	Barmer	POPATRAM	7557	no	IND	Independent
Rajasthan	Barmer	MANGILAL GAUR	14906	no	AAAP	Aam Aadmi Party
Rajasthan	Barmer	KHET SINGH RAJPUROHIT	12366	no	IND	Independent
Rajasthan	Barmer	RAMA RAM	17563	no	IND	Independent
Rajasthan	Barmer	MAHANT ARJUNPURI	6380	no	IND	Independent
Rajasthan	Barmer	BABULAL	6737	no	IND	Independent
Rajasthan	Barmer	MALAM SINGH	12883	no	BSP	Bahujan Samaj Party
Rajasthan	Barmer	None of the Above	15889	no	NOTA	None of the Above
Rajasthan	Jalore	DEVJI PATEL	580508	yes	BJP	Bharatiya Janata Party
Rajasthan	Jalore	None of the Above	11183	no	NOTA	None of the Above
Rajasthan	Jalore	MANARA RAM	6146	no	IND	Independent
Rajasthan	Jalore	S. BUTA SINGH	175344	no	IND	Independent
Rajasthan	Jalore	SHANKAR LAL DARJI	16399	no	IND	Independent
Rajasthan	Jalore	MOOLA RAM	10316	no	IND	Independent
Rajasthan	Jalore	OTARAM	29998	no	BSP	Bahujan Samaj Party
Rajasthan	Jalore	CHHAGANARAM / BHALARAM	3960	no	BMUP	Bahujan Mukti Party
Rajasthan	Jalore	RAN SINGH RAJPUROHIT	2731	no	JGP	Jago Party
Rajasthan	Jalore	MAHENDRA K. CHOUDHARY	13008	no	IND	Independent
Rajasthan	Jalore	NARINGA RAM YADAV	3200	no	IND	Independent
Rajasthan	Jalore	ANJANA UDAI LAL	199363	no	INC	Indian National Congress
Rajasthan	Jalore	CHHAGANARAM / PRHALAD	15811	no	HJP	Hindustan Janta Party
Rajasthan	Jalore	SUKH DEV	2017	no	AAP	awami aamjan party
Rajasthan	Jalore	BHANWAR LAL WAGHELA	4817	no	IND	Independent
Rajasthan	Jalore	NARAYAN LAL	9875	no	IND	Independent
Rajasthan	Jalore	TARUN KUMAR	2596	no	IND	Independent
Rajasthan	Udaipur	ARJUNLAL MEENA	660373	yes	BJP	Bharatiya Janata Party
Rajasthan	Udaipur	LAXMILAL GAMETI	6680	no	SP(I)	Socialist Party (India)
Rajasthan	Udaipur	DR. VELARAM MEENA	11449	no	AAAP	Aam Aadmi Party
Rajasthan	Udaipur	PRABHU LAL MEENA	4529	no	BMUP	Bahujan Mukti Party
Rajasthan	Udaipur	LAXMAN BHIL	20690	no	BSP	Bahujan Samaj Party
Rajasthan	Udaipur	MEGHRAJ TAWAR	33743	no	CPI	Communist Party of India
Rajasthan	Udaipur	GOTAM LAL MEENA	5442	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Rajasthan	Udaipur	RAGHUVIR SINGH	423611	no	INC	Indian National Congress
Rajasthan	Udaipur	None of the Above	26685	no	NOTA	None of the Above
Rajasthan	Banswara	MANSHANKAR NINAMA	577433	yes	BJP	Bharatiya Janata Party
Rajasthan	Banswara	None of the Above	34404	no	NOTA	None of the Above
Rajasthan	Banswara	BHANJI BHAI	21760	no	JD(U)	Janata Dal  (United)
Rajasthan	Banswara	RESHAM MALVIYA	485517	no	INC	Indian National Congress
Rajasthan	Banswara	VIJAYPAL	27886	no	BSP	Bahujan Samaj Party
Rajasthan	Banswara	SOMA LAL KOTED	10541	no	BMUP	Bahujan Mukti Party
Rajasthan	Banswara	HARISH RAWAT	13647	no	AAAP	Aam Aadmi Party
Rajasthan	Chittorgarh	CHANDRA PRAKASH JOSHI	703236	yes	BJP	Bharatiya Janata Party
Rajasthan	Chittorgarh	MANGI LAL SOLANKI	1291	no	BMUP	Bahujan Mukti Party
Rajasthan	Chittorgarh	SHANTI LAL TRIVEDI	1564	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Rajasthan	Chittorgarh	GULAB CHAND SAHLOT	1692	no	IND	Independent
Rajasthan	Chittorgarh	DR. NARENDRA KUMAR GUPTA	5939	no	AAAP	Aam Aadmi Party
Rajasthan	Chittorgarh	SANJAY KUMAR	4467	no	BSP	Bahujan Samaj Party
Rajasthan	Chittorgarh	BABU MEGHWAL	3069	no	IND	Independent
Rajasthan	Chittorgarh	RADHA DEVI	21593	no	CPI	Communist Party of India
Rajasthan	Chittorgarh	GIRIJA VYAS	386379	no	INC	Indian National Congress
Rajasthan	Chittorgarh	YASHWANT PURI	12675	no	IND	Independent
Rajasthan	Chittorgarh	MOHAMMAD IMTIYAZ	4971	no	IND	Independent
Rajasthan	Chittorgarh	BHANWAR LAL	3263	no	IND	Independent
Rajasthan	Chittorgarh	VIJAYA RAY	1240	no	RJVP	Rajasthan Vikas Party
Rajasthan	Chittorgarh	PRABHU LAL GUJJAR	1216	no	JMBP	Jai Maha Bharath Party
Rajasthan	Chittorgarh	None of the Above	20034	no	NOTA	None of the Above
Rajasthan	Rajsamand	HARIOM SINGH RATHORE	644794	yes	BJP	Bharatiya Janata Party
Rajasthan	Rajsamand	None of the Above	17182	no	NOTA	None of the Above
Rajasthan	Rajsamand	IDRISH MOHAMMAD	10407	no	JGP	Jago Party
Rajasthan	Rajsamand	KHIMARAM SALVI	2559	no	IND	Independent
Rajasthan	Rajsamand	BHANWAR LAL MALI	6418	no	IND	Independent
Rajasthan	Rajsamand	SANJAY CHOUDHARY	12479	no	IND	Independent
Rajasthan	Rajsamand	LAXMAN	1392	no	JMBP	Jai Maha Bharath Party
Rajasthan	Rajsamand	PRADEEP SINGH BHATNAGAR	4897	no	AAAP	Aam Aadmi Party
Rajasthan	Rajsamand	SURYA BHAWANI SINGH CHAWARA	10160	no	IND	Independent
Rajasthan	Rajsamand	GULAM FARID	2977	no	SP	Samajwadi Party
Rajasthan	Rajsamand	GOPAL SINGH SHEKHAWAT	249089	no	INC	Indian National Congress
Rajasthan	Rajsamand	NEERU RAM JAT	14579	no	BSP	Bahujan Samaj Party
Rajasthan	Rajsamand	HEERA KATHAT	5186	no	IND	Independent
Rajasthan	Bhilwara	SUBHASH BAHERIA	630317	yes	BJP	Bharatiya Janata Party
Rajasthan	Bhilwara	SURENDAR SINGH RANAWAT	6737	no	IND	Independent
Rajasthan	Bhilwara	ASLAM SHEKH ADVOCATE	1794	no	IND	Independent
Rajasthan	Bhilwara	SUNIL AGIWAL	5909	no	AAAP	Aam Aadmi Party
Rajasthan	Bhilwara	ASHOK CHANDNA	384053	no	INC	Indian National Congress
Rajasthan	Bhilwara	SHYAM LAL SALVI	5374	no	IND	Independent
Rajasthan	Bhilwara	RADHY SHYAM VAISHNAV	5684	no	BA S D	Bahujan Sangharshh Dal
Rajasthan	Bhilwara	RATAN LAL DHOBI	7110	no	IND	Independent
Rajasthan	Bhilwara	GOVIND KUMAR BAIRWA	15088	no	BSP	Bahujan Samaj Party
Rajasthan	Bhilwara	CHANDRA PRAKASH GOCHER	10344	no	BYS	Bharatiya Yuva Shakti
Rajasthan	Bhilwara	SATYA PRAKASH SHARMA	11982	no	IND	Independent
Rajasthan	Bhilwara	None of the Above	19698	no	NOTA	None of the Above
Rajasthan	Kota	OM BIRLA	644822	yes	BJP	Bharatiya Janata Party
Rajasthan	Kota	None of the Above	12760	no	NOTA	None of the Above
Rajasthan	Kota	RAJESH KUMAR	1168	no	JMBP	Jai Maha Bharath Party
Rajasthan	Kota	AJMAT ALI	7559	no	BYS	Bharatiya Yuva Shakti
Rajasthan	Kota	MAHESH KUMAR RANIWAL	1010	no	AVIRP	AARAKSHAN VIRODHI PARTY
Rajasthan	Kota	MOHAN LAL MAHAWAR	4822	no	IND	Independent
Rajasthan	Kota	ASHOK KUMAR JAIN	16981	no	AAAP	Aam Aadmi Party
Rajasthan	Kota	SHYAM SUNDER SHARMA	5091	no	IND	Independent
Rajasthan	Kota	IJYARAJ SINGH	444040	no	INC	Indian National Congress
Rajasthan	Kota	ASHOK KUMAR	10862	no	BSP	Bahujan Samaj Party
Rajasthan	Kota	TEJPAL	1825	no	IND	Independent
Rajasthan	Kota	MAHENDRA KUMAR	2307	no	IND	Independent
Rajasthan	Kota	ASFAK ALI	1713	no	IND	Independent
Rajasthan	JHALAWAR-BARAN	DUSHYANT SINGH	676102	yes	BJP	Bharatiya Janata Party
Rajasthan	JHALAWAR-BARAN	JAVED KHAN	13617	no	BYS	Bharatiya Yuva Shakti
Rajasthan	JHALAWAR-BARAN	CHANDRA SINGH	23587	no	BSP	Bahujan Samaj Party
Rajasthan	JHALAWAR-BARAN	SULEMAN PUTA	11602	no	IND	Independent
Rajasthan	JHALAWAR-BARAN	PRAMOD BHAYA	394556	no	INC	Indian National Congress
Rajasthan	JHALAWAR-BARAN	BALDAR	7836	no	IND	Independent
Rajasthan	JHALAWAR-BARAN	None of the Above	19064	no	NOTA	None of the Above
Sikkim	Sikkim	PREM DAS RAI	163698	yes	SDF	Sikkim Democratic Front
Sikkim	Sikkim	None of the Above	4332	no	NOTA	None of the Above
Sikkim	Sikkim	NAR BAHADUR KHATIWADA	7279	no	BJP	Bharatiya Janata Party
Sikkim	Sikkim	TEK NATH DHAKAL	121956	no	SKM	Sikkim Krantikari Morcha
Sikkim	Sikkim	AKAR DHOJ LIMBU	7189	no	INC	Indian National Congress
Sikkim	Sikkim	NAKUL DAS RAI	1972	no	AITC	All India Trinamool Congress
Sikkim	Sikkim	KAUSHAL RAI	2541	no	AAAP	Aam Aadmi Party
Tamil Nadu	Thiruvallur	VENUGOPAL.P.(DR)	628499	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Thiruvallur	None of the Above	23598	no	NOTA	None of the Above
Tamil Nadu	Thiruvallur	BALAMURUGAN.B	8260	no	AAAP	Aam Aadmi Party
Tamil Nadu	Thiruvallur	SRINIVASAN.D	2642	no	SP	Samajwadi Party
Tamil Nadu	Thiruvallur	CHANDRASEKARAN.M	1597	no	IND	Independent
Tamil Nadu	Thiruvallur	SATHYAMURTHY.C	11137	no	BSP	Bahujan Samaj Party
Tamil Nadu	Thiruvallur	YUVARAJ.V	204734	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Thiruvallur	KANNAN A.S	13794	no	CPI	Communist Party of India
Tamil Nadu	Thiruvallur	JAYAKUMAR.M	43960	no	INC	Indian National Congress
Tamil Nadu	Thiruvallur	RAVIKUMAR.D	305069	no	VCK	Viduthalai Chiruthaigal Katchi
Tamil Nadu	Thiruvallur	VETRISELVAM.K	2441	no	IND	Independent
Tamil Nadu	Thiruvallur	VENUGOPAL.P	3085	no	IND	Independent
Tamil Nadu	Thiruvallur	RAVIKUMAR.M	2966	no	IND	Independent
Tamil Nadu	Thiruvallur	SURESH KUMAR.S	1062	no	IND	Independent
Tamil Nadu	Thiruvallur	DOSS.R	1596	no	IND	Independent
Tamil Nadu	Chennai North	VENKATESH BABU .T.G	406704	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Chennai North	SOUNDARAPANDIAN .M	86989	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Chennai North	BIJU CHACKO	24190	no	INC	Indian National Congress
Tamil Nadu	Chennai North	JANARTHANAN .J	4960	no	BSP	Bahujan Samaj Party
Tamil Nadu	Chennai North	VIJAYA KUMAR .K	1145	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Chennai North	SRINIVASON .S	6819	no	AAAP	Aam Aadmi Party
Tamil Nadu	Chennai North	ANBAZHAGAN .G	208	no	IND	Independent
Tamil Nadu	Chennai North	GIRIRAJ. D	414	no	IND	Independent
Tamil Nadu	Chennai North	KESAVAN. L	1198	no	IND	Independent
Tamil Nadu	Chennai North	KOLANGI. M	1494	no	IND	Independent
Tamil Nadu	Chennai North	CHARAVANAN. M.P.	475	no	IND	Independent
Tamil Nadu	Chennai North	SIVASHANMUGA PERUMAL. S	712	no	IND	Independent
Tamil Nadu	Chennai North	SEETHA MALLAR. A	469	no	IND	Independent
Tamil Nadu	Chennai North	SRINIVASAN. J	964	no	IND	Independent
Tamil Nadu	Chennai North	SUNDAR. G	870	no	IND	Independent
Tamil Nadu	Chennai North	SENTHIL KUMAR. K	472	no	IND	Independent
Tamil Nadu	Chennai North	DEEPAK. C	475	no	IND	Independent
Tamil Nadu	Chennai North	DURAI BABU. R	297	no	IND	Independent
Tamil Nadu	Chennai North	DEVARAJ. G	386	no	IND	Independent
Tamil Nadu	Chennai North	SIVAKUMAR . V	502	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Tamil Nadu	Chennai North	NIJAM MOHAIDEEN .M	14585	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Tamil Nadu	Chennai North	BALAJI. E	311	no	IND	Independent
Tamil Nadu	Chennai North	PRABHAKARAN. K	457	no	IND	Independent
Tamil Nadu	Chennai North	PONRAJ. P	208	no	IND	Independent
Tamil Nadu	Chennai North	PAULRAJ .T	387	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Chennai North	BABU MAILAN .A	569	no	LSP	Lok Satta Party
Tamil Nadu	Chennai North	PAUL KANAGARAJ. R.C	1589	no	IND	Independent
Tamil Nadu	Chennai North	RAMASAMY. M.D.	234	no	IND	Independent
Tamil Nadu	Chennai North	RAJ. N	216	no	IND	Independent
Tamil Nadu	Chennai North	RAJA. B	337	no	IND	Independent
Tamil Nadu	Chennai North	MAHENDRAN. K.P.P.	343	no	IND	Independent
Tamil Nadu	Chennai North	RAMESH KANNAN .G	257	no	RPI	Republican Party of India
Tamil Nadu	Chennai North	MARIMUTHU. P	777	no	IND	Independent
Tamil Nadu	Chennai North	MARIRAJ. T.R	559	no	IND	Independent
Tamil Nadu	Chennai North	MOHAMED MAJASUDDIN	302	no	IND	Independent
Tamil Nadu	Chennai North	YOGARAJ. S	206	no	IND	Independent
Tamil Nadu	Chennai North	RAVIKUMAR. T	211	no	IND	Independent
Tamil Nadu	Chennai North	RAVI PARIYANAR. D	600	no	IND	Independent
Tamil Nadu	Chennai North	None of the Above	17472	no	NOTA	None of the Above
Tamil Nadu	Chennai North	VASUKI .U	23751	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Chennai North	GIRIRAJAN .R	307000	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Chennai South	DR. J. JAYAVARDHAN	438404	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Chennai South	GANESAN  S	1730	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Tamil Nadu	Chennai South	K.R.RAMASWAMY@TRAFFIC RAMASWAMY	1670	no	IND	Independent
Tamil Nadu	Chennai South	(KUPPAL)G.DEVADOSS	514	no	IND	Independent
Tamil Nadu	Chennai South	RAJINI NIVETHA	142	no	IND	Independent
Tamil Nadu	Chennai South	S. BALU	264	no	IND	Independent
Tamil Nadu	Chennai South	R. RAVICHANDRAN	216	no	IND	Independent
Tamil Nadu	Chennai South	V. BALAJI	1540	no	BSP	Bahujan Samaj Party
Tamil Nadu	Chennai South	S.S.RADHAKRISHNAN	376	no	IND	Independent
Tamil Nadu	Chennai South	THANGARAJ	189	no	IND	Independent
Tamil Nadu	Chennai South	S. DEVADOSS	235	no	IND	Independent
Tamil Nadu	Chennai South	S. PRAGASAM	648	no	IND	Independent
Tamil Nadu	Chennai South	T.K.S.ELANGOVAN	301779	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Chennai South	S. SEETHALAKSHMI	1303	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Chennai South	JAHIR HUSSAIN. M	17312	no	AAAP	Aam Aadmi Party
Tamil Nadu	Chennai South	S. PARTHIBAN	175	no	IND	Independent
Tamil Nadu	Chennai South	LOGANATHAN	117	no	IND	Independent
Tamil Nadu	Chennai South	A. IRUDAYADASS	963	no	IND	Independent
Tamil Nadu	Chennai South	J.S. MAHALINGAM	269	no	IND	Independent
Tamil Nadu	Chennai South	M. ARUMUGAM	219	no	IND	Independent
Tamil Nadu	Chennai South	K. MANI	275	no	JD(U)	Janata Dal  (United)
Tamil Nadu	Chennai South	KANTHAVEL  D	878	no	IND	Independent
Tamil Nadu	Chennai South	S. BALAMURALI	420	no	IND	Independent
Tamil Nadu	Chennai South	S. RAJESHWARI	155	no	IND	Independent
Tamil Nadu	Chennai South	S. VEERAMANI	105	no	IND	Independent
Tamil Nadu	Chennai South	S.V. RAMANI	24420	no	INC	Indian National Congress
Tamil Nadu	Chennai South	B. KUMAR SRI SRI	236	no	IND	Independent
Tamil Nadu	Chennai South	BHAVANI JANAKIRAM	247	no	IND	Independent
Tamil Nadu	Chennai South	COLONEL KADAR BASHA	434	no	IND	Independent
Tamil Nadu	Chennai South	A. JAIGANESH	4501	no	LSP	Lok Satta Party
Tamil Nadu	Chennai South	LA.GANESAN	256786	no	BJP	Bharatiya Janata Party
Tamil Nadu	Chennai South	T. DHANASEKAR	332	no	NOC	National Organisation Congress
Tamil Nadu	Chennai South	T.K. RAMAJAYAM	1194	no	IND	Independent
Tamil Nadu	Chennai South	R. KANDASAMY@BABU	374	no	APM	Ambedkar People's Movement
Tamil Nadu	Chennai South	M. JAYARAMAN	194	no	IND	Independent
Tamil Nadu	Chennai South	JOSEPH GUNASEKAR	245	no	IND	Independent
Tamil Nadu	Chennai South	T. PONNUSAMY	539	no	IND	Independent
Tamil Nadu	Chennai South	A. MAGESH	121	no	IND	Independent
Tamil Nadu	Chennai South	PON. SIVA KUMAR	274	no	IND	Independent
Tamil Nadu	Chennai South	S. CHARUMATHI	261	no	IND	Independent
Tamil Nadu	Chennai South	S. SESU RAJ	201	no	IND	Independent
Tamil Nadu	Chennai South	K. SHANKAR	504	no	IND	Independent
Tamil Nadu	Chennai South	None of the Above	20402	no	NOTA	None of the Above
Tamil Nadu	Chennai central	S.R. VIJAYAKUMAR	333296	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Chennai central	DAYANIDHI MARAN	287455	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Chennai central	S.D. KRISHNAKUMAR	1528	no	RPI(A)	Republican Party of India (A)
Tamil Nadu	Chennai central	C.D. MEYYAPPAN	25981	no	INC	Indian National Congress
Tamil Nadu	Chennai central	J. CONSTANDINE RAVINDRAN	114798	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Chennai central	MURALIKRISHNAN (A) SAMARAN	2531	no	BSP	Bahujan Samaj Party
Tamil Nadu	Chennai central	AUDITOR GOPINARAYANAN	805	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Chennai central	B. AMBETH VENKATESH	390	no	IND	Independent
Tamil Nadu	Chennai central	D. RAMESH	259	no	IND	Independent
Tamil Nadu	Chennai central	V. CHANDRAMOHAN	324	no	IND	Independent
Tamil Nadu	Chennai central	D. BABU	261	no	IND	Independent
Tamil Nadu	Chennai central	J. PRABHAKAR	19553	no	AAAP	Aam Aadmi Party
Tamil Nadu	Chennai central	P. AHILA	579	no	IND	Independent
Tamil Nadu	Chennai central	M. PRABAKARAN	336	no	IND	Independent
Tamil Nadu	Chennai central	P. MANIMARAN	391	no	IND	Independent
Tamil Nadu	Chennai central	M. MURUGAN	697	no	IND	Independent
Tamil Nadu	Chennai central	T. RAVIKUMAR	973	no	IND	Independent
Tamil Nadu	Chennai central	K.RAVEENDRAN	369	no	IND	Independent
Tamil Nadu	Chennai central	ARUNDHATHI JAIGANESH (A) S.JAIGANESH	390	no	IND	Independent
Tamil Nadu	Chennai central	M. MOHAMED RASIK	2019	no	IND	Independent
Tamil Nadu	Chennai central	None of the Above	21959	no	NOTA	None of the Above
Tamil Nadu	Sriperumbudur	RAMACHANDRAN, K.N. THIRU	545820	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Sriperumbudur	None of the Above	27676	no	NOTA	None of the Above
Tamil Nadu	Sriperumbudur	PALANI, A. THIRU	781	no	IND	Independent
Tamil Nadu	Sriperumbudur	BHARATHIDASAN R. THIRU.	811	no	IND	Independent
Tamil Nadu	Sriperumbudur	MADHAVARAJ, K.V. THIRU.	1165	no	IND	Independent
Tamil Nadu	Sriperumbudur	PUGAZHENDI, A. THIRU	1726	no	IND	Independent
Tamil Nadu	Sriperumbudur	RAMESH, THIRU	2782	no	IND	Independent
Tamil Nadu	Sriperumbudur	RAMACHANDIRAN, K. THIRU	791	no	IND	Independent
Tamil Nadu	Sriperumbudur	RAJAGOPAL MALLAR, P. THIRU	2622	no	IND	Independent
Tamil Nadu	Sriperumbudur	SRIDHAR,M.THIRU	477	no	IND	Independent
Tamil Nadu	Sriperumbudur	SRIDHAR, C. THIRU	497	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Sriperumbudur	AYODHI, L.( DR).	699	no	IND	Independent
Tamil Nadu	Sriperumbudur	SAMPATH. K. THIRU	1301	no	IND	Independent
Tamil Nadu	Sriperumbudur	SHANMUGAM, K. THIRU	291	no	IND	Independent
Tamil Nadu	Sriperumbudur	SHANMUGAM, G. THIRU	372	no	IND	Independent
Tamil Nadu	Sriperumbudur	JAGATHRAKSHAKAN, S. THIRU	443174	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Sriperumbudur	ARUL ANBARASU	39015	no	INC	Indian National Congress
Tamil Nadu	Sriperumbudur	MOHAMED ABBAS, S. THIRU	4977	no	BSP	Bahujan Samaj Party
Tamil Nadu	Sriperumbudur	PULAVAR. E.SU. MANI. THIRU	870	no	NOC	National Organisation Congress
Tamil Nadu	Sriperumbudur	BHARATHI, K. THIRU	4743	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tamil Nadu	Sriperumbudur	MASILAMANI, R. (DR).	187094	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Sriperumbudur	VASIGARAN, S.A.N. THIRU	18963	no	AAAP	Aam Aadmi Party
Tamil Nadu	Kancheepuram	MARAGATHAM  K	499395	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Kancheepuram	SATHIYARAJ  J.S	6807	no	BSP	Bahujan Samaj Party
Tamil Nadu	Kancheepuram	SELVAM  G	352529	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Kancheepuram	SATHIYANATHAN, N.	1089	no	IND	Independent
Tamil Nadu	Kancheepuram	PARGUNAN  A.	1368	no	IND	Independent
Tamil Nadu	Kancheepuram	BALAMURUGAN, R	2895	no	IND	Independent
Tamil Nadu	Kancheepuram	SRINIVASAN C.	1090	no	IND	Independent
Tamil Nadu	Kancheepuram	MARAGATHAM M	3618	no	IND	Independent
Tamil Nadu	Kancheepuram	VISWANATHAN  P	33313	no	INC	Indian National Congress
Tamil Nadu	Kancheepuram	ULAGANATHAN  .A	1479	no	IND	Independent
Tamil Nadu	Kancheepuram	SATHYA  C.E.	207080	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Kancheepuram	None of the Above	17736	no	NOTA	None of the Above
Tamil Nadu	Arakkonam	HARI, G.	493534	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Arakkonam	None of the Above	10370	no	NOTA	None of the Above
Tamil Nadu	Arakkonam	PANCHU.UDAYAKUMAR	6025	no	IND	Independent
Tamil Nadu	Arakkonam	GAJENDRAN, S.	6570	no	IND	Independent
Tamil Nadu	Arakkonam	ELANGO, D.	517	no	IND	Independent
Tamil Nadu	Arakkonam	ELANGO, N.	918	no	IND	Independent
Tamil Nadu	Arakkonam	ELANGOVAN, A.	804	no	IND	Independent
Tamil Nadu	Arakkonam	ELANGOVAN, D.	1556	no	IND	Independent
Tamil Nadu	Arakkonam	SADHU MUTHU KRISTNAN ERAJANDRAN, T.M.S.	484	no	IND	Independent
Tamil Nadu	Arakkonam	SHETTU, S.	570	no	IND	Independent
Tamil Nadu	Arakkonam	GOPINATHAN, G.	1955	no	IND	Independent
Tamil Nadu	Arakkonam	HARI, S.	1458	no	IND	Independent
Tamil Nadu	Arakkonam	ARUN, N.	610	no	IND	Independent
Tamil Nadu	Arakkonam	RAVI, V.	2198	no	IND	Independent
Tamil Nadu	Arakkonam	HARI, S.	599	no	IND	Independent
Tamil Nadu	Arakkonam	HARIKRISHNAN, P.	541	no	IND	Independent
Tamil Nadu	Arakkonam	DASS, D.	7354	no	BSP	Bahujan Samaj Party
Tamil Nadu	Arakkonam	VELU, R.	233762	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Arakkonam	RAJESH, R	56337	no	INC	Indian National Congress
Tamil Nadu	Arakkonam	RAJESH, S.	4021	no	AAAP	Aam Aadmi Party
Tamil Nadu	Arakkonam	ELANGO, N.R	252768	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Arakkonam	VELU, D.	920	no	IND	Independent
Tamil Nadu	Arakkonam	VELU, V.	783	no	IND	Independent
Tamil Nadu	Arakkonam	SRIDHAR, R.S	723	no	IND	Independent
Tamil Nadu	Arakkonam	RAVI, N.D.	471	no	IND	Independent
Tamil Nadu	Arakkonam	VELU, R.	747	no	IND	Independent
Tamil Nadu	Arakkonam	VADIVEL, A.	1023	no	IND	Independent
Tamil Nadu	Arakkonam	VISWANATHAN, P.	1434	no	IND	Independent
Tamil Nadu	Vellore	SENGUTTUVAN, B.	383719	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Vellore	IMDAD SHARIFF, A.	3742	no	AAAP	Aam Aadmi Party
Tamil Nadu	Vellore	THIRUPATHY, T.	1293	no	IND	Independent
Tamil Nadu	Vellore	ABDUL RAHMAN, M.	205896	no	IUML	Indian Union Muslim League
Tamil Nadu	Vellore	GANGADARAN, K.	991	no	IND	Independent
Tamil Nadu	Vellore	SHANMUGAM, A.C.	324326	no	BJP	Bharatiya Janata Party
Tamil Nadu	Vellore	GOPU, C.	6056	no	IND	Independent
Tamil Nadu	Vellore	LALITHA, K.	561	no	IND	Independent
Tamil Nadu	Vellore	SUGUMAR, K.	828	no	IND	Independent
Tamil Nadu	Vellore	SHAREEF BASHA, H.	496	no	SAP	Samata Party
Tamil Nadu	Vellore	MANIMARAN, M.	430	no	IND	Independent
Tamil Nadu	Vellore	DHANDAPANI, M.	558	no	IND	Independent
Tamil Nadu	Vellore	GANGADHARAN, K.	430	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Vellore	GOVINDAN, C.	4169	no	IND	Independent
Tamil Nadu	Vellore	VENKATESAN, R.	764	no	IND	Independent
Tamil Nadu	Vellore	SIVAPRAKASAM, R.S.	3603	no	IND	Independent
Tamil Nadu	Vellore	JAYAPRAKASH	2791	no	BSP	Bahujan Samaj Party
Tamil Nadu	Vellore	ESWARAN, R.	790	no	IND	Independent
Tamil Nadu	Vellore	KESAVAN, M.	1708	no	IND	Independent
Tamil Nadu	Vellore	VIJAY ELANCHEZIAN, J.	21650	no	INC	Indian National Congress
Tamil Nadu	Vellore	NANDHA KUMAR, V.	745	no	SP	Samajwadi Party
Tamil Nadu	Vellore	ABDUL RAHMAN, N.	581	no	IND	Independent
Tamil Nadu	Vellore	ABDUL RAHMAN, B.	1052	no	IND	Independent
Tamil Nadu	Vellore	GEORGE, V.A.	491	no	BDBRAJP	Bharatiya Dr. B.R.Ambedkar Janta Party
Tamil Nadu	Vellore	None of the Above	7100	no	NOTA	None of the Above
Tamil Nadu	Krishnagiri	ASHOK KUMAR.K	480491	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Krishnagiri	None of the Above	16020	no	NOTA	None of the Above
Tamil Nadu	Krishnagiri	CHINNA PILLAPPA.P	273900	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Krishnagiri	VIRGINIYA BENATIN FRANCILES	2530	no	IND	Independent
Tamil Nadu	Krishnagiri	MANI.G.K	224963	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Krishnagiri	MUNIRAJ.K	2135	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Krishnagiri	CHANBASHA.M	8618	no	BSP	Bahujan Samaj Party
Tamil Nadu	Krishnagiri	VINOTHKUMAR.P	2261	no	IND	Independent
Tamil Nadu	Krishnagiri	GOWDA.NSM	3033	no	JD(S)	Janata Dal  (Secular)
Tamil Nadu	Krishnagiri	SARAVANAN.V	2737	no	IND	Independent
Tamil Nadu	Krishnagiri	CHELLAKUMAR. DR. A	38885	no	INC	Indian National Congress
Tamil Nadu	Krishnagiri	SOUNDARAJAN.M	1744	no	IND	Independent
Tamil Nadu	Krishnagiri	ASHOK KUMAR.A	3875	no	IND	Independent
Tamil Nadu	Krishnagiri	GANDHI.TVS	1519	no	IND	Independent
Tamil Nadu	Krishnagiri	SUNDARAM.S	3665	no	UCPI	United Communist Party of India
Tamil Nadu	Krishnagiri	PATTABIRAMA.C	2015	no	IND	Independent
Tamil Nadu	Dharmapuri	ANBUMANI RAMADOSS	468194	yes	PMK	Pattali Makkal Katchi
Tamil Nadu	Dharmapuri	SAVITHIRI.K	3164	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Dharmapuri	ANANDKUMAR.K	1978	no	IND	Independent
Tamil Nadu	Dharmapuri	RAMA SUGANTHAN	15455	no	INC	Indian National Congress
Tamil Nadu	Dharmapuri	ANBUMANI.G	1370	no	IND	Independent
Tamil Nadu	Dharmapuri	ARIVUDAINAMBI.M	1403	no	IND	Independent
Tamil Nadu	Dharmapuri	SAMBATH.M	1310	no	IND	Independent
Tamil Nadu	Dharmapuri	RAJINIKANTH.S	8180	no	BSP	Bahujan Samaj Party
Tamil Nadu	Dharmapuri	ANGURAJ.P	3905	no	IND	Independent
Tamil Nadu	Dharmapuri	NATARAJAN.R	2199	no	IND	Independent
Tamil Nadu	Dharmapuri	THAMARAISELVAN.R	180297	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Dharmapuri	KUMARAN.V	2413	no	IND	Independent
Tamil Nadu	Dharmapuri	None of the Above	12693	no	NOTA	None of the Above
Tamil Nadu	Dharmapuri	PADMARAJAN.K.DR	4934	no	IND	Independent
Tamil Nadu	Dharmapuri	ARIVAZHAGAN.P	2802	no	IND	Independent
Tamil Nadu	Dharmapuri	MOHAN.P.S	391048	no	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Tiruvannamalai	VANAROJA R	500751	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Tiruvannamalai	None of the Above	9595	no	NOTA	None of the Above
Tamil Nadu	Tiruvannamalai	MANIKKAVEL M R	1555	no	JD(S)	Janata Dal  (Secular)
Tamil Nadu	Tiruvannamalai	ARULJOTHI A	904	no	IND	Independent
Tamil Nadu	Tiruvannamalai	PRABAKARAN L	679	no	IND	Independent
Tamil Nadu	Tiruvannamalai	ARUMUGAM R	973	no	IND	Independent
Tamil Nadu	Tiruvannamalai	LOGANATHAN P	1729	no	IND	Independent
Tamil Nadu	Tiruvannamalai	ANNADURAI C N	332145	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Tiruvannamalai	VAITHIYANATHAN S	931	no	IND	Independent
Tamil Nadu	Tiruvannamalai	MURUGESAN K	10708	no	IND	Independent
Tamil Nadu	Tiruvannamalai	VELMURUGAN A	724	no	IND	Independent
Tamil Nadu	Tiruvannamalai	SUBRAMANIYAN A	17854	no	INC	Indian National Congress
Tamil Nadu	Tiruvannamalai	ARUNACHALAM P	736	no	IND	Independent
Tamil Nadu	Tiruvannamalai	RAMESH P	2445	no	IND	Independent
Tamil Nadu	Tiruvannamalai	EDIROLI MANIAN G	157954	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Tiruvannamalai	ANBALAGAN D	1686	no	IND	Independent
Tamil Nadu	Tiruvannamalai	KOTHANDAPANI B	4688	no	IND	Independent
Tamil Nadu	Tiruvannamalai	MURUGAN P	991	no	IND	Independent
Tamil Nadu	Tiruvannamalai	BASKARAN  M R	4328	no	BSP	Bahujan Samaj Party
Tamil Nadu	Tiruvannamalai	SENTHIL S	3867	no	IND	Independent
Tamil Nadu	Tiruvannamalai	KAMARAJ M	1369	no	IND	Independent
Tamil Nadu	Tiruvannamalai	ANNADURAI B	1693	no	IND	Independent
Tamil Nadu	Tiruvannamalai	ANNADURAI A	865	no	IND	Independent
Tamil Nadu	Tiruvannamalai	ARUMUGAM C	6278	no	SP	Samajwadi Party
Tamil Nadu	Tiruvannamalai	RAVINDRAN T	1999	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Arani	V.ELUMALAI	502721	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Arani	A.GANESAN	5573	no	BSP	Bahujan Samaj Party
Tamil Nadu	Arani	R.SIVANANDAM	258877	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Arani	M.K.VISHNU PRASAD	27717	no	INC	Indian National Congress
Tamil Nadu	Arani	V.ANNA VENKATESH	2372	no	ADSMK	Anaithindia Dravidar Samudaya Munnetra Kazhagam
Tamil Nadu	Arani	A.K.MOORTHY	253332	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Arani	P.LOGANATHAN	4331	no	IND	Independent
Tamil Nadu	Arani	S.ELUMALAI	513	no	IND	Independent
Tamil Nadu	Arani	K.ELUMALAI	1243	no	IND	Independent
Tamil Nadu	Arani	R.SARASWATHI	668	no	IND	Independent
Tamil Nadu	Arani	H.SENTHILKUMAR	798	no	IND	Independent
Tamil Nadu	Arani	S.SENTHILNATHAN	1746	no	IND	Independent
Tamil Nadu	Arani	M.DURAIRAJ	910	no	IND	Independent
Tamil Nadu	Arani	A.MAHADEVAN	1309	no	IND	Independent
Tamil Nadu	Arani	V.MOORTHY	8059	no	IND	Independent
Tamil Nadu	Arani	S.RAMAMOORTHY	1760	no	IND	Independent
Tamil Nadu	Arani	K.MOORTHY	10263	no	IND	Independent
Tamil Nadu	Arani	S.MANIKANDAN	1952	no	IND	Independent
Tamil Nadu	Arani	M.MUNUSAMY	2598	no	IND	Independent
Tamil Nadu	Arani	None of the Above	9304	no	NOTA	None of the Above
Tamil Nadu	Viluppuram	RAJENDRAN S	482704	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Viluppuram	None of the Above	11440	no	NOTA	None of the Above
Tamil Nadu	Viluppuram	VENKATESAN S	2798	no	IND	Independent
Tamil Nadu	Viluppuram	KADRVEL M	3430	no	IND	Independent
Tamil Nadu	Viluppuram	UMASANKAR K	209663	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Viluppuram	MUTHAIYAN N	4780	no	IND	Independent
Tamil Nadu	Viluppuram	UMASANKAR M	1212	no	IND	Independent
Tamil Nadu	Viluppuram	MUTHIYAN	1774	no	IND	Independent
Tamil Nadu	Viluppuram	DEVI S	3443	no	IND	Independent
Tamil Nadu	Viluppuram	RAJAMANICKAM K	1197	no	IND	Independent
Tamil Nadu	Viluppuram	MUTHAIYAN K DR	289337	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Viluppuram	ANANDAN G	17408	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Viluppuram	ARASAN K	811	no	IND	Independent
Tamil Nadu	Viluppuram	SRINIVASAN K	2294	no	IND	Independent
Tamil Nadu	Viluppuram	KARUNSIRUTHAI KALIYAMURTHI G	2683	no	BSP	Bahujan Samaj Party
Tamil Nadu	Viluppuram	DESINGU K	1607	no	IND	Independent
Tamil Nadu	Viluppuram	ELUMALAI D	2337	no	IND	Independent
Tamil Nadu	Viluppuram	RANI K	21461	no	INC	Indian National Congress
Tamil Nadu	Viluppuram	RAMESH A	2663	no	IND	Independent
Tamil Nadu	Viluppuram	ARUMUGAM R	693	no	IND	Independent
Tamil Nadu	Viluppuram	VENKATESAN M	3906	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tamil Nadu	Kallakurichi	KAMARAJ. K	533383	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Kallakurichi	GOVINDASAMY.A	1015	no	IND	Independent
Tamil Nadu	Kallakurichi	ESWARAN. V. P	164183	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Kallakurichi	BOJAN. G	2255	no	IND	Independent
Tamil Nadu	Kallakurichi	LAKSHMANAN. S	3261	no	IND	Independent
Tamil Nadu	Kallakurichi	VELLIYAN. K	946	no	IND	Independent
Tamil Nadu	Kallakurichi	VARADAN. M	5054	no	IND	Independent
Tamil Nadu	Kallakurichi	SEKAR. K	962	no	IND	Independent
Tamil Nadu	Kallakurichi	PERUMAL. A. K.	3512	no	JMM(U)	Jharkhand Mukti Morcha  (Ulgulan)
Tamil Nadu	Kallakurichi	SHAJAHAN. A	1499	no	IND	Independent
Tamil Nadu	Kallakurichi	SAKTHIVEL.S	5929	no	BSP	Bahujan Samaj Party
Tamil Nadu	Kallakurichi	VETRIVEL. K	2029	no	IND	Independent
Tamil Nadu	Kallakurichi	MOHAMMED YASIN. K	2910	no	AAAP	Aam Aadmi Party
Tamil Nadu	Kallakurichi	MANIMARAN. R	309876	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Kallakurichi	RAMACHANDIRAN. T	1013	no	IND	Independent
Tamil Nadu	Kallakurichi	MADAIYAN. S	7314	no	IND	Independent
Tamil Nadu	Kallakurichi	MANNAN. M. P	6186	no	IND	Independent
Tamil Nadu	Kallakurichi	NARAYANSAMY. P	1684	no	IND	Independent
Tamil Nadu	Kallakurichi	PANDIAN. R	1221	no	IND	Independent
Tamil Nadu	Kallakurichi	KAMARAJ. R	1334	no	IND	Independent
Tamil Nadu	Kallakurichi	DEVADASS. R	39677	no	INC	Indian National Congress
Tamil Nadu	Kallakurichi	VENNILA.  K	1097	no	IND	Independent
Tamil Nadu	Kallakurichi	None of the Above	10901	no	NOTA	None of the Above
Tamil Nadu	Salem	PANNERSELVAM.V	556546	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Salem	None of the Above	20601	no	NOTA	None of the Above
Tamil Nadu	Salem	SHANMUGAM G	1623	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Salem	VENKATESAN R	623	no	RPI(A)	Republican Party of India (A)
Tamil Nadu	Salem	RAJA A	2203	no	IND	Independent
Tamil Nadu	Salem	THIAGARAJAN K	777	no	IND	Independent
Tamil Nadu	Salem	RAJAKANNU A	1151	no	IND	Independent
Tamil Nadu	Salem	SENTHIL KUMAR M	2903	no	IND	Independent
Tamil Nadu	Salem	VINAYAGAMOORTHI T	1619	no	BSP	Bahujan Samaj Party
Tamil Nadu	Salem	MASS GANESH A	474	no	TTNC	Thrinamool Tamil Nadu Congress
Tamil Nadu	Salem	PRAKASAM R	484	no	IND	Independent
Tamil Nadu	Salem	KUPPAYEE R	1701	no	IND	Independent
Tamil Nadu	Salem	ARUMUGAM M	731	no	IND	Independent
Tamil Nadu	Salem	UMARANI. S	288936	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Salem	GOVINDARAJU K	3261	no	IND	Independent
Tamil Nadu	Salem	SAKTHIVEL S	2581	no	IND	Independent
Tamil Nadu	Salem	NANDAKUMAR G	357	no	IND	Independent
Tamil Nadu	Salem	KANDASAMY R	2550	no	IND	Independent
Tamil Nadu	Salem	ABDUL WAHID I	526	no	IND	Independent
Tamil Nadu	Salem	SATHEESH KUMAR E	5198	no	AAAP	Aam Aadmi Party
Tamil Nadu	Salem	SUDHISH L K	201265	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Salem	UMARANI S	1232	no	IND	Independent
Tamil Nadu	Salem	CHINNUSAMY N	5374	no	IND	Independent
Tamil Nadu	Salem	RANGARAJAN MOHAN KUMARAMANGALAM	46477	no	INC	Indian National Congress
Tamil Nadu	Salem	PANNEERSELVAM N	574	no	IND	Independent
Tamil Nadu	Salem	M AHAMED SHAHJAHAN	529	no	IND	Independent
Tamil Nadu	Namakkal	SUNDARAM P.R	563272	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Namakkal	GNANAPANDITHAN.P	700	no	RPI(A)	Republican Party of India (A)
Tamil Nadu	Namakkal	PANNEERSELVAM.S	823	no	IND	Independent
Tamil Nadu	Namakkal	DHARMALINGAM.M	473	no	IND	Independent
Tamil Nadu	Namakkal	MURALI.V	2487	no	IND	Independent
Tamil Nadu	Namakkal	SIVAJI.S.M	4808	no	IND	Independent
Tamil Nadu	Namakkal	KALAIVANAN.M	1711	no	JD(S)	Janata Dal  (Secular)
Tamil Nadu	Namakkal	NATARAJAN.M	1182	no	IND	Independent
Tamil Nadu	Namakkal	RAMAJEYAM.G	915	no	IND	Independent
Tamil Nadu	Namakkal	SAKTHIVEL.S.P	3920	no	IND	Independent
Tamil Nadu	Namakkal	VELUSAMY.S	4827	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Namakkal	SUBRAMANI.S	3070	no	IND	Independent
Tamil Nadu	Namakkal	SELVARAJ.S	835	no	IND	Independent
Tamil Nadu	Namakkal	SELVAM.G	3362	no	IND	Independent
Tamil Nadu	Namakkal	VEL S.K	146882	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Namakkal	SUBRAMANIYAN G.R	19800	no	INC	Indian National Congress
Tamil Nadu	Namakkal	PERUMAL.A	858	no	IND	Independent
Tamil Nadu	Namakkal	KRISHNAN.M	2079	no	IND	Independent
Tamil Nadu	Namakkal	RAMASAMY.N	1874	no	IND	Independent
Tamil Nadu	Namakkal	VADIVEL.R	737	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Namakkal	GANDHISELVAN.S	268898	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Namakkal	RAMASAMY.P	717	no	IND	Independent
Tamil Nadu	Namakkal	SELVARAJ.R	1788	no	BSP	Bahujan Samaj Party
Tamil Nadu	Namakkal	PONMUDI.P	787	no	IND	Independent
Tamil Nadu	Namakkal	CHELLAKUMARASAMY.T.S(DR)	4348	no	AAAP	Aam Aadmi Party
Tamil Nadu	Namakkal	GUNASEKARAN.S	1600	no	IND	Independent
Tamil Nadu	Namakkal	None of the Above	16002	no	NOTA	None of the Above
Tamil Nadu	Erode	SELVAKUMARA CHINNAYAN S	466995	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Erode	MAYILSAMY K	1592	no	IND	Independent
Tamil Nadu	Erode	SETHUPATHY	5917	no	BSP	Bahujan Samaj Party
Tamil Nadu	Erode	KUMARASAMY K.K	4654	no	AAAP	Aam Aadmi Party
Tamil Nadu	Erode	GANESHAMURTHI.A	255432	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Erode	VIJAYAKUMAR S	7478	no	IND	Independent
Tamil Nadu	Erode	GOPI P	26726	no	INC	Indian National Congress
Tamil Nadu	Erode	PALANIAPPAN JOSIYAR C.M	519	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Erode	GUNASEKARAN T.K	1298	no	UCPI	United Communist Party of India
Tamil Nadu	Erode	THANGARAJ V	818	no	IND	Independent
Tamil Nadu	Erode	DURAISAMY.A	610	no	IND	Independent
Tamil Nadu	Erode	RAJAGOPAL V	1969	no	IND	Independent
Tamil Nadu	Erode	PAVITHRAVALLI H	217260	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Erode	None of the Above	16268	no	NOTA	None of the Above
Tamil Nadu	Erode	MURUGESAN_S	1046	no	IND	Independent
Tamil Nadu	Tiruppur	V.SATHYABAMA	442778	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Tiruppur	A.K.SHEIKDAVOOD	788	no	IND	Independent
Tamil Nadu	Tiruppur	K.SUBBARAYAN	33331	no	CPI	Communist Party of India
Tamil Nadu	Tiruppur	E.V.K.S. ELANGOVAN	47554	no	INC	Indian National Congress
Tamil Nadu	Tiruppur	R.CHAKRAVARTHI RAJA GOPALA KRISHNAN	3087	no	AAAP	Aam Aadmi Party
Tamil Nadu	Tiruppur	M.PALANISAMY	4074	no	IND	Independent
Tamil Nadu	Tiruppur	R.GOPALSAMY	2167	no	LSP	Lok Satta Party
Tamil Nadu	Tiruppur	P.SELVABOOBATHI	4264	no	IND	Independent
Tamil Nadu	Tiruppur	N.SIVAKUMAR	949	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Tiruppur	M.RAJENDRAN	1991	no	IND	Independent
Tamil Nadu	Tiruppur	N.DINESHKUMAR	263463	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Tiruppur	K.SENTHILKUMAR	1926	no	IND	Independent
Tamil Nadu	Tiruppur	R.NANDHAKUMAR	900	no	IND	Independent
Tamil Nadu	Tiruppur	A.PONNUSAMY	1193	no	IND	Independent
Tamil Nadu	Tiruppur	P.BALASUBRAMANIYAN	916	no	IND	Independent
Tamil Nadu	Tiruppur	M.SENTHILNATHAN	205411	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Tiruppur	PASTOR A.JAMES	1153	no	IND	Independent
Tamil Nadu	Tiruppur	R.R.MURUGAN	6422	no	IND	Independent
Tamil Nadu	Tiruppur	R.SATHYABAMA	1097	no	IND	Independent
Tamil Nadu	Tiruppur	R.SATHYABHAMA	1193	no	IND	Independent
Tamil Nadu	Tiruppur	J.LAXMI	908	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Tiruppur	N.GURUSAMY	4665	no	BSP	Bahujan Samaj Party
Tamil Nadu	Tiruppur	S.K.DINESHKUMAR	1820	no	IND	Independent
Tamil Nadu	Tiruppur	R.MURUGESAN	2010	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Tiruppur	A.MOGHEETH KHAN	1038	no	IND	Independent
Tamil Nadu	Tiruppur	T.S.SENTHILKUMAR	1151	no	IND	Independent
Tamil Nadu	Tiruppur	None of the Above	13941	no	NOTA	None of the Above
Tamil Nadu	Nilgiris	GOPALAKRISHNAN, C.	463700	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Nilgiris	None of the Above	46559	no	NOTA	None of the Above
Tamil Nadu	Nilgiris	RAJA, A.	358760	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Nilgiris	DR.BALAN, P.P.	2704	no	IND	Independent
Tamil Nadu	Nilgiris	PONNUSAMY, P.	2733	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Nilgiris	GUNASEKARAN, K.	1650	no	RPI(A)	Republican Party of India (A)
Tamil Nadu	Nilgiris	SUBRAMANIYAM, P.	1711	no	IND	Independent
Tamil Nadu	Nilgiris	ESWARAN, T.	1655	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Nilgiris	RANI, M.T.	12525	no	AAAP	Aam Aadmi Party
Tamil Nadu	Nilgiris	GANDHI, P.	37702	no	INC	Indian National Congress
Tamil Nadu	Nilgiris	KALA, M.	3377	no	BSP	Bahujan Samaj Party
Tamil Nadu	Coimbatore	NAGARAJAN, P.	431717	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Coimbatore	GANESHKUMAR, K.	217083	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Coimbatore	TAMILNADU SELVAM, ERA.	1959	no	BSP	Bahujan Samaj Party
Tamil Nadu	Coimbatore	NATARAJAN, P.R.	34197	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Coimbatore	PRABHU, R.	56962	no	INC	Indian National Congress
Tamil Nadu	Coimbatore	RADHAKRISHNAN, C.P.	389701	no	BJP	Bharatiya Janata Party
Tamil Nadu	Coimbatore	DORAISAMY, A.	550	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Coimbatore	JAGADEESWARAN, S.	596	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Coimbatore	ABDUL SALAM, A.	766	no	IND	Independent
Tamil Nadu	Coimbatore	KALAPIRAR KUPPUSAMY, C.	1051	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Coimbatore	CHANDRAN, D.	1360	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tamil Nadu	Coimbatore	PON CHANDRAN.	6680	no	AAAP	Aam Aadmi Party
Tamil Nadu	Coimbatore	ALPHONESRAJ, M.	1306	no	IND	Independent
Tamil Nadu	Coimbatore	ANBUSELVAN.	799	no	IND	Independent
Tamil Nadu	Coimbatore	RAJAGOPAL, M.	1232	no	IND	Independent
Tamil Nadu	Coimbatore	KANAGASABAPATHY, G.	2329	no	IND	Independent
Tamil Nadu	Coimbatore	KITTUSAMY, P.	2122	no	IND	Independent
Tamil Nadu	Coimbatore	SUBASH, K.R.	1756	no	IND	Independent
Tamil Nadu	Coimbatore	TAMILSELVAN, K.	640	no	IND	Independent
Tamil Nadu	Coimbatore	RANJITHKUMAR, M.	1100	no	IND	Independent
Tamil Nadu	Coimbatore	RADHAKRISHNAN, R.	1672	no	IND	Independent
Tamil Nadu	Coimbatore	NAGARAJ, S.	1699	no	IND	Independent
Tamil Nadu	Coimbatore	MOHANRAJ, K.	607	no	IND	Independent
Tamil Nadu	Coimbatore	JERLLD AMALAJOTHI, I.	858	no	IND	Independent
Tamil Nadu	Coimbatore	SRIDHARAN, S.	450	no	IND	Independent
Tamil Nadu	Coimbatore	None of the Above	17428	no	NOTA	None of the Above
Tamil Nadu	Pollachi	MAHENDRAN.C	417092	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Pollachi	None of the Above	12947	no	NOTA	None of the Above
Tamil Nadu	Pollachi	MARY STELLA. M	4942	no	IND	Independent
Tamil Nadu	Pollachi	PALANISAMY. N	2192	no	IND	Independent
Tamil Nadu	Pollachi	RAMASAMY. K	2071	no	IND	Independent
Tamil Nadu	Pollachi	MAHENDRAN. M	1171	no	IND	Independent
Tamil Nadu	Pollachi	PRABHU.  J	1599	no	IND	Independent
Tamil Nadu	Pollachi	RAJENDRAN. M	1004	no	IND	Independent
Tamil Nadu	Pollachi	KATHIRESAN. L	1523	no	IND	Independent
Tamil Nadu	Pollachi	EASWARAN . R	891	no	IND	Independent
Tamil Nadu	Pollachi	SELVARAJ	30014	no	INC	Indian National Congress
Tamil Nadu	Pollachi	SATHISH KUMAR .L	895	no	SP	Samajwadi Party
Tamil Nadu	Pollachi	PALANISAMY. V	2472	no	IND	Independent
Tamil Nadu	Pollachi	KATHIRAVAN. K	626	no	IND	Independent
Tamil Nadu	Pollachi	KARTHIKEYAN. PON	494	no	IND	Independent
Tamil Nadu	Pollachi	DEIVASIGAMANI.K	834	no	IND	Independent
Tamil Nadu	Pollachi	MANOHARAN.M	3953	no	BSP	Bahujan Samaj Party
Tamil Nadu	Pollachi	PONGALUR PALANISAMY.N	251829	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Pollachi	ESWARAN.E.R.	276118	no	BJP	Bharatiya Janata Party
Tamil Nadu	Dindigul	UDHAYA KUMAR .M	510462	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Dindigul	None of the Above	10591	no	NOTA	None of the Above
Tamil Nadu	Dindigul	KRISHNAMOORTHY A	93794	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Dindigul	MANIVANNAN N	5859	no	IND	Independent
Tamil Nadu	Dindigul	RAJENDHRAN. R	7580	no	IND	Independent
Tamil Nadu	Dindigul	PAULDURAI  V K	2604	no	IND	Independent
Tamil Nadu	Dindigul	CHANDRAMOHAN P	1261	no	IND	Independent
Tamil Nadu	Dindigul	KUPPURAJ C	627	no	IND	Independent
Tamil Nadu	Dindigul	GANDHIRAJAN S	382617	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Dindigul	NAGARAJAN V	1202	no	IND	Independent
Tamil Nadu	Dindigul	ELANCHEZHIAN.S	3395	no	AAAP	Aam Aadmi Party
Tamil Nadu	Dindigul	THANGAPANDIAN.R	786	no	IND	Independent
Tamil Nadu	Dindigul	DHANDAPANI.K	863	no	IND	Independent
Tamil Nadu	Dindigul	BAGATSINGH PALANICHAMY M	1903	no	BSP	Bahujan Samaj Party
Tamil Nadu	Dindigul	CHITTHAN N.S.V	35632	no	INC	Indian National Congress
Tamil Nadu	Dindigul	VENKATESAN G	3588	no	IND	Independent
Tamil Nadu	Dindigul	SUDHARSHNAR.K	592	no	IND	Independent
Tamil Nadu	Dindigul	PANDI.N	19455	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Dindigul	BALASUBRAMANI.R	553	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Karur	THAMBIDURAI,M.	540722	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Karur	KRISHNAN, N.S.	76560	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Karur	BALASUBRAMANIAN,  G.	4486	no	IND	Independent
Tamil Nadu	Karur	RAVICHANDRAN, V.	563	no	UMK	Ulzaipali Makkal Katchy
Tamil Nadu	Karur	GOWRIMANI, S.	1433	no	IND	Independent
Tamil Nadu	Karur	KANNAN, P.	1436	no	IND	Independent
Tamil Nadu	Karur	JOTHIMANI, S.	30459	no	INC	Indian National Congress
Tamil Nadu	Karur	CHINNASAMY, M.	345475	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Karur	MAHENDRAN, T.	1024	no	IND	Independent
Tamil Nadu	Karur	MURUGESAN, M.	848	no	SP	Samajwadi Party
Tamil Nadu	Karur	None of the Above	13763	no	NOTA	None of the Above
Tamil Nadu	Karur	VIJAY, J.	847	no	IND	Independent
Tamil Nadu	Karur	RAVI, S.P.	732	no	IND	Independent
Tamil Nadu	Karur	VIGNESHWARAN, M.	981	no	IND	Independent
Tamil Nadu	Karur	THAMILALAGAN, A.	1387	no	IND	Independent
Tamil Nadu	Karur	SELVARAJ, S.	3552	no	IND	Independent
Tamil Nadu	Karur	MARUTHAIVEERAN, V.	5694	no	BSP	Bahujan Samaj Party
Tamil Nadu	Karur	MANICKAVASAGAM, A.	831	no	IND	Independent
Tamil Nadu	Karur	VALAIYAPATHI, R.	2440	no	AAAP	Aam Aadmi Party
Tamil Nadu	Karur	SARASWATHY, K.	813	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Karur	CHINNASAMY, M.	3719	no	IND	Independent
Tamil Nadu	Karur	KANNAN, S.	668	no	IND	Independent
Tamil Nadu	Karur	MANEESHANKAR, M.G.	1276	no	RSPS	Rashtriya Samaj Paksha
Tamil Nadu	Karur	PRAKASH, N.	4280	no	IND	Independent
Tamil Nadu	Karur	MURUGAAN, N.	437	no	IND	Independent
Tamil Nadu	Karur	CHINNASAMY, V.	2108	no	IND	Independent
Tamil Nadu	Tiruchirappalli	KUMAR.P	458478	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Tiruchirappalli	SRIDEVI.S	2348	no	IND	Independent
Tamil Nadu	Tiruchirappalli	VEERAMALAI.A	589	no	IND	Independent
Tamil Nadu	Tiruchirappalli	THADDAUS FERNANDEZ,	751	no	IND	Independent
Tamil Nadu	Tiruchirappalli	ASRAF ALI.M	734	no	IND	Independent
Tamil Nadu	Tiruchirappalli	VIJAYKUMAR.AMG	94785	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Tiruchirappalli	RAVI.P	4885	no	AAAP	Aam Aadmi Party
Tamil Nadu	Tiruchirappalli	ARUNACHALAM.A	3239	no	IND	Independent
Tamil Nadu	Tiruchirappalli	ESWARAN.J	2484	no	IND	Independent
Tamil Nadu	Tiruchirappalli	BALA KRISHNAN.K	485	no	IND	Independent
Tamil Nadu	Tiruchirappalli	SUNDARRAJ.S	784	no	IND	Independent
Tamil Nadu	Tiruchirappalli	RAGAVAN. R	1283	no	IND	Independent
Tamil Nadu	Tiruchirappalli	PALANIVEL.P	687	no	IND	Independent
Tamil Nadu	Tiruchirappalli	None of the Above	22848	no	NOTA	None of the Above
Tamil Nadu	Tiruchirappalli	ANWAR DEEN.S	731	no	SAP	Samata Party
Tamil Nadu	Tiruchirappalli	SARUBALA  R THONDAIMAN	51537	no	INC	Indian National Congress
Tamil Nadu	Tiruchirappalli	KUMAR.P	4077	no	IND	Independent
Tamil Nadu	Tiruchirappalli	DINESHKUMAR.G	1119	no	IND	Independent
Tamil Nadu	Tiruchirappalli	VIJAYAKUMAR.P	680	no	IND	Independent
Tamil Nadu	Tiruchirappalli	JOTHI.P	1003	no	SP	Samajwadi Party
Tamil Nadu	Tiruchirappalli	ANBALAGAN.M	636	no	IND	Independent
Tamil Nadu	Tiruchirappalli	LARANCE.Y	1198	no	IND	Independent
Tamil Nadu	Tiruchirappalli	ANBHALAGAN.MU	308002	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Tiruchirappalli	MOHANDASS.M	355	no	IND	Independent
Tamil Nadu	Tiruchirappalli	NATARAJAN.S	1710	no	BSP	Bahujan Samaj Party
Tamil Nadu	Tiruchirappalli	BADHUSHA.M	669	no	IND	Independent
Tamil Nadu	Tiruchirappalli	SRIDHAR.S	17039	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Tiruchirappalli	SHADHICK BATCHA.A	3083	no	IND	Independent
Tamil Nadu	Tiruchirappalli	KARUNAKARAN.M	606	no	LSP	Lok Satta Party
Tamil Nadu	Tiruchirappalli	ASAITHAMBI.PALA	1425	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tamil Nadu	Perambalur	MARUTHARAJAA, R.P.	462693	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Perambalur	RAMAR YADAV,  K.	6324	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Perambalur	ABDUL RAHMAN,  R.	1018	no	IND	Independent
Tamil Nadu	Perambalur	RENGARAJU, M.	633	no	IND	Independent
Tamil Nadu	Perambalur	VELAYUTHAM, C.	2995	no	IND	Independent
Tamil Nadu	Perambalur	PARIVENDAR  PACHAMUTHU, T.R.	238887	no	BJP	Bharatiya Janata Party
Tamil Nadu	Perambalur	SELVARAJ, V.M.	2924	no	BSP	Bahujan Samaj Party
Tamil Nadu	Perambalur	SEEMANUR  PRABU, S.	249645	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Perambalur	None of the Above	11605	no	NOTA	None of the Above
Tamil Nadu	Perambalur	SIVAPERUMAL,  K.	986	no	IND	Independent
Tamil Nadu	Perambalur	ANBIL  THANGAMANI, K.	1636	no	IND	Independent
Tamil Nadu	Perambalur	THOZHAR  TAMILSELVAN,  P.	1509	no	UCPI	United Communist Party of India
Tamil Nadu	Perambalur	RAJASEKARAN,  M.,	31998	no	INC	Indian National Congress
Tamil Nadu	Perambalur	SUBRAMANI, D.	1201	no	IND	Independent
Tamil Nadu	Perambalur	CHINNA RAJENDRAN, C.	877	no	IND	Independent
Tamil Nadu	Perambalur	VISWANATHAN, A.	2369	no	IND	Independent
Tamil Nadu	Perambalur	VADIVELU, P.	1821	no	IND	Independent
Tamil Nadu	Perambalur	RAJASEKAR, T.	1490	no	IND	Independent
Tamil Nadu	Perambalur	PRABU,  S.	2211	no	IND	Independent
Tamil Nadu	Perambalur	PACHAMUTHU, N.	2762	no	IND	Independent
Tamil Nadu	Perambalur	MARUTHARAJ, P.	3940	no	IND	Independent
Tamil Nadu	Perambalur	MANI, P.	1302	no	IND	Independent
Tamil Nadu	Cuddalore	ARUNMOZHITHEVAN.A	481429	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Cuddalore	SHRUTI MISHRA	944	no	UPI	Union Party of India
Tamil Nadu	Cuddalore	ALAGIRI.S	26650	no	INC	Indian National Congress
Tamil Nadu	Cuddalore	SENGUTTUVAN.R	862	no	IND	Independent
Tamil Nadu	Cuddalore	SENTHILMURUGAN.S	2642	no	BSP	Bahujan Samaj Party
Tamil Nadu	Cuddalore	THIRUNAVUKKARASU	1782	no	IND	Independent
Tamil Nadu	Cuddalore	NANDAGOPALAKRISHNAN.K.	278304	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Cuddalore	RAJKUMAR.V	7678	no	IND	Independent
Tamil Nadu	Cuddalore	SYED MOHIDEEN.A.G.	3473	no	AAAP	Aam Aadmi Party
Tamil Nadu	Cuddalore	JAISHANKAR.P.	6250	no	IND	Independent
Tamil Nadu	Cuddalore	RADHAKRISHNAN.K.R	1370	no	IND	Independent
Tamil Nadu	Cuddalore	ANANDHARAJAN.S	1309	no	IND	Independent
Tamil Nadu	Cuddalore	None of the Above	10338	no	NOTA	None of the Above
Tamil Nadu	Cuddalore	BASKAR.A	872	no	IND	Independent
Tamil Nadu	Cuddalore	JAYASANKAR.C.R	147606	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Cuddalore	SIVAGNANASAMBANDAN.T	1257	no	IND	Independent
Tamil Nadu	Cuddalore	BALASUBRAMANIAN.K	11122	no	CPI	Communist Party of India
Tamil Nadu	Cuddalore	GIRIJA.S	650	no	IND	Independent
Tamil Nadu	Chidambaram	CHANDRAKASI, M	429536	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Chidambaram	PARVATHI, S	1543	no	IND	Independent
Tamil Nadu	Chidambaram	SENTHAMIZHSELVI, K	1469	no	IND	Independent
Tamil Nadu	Chidambaram	VALLAL PERUMAN, P	28988	no	INC	Indian National Congress
Tamil Nadu	Chidambaram	THIRUMAAVALAVAN, THOL	301041	no	VCK	Viduthalai Chiruthaigal Katchi
Tamil Nadu	Chidambaram	None of the Above	12138	no	NOTA	None of the Above
Tamil Nadu	Chidambaram	THIRUSANGU, A	3370	no	IND	Independent
Tamil Nadu	Chidambaram	EZHUMALAI, V	6824	no	ABHM	Akhil Bharat Hindu Mahasabha
Tamil Nadu	Chidambaram	SUDHAMANIRATHINEM	279016	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Chidambaram	CHANDRAGASAN, N	1182	no	IND	Independent
Tamil Nadu	Chidambaram	PAZHANIVEL, P	1772	no	IND	Independent
Tamil Nadu	Chidambaram	ANANDAN ALIAS KAVIGNAR ANANTHAMAALAI, S	8875	no	IND	Independent
Tamil Nadu	Chidambaram	RAJENDRAN, K	5179	no	BSP	Bahujan Samaj Party
Tamil Nadu	Chidambaram	RAJKUMAR, S	4896	no	IND	Independent
Tamil Nadu	Chidambaram	SANMUGAM, A	1071	no	IND	Independent
Tamil Nadu	Chidambaram	PUSHPARAJ, S	1523	no	IND	Independent
Tamil Nadu	Mayiladuthurai	BHARATHI MOHAN R.K	513729	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Mayiladuthurai	None of the Above	13181	no	NOTA	None of the Above
Tamil Nadu	Mayiladuthurai	MUNUSAMY.V	1994	no	IND	Independent
Tamil Nadu	Mayiladuthurai	GUNASEKARAN.N	4149	no	IND	Independent
Tamil Nadu	Mayiladuthurai	MANI SHANKAR AIYAR	58465	no	INC	Indian National Congress
Tamil Nadu	Mayiladuthurai	TIMOTHY.T	2327	no	IND	Independent
Tamil Nadu	Mayiladuthurai	PANDIYAN .R	11613	no	IND	Independent
Tamil Nadu	Mayiladuthurai	RASAVELU.K	3654	no	BSP	Bahujan Samaj Party
Tamil Nadu	Mayiladuthurai	ARUMUGAM. C	2028	no	IND	Independent
Tamil Nadu	Mayiladuthurai	AGORAM. K	144085	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Mayiladuthurai	IRULAPPAN.V	2643	no	SP	Samajwadi Party
Tamil Nadu	Mayiladuthurai	THIRUNAVUKKARASU. R	4714	no	IND	Independent
Tamil Nadu	Mayiladuthurai	SHAMSUDEEN. M	2303	no	IND	Independent
Tamil Nadu	Mayiladuthurai	NAGARAJAN. M	1066	no	IND	Independent
Tamil Nadu	Mayiladuthurai	FATHIMA ALIAS BHARATHI. R	7582	no	IND	Independent
Tamil Nadu	Mayiladuthurai	RAGURAMAN. A	7214	no	IND	Independent
Tamil Nadu	Mayiladuthurai	REVATHI.A	6286	no	IND	Independent
Tamil Nadu	Mayiladuthurai	JARINA BEGAM. N	1056	no	IND	Independent
Tamil Nadu	Mayiladuthurai	JEYAPRAKASH. B	770	no	IND	Independent
Tamil Nadu	Mayiladuthurai	HYDER ALI.S	236679	no	MAMAK	Manithaneya Makkal Katchi
Tamil Nadu	Nagapattinam	GOPAL. DR. K	434174	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Nagapattinam	SENTHILPANDIAN. THALAI. T.A.P	23967	no	INC	Indian National Congress
Tamil Nadu	Nagapattinam	PALANISAMY. G	90313	no	CPI	Communist Party of India
Tamil Nadu	Nagapattinam	VIJAYAN. A.K.S	328095	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Nagapattinam	VADIVEL RAVANAN	43506	no	PMK	Pattali Makkal Katchi
Tamil Nadu	Nagapattinam	MOHAN. G	1754	no	IND	Independent
Tamil Nadu	Nagapattinam	MURUGAIYAN. K	1033	no	IND	Independent
Tamil Nadu	Nagapattinam	DEVAKI. G	1847	no	IND	Independent
Tamil Nadu	Nagapattinam	THANGASAMY. V	1851	no	BSP	Bahujan Samaj Party
Tamil Nadu	Nagapattinam	None of the Above	15662	no	NOTA	None of the Above
Tamil Nadu	Thanjavur	PARASURAMAN.K	510307	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Thanjavur	None of the Above	12218	no	NOTA	None of the Above
Tamil Nadu	Thanjavur	ARANGARAJAN.M	2253	no	BSP	Bahujan Samaj Party
Tamil Nadu	Thanjavur	RAJKUMAR.R	1409	no	IND	Independent
Tamil Nadu	Thanjavur	GUNASEKARAN.T	894	no	IND	Independent
Tamil Nadu	Thanjavur	PANASAI ARANGAN.N	1830	no	IND	Independent
Tamil Nadu	Thanjavur	MURUGANANTHAM.M	58521	no	BJP	Bharatiya Janata Party
Tamil Nadu	Thanjavur	TAMILSELVI.S	23215	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Thanjavur	SIVAKUMAR.V	1511	no	IND	Independent
Tamil Nadu	Thanjavur	KRISHNASAMY VANDAYAR.T	30232	no	INC	Indian National Congress
Tamil Nadu	Thanjavur	BAALU.T.R	366188	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Thanjavur	MUMMOORTHI.S@S.M.MOORTHY	2511	no	NMK	Namadhu Makkal Katchi
Tamil Nadu	Thanjavur	MADHAVAN.T	1169	no	IND	Independent
Tamil Nadu	Sivaganga	SENTHILNATHAN PR	475993	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Sivaganga	PALANI S	1699	no	IND	Independent
Tamil Nadu	Sivaganga	THAMILARIMA S	2131	no	AAAP	Aam Aadmi Party
Tamil Nadu	Sivaganga	NATCHIAPPAN SP	794	no	IND	Independent
Tamil Nadu	Sivaganga	LENIN M	741	no	IND	Independent
Tamil Nadu	Sivaganga	NELSON A	2987	no	IND	Independent
Tamil Nadu	Sivaganga	RAJA A	1337	no	IND	Independent
Tamil Nadu	Sivaganga	DHURAI RAAJ SUBHA	246608	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Sivaganga	RADHAKRISHNAN A	641	no	IND	Independent
Tamil Nadu	Sivaganga	SELVARAJ A	2050	no	IND	Independent
Tamil Nadu	Sivaganga	KARTI P CHIDAMBARAM	104678	no	INC	Indian National Congress
Tamil Nadu	Sivaganga	MURUGAN P	736	no	IND	Independent
Tamil Nadu	Sivaganga	None of the Above	8042	no	NOTA	None of the Above
Tamil Nadu	Sivaganga	MEGARAJ MALLAR	565	no	IND	Independent
Tamil Nadu	Sivaganga	GUNASEKARAN P	1269	no	IND	Independent
Tamil Nadu	Sivaganga	KRISHNAN S	20473	no	CPI	Communist Party of India
Tamil Nadu	Sivaganga	ARIMAZHAM THIYAGI SUBRAMANIAN MUTHURAJA M	3021	no	IND	Independent
Tamil Nadu	Sivaganga	VELLADHURAI A	3023	no	ETMK	Ezhuchi Tamilargal Munnetra Kazhagam
Tamil Nadu	Sivaganga	BALAKRISHNAN P	949	no	IND	Independent
Tamil Nadu	Sivaganga	BALU S	2196	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Sivaganga	RAJA H	133763	no	BJP	Bharatiya Janata Party
Tamil Nadu	Sivaganga	PERIYAKARUPPAN S	837	no	IND	Independent
Tamil Nadu	Sivaganga	RAKKAMUTHU V	3056	no	BSP	Bahujan Samaj Party
Tamil Nadu	Sivaganga	SIVAGNANAM AL	1007	no	IND	Independent
Tamil Nadu	Sivaganga	KARTHIKARAJAN T	873	no	IND	Independent
Tamil Nadu	Sivaganga	RAJASEKAR.S @ S.R.THEVAR	4029	no	DFBL	Desiya Forward Bloc
Tamil Nadu	Sivaganga	SARAVANAN M	2100	no	IND	Independent
Tamil Nadu	Sivaganga	KALAIMANI K	1438	no	MMKA	Makkal Manadu Katchi
Tamil Nadu	Madurai	R.GOPALAKRISHNAN	454167	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Madurai	BHARATHI KANNAMMA	1232	no	IND	Independent
Tamil Nadu	Madurai	K.K.RAMESH	432	no	IND	Independent
Tamil Nadu	Madurai	B.VIKRAMAN	30108	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Madurai	G.MARIA JOSEPH XAVIOUR	568	no	IND	Independent
Tamil Nadu	Madurai	S.MANICKAVASAGAM	341	no	IND	Independent
Tamil Nadu	Madurai	P.RAMASUBRAMANIAN	565	no	IND	Independent
Tamil Nadu	Madurai	M.GOPALAKRISHNAN	2770	no	IND	Independent
Tamil Nadu	Madurai	M.KAMACIS	5423	no	AAAP	Aam Aadmi Party
Tamil Nadu	Madurai	M.MOHAMMED ALI	904	no	SAP	Samata Party
Tamil Nadu	Madurai	V.ANDICHAMY	4461	no	IND	Independent
Tamil Nadu	Madurai	P.CHANDRABOSE	728	no	IND	Independent
Tamil Nadu	Madurai	T.N.BHARATH NACHIAPPAN	32143	no	INC	Indian National Congress
Tamil Nadu	Madurai	N ALAGU MUTHU VELAYUDAM	487	no	IND	Independent
Tamil Nadu	Madurai	M VENKATESAN	394	no	IND	Independent
Tamil Nadu	Madurai	D SIVAMUTHU KUMAR	147300	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Madurai	A THAVAMANI	1255	no	BSP	Bahujan Samaj Party
Tamil Nadu	Madurai	V VELUSAMY	256731	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Madurai	None of the Above	19866	no	NOTA	None of the Above
Tamil Nadu	Madurai	M.MEENAKSHISUNDARAM	429	no	IND	Independent
Tamil Nadu	Madurai	P.JEYAKUMAR	985	no	IND	Independent
Tamil Nadu	Madurai	S.ARIVUDAINAMBI	441	no	IND	Independent
Tamil Nadu	Madurai	V.KARUPPAIAH	2039	no	IND	Independent
Tamil Nadu	Madurai	T.RAMASAMY	432	no	IND	Independent
Tamil Nadu	Madurai	J.MANIKANDESWARAN	1576	no	IND	Independent
Tamil Nadu	Madurai	S.GANESAN	363	no	IND	Independent
Tamil Nadu	Madurai	V.G.RAMDOSS	2745	no	IND	Independent
Tamil Nadu	Madurai	A.KANNAN	420	no	IND	Independent
Tamil Nadu	Madurai	G.KANNAN	555	no	IND	Independent
Tamil Nadu	Madurai	P.GOPALAKRISHNAN	6337	no	IND	Independent
Tamil Nadu	Madurai	K.THANGAPANDI	666	no	IND	Independent
Tamil Nadu	Madurai	K. RAJA	1483	no	DFBL	Desiya Forward Bloc
Tamil Nadu	Theni	PARTHIPAN, R.	571254	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Theni	None of the Above	10312	no	NOTA	None of the Above
Tamil Nadu	Theni	KANNAN, S.	1273	no	IND	Independent
Tamil Nadu	Theni	INNASIMUTHU, M.S.	991	no	IND	Independent
Tamil Nadu	Theni	INIYAVAN, R.	739	no	IND	Independent
Tamil Nadu	Theni	KARUPPIAH, R.	679	no	IND	Independent
Tamil Nadu	Theni	MAMALLAN THA. KANAGAMUTHU	2681	no	IND	Independent
Tamil Nadu	Theni	SENTHIL RAJAN MALLAR, K.	2908	no	IND	Independent
Tamil Nadu	Theni	MANI, S.V.	1349	no	IND	Independent
Tamil Nadu	Theni	RABEEK, K.	767	no	IND	Independent
Tamil Nadu	Theni	RAJA, V.	695	no	IND	Independent
Tamil Nadu	Theni	MARIAPPAN, S.K.	3521	no	IND	Independent
Tamil Nadu	Theni	MUTHURAMALINGAM, M.	859	no	IND	Independent
Tamil Nadu	Theni	MOHAMED MIAKHAN, P.	1954	no	IND	Independent
Tamil Nadu	Theni	RAMPRAKASH, J.	3213	no	AAAP	Aam Aadmi Party
Tamil Nadu	Theni	ASATH, M.M.	1082	no	IND	Independent
Tamil Nadu	Theni	RAMAYA, R.	742	no	IND	Independent
Tamil Nadu	Theni	KRISHNAMOORTHY, K.	946	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Theni	MEENAKSHI SUNDARAM, G.K.	1107	no	SP	Samajwadi Party
Tamil Nadu	Theni	VOLTAIRE, M.J.	696	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Tamil Nadu	Theni	AARON RASHID, J.M.	71432	no	INC	Indian National Congress
Tamil Nadu	Theni	THANGADURAI, V.	5299	no	BSP	Bahujan Samaj Party
Tamil Nadu	Theni	PON. MUTHURAMALINGAM	256722	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Theni	ALAGUSUNDARAM, K.	134362	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Virudhunagar	RADHAKRISHNAN T	406694	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Virudhunagar	None of the Above	12225	no	NOTA	None of the Above
Tamil Nadu	Virudhunagar	SAMUELRAJ K	20157	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Virudhunagar	MANICKAM TAGORE B	38482	no	INC	Indian National Congress
Tamil Nadu	Virudhunagar	KARTHIKAISAMY P	2249	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Virudhunagar	SANTHANA KRISHNAN G	3411	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Virudhunagar	PRADEEPKUMAR D	681	no	LSP	Lok Satta Party
Tamil Nadu	Virudhunagar	MAHALINGAM T	1017	no	DFBL	Desiya Forward Bloc
Tamil Nadu	Virudhunagar	MOHANRAJ J	1517	no	JJ	Jebamani Janata
Tamil Nadu	Virudhunagar	VAIKO	261143	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Virudhunagar	KANNAN G	1133	no	IND	Independent
Tamil Nadu	Virudhunagar	SANKAR A	532	no	IND	Independent
Tamil Nadu	Virudhunagar	SATHESH M	903	no	IND	Independent
Tamil Nadu	Virudhunagar	SURESH GANDHI C	711	no	IND	Independent
Tamil Nadu	Virudhunagar	SOLAIRAJ K	2721	no	IND	Independent
Tamil Nadu	Virudhunagar	SOUNDRAPANDIYAN K	3470	no	IND	Independent
Tamil Nadu	Virudhunagar	DHANUSHKODI M	2522	no	IND	Independent
Tamil Nadu	Virudhunagar	PALANICHAMY MALLAR P	700	no	IND	Independent
Tamil Nadu	Virudhunagar	RETHINAVELU S	241505	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Virudhunagar	KAMALAVELSELVAN	1685	no	BSP	Bahujan Samaj Party
Tamil Nadu	Virudhunagar	PANDIARAJAN R	922	no	IND	Independent
Tamil Nadu	Virudhunagar	PALPANDI S DR	2342	no	IND	Independent
Tamil Nadu	Virudhunagar	MANMATHAN M	714	no	IND	Independent
Tamil Nadu	Virudhunagar	MADHAVAN P	642	no	IND	Independent
Tamil Nadu	Virudhunagar	MARIMUTHU C	629	no	IND	Independent
Tamil Nadu	Virudhunagar	RADHAKRISHNAN PV	1068	no	IND	Independent
Tamil Nadu	Virudhunagar	LAKSHMI KANDHAN S	1155	no	IND	Independent
Tamil Nadu	Ramanathapuram	ANWHAR RAAJHAA.A	405945	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Ramanathapuram	None of the Above	6279	no	NOTA	None of the Above
Tamil Nadu	Ramanathapuram	KUPPURAMU .D	171082	no	BJP	Bharatiya Janata Party
Tamil Nadu	Ramanathapuram	PERIYASAMY .P	985	no	IND	Independent
Tamil Nadu	Ramanathapuram	THIRUNNAVUKKARASAR .SU	62160	no	INC	Indian National Congress
Tamil Nadu	Ramanathapuram	VELCHSAMY .R	577	no	IND	Independent
Tamil Nadu	Ramanathapuram	KASIRAJAN .N	1415	no	IND	Independent
Tamil Nadu	Ramanathapuram	AZHAGU MEENA .A	835	no	IND	Independent
Tamil Nadu	Ramanathapuram	MURUGESWARI	814	no	IND	Independent
Tamil Nadu	Ramanathapuram	AMIRTHALINGAM .U	941	no	IND	Independent
Tamil Nadu	Ramanathapuram	SATHIAH .G	1129	no	IND	Independent
Tamil Nadu	Ramanathapuram	THENNARASU .P	1321	no	IND	Independent
Tamil Nadu	Ramanathapuram	KANNATHASAN .R	999	no	IND	Independent
Tamil Nadu	Ramanathapuram	MOOKAIYA .S	423	no	IND	Independent
Tamil Nadu	Ramanathapuram	RAVI .K	478	no	IND	Independent
Tamil Nadu	Ramanathapuram	BALAKRISHNAN .S.S.	1171	no	IND	Independent
Tamil Nadu	Ramanathapuram	VETRIVEL .S	914	no	IND	Independent
Tamil Nadu	Ramanathapuram	GOPAL .R	3969	no	IND	Independent
Tamil Nadu	Ramanathapuram	SASI KUMAR .S	1220	no	IND	Independent
Tamil Nadu	Ramanathapuram	THIAGARAJAN .R	1037	no	IND	Independent
Tamil Nadu	Ramanathapuram	AYYAPPAN .S	785	no	IND	Independent
Tamil Nadu	Ramanathapuram	NOOR JIYAVUDEEN.M.I	12541	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Tamil Nadu	Ramanathapuram	PALPANDI .C	1355	no	IND	Independent
Tamil Nadu	Ramanathapuram	GOKILA .K	3471	no	IND	Independent
Tamil Nadu	Ramanathapuram	SUBBIAH .M	645	no	IND	Independent
Tamil Nadu	Ramanathapuram	MANIVASAGAM	2684	no	IND	Independent
Tamil Nadu	Ramanathapuram	ARASAKUMAR B.T.	10945	no	DFBL	Desiya Forward Bloc
Tamil Nadu	Ramanathapuram	SIVAGURUNATHAN .K	2123	no	BSP	Bahujan Samaj Party
Tamil Nadu	Ramanathapuram	ANNAMALAI .S	3125	no	IND	Independent
Tamil Nadu	Ramanathapuram	ALLAPICHAI .E	747	no	IND	Independent
Tamil Nadu	Ramanathapuram	UMA MAGESWARI R.T.	12312	no	CPI	Communist Party of India
Tamil Nadu	Ramanathapuram	MOHAMED JALEEL .S	286621	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Thoothukkudi	JEYASINGH THIYAGARAJ NATTERJEE.J	366052	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Thoothukkudi	JOEL. S	182191	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Thoothukkudi	MOHANRAJ. A	14993	no	CPI	Communist Party of India
Tamil Nadu	Thoothukkudi	ANANDHARAJ, M.	739	no	IND	Independent
Tamil Nadu	Thoothukkudi	ALWARSAMY KARTHIKEYAN.L	1727	no	IND	Independent
Tamil Nadu	Thoothukkudi	RAMKUMAR. V	784	no	IND	Independent
Tamil Nadu	Thoothukkudi	PANNEERSELVAM.C	1129	no	IND	Independent
Tamil Nadu	Thoothukkudi	SANTHADEVI. S	765	no	IND	Independent
Tamil Nadu	Thoothukkudi	SAMUEL. A.S.	1157	no	IND	Independent
Tamil Nadu	Thoothukkudi	VINSTON ANTO. S	983	no	IND	Independent
Tamil Nadu	Thoothukkudi	JEGAN. P	242050	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Thoothukkudi	SHANMUGAM, A.P.C.V.	63080	no	INC	Indian National Congress
Tamil Nadu	Thoothukkudi	AYYADURAI. S	3205	no	BSP	Bahujan Samaj Party
Tamil Nadu	Thoothukkudi	None of the Above	11447	no	NOTA	None of the Above
Tamil Nadu	Thoothukkudi	PUSHPARAYAN.M.	26476	no	AAAP	Aam Aadmi Party
Tamil Nadu	Tenkasi	VASANTHI.M	424586	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Tenkasi	None of the Above	14492	no	NOTA	None of the Above
Tamil Nadu	Tenkasi	JANAGAR RAJA	1777	no	IND	Independent
Tamil Nadu	Tenkasi	KANNAN	2917	no	BSP	Bahujan Samaj Party
Tamil Nadu	Tenkasi	LINGAM.P	23528	no	CPI	Communist Party of India
Tamil Nadu	Tenkasi	GUNASEKARAN.K.	8022	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Tenkasi	DR.KRISHNASAMY.K.	262812	no	PT	Puthiya Tamilagam
Tamil Nadu	Tenkasi	DR.JAYAKUMAR.K.	58963	no	INC	Indian National Congress
Tamil Nadu	Tenkasi	MANMATHAN,M.	3331	no	IND	Independent
Tamil Nadu	Tenkasi	MAGESH.S.	2163	no	IND	Independent
Tamil Nadu	Tenkasi	MUNEESWARAN	2513	no	IND	Independent
Tamil Nadu	Tenkasi	RADHAKRISHNAN.S.	3904	no	IND	Independent
Tamil Nadu	Tenkasi	VELLADURAI.P.	1216	no	IND	Independent
Tamil Nadu	Tenkasi	KRISHNASAMY.A.	12115	no	IND	Independent
Tamil Nadu	Tenkasi	GANESAN.S.	1809	no	IND	Independent
Tamil Nadu	Tenkasi	ALAGUMALAI.V.	1215	no	IND	Independent
Tamil Nadu	Tenkasi	MEENASHISUNDARAVADIVEL.T.	888	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Tenkasi	DR.SADHAN THIRUMALAIKUMAR.T.	190233	no	MDMK	Marumalarchi Dravida Munnetra Kazhagam
Tamil Nadu	Tenkasi	POONGANI.J.	1454	no	SAP	Samata Party
Tamil Nadu	Tirunelveli	PRABAKARAN.K.R.P	398139	yes	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Tirunelveli	DHANARAJ.R.	3062	no	ICSP	Indian Christian Secular Party
Tamil Nadu	Tirunelveli	DHEVANATHAN YADAV.T.	19381	no	TNMC	Tamil Nadu Makkal Congress
Tamil Nadu	Tirunelveli	MOHAMED MUBARAK.V.M.S	14877	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
Tamil Nadu	Tirunelveli	SESURAJ.M.P	18353	no	AAAP	Aam Aadmi Party
Tamil Nadu	Tirunelveli	SIVANANENTHAPERUMAL.S.	127370	no	DMDK	Desiya Murpokku Dravida Kazhagam
Tamil Nadu	Tirunelveli	DEVADASA SUNDARAM	272040	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Tirunelveli	THEVENDRAN.T.	4692	no	BSP	Bahujan Samaj Party
Tamil Nadu	Tirunelveli	RAMASUBBU.S.S	62863	no	INC	Indian National Congress
Tamil Nadu	Tirunelveli	ELIZABETH.D	651	no	IND	Independent
Tamil Nadu	Tirunelveli	DR.GANESAN.M	1262	no	IND	Independent
Tamil Nadu	Tirunelveli	SURESH KUMAR.S	1281	no	IND	Independent
Tamil Nadu	Tirunelveli	SELVAGANAPATHY.G	3233	no	IND	Independent
Tamil Nadu	Tirunelveli	DEVADOSS.A.R	3394	no	IND	Independent
Tamil Nadu	Tirunelveli	PATHMANABAN.K	1823	no	IND	Independent
Tamil Nadu	Tirunelveli	MURUGAN.S	1159	no	IND	Independent
Tamil Nadu	Tirunelveli	MOHAN.T	2518	no	IND	Independent
Tamil Nadu	Tirunelveli	RAJAMARAVAN @ MANICKAVASAGAM.S	1391	no	IND	Independent
Tamil Nadu	Tirunelveli	RAJI YADAV	1579	no	IND	Independent
Tamil Nadu	Tirunelveli	RAJESHKANNAN	947	no	IND	Independent
Tamil Nadu	Tirunelveli	LAKSHMANAN @ LAKKAN.A	1850	no	IND	Independent
Tamil Nadu	Tirunelveli	VENKATESH.A	1052	no	IND	Independent
Tamil Nadu	Tirunelveli	SARAVANAN.P	740	no	IND	Independent
Tamil Nadu	Tirunelveli	SUBRAMANIAN.K	1196	no	IND	Independent
Tamil Nadu	Tirunelveli	SUBRAMANIAN @ S.S.S.MANI.S	1751	no	IND	Independent
Tamil Nadu	Tirunelveli	VEILUMUTHUKUMAR.P	1387	no	IND	Independent
Tamil Nadu	Tirunelveli	GERMANUS.S	1446	no	IND	Independent
Tamil Nadu	Tirunelveli	None of the Above	12893	no	NOTA	None of the Above
Tamil Nadu	Kanniyakumari	RADHAKRISHNAN P.	372906	yes	BJP	Bharatiya Janata Party
Tamil Nadu	Kanniyakumari	None of the Above	4150	no	NOTA	None of the Above
Tamil Nadu	Kanniyakumari	WILSON I.	421	no	IND	Independent
Tamil Nadu	Kanniyakumari	JOSE BILBIN J.	504	no	IND	Independent
Tamil Nadu	Kanniyakumari	KAMARAJ K.	955	no	IND	Independent
Tamil Nadu	Kanniyakumari	KAMARAJ G.	591	no	IND	Independent
Tamil Nadu	Kanniyakumari	SARASWATHI S.	536	no	IND	Independent
Tamil Nadu	Kanniyakumari	SEETHARAMADHAS P.	930	no	IND	Independent
Tamil Nadu	Kanniyakumari	NALLA THAMPY C.	2391	no	IND	Independent
Tamil Nadu	Kanniyakumari	NAGOOR MEERAN PEER MOHAMEAD U.	3851	no	IND	Independent
Tamil Nadu	Kanniyakumari	PADMANABHAN S.	1246	no	IND	Independent
Tamil Nadu	Kanniyakumari	BALA SUBRAMANIAN T.	1737	no	IND	Independent
Tamil Nadu	Kanniyakumari	MANIKANDAN I.	1557	no	IND	Independent
Tamil Nadu	Kanniyakumari	RAJESH KUMAR K.R.	1437	no	IND	Independent
Tamil Nadu	Kanniyakumari	VASANTHA KUMAR L.	1474	no	IND	Independent
Tamil Nadu	Kanniyakumari	VASANTHEESVARAN E.	791	no	IND	Independent
Tamil Nadu	Kanniyakumari	RAAJARATHINUM F.M.	117933	no	DMK	Dravida Munnetra Kazhagam
Tamil Nadu	Kanniyakumari	BELLARMIN A.V.	35284	no	CPM	Communist Party of India  (Marxist)
Tamil Nadu	Kanniyakumari	VASANTHA KUMAR H.	244244	no	INC	Indian National Congress
Tamil Nadu	Kanniyakumari	JAWAHAR J.	3236	no	BSP	Bahujan Samaj Party
Tamil Nadu	Kanniyakumari	JOHN THANKAM D.	176239	no	ADMK	All India Anna Dravida Munnetra Kazhagam
Tamil Nadu	Kanniyakumari	ANTHONY MUTHU	1647	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tamil Nadu	Kanniyakumari	UDAYAKUMAR S.P.	15314	no	AAAP	Aam Aadmi Party
Tamil Nadu	Kanniyakumari	PAULRAJ C.M.	436	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Tamil Nadu	Kanniyakumari	SHALIN RICHARD J.	398	no	IVP	Indians Victory Party
Tamil Nadu	Kanniyakumari	RADHAKRISHNAN T.	534	no	IND	Independent
Tripura	Tripura West	SANKAR PRASAD DATTA	671665	yes	CPM	Communist Party of India  (Marxist)
Tripura	Tripura West	SALIL SAHA	4981	no	AAAP	Aam Aadmi Party
Tripura	Tripura West	PJECH DEBBARMA	2061	no	JMBP	Jai Maha Bharath Party
Tripura	Tripura West	PARTHA KARMAKAR	4226	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tripura	Tripura West	BINAY DEBBARMA	11040	no	IPFT	Indigenousn People's Front Of Tripura
Tripura	Tripura West	SUDHINDRA CHANDRA DASGUPTA	54706	no	BJP	Bharatiya Janata Party
Tripura	Tripura West	ARUN KUMAR BHOWMIK	8202	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Tripura	Tripura West	KAMAL KANTI DEBNATH	3747	no	IND	Independent
Tripura	Tripura West	RATAN CHAKRABORTI	117727	no	AITC	All India Trinamool Congress
Tripura	Tripura West	SUBAL BHOWMIK	4690	no	TPGC	Tripura Pragatishil Gramin Congress
Tripura	Tripura West	RAKHAL RAJ DATTA	2879	no	AMB	Amra Bangalee
Tripura	Tripura West	JOGANATI DATTA	5947	no	IND	Independent
Tripura	Tripura West	ARUNODAY SAHA	168179	no	INC	Indian National Congress
Tripura	Tripura West	None of the Above	12699	no	NOTA	None of the Above
Tripura	Tripura East	JITENDRA CHOUDHURY	623771	yes	CPM	Communist Party of India  (Marxist)
Tripura	Tripura East	NARENDRA CHANDRA DEB BARMA	10286	no	IPFT	Indigenousn People's Front Of Tripura
Tripura	Tripura East	PATAL KANYA JAMATIA	3004	no	IND	Independent
Tripura	Tripura East	BIKASH DEBBARMA	3116	no	JMBP	Jai Maha Bharath Party
Tripura	Tripura East	PRABIDDHA REANG	5066	no	IND	Independent
Tripura	Tripura East	SACHITRA DEBBARMA	139413	no	INC	Indian National Congress
Tripura	Tripura East	FALGUNI TRIPURA	4444	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Tripura	Tripura East	BHRIGURAM REANG	77028	no	AITC	All India Trinamool Congress
Tripura	Tripura East	SUBARNA MALA DEBBARMA	4152	no	AMB	Amra Bangalee
Tripura	Tripura East	BASU MOG	4262	no	TPGC	Tripura Pragatishil Gramin Congress
Tripura	Tripura East	None of the Above	11084	no	NOTA	None of the Above
Tripura	Tripura East	KARNA BIJOY JAMATIA	4871	no	AAAP	Aam Aadmi Party
Tripura	Tripura East	PARIKSHIT DEBBARMA	60613	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Saharanpur	RAGHAV LAKHANPAL	472999	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Saharanpur	None of the Above	6267	no	NOTA	None of the Above
Uttar Pradesh	Saharanpur	JAGDISH SINGH RANA	235033	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Saharanpur	IMRAN MASOOD	407909	no	INC	Indian National Congress
Uttar Pradesh	Saharanpur	RAGHUVEER SINGH BODH	1066	no	IND	Independent
Uttar Pradesh	Saharanpur	SHAHANAVAJ	553	no	RJ	Rashtriya Janmorcha
Uttar Pradesh	Saharanpur	SHAZAN MASOOD URF SHADAN MASOOD	52765	no	SP	Samajwadi Party
Uttar Pradesh	Saharanpur	YOGESH KUMAR	3065	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Saharanpur	CP VERMA	2114	no	IND	Independent
Uttar Pradesh	Saharanpur	SESHRAJ	4057	no	IND	Independent
Uttar Pradesh	Saharanpur	RATAN SINGH	1366	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Saharanpur	HANSRAJ SAINI	4068	no	IND	Independent
Uttar Pradesh	Saharanpur	RAJENDER KUMAR	466	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Saharanpur	MOHD. FIROZ AFTAB	692	no	IND	Independent
Uttar Pradesh	Saharanpur	SHIV KUMAR	1888	no	ASP	Ambedkar Samaj Party
Uttar Pradesh	Kairana	HUKUM SINGH	565909	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Kairana	DIVAKAR KASHYAP	6562	no	AITC	All India Trinamool Congress
Uttar Pradesh	Kairana	RAMKARAN KASHYAP	1378	no	BaVaP	Bhartiya Vanchitsamaj Party
Uttar Pradesh	Kairana	PARDIP	1050	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Kairana	SUNITA	946	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Kairana	KARTAR SINGH BHADANA	42706	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Kairana	MOHANLAL	1741	no	IND	Independent
Uttar Pradesh	Kairana	NAHID HASAN	329081	no	SP	Samajwadi Party
Uttar Pradesh	Kairana	KANWAR HASAN	160414	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kairana	SUNIL KUMAR KASANA	2663	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kairana	KRISHANPAL	2971	no	IND	Independent
Uttar Pradesh	Kairana	None of the Above	3903	no	NOTA	None of the Above
Uttar Pradesh	Muzaffarnagar	(DR.) SANJEEV KUMAR BALYAN	653391	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Muzaffarnagar	None of the Above	4739	no	NOTA	None of the Above
Uttar Pradesh	Muzaffarnagar	MOHAMMED YAMIN	1931	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Muzaffarnagar	MANGE RAM KASHYAP	362	no	MKUP	Majdoor Kisan Union Party
Uttar Pradesh	Muzaffarnagar	PANKAJ AGARWAL	12937	no	INC	Indian National Congress
Uttar Pradesh	Muzaffarnagar	KADIR RANA	252241	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Muzaffarnagar	GUFRAN AHMAD	1682	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	SHANDAR GUFRAN	1864	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	RAKESH	3482	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	SHABNAM	487	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	CHATAR SINGH KASHYAP	350	no	VAJP	Vanchit Jamat Party
Uttar Pradesh	Muzaffarnagar	PHOOL SINGH PAL	3431	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	MO. YAMIN	543	no	AIRSP	All India Ravidas Samata Party
Uttar Pradesh	Muzaffarnagar	ANKUR	723	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	ANIL KUMAR	2238	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Muzaffarnagar	RAJESH DEVI ALIAS RADHIKA BALIYAN	4129	no	AITC	All India Trinamool Congress
Uttar Pradesh	Muzaffarnagar	VIRENDER SINGH	160810	no	SP	Samajwadi Party
Uttar Pradesh	Muzaffarnagar	NASEEM AHMAD ANSARI	750	no	IND	Independent
Uttar Pradesh	Muzaffarnagar	LALIT CHAUHAN	499	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Muzaffarnagar	JAGBIR SINGH	845	no	IND	Independent
Uttar Pradesh	Bijnor	KUNWAR BHARTENDRA	486913	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Bijnor	MOHAMAD ADEEB	1756	no	RWSP	Rashtrawadi Samaj Party
Uttar Pradesh	Bijnor	KAMAL	2545	no	IND	Independent
Uttar Pradesh	Bijnor	MOHAMMAD MOOSA	15801	no	PECP	Peace Party
Uttar Pradesh	Bijnor	GAURAV	2483	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Bijnor	JAYA PRADA NAHATA	24348	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Bijnor	SHAHNAWAZ RANA	281139	no	SP	Samajwadi Party
Uttar Pradesh	Bijnor	GAJRESH KUMAR	3317	no	IND	Independent
Uttar Pradesh	Bijnor	UMESH ALIES PANI	2967	no	IND	Independent
Uttar Pradesh	Bijnor	MALOOK NAGAR	230124	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bijnor	UDAY ALIES UDAYVEER	2456	no	BMF	Bharatiya Momin Front
Uttar Pradesh	Bijnor	PARVEZ AQIL	722	no	AIMF	All India Minorities Front
Uttar Pradesh	Bijnor	None of the Above	5775	no	NOTA	None of the Above
Uttar Pradesh	Nagina	YASHWANT SINGH	367825	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Nagina	SHARISHTHA KUMAR SINGH	861	no	JMBP	Jai Maha Bharath Party
Uttar Pradesh	Nagina	SHAMSHER	3577	no	IND	Independent
Uttar Pradesh	Nagina	OMPRAKASH RAVI	2967	no	SHS	Shivsena
Uttar Pradesh	Nagina	None of the Above	6470	no	NOTA	None of the Above
Uttar Pradesh	Nagina	BHAGWAN DASS RATHOR	4581	no	MD	Mahan Dal
Uttar Pradesh	Nagina	VINOD KUMAR	852	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Nagina	MANJU DEVI	1027	no	LD	Lok Dal
Uttar Pradesh	Nagina	TEJ SINGH	1845	no	ASP	Ambedkar Samaj Party
Uttar Pradesh	Nagina	YASHVIR SINGH	275435	no	SP	Samajwadi Party
Uttar Pradesh	Nagina	SARIKA	3695	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Nagina	GIRISH CHANDRA	245685	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Nagina	SHEESHRAM SINGH RAVI	21334	no	PECP	Peace Party
Uttar Pradesh	Nagina	SUNIL	4328	no	IND	Independent
Uttar Pradesh	Nagina	ASARPAT SINGH	1714	no	IND	Independent
Uttar Pradesh	Moradabad	KUNWER SARVESH KUMAR	485224	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Moradabad	ROOPA CHANDRA SINGH	1282	no	IND	Independent
Uttar Pradesh	Moradabad	VIMAL KUMAR	4469	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Moradabad	BEGUM NOOR BANO URF MEHTAB	19732	no	INC	Indian National Congress
Uttar Pradesh	Moradabad	RAJ KUMAR GUPTA	1278	no	IND	Independent
Uttar Pradesh	Moradabad	SATISH CHANDRA SHARMA	2267	no	NAP	Naitik Party
Uttar Pradesh	Moradabad	KAMAR ALAM	1059	no	IBSP	Indian Bahujan Samajwadi Party
Uttar Pradesh	Moradabad	SAURABH PRATAP SINGH	5647	no	AITC	All India Trinamool Congress
Uttar Pradesh	Moradabad	AMIT KUMAR	3047	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Moradabad	JAIPAL SINGH SAINI	1334	no	SHS	Shivsena
Uttar Pradesh	Moradabad	TEAJ SINGH SAINI	1710	no	RSMD	Rashtriya Samanta Dal
Uttar Pradesh	Moradabad	SANDEEP KUMAR	2971	no	SSKP	Sarva Samaj Kalyan Party
Uttar Pradesh	Moradabad	NAFISUDDIN	3180	no	CPM	Communist Party of India  (Marxist)
Uttar Pradesh	Moradabad	HAJI MOHD YAQOOB	160945	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Moradabad	MOHD ZAFAR IQBAL	956	no	ABML(S)	Akhil Bharatiya Muslim League (Secular)
Uttar Pradesh	Moradabad	DR S T HASAN	397720	no	SP	Samajwadi Party
Uttar Pradesh	Moradabad	HARIOM	4097	no	NADP	Naya Daur Party
Uttar Pradesh	Moradabad	ENGINEER MOHAMMAD IRFAN	25840	no	PECP	Peace Party
Uttar Pradesh	Moradabad	None of the Above	5207	no	NOTA	None of the Above
Uttar Pradesh	Rampur	DR. NEPAL SINGH	358616	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Rampur	None of the Above	6905	no	NOTA	None of the Above
Uttar Pradesh	Rampur	NAVED MIAN	2448	no	IND	Independent
Uttar Pradesh	Rampur	JANNAT NISHA	4473	no	AIMF	All India Minorities Front
Uttar Pradesh	Rampur	JAGDISH SARAN SAGAR	1834	no	IND	Independent
Uttar Pradesh	Rampur	VIJENDRA SINGH	2079	no	RAC	Rashtriya Congress (Babu Jagjivanram)
Uttar Pradesh	Rampur	TEJPAL SINGH	2063	no	IND	Independent
Uttar Pradesh	Rampur	NAWAB KAZIM ALI KHAN	156466	no	INC	Indian National Congress
Uttar Pradesh	Rampur	SYED SALMAN ALI	1996	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Rampur	AKBAR MASOOD	817	no	IND	Independent
Uttar Pradesh	Rampur	AKBAR HUSAIN	81006	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Rampur	NASEER AHMAD KHAN	335181	no	SP	Samajwadi Party
Uttar Pradesh	Rampur	JAGMOHAN	2505	no	IND	Independent
Uttar Pradesh	Sambhal	SATYAPAL SINGH	360242	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Sambhal	DR SHAFIQ- UR RAHMAN BARQ	355068	no	SP	Samajwadi Party
Uttar Pradesh	Sambhal	ACHARYA PRAMOD KRISHNAM	16034	no	INC	Indian National Congress
Uttar Pradesh	Sambhal	DHARAM YADAV URF D.P.YADAV	36134	no	RPD	Rashtriya Parivartan Dal
Uttar Pradesh	Sambhal	DR. RASHID ALI	6376	no	PECP	Peace Party
Uttar Pradesh	Sambhal	AQEEL-UR -REHMAN KHAN	252640	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Sambhal	RASHID	2791	no	IND	Independent
Uttar Pradesh	Sambhal	AQEEL AHMAD	2005	no	IND	Independent
Uttar Pradesh	Sambhal	FIROZ HAIDER	3648	no	IND	Independent
Uttar Pradesh	Sambhal	NANAD RAM KHADAGVANSHI	2322	no	IND	Independent
Uttar Pradesh	Sambhal	MOHD ASLAM URF PASHA	3833	no	RPI(A)	Republican Party of India (A)
Uttar Pradesh	Sambhal	NUTAN VIJAY ADVOCATE	6686	no	IND	Independent
Uttar Pradesh	Sambhal	REKHA DEVI YADAV	1565	no	SSAD	Sanyukt Samajwadi Dal
Uttar Pradesh	Sambhal	None of the Above	7658	no	NOTA	None of the Above
Uttar Pradesh	Amroha	KANWAR SINGH TANWAR	528880	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Amroha	None of the Above	7779	no	NOTA	None of the Above
Uttar Pradesh	Amroha	KHAJAN SINGH	2448	no	RAC	Rashtriya Congress (Babu Jagjivanram)
Uttar Pradesh	Amroha	SARVESH KUMAR SAINI	980	no	SHS	Shivsena
Uttar Pradesh	Amroha	SURENDRA SINGH	2425	no	IND	Independent
Uttar Pradesh	Amroha	RAKESH	749	no	PECP	Peace Party
Uttar Pradesh	Amroha	HUSNEIN RAZAA	858	no	aicp	New All India Congress Party
Uttar Pradesh	Amroha	MAHESH	713	no	RPI(A)	Republican Party of India (A)
Uttar Pradesh	Amroha	RAKESH TIKAIT	9539	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Amroha	RAJPAL	478	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Amroha	FARHAT HASAN	162983	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Amroha	NARESH CHAND	1431	no	IND	Independent
Uttar Pradesh	Amroha	NARESH KUMAR	2493	no	IND	Independent
Uttar Pradesh	Amroha	SYED KALBE RUSHAID RIZVI	2601	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Amroha	BRAJLAL	837	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Amroha	HUMERA AKHTAR	370666	no	SP	Samajwadi Party
Uttar Pradesh	Meerut	RAJENDRA AGARWAL	532981	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Meerut	MOHD.SHAHID AKHLAK	300655	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Meerut	SUNIL DUTT	1734	no	IND	Independent
Uttar Pradesh	Meerut	MUKESH KUMAR	1542	no	IND	Independent
Uttar Pradesh	Meerut	DR. (EX. MAJ) HIMANSHU SINGH	11793	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Meerut	APHAJAL	382	no	IND	Independent
Uttar Pradesh	Meerut	MOHD. SAJID SAIFI	1063	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Meerut	MOHD.USMAN GHAZI	1451	no	RsAD	Rashtriya Apna Dal
Uttar Pradesh	Meerut	ATUL KHODAWAL	818	no	IND	Independent
Uttar Pradesh	Meerut	ANIL	348	no	IND	Independent
Uttar Pradesh	Meerut	IQRA CHOUDHARY	734	no	IND	Independent
Uttar Pradesh	Meerut	NAGMA	42911	no	INC	Indian National Congress
Uttar Pradesh	Meerut	SHAHID MANZOOR	211759	no	SP	Samajwadi Party
Uttar Pradesh	Meerut	None of the Above	5213	no	NOTA	None of the Above
Uttar Pradesh	Baghpat	DR. SATYA PAL SINGH	423475	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Baghpat	GHULAM MOHAMMED	213609	no	SP	Samajwadi Party
Uttar Pradesh	Baghpat	SOMENDAR DHAKA	5828	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Baghpat	PRASHANT CHAUDHRI	141743	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Baghpat	KRISHAN PAL	601	no	NAP	Naitik Party
Uttar Pradesh	Baghpat	AJAY PAL	440	no	IND	Independent
Uttar Pradesh	Baghpat	ANIL KUMAR	3025	no	PECP	Peace Party
Uttar Pradesh	Baghpat	AJIT SINGH	199516	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Baghpat	ARVIND	992	no	IND	Independent
Uttar Pradesh	Baghpat	SUBHASH CHAND KASHYAP	1967	no	IND	Independent
Uttar Pradesh	Baghpat	JAIVINDRA KUMAR	3915	no	IND	Independent
Uttar Pradesh	Baghpat	NISHANT	2708	no	IND	Independent
Uttar Pradesh	Baghpat	RUKSANA	2092	no	IND	Independent
Uttar Pradesh	Baghpat	YUNUSH	441	no	NNP	Navbharat Nirman Party
Uttar Pradesh	Baghpat	None of the Above	3911	no	NOTA	None of the Above
Uttar Pradesh	Ghaziabad	VIJAY KUMAR SINGH	758482	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Ghaziabad	SURESH PAL	1591	no	BSKP	Bharatiya Sarvodaya Kranti Party
Uttar Pradesh	Ghaziabad	SHAZIA ILMI MALIK	89147	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ghaziabad	MUKUL	173085	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Ghaziabad	SUDHAN KUMAR	106984	no	SP	Samajwadi Party
Uttar Pradesh	Ghaziabad	KESHAV CHOUDHARY	627	no	IND	Independent
Uttar Pradesh	Ghaziabad	SABIR	3409	no	IND	Independent
Uttar Pradesh	Ghaziabad	SANJEEV KUMAR	4639	no	IND	Independent
Uttar Pradesh	Ghaziabad	SALEEM MALIK	1486	no	IND	Independent
Uttar Pradesh	Ghaziabad	NARESH GAUTAM	1548	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Ghaziabad	NITIN	624	no	IND	Independent
Uttar Pradesh	Ghaziabad	RINKU KUMAR TYAGI	1185	no	IND	Independent
Uttar Pradesh	Ghaziabad	RAJ BABBAR	191222	no	INC	Indian National Congress
Uttar Pradesh	Ghaziabad	VINOD KUMAR TYAGI	992	no	IND	Independent
Uttar Pradesh	Ghaziabad	RAMPHAL SHAKYA	1094	no	IND	Independent
Uttar Pradesh	Ghaziabad	None of the Above	6205	no	NOTA	None of the Above
Uttar Pradesh	Gautam Buddha Nagar	DR.MAHESH  SHARMA	599702	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Gautam Buddha Nagar	RAVINDER SHARMA	503	no	RTKP	Rashtriya Kranti Party
Uttar Pradesh	Gautam Buddha Nagar	KADIR KHAN JAISWAL	2684	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	GRISA KUMAR	3496	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	RAKESH	1225	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	MAHESH	794	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	SUNIL GAUTAM	623	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	KADEEM	4198	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	MOHD. SABIR ANSARI	4506	no	AITC	All India Trinamool Congress
Uttar Pradesh	Gautam Buddha Nagar	SAFRUDDIN	491	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	SAVITRI	996	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	LT COL SP SINGH	1183	no	NBDP	Nav Bharat Democratic Party
Uttar Pradesh	Gautam Buddha Nagar	SEEMA	977	no	RND	Rashtriya Naujawan Dal
Uttar Pradesh	Gautam Buddha Nagar	UDAYVEER	1526	no	RtrJP	Rashtriya Janta Party
Uttar Pradesh	Gautam Buddha Nagar	GAJENDRA	649	no	IOP	Indian Oceanic Party
Uttar Pradesh	Gautam Buddha Nagar	RAM VILAS	656	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Gautam Buddha Nagar	SONIA SHARMA	2530	no	ABSC	Akhil Bharatiya Samajwadi Congress
Uttar Pradesh	Gautam Buddha Nagar	RAMESH CHAND TOMAR	12696	no	INC	Indian National Congress
Uttar Pradesh	Gautam Buddha Nagar	KISHAN PAL SINGH	32358	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Gautam Buddha Nagar	SATISH KUMAR	198237	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Gautam Buddha Nagar	SUMITI	978	no	IND	Independent
Uttar Pradesh	Gautam Buddha Nagar	DHARMENDER	4130	no	DND	Dharam Nirpeksh Dal
Uttar Pradesh	Gautam Buddha Nagar	NARENDRA BHATI	319490	no	SP	Samajwadi Party
Uttar Pradesh	Gautam Buddha Nagar	RUVEZ ALAM RAHI	773	no	NLP	National Loktantrik Party
Uttar Pradesh	Gautam Buddha Nagar	None of the Above	3837	no	NOTA	None of the Above
Uttar Pradesh	Bulandshahr	BHOLA SINGH	604449	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Bulandshahr	None of the Above	6915	no	NOTA	None of the Above
Uttar Pradesh	Bulandshahr	VIJAI PAL	4117	no	RtJP	Rashtriya Janpriya Party
Uttar Pradesh	Bulandshahr	PYARE LAL	4448	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Bulandshahr	HARVIR	5690	no	IND	Independent
Uttar Pradesh	Bulandshahr	PRADEEP KUMAR JATAV	182476	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bulandshahr	DR. RAHUL DIPANKAR	9727	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Bulandshahr	ANJU URF MUSKAN	59116	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Bulandshahr	KAMLESH	128737	no	SP	Samajwadi Party
Uttar Pradesh	Bulandshahr	SURESH	2580	no	JMBP	Jai Maha Bharath Party
Uttar Pradesh	Bulandshahr	RAGHURAJ PRATAP SINGH	1455	no	RTKP	Rashtriya Kranti Party
Uttar Pradesh	Aligarh	SATISH KUMAR	514622	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Aligarh	VINOD KUMAR BAGHEL	1723	no	RSPS	Rashtriya Samaj Paksha
Uttar Pradesh	Aligarh	SANJAY KUMAR	1842	no	IND	Independent
Uttar Pradesh	Aligarh	NATTHI LAL SHARMA	4659	no	IND	Independent
Uttar Pradesh	Aligarh	BADSHAH	978	no	PECP	Peace Party
Uttar Pradesh	Aligarh	RAKESH CHANDRA RAWAT	665	no	RSD	Rashtriya Sawarn Dal
Uttar Pradesh	Aligarh	DEVANAND	785	no	LD	Lok Dal
Uttar Pradesh	Aligarh	PRADEEP KUMAR	626	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Aligarh	DURGESH CHANDRA GAUTAM	3076	no	AITC	All India Trinamool Congress
Uttar Pradesh	Aligarh	MOHD. SABIR RAHI	8978	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Aligarh	AMITA SINGH JAT	3376	no	SSKP	Sarva Samaj Kalyan Party
Uttar Pradesh	Aligarh	ZAFAR ALAM	226284	no	SP	Samajwadi Party
Uttar Pradesh	Aligarh	DR. ARVIND KUMAR SINGH	227886	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Aligarh	BIJENDRA SINGH	62674	no	INC	Indian National Congress
Uttar Pradesh	Aligarh	None of the Above	6183	no	NOTA	None of the Above
Uttar Pradesh	Hathras	RAJESH KUMAR DIWAKER	544277	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Hathras	None of the Above	5669	no	NOTA	None of the Above
Uttar Pradesh	Hathras	DALVIR SINGH	1599	no	IND	Independent
Uttar Pradesh	Hathras	NIRANJAN SINGH	2847	no	IND	Independent
Uttar Pradesh	Hathras	RAJA RAM	2169	no	IND	Independent
Uttar Pradesh	Hathras	NIRANJAN SINGH DHANGAR	86109	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Hathras	MANOJ KUMAR SONI	217891	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Hathras	OMPRKASH	2778	no	IND	Independent
Uttar Pradesh	Hathras	SUNHARI LAL	5043	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Hathras	RAMJI LAL SUMAN	180891	no	SP	Samajwadi Party
Uttar Pradesh	Mathura	HEMA MALINI	574633	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Mathura	MAHESH PRATAP SHARMA	2396	no	RVP	Rashtriya Vikas Party
Uttar Pradesh	Mathura	MAHARAM SINGH	655	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Mathura	PT. UDYAN SHARMA 'MUNNA'	1066	no	PECP	Peace Party
Uttar Pradesh	Mathura	RAM BABU SAINI	2082	no	IND	Independent
Uttar Pradesh	Mathura	BHUPENDRA KUMAR  DHANGAR	1054	no	RSOSP	Rashtriya Shoshit Samaj Party
Uttar Pradesh	Mathura	PT. YOGESH KUMAR DWIVEDI	173572	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Mathura	CHANDAN SINGH	36673	no	SP	Samajwadi Party
Uttar Pradesh	Mathura	ANUJ KUMAR GARG	7858	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Mathura	THAKUR HEMENDRA VEER SINGH	1858	no	SSKP	Sarva Samaj Kalyan Party
Uttar Pradesh	Mathura	JAYANT CHAUDHARY	243890	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Mathura	MOHAN SHYAM	731	no	GaAP	Gareeb Aadmi Party
Uttar Pradesh	Mathura	FAKKAR BABA 'RAMAYANI'	2160	no	IND	Independent
Uttar Pradesh	Mathura	OM PRAKASH	2847	no	IND	Independent
Uttar Pradesh	Mathura	HEMA MALINI	10158	no	IND	Independent
Uttar Pradesh	Mathura	PARSOTTAM	1338	no	RMGP	Rashtriya mahan Gantantra Party
Uttar Pradesh	Mathura	BHUDEV	2715	no	IND	Independent
Uttar Pradesh	Mathura	RADHACHARAN	4795	no	IND	Independent
Uttar Pradesh	Mathura	PRAMOD KRISHNA	1759	no	IND	Independent
Uttar Pradesh	Mathura	SATISH CHANDRA SHARMA	2675	no	IND	Independent
Uttar Pradesh	Mathura	None of the Above	1953	no	NOTA	None of the Above
Uttar Pradesh	Agra	DR. RAM SHANKAR KATHERIA	583716	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Agra	None of the Above	5191	no	NOTA	None of the Above
Uttar Pradesh	Agra	ANIL	3646	no	BKPP	Bharatiya Kisan Parivartan Party
Uttar Pradesh	Agra	OM PRAKASH	4243	no	IND	Independent
Uttar Pradesh	Agra	AMAR SINGH	2453	no	IND	Independent
Uttar Pradesh	Agra	RAMESH	602	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Agra	RAVINDRA SINGH	7804	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Agra	ANITA	765	no	VIP	VANCHITSAMAJ INSAAF PARTY
Uttar Pradesh	Agra	VINOD KUMAR SINGH	2584	no	IND	Independent
Uttar Pradesh	Agra	NARAYAN SINGH SUMAN	283453	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Agra	DINESH KUMAR GAUTAM	3657	no	IND	Independent
Uttar Pradesh	Agra	ASHOK KUMAR	1416	no	IND	Independent
Uttar Pradesh	Agra	CHANDRA PAL	557	no	A S P	Adarsh Samaj Party
Uttar Pradesh	Agra	PREM SINGH	771	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Agra	MAHARAJ SINGH DHANGAR	134708	no	SP	Samajwadi Party
Uttar Pradesh	Agra	UPENDRA SINGH	34834	no	INC	Indian National Congress
Uttar Pradesh	Fatehpur Sikri	BABULAL	426589	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Fatehpur Sikri	RANI PAKSHALIKA SINGH	213397	no	SP	Samajwadi Party
Uttar Pradesh	Fatehpur Sikri	PITAM SINGH	691	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Fatehpur Sikri	VIJAY SINGH KUSHWAHA	5223	no	SSAD	Sanyukt Samajwadi Dal
Uttar Pradesh	Fatehpur Sikri	MANIK	643	no	LD	Lok Dal
Uttar Pradesh	Fatehpur Sikri	PANKAJ KUMAR SHARMA	2052	no	RSMD	Rashtriya Samanta Dal
Uttar Pradesh	Fatehpur Sikri	SEEMA UPADHYAY	253483	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Fatehpur Sikri	PEETAM SINGH	872	no	RTKP	Rashtriya Kranti Party
Uttar Pradesh	Fatehpur Sikri	AMAR SINGH	24185	no	RLD	Rashtriya Lok Dal
Uttar Pradesh	Fatehpur Sikri	KHEMRAJ SINGH	951	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	AMBEDKARI HASNURAM AMBEDKARI	3512	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	MEGH SINGH	1529	no	PECP	Peace Party
Uttar Pradesh	Fatehpur Sikri	RAM KUMAR DIXIT	1657	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	SANTOSH KUMAR	3184	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	MAHAVIR SINGH SOLANKI	583	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	ASHOK KUMAR	2553	no	GaAP	Gareeb Aadmi Party
Uttar Pradesh	Fatehpur Sikri	ROHAN SINGH	1063	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Fatehpur Sikri	DIWAN SINGH	3101	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	RAKESH SINGH DHANKAR	650	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	ANIL KUMAR	3809	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	LAXMI	3117	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Fatehpur Sikri	BRAJENDR SINGH	2725	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	RAM BRAJ YADAV	2057	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	SEEMA DEVI	1571	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	SATISH KUMAR	2235	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	MUNNA LAL	497	no	RMGP	Rashtriya mahan Gantantra Party
Uttar Pradesh	Fatehpur Sikri	SHOBHNA BAUDDHA	1006	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	NATHOLI	2356	no	IND	Independent
Uttar Pradesh	Fatehpur Sikri	None of the Above	2677	no	NOTA	None of the Above
Uttar Pradesh	Firozabad	AKSHAY YADAV	534583	yes	SP	Samajwadi Party
Uttar Pradesh	Firozabad	None of the Above	4654	no	NOTA	None of the Above
Uttar Pradesh	Firozabad	RAJVEER	2425	no	IND	Independent
Uttar Pradesh	Firozabad	MIRJA MUKARRAM BAIG	1470	no	IND	Independent
Uttar Pradesh	Firozabad	SHAMSHER KHA	3526	no	PECP	Peace Party
Uttar Pradesh	Firozabad	RAKESH YADAV	2808	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Firozabad	KHAJAN SINGH	753	no	LD	Lok Dal
Uttar Pradesh	Firozabad	LAKSHMI  NARAYAN	939	no	RJPK	Rashtriya Janwadi Party (Krantikari)
Uttar Pradesh	Firozabad	LAXMI	620	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Firozabad	PROF. S.P. SINGH BAGHEL	420524	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Firozabad	ATUL CHATURVEDI	7447	no	INC	Indian National Congress
Uttar Pradesh	Firozabad	JAY PRAKASH	1248	no	SHS	Shivsena
Uttar Pradesh	Firozabad	TH. VISHWADEEP SINGH	118909	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Firozabad	RAHAT AFROZ	498	no	IUML	Indian Union Muslim League
Uttar Pradesh	Firozabad	RAJENDRA PRASAD	506	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Firozabad	UPENDRA SINGH	3696	no	BKPP	Bharatiya Kisan Parivartan Party
Uttar Pradesh	Mainpuri	MULAYAM SINGH YADAV	595918	yes	SP	Samajwadi Party
Uttar Pradesh	Mainpuri	ABU NASIM PATHAN	2095	no	PECP	Peace Party
Uttar Pradesh	Mainpuri	MUKESH KUMAR	916	no	IND	Independent
Uttar Pradesh	Mainpuri	RAJESHWARI	1746	no	IND	Independent
Uttar Pradesh	Mainpuri	SATENDRA VEER SINGH CHAUHAN	872	no	AIFB	All India Forward Bloc
Uttar Pradesh	Mainpuri	SHATRUGHAN SINGH CHAUHAN	231252	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Mainpuri	BABA HARDEV SINGH	5588	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Mainpuri	SHISHUPAL SINGH	561	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Mainpuri	ALOK NANDAN	5645	no	IND	Independent
Uttar Pradesh	Mainpuri	DR. SANGHMITRA MAURYA	142833	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Mainpuri	MUKESH KUMAR SHAKYA	2542	no	IND	Independent
Uttar Pradesh	Mainpuri	HAKIM SINGH	2279	no	IND	Independent
Uttar Pradesh	Mainpuri	ASHOK KUMAR	695	no	IND	Independent
Uttar Pradesh	Mainpuri	None of the Above	6323	no	NOTA	None of the Above
Uttar Pradesh	Etah	RAJVEER SINGH (RAJU BHAIYA)	474978	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Etah	None of the Above	6201	no	NOTA	None of the Above
Uttar Pradesh	Etah	RAJVEER SINGH	5848	no	IND	Independent
Uttar Pradesh	Etah	KU. DEVENDRA SINGH YADAV	273977	no	SP	Samajwadi Party
Uttar Pradesh	Etah	RAJENDRA	1021	no	BRAVP	Braj Vikas Party
Uttar Pradesh	Etah	RAKESH GANDHI	7123	no	IND	Independent
Uttar Pradesh	Etah	DANVEER	1333	no	IND	Independent
Uttar Pradesh	Etah	NOOR MOHAMMAD KHAN	137127	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Etah	THAKUR HEMENDRA VEER SINGH	2034	no	SSKP	Sarva Samaj Kalyan Party
Uttar Pradesh	Etah	VINAY	2008	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Etah	TH. JOGINDRA SINGH BHADORIYA	12445	no	MD	Mahan Dal
Uttar Pradesh	Etah	HANS  RAJ SINGH	2186	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Badaun	DHARMENDRA YADAV	498378	yes	SP	Samajwadi Party
Uttar Pradesh	Badaun	AJEET PAL SINGH	3421	no	PECP	Peace Party
Uttar Pradesh	Badaun	PAGLANAND	5748	no	MD	Mahan Dal
Uttar Pradesh	Badaun	MAHENDRA PAL	860	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Badaun	REENA	2065	no	RSMD	Rashtriya Samanta Dal
Uttar Pradesh	Badaun	SANTOSH KUMAR GUPTA (SATYMARGI)	1448	no	RPI(A)	Republican Party of India (A)
Uttar Pradesh	Badaun	VAGISH PATHAK	332031	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Badaun	MUNSHI LAL	5651	no	IND	Independent
Uttar Pradesh	Badaun	ATUL KUMAR	4195	no	IND	Independent
Uttar Pradesh	Badaun	HAR BHAGWAN	4083	no	IND	Independent
Uttar Pradesh	Badaun	MADAN LAL	791	no	NP	National Party
Uttar Pradesh	Badaun	VIRENDRA PAL GUPTA	810	no	RVNP	Rashtravadi Janata Party
Uttar Pradesh	Badaun	ANIL KUMAR SHARMA	1993	no	LPI(V)	Labour Party of India (V.V. Prasad)
Uttar Pradesh	Badaun	AKMAL KHAN URF CHAMAN	156973	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Badaun	HEMA MEHRA	2861	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Badaun	None of the Above	6286	no	NOTA	None of the Above
Uttar Pradesh	Aonla	DHARMENDRA KUMAR	409907	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Aonla	MOHD. ZARRAR KHAN	1440	no	BNIP	Bhartiya Naujawan Inklav Party
Uttar Pradesh	Aonla	PRAMOD KUMAR YADAV	1189	no	BKrD	Bhartiya Krishak Dal
Uttar Pradesh	Aonla	NARESH SINGH SOLANKI	2304	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Aonla	KUNWAR SARVRAJ SINGH	271478	no	SP	Samajwadi Party
Uttar Pradesh	Aonla	JITENDRA SINGH YADAV	855	no	NAP	Naitik Party
Uttar Pradesh	Aonla	ARSHADURRAZA KHAN	3019	no	IND	Independent
Uttar Pradesh	Aonla	YOGENDRA KUMAR VERMA	4332	no	ABHM	Akhil Bharat Hindu Mahasabha
Uttar Pradesh	Aonla	SALEEM IQBAL SHERVANI	93861	no	INC	Indian National Congress
Uttar Pradesh	Aonla	SUNITA SHAKYA	190200	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Aonla	None of the Above	10496	no	NOTA	None of the Above
Uttar Pradesh	Aonla	OM VEER	1152	no	BRVP	Bhartiya Vikas Party
Uttar Pradesh	Aonla	CAPTAIN P.C. SHARMA	2711	no	JKM	Jawan Kisan Morcha
Uttar Pradesh	Aonla	IRSHAD ANSARI ADVOCATE	2697	no	PECP	Peace Party
Uttar Pradesh	Bareilly	SANTOSH KUMAR GANGWAR	518258	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Bareilly	NETRAM	2038	no	IND	Independent
Uttar Pradesh	Bareilly	LOTAN SINGH PATEL	1402	no	IND	Independent
Uttar Pradesh	Bareilly	KAMAL KISHORE ENGINEER	1806	no	IND	Independent
Uttar Pradesh	Bareilly	JAVED KHAN	964	no	IND	Independent
Uttar Pradesh	Bareilly	MANOJ VIKAT	1567	no	IND	Independent
Uttar Pradesh	Bareilly	PRAVEEN SINGH ARON	84213	no	INC	Indian National Congress
Uttar Pradesh	Bareilly	SUNIL KUMAR	3703	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Bareilly	MASSARAT WARSI (PAPPU BHAI)	7639	no	CPI	Communist Party of India
Uttar Pradesh	Bareilly	ASLAM ADVOCATE	3338	no	PECP	Peace Party
Uttar Pradesh	Bareilly	AYESHA ISLAM	277573	no	SP	Samajwadi Party
Uttar Pradesh	Bareilly	SAYAD RASHID ALI	1885	no	IND	Independent
Uttar Pradesh	Bareilly	LAEEK AHMAD	719	no	NAP	Naitik Party
Uttar Pradesh	Bareilly	UMESH GAUTAM	106049	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bareilly	None of the Above	6737	no	NOTA	None of the Above
Uttar Pradesh	Pilibhit	MANEKA SANJAY GANDHI	546934	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Pilibhit	None of the Above	11521	no	NOTA	None of the Above
Uttar Pradesh	Pilibhit	RAJEEV AGRAWAL	5547	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Pilibhit	SANJAY KAPOOR	29169	no	INC	Indian National Congress
Uttar Pradesh	Pilibhit	MUNESH SINGH	6151	no	IND	Independent
Uttar Pradesh	Pilibhit	ARAMAN KHAN	2170	no	IND	Independent
Uttar Pradesh	Pilibhit	RAVINDRA KUMAR DEVNATH	3145	no	IND	Independent
Uttar Pradesh	Pilibhit	ANIS AHMAD KHAN ALIAS PHOOL BABU	196294	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Pilibhit	SANJAY KUMAR	1260	no	NAP	Naitik Party
Uttar Pradesh	Pilibhit	NILIMA SHARMA	2670	no	PECP	Peace Party
Uttar Pradesh	Pilibhit	RAM AUTAR	3187	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Pilibhit	BUDHSEN VERMA	239882	no	SP	Samajwadi Party
Uttar Pradesh	Pilibhit	MOHD NAEEM ANSARI	2617	no	IND	Independent
Uttar Pradesh	Shahjahanpur	KRISHNA RAJ	525132	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Shahjahanpur	PYARE ALIAS RAJKUMAR GAUTAM	1378	no	VIP	VANCHITSAMAJ INSAAF PARTY
Uttar Pradesh	Shahjahanpur	RAKESH	2522	no	IND	Independent
Uttar Pradesh	Shahjahanpur	ROSHAN LAL ALIAS ROSHAN KANAUJIYA	7871	no	IND	Independent
Uttar Pradesh	Shahjahanpur	MEVARAM	1105	no	NAP	Naitik Party
Uttar Pradesh	Shahjahanpur	None of the Above	9964	no	NOTA	None of the Above
Uttar Pradesh	Shahjahanpur	SMT REETA SINGH ALIAS REETA BHARTI	3610	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Shahjahanpur	RAMLAL VERMA	1613	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Shahjahanpur	KISHAN LAL	2790	no	LD	Lok Dal
Uttar Pradesh	Shahjahanpur	MITHILESH KUMAR	242913	no	SP	Samajwadi Party
Uttar Pradesh	Shahjahanpur	RAMMURTI	1384	no	BKrD	Bhartiya Krishak Dal
Uttar Pradesh	Shahjahanpur	SUNITA SUJATA NAGPAL	12945	no	PECP	Peace Party
Uttar Pradesh	Shahjahanpur	UMED SINGH KASHYAP	289603	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Shahjahanpur	CHETRAM	27011	no	INC	Indian National Congress
Uttar Pradesh	Kheri	AJAY KUMAR	398578	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Kheri	RAVI PRAKASH VERMA	160112	no	SP	Samajwadi Party
Uttar Pradesh	Kheri	VIPNESH	16401	no	CPI	Communist Party of India
Uttar Pradesh	Kheri	ARVIND GIRI	288304	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kheri	RAMGOPAL MISHRA	2608	no	IND	Independent
Uttar Pradesh	Kheri	ZAFAR ALI NAQVI	183940	no	INC	Indian National Congress
Uttar Pradesh	Kheri	None of the Above	12031	no	NOTA	None of the Above
Uttar Pradesh	Kheri	KRISHNA ADHIKARI	3230	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Kheri	ASHRAF ALI	3124	no	PECP	Peace Party
Uttar Pradesh	Kheri	RAMESHWARI	3514	no	IND	Independent
Uttar Pradesh	Kheri	ASHOK	3165	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kheri	DINESH KUMAR	2861	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Dhaurahra	REKHA	360357	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Dhaurahra	ANAND KUMAR	9967	no	NCP	Nationalist Congress Party
Uttar Pradesh	Dhaurahra	KIRTI SINGH	2788	no	SHS	Shivsena
Uttar Pradesh	Dhaurahra	AJAY VERMA	6144	no	BJKD	Bharatiya Jan Kranti Dal (Democratic)
Uttar Pradesh	Dhaurahra	ANAND BHADAURIYA	234032	no	SP	Samajwadi Party
Uttar Pradesh	Dhaurahra	SWAMI DAYAL	3491	no	IND	Independent
Uttar Pradesh	Dhaurahra	DAUD AHMAD	234682	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Dhaurahra	LEKHRAJ	12776	no	AITC	All India Trinamool Congress
Uttar Pradesh	Dhaurahra	KUWAR JITIN PRASAD	170994	no	INC	Indian National Congress
Uttar Pradesh	Dhaurahra	None of the Above	8138	no	NOTA	None of the Above
Uttar Pradesh	Dhaurahra	RAM BALI	2144	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Uttar Pradesh	Dhaurahra	SUKHVINDRA SINGH	4021	no	NBDP	Nav Bharat Democratic Party
Uttar Pradesh	Dhaurahra	RAJENDRA KUMAR SINGH	1730	no	PECP	Peace Party
Uttar Pradesh	Dhaurahra	VIVEK MISHRA	3745	no	NAP	Naitik Party
Uttar Pradesh	Dhaurahra	MALA DEVI	1929	no	VIP	VANCHITSAMAJ INSAAF PARTY
Uttar Pradesh	Dhaurahra	VIJAY KUMAR TIWARI	3333	no	IND	Independent
Uttar Pradesh	Sitapur	RAJESH VERMA	417546	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Sitapur	TURAB ALI	6646	no	IND	Independent
Uttar Pradesh	Sitapur	JAGMOHAN SINGH VERMA	2550	no	AIFB	All India Forward Bloc
Uttar Pradesh	Sitapur	SANJAY TIWARI	2373	no	NAP	Naitik Party
Uttar Pradesh	Sitapur	RAM AVTAR NISHAD	2504	no	jhspt	Jai Hind Samaj Party
Uttar Pradesh	Sitapur	SANT KUMAR	1544	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Uttar Pradesh	Sitapur	SHEFALI MISRA	2319	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Sitapur	None of the Above	12682	no	NOTA	None of the Above
Uttar Pradesh	Sitapur	PUSHPA DEVI	3575	no	IND	Independent
Uttar Pradesh	Sitapur	ERAJ BEG	2195	no	NEP	Nagrik Ekta Party
Uttar Pradesh	Sitapur	JAGDISH NARAYAN SHUKLA	8089	no	AITC	All India Trinamool Congress
Uttar Pradesh	Sitapur	BHARAT TRIPATHI	156170	no	SP	Samajwadi Party
Uttar Pradesh	Sitapur	R. K. MISRA	2205	no	NBDP	Nav Bharat Democratic Party
Uttar Pradesh	Sitapur	CHAUDHARY LAL BAGHEL	1992	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Sitapur	VAISHALI ALI	29104	no	INC	Indian National Congress
Uttar Pradesh	Sitapur	KAISER JAHAN	366519	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Sitapur	SUCHITA KUMAR	8959	no	IND	Independent
Uttar Pradesh	Hardoi	ANSHUL VERMA	360501	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Hardoi	USHA VERMA	276543	no	SP	Samajwadi Party
Uttar Pradesh	Hardoi	SHIVE PRASAD VERMA	279158	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Hardoi	SHIVEKUMAR	4737	no	IND	Independent
Uttar Pradesh	Hardoi	SUNIL BHARTI	4045	no	IND	Independent
Uttar Pradesh	Hardoi	SANJAY KUMAR	830	no	BKrD	Bhartiya Krishak Dal
Uttar Pradesh	Hardoi	SARVESH KUMAR	23298	no	INC	Indian National Congress
Uttar Pradesh	Hardoi	RAMPAL	1326	no	IND	Independent
Uttar Pradesh	Hardoi	RAMDAYAL	1576	no	PECP	Peace Party
Uttar Pradesh	Hardoi	SAROJ KUMAR	3287	no	IND	Independent
Uttar Pradesh	Hardoi	SARVESH	4130	no	IND	Independent
Uttar Pradesh	Hardoi	CHANDRAPAL	2003	no	BRPP	Bharatiya Republican Paksha
Uttar Pradesh	Hardoi	HIRA LAL VERMA	1670	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Hardoi	RAMLAKHAN GAUTAM	834	no	ISSP	Indian Savarn Samaj Party
Uttar Pradesh	Hardoi	SANTRAM	1296	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Hardoi	None of the Above	7660	no	NOTA	None of the Above
Uttar Pradesh	Misrikh	ANJU BALA	412575	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Misrikh	None of the Above	9633	no	NOTA	None of the Above
Uttar Pradesh	Misrikh	SWADESH KUMAR	2970	no	IND	Independent
Uttar Pradesh	Misrikh	SHABNUM	1785	no	IND	Independent
Uttar Pradesh	Misrikh	MANSHARAM GAUTAM	704	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Uttar Pradesh	Misrikh	RAJESH KUMAR S/O BABOORAM	951	no	IND	Independent
Uttar Pradesh	Misrikh	RAJESH KUMAR S/O PARMESHWAR DEEN	1158	no	BRPP	Bharatiya Republican Paksha
Uttar Pradesh	Misrikh	KANEDI	2773	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Misrikh	OM PRAKASH	33075	no	INC	Indian National Congress
Uttar Pradesh	Misrikh	HANS MUKHI KATHERIA	8635	no	IND	Independent
Uttar Pradesh	Misrikh	RAMPAL	2234	no	IND	Independent
Uttar Pradesh	Misrikh	JAI PRAKASH	194759	no	SP	Samajwadi Party
Uttar Pradesh	Misrikh	HARINAM	1003	no	SP(I)	Socialist Party (India)
Uttar Pradesh	Misrikh	ASHOK KUMAR RAWAT	325212	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Misrikh	NAGPAL	847	no	BKrD	Bhartiya Krishak Dal
Uttar Pradesh	Unnao	SWAMI SACHCHIDANAND HARI SAKSHI	518834	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Unnao	BRAJESH PATHAK	200176	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Unnao	ARUN SHANKAR SHUKLA	208661	no	SP	Samajwadi Party
Uttar Pradesh	Unnao	ANNU TANDON	197098	no	INC	Indian National Congress
Uttar Pradesh	Unnao	ARVIND KUMAR	4214	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Unnao	ROOP NARAYAN SINGH	1818	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Unnao	ANIL KUMAR MISHRA	1881	no	SP(I)	Socialist Party (India)
Uttar Pradesh	Unnao	SHALU DHARMENDRA	2440	no	SARP	Samtawadi Republican Party
Uttar Pradesh	Unnao	ANIL KUMAR CHAUDHARY	9618	no	IND	Independent
Uttar Pradesh	Unnao	SUKHDEV	4401	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Unnao	SUMIT TEWARI	3398	no	NBDP	Nav Bharat Democratic Party
Uttar Pradesh	Unnao	RAKESH KUMAR	2771	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Unnao	ANIL KUMAR	3214	no	IND	Independent
Uttar Pradesh	Unnao	RAJNIKANT	3987	no	IND	Independent
Uttar Pradesh	Unnao	MOHAMMAD AMIR KHAN	4148	no	IND	Independent
Uttar Pradesh	Unnao	RAMESHWAR	9277	no	IND	Independent
Uttar Pradesh	Unnao	CHETRAM PASI	2592	no	RPI(A)	Republican Party of India (A)
Uttar Pradesh	Unnao	AKHILESH KUMAR	5128	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Uttar Pradesh	Unnao	SHIVKANT	3978	no	IND	Independent
Uttar Pradesh	Unnao	SHIVNATH	3472	no	IND	Independent
Uttar Pradesh	Unnao	GIRJA SHANKAR @ RAJU	5871	no	SWPI	Swarajya Party Of India
Uttar Pradesh	Unnao	None of the Above	4626	no	NOTA	None of the Above
Uttar Pradesh	Mohanlalganj	KAUSHAL KISHORE	455274	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Mohanlalganj	OM PRAKASH	1427	no	PECP	Peace Party
Uttar Pradesh	Mohanlalganj	REENA CHAUDHARY	6304	no	IND	Independent
Uttar Pradesh	Mohanlalganj	SUNIL GAUTAM	10031	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Mohanlalganj	MAHENDRA KUMAR RAWAT	1617	no	ASaP	Awami Samta Party
Uttar Pradesh	Mohanlalganj	SANTOSH KUMAR DHARKAR	4280	no	INL	Indian National League
Uttar Pradesh	Mohanlalganj	MEERA DEVI	2425	no	MaSPa	Mahila Swabhiman Party
Uttar Pradesh	Mohanlalganj	SHATROHANLAL RAWAT	4214	no	IND	Independent
Uttar Pradesh	Mohanlalganj	None of the Above	4708	no	NOTA	None of the Above
Uttar Pradesh	Mohanlalganj	GAUTAM BHARTI	2225	no	RRP	Rashtriya Rashtrawadi Party
Uttar Pradesh	Mohanlalganj	BRIJLAL	1632	no	SARP	Samtawadi Republican Party
Uttar Pradesh	Mohanlalganj	GOPAL KANAUJIYA	1826	no	RPI(A)	Republican Party of India (A)
Uttar Pradesh	Mohanlalganj	NARENDRA GAUTAM	52598	no	INC	Indian National Congress
Uttar Pradesh	Mohanlalganj	MAHESH KUMAR	7412	no	IND	Independent
Uttar Pradesh	Mohanlalganj	AMIT RAWAT	5115	no	AITC	All India Trinamool Congress
Uttar Pradesh	Mohanlalganj	MEENA RAWAT	1782	no	NAP	Naitik Party
Uttar Pradesh	Mohanlalganj	SUSHILA SAROJ	242366	no	SP	Samajwadi Party
Uttar Pradesh	Mohanlalganj	JAGDISH PRASAD GAUTAM	1494	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Mohanlalganj	R.K CHAUDHARY	309858	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Lucknow	RAJ NATH SINGH	561106	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Lucknow	SHISHU PAL YADAV	851	no	RVLP	RASHTRIYA VIKLANG PARTY
Uttar Pradesh	Lucknow	MOHD. SARWAR MALIK	1486	no	AITC	All India Trinamool Congress
Uttar Pradesh	Lucknow	RAMPHAL RAWAT	502	no	BHNKP	Bharatiya Nav Kranti Party
Uttar Pradesh	Lucknow	VIJAY BAHADUR SINGH	575	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Lucknow	VINAY PRAKASH MISHRA	441	no	IND	Independent
Uttar Pradesh	Lucknow	MANOJ SINGH	572	no	IND	Independent
Uttar Pradesh	Lucknow	GHANSHYAM SINGH	282	no	IND	Independent
Uttar Pradesh	Lucknow	NAKUL DUBEY	64449	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Lucknow	ASOK PANDE	930	no	IND	Independent
Uttar Pradesh	Lucknow	TARA PATKAR	743	no	SWPI	Swarajya Party Of India
Uttar Pradesh	Lucknow	RAJ KUMAR SINGH	523	no	NYP	National Youth Party
Uttar Pradesh	Lucknow	RADHA KRISHNAN	542	no	UPRP	Uttar Pradesh Republican Party
Uttar Pradesh	Lucknow	PADAM CHANDRA GUPTA	661	no	IND	Independent
Uttar Pradesh	Lucknow	PROF. RITA BAHUGUNA JOSHI	288357	no	INC	Indian National Congress
Uttar Pradesh	Lucknow	NAND KUMAR LODHI RAJPOOT	445	no	RKSP	Rashtriya Krantikari Samajwadi Party
Uttar Pradesh	Lucknow	HARI RAM ALIAS HARIBHAI PATEL	694	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Lucknow	None of the Above	4596	no	NOTA	None of the Above
Uttar Pradesh	Lucknow	SUBHASH CHANDRA LEHRI	719	no	IND	Independent
Uttar Pradesh	Lucknow	SHAILENDRA SINGH YADAV	607	no	IND	Independent
Uttar Pradesh	Lucknow	SANJEEV MEHROTRA ALIAS PIKKU BHAI	1174	no	BKP	Bahujan Kranti Party (Marxwad-Ambedkarwad)
Uttar Pradesh	Lucknow	RAJEEV RANJAN TIWARI ALIAS RAJ BEHARI	391	no	IND	Independent
Uttar Pradesh	Lucknow	ABHISHEK MISRA	56771	no	SP	Samajwadi Party
Uttar Pradesh	Lucknow	SUMAN VERMA	1901	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Lucknow	SUNIL SINGH	849	no	LD	Lok Dal
Uttar Pradesh	Lucknow	YOGESH CHANDRA BHATT	337	no	IND	Independent
Uttar Pradesh	Lucknow	ADARSH SHASTRI	389	no	IND	Independent
Uttar Pradesh	Lucknow	RABI DUBEY	361	no	NEP	Nagrik Ekta Party
Uttar Pradesh	Lucknow	SYED JAVED AHMED JAFRI	41429	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Lucknow	SYED ALI HUSAIN	1100	no	IND	Independent
Uttar Pradesh	Rae Bareli	SONIA GANDHI	526434	yes	INC	Indian National Congress
Uttar Pradesh	Rae Bareli	SHAKIL AHAMAD	7914	no	IND	Independent
Uttar Pradesh	Rae Bareli	MANGAL SAIN	4418	no	IND	Independent
Uttar Pradesh	Rae Bareli	MOHAMMAD ASHRAF	1586	no	VIP	VANCHITSAMAJ INSAAF PARTY
Uttar Pradesh	Rae Bareli	JAWAHAR LAL	1463	no	NJC	Nehru Janhit Congress
Uttar Pradesh	Rae Bareli	ARCHANA SRIVASTAVA	10383	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Rae Bareli	RATIRAM	1661	no	SARP	Samtawadi Republican Party
Uttar Pradesh	Rae Bareli	SAHAR NAQVI	2108	no	LD	Lok Dal
Uttar Pradesh	Rae Bareli	PRAMOD KUREEL	6688	no	IND	Independent
Uttar Pradesh	Rae Bareli	AJAY AGRAWAL	173721	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Rae Bareli	KAILASH KUMAR	2645	no	HKRD	Hindustan Krantikari Dal
Uttar Pradesh	Rae Bareli	KIRAN CHAUDHARI	5205	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Rae Bareli	None of the Above	5409	no	NOTA	None of the Above
Uttar Pradesh	Rae Bareli	PRAVESH SINGH	63633	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Rae Bareli	SHIV SEWAK	1493	no	RAHM	Rashtriya Ahinsa Manch
Uttar Pradesh	Rae Bareli	SURESH CHANDRA	3330	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Rae Bareli	SAHADAT ALI KHAN	3538	no	IND	Independent
Uttar Pradesh	Rae Bareli	ANJU SINGH	3507	no	AITC	All India Trinamool Congress
Uttar Pradesh	Amethi	RAHUL GANDHI	408651	yes	INC	Indian National Congress
Uttar Pradesh	Amethi	None of the Above	1784	no	NOTA	None of the Above
Uttar Pradesh	Amethi	MITHILESH KUMARI	2726	no	IND	Independent
Uttar Pradesh	Amethi	GANGESH KUMAR GUPTA	1307	no	IND	Independent
Uttar Pradesh	Amethi	SWAMINATH	5537	no	IND	Independent
Uttar Pradesh	Amethi	CHHAYA SINGH	4109	no	AITC	All India Trinamool Congress
Uttar Pradesh	Amethi	RAMSIDDH YADAV	2189	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Amethi	DR. KUMAR VISHVAS	25527	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Amethi	DHARMENDRA PRATAP SINGH	57716	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Amethi	SONAM KINNAR	1252	no	IND	Independent
Uttar Pradesh	Amethi	ZAHIDA BEGAM	1387	no	PECP	Peace Party
Uttar Pradesh	Amethi	GOPAL SWAROOP GANDHI	5467	no	KMBS	Kisan Majdoor Berojgar Sangh
Uttar Pradesh	Amethi	SMRITI ZUBIN IRANI	300748	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Amethi	BHAGWAN DEEN	1299	no	IND	Independent
Uttar Pradesh	Amethi	RAVI KUMAR	1064	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Amethi	RAM SAJIWAN	2384	no	IND	Independent
Uttar Pradesh	Amethi	UMAKANT	3305	no	IND	Independent
Uttar Pradesh	Amethi	NARESH SINGH BHADAURIYA	1255	no	IND	Independent
Uttar Pradesh	Amethi	RADHE MOHAN	1753	no	ssrd	Sanatan Sanskriti Raksha Dal
Uttar Pradesh	Amethi	SANJAY KUMAR	1015	no	IND	Independent
Uttar Pradesh	Amethi	RAM ASREY	1572	no	RaMSP	Rashtriya Manav samman Party
Uttar Pradesh	Amethi	GIRIJA BAKS	2454	no	IND	Independent
Uttar Pradesh	Amethi	SHIV KUMAR	2289	no	IND	Independent
Uttar Pradesh	Amethi	JAGAT SINGH CHAUDHARY	1043	no	IND	Independent
Uttar Pradesh	Amethi	BABU LAL	1617	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Amethi	JITENDRA SINGH	2201	no	IND	Independent
Uttar Pradesh	Amethi	LAL BAHADUR	2845	no	IND	Independent
Uttar Pradesh	Amethi	C L MAURYA	1871	no	PGSP	Pragatisheel Samaj Party
Uttar Pradesh	Amethi	RAMESH CHANDRA	6422	no	IND	Independent
Uttar Pradesh	Amethi	MOHD HASAN LAHRI	4457	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Amethi	VASDEO	2546	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Uttar Pradesh	Amethi	RAM NEWAJ	1140	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Amethi	DWARIKA PRASAD	2345	no	IND	Independent
Uttar Pradesh	Amethi	VEDPAL SHASATRI	6464	no	VIP	VANCHITSAMAJ INSAAF PARTY
Uttar Pradesh	Amethi	TULSIRAM	4884	no	IND	Independent
Uttar Pradesh	Sultanpur	FEROZE VARUN GANDHI	410348	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Sultanpur	VARUN GANDHI	14021	no	IND	Independent
Uttar Pradesh	Sultanpur	RAM MILAN	1887	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Sultanpur	MOHD SHAMIM	1637	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Sultanpur	PAWAN PANDEY	231446	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Sultanpur	SHAKEEL AHMED	228144	no	SP	Samajwadi Party
Uttar Pradesh	Sultanpur	BRIJESH KUMAR	5752	no	IND	Independent
Uttar Pradesh	Sultanpur	SARTAJ KHAN	2363	no	ADPT	Apna Desh Party
Uttar Pradesh	Sultanpur	AMEETA SINGH	41983	no	INC	Indian National Congress
Uttar Pradesh	Sultanpur	GIRISH LAL	3804	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Sultanpur	DR. NAFEES AHMAD	3381	no	RaIP	Rastriya Insaaf Party
Uttar Pradesh	Sultanpur	SHAILENDRA PRATAP SINGH	5835	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Sultanpur	SHIV SHANKAR	1601	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Sultanpur	None of the Above	5412	no	NOTA	None of the Above
Uttar Pradesh	Sultanpur	VIJAY	4630	no	IND	Independent
Uttar Pradesh	Sultanpur	PITAMBAR NISHAD	2737	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Pratapgarh	KUWAR HARIVANSH SINGH	375789	yes	AD	Apna Dal
Uttar Pradesh	Pratapgarh	None of the Above	9098	no	NOTA	None of the Above
Uttar Pradesh	Pratapgarh	ASIF NIZAMUDDIN SIDDIQUE	207567	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Pratapgarh	RAJENDRA SHARMA	1571	no	RshJP	Rashtriya Janshanti Party
Uttar Pradesh	Pratapgarh	SHESHNATH	2458	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Uttar Pradesh	Pratapgarh	SANTOSH KUMAR SINGH	6309	no	AITC	All India Trinamool Congress
Uttar Pradesh	Pratapgarh	KIRAN MISHRA	3002	no	IND	Independent
Uttar Pradesh	Pratapgarh	FOOLCHANDRA	1859	no	SHS	Shivsena
Uttar Pradesh	Pratapgarh	RAJKUMARI RATNA SINGH	138620	no	INC	Indian National Congress
Uttar Pradesh	Pratapgarh	BAL KRISHNA	2160	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Pratapgarh	RAM SEWAK	4207	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Pratapgarh	AJMAL KHAN	3342	no	IND	Independent
Uttar Pradesh	Pratapgarh	NIRMALA DEVI	2766	no	BaVaP	Bhartiya Vanchitsamaj Party
Uttar Pradesh	Pratapgarh	HARIVANSH	5618	no	IND	Independent
Uttar Pradesh	Pratapgarh	PRAMOD KUMAR SINGH PATEL	120107	no	SP	Samajwadi Party
Uttar Pradesh	Pratapgarh	"ASHOK SHUKLA "" SENANI"""	9983	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Farrukhabad	MUKESH RAJPUT	406195	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Farrukhabad	None of the Above	1986	no	NOTA	None of the Above
Uttar Pradesh	Farrukhabad	RAMNIWAS KATHERIA	941	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Farrukhabad	RAM KISHORE DIWEDI	4974	no	IND	Independent
Uttar Pradesh	Farrukhabad	KISHAN PAL SINGH	2933	no	IND	Independent
Uttar Pradesh	Farrukhabad	ANSHU MAHAN	2066	no	IND	Independent
Uttar Pradesh	Farrukhabad	ANOKHEY LAL	1202	no	IND	Independent
Uttar Pradesh	Farrukhabad	MUKESH RAJPUT	2420	no	IND	Independent
Uttar Pradesh	Farrukhabad	SACHIN SINGH YADAV	58703	no	IND	Independent
Uttar Pradesh	Farrukhabad	VIMLA DEVI	2140	no	IND	Independent
Uttar Pradesh	Farrukhabad	ANURAG RAJPUT	1605	no	IND	Independent
Uttar Pradesh	Farrukhabad	MALIKHAN SINGH ADVOCATE	4659	no	IND	Independent
Uttar Pradesh	Farrukhabad	TARIK PARVEZ	1857	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Farrukhabad	SURESH SARASWAT ALAIS ILLAYCHIWALA	1013	no	IND	Independent
Uttar Pradesh	Farrukhabad	RAMESHWAR SINGH YADAV	255693	no	SP	Samajwadi Party
Uttar Pradesh	Farrukhabad	MO. TAMAJID	2655	no	PECP	Peace Party
Uttar Pradesh	Farrukhabad	ANJU	2254	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Farrukhabad	SALMAN KHURSHID	95543	no	INC	Indian National Congress
Uttar Pradesh	Farrukhabad	VRAHMA SARAN JATAV	1809	no	IND	Independent
Uttar Pradesh	Farrukhabad	JAIVEER SINGH	114521	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Farrukhabad	KRISHAN KUMAR DIXIT ALIAS K. K. DIXIT	2107	no	RVLP	RASHTRIYA VIKLANG PARTY
Uttar Pradesh	Farrukhabad	IMAMUDDIN HUSAIN HUSAIN	1537	no	IND	Independent
Uttar Pradesh	Farrukhabad	DAYA SHANKAR	1864	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Etawah	ASHOK KUMAR DOHAREY	439646	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Etawah	PREMDAS KATERIYA	266700	no	SP	Samajwadi Party
Uttar Pradesh	Etawah	DR MUKESH SINGH DOHARE	3178	no	PECP	Peace Party
Uttar Pradesh	Etawah	HAR BILASH DOHERY	2677	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Etawah	UJIYARE LAL DOHARE	5923	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Etawah	SIYA RAM PASWAN	1788	no	ALHP	Al-Hind Party
Uttar Pradesh	Etawah	AJAY PAL SINGH JATAV	192804	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Etawah	HANS MUKHI KORI	13397	no	INC	Indian National Congress
Uttar Pradesh	Etawah	VINEET KUMAR GAUTAM	4954	no	ASP	Ambedkar Samaj Party
Uttar Pradesh	Etawah	None of the Above	6165	no	NOTA	None of the Above
Uttar Pradesh	Etawah	PREM CHANDRA	2372	no	KMBS	Kisan Majdoor Berojgar Sangh
Uttar Pradesh	Kannauj	DIMPLE YADAV	489164	yes	SP	Samajwadi Party
Uttar Pradesh	Kannauj	SUBODH KUMAR	927	no	PECP	Peace Party
Uttar Pradesh	Kannauj	PRADEEP KUMAR	964	no	IND	Independent
Uttar Pradesh	Kannauj	IMRAN BIN ZAFAR	4826	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kannauj	GANGA CHARAN	1282	no	RTKP	Rashtriya Kranti Party
Uttar Pradesh	Kannauj	None of the Above	7380	no	NOTA	None of the Above
Uttar Pradesh	Kannauj	MUHAR SINGH AMVADI	2891	no	IND	Independent
Uttar Pradesh	Kannauj	NIRMAL TIWARI	127785	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kannauj	SUBRAT PATHAK	469257	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Kannauj	PUSHPA DEVI	806	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Kannauj	SURENDRA NATH	888	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Kannauj	SANJAY SHAKYA	2567	no	IND	Independent
Uttar Pradesh	Kannauj	RAKESH KUMAR TIWARI ADVOCATE	5723	no	IND	Independent
Uttar Pradesh	Kanpur	DR.MURLI MANOHAR JOSHI	474712	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Kanpur	MUZAFFAR HUSAIN KHAN	1069	no	IND	Independent
Uttar Pradesh	Kanpur	MAHMOOD HUSAIN RAHMANI	11925	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kanpur	ANIL AWASTHI	1701	no	AITC	All India Trinamool Congress
Uttar Pradesh	Kanpur	SALEEM AHMAD	53218	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kanpur	ASHOK PASWAN ADVOCATE	1398	no	IND	Independent
Uttar Pradesh	Kanpur	SRIPRAKASH JAISWAL	251766	no	INC	Indian National Congress
Uttar Pradesh	Kanpur	SURENDRA MOHAN AGARWAL	25723	no	SP	Samajwadi Party
Uttar Pradesh	Kanpur	KUMARI SARASWATI	475	no	IND	Independent
Uttar Pradesh	Kanpur	PREM CHANDRA PANDEY	2350	no	IND	Independent
Uttar Pradesh	Kanpur	HIMMAT LAL	683	no	ABRS	Akhil Bharatiya Rajarya Sabha
Uttar Pradesh	Kanpur	BETALAL DIWAKAR	665	no	IND	Independent
Uttar Pradesh	Kanpur	VINAYAK RAO TOPE	723	no	SWPI	Swarajya Party Of India
Uttar Pradesh	Kanpur	None of the Above	2278	no	NOTA	None of the Above
Uttar Pradesh	Kanpur	KISHOR BAJPAI	3005	no	IND	Independent
Uttar Pradesh	Kanpur	VISHWANATH KATARIA	929	no	IND	Independent
Uttar Pradesh	Kanpur	ANAND MEHROTRA	361	no	NEP	Nagrik Ekta Party
Uttar Pradesh	Kanpur	DR.NIKHIL GUPTA	580	no	NBDP	Nav Bharat Democratic Party
Uttar Pradesh	Kanpur	DEVENDRA KUMAR SHARMA	296	no	ABAS	Akhil Bharatiya Ashok Sena
Uttar Pradesh	Kanpur	RAMESH KUMAR GUPTA	433	no	PSJP	Parivartan Samaj Party
Uttar Pradesh	Kanpur	MOHD.NASIR KHAN	364	no	PECP	Peace Party
Uttar Pradesh	Kanpur	MOHAMMAD.ATEEQ	425	no	RWSP	Rashtrawadi Samaj Party
Uttar Pradesh	Akbarpur	DEVENDRA SINGH @ BHOLE SINGH	481584	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Akbarpur	ASHOK YADAV	1853	no	IND	Independent
Uttar Pradesh	Akbarpur	JASWANT SINGH	1037	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Akbarpur	None of the Above	3948	no	NOTA	None of the Above
Uttar Pradesh	Akbarpur	BHARTI DEVI PASI ADVOCATE	5432	no	IND	Independent
Uttar Pradesh	Akbarpur	GOPI RAMAN	523	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Akbarpur	RAJARAM PAL	96827	no	INC	Indian National Congress
Uttar Pradesh	Akbarpur	DR. OMKAR NATH KATIYAR	597	no	ALHP	Al-Hind Party
Uttar Pradesh	Akbarpur	VIJAY KUMAR SHARMA	4285	no	IND	Independent
Uttar Pradesh	Akbarpur	NEERAJ KUMAR ADVOCATE	2259	no	IND	Independent
Uttar Pradesh	Akbarpur	MUNNI DEVI	5331	no	IND	Independent
Uttar Pradesh	Akbarpur	DR. BAJARANG PRATAP SONKAR	799	no	SARP	Samtawadi Republican Party
Uttar Pradesh	Akbarpur	RAMENDRA SINGH TOMAR	2754	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Akbarpur	RAM SEVAK	3403	no	IND	Independent
Uttar Pradesh	Akbarpur	ARVIND KUMAR	7914	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Akbarpur	AMAR SINGH	1890	no	JPS	Janvadi Party(Socialist)
Uttar Pradesh	Akbarpur	ANIL SHUKLA WARSI	202587	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Akbarpur	LAL SINGH TOMAR	147002	no	SP	Samajwadi Party
Uttar Pradesh	Akbarpur	RAMA KANT VERMA	1350	no	PECP	Peace Party
Uttar Pradesh	Jalaun	BHANU PRATAP SINGH VERMA	548631	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Jalaun	None of the Above	10291	no	NOTA	None of the Above
Uttar Pradesh	Jalaun	GHANSHAYAM ANURAGI	180921	no	SP	Samajwadi Party
Uttar Pradesh	Jalaun	MANOJ KUMAR	4561	no	BRPP	Bharatiya Republican Paksha
Uttar Pradesh	Jalaun	GANGASINGH	2525	no	ALHP	Al-Hind Party
Uttar Pradesh	Jalaun	RAMNARAYAN	4944	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Jalaun	BRIJLAL KHABRI	261429	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Jalaun	RAM SINGH	4761	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Jalaun	DALCHANDRA ANURAGI	3337	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Jalaun	RAJKUMAR	2816	no	PECP	Peace Party
Uttar Pradesh	Jalaun	VIJAY CHAUDHARI	82903	no	INC	Indian National Congress
Uttar Pradesh	Jalaun	JAYRAM	2047	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Jhansi	UMA BHARATI	575889	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Jhansi	RACHNA SHARMA	4522	no	IND	Independent
Uttar Pradesh	Jhansi	MAULANA MOHAMMAD	1819	no	PECP	Peace Party
Uttar Pradesh	Jhansi	ARCHANA	5536	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Jhansi	DR. CHANDRAPAL SINGH YADAV	385422	no	SP	Samajwadi Party
Uttar Pradesh	Jhansi	PRADEEP JAIN 'ADITYA'	84089	no	INC	Indian National Congress
Uttar Pradesh	Jhansi	None of the Above	13959	no	NOTA	None of the Above
Uttar Pradesh	Jhansi	RAM KUMAR 'ANK SHASTRI'	11443	no	IND	Independent
Uttar Pradesh	Jhansi	CHINTAMANI	2029	no	ARWP	Akhil Rashtrawadi Party
Uttar Pradesh	Jhansi	NARESH KUMAR	4215	no	RrSP	Rashtriya Sarvajan Party
Uttar Pradesh	Jhansi	KULDEEP KUMAR	2036	no	IND	Independent
Uttar Pradesh	Jhansi	DILSHAD AHMAD	2727	no	IND	Independent
Uttar Pradesh	Jhansi	RAJENDRA KUMAR	8600	no	IND	Independent
Uttar Pradesh	Jhansi	SUNIL MASSEY	2121	no	SHS	Shivsena
Uttar Pradesh	Jhansi	TULA RAM	2605	no	IND	Independent
Uttar Pradesh	Jhansi	ANURADHA SHARMA	213792	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Hamirpur	KUNWAR PUSHPENDRA SINGH CHANDEL	453884	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Hamirpur	KIRAN	4079	no	IND	Independent
Uttar Pradesh	Hamirpur	VIMLESH	23137	no	IND	Independent
Uttar Pradesh	Hamirpur	ATUL KUMAR PRAJAPATI	9663	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Hamirpur	AVINASH KUMAR MISHRA	6297	no	AITC	All India Trinamool Congress
Uttar Pradesh	Hamirpur	"PRITAM SINGH LODHI ""KISAAN"""	78229	no	INC	Indian National Congress
Uttar Pradesh	Hamirpur	RAKESH KUMAR GOSWAMI	176356	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Hamirpur	None of the Above	10449	no	NOTA	None of the Above
Uttar Pradesh	Hamirpur	RAMSAJIVAN	2955	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Hamirpur	KAILASH PRASAD NISHAD	1756	no	EKSP	Eklavya Samaj Party
Uttar Pradesh	Hamirpur	BISHAMBHAR PRASAD NISHAD	187096	no	SP	Samajwadi Party
Uttar Pradesh	Hamirpur	RAMESH PRAJAPATI	2981	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Hamirpur	VIVEK KUMAR	5749	no	RKSP	Rashtriya Krantikari Samajwadi Party
Uttar Pradesh	Hamirpur	PRAVESH KUMAR VYAS	4314	no	IND	Independent
Uttar Pradesh	Hamirpur	ANIL KUMAR	4860	no	ASP	Ambedkar Samaj Party
Uttar Pradesh	Hamirpur	SHRIPAL	3435	no	BC	Bundelkhand Congress
Uttar Pradesh	Banda	BHAIRON PRASAD MISHRA	342066	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Banda	SUSHEELA SINGH	2931	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Banda	BAL KUMAR PATEL ALIAS RAJKUMAR	189730	no	SP	Samajwadi Party
Uttar Pradesh	Banda	R. K. SINGH PATEL	226278	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Banda	None of the Above	13824	no	NOTA	None of the Above
Uttar Pradesh	Banda	MO. SAGEER	3201	no	PECP	Peace Party
Uttar Pradesh	Banda	NARENDRA KUMAR SHRIVASTAVA	5115	no	IND	Independent
Uttar Pradesh	Banda	PRABHAKAR PRASAD	2605	no	BKSL	Bharatiya Kisan Sena Loktantrik
Uttar Pradesh	Banda	AMAR SINGH RATHAUR	6439	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Banda	VIJAY PRAKASH	7140	no	IND	Independent
Uttar Pradesh	Banda	PAPPU YADAV	2436	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Banda	VISHVAMBHAR NATH	4755	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Banda	VIVEK KUMAR SINGH	36650	no	INC	Indian National Congress
Uttar Pradesh	Banda	RAMCHANDRA SARAS	15156	no	CPI	Communist Party of India
Uttar Pradesh	Fatehpur	NIRANJAN JYOTI	485994	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Fatehpur	ASHOK MISHRA	3029	no	SARP	Samtawadi Republican Party
Uttar Pradesh	Fatehpur	RUKMINI DEVI NISHAD	1345	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Fatehpur	MOHAMMAD JAVED	1657	no	NBDP	Nav Bharat Democratic Party
Uttar Pradesh	Fatehpur	RANJANA KAKKAR	5448	no	IND	Independent
Uttar Pradesh	Fatehpur	CHANDRABHAN SINGH	2498	no	IND	Independent
Uttar Pradesh	Fatehpur	ARVIND KUMAR	1967	no	IND	Independent
Uttar Pradesh	Fatehpur	None of the Above	11607	no	NOTA	None of the Above
Uttar Pradesh	Fatehpur	RAKESH SACHAN	179724	no	SP	Samajwadi Party
Uttar Pradesh	Fatehpur	SRIDEVI	5838	no	IND	Independent
Uttar Pradesh	Fatehpur	AFZAL SIDDIQUI	298788	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Fatehpur	RAMKRISHNA TRIPATHI	7578	no	AITC	All India Trinamool Congress
Uttar Pradesh	Fatehpur	RAMLOCHAN SEN	2173	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Fatehpur	ZAINUL ABDIN	2454	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Fatehpur	USHA MAURYA	46588	no	INC	Indian National Congress
Uttar Pradesh	Kaushambi	VINOD KUMAR SONKAR	331593	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Kaushambi	BEENA RANI	4957	no	IND	Independent
Uttar Pradesh	Kaushambi	RAJENDRA SONKAR	6386	no	LD	Lok Dal
Uttar Pradesh	Kaushambi	CHHEDDOO	3340	no	IND	Independent
Uttar Pradesh	Kaushambi	MALTI DEVI	6548	no	RSMD	Rashtriya Samanta Dal
Uttar Pradesh	Kaushambi	SURESH PASI	201196	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kaushambi	SWARNALATA SUMAN	3692	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kaushambi	SHRIPAL	7341	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Kaushambi	MOHAN LAL	9180	no	IND	Independent
Uttar Pradesh	Kaushambi	SHAILENDRA KUMAR	288746	no	SP	Samajwadi Party
Uttar Pradesh	Kaushambi	MAHENDRA KUMAR	31904	no	INC	Indian National Congress
Uttar Pradesh	Kaushambi	None of the Above	15155	no	NOTA	None of the Above
Uttar Pradesh	Phulpur	KESHAV PRASAD MAURYA	503564	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Phulpur	None of the Above	8424	no	NOTA	None of the Above
Uttar Pradesh	Phulpur	NATHU RAM BOUDHA	1306	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Phulpur	DHARAM RAJ SINGH PATEL	195256	no	SP	Samajwadi Party
Uttar Pradesh	Phulpur	MOHD. KAIF	58127	no	INC	Indian National Congress
Uttar Pradesh	Phulpur	DAKKHINI PRASAD KUSHWAHA	1111	no	RGD	Rashtriya Garib Dal
Uttar Pradesh	Phulpur	MILAN MUKERJEE	5652	no	IND	Independent
Uttar Pradesh	Phulpur	GANESHJI TRIPATHI	1521	no	RaBaP	Rashtriya Bandhutwa Party
Uttar Pradesh	Phulpur	SHIMALA SHRI	7384	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Phulpur	PARVEZ ASHFAQUE SIDDIQUI	1302	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Phulpur	SURENDRA KUMAR MISHRA	1585	no	LD	Lok Dal
Uttar Pradesh	Phulpur	GYANENDRA KUMAR SRIVASTAVA (GYANI BHAI)	2706	no	NCP	Nationalist Congress Party
Uttar Pradesh	Phulpur	ZIAOUL HAQUE	3392	no	IND	Independent
Uttar Pradesh	Phulpur	KAPIL MUNI KARWARIYA	163710	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Phulpur	DR. NEERAJ	2187	no	IND	Independent
Uttar Pradesh	Phulpur	MITHILESH KUMAR	3114	no	IND	Independent
Uttar Pradesh	Allahabad	SHYAMA CHARAN GUPTA	313772	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Allahabad	SHYAM SUNDAR DAS	2115	no	ssrd	Sanatan Sanskriti Raksha Dal
Uttar Pradesh	Allahabad	SHYAM CHARAN	2593	no	IND	Independent
Uttar Pradesh	Allahabad	RAJENDRA PRASAD	1730	no	kajp	Kalyankari Jantantrik Party
Uttar Pradesh	Allahabad	CHANDRA PRAKASH TIWARI ALIAS C. P. TIWARI (ADVOCATE)	1221	no	BKSL	Bharatiya Kisan Sena Loktantrik
Uttar Pradesh	Allahabad	MOHD. AMEEN AZHAR ANSARI	4426	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Allahabad	SANJAY ROBINSON	3315	no	NAP	Naitik Party
Uttar Pradesh	Allahabad	SHAILENDRA KUMAR PRAJAPATI	1212	no	IND	Independent
Uttar Pradesh	Allahabad	ADARSH SHASTRI	6439	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Allahabad	BRIJESH KUMAR	1438	no	IND	Independent
Uttar Pradesh	Allahabad	NISTHA DEV MAURYA	2181	no	PGSP	Pragatisheel Samaj Party
Uttar Pradesh	Allahabad	DRAUPADI DEVI	8321	no	IND	Independent
Uttar Pradesh	Allahabad	GAYTRI PRASAD	1293	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Allahabad	MAHENDRA KUMAR	4190	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Allahabad	SHIV PRASAD AGRAHARI	4323	no	IND	Independent
Uttar Pradesh	Allahabad	KUNWAR REWATI RAMAN SINGH ALIAS MANI	251763	no	SP	Samajwadi Party
Uttar Pradesh	Allahabad	NAND GOPAL GUPTA NANDI	102453	no	INC	Indian National Congress
Uttar Pradesh	Allahabad	HIRA LAL	1711	no	IND	Independent
Uttar Pradesh	Allahabad	AJEET KUMAR	3388	no	IND	Independent
Uttar Pradesh	Allahabad	KESHRI DEVI	162073	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Allahabad	WAHID ALI ALIAS WAJID ALI	2332	no	RUC	Rashtriya Ulama Council
Uttar Pradesh	Allahabad	HANSRAJ	1928	no	IND	Independent
Uttar Pradesh	Allahabad	PARMANAND	3744	no	IND	Independent
Uttar Pradesh	Allahabad	None of the Above	3621	no	NOTA	None of the Above
Uttar Pradesh	Barabanki	PRIYANKA SINGH RAWAT	454214	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Barabanki	None of the Above	8726	no	NOTA	None of the Above
Uttar Pradesh	Barabanki	ABHILAKH RAWAT	3038	no	IND	Independent
Uttar Pradesh	Barabanki	BABU LAL	2035	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Barabanki	NITIN SINGH	1315	no	NAP	Naitik Party
Uttar Pradesh	Barabanki	MEDI LAL	2171	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Barabanki	DINESH CHANDRA GAUTAM	3126	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Barabanki	RAJRANI RAWAT	159284	no	SP	Samajwadi Party
Uttar Pradesh	Barabanki	MAHESH	3836	no	IND	Independent
Uttar Pradesh	Barabanki	KHANJAN LAL	7136	no	IND	Independent
Uttar Pradesh	Barabanki	P. L. PUNIA	242336	no	INC	Indian National Congress
Uttar Pradesh	Barabanki	KISHAN LAL	3261	no	SHS	Shivsena
Uttar Pradesh	Barabanki	RAJESH KUMAR VIDYARTHI	7980	no	PECP	Peace Party
Uttar Pradesh	Barabanki	P. C. KUREEL	2560	no	INL	Indian National League
Uttar Pradesh	Barabanki	KAMLA PRASAD RAWAT	167150	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Faizabad	LALLU SINGH	491761	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Faizabad	JITENDRA KUMAR SINGH (BABLU BHAIYA)	141827	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Faizabad	MITRASEN YADAV	208986	no	SP	Samajwadi Party
Uttar Pradesh	Faizabad	None of the Above	11537	no	NOTA	None of the Above
Uttar Pradesh	Faizabad	MAKASUD ALI	5963	no	PECP	Peace Party
Uttar Pradesh	Faizabad	JAI RAM SINGH JAI	5702	no	LPSP	Lokpriya Samaj Party
Uttar Pradesh	Faizabad	NIRMAL KHATRI	129917	no	INC	Indian National Congress
Uttar Pradesh	Faizabad	MAYURI TIWARI	4795	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Faizabad	RAM JANAM	6308	no	MADP	Moulik Adhikar Party
Uttar Pradesh	Faizabad	RADHIKA PRASAD	9055	no	ISSP	Indian Savarn Samaj Party
Uttar Pradesh	Faizabad	NASREEN BANO	2678	no	RUC	Rashtriya Ulama Council
Uttar Pradesh	Faizabad	SHAILENDRA KUMAR SHUKLA	4187	no	NAP	Naitik Party
Uttar Pradesh	Ambedkar Nagar	HARI OM PANDEY	432104	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Ambedkar Nagar	RAM BAHAL VERMA	1137	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Ambedkar Nagar	ASHOK SINGH	22775	no	INC	Indian National Congress
Uttar Pradesh	Ambedkar Nagar	TIGER RAMNIHOR PATEL	7040	no	IND	Independent
Uttar Pradesh	Ambedkar Nagar	J.S. KASHYAP(JAHAR SINGH KASHYAP)	6896	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Ambedkar Nagar	SURESH KUMAR BAUDDH	7694	no	MADP	Moulik Adhikar Party
Uttar Pradesh	Ambedkar Nagar	SREERAM	4138	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Ambedkar Nagar	BRIJESH KUMAR	3025	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ambedkar Nagar	VINOD KUMAR TIWARI	4707	no	IND	Independent
Uttar Pradesh	Ambedkar Nagar	RAM MURTI VERMA	234467	no	SP	Samajwadi Party
Uttar Pradesh	Ambedkar Nagar	GOPAL NISHAD	5792	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Ambedkar Nagar	SURENDRA KUMAR	2590	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Ambedkar Nagar	BALMUKUND DHURIYA	1942	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Ambedkar Nagar	None of the Above	7422	no	NOTA	None of the Above
Uttar Pradesh	Ambedkar Nagar	RAKESH PANDEY	292675	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bahraich	SADHVI SAVITRI BAI FOOLE	432392	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Bahraich	None of the Above	13498	no	NOTA	None of the Above
Uttar Pradesh	Bahraich	DR.VIJAY KUMAR	96904	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bahraich	COMANDO KAMAL KISHOR	24421	no	INC	Indian National Congress
Uttar Pradesh	Bahraich	SHABBIR AHMAD	336747	no	SP	Samajwadi Party
Uttar Pradesh	Bahraich	HIRA LAL RAO	4026	no	ANC	Ambedkar National Congress
Uttar Pradesh	Bahraich	RAM NARESH	5826	no	IND	Independent
Uttar Pradesh	Bahraich	RAMASHANKER GOND	5735	no	jhspt	Jai Hind Samaj Party
Uttar Pradesh	Bahraich	MADHAV	2676	no	LD	Lok Dal
Uttar Pradesh	Bahraich	JAGAT RAM	8319	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Bahraich	VINOD KUMAR	3719	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Kaiserganj	BRIJ BHUSAN SHARAN SINGH	381500	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Kaiserganj	CHAMAN LAL KASAUNDHAN	1822	no	LD	Lok Dal
Uttar Pradesh	Kaiserganj	PRAHLAD  NATH MISHRA	3867	no	IND	Independent
Uttar Pradesh	Kaiserganj	NIRHU	1466	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Uttar Pradesh	Kaiserganj	NISHANT KANCHAN	3246	no	IND	Independent
Uttar Pradesh	Kaiserganj	VINOD KUMAR ALIAS  PANDIT SINGH	303282	no	SP	Samajwadi Party
Uttar Pradesh	Kaiserganj	MUKESH SRIVASTAV ALIAS GYANENDRA PRATAP	57401	no	INC	Indian National Congress
Uttar Pradesh	Kaiserganj	MUNNI	5245	no	IND	Independent
Uttar Pradesh	Kaiserganj	NIHALUDDIN AHAMAD	2119	no	AIPF	All India Peoples' Front (Radical)
Uttar Pradesh	Kaiserganj	KRISHNA KUMAR OJHA	146726	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kaiserganj	JAMEER AHMAD	3905	no	IND	Independent
Uttar Pradesh	Kaiserganj	RAMPHER  ALIAS  CHUNTI	11366	no	IND	Independent
Uttar Pradesh	Kaiserganj	RIZWAN AHMAD	3710	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kaiserganj	GOVIND PRASAD	5849	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Kaiserganj	None of the Above	11853	no	NOTA	None of the Above
Uttar Pradesh	Shrawasti	DADDAN MISHRA	345964	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Shrawasti	None of the Above	14587	no	NOTA	None of the Above
Uttar Pradesh	Shrawasti	ARJUN YADAV	3730	no	IND	Independent
Uttar Pradesh	Shrawasti	RAM ADHAR	6278	no	IND	Independent
Uttar Pradesh	Shrawasti	ATIQ AHMAD	260051	no	SP	Samajwadi Party
Uttar Pradesh	Shrawasti	VINAY KUMAR PANDEY 'VINNU'	20006	no	INC	Indian National Congress
Uttar Pradesh	Shrawasti	RAM SUDHI	2961	no	JSEP	Jan Shakti Ekta Party
Uttar Pradesh	Shrawasti	RATNESH	2578	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Shrawasti	LAL JI VERMA	194890	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Shrawasti	RIZWAN ZAHEER	101817	no	PECP	Peace Party
Uttar Pradesh	Shrawasti	PRAKSH CHANDRA MISHRA	4489	no	JSK	Jan Sevak Party
Uttar Pradesh	Shrawasti	SAXEN	3188	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Shrawasti	TEJ BAHADUR	3474	no	IND	Independent
Uttar Pradesh	Shrawasti	VIJAY KUMAR	5542	no	IND	Independent
Uttar Pradesh	Shrawasti	ARUN KUMAR	10083	no	IND	Independent
Uttar Pradesh	Gonda	KIRTI VARDHAN SINGH	359643	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Gonda	TILAK RAM	5374	no	IND	Independent
Uttar Pradesh	Gonda	MAHESH KUMAR	2555	no	JPS	Janvadi Party(Socialist)
Uttar Pradesh	Gonda	AJAY  KUMAR  PRABHAKAR	3200	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Gonda	RAJESH  KUMAR  MAURYA	2199	no	SP(I)	Socialist Party (India)
Uttar Pradesh	Gonda	SATYA PRAKASH	4748	no	SHS	Shivsena
Uttar Pradesh	Gonda	MASOOD ALAM KHAN	42744	no	PECP	Peace Party
Uttar Pradesh	Gonda	AKBAR AHMAD DUMPY	116178	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Gonda	NANDITA SHUKLA	199227	no	SP	Samajwadi Party
Uttar Pradesh	Gonda	OM PRAKASH	18450	no	CPI	Communist Party of India
Uttar Pradesh	Gonda	PREM KUMAR	1900	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Gonda	MASHOOQ AHMAD	3358	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Gonda	RAJENDRA PRASAD	1519	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Gonda	BENI PRASAD VERMA	102254	no	INC	Indian National Congress
Uttar Pradesh	Gonda	BRIJNANDAN TIWARI	2395	no	IND	Independent
Uttar Pradesh	Gonda	None of the Above	7988	no	NOTA	None of the Above
Uttar Pradesh	Domariyaganj	JAGDAMBIKA PAL	298845	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Domariyaganj	None of the Above	6775	no	NOTA	None of the Above
Uttar Pradesh	Domariyaganj	RAM UGRAH	7762	no	IND	Independent
Uttar Pradesh	Domariyaganj	BADRE ALAM	3174	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Domariyaganj	KALI RAM	4939	no	IND	Independent
Uttar Pradesh	Domariyaganj	RAVI KUMAR	2535	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Domariyaganj	PINGLE PRASAD	5521	no	S(J)	Swaraj (J)
Uttar Pradesh	Domariyaganj	MANOJ KUMAR	2709	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Domariyaganj	VASUNDHARA	88117	no	INC	Indian National Congress
Uttar Pradesh	Domariyaganj	HAMIDULLAH CHAUDHARY	7159	no	NCP	Nationalist Congress Party
Uttar Pradesh	Domariyaganj	MUHAMMAD MUQEEM	195257	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Domariyaganj	SANTRAM	3238	no	BRABSVP	Bharatiya Rashtriya Bahujan Samaj Vikas Party
Uttar Pradesh	Domariyaganj	MUKESH NARAYAN SHUKLA URF GYANESH NARAYAN SHUKLA	10952	no	IND	Independent
Uttar Pradesh	Domariyaganj	ARJUN SINGH	9529	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Domariyaganj	DR. MOHD. AYUB	99242	no	PECP	Peace Party
Uttar Pradesh	Domariyaganj	DR. SATYENDRA  NATH PANDEY	9372	no	AAMJP	Aam Janata Party
Uttar Pradesh	Domariyaganj	MATA PRASAD PANDEY	174778	no	SP	Samajwadi Party
Uttar Pradesh	Domariyaganj	DIWAKAR PRASAD	5123	no	IND	Independent
Uttar Pradesh	Basti	HARISH CHANDRA  ALIAS  HARISH DWIVEDI	357680	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Basti	SITA RAM	2240	no	JPS	Janvadi Party(Socialist)
Uttar Pradesh	Basti	RAM SHANKAR GUPTA	4521	no	IND	Independent
Uttar Pradesh	Basti	AMBIKA SINGH	27673	no	INC	Indian National Congress
Uttar Pradesh	Basti	BHAGWANDAS	3532	no	BSP(K)	Bahujan Sangharsh Party (Kanshiram)
Uttar Pradesh	Basti	RAM PRASAD CHAUDHARY	283747	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Basti	PURUSHOTTAM PANDEY	8172	no	PECP	Peace Party
Uttar Pradesh	Basti	ARUN KUMAR	3587	no	IND	Independent
Uttar Pradesh	Basti	RAM KARAN ALIAS R.K. GAUTAM	2803	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Basti	KRISHNA  KUMAR UPADHYAY ADVOCATE	4823	no	IND	Independent
Uttar Pradesh	Basti	SHIVPOOJAN RAJBHAR	7063	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Basti	ANAND RAJPAL	8407	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Basti	"BRIJ KISHOR SINGH ""DIMPAL"""	324118	no	SP	Samajwadi Party
Uttar Pradesh	Basti	None of the Above	10168	no	NOTA	None of the Above
Uttar Pradesh	Sant Kabir Nagar	SHARAD TRIPATHI	348892	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Sant Kabir Nagar	None of the Above	4747	no	NOTA	None of the Above
Uttar Pradesh	Sant Kabir Nagar	SANT SIROMANI BABOOLAL YADAV MAHARAJ JI	3900	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Sant Kabir Nagar	AMIT	2796	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	BHISM SHANKAR ALIAS KUSHAL TIWARI	250914	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Sant Kabir Nagar	SHAYAM LAL	1477	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	SHABBEER HUSEN	8993	no	jhspt	Jai Hind Samaj Party
Uttar Pradesh	Sant Kabir Nagar	ROHIT KUMAR PANDEY	22029	no	INC	Indian National Congress
Uttar Pradesh	Sant Kabir Nagar	RAJARAM	69193	no	PECP	Peace Party
Uttar Pradesh	Sant Kabir Nagar	RAJMANI	5036	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Sant Kabir Nagar	JYOTI SINGH	1884	no	BSP(K)	Bahujan Sangharsh Party (Kanshiram)
Uttar Pradesh	Sant Kabir Nagar	AFTAB ALAM KHAN	7458	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Sant Kabir Nagar	JANTRI LAL CHAUHAN	2439	no	JPS	Janvadi Party(Socialist)
Uttar Pradesh	Sant Kabir Nagar	LOTAN URF LAUTAN PRASAD	3618	no	SSD	Shoshit Samaj Dal
Uttar Pradesh	Sant Kabir Nagar	KAILASH NATH BELDAR	3181	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	BHAL CHANDRA YADAV	240169	no	SP	Samajwadi Party
Uttar Pradesh	Sant Kabir Nagar	KRIPANARAYAN RAI	1215	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	JAIYANTRI PRASAD	1335	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	MOHAMMAD ALI	4082	no	PPI	Parcham Party of India
Uttar Pradesh	Sant Kabir Nagar	MOHAMMAD IRFAN	2164	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	APARNA	11046	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	RAVIKANT	1721	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Sant Kabir Nagar	SURENDRA KUMAR CHAUDHARY	5866	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	RAJENDRA KUMAR	2278	no	RJPK	Rashtriya Janwadi Party (Krantikari)
Uttar Pradesh	Sant Kabir Nagar	SUBODH CHANDRA	3061	no	IND	Independent
Uttar Pradesh	Sant Kabir Nagar	PREM PRAKASH	2155	no	IPP	Indian Peace Party
Uttar Pradesh	Maharajganj	PANKAJ	471542	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Maharajganj	ARAT KHARVAR	3752	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Maharajganj	MANGAL LAL	1491	no	LD	Lok Dal
Uttar Pradesh	Maharajganj	RAKESH KUMAR GIRI	2545	no	IND	Independent
Uttar Pradesh	Maharajganj	SHREE AKHILESH KUMAR	5422	no	ADUP	Apna Dal United Party
Uttar Pradesh	Maharajganj	AKHILESH	213974	no	SP	Samajwadi Party
Uttar Pradesh	Maharajganj	KAVILAS	5195	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Maharajganj	MUMMATAJ	3582	no	PECP	Peace Party
Uttar Pradesh	Maharajganj	KASHI NATH SHUKLA	231084	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Maharajganj	ANIL KUMAR SHAHANI	15517	no	IND	Independent
Uttar Pradesh	Maharajganj	HARSH VARDHAN	57193	no	INC	Indian National Congress
Uttar Pradesh	Maharajganj	None of the Above	4545	no	NOTA	None of the Above
Uttar Pradesh	Maharajganj	AJAYDHAR	4079	no	IND	Independent
Uttar Pradesh	Maharajganj	TEJ PRATAP	6131	no	IND	Independent
Uttar Pradesh	Maharajganj	SHIVCHARAN	3455	no	jhspt	Jai Hind Samaj Party
Uttar Pradesh	Maharajganj	RAKESH KUMAR GUPTA	1828	no	IND	Independent
Uttar Pradesh	Maharajganj	RADHEYSHYAM	1961	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Maharajganj	VIMLESH TRIPATHI	2339	no	NAP	Naitik Party
Uttar Pradesh	Maharajganj	DAYARAM NISHAD	4210	no	IND	Independent
Uttar Pradesh	Maharajganj	DINANATH	2149	no	IND	Independent
Uttar Pradesh	Maharajganj	DINESH KUMAR	1671	no	IND	Independent
Uttar Pradesh	Maharajganj	RAMSAMUJH	5001	no	S(J)	Swaraj (J)
Uttar Pradesh	Maharajganj	GOVIND DAS	4216	no	IND	Independent
Uttar Pradesh	Maharajganj	PASHUPATI NATH GUPTA	3196	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Gorakhpur	ADITYANATH	539127	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Gorakhpur	ACHHE LAL GUPTA	5339	no	IND	Independent
Uttar Pradesh	Gorakhpur	ASTBHUJA PRASAD TRIPATHI	45719	no	INC	Indian National Congress
Uttar Pradesh	Gorakhpur	FAIZ AHAMAD FAIZI	5046	no	PECP	Peace Party
Uttar Pradesh	Gorakhpur	INDRA DEO YADAVA	2398	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Gorakhpur	RAMKISHUN	4387	no	IND	Independent
Uttar Pradesh	Gorakhpur	MOHD. WASEEM KHAN	2016	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Gorakhpur	SUBASH KUMAR MAURYA	2123	no	RJPK	Rashtriya Janwadi Party (Krantikari)
Uttar Pradesh	Gorakhpur	RAJIT MISHRA	1171	no	NAP	Naitik Party
Uttar Pradesh	Gorakhpur	ASHOK	4194	no	IND	Independent
Uttar Pradesh	Gorakhpur	None of the Above	8153	no	NOTA	None of the Above
Uttar Pradesh	Gorakhpur	RAJMATI NISHAD	226344	no	SP	Samajwadi Party
Uttar Pradesh	Gorakhpur	RADHE MOHAN MISRA	11873	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Gorakhpur	RAM BHUAL NISHAD	176412	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Gorakhpur	MUNNA	4024	no	SWPI	Swarajya Party Of India
Uttar Pradesh	Gorakhpur	RAJESH	1873	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Kushi Nagar	RAJESH PANDEY URF GUDDU	370051	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Kushi Nagar	MARKANDE	4270	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Kushi Nagar	AKHAND	3802	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Kushi Nagar	RADHE SHYAM SINGH	111256	no	SP	Samajwadi Party
Uttar Pradesh	Kushi Nagar	KASHEEM ALI	9024	no	PECP	Peace Party
Uttar Pradesh	Kushi Nagar	AMEERUDDIN	3843	no	ADUP	Apna Dal United Party
Uttar Pradesh	Kushi Nagar	None of the Above	10102	no	NOTA	None of the Above
Uttar Pradesh	Kushi Nagar	JAHID	2677	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Kushi Nagar	RAMDHANEE	5485	no	DNP	Deshbhakt Nirman Party
Uttar Pradesh	Kushi Nagar	DR. SANGAM MISHRA	132881	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Kushi Nagar	RAMPRATAP	4805	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Kushi Nagar	BACHCHOOLAAL	2145	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Kushi Nagar	ARVIND	3630	no	S(J)	Swaraj (J)
Uttar Pradesh	Kushi Nagar	KUNWAR RATANJIT PRATAP NARAIN SINGH	284511	no	INC	Indian National Congress
Uttar Pradesh	Kushi Nagar	GOBARDHAN PRASAD	1963	no	SP(I)	Socialist Party (India)
Uttar Pradesh	Deoria	KALRAJ MISHRA	496500	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Deoria	NIYAJ AHMAD	231114	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Deoria	SHYAM SUNDAR	5130	no	PECP	Peace Party
Uttar Pradesh	Deoria	UMESH	4343	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Deoria	BALESHWAR YADAV	150852	no	SP	Samajwadi Party
Uttar Pradesh	Deoria	RAJU	5406	no	MOP	Moderate Party
Uttar Pradesh	Deoria	RAM PRAVESH	6408	no	BED	Bharatiya Ekta Dal
Uttar Pradesh	Deoria	SABHA KUNWAR	37752	no	INC	Indian National Congress
Uttar Pradesh	Deoria	AWADH KISHOR NIGAM	1153	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Deoria	RAM PREET	1906	no	MBCOI	Most Backward Classes Of India
Uttar Pradesh	Deoria	INTZARUL	1833	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Deoria	None of the Above	12405	no	NOTA	None of the Above
Uttar Pradesh	Deoria	VED PRAKASH MALL	1916	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Deoria	HEMANT KISHOR SRIVASTAVA	5763	no	IND	Independent
Uttar Pradesh	Deoria	BARKHA KINNAR	4124	no	IND	Independent
Uttar Pradesh	Deoria	AROON KUMAR TRIPATHI	4952	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Bansgaon	KAMLESH PASWAN	417959	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Bansgaon	KUNJAWATI	4346	no	IND	Independent
Uttar Pradesh	Bansgaon	None of the Above	13495	no	NOTA	None of the Above
Uttar Pradesh	Bansgaon	SADAL PRASAD	228443	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bansgaon	CHANDRIKA PRASAD BHARATI	1327	no	RJPK	Rashtriya Janwadi Party (Krantikari)
Uttar Pradesh	Bansgaon	ARJUN PRASAD RAW	2896	no	BaSaP	Bhartiya  Sarvjan Party
Uttar Pradesh	Bansgaon	RAMPRIT JAKHMEE	3080	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Bansgaon	SANJAI KUMAR	50675	no	INC	Indian National Congress
Uttar Pradesh	Bansgaon	LALCHAND PRASAD JATAV	9769	no	ASP	Ambedkar Samaj Party
Uttar Pradesh	Bansgaon	RAMNARESH	2963	no	ISSP	Indian Savarn Samaj Party
Uttar Pradesh	Bansgaon	GORAKH PRASAD PASWAN	133675	no	SP	Samajwadi Party
Uttar Pradesh	Bansgaon	SHEO SHANKAR	4013	no	IND	Independent
Uttar Pradesh	Bansgaon	RAMNIVAS  PASWAN	5236	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Lalganj	NEELAM SONKAR	324016	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Lalganj	JIYA LAL	5668	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Lalganj	LAL BAHADUR	1911	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Lalganj	BECHAI SAROJ	260930	no	SP	Samajwadi Party
Uttar Pradesh	Lalganj	HARIPRASAD SONKAR	10523	no	CPI	Communist Party of India
Uttar Pradesh	Lalganj	BALIHARI BABU	21832	no	INC	Indian National Congress
Uttar Pradesh	Lalganj	SUKHANAYAN	1398	no	JSEP	Jan Shakti Ekta Party
Uttar Pradesh	Lalganj	DINESH KUMAR	1733	no	Aa S P	Asankhya Samaj Party
Uttar Pradesh	Lalganj	ACHCHHELAL	4536	no	IND	Independent
Uttar Pradesh	Lalganj	AVADHESH	2129	no	IND	Independent
Uttar Pradesh	Lalganj	PRADEEP KUMAR	14368	no	RUC	Rashtriya Ulama Council
Uttar Pradesh	Lalganj	None of the Above	7713	no	NOTA	None of the Above
Uttar Pradesh	Lalganj	SHRIPATI	4620	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Lalganj	JANGBAHADUR	3307	no	IND	Independent
Uttar Pradesh	Lalganj	DR. BALIRAM	233971	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Azamgarh	MULAYAM SINGH YADAV	340306	yes	SP	Samajwadi Party
Uttar Pradesh	Azamgarh	SHAMSHAD AHMAD	4875	no	IND	Independent
Uttar Pradesh	Azamgarh	SHAH ALAM ALIAS GUDDU JAMALI	266528	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Azamgarh	None of the Above	5660	no	NOTA	None of the Above
Uttar Pradesh	Azamgarh	M. AMIR RASHADI	13271	no	RUC	Rashtriya Ulama Council
Uttar Pradesh	Azamgarh	PRAMOD SINGH	3752	no	IND	Independent
Uttar Pradesh	Azamgarh	RAJDHARI RAJBHAR	1695	no	MOSP	Moolniwasi Samaj Party
Uttar Pradesh	Azamgarh	MADAN	5207	no	AITC	All India Trinamool Congress
Uttar Pradesh	Azamgarh	SANJAY KUMAR CHATURVEDI	4051	no	ISSP	Indian Savarn Samaj Party
Uttar Pradesh	Azamgarh	DURG VIJAY YADAV	1566	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Azamgarh	ARVIND KUMAR JAISWAL	17950	no	INC	Indian National Congress
Uttar Pradesh	Azamgarh	KIRAN SHARAMA	1298	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Azamgarh	SANJAY CHAUHAN	3517	no	IND	Independent
Uttar Pradesh	Azamgarh	SANTOSH KUMAR GAUTAM	3866	no	MwSP	Manavtawadi Samaj Party
Uttar Pradesh	Azamgarh	RAM SINGH CHAUHAN	2507	no	IND	Independent
Uttar Pradesh	Azamgarh	PRAKASH CHANDRA	2103	no	ssrd	Sanatan Sanskriti Raksha Dal
Uttar Pradesh	Azamgarh	RAMAKANT YADAV	277102	no	BJP	Bharatiya Janata Party
Uttar Pradesh	Azamgarh	SUGRIV CHAUHAN	1882	no	IND	Independent
Uttar Pradesh	Azamgarh	RAJESH YADAV	3082	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ghosi	HARINARAYAN RAJBHAR	379797	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Ghosi	HARINATH	1786	no	NAP	Naitik Party
Uttar Pradesh	Ghosi	RASHTRA KUNWAR SINGH	19315	no	INC	Indian National Congress
Uttar Pradesh	Ghosi	HARISH CHANDRA	4130	no	RJPK	Rashtriya Janwadi Party (Krantikari)
Uttar Pradesh	Ghosi	VISHUN DAYAL	2721	no	IND	Independent
Uttar Pradesh	Ghosi	RAJKUMAR CHAUHAN	2246	no	JPS	Janvadi Party(Socialist)
Uttar Pradesh	Ghosi	None of the Above	4457	no	NOTA	None of the Above
Uttar Pradesh	Ghosi	BADRI NATH YADAV	7650	no	RaCP	Rashtriya Congress(J) Party
Uttar Pradesh	Ghosi	HEMANT KUMAR	2949	no	BaSaP	Bhartiya  Sarvjan Party
Uttar Pradesh	Ghosi	ATUL KUMAR SINGH ANJAAN	18162	no	CPI	Communist Party of India
Uttar Pradesh	Ghosi	VIJAY KUMAR UPADHYAY	3826	no	RKSP	Rashtriya Krantikari Samajwadi Party
Uttar Pradesh	Ghosi	DOODHNATH CHAUHAN	8163	no	IND	Independent
Uttar Pradesh	Ghosi	MOKHTAR ANSARI	166443	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Ghosi	MANISH KUMAR RAI	4188	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ghosi	DARA SINGH CHAUHAN	233782	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Ghosi	RAJEEV KUMAR RAI	165887	no	SP	Samajwadi Party
Uttar Pradesh	Ghosi	SURYA KUMAR	4030	no	IND	Independent
Uttar Pradesh	Ghosi	RAVI SHANKAR BHARAT	2543	no	JaRaP	Jan Raajya Party
Uttar Pradesh	Ghosi	DR. SHAMEEM ABIDI	7581	no	PECP	Peace Party
Uttar Pradesh	Salempur	RAVINDRA KUSHAWAHA	392213	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Salempur	SUSHANT RAJ PATHAK	3631	no	IND	Independent
Uttar Pradesh	Salempur	OM PRAKASH RAJBHAR	66084	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Salempur	HARIBANSH SAHAI KUSHWAHA	159688	no	SP	Samajwadi Party
Uttar Pradesh	Salempur	RAM KRIPAL	4377	no	IND	Independent
Uttar Pradesh	Salempur	DR.BHOLA PANDEY	41890	no	INC	Indian National Congress
Uttar Pradesh	Salempur	"RAVI SHANKER SINGH""PAPPU"""	159871	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Salempur	SHRIRAM CHAUDHARY	3572	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Salempur	DR SANJAY KR. SINGH	1675	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Salempur	ONKAR SINGH	4947	no	SaSaP	Samajwadi Samaj Party
Uttar Pradesh	Salempur	SIYARAM DAS	2709	no	IND	Independent
Uttar Pradesh	Salempur	None of the Above	8322	no	NOTA	None of the Above
Uttar Pradesh	Salempur	FIROZ AKHTAR	1507	no	RUC	Rashtriya Ulama Council
Uttar Pradesh	Salempur	CHANDRA PRATAP SINGH	4263	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ballia	BHARAT SINGH	359758	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Ballia	SUCHIT GOND	2506	no	GGP	Gondvana Gantantra Party
Uttar Pradesh	Ballia	COL(RETD) BHARAT SINGH SHAURYA CHAKRA	3297	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ballia	MANNU YADAV	980	no	NAP	Naitik Party
Uttar Pradesh	Ballia	AVADHESH VERMA	2529	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Ballia	NEERAJ SHEKHAR	220324	no	SP	Samajwadi Party
Uttar Pradesh	Ballia	VIRENDRA KUMAR PATHAK	141684	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Ballia	SUDHA RAI	13501	no	INC	Indian National Congress
Uttar Pradesh	Ballia	RAMROOP	6435	no	jhspt	Jai Hind Samaj Party
Uttar Pradesh	Ballia	None of the Above	6670	no	NOTA	None of the Above
Uttar Pradesh	Ballia	RAMJI	1096	no	BSD	Bharatiya Samaj Dal
Uttar Pradesh	Ballia	MANISH	6166	no	IND	Independent
Uttar Pradesh	Ballia	SHESHNATH	3674	no	IND	Independent
Uttar Pradesh	Ballia	MANOJ	6833	no	IND	Independent
Uttar Pradesh	Ballia	AFZAL ANSARI	163943	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Ballia	BHOLA NATH	2815	no	IND	Independent
Uttar Pradesh	Jaunpur	KRISHNA PRATAP 'K.P.'	367149	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Jaunpur	SUBHASH PANDEY	220839	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Jaunpur	RAVISHANKAR MAURYA	2608	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Uttar Pradesh	Jaunpur	DR. K. P. YADAV	43471	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Jaunpur	GULAB CHAND DUBEY	1751	no	SHS	Shivsena
Uttar Pradesh	Jaunpur	JOGENDRA PRASAD	7281	no	IND	Independent
Uttar Pradesh	Jaunpur	PRAMOD KUMAR ALIAS PRAMOD BHAI PATEL	7206	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Jaunpur	DR. SANJIVAN BIND	1863	no	IND	Independent
Uttar Pradesh	Jaunpur	VIMAL KUMAR YADAV	2109	no	IND	Independent
Uttar Pradesh	Jaunpur	YOGISH CHANDRA DUBEY	2773	no	IND	Independent
Uttar Pradesh	Jaunpur	RAJESH PRAJAPATI	2329	no	SaDa	Sarvshreshth Dal
Uttar Pradesh	Jaunpur	ANIL KUMAR SINGH	2694	no	IND	Independent
Uttar Pradesh	Jaunpur	None of the Above	2595	no	NOTA	None of the Above
Uttar Pradesh	Jaunpur	ARVIND	2204	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Jaunpur	RAVI KISHAN	42759	no	INC	Indian National Congress
Uttar Pradesh	Jaunpur	DHANANJAY SINGH	64137	no	IND	Independent
Uttar Pradesh	Jaunpur	PREMCHAND BIND	6814	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Jaunpur	ANUPATI RAM YADAV	4026	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Jaunpur	RAVIKANT YADAV	20832	no	IND	Independent
Uttar Pradesh	Jaunpur	SARFARAJ	2064	no	PECP	Peace Party
Uttar Pradesh	Jaunpur	PARASNATH YADAV	180003	no	SP	Samajwadi Party
Uttar Pradesh	Jaunpur	SHAHABUDDIN	19636	no	RUC	Rashtriya Ulama Council
Uttar Pradesh	Machhlishahr	RAM CHARITRA NISHAD	438210	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Machhlishahr	SHRAVAN KUMAR PASWAN	7833	no	IND	Independent
Uttar Pradesh	Machhlishahr	LALJI	1543	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Machhlishahr	SUBAS CHANDRA	18777	no	CPI	Communist Party of India
Uttar Pradesh	Machhlishahr	HARINATH	5279	no	IND	Independent
Uttar Pradesh	Machhlishahr	SURESH GAUTAM	1467	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Machhlishahr	SIKANDAR	2114	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Machhlishahr	HARI	2669	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Machhlishahr	TUFANI NISHAD	36275	no	INC	Indian National Congress
Uttar Pradesh	Machhlishahr	None of the Above	8448	no	NOTA	None of the Above
Uttar Pradesh	Machhlishahr	OMPRAKASH ALIAS AMBEDKAR	2708	no	IND	Independent
Uttar Pradesh	Machhlishahr	RAJESH	1552	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Machhlishahr	RAJESH SONKAR	4362	no	IND	Independent
Uttar Pradesh	Machhlishahr	TUFANI	191387	no	SP	Samajwadi Party
Uttar Pradesh	Machhlishahr	BHOLANATH ALIAS B.P. SAROJ	266055	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Machhlishahr	RAJ ALIAS VIRENDRA KUMAR SONKAR	9223	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ghazipur	MANOJ SINHA	306929	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Ghazipur	SATYA DEV YADAV	6381	no	NAP	Naitik Party
Uttar Pradesh	Ghazipur	BRIJ BHUSHAN	6925	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Ghazipur	RAJENDRA NATH RAM	3468	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Ghazipur	KAILASH NATH SINGH YADAV	241645	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Ghazipur	KUBER	4889	no	IND	Independent
Uttar Pradesh	Ghazipur	ARUN KUMAR SINGH	34093	no	IND	Independent
Uttar Pradesh	Ghazipur	DEENDAYAL	2122	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Ghazipur	LAKSHAMAN	1588	no	IND	Independent
Uttar Pradesh	Ghazipur	SMT SHIVKANYA KUSHWAHA	274477	no	SP	Samajwadi Party
Uttar Pradesh	Ghazipur	ISHWARI	6512	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Ghazipur	BHARAT	2484	no	JRPa	Janta Raj Party
Uttar Pradesh	Ghazipur	DHARM YADAV ALIAS D.P.YADAV	59510	no	RPD	Rashtriya Parivartan Dal
Uttar Pradesh	Ghazipur	ISHWAR DEV	4019	no	ARVP	Adarsh Rashtriya Vikas Party
Uttar Pradesh	Ghazipur	GUPTESHWAR	1111	no	LD	Lok Dal
Uttar Pradesh	Ghazipur	None of the Above	5611	no	NOTA	None of the Above
Uttar Pradesh	Ghazipur	OMKAR CHAUHAN	2416	no	JPS	Janvadi Party(Socialist)
Uttar Pradesh	Ghazipur	PAPPU	3585	no	EKSP	Eklavya Samaj Party
Uttar Pradesh	Ghazipur	MO. MAKSUD KHAN	18908	no	INC	Indian National Congress
Uttar Pradesh	Chandauli	DR MAHENDRA NATH PANDEY	414135	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Chandauli	SANJAY	2148	no	IND	Independent
Uttar Pradesh	Chandauli	RAMASHANKER SINGH MAURYA	1423	no	BSKD	Bhartiya Samajik Kranti Dal
Uttar Pradesh	Chandauli	RAJA JAISWAL	6170	no	AITC	All India Trinamool Congress
Uttar Pradesh	Chandauli	CHANCHAL	1362	no	JRPa	Janta Raj Party
Uttar Pradesh	Chandauli	GULAVAS	1482	no	IND	Independent
Uttar Pradesh	Chandauli	RAJENDRA	7767	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Chandauli	IRSHAD	15598	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Chandauli	SHASHI SINHA	2988	no	ESKP	Ex-Sainik Kissan Party
Uttar Pradesh	Chandauli	BINDU	2769	no	IND	Independent
Uttar Pradesh	Chandauli	DR. HARIDWAR SINGH YADAV	8756	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Chandauli	ANIL KUMAR MAURYA	257379	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Chandauli	None of the Above	3186	no	NOTA	None of the Above
Uttar Pradesh	Chandauli	JITENDRA	2502	no	GGP	Gondvana Gantantra Party
Uttar Pradesh	Chandauli	SANJEEV SINGH	4993	no	SBSP	Suheldev Bhartiya Samaj Party
Uttar Pradesh	Chandauli	TARUN PATEL URF TARUNENDRA CHAND PATEL	27194	no	INC	Indian National Congress
Uttar Pradesh	Chandauli	RAM DULAR	2386	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Chandauli	VINAI KUMAR SINGH	3882	no	INL	Indian National League
Uttar Pradesh	Chandauli	RAJNARAYAN	4221	no	IND	Independent
Uttar Pradesh	Chandauli	HEERALAL	4768	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Chandauli	RAMKISHUN	204145	no	SP	Samajwadi Party
Uttar Pradesh	Chandauli	MRITUNJAYA PANDEY	1318	no	JaRaPa	Jan Raksha Party
Uttar Pradesh	Varanasi	NARENDRA MODI	581022	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Varanasi	VIJAY PRAKASH JAISAWAL	60579	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Varanasi	MANOJ KUMAR CHAUBEY	1074	no	IND	Independent
Uttar Pradesh	Varanasi	DEVI PRASAD NAND	1740	no	MADP	Moulik Adhikar Party
Uttar Pradesh	Varanasi	HIRALAL YADAV	2457	no	CPM	Communist Party of India  (Marxist)
Uttar Pradesh	Varanasi	A.K. AGGARWAL	2037	no	AGRJP	Agar Jan Party
Uttar Pradesh	Varanasi	KAILASH CHAURASIYA	45291	no	SP	Samajwadi Party
Uttar Pradesh	Varanasi	SATYAPRAKASH SRIVASTAVA	599	no	IND	Independent
Uttar Pradesh	Varanasi	LALLAN	997	no	MaJP	Manavadhikar Janshakti Party
Uttar Pradesh	Varanasi	NARENDRA NATH DUBEY ADIG	2277	no	JSEP	Jan Shakti Ekta Party
Uttar Pradesh	Varanasi	PRABHAT KUMAR	1089	no	IND	Independent
Uttar Pradesh	Varanasi	BASEER KINNAR	2850	no	IND	Independent
Uttar Pradesh	Varanasi	INDIRA TIWARI	2674	no	AITC	All India Trinamool Congress
Uttar Pradesh	Varanasi	RAM LAKHAN GUPTA	1376	no	JAP	Jantantra Party
Uttar Pradesh	Varanasi	AJAY RAI	75614	no	INC	Indian National Congress
Uttar Pradesh	Varanasi	SHIVHARI AGARWAL	2000	no	BhNP	Bharat Nirman Party
Uttar Pradesh	Varanasi	RAMPYARE SINGH	423	no	IND	Independent
Uttar Pradesh	Varanasi	PRAMOD KUMAR	1807	no	IND	Independent
Uttar Pradesh	Varanasi	JOHNSON THOMAS	1524	no	IND	Independent
Uttar Pradesh	Varanasi	BACHCHELAL	1172	no	LPSP	Lokpriya Samaj Party
Uttar Pradesh	Varanasi	IFTEKHAR KURAISHI	2323	no	IND	Independent
Uttar Pradesh	Varanasi	SHIV KUMAR SHAH	312	no	IND	Independent
Uttar Pradesh	Varanasi	None of the Above	2051	no	NOTA	None of the Above
Uttar Pradesh	Varanasi	NARENDRA BAHADUR SINGH	1306	no	IND	Independent
Uttar Pradesh	Varanasi	AHMED SOHEL SIDDIQUI	937	no	IND	Independent
Uttar Pradesh	Varanasi	HARI LAL	838	no	RaIP	Rastriya Insaaf Party
Uttar Pradesh	Varanasi	MAHENDRA MISHRA	573	no	IND	Independent
Uttar Pradesh	Varanasi	RAJESH BHARTI SURYA	4327	no	RaAD	Rashtriya Ambedkar Dal
Uttar Pradesh	Varanasi	SARVESH KUMAR GUPTA	3023	no	IND	Independent
Uttar Pradesh	Varanasi	SATISH SHANKAR JAYASWAL	459	no	IND	Independent
Uttar Pradesh	Varanasi	RAJENDRA PRASAD (GARIB DAS)	1367	no	IND	Independent
Uttar Pradesh	Varanasi	GHANSHYAM	1151	no	IND	Independent
Uttar Pradesh	Varanasi	PRAKASH PRASAD	1361	no	IND	Independent
Uttar Pradesh	Varanasi	ABHIMANYU SINGH PATEL	1644	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Varanasi	ARUN	3634	no	BSCP	Bhartiya Shakti Chetna Party
Uttar Pradesh	Varanasi	BACHCHAN PRASAD YADAV	1192	no	IND	Independent
Uttar Pradesh	Varanasi	HEMANT KUMAR YADAV	1202	no	INL	Indian National League
Uttar Pradesh	Varanasi	RAJIV KUMAR MISHRA	677	no	IND	Independent
Uttar Pradesh	Varanasi	SANTOSH KUMAR	1434	no	RVMP	Rashtriya Vikas Manch Party
Uttar Pradesh	Varanasi	OM GURU CHARAN DAS ALIAS VIMAL KUMAR SINGH	1144	no	ssrd	Sanatan Sanskriti Raksha Dal
Uttar Pradesh	Varanasi	ARVIND KEJRIWAL	209238	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Varanasi	USMAN	878	no	RsAD	Rashtriya Apna Dal
Uttar Pradesh	Varanasi	RAVINDRA KUMAR	1012	no	IND	Independent
Uttar Pradesh	Bhadohi	VIRENDRA SINGH	403544	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Bhadohi	None of the Above	8966	no	NOTA	None of the Above
Uttar Pradesh	Bhadohi	RAKESH DHAR TRIPATHI	245505	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Bhadohi	JEEVAN KUMAR MALLAH	4164	no	IND	Independent
Uttar Pradesh	Bhadohi	SEEMA MISHRA	238615	no	SP	Samajwadi Party
Uttar Pradesh	Bhadohi	DR. AKHILESH KUMAR DWIVEDI	7730	no	NCP	Nationalist Congress Party
Uttar Pradesh	Bhadohi	SARTAJ IMAM	22574	no	INC	Indian National Congress
Uttar Pradesh	Bhadohi	TEJ BAHADUR YADAV ADVOCATE	26995	no	JD(U)	Janata Dal  (United)
Uttar Pradesh	Bhadohi	AJAY KUMAR MAURYA	5807	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Bhadohi	JAGJEEVAN RAM	1963	no	IND	Independent
Uttar Pradesh	Bhadohi	CHANDRASHEKHAR	2191	no	IND	Independent
Uttar Pradesh	Bhadohi	RAJESH KUMAR ALIAS KHANNA MAURYA	1919	no	AMP	Adarsh Manavtawadi Party
Uttar Pradesh	Bhadohi	SANTRAJ SINGH	4823	no	IND	Independent
Uttar Pradesh	Bhadohi	RAM SAGAR BIND	2443	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Bhadohi	RAM SAKHA TRIPATHI	4313	no	IND	Independent
Uttar Pradesh	Mirzapur	ANUPRIYA SINGH PATEL	436536	yes	AD	Apna Dal
Uttar Pradesh	Mirzapur	DR. KARNAIL SINGH	3627	no	IND	Independent
Uttar Pradesh	Mirzapur	BHAGWAN DAS	3324	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Mirzapur	SURENDRA SINGH PATEL	108859	no	SP	Samajwadi Party
Uttar Pradesh	Mirzapur	LALITESH PATI TRIPATHI	152666	no	INC	Indian National Congress
Uttar Pradesh	Mirzapur	JEERA BHARTI	4148	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttar Pradesh	Mirzapur	VINEET SAHAI	4548	no	AITC	All India Trinamool Congress
Uttar Pradesh	Mirzapur	None of the Above	4539	no	NOTA	None of the Above
Uttar Pradesh	Mirzapur	GIRJA SHANKAR URF CHUNMUN	2363	no	IND	Independent
Uttar Pradesh	Mirzapur	BRIJESH KUMAR RAI	4318	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Mirzapur	RADHESHYAM INSAN	2138	no	BRPI	Bhartiya Republican Party (Insan)
Uttar Pradesh	Mirzapur	AJAD	6762	no	GGP	Gondvana Gantantra Party
Uttar Pradesh	Mirzapur	RAJA RAM	2010	no	IND	Independent
Uttar Pradesh	Mirzapur	SANTOSH KUMAR	6811	no	SPP	Samyak Parivartan Party
Uttar Pradesh	Mirzapur	SARVESH DUBEY	4304	no	RPI(A)	Republican Party of India (A)
Uttar Pradesh	Mirzapur	DEVI	4639	no	IND	Independent
Uttar Pradesh	Mirzapur	RAM MURAT	7947	no	IND	Independent
Uttar Pradesh	Mirzapur	SHIVRAJ SAHNI	4405	no	PMSP	Pragatisheel Manav Samaj Party
Uttar Pradesh	Mirzapur	HINCHH LAL	2685	no	IND	Independent
Uttar Pradesh	Mirzapur	SUKCHARA NAND URF AALOO BABA	4787	no	SHS	Shivsena
Uttar Pradesh	Mirzapur	SAMUDRA BIND	217457	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Mirzapur	JITTOO RAM RATNA	4209	no	LD	Lok Dal
Uttar Pradesh	Mirzapur	ASHOK KUMAR	11245	no	IND	Independent
Uttar Pradesh	Mirzapur	ADESH TYAGI	3300	no	IND	Independent
Uttar Pradesh	Robertsganj	CHHOTELAL	378211	yes	BJP	Bharatiya Janata Party
Uttar Pradesh	Robertsganj	RAMBRIKSHA	9408	no	IND	Independent
Uttar Pradesh	Robertsganj	ASHOK KUMAR KANNAOUJIYA	24363	no	CPI	Communist Party of India
Uttar Pradesh	Robertsganj	LAWRENCE ANTHONY	7703	no	IND	Independent
Uttar Pradesh	Robertsganj	GUN DEVI ( GYANI DEVI)	5307	no	QED	Qaumi Ekta Dal
Uttar Pradesh	Robertsganj	MANGOI	2966	no	BMUP	Bahujan Mukti Party
Uttar Pradesh	Robertsganj	RAJKUMAR	2982	no	shsap	Shoshit Sandesh Party
Uttar Pradesh	Robertsganj	None of the Above	18489	no	NOTA	None of the Above
Uttar Pradesh	Robertsganj	SHARADA PRASAD	187725	no	BSP	Bahujan Samaj Party
Uttar Pradesh	Robertsganj	ARUN SINGH(CHERO)	5613	no	IND	Independent
Uttar Pradesh	Robertsganj	S. R. DARAPURI	4891	no	AIPF	All India Peoples' Front (Radical)
Uttar Pradesh	Robertsganj	BHAGWATI PRASAD CHAUDHARY	86235	no	INC	Indian National Congress
Uttar Pradesh	Robertsganj	RAVI SHANKAR KANNAUJIA	9913	no	AAAP	Aam Aadmi Party
Uttar Pradesh	Robertsganj	PAKAUDI LAL KOL	135966	no	SP	Samajwadi Party
Uttar Pradesh	Robertsganj	SHANKAR KOL	6101	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
West Bengal	Cooch behar	RENUKA SINHA	526499	yes	AITC	All India Trinamool Congress
West Bengal	Cooch behar	None of the Above	11409	no	NOTA	None of the Above
West Bengal	Cooch behar	DALENDRA NATH RAY	4169	no	AMB	Amra Bangalee
West Bengal	Cooch behar	KAMAL KRISHNA BAIRAGI	7125	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Cooch behar	GIRINDRA NATH BARMAN	15683	no	BSP	Bahujan Samaj Party
West Bengal	Cooch behar	HAREKRISHNA SARKAR	5796	no	RPI(A)	Republican Party of India (A)
West Bengal	Cooch behar	PIJUSH BARMAN	5137	no	WPOI	Welfare Party Of India
West Bengal	Cooch behar	KESHAB CHANDRA RAY	74540	no	INC	Indian National Congress
West Bengal	Cooch behar	HITENDRA DAS	5059	no	IND	Independent
West Bengal	Cooch behar	HEM CHANDRA BARMAN	217653	no	BJP	Bharatiya Janata Party
West Bengal	Cooch behar	DIPAK KUMAR ROY	439392	no	AIFB	All India Forward Bloc
West Bengal	Cooch behar	NRIPEN KARJEE	6742	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Cooch behar	BANGSHI BADAN BARMAN	13205	no	IND	Independent
West Bengal	Alipurduars	DASRATH TIRKEY	362453	yes	AITC	All India Trinamool Congress
West Bengal	Alipurduars	ISHABELA KUJUR	5961	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Alipurduars	BILKAN BARA	5340	no	SWJP	Samajwadi Jan Parishad
West Bengal	Alipurduars	PAUL DEXION KHARIYA	10450	no	IND	Independent
West Bengal	Alipurduars	MANOHAR TIRKEY	341056	no	RSP	Revolutionary Socialist Party
West Bengal	Alipurduars	BIRENDRA BARA (ORAON)	335857	no	BJP	Bharatiya Janata Party
West Bengal	Alipurduars	SUBASH CHIK BARAIK	7131	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Alipurduars	JOSEPH MUNDA	116718	no	INC	Indian National Congress
West Bengal	Alipurduars	ELIASH NARJINARY	11039	no	BSP	Bahujan Samaj Party
West Bengal	Alipurduars	DR. KAPIN CH. BORO	7676	no	IND	Independent
West Bengal	Alipurduars	None of the Above	19885	no	NOTA	None of the Above
West Bengal	Jalpaiguri	BIJOY CHANDRA BARMAN	494773	yes	AITC	All India Trinamool Congress
West Bengal	Jalpaiguri	None of the Above	16541	no	NOTA	None of the Above
West Bengal	Jalpaiguri	HAREKRISHNA SARKAR	7636	no	RPI(A)	Republican Party of India (A)
West Bengal	Jalpaiguri	SUBHAS BISWAS	8545	no	IND	Independent
West Bengal	Jalpaiguri	HARIPADA BARMAN	6608	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Jalpaiguri	DILIP SARKAR	4900	no	BMUP	Bahujan Mukti Party
West Bengal	Jalpaiguri	SUKHBILAS BARMA	87588	no	INC	Indian National Congress
West Bengal	Jalpaiguri	DHIRENDRA NATH ROY	2835	no	AMB	Amra Bangalee
West Bengal	Jalpaiguri	SATYALAL SARKAR	221593	no	BJP	Bharatiya Janata Party
West Bengal	Jalpaiguri	KSHITISH CHANDRA MANDAL	12147	no	BSP	Bahujan Samaj Party
West Bengal	Jalpaiguri	MAHENDRA KUMAR ROY	425167	no	CPM	Communist Party of India  (Marxist)
West Bengal	Jalpaiguri	DHRITIMAN ROY	4501	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Jalpaiguri	HARIBHAKTA SARDAR	9283	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Darjeeling	S.S.AHLUWALIA	488257	yes	BJP	Bharatiya Janata Party
West Bengal	Darjeeling	KAKALI MAJUMDAR (ROY)	6040	no	BSP	Bahujan Samaj Party
West Bengal	Darjeeling	SAMAN PATHAK (SURAJ)	167186	no	CPM	Communist Party of India  (Marxist)
West Bengal	Darjeeling	SUJAY GHATAK	90076	no	INC	Indian National Congress
West Bengal	Darjeeling	GOUTAM BHATTACHARYA	3993	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Darjeeling	BHAI CHUNG BHUTIA	291018	no	AITC	All India Trinamool Congress
West Bengal	Darjeeling	MAHENDRA P. LAMA	55767	no	IND	Independent
West Bengal	Darjeeling	NIMA LAMA	2906	no	grac	Gorkha Rashtriya Congress
West Bengal	Darjeeling	ARUN KUMAR AGARWAL	5449	no	IND	Independent
West Bengal	Darjeeling	LALIT SINGHA	2100	no	BMUP	Bahujan Mukti Party
West Bengal	Darjeeling	NIRANJAN SAHA	2029	no	AMB	Amra Bangalee
West Bengal	Darjeeling	RABINDRA ROY BASUNIA	6100	no	IND	Independent
West Bengal	Darjeeling	SUNIL PANDIT	3043	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Darjeeling	None of the Above	18045	no	NOTA	None of the Above
West Bengal	Raiganj	MD. SALIM	317515	yes	CPM	Communist Party of India  (Marxist)
West Bengal	Raiganj	ZAMEERUL HASAN	3471	no	AIUDF	All India United Democratic Front
West Bengal	Raiganj	NIMU BHOWMICK	203131	no	BJP	Bharatiya Janata Party
West Bengal	Raiganj	PASARUL ALAM	3614	no	AAAP	Aam Aadmi Party
West Bengal	Raiganj	PABITRA RANJAN DASMUNSHI (SATYA)	192698	no	AITC	All India Trinamool Congress
West Bengal	Raiganj	DEEPA DASMUNSI	315881	no	INC	Indian National Congress
West Bengal	Raiganj	DULAL RAJBANSHI	8592	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Raiganj	SWAPAN KUMAR DAS	5629	no	BSP	Bahujan Samaj Party
West Bengal	Raiganj	BABLU SOREN	5544	no	JDP	Jharkhand Disom Party
West Bengal	Raiganj	NANDAKISHOR SINGHA	3338	no	AMB	Amra Bangalee
West Bengal	Raiganj	SUDIIP RANJAN SEN	32303	no	SP	Samajwadi Party
West Bengal	Raiganj	None of the Above	10929	no	NOTA	None of the Above
West Bengal	Raiganj	SUBRATA ADHIKARY	5548	no	IND	Independent
West Bengal	Balurghat	ARPITA GHOSH	409641	yes	AITC	All India Trinamool Congress
West Bengal	Balurghat	None of the Above	11691	no	NOTA	None of the Above
West Bengal	Balurghat	SIBEN SARKAR	5647	no	IND	Independent
West Bengal	Balurghat	ISLAM NURAL	2644	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Balurghat	ABDUS SADEK SARKAR	10547	no	AIUDF	All India United Democratic Front
West Bengal	Balurghat	BIMALENDU SARKAR (BIMAL)	302677	no	RSP	Revolutionary Socialist Party
West Bengal	Balurghat	OM PRAKAS MISHRA	80715	no	INC	Indian National Congress
West Bengal	Balurghat	BISWAPRIYA ROY CHOUDHURY	223014	no	BJP	Bharatiya Janata Party
West Bengal	Balurghat	SASTI CHANDRA SARKAR	3969	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Balurghat	MANAS CHAKRABORTY (GANESH)	1466	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
West Bengal	Balurghat	SOM HEMRAM	4774	no	JDP	Jharkhand Disom Party
West Bengal	Balurghat	RANENDRANATH MALI	1687	no	BMUP	Bahujan Mukti Party
West Bengal	Balurghat	RAJEN HANSDA	4581	no	BSP	Bahujan Samaj Party
West Bengal	Maldaha Uttar	MAUSAM NOOR	388609	yes	INC	Indian National Congress
West Bengal	Maldaha Uttar	NURUL ISLAM MAJIDI	7128	no	WPOI	Welfare Party Of India
West Bengal	Maldaha Uttar	BISHNUPADA BARMAN	4225	no	AMB	Amra Bangalee
West Bengal	Maldaha Uttar	MILAN DAS	12163	no	SP	Samajwadi Party
West Bengal	Maldaha Uttar	ABDUL KHALEQUE	4650	no	AIUDF	All India United Democratic Front
West Bengal	Maldaha Uttar	SUBHASH KRISHNA GOSWAMI	179000	no	BJP	Bharatiya Janata Party
West Bengal	Maldaha Uttar	KHAGEN MURMU	322904	no	CPM	Communist Party of India  (Marxist)
West Bengal	Maldaha Uttar	GAUTAM SARKAR	8988	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Maldaha Uttar	IMANUYEL HEMRAM (BAIDYA)	6805	no	JDP	Jharkhand Disom Party
West Bengal	Maldaha Uttar	MINARA KHATUN	3348	no	HKRD	Hindustan Krantikari Dal
West Bengal	Maldaha Uttar	CHITTA RANJAN KIRTTANIA	6229	no	BSP	Bahujan Samaj Party
West Bengal	Maldaha Uttar	SOUMITRA RAY	197313	no	AITC	All India Trinamool Congress
West Bengal	Maldaha Uttar	MONATAN HEMBRAM	4696	no	BMUP	Bahujan Mukti Party
West Bengal	Maldaha Uttar	None of the Above	10481	no	NOTA	None of the Above
West Bengal	Maldaha Uttar	SUREN MURMU	6446	no	IND	Independent
West Bengal	Maldaha Dakshin	ABU HASEM KHAN CHOWDHURY	380291	yes	INC	Indian National Congress
West Bengal	Maldaha Dakshin	None of the Above	8392	no	NOTA	None of the Above
West Bengal	Maldaha Dakshin	NARESH RISHI	6616	no	IND	Independent
West Bengal	Maldaha Dakshin	MD EZARUDDIN	3377	no	JeSM	Jamat-E-Seratul Mustakim
West Bengal	Maldaha Dakshin	DIPAK SINGH	1449	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Maldaha Dakshin	MD NAJRUL ISLAM	21207	no	AIUDF	All India United Democratic Front
West Bengal	Maldaha Dakshin	NIKHIL CHANDRA MANDAL	6138	no	BSP	Bahujan Samaj Party
West Bengal	Maldaha Dakshin	NASMUL HOQUE	2466	no	PDS	Party for Democratic Socialism
West Bengal	Maldaha Dakshin	SADHAN CHATTERJEE	10536	no	IND	Independent
West Bengal	Maldaha Dakshin	MD MOAZZEM HOSSAIN	192632	no	AITC	All India Trinamool Congress
West Bengal	Maldaha Dakshin	ABUL HASNAT KHAN	209480	no	CPM	Communist Party of India  (Marxist)
West Bengal	Maldaha Dakshin	BISNU PADA ROY	216180	no	BJP	Bharatiya Janata Party
West Bengal	Maldaha Dakshin	MURSED SEKH	2720	no	RPI(A)	Republican Party of India (A)
West Bengal	Maldaha Dakshin	MD FARUQUE HOSSAIN (SAHITYARATNA)	3282	no	IUML	Indian Union Muslim League
West Bengal	Maldaha Dakshin	FIROZ AKHTAR	1342	no	BMUP	Bahujan Mukti Party
West Bengal	Maldaha Dakshin	MANJUR ALAHI MUNSHI	11426	no	IND	Independent
West Bengal	Maldaha Dakshin	BIMAL MARDI	1921	no	JDP	Jharkhand Disom Party
West Bengal	Maldaha Dakshin	IMTIAZ AHMED MOLLAH	12952	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
West Bengal	Jangipur	ABHIJIT MUKHERJEE	378201	yes	INC	Indian National Congress
West Bengal	Jangipur	MD. SAHABUDDIN	17257	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
West Bengal	Jangipur	SANJIT SINGH	5507	no	BSP	Bahujan Samaj Party
West Bengal	Jangipur	SK NURUL ISLAM	207455	no	AITC	All India Trinamool Congress
West Bengal	Jangipur	MD. GINNATULLA SK	2318	no	JeSM	Jamat-E-Seratul Mustakim
West Bengal	Jangipur	ABDUS SAYEED	7926	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Jangipur	SAMRAT GHOSH	96751	no	BJP	Bharatiya Janata Party
West Bengal	Jangipur	DHANANJAY BANERJEE	3019	no	AMB	Amra Bangalee
West Bengal	Jangipur	MONIRUL ISLAM	9476	no	WPOI	Welfare Party Of India
West Bengal	Jangipur	MUZAFFAR HOSSAIN	370040	no	CPM	Communist Party of India  (Marxist)
West Bengal	Jangipur	None of the Above	11079	no	NOTA	None of the Above
West Bengal	Jangipur	ABHIJIT SARKAR	10055	no	IND	Independent
West Bengal	Baharampur	ADHIR RANJAN CHOWDHURY	583549	yes	INC	Indian National Congress
West Bengal	Baharampur	None of the Above	11192	no	NOTA	None of the Above
West Bengal	Baharampur	ABDUS SUKUR	4322	no	IUML	Indian Union Muslim League
West Bengal	Baharampur	INDRANIL SEN	226982	no	AITC	All India Trinamool Congress
West Bengal	Baharampur	DEBES ADHIKARI	81656	no	BJP	Bharatiya Janata Party
West Bengal	Baharampur	BADRUL SEKH	11642	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
West Bengal	Baharampur	PRAMOTHES MUKHERJEE	225699	no	RSP	Revolutionary Socialist Party
West Bengal	Baharampur	KUSHADHWAJ BALA	4676	no	BSP	Bahujan Samaj Party
West Bengal	Baharampur	MD. EZARUDDIN	4869	no	JeSM	Jamat-E-Seratul Mustakim
West Bengal	Murshidabad	BADARUDDOZA KHAN	426947	yes	CPM	Communist Party of India  (Marxist)
West Bengal	Murshidabad	ABDUL BARI	5383	no	IUML	Indian Union Muslim League
West Bengal	Murshidabad	None of the Above	10156	no	NOTA	None of the Above
West Bengal	Murshidabad	ASIM KUMAR DAS	4539	no	IND	Independent
West Bengal	Murshidabad	SWAPAN KUMAR MANDAL	4260	no	AMB	Amra Bangalee
West Bengal	Murshidabad	MD. NAJMUL HOQUE	11333	no	AIUDF	All India United Democratic Front
West Bengal	Murshidabad	MD. KHODABOX SEKH	6445	no	WPOI	Welfare Party Of India
West Bengal	Murshidabad	ABDUL MANNAN HOSSAIN	408494	no	INC	Indian National Congress
West Bengal	Murshidabad	KAMARUJJAMAN KHANDEKAR(BAKUL)	8378	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Murshidabad	SUJIT KUMAR GHOSH	101069	no	BJP	Bharatiya Janata Party
West Bengal	Murshidabad	ALI MOHAMMAD	289027	no	AITC	All India Trinamool Congress
West Bengal	Murshidabad	JITENDRA NATH HALDER	5722	no	BSP	Bahujan Samaj Party
West Bengal	Murshidabad	MASUDUL ISLAM	6010	no	SDPI	SOCIAL DEMOCRATIC PARTY OF INDIA
West Bengal	Krishnanagar	TAPAS PAUL	438789	yes	AITC	All India Trinamool Congress
West Bengal	Krishnanagar	SUBIMAL SENGUPTA	7777	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
West Bengal	Krishnanagar	HARASHIT BISWAS	6168	no	BSP	Bahujan Samaj Party
West Bengal	Krishnanagar	KAMAL DATTA	7214	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Krishnanagar	SATYA BRATA MOOKHERJEE	329387	no	BJP	Bharatiya Janata Party
West Bengal	Krishnanagar	AHMED RAZIA	74789	no	INC	Indian National Congress
West Bengal	Krishnanagar	MOLLA JAFORULLA	4865	no	BMUP	Bahujan Mukti Party
West Bengal	Krishnanagar	JHA SHANTANU	367534	no	CPM	Communist Party of India  (Marxist)
West Bengal	Krishnanagar	AHAMED HOSSAIN	3749	no	IUML	Indian Union Muslim League
West Bengal	Krishnanagar	None of the Above	7642	no	NOTA	None of the Above
West Bengal	Ranaghat	TAPAS MANDAL	590451	yes	AITC	All India Trinamool Congress
West Bengal	Ranaghat	None of the Above	14626	no	NOTA	None of the Above
West Bengal	Ranaghat	SUBHAS CHANDRA SARKAR	8658	no	IND	Independent
West Bengal	Ranaghat	PRATAP KANTI RAY	92218	no	INC	Indian National Congress
West Bengal	Ranaghat	SUPRAVAT BISWAS	233670	no	BJP	Bharatiya Janata Party
West Bengal	Ranaghat	PARESH HALDER	7952	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Ranaghat	UTPALA BISWAS	9116	no	BSP	Bahujan Samaj Party
West Bengal	Ranaghat	CHAITANYA BARAI	3829	no	NSBP	Nirjatita Samaj Biplabi Party
West Bengal	Ranaghat	NADIYAR CHAND MONDAL	3992	no	RPI(A)	Republican Party of India (A)
West Bengal	Ranaghat	ARCHANA BISWAS	388684	no	CPM	Communist Party of India  (Marxist)
West Bengal	Bangaon	KAPIL KRISHNA THAKUR	551213	yes	AITC	All India Trinamool Congress
West Bengal	Bangaon	PINAKI RANJAN BHARATI	1071	no	TRMRPPI	The Religion of Man Revolving Political Party of India
West Bengal	Bangaon	ILA MONDAL	43866	no	INC	Indian National Congress
West Bengal	Bangaon	DEBESH DAS	404612	no	CPM	Communist Party of India  (Marxist)
West Bengal	Bangaon	PRANITA MANDAL	8738	no	IND	Independent
West Bengal	Bangaon	CHANDAN MALLICK	9207	no	BSP	Bahujan Samaj Party
West Bengal	Bangaon	SWAPAN MONDAL	3589	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Bangaon	TARAPADA BISWAS	2848	no	AMB	Amra Bangalee
West Bengal	Bangaon	K.D. BISWAS	244783	no	BJP	Bharatiya Janata Party
West Bengal	Bangaon	SHYAM PRASAD MONDAL	2624	no	PDS	Party for Democratic Socialism
West Bengal	Bangaon	SARAT CHANDRA MANDAL	1172	no	RAHM	Rashtriya Ahinsa Manch
West Bengal	Bangaon	None of the Above	9965	no	NOTA	None of the Above
West Bengal	Barrackpore	DINESH TRIVEDI	479206	yes	AITC	All India Trinamool Congress
West Bengal	Barrackpore	None of the Above	10979	no	NOTA	None of the Above
West Bengal	Barrackpore	MIHIR BISWAS	1787	no	AAAP	Aam Aadmi Party
West Bengal	Barrackpore	RUMESH KUMAR HANDA	230401	no	BJP	Bharatiya Janata Party
West Bengal	Barrackpore	DINA SHANKAR SINGH	5036	no	IND	Independent
West Bengal	Barrackpore	SHARMISTHA CHOUDHURY	2036	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
West Bengal	Barrackpore	OM PRAKASH SHAW	3555	no	IND	Independent
West Bengal	Barrackpore	SAMRAT TOPADAR	30491	no	INC	Indian National Congress
West Bengal	Barrackpore	PRADIP CHAUDHURI	2200	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Barrackpore	GIRISH CHANDRA SINGH	3132	no	IND	Independent
West Bengal	Barrackpore	SUBHASHINI ALI	272433	no	CPM	Communist Party of India  (Marxist)
West Bengal	Barrackpore	TAPAS SARKAR	5458	no	BSP	Bahujan Samaj Party
West Bengal	Barrackpore	OMPRAKASH RAJBHAR	4416	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
West Bengal	Dum dum	SAUGATA ROY	483244	yes	AITC	All India Trinamool Congress
West Bengal	Dum dum	ASIM KUMAR DASGUPTA	328310	no	CPM	Communist Party of India  (Marxist)
West Bengal	Dum dum	SATYABRATA BANDYOPADHYAY	1618	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Dum dum	ASHOK KUNDU	4823	no	PDS	Party for Democratic Socialism
West Bengal	Dum dum	DHANANJAY (SHAKTI) MOITRA	34116	no	INC	Indian National Congress
West Bengal	Dum dum	SIKHA SEN ROY	1544	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
West Bengal	Dum dum	NARENDRA NATH GHOSH (NAREN GHOSH)	4077	no	BSP	Bahujan Samaj Party
West Bengal	Dum dum	TAPAN SIKDAR	254819	no	BJP	Bharatiya Janata Party
West Bengal	Dum dum	SUKHA RANJAN BANIK	3256	no	IND	Independent
West Bengal	Dum dum	None of the Above	16837	no	NOTA	None of the Above
West Bengal	Barasat	DR. KAKALI GHOSHDOSTIDAR	525387	yes	AITC	All India Trinamool Congress
West Bengal	Barasat	None of the Above	12700	no	NOTA	None of the Above
West Bengal	Barasat	BIKASH HAZRA	1478	no	BMUP	Bahujan Mukti Party
West Bengal	Barasat	MORTTAJA HOSEN	6911	no	IND	Independent
West Bengal	Barasat	SUKLA BHUIMALI	1332	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
West Bengal	Barasat	KALYAN BASAK	4122	no	IND	Independent
West Bengal	Barasat	SHRI KAUSHIK DAS GUPTA	6375	no	IND	Independent
West Bengal	Barasat	RIJU GHOSAL	40660	no	INC	Indian National Congress
West Bengal	Barasat	PARESH CHANDRA GHOSH	2439	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Barasat	MD. RAFIKUL ISLAM	6895	no	WPOI	Welfare Party Of India
West Bengal	Barasat	P.C. SORCAR JUNIOR	296608	no	BJP	Bharatiya Janata Party
West Bengal	Barasat	DR. MORTOZA HOSSAIN	352246	no	AIFB	All India Forward Bloc
West Bengal	Barasat	SUKUMAR BALA	12178	no	BSP	Bahujan Samaj Party
West Bengal	Basirhat	IDRIS ALI	492326	yes	AITC	All India Trinamool Congress
West Bengal	Basirhat	NURUL HUDA	382667	no	CPI	Communist Party of India
West Bengal	Basirhat	SAMIK BHATTACHARYA	233887	no	BJP	Bharatiya Janata Party
West Bengal	Basirhat	AJAY KUMAR BAIN	6532	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Basirhat	CHOWDHURY SIDDIQULLAH	25178	no	AIUDF	All India United Democratic Front
West Bengal	Basirhat	MD. HAFIZ	5976	no	IND	Independent
West Bengal	Basirhat	GOPAL DAS	7016	no	BSP	Bahujan Samaj Party
West Bengal	Basirhat	ABDUR RAHIM QUAZI	102130	no	INC	Indian National Congress
West Bengal	Basirhat	RANJIT GAYEN	8088	no	IND	Independent
West Bengal	Basirhat	None of the Above	9971	no	NOTA	None of the Above
West Bengal	Joynagar	PRATIMA MONDAL	494746	yes	AITC	All India Trinamool Congress
West Bengal	Joynagar	None of the Above	5823	no	NOTA	None of the Above
West Bengal	Joynagar	SUBHAS NASKAR	5161	no	IND	Independent
West Bengal	Joynagar	TARANGA MONDAL	13333	no	AIUDF	All India United Democratic Front
West Bengal	Joynagar	DR. RABIN NASKAR	2657	no	BMUP	Bahujan Mukti Party
West Bengal	Joynagar	MANIKLAL SARDAR	5908	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Joynagar	SUBHAS NASKAR	386362	no	RSP	Revolutionary Socialist Party
West Bengal	Joynagar	ARNAB ROY	38493	no	INC	Indian National Congress
West Bengal	Joynagar	ANANYA SARKAR	2909	no	BSP	Bahujan Samaj Party
West Bengal	Joynagar	KRISHNAPADA MAJUMDER	113206	no	BJP	Bharatiya Janata Party
West Bengal	Joynagar	DR. TARUN MANDAL	117454	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Mathurapur	CHOUDHURY MOHAN JATUA	627761	yes	AITC	All India Trinamool Congress
West Bengal	Mathurapur	PURNA CHANDRA NAIYA	10191	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Mathurapur	SHRI MANORANJAN HALDER	47277	no	INC	Indian National Congress
West Bengal	Mathurapur	TAPAN NASKAR	65965	no	BJP	Bharatiya Janata Party
West Bengal	Mathurapur	RINKU NASKAR	489325	no	CPM	Communist Party of India  (Marxist)
West Bengal	Mathurapur	RABINDRANATH MISTRI	3212	no	PDS	Party for Democratic Socialism
West Bengal	Mathurapur	MANTU RAM HALDER	4674	no	WPOI	Welfare Party Of India
West Bengal	Mathurapur	SOUMEN SARADAR	4364	no	BSP	Bahujan Samaj Party
West Bengal	Mathurapur	ABANINDRA NATH BAIDYA	2088	no	IND	Independent
West Bengal	Mathurapur	NANDADULAL MANDAL	1357	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Mathurapur	None of the Above	9317	no	NOTA	None of the Above
West Bengal	Diamond harbour	ABHISHEK BANERJEE	508481	yes	AITC	All India Trinamool Congress
West Bengal	Diamond harbour	None of the Above	10657	no	NOTA	None of the Above
West Bengal	Diamond harbour	JAYNAL ABEDIN MONDAL	1856	no	IUML	Indian Union Muslim League
West Bengal	Diamond harbour	HABIBUR RAHAMAN MOLLA	1260	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Diamond harbour	ALOK SEN	977	no	IND	Independent
West Bengal	Diamond harbour	SOUGATA GHOSH	6634	no	IND	Independent
West Bengal	Diamond harbour	NAZMA YASMEEN	3272	no	WPOI	Welfare Party Of India
West Bengal	Diamond harbour	ABHIJIT DAS (BOBBY)	200858	no	BJP	Bharatiya Janata Party
West Bengal	Diamond harbour	MD. QAMRUZZAMAN QAMAR	63047	no	INC	Indian National Congress
West Bengal	Diamond harbour	BECHU MONDAL	3607	no	IND	Independent
West Bengal	Diamond harbour	BIJAN MAJUMDER	1348	no	IND	Independent
West Bengal	Diamond harbour	SAILEN SADHUKHAN	6633	no	IND	Independent
West Bengal	Diamond harbour	DR. ABUL HASNAT	437183	no	CPM	Communist Party of India  (Marxist)
West Bengal	Diamond harbour	SUDIP KUMAR SAHA	8740	no	IND	Independent
West Bengal	Diamond harbour	AJAY GHOSH	2529	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Diamond harbour	DILIP KUMAR MANDAL	2892	no	BSP	Bahujan Samaj Party
West Bengal	Diamond harbour	SAMIR PUTATUNDA	1322	no	PDS	Party for Democratic Socialism
West Bengal	Jadavpur	SUGATA BOSE	584244	yes	AITC	All India Trinamool Congress
West Bengal	Jadavpur	DR. ASOK KUMAR SAMANTA	11317	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Jadavpur	SANDHYA MANDAL	3945	no	BSP	Bahujan Samaj Party
West Bengal	Jadavpur	SUJAN CHAKRABORTY	459041	no	CPM	Communist Party of India  (Marxist)
West Bengal	Jadavpur	SAMIR AICH	26344	no	INC	Indian National Congress
West Bengal	Jadavpur	DR. SARUP PRASAD GHOSH	155511	no	BJP	Bharatiya Janata Party
West Bengal	Jadavpur	MANGAL KUMAR SARDAR	3406	no	IND	Independent
West Bengal	Jadavpur	KARTIK KAYAL	2837	no	IND	Independent
West Bengal	Jadavpur	PINTU SANPUI	1340	no	BMUP	Bahujan Mukti Party
West Bengal	Jadavpur	SHAMALI DAS	2885	no	IND	Independent
West Bengal	Jadavpur	HASIBUL ISLAM MIR	1464	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Jadavpur	SUSANTA KUMAR NASKAR	3067	no	IND	Independent
West Bengal	Jadavpur	ASHOK KUMAR SHAW	1294	no	IND	Independent
West Bengal	Jadavpur	None of the Above	15667	no	NOTA	None of the Above
West Bengal	Kolkata Dakshin	SUBRATA BAKSHI	431715	yes	AITC	All India Trinamool Congress
West Bengal	Kolkata Dakshin	None of the Above	19504	no	NOTA	None of the Above
West Bengal	Kolkata Dakshin	SYED MD WASIM RAZA	1195	no	AIMF	All India Minorities Front
West Bengal	Kolkata Dakshin	KARAN SINGH	1103	no	IJP	Indian Justice Party
West Bengal	Kolkata Dakshin	HITANGSHU BANERJEE	2921	no	AMB	Amra Bangalee
West Bengal	Kolkata Dakshin	MALA ROY	113453	no	INC	Indian National Congress
West Bengal	Kolkata Dakshin	K. P. MD. SHARIF	3776	no	IUML	Indian Union Muslim League
West Bengal	Kolkata Dakshin	TATHAGATA ROY	295376	no	BJP	Bharatiya Janata Party
West Bengal	Kolkata Dakshin	MD. HESHAMUDDIN	1554	no	WPOI	Welfare Party Of India
West Bengal	Kolkata Dakshin	BADRI MONDAL	1844	no	IND	Independent
West Bengal	Kolkata Dakshin	SHAMALI DAS	3356	no	IND	Independent
West Bengal	Kolkata Dakshin	NANDINI MUKHERJEE	278414	no	CPM	Communist Party of India  (Marxist)
West Bengal	Kolkata Dakshin	ASHOKE BISWAS	4335	no	IND	Independent
West Bengal	Kolkata Dakshin	OMPRAKASH PRAJAPATI	4564	no	BSP	Bahujan Samaj Party
West Bengal	Kolkata Dakshin	RAVEENDRAN. T.P (RAVI PALOOR)	875	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
West Bengal	Kolkata Dakshin	ZUBAIR RABBANI	3994	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Kolkata Uttar	SUDIP BANDYOPADHYAY	343687	yes	AITC	All India Trinamool Congress
West Bengal	Kolkata Uttar	PHUL CHAND RAM	2127	no	BSP	Bahujan Samaj Party
West Bengal	Kolkata Uttar	SOMENDRANATH MITRA	130783	no	INC	Indian National Congress
West Bengal	Kolkata Uttar	RAHUL (BISWAJIT) SINHA	247461	no	BJP	Bharatiya Janata Party
West Bengal	Kolkata Uttar	SANJOY SAHA	4808	no	IND	Independent
West Bengal	Kolkata Uttar	MD. IBRAHIM KHAN	749	no	IUML	Indian Union Muslim League
West Bengal	Kolkata Uttar	KALI PADA JANA	1860	no	IND	Independent
West Bengal	Kolkata Uttar	KAUSHIK BISWAS	1810	no	IND	Independent
West Bengal	Kolkata Uttar	DR. DEVENDRA PANDEY	4168	no	IND	Independent
West Bengal	Kolkata Uttar	BAGCHI RUPA	196053	no	CPM	Communist Party of India  (Marxist)
West Bengal	Kolkata Uttar	DEBAJYOTI SENGUPTA	1435	no	IND	Independent
West Bengal	Kolkata Uttar	ALOK CHATURVEDI	4399	no	AAAP	Aam Aadmi Party
West Bengal	Kolkata Uttar	SUNIRMAL BASU	518	no	IND	Independent
West Bengal	Kolkata Uttar	SAUMEN MITRA	3752	no	IND	Independent
West Bengal	Kolkata Uttar	RATHINDRA NATH ROY	1149	no	IND	Independent
West Bengal	Kolkata Uttar	MITUL KUMAR SINGH	548	no	RAHM	Rashtriya Ahinsa Manch
West Bengal	Kolkata Uttar	S.M. HABIBULLAH	660	no	GaAP	Gareeb Aadmi Party
West Bengal	Kolkata Uttar	TARUN MUKHERJEE	708	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Kolkata Uttar	None of the Above	9103	no	NOTA	None of the Above
West Bengal	Howrah	PRASUN BANERJEE	488461	yes	AITC	All India Trinamool Congress
West Bengal	Howrah	None of the Above	9929	no	NOTA	None of the Above
West Bengal	Howrah	SONIA DAS	4441	no	IND	Independent
West Bengal	Howrah	DINESH KUMAR SHARMA	2343	no	IND	Independent
West Bengal	Howrah	SAJAL KUMAR DAS	1572	no	IND	Independent
West Bengal	Howrah	SUKUMAR BARAL	635	no	BMUP	Bahujan Mukti Party
West Bengal	Howrah	MOHAMMAD SIRAJUDDIN SEKH	1079	no	GaAP	Gareeb Aadmi Party
West Bengal	Howrah	SANJIB SARKAR	2105	no	IND	Independent
West Bengal	Howrah	SURAJ NARAYAN SINGH	2186	no	AAAP	Aam Aadmi Party
West Bengal	Howrah	MRITYUNJAY SARKAR	2034	no	IND	Independent
West Bengal	Howrah	MANOJ KUMAR PANDEY	63254	no	INC	Indian National Congress
West Bengal	Howrah	SHIB CHANDRA RAM	3241	no	BSP	Bahujan Samaj Party
West Bengal	Howrah	SOUMITRA SENGUPTA	1814	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Howrah	GEORGE BAKER	248120	no	BJP	Bharatiya Janata Party
West Bengal	Howrah	GOBARDDHAN MANNA	2680	no	IUC	Indian Unity Centre
West Bengal	Howrah	SRIDIP BHATTACHARYA	291505	no	CPM	Communist Party of India  (Marxist)
West Bengal	Uluberia	SULTAN AHMED	570785	yes	AITC	All India Trinamool Congress
West Bengal	Uluberia	SABIR UDDIN MOLLA	369563	no	CPM	Communist Party of India  (Marxist)
West Bengal	Uluberia	ABUL QASEM	6607	no	IUC	Indian Unity Centre
West Bengal	Uluberia	RANJIT KISHORE MOHANTY	137137	no	BJP	Bharatiya Janata Party
West Bengal	Uluberia	MINATI SARKAR	2902	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Uluberia	HASAN NAWAJ	5709	no	AIUDF	All India United Democratic Front
West Bengal	Uluberia	REKHA DAS	3918	no	BSP	Bahujan Samaj Party
West Bengal	Uluberia	ASHIS KUMAR DAS	2437	no	IND	Independent
West Bengal	Uluberia	SUJATA DUTTA	1400	no	BMUP	Bahujan Mukti Party
West Bengal	Uluberia	RAMESH DHARA	2529	no	IND	Independent
West Bengal	Uluberia	SUSANTA KUMAR DALUI	2990	no	IND	Independent
West Bengal	Uluberia	ASIT MITRA	67826	no	INC	Indian National Congress
West Bengal	Uluberia	DILIP KUMAR HAIT	3947	no	IND	Independent
West Bengal	Uluberia	None of the Above	8277	no	NOTA	None of the Above
West Bengal	Srerampur	KALYAN BANERJEE	514933	yes	AITC	All India Trinamool Congress
West Bengal	Srerampur	None of the Above	15374	no	NOTA	None of the Above
West Bengal	Srerampur	RAJU DEY SARKAR	4277	no	IND	Independent
West Bengal	Srerampur	KAILASH NASKAR	2674	no	IND	Independent
West Bengal	Srerampur	MANASA SEN	3406	no	WPOI	Welfare Party Of India
West Bengal	Srerampur	DR. SOMNATH GHOSH	6613	no	IND	Independent
West Bengal	Srerampur	BAPPI LAHIRI	287712	no	BJP	Bharatiya Janata Party
West Bengal	Srerampur	NASIRUDDIN MIR	4240	no	IUC	Indian Unity Centre
West Bengal	Srerampur	TIRTHANKAR RAY	362407	no	CPM	Communist Party of India  (Marxist)
West Bengal	Srerampur	MD. SHANAWAZ	2698	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Srerampur	ABDUL MANNAN	86099	no	INC	Indian National Congress
West Bengal	Hooghly	DR. RATNA DE (NAG)	614312	yes	AITC	All India Trinamool Congress
West Bengal	Hooghly	SAJAL ADHIKARI	9152	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
West Bengal	Hooghly	PABAN MAZUMDER	7682	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Hooghly	VIJAY KUMAR MAHATO	5519	no	BSP	Bahujan Samaj Party
West Bengal	Hooghly	SHUKCHAND MURMU	3197	no	JDP	Jharkhand Disom Party
West Bengal	Hooghly	PRITAM GHOSH	42226	no	INC	Indian National Congress
West Bengal	Hooghly	SANGHAMITRA MUKHERJEE	3766	no	RJNP	Rashtriya Janasachetan Party (R.J.P.)
West Bengal	Hooghly	PRADIP SAHA	425228	no	CPM	Communist Party of India  (Marxist)
West Bengal	Hooghly	DR. CHANDAN MITRA	221271	no	BJP	Bharatiya Janata Party
West Bengal	Hooghly	None of the Above	16517	no	NOTA	None of the Above
West Bengal	Arambagh	APARUPA PODDAR (AFRIN ALI)	748764	yes	AITC	All India Trinamool Congress
West Bengal	Arambagh	None of the Above	17837	no	NOTA	None of the Above
West Bengal	Arambagh	GANESH BAG	7062	no	JDP	Jharkhand Disom Party
West Bengal	Arambagh	SAKTIMOHAN MALIK	401919	no	CPM	Communist Party of India  (Marxist)
West Bengal	Arambagh	SAMBHUNATH MALIK	27872	no	INC	Indian National Congress
West Bengal	Arambagh	MADHUSUDAN BAG	158480	no	BJP	Bharatiya Janata Party
West Bengal	Tamluk	ADHIKARI SUVENDU	716928	yes	AITC	All India Trinamool Congress
West Bengal	Tamluk	None of the Above	11643	no	NOTA	None of the Above
West Bengal	Tamluk	KAMAL BAG	3110	no	BSP	Bahujan Samaj Party
West Bengal	Tamluk	MANIK CHANDRA MONDAL	1500	no	IUML	Indian Union Muslim League
West Bengal	Tamluk	KALISANKAR JANA	2658	no	IND	Independent
West Bengal	Tamluk	ANWAR ALI SK	29645	no	INC	Indian National Congress
West Bengal	Tamluk	ABDUR REZAK SK	1682	no	IND	Independent
West Bengal	Tamluk	BADSHA ALAM	86265	no	BJP	Bharatiya Janata Party
West Bengal	Tamluk	BIBEKANANDA RAY	10197	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Tamluk	RAJYASHREE CHAUDHURI	3609	no	IND	Independent
West Bengal	Tamluk	SEKH IBRAHIM ALI	470447	no	CPM	Communist Party of India  (Marxist)
West Bengal	Kanthi	ADHIKARI SISIR KUMAR	676749	yes	AITC	All India Trinamool Congress
West Bengal	Kanthi	SINHA TAPAS	447259	no	CPM	Communist Party of India  (Marxist)
West Bengal	Kanthi	MANAS PRADHAN	7335	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Kanthi	ANSAR ALI SK	8298	no	BSP	Bahujan Samaj Party
West Bengal	Kanthi	KAMALENDU PAHARI	111082	no	BJP	Bharatiya Janata Party
West Bengal	Kanthi	KUNAL BANERJEE	27230	no	INC	Indian National Congress
West Bengal	Kanthi	SK GOLAM NABI AJAD	3260	no	IND	Independent
West Bengal	Kanthi	None of the Above	9598	no	NOTA	None of the Above
West Bengal	Ghatal	ADHIKARI DEEPAK (DEV)	685696	yes	AITC	All India Trinamool Congress
West Bengal	Ghatal	GOBARDHAN GHOSH	1983	no	RaJSP	Rashtriya Janadhikar Suraksha Party
West Bengal	Ghatal	GOPAL MONDAL	4498	no	IND	Independent
West Bengal	Ghatal	None of the Above	14418	no	NOTA	None of the Above
West Bengal	Ghatal	MANAS RANJAN BHUNIA	122928	no	INC	Indian National Congress
West Bengal	Ghatal	GOPAL MURMU	2154	no	JHAP	Jharkhand Anushilan Party
West Bengal	Ghatal	SANTOSH RANA	424805	no	CPI	Communist Party of India
West Bengal	Ghatal	ANJAN JANA	8080	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Ghatal	MD. ALAM	94842	no	BJP	Bharatiya Janata Party
West Bengal	Ghatal	GOUTAM KOURI	5547	no	AMB	Amra Bangalee
West Bengal	Ghatal	GANGARAM SASMAL	1758	no	PDS	Party for Democratic Socialism
West Bengal	Jhargram	UMA SAREN	674504	yes	AITC	All India Trinamool Congress
West Bengal	Jhargram	BUDDHADEB MANDI	15114	no	JMM	Jharkhand Mukti Morcha
West Bengal	Jhargram	MILAN MANDI	6013	no	JDP	Jharkhand Disom Party
West Bengal	Jhargram	HIRANMOY MAJHI	8616	no	IND	Independent
West Bengal	Jhargram	GOMASTA PRASAD SOREN	8364	no	AKBJHP	Akhil Bhartiya Jharkhand Party
West Bengal	Jhargram	RAJIB MUDI	5058	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Jhargram	DR. PULIN BIHARI BASKE	326621	no	CPM	Communist Party of India  (Marxist)
West Bengal	Jhargram	CHUNIBALA HANSDA	7608	no	JKP(N)	Jharkhand Party  (Naren)
West Bengal	Jhargram	SUBODH KUMAR MANDI	7453	no	IND	Independent
West Bengal	Jhargram	ANITA HANSDA	40513	no	INC	Indian National Congress
West Bengal	Jhargram	RAMPADA HANSDA	8478	no	AMB	Amra Bangalee
West Bengal	Jhargram	BIKASH MUDI	122459	no	BJP	Bharatiya Janata Party
West Bengal	Jhargram	MURARI MOHAN BASKE	3877	no	JHAP	Jharkhand Anushilan Party
West Bengal	Jhargram	None of the Above	22935	no	NOTA	None of the Above
West Bengal	Medinipur	SANDHYA ROY	579860	yes	AITC	All India Trinamool Congress
West Bengal	Medinipur	None of the Above	13626	no	NOTA	None of the Above
West Bengal	Medinipur	DR. BIMAL KUMAR RAJ	48883	no	INC	Indian National Congress
West Bengal	Medinipur	PRABHAKAR TEWARI	180112	no	BJP	Bharatiya Janata Party
West Bengal	Medinipur	TUSHAR JANA	9783	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Medinipur	PRABODH PANDA	395194	no	CPI	Communist Party of India
West Bengal	Medinipur	KAVITA RANI	8639	no	BSP	Bahujan Samaj Party
West Bengal	Medinipur	RAMKRISNA SARKAR	4655	no	IND	Independent
West Bengal	Medinipur	SOUMEN SAMANTA	6062	no	PDS	Party for Democratic Socialism
West Bengal	Medinipur	ASHOK BERA	7883	no	AMB	Amra Bangalee
West Bengal	Medinipur	MUKIM KHAN	5335	no	JMM	Jharkhand Mukti Morcha
West Bengal	Purulia	DR. MRIGANKA MAHATO	468277	yes	AITC	All India Trinamool Congress
West Bengal	Purulia	MONBODH MAHATO	6774	no	IND	Independent
West Bengal	Purulia	PURNA CHANDRA TUDU	4073	no	IND	Independent
West Bengal	Purulia	TAPAN KUMAR MAHATO	7386	no	JMM	Jharkhand Mukti Morcha
West Bengal	Purulia	DHIREN RAJAK	5584	no	IND	Independent
West Bengal	Purulia	NEPAL MAHATA	257923	no	INC	Indian National Congress
West Bengal	Purulia	MIHIR KUMAR RAJWAR	9850	no	BSP	Bahujan Samaj Party
West Bengal	Purulia	AJIT PRASAD MAHATA	9012	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
West Bengal	Purulia	BAIDYANATH HANSDA	7168	no	JDP	Jharkhand Disom Party
West Bengal	Purulia	SUBARNA  KUMAR	11266	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Purulia	NARAHARI  MAHATO	314400	no	AIFB	All India Forward Bloc
West Bengal	Purulia	BIKASH BANERJEE	86236	no	BJP	Bharatiya Janata Party
West Bengal	Purulia	None of the Above	16726	no	NOTA	None of the Above
West Bengal	Bankura	SREEMATI  DEV VARMA (MOON MOON SEN)	483455	yes	AITC	All India Trinamool Congress
West Bengal	Bankura	None of the Above	23682	no	NOTA	None of the Above
West Bengal	Bankura	SUDHIR KUMAR MURMU	7579	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
West Bengal	Bankura	SUBHAS KUMAR SARKAR	251183	no	BJP	Bharatiya Janata Party
West Bengal	Bankura	NILMADHAB GUPTA	22021	no	INC	Indian National Congress
West Bengal	Bankura	HULU KHETRAPAL	7564	no	BMUP	Bahujan Mukti Party
West Bengal	Bankura	SHIB SANKAR GHOSH	3105	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
West Bengal	Bankura	PANMANI BESRA	5909	no	JDP	Jharkhand Disom Party
West Bengal	Bankura	SHYAMAL SIKDAR	9045	no	IND	Independent
West Bengal	Bankura	BINOY CHOWDHURY	7388	no	BSP	Bahujan Samaj Party
West Bengal	Bankura	PARESH MARANDI	10229	no	JMM	Jharkhand Mukti Morcha
West Bengal	Bankura	BANERJEE PRABIR	6209	no	JHAP	Jharkhand Anushilan Party
West Bengal	Bankura	GOUR CHANDRA HEMBRAM	4876	no	IND	Independent
West Bengal	Bankura	KABITA SINGHABABU	9125	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Bankura	ACHARIA BASUDEB	384949	no	CPM	Communist Party of India  (Marxist)
West Bengal	Bishnupur	KHAN SAUMITRA	578870	yes	AITC	All India Trinamool Congress
West Bengal	Bishnupur	SUSMITA BAURI	429185	no	CPM	Communist Party of India  (Marxist)
West Bengal	Bishnupur	JOYDEB BAURI	7816	no	BMUP	Bahujan Mukti Party
West Bengal	Bishnupur	JAGADANANDA ROY	10127	no	BSP	Bahujan Samaj Party
West Bengal	Bishnupur	TARANI ROY	6854	no	IND	Independent
West Bengal	Bishnupur	JAYANTA MONDAL	179530	no	BJP	Bharatiya Janata Party
West Bengal	Bishnupur	NARAYAN CHANDRA KHAN	27054	no	INC	Indian National Congress
West Bengal	Bishnupur	SADANANDA MANDAL	4886	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Bishnupur	DINESH LOHAR	6820	no	IND	Independent
West Bengal	Bishnupur	None of the Above	20928	no	NOTA	None of the Above
West Bengal	Bardhaman Purba	SUNIL KUMAR MONDAL	574660	yes	AITC	All India Trinamool Congress
West Bengal	Bardhaman Purba	None of the Above	13951	no	NOTA	None of the Above
West Bengal	Bardhaman Purba	PROSANTA BAG	3410	no	JDP	Jharkhand Disom Party
West Bengal	Bardhaman Purba	SWAPAN MALIK	9666	no	JMM	Jharkhand Mukti Morcha
West Bengal	Bardhaman Purba	CHANDANA MAJHI	68884	no	INC	Indian National Congress
West Bengal	Bardhaman Purba	ISWAR CHANDRA DAS	460181	no	CPM	Communist Party of India  (Marxist)
West Bengal	Bardhaman Purba	SANTOSH ROY	170828	no	BJP	Bharatiya Janata Party
West Bengal	Bardhaman Purba	MUKUL BISWAS	5442	no	BSP	Bahujan Samaj Party
West Bengal	Bardhaman Purba	KALICHARAN SARDAR	7981	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Bardhaman Purba	PEJUSH KUMAR SAHANA	5919	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
West Bengal	Burdwan - durgapur	DR. MAMTAZ SANGHAMITA	554521	yes	AITC	All India Trinamool Congress
West Bengal	Burdwan - durgapur	MD. HARUN	11862	no	BSP	Bahujan Samaj Party
West Bengal	Burdwan - durgapur	SUNIL KUMAR PURKAIT	7574	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Burdwan - durgapur	DEBASREE CHAUDHURI	237205	no	BJP	Bharatiya Janata Party
West Bengal	Burdwan - durgapur	SK. SAIDUL HAQUE	447190	no	CPM	Communist Party of India  (Marxist)
West Bengal	Burdwan - durgapur	None of the Above	16886	no	NOTA	None of the Above
West Bengal	Burdwan - durgapur	AGASTY PRADIP	44355	no	INC	Indian National Congress
West Bengal	Burdwan - durgapur	DR. DHANAPATI DAS	6665	no	BMUP	Bahujan Mukti Party
West Bengal	Burdwan - durgapur	SARADA MONI SAMANTA	4984	no	IND	Independent
West Bengal	Asansol	BABUL SUPRIYA BARAL (BABUL SUPRIYO)	419983	yes	BJP	Bharatiya Janata Party
West Bengal	Asansol	None of the Above	11479	no	NOTA	None of the Above
West Bengal	Asansol	ATUL CHANDRA  BOURI	4256	no	BMUP	Bahujan Mukti Party
West Bengal	Asansol	BURO MURMU	2434	no	JDP	Jharkhand Disom Party
West Bengal	Asansol	INDRANI MISHRA	48502	no	INC	Indian National Congress
West Bengal	Asansol	JARASANDHA SINHA	4663	no	BSP	Bahujan Samaj Party
West Bengal	Asansol	DOLA SEN	349503	no	AITC	All India Trinamool Congress
West Bengal	Asansol	BANSA GOPAL CHOUDHURY	255829	no	CPM	Communist Party of India  (Marxist)
West Bengal	Asansol	MD. REYAZUDDIN	4947	no	IUML	Indian Union Muslim League
West Bengal	Asansol	KANAI BANERJEE	5728	no	JMM	Jharkhand Mukti Morcha
West Bengal	Asansol	ANANTA LAL GUPTA	3115	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Asansol	SUJIT KAR	5016	no	IND	Independent
West Bengal	Asansol	MD. MUSTAQIM	2450	no	GaAP	Gareeb Aadmi Party
West Bengal	Asansol	JYOTIRMOY MAITI	10227	no	IND	Independent
West Bengal	Asansol	MANASH SARKAR	14263	no	IND	Independent
West Bengal	Bolpur	ANUPAM HAZRA	630693	yes	AITC	All India Trinamool Congress
West Bengal	Bolpur	None of the Above	17322	no	NOTA	None of the Above
West Bengal	Bolpur	TAPAN KUMAR SAHA	46953	no	INC	Indian National Congress
West Bengal	Bolpur	SAMIRAN DAS	5952	no	BSP	Bahujan Samaj Party
West Bengal	Bolpur	KAMINI MOHAN SARKAR	197474	no	BJP	Bharatiya Janata Party
West Bengal	Bolpur	SANJIB MALIK	6371	no	JDP	Jharkhand Disom Party
West Bengal	Bolpur	BIJOY DOLUI	5410	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Bolpur	DOME RAMCHANDRA	394581	no	CPM	Communist Party of India  (Marxist)
West Bengal	Birbhum	SATABDI ROY	460568	yes	AITC	All India Trinamool Congress
West Bengal	Birbhum	DR. ELAHI KAMRE MAHAMMAD	393305	no	CPM	Communist Party of India  (Marxist)
West Bengal	Birbhum	PRASANTA ROY	7194	no	SP	Samajwadi Party
West Bengal	Birbhum	AYESHA KHATUN	6593	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
West Bengal	Birbhum	NURUL ISLAM	2205	no	JD(U)	Janata Dal  (United)
West Bengal	Birbhum	SAJAL DAS	2982	no	BMUP	Bahujan Mukti Party
West Bengal	Birbhum	SHIBRATAN SHARMA	7989	no	JMM	Jharkhand Mukti Morcha
West Bengal	Birbhum	SUTTAM DAS	5448	no	IND	Independent
West Bengal	Birbhum	PRABIR MUKHOPADHYAY	7141	no	BSP	Bahujan Samaj Party
West Bengal	Birbhum	JOY BANERJEE	235753	no	BJP	Bharatiya Janata Party
West Bengal	Birbhum	SYED SIRAJ JIMMI	132084	no	INC	Indian National Congress
West Bengal	Birbhum	None of the Above	14557	no	NOTA	None of the Above
Chhattisgarh	SARGUJA	KAMALBHAN SINGH MARABI	585336	yes	BJP	Bharatiya Janata Party
Chhattisgarh	SARGUJA	None of the Above	31104	no	NOTA	None of the Above
Chhattisgarh	SARGUJA	RAMASAPNA SINGH	11691	no	APoI	Ambedkarite Party of India
Chhattisgarh	SARGUJA	DUBRAJ SINGH MARKAM	7577	no	IND	Independent
Chhattisgarh	SARGUJA	SHIVKUMAR SINGH SHYAM	7374	no	RGOP	Rashtriya Gondvana Party
Chhattisgarh	SARGUJA	BAHADUR RAM	10861	no	IND	Independent
Chhattisgarh	SARGUJA	RAJNATH	15515	no	IND	Independent
Chhattisgarh	SARGUJA	KUMAIT RAM SANDIL	6146	no	IND	Independent
Chhattisgarh	SARGUJA	COMRADE SURENDRA LAL SINGH	14849	no	CPM	Communist Party of India  (Marxist)
Chhattisgarh	SARGUJA	ADVOCATE KUNJ BIHARI SINGH PAIKRA	4998	no	BMUP	Bahujan Mukti Party
Chhattisgarh	SARGUJA	DHARMJEET SINGH MARKAM	21633	no	BSP	Bahujan Samaj Party
Chhattisgarh	SARGUJA	TULESHWAR SINGH	11384	no	AITC	All India Trinamool Congress
Chhattisgarh	SARGUJA	BALDEO PRASAD SINGH MARAVI	14702	no	GGP	Gondvana Gantantra Party
Chhattisgarh	SARGUJA	RAMNATH CHEARWA	6051	no	SSD	Shoshit Samaj Dal
Chhattisgarh	SARGUJA	RAM DEV RAM	438100	no	INC	Indian National Congress
Chhattisgarh	RAIGARH	VISHNU DEO SAI	662478	yes	BJP	Bharatiya Janata Party
Chhattisgarh	RAIGARH	PAULUS KUJUR	5094	no	BMUP	Bahujan Mukti Party
Chhattisgarh	RAIGARH	PRAKASH KUMAR URANW	12860	no	IND	Independent
Chhattisgarh	RAIGARH	KRIPARAM SIDAR	11227	no	GGP	Gondvana Gantantra Party
Chhattisgarh	RAIGARH	TARA SINGH RATHIYA	12133	no	AAAP	Aam Aadmi Party
Chhattisgarh	RAIGARH	ARTI SINGH	445728	no	INC	Indian National Congress
Chhattisgarh	RAIGARH	KRIPA SHANKAR BHAGAT	27152	no	BSP	Bahujan Samaj Party
Chhattisgarh	RAIGARH	JOSEPH BARA	5168	no	IND	Independent
Chhattisgarh	RAIGARH	RAM NARAYAN AYAM	8747	no	APoI	Ambedkarite Party of India
Chhattisgarh	RAIGARH	DHANANJAY RATHIYA	8046	no	IND	Independent
Chhattisgarh	RAIGARH	BAJARANG SIDAR	11098	no	IND	Independent
Chhattisgarh	RAIGARH	AMRIT TIRKEY	7975	no	IND	Independent
Chhattisgarh	RAIGARH	None of the Above	28480	no	NOTA	None of the Above
Chhattisgarh	JANJGIR-CHAMPA	KAMLA PATLE	518909	yes	BJP	Bharatiya Janata Party
Chhattisgarh	JANJGIR-CHAMPA	PREMSHANKAR MAHILANGE	5309	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	DEEPAK NARANG	3977	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	KAUSHAL PRASAD NAVARANG	2724	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	LAKHAN LAL CHAUHAN	2859	no	BMUP	Bahujan Mukti Party
Chhattisgarh	JANJGIR-CHAMPA	NOHAR SINGH SONWANI	2852	no	SP	Samajwadi Party
Chhattisgarh	JANJGIR-CHAMPA	CHAITRAM SURYAVANSHI ADHIWAKTA	3208	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	SATYENDRA BHANDARI	6275	no	APoI	Ambedkarite Party of India
Chhattisgarh	JANJGIR-CHAMPA	None of the Above	18438	no	NOTA	None of the Above
Chhattisgarh	JANJGIR-CHAMPA	JEEVAN LAL BHARATI	2962	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	LAKHAN LAL LAHRE	7535	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	PREM CHAND JAYASI	343948	no	INC	Indian National Congress
Chhattisgarh	JANJGIR-CHAMPA	DADOO RAM MANHAR	16862	no	AAAP	Aam Aadmi Party
Chhattisgarh	JANJGIR-CHAMPA	DUJ RAM BOUDDH	125617	no	BSP	Bahujan Samaj Party
Chhattisgarh	JANJGIR-CHAMPA	VRINDA CHAUHAN	8982	no	IND	Independent
Chhattisgarh	JANJGIR-CHAMPA	JAGDISH PRASAD NIRALA	2890	no	IND	Independent
Chhattisgarh	KORBA	DR. BANSHILAL MAHTO	439002	yes	BJP	Bharatiya Janata Party
Chhattisgarh	KORBA	MOHAN LAL BARGAHI	2679	no	IND	Independent
Chhattisgarh	KORBA	RAJESH KUMAR MAHANT	2333	no	JD(U)	Janata Dal  (United)
Chhattisgarh	KORBA	RAJENDRA SINGH KANWAR	2700	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	KORBA	CHARAN DAS MAHANT	434737	no	INC	Indian National Congress
Chhattisgarh	KORBA	FATTE SINGH SHYAM	2671	no	IND	Independent
Chhattisgarh	KORBA	LAXMAN LAL KAIWART	4265	no	chp	Chhattisgarhiya Party
Chhattisgarh	KORBA	PRATAP BHANU	9495	no	IND	Independent
Chhattisgarh	KORBA	KAMAL DEO	1998	no	BSCP	Bhartiya Shakti Chetna Party
Chhattisgarh	KORBA	HIRASINGH MARKAM	52753	no	GGP	Gondvana Gantantra Party
Chhattisgarh	KORBA	PHULESHWAR PRASAD SURAJIHA	4111	no	IND	Independent
Chhattisgarh	KORBA	DR.URMILA SINGH MARKO	2768	no	RGOP	Rashtriya Gondvana Party
Chhattisgarh	KORBA	DHANSINGH DHURVE	14198	no	BSP	Bahujan Samaj Party
Chhattisgarh	KORBA	RAJESH KUMAR PANDEY	3676	no	IND	Independent
Chhattisgarh	KORBA	CHANDRA BHUSHAN PRATAP SINGH KANWAR	5238	no	APoI	Ambedkarite Party of India
Chhattisgarh	KORBA	SHAMBHU PRASAD SHARMA ADVOCATE	1808	no	IND	Independent
Chhattisgarh	KORBA	KEWAL GOSWAMI	2478	no	SP	Samajwadi Party
Chhattisgarh	KORBA	AMAR NATH PANDEY	14594	no	AAAP	Aam Aadmi Party
Chhattisgarh	KORBA	CHARAN BHAYA	6919	no	IND	Independent
Chhattisgarh	KORBA	JEEVAN LAL RAOUTEL	18459	no	IND	Independent
Chhattisgarh	KORBA	S.R. ANCHAL	5657	no	IND	Independent
Chhattisgarh	KORBA	JATA SHANKAR PANDAY	6461	no	IND	Independent
Chhattisgarh	KORBA	HARBHAJAN SINGH SHYAM	2728	no	IND	Independent
Chhattisgarh	KORBA	SEKH RAUF	2422	no	IND	Independent
Chhattisgarh	KORBA	None of the Above	8570	no	NOTA	None of the Above
Chhattisgarh	BILASPUR	LAKHAN LAL SAHU	561387	yes	BJP	Bharatiya Janata Party
Chhattisgarh	BILASPUR	DINESH KUMAR SINGH KHUSRO NIKHIL	12024	no	IND	Independent
Chhattisgarh	BILASPUR	RAMESHWAR KEWAT	1661	no	IND	Independent
Chhattisgarh	BILASPUR	DAYADAS LAHRE	2988	no	IND	Independent
Chhattisgarh	BILASPUR	GANESH YADAV	4279	no	GGP	Gondvana Gantantra Party
Chhattisgarh	BILASPUR	MANIK DAHARIYA	2059	no	IND	Independent
Chhattisgarh	BILASPUR	AJAY PALI	2237	no	IND	Independent
Chhattisgarh	BILASPUR	LAKHANLAL SAHU	910	no	IND	Independent
Chhattisgarh	BILASPUR	LAKHAN LAL SAHU	910	no	IND	Independent
Chhattisgarh	BILASPUR	LAKHAN LAL SAHU	1458	no	IND	Independent
Chhattisgarh	BILASPUR	DHANIRAM YADAV	1706	no	SP	Samajwadi Party
Chhattisgarh	BILASPUR	None of the Above	7566	no	NOTA	None of the Above
Chhattisgarh	BILASPUR	SALIKRAM JOGI	1836	no	IND	Independent
Chhattisgarh	BILASPUR	RAM SINGH PANDRAM (PENDRO)	4034	no	IND	Independent
Chhattisgarh	BILASPUR	SARITA VIJAY SONI	2244	no	IND	Independent
Chhattisgarh	BILASPUR	DHANIRAM KHARE	1287	no	RP(K)	Republican Paksha (Khoripa)
Chhattisgarh	BILASPUR	DHARAM BHARGAV	26340	no	BSP	Bahujan Samaj Party
Chhattisgarh	BILASPUR	RAMJI SAHU	1776	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	BILASPUR	KARUNA SHUKLA	384951	no	INC	Indian National Congress
Chhattisgarh	BILASPUR	DILIP KOSHLE	6911	no	IND	Independent
Chhattisgarh	BILASPUR	JAITRAM KHANDE	2369	no	IND	Independent
Chhattisgarh	BILASPUR	LAKHANLAL SAHU	855	no	IND	Independent
Chhattisgarh	BILASPUR	BALDAU PRASAD SAHU	13339	no	IND	Independent
Chhattisgarh	BILASPUR	ENG. RAMFAL MANDREY	4294	no	APoI	Ambedkarite Party of India
Chhattisgarh	BILASPUR	PRAKASH SURYA	14918	no	IND	Independent
Chhattisgarh	BILASPUR	DAOSINGH MARAVI	5385	no	IND	Independent
Chhattisgarh	BILASPUR	ANAND MISHRA	20733	no	AAAP	Aam Aadmi Party
Chhattisgarh	RAJNANDGAON	ABHISHEK SINGH	643473	yes	BJP	Bharatiya Janata Party
Chhattisgarh	RAJNANDGAON	None of the Above	32384	no	NOTA	None of the Above
Chhattisgarh	RAJNANDGAON	BHASKAR DWIVEDI	10733	no	AAAP	Aam Aadmi Party
Chhattisgarh	RAJNANDGAON	KHEMRAJ LODHI	5546	no	IND	Independent
Chhattisgarh	RAJNANDGAON	BHAGAT SONI	3636	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	RAJNANDGAON	SANTOSH CHANDRAKAR	6007	no	GGP	Gondvana Gantantra Party
Chhattisgarh	RAJNANDGAON	MURAD BHAI	5255	no	IND	Independent
Chhattisgarh	RAJNANDGAON	WAMANRAO BAGDE	10891	no	IND	Independent
Chhattisgarh	RAJNANDGAON	SAHUKAR BHASKAR	11478	no	IND	Independent
Chhattisgarh	RAJNANDGAON	LODHI SEEMA KAUSHIK	3228	no	SP	Samajwadi Party
Chhattisgarh	RAJNANDGAON	RAMESH YADAV	5941	no	IND	Independent
Chhattisgarh	RAJNANDGAON	ANAND SAHU	20458	no	BSP	Bahujan Samaj Party
Chhattisgarh	RAJNANDGAON	KAMLESHWAR VERMA	407562	no	INC	Indian National Congress
Chhattisgarh	RAJNANDGAON	NARENDRA BANSOD ADVOCATE	11704	no	APoI	Ambedkarite Party of India
Chhattisgarh	DURG	TAMRADHWAJ SAHU	570687	yes	INC	Indian National Congress
Chhattisgarh	DURG	ADHIWAKTA NARENDRA GUPTA	1413	no	bhmm	Bhrashtachar Mukti Morcha
Chhattisgarh	DURG	JINENDRA GHRITLAHARE	4537	no	IND	Independent
Chhattisgarh	DURG	VISHWA RATNA SINHA	17454	no	AAAP	Aam Aadmi Party
Chhattisgarh	DURG	DIWAKAR BAIRAGI	1150	no	IND	Independent
Chhattisgarh	DURG	SAROJ PANDEY	553839	no	BJP	Bharatiya Janata Party
Chhattisgarh	DURG	MANNU LAL PARGANIHA	2159	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	DURG	ENGG. CHANDRAPRAKASH MANDLE	2301	no	IND	Independent
Chhattisgarh	DURG	JAI PRAKASH NAIR	2560	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Chhattisgarh	DURG	MUNNA CHANDRAKAR	15600	no	BSP	Bahujan Samaj Party
Chhattisgarh	DURG	ARUN JOSHI	16529	no	IND	Independent
Chhattisgarh	DURG	AATMA RAM SAHU	10371	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Chhattisgarh	DURG	SHAILENDRA BANJARE (SHAKTIPURTA)	2903	no	SSBD	Shakti Sena (Bharat Desh)
Chhattisgarh	DURG	SUBODH KUMAR GUPTA	4711	no	RPI(A)	Republican Party of India (A)
Chhattisgarh	DURG	RADHA BAI YADAV	1011	no	IND	Independent
Chhattisgarh	DURG	None of the Above	11907	no	NOTA	None of the Above
Chhattisgarh	DURG	BHARAT BHUSHAN PANDEY	1427	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Chhattisgarh	DURG	KSHITIJ KUMAR SHAMBHARKAR	8120	no	RP(K)	Republican Paksha (Khoripa)
Chhattisgarh	DURG	A.K.MOHAN	2286	no	AITC	All India Trinamool Congress
Chhattisgarh	DURG	SHITKARAN MAHILVAR	1225	no	IND	Independent
Chhattisgarh	DURG	BALDAU PRASAD SAHU	859	no	IND	Independent
Chhattisgarh	DURG	ALFRED VIKAS LOUISE	7098	no	IND	Independent
Chhattisgarh	DURG	SAHETRU SINGH	2736	no	IND	Independent
Chhattisgarh	DURG	HIRALAL ANNANT	2901	no	IND	Independent
Chhattisgarh	DURG	SHRIKANT SHAH	5340	no	JD(U)	Janata Dal  (United)
Chhattisgarh	DURG	SARJU PRASAD SHUKLA	2114	no	IND	Independent
Chhattisgarh	DURG	SHEEMA RAHANGDALE	5104	no	APoI	Ambedkarite Party of India
Chhattisgarh	RAIPUR	RAMESH BAIS	654922	yes	BJP	Bharatiya Janata Party
Chhattisgarh	RAIPUR	SHAMEEM KHAN	6750	no	IND	Independent
Chhattisgarh	RAIPUR	IMRRAN PASHA	937	no	AKAAD	Aazadi Ka Antim Aandolan Dal
Chhattisgarh	RAIPUR	KRANTI CHANDRAKAR	2521	no	IND	Independent
Chhattisgarh	RAIPUR	LALIT KUMAR PATEL	2184	no	IND	Independent
Chhattisgarh	RAIPUR	TARZAN JANGDE	962	no	IND	Independent
Chhattisgarh	RAIPUR	KRISHNANANDAN SINGH	1527	no	IND	Independent
Chhattisgarh	RAIPUR	UMESH KUMAR VERMA	3527	no	IND	Independent
Chhattisgarh	RAIPUR	RAMGOPAL VERMA	2619	no	AITC	All India Trinamool Congress
Chhattisgarh	RAIPUR	ANIL KUMAR UMRE	2700	no	APoI	Ambedkarite Party of India
Chhattisgarh	RAIPUR	HORILAL YADAV	662	no	IND	Independent
Chhattisgarh	RAIPUR	None of the Above	5796	no	NOTA	None of the Above
Chhattisgarh	RAIPUR	KAMALUDDIN	4316	no	IND	Independent
Chhattisgarh	RAIPUR	BASMATI KSHATRIYA	817	no	IND	Independent
Chhattisgarh	RAIPUR	PRAVIN JAIN	729	no	IND	Independent
Chhattisgarh	RAIPUR	SHANKAR LAL VARANDANI	2726	no	IND	Independent
Chhattisgarh	RAIPUR	RIJHENDRA DAHARIYA	3398	no	IND	Independent
Chhattisgarh	RAIPUR	BISAHAT KURRE	1365	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Chhattisgarh	RAIPUR	MEERA SHARMA	1405	no	IND	Independent
Chhattisgarh	RAIPUR	AJAY VERMA	4963	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	RAIPUR	GURUJI VIRENDRA KUMAR DAHARIYA	13147	no	BSP	Bahujan Samaj Party
Chhattisgarh	RAIPUR	SATYA NARAYAN SHARMA (SATTU BHAIYA)	483276	no	INC	Indian National Congress
Chhattisgarh	RAIPUR	RAMKRISHNA VERMA	1596	no	IND	Independent
Chhattisgarh	RAIPUR	MAHESH HARPAL	1562	no	SSBD	Shakti Sena (Bharat Desh)
Chhattisgarh	RAIPUR	NAARAD PRASAD NISHAD	2215	no	IND	Independent
Chhattisgarh	RAIPUR	ABDUL SAFIQ KHAN	6250	no	IND	Independent
Chhattisgarh	RAIPUR	NARESH TANDI	1329	no	IND	Independent
Chhattisgarh	RAIPUR	YASMIN BEG	1086	no	IND	Independent
Chhattisgarh	RAIPUR	MOINUDIN TANVAR(MONU)	922	no	IND	Independent
Chhattisgarh	RAIPUR	TEEKAM KUMAR TONDARE	741	no	IND	Independent
Chhattisgarh	RAIPUR	SATANAMI SANTOSH DAHARIYA	1657	no	IND	Independent
Chhattisgarh	RAIPUR	OMPRAKASH MESHRAM	1026	no	RP(K)	Republican Paksha (Khoripa)
Chhattisgarh	RAIPUR	NAVEEN GUPTA	1507	no	SP	Samajwadi Party
Chhattisgarh	RAIPUR	SAIYYAD JAVED ALI	724	no	IND	Independent
Chhattisgarh	RAIPUR	SANDEEP TIWARI	15139	no	AAAP	Aam Aadmi Party
Chhattisgarh	RAIPUR	OM PRAKASH DANDE	12699	no	IND	Independent
Chhattisgarh	RAIPUR	MADAN LAL SAHU	1143	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU (CHANDU BHAIYA)	503514	yes	BJP	Bharatiya Janata Party
Chhattisgarh	MAHASAMUND	None of the Above	9955	no	NOTA	None of the Above
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU	10797	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDOO LAL SAHU	4718	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDURAM SAHU	2167	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDU RAM SAHU	3732	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDU RAM SAHU	1628	no	IND	Independent
Chhattisgarh	MAHASAMUND	DR. RUPANAND SOI	1338	no	IND	Independent
Chhattisgarh	MAHASAMUND	PREETI DHRUW	3090	no	GGP	Gondvana Gantantra Party
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU	12308	no	IND	Independent
Chhattisgarh	MAHASAMUND	MOTILAL SAHU	753	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU	2268	no	IND	Independent
Chhattisgarh	MAHASAMUND	MOTI LAL SAHU	904	no	IND	Independent
Chhattisgarh	MAHASAMUND	MOHAN LAL PATEL	1395	no	BSCP	Bhartiya Shakti Chetna Party
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU	7091	no	IND	Independent
Chhattisgarh	MAHASAMUND	DEV PRASAD KELKAR	1053	no	IND	Independent
Chhattisgarh	MAHASAMUND	SUKHNANDAN DESHKAR	3670	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU	5497	no	IND	Independent
Chhattisgarh	MAHASAMUND	GANGADHAR PATEL	1279	no	IND	Independent
Chhattisgarh	MAHASAMUND	VIJAY KUMAR PATEL	1523	no	IND	Independent
Chhattisgarh	MAHASAMUND	CHANDU LAL SAHU	20255	no	IND	Independent
Chhattisgarh	MAHASAMUND	AJIT JOGI	502297	no	INC	Indian National Congress
Chhattisgarh	MAHASAMUND	KANHAIYA LAL SAHU	10600	no	BSP	Bahujan Samaj Party
Chhattisgarh	MAHASAMUND	CHAMPA LAL PATEL	2409	no	IND	Independent
Chhattisgarh	MAHASAMUND	PATEL SHRI DHAR CHANDRAKAR	1665	no	AD	Apna Dal
Chhattisgarh	MAHASAMUND	LAKSHMAN MASTURIYA	5524	no	AAAP	Aam Aadmi Party
Chhattisgarh	MAHASAMUND	ABHA PANDEY (GUDIA)	5868	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	MAHASAMUND	HEMANT PRADHAN	3911	no	IND	Independent
Chhattisgarh	BASTAR	DINESH KASHYAP	385829	yes	BJP	Bharatiya Janata Party
Chhattisgarh	BASTAR	BIMALA SORI	33883	no	CPI	Communist Party of India
Chhattisgarh	BASTAR	COM. DEVCHAND DURUV	6180	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Chhattisgarh	BASTAR	SONI SORI	16903	no	AAAP	Aam Aadmi Party
Chhattisgarh	BASTAR	DEEPAK KARMA (BUNTY)	261470	no	INC	Indian National Congress
Chhattisgarh	BASTAR	ARJUN SINGH THAKUR	8966	no	APoI	Ambedkarite Party of India
Chhattisgarh	BASTAR	AD. MANBODH BAGHEL	9774	no	BSP	Bahujan Samaj Party
Chhattisgarh	BASTAR	SHANKAR THAKUR	8136	no	SP	Samajwadi Party
Chhattisgarh	BASTAR	None of the Above	38772	no	NOTA	None of the Above
Chhattisgarh	KANKER	VIKRAM DEV USENDI	465215	yes	BJP	Bharatiya Janata Party
Chhattisgarh	KANKER	None of the Above	31917	no	NOTA	None of the Above
Chhattisgarh	KANKER	FOOLODEVI NETAM	430057	no	INC	Indian National Congress
Chhattisgarh	KANKER	RAMESH KUMAR GAWDE	23482	no	CPI	Communist Party of India
Chhattisgarh	KANKER	RAMSAY KORRAM	9391	no	BSP	Bahujan Samaj Party
Chhattisgarh	KANKER	JAMUNA MANDAVI	8972	no	CSM	Chhattisgarh Swabhiman Manch
Chhattisgarh	KANKER	MALIK RAM THAKUR	8963	no	APoI	Ambedkarite Party of India
Chhattisgarh	KANKER	HEMLAL MARKAM	7586	no	GGP	Gondvana Gantantra Party
Chhattisgarh	KANKER	AMAL SAY SORI	6618	no	IND	Independent
Chhattisgarh	KANKER	MAHENDRA GAWADE	14136	no	IND	Independent
Chhattisgarh	KANKER	SANTOSH KUMAR DHRUW	10606	no	AAAP	Aam Aadmi Party
Jharkhand	Rajmahal	VIJAY KUMAR HANSDAK	379507	yes	JMM	Jharkhand Mukti Morcha
Jharkhand	Rajmahal	DR. ANIL MURMU	97374	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Rajmahal	SUNIRAM HEMBROM	6460	no	IND	Independent
Jharkhand	Rajmahal	DR. SURAE SOREN	7906	no	IND	Independent
Jharkhand	Rajmahal	HEMLAL MURMU	338170	no	BJP	Bharatiya Janata Party
Jharkhand	Rajmahal	ARJUN PRASAD SINGH	6761	no	AJSUP	AJSU Party
Jharkhand	Rajmahal	KRISHNA SINGH	16354	no	IUML	Indian Union Muslim League
Jharkhand	Rajmahal	JYOTIN SOREN	58034	no	CPM	Communist Party of India  (Marxist)
Jharkhand	Rajmahal	None of the Above	19875	no	NOTA	None of the Above
Jharkhand	Rajmahal	BARNARD HEMBROM	8686	no	RKSP	Rashtriya Krantikari Samajwadi Party
Jharkhand	Rajmahal	TALA HANSDA	7661	no	IND	Independent
Jharkhand	Rajmahal	ARUN MARANDI	4775	no	BSP	Bahujan Samaj Party
Jharkhand	Dumka	SHIBU SOREN	335815	yes	JMM	Jharkhand Mukti Morcha
Jharkhand	Dumka	STEPHAN BESRA	4310	no	JDP	Jharkhand Disom Party
Jharkhand	Dumka	PULIS HEMRAM	10283	no	IND	Independent
Jharkhand	Dumka	UMANATH KOLE	3733	no	IND	Independent
Jharkhand	Dumka	None of the Above	18325	no	NOTA	None of the Above
Jharkhand	Dumka	CHHAYA KOLE	26442	no	CPI	Communist Party of India
Jharkhand	Dumka	BAGHRAI SOREN	9436	no	IND	Independent
Jharkhand	Dumka	SOKOL HANSDA	9059	no	BSP	Bahujan Samaj Party
Jharkhand	Dumka	SUNIL SOREN	296785	no	BJP	Bharatiya Janata Party
Jharkhand	Dumka	BABU LAL MARANDI	158122	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Dumka	BITIYA MANJHI	5813	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Dumka	SAHADEO SOREN	3739	no	AAAP	Aam Aadmi Party
Jharkhand	Dumka	SHRILAL KISKU	10347	no	IND	Independent
Jharkhand	Dumka	BABULAL SOREN	6284	no	AITC	All India Trinamool Congress
Jharkhand	Dumka	JOSHIL MURMU	4567	no	IND	Independent
Jharkhand	Godda	NISHIKANT DUBEY	380500	yes	BJP	Bharatiya Janata Party
Jharkhand	Godda	MANRAJ	20871	no	BSP	Bahujan Samaj Party
Jharkhand	Godda	RAJVARDHAN AZAD	14034	no	JD(U)	Janata Dal  (United)
Jharkhand	Godda	NIRANJAN PRASAD YADAV	9059	no	IND	Independent
Jharkhand	Godda	SUBODH PRASAD	10998	no	AJSUP	AJSU Party
Jharkhand	Godda	PRADIP YADAV	193506	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Godda	MAHENDRA MURMU	3904	no	JVD	Jharkhand Vikas Dal
Jharkhand	Godda	SUNIL KUMAR GUPTA	22349	no	IND	Independent
Jharkhand	Godda	DAMODAR SINGH	28246	no	AITC	All India Trinamool Congress
Jharkhand	Godda	GITA MANDAL	4344	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Godda	FURKAN ANSARI	319818	no	INC	Indian National Congress
Jharkhand	Godda	MD. JENUL	4466	no	BMUP	Bahujan Mukti Party
Jharkhand	Godda	MD. NOOR HASSAN	5132	no	SAP	Samata Party
Jharkhand	Godda	NAVIN CHANDRA JHA	7278	no	IND	Independent
Jharkhand	Godda	MANOJ KUMAR RAY	7447	no	IND	Independent
Jharkhand	Godda	JAI SHANKAR JHA	5080	no	AAAP	Aam Aadmi Party
Jharkhand	Godda	None of the Above	12410	no	NOTA	None of the Above
Jharkhand	Chatra	SUNIL KUMAR SINGH	295862	yes	BJP	Bharatiya Janata Party
Jharkhand	Chatra	None of the Above	5791	no	NOTA	None of the Above
Jharkhand	Chatra	BANWARI SAW	21261	no	CPI	Communist Party of India
Jharkhand	Chatra	HAJI JAINUL ABEDIN	14929	no	BSP	Bahujan Samaj Party
Jharkhand	Chatra	RAMNARESH SINGH	8341	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Chatra	CHANDRADEO THAKUR	4725	no	SP(I)	Socialist Party (India)
Jharkhand	Chatra	MAHESH SINGH YADAV	5783	no	JD(U)	Janata Dal  (United)
Jharkhand	Chatra	DHIRAJ PRASAD SAHU	117836	no	INC	Indian National Congress
Jharkhand	Chatra	BALWANT KUMAR	6845	no	JKP	Jharkhand Party
Jharkhand	Chatra	NILAM DEVI	104176	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Chatra	KESHWAR YADAV	29754	no	SP	Samajwadi Party
Jharkhand	Chatra	SAGAR RAM	5743	no	ABHM	Akhil Bharat Hindu Mahasabha
Jharkhand	Chatra	SUDHANSHU SUMAN	5843	no	IND	Independent
Jharkhand	Chatra	NAGMANI	35674	no	AJSUP	AJSU Party
Jharkhand	Chatra	ROBIN SINGH	2016	no	IND	Independent
Jharkhand	Chatra	NAUSHAD ALAM	7841	no	AITC	All India Trinamool Congress
Jharkhand	Chatra	MISTAR ALAM ASRAFEE	4647	no	RJM	Rashtriya Jankranti Morcha
Jharkhand	Chatra	SIMA CHANDRAVANSI	17980	no	AAAP	Aam Aadmi Party
Jharkhand	Chatra	RAJ KUMAR PAHAN	10771	no	RADP	Rashtriya Deshaj Party
Jharkhand	Chatra	ASHISH BHUSHAN PATHAK	2944	no	IND	Independent
Jharkhand	Chatra	ANIL KUMAR ORAON	4209	no	IND	Independent
Jharkhand	Kodarma	RAVINDRA KR. RAY	365410	yes	BJP	Bharatiya Janata Party
Jharkhand	Kodarma	YOGENDRA RAM	4516	no	ABJS	Akhil Bharatiya Jan Sangh
Jharkhand	Kodarma	DR. SURAJ MANDAL	6966	no	JVD	Jharkhand Vikas Dal
Jharkhand	Kodarma	SHIVANATH SAV	11117	no	IND	Independent
Jharkhand	Kodarma	KRISHNA SINGH	18727	no	JD(U)	Janata Dal  (United)
Jharkhand	Kodarma	JAIDEO CHAUDHARY	7838	no	SP	Samajwadi Party
Jharkhand	Kodarma	MD. JALIL ANSARI	4245	no	NLP	National Loktantrik Party
Jharkhand	Kodarma	MANOJ KUMAR	2873	no	IND	Independent
Jharkhand	Kodarma	VIJAY KUMAR CHOURASIYA	11880	no	AAAP	Aam Aadmi Party
Jharkhand	Kodarma	MAHENDRA RAVIDAS	6141	no	BMUP	Bahujan Mukti Party
Jharkhand	Kodarma	KANCHAN KUMARI	20669	no	IND	Independent
Jharkhand	Kodarma	PRANAV KUMAR VERMA	160638	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Kodarma	RAGHUNANDAN PRASAD VISHWAKARMA	6992	no	AIFB	All India Forward Bloc
Jharkhand	Kodarma	PHALJIT MAHTO	5996	no	IND	Independent
Jharkhand	Kodarma	RAMESHWAR PRASAD YADAV	6334	no	IND	Independent
Jharkhand	Kodarma	NAJRUL HASSAN HASHMI	25522	no	AJSUP	AJSU Party
Jharkhand	Kodarma	TILAKDHARI PD SINGH	60330	no	INC	Indian National Congress
Jharkhand	Kodarma	BISHESHWAR PRASAD	12459	no	BSP	Bahujan Samaj Party
Jharkhand	Kodarma	RAJKUMAR YADAV	266756	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Kodarma	MANJU KUMARI	12785	no	AITC	All India Trinamool Congress
Jharkhand	Kodarma	None of the Above	6712	no	NOTA	None of the Above
Jharkhand	Giridih	RAVINDRA KUMAR PANDEY	391913	yes	BJP	Bharatiya Janata Party
Jharkhand	Giridih	SHIVA MAHTO	3660	no	IND	Independent
Jharkhand	Giridih	SOHRAB SHAH	5072	no	BMUP	Bahujan Mukti Party
Jharkhand	Giridih	KARTIK MAHTO	6341	no	BSP	Bahujan Samaj Party
Jharkhand	Giridih	DILIP KR RAJAK	6406	no	IND	Independent
Jharkhand	Giridih	SAJJADUR RAB HASAMI	3512	no	IND	Independent
Jharkhand	Giridih	GURJEET SINGH	7071	no	AAAP	Aam Aadmi Party
Jharkhand	Giridih	None of the Above	4879	no	NOTA	None of the Above
Jharkhand	Giridih	JAGARNATH MAHTO	351600	no	JMM	Jharkhand Mukti Morcha
Jharkhand	Giridih	SABA AHMAD	57380	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Giridih	JALESHWAR MAHATO	40010	no	JD(U)	Janata Dal  (United)
Jharkhand	Giridih	SANJEEV KUMAR SINHA	2988	no	AIFB	All India Forward Bloc
Jharkhand	Giridih	MRINAL KANTI DEV	1997	no	SLP(L)	Socialist Party (Lohia)
Jharkhand	Giridih	ASHUTOSH VERMA	11355	no	AITC	All India Trinamool Congress
Jharkhand	Giridih	BIDESHI MAHTO	4325	no	JDP	Jharkhand Disom Party
Jharkhand	Giridih	JEETENDRA KUMAR	2825	no	SP	Samajwadi Party
Jharkhand	Giridih	SANJEEV KUMAR MAHATO	13132	no	IND	Independent
Jharkhand	Giridih	UMESH CHANDRA MEHTA	55531	no	AJSUP	AJSU Party
Jharkhand	Dhanbad	PASHUPATI NATH SINGH	543491	yes	BJP	Bharatiya Janata Party
Jharkhand	Dhanbad	RANBIR SINGH	1516	no	IND	Independent
Jharkhand	Dhanbad	DR. KRISHNA CHANDRA SINGHRAJ	13200	no	IND	Independent
Jharkhand	Dhanbad	CHANDRA SHEKHAR DUBEY	29937	no	AITC	All India Trinamool Congress
Jharkhand	Dhanbad	PARSHURAM PASWAN	1359	no	IND	Independent
Jharkhand	Dhanbad	ANAND MAHATO	110185	no	MCO	Marxist Co-Ordination
Jharkhand	Dhanbad	HEMLATA S. MOHAN	21277	no	AJSUP	AJSU Party
Jharkhand	Dhanbad	SAMARESH SINGH	90926	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Dhanbad	KISHOR KUMAR MURMU	4531	no	BMUP	Bahujan Mukti Party
Jharkhand	Dhanbad	AJAY KUMAR DUBEY	250537	no	INC	Indian National Congress
Jharkhand	Dhanbad	AKLU RAM MAHTO	4169	no	SP	Samajwadi Party
Jharkhand	Dhanbad	GANPAT MAHTO	5441	no	IND	Independent
Jharkhand	Dhanbad	RAMESH BHARATI	2052	no	IND	Independent
Jharkhand	Dhanbad	JAYANT PANDEY	2518	no	AIFB	All India Forward Bloc
Jharkhand	Dhanbad	HIRALAL SANKHWAR	8580	no	JKP	Jharkhand Party
Jharkhand	Dhanbad	PAWAN MAHATO	811	no	IND	Independent
Jharkhand	Dhanbad	RAMESH KUMAR RAY	965	no	IND	Independent
Jharkhand	Dhanbad	SUMAN BANERJEE	2104	no	IND	Independent
Jharkhand	Dhanbad	VIJAY KANT MISHRA	1537	no	IND	Independent
Jharkhand	Dhanbad	MANILAL MAHTO	1888	no	IND	Independent
Jharkhand	Dhanbad	TARCH MURMU	1190	no	IND	Independent
Jharkhand	Dhanbad	BINOD SINGH	1457	no	IND	Independent
Jharkhand	Dhanbad	MANTOSH KUMAR MANDAL	3413	no	AMB	Amra Bangalee
Jharkhand	Dhanbad	AHMAD HUSSAIN ANSARI	3120	no	JDP	Jharkhand Disom Party
Jharkhand	Dhanbad	RAMA KANT VERMA	8948	no	AAAP	Aam Aadmi Party
Jharkhand	Dhanbad	None of the Above	7675	no	NOTA	None of the Above
Jharkhand	Dhanbad	GAUTAM KUMAR MAHATO	3000	no	IND	Independent
Jharkhand	Dhanbad	MD. ZUBAIR	3094	no	AIMF	All India Minorities Front
Jharkhand	Dhanbad	MUNSHI HEMBRAM	1486	no	IND	Independent
Jharkhand	Dhanbad	RITU RANI SINGH	3814	no	IND	Independent
Jharkhand	Dhanbad	PREM PRAKASH PASWAN	1226	no	IND	Independent
Jharkhand	Dhanbad	MADAN MOHAN RAM	8455	no	BSP	Bahujan Samaj Party
Jharkhand	Ranchi	RAM TAHAL CHOUDHARY	448729	yes	BJP	Bharatiya Janata Party
Jharkhand	Ranchi	SAJIYA HAIDAR	2195	no	IND	Independent
Jharkhand	Ranchi	MAN SINGH MARDI	4240	no	JKP(N)	Jharkhand Party  (Naren)
Jharkhand	Ranchi	THADDEUS LAKRA	1504	no	IND	Independent
Jharkhand	Ranchi	VIKAS CHANDRA SHARMA	14898	no	CPIM	Communist Party of India (Marxist-Leninist) Red Star
Jharkhand	Ranchi	AMIT KUMAR SINGH	1987	no	IND	Independent
Jharkhand	Ranchi	BALRAM KUMAR BEDIA	2889	no	IND	Independent
Jharkhand	Ranchi	RANJIT MAHTO	3823	no	BMUP	Bahujan Mukti Party
Jharkhand	Ranchi	SURESH TOPPO	4085	no	JDP	Jharkhand Disom Party
Jharkhand	Ranchi	None of the Above	6900	no	NOTA	None of the Above
Jharkhand	Ranchi	AMANULLAH	8497	no	AAAP	Aam Aadmi Party
Jharkhand	Ranchi	LAL JOTINDRA DEV	3411	no	AKBJHP	Akhil Bhartiya Jharkhand Party
Jharkhand	Ranchi	BISHESHWAR MAHTO	2130	no	SAP	Samata Party
Jharkhand	Ranchi	"YUGESHWAR MARAR ""DEEN"""	4409	no	MMM	Manav Mukti Morcha
Jharkhand	Ranchi	BIRENDRA KUMAR JAISWAL	1366	no	IND	Independent
Jharkhand	Ranchi	DURGA MUNDA	2502	no	BSP	Bahujan Samaj Party
Jharkhand	Ranchi	BANDHU TIRKEY	46126	no	AITC	All India Trinamool Congress
Jharkhand	Ranchi	BAHADUR  URAON	3877	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Ranchi	RAMPODO MAHTO	1296	no	IND	Independent
Jharkhand	Ranchi	ABUL HASAN	1219	no	IND	Independent
Jharkhand	Ranchi	RAM LAL MAHTO	4190	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Jharkhand	Ranchi	KAFILUR RAHMAN	4696	no	IND	Independent
Jharkhand	Ranchi	RAJENDRA SINGH MUNDA	10178	no	CPM	Communist Party of India  (Marxist)
Jharkhand	Ranchi	ARSAD AYUB	2083	no	IND	Independent
Jharkhand	Ranchi	CHINTAMANI MAHTO	1605	no	IND	Independent
Jharkhand	Ranchi	SUBODH KANT SAHAY	249426	no	INC	Indian National Congress
Jharkhand	Ranchi	ANJANI PANDEY	1250	no	IND	Independent
Jharkhand	Ranchi	AMITABH CHOUDHARY	67712	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Ranchi	SUDESH KUMAR MAHTO	142560	no	AJSUP	AJSU Party
Jharkhand	Jamshedpur	BIDYUT BARAN MAHATO	464153	yes	BJP	Bharatiya Janata Party
Jharkhand	Jamshedpur	AJAY KUMAR	364277	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Jamshedpur	SEKH AKHIRUDDIN	4822	no	JKP(N)	Jharkhand Party  (Naren)
Jharkhand	Jamshedpur	UPENDRA KARMAKAR	4429	no	IND	Independent
Jharkhand	Jamshedpur	ANGAD MAHATO	12632	no	AMB	Amra Bangalee
Jharkhand	Jamshedpur	MOBIN KHAN	5406	no	IND	Independent
Jharkhand	Jamshedpur	HARI ORAWN	4485	no	IND	Independent
Jharkhand	Jamshedpur	OM PRAKASH ANAND	5655	no	IND	Independent
Jharkhand	Jamshedpur	NIROOP MAHANTY	138109	no	JMM	Jharkhand Mukti Morcha
Jharkhand	Jamshedpur	None of the Above	15629	no	NOTA	None of the Above
Jharkhand	Jamshedpur	D.G.RAJA	3414	no	SP	Samajwadi Party
Jharkhand	Jamshedpur	DAMAN CHANDRA BHAKAT	4606	no	BSP	Bahujan Samaj Party
Jharkhand	Jamshedpur	KUMAR CHANDRA MARDI	7145	no	AAAP	Aam Aadmi Party
Jharkhand	Jamshedpur	JOSAI MARDI	5378	no	RADP	Rashtriya Deshaj Party
Jharkhand	Jamshedpur	SITA RAM TUDU	3700	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
Jharkhand	Jamshedpur	DINESH MAHATO	5300	no	IND	Independent
Jharkhand	Singhbhum	LAXMAN GILUWA	303131	yes	BJP	Bharatiya Janata Party
Jharkhand	Singhbhum	RAJENDRA PURTY	11507	no	JKP	Jharkhand Party
Jharkhand	Singhbhum	SNEHLATA JOJO	12877	no	IND	Independent
Jharkhand	Singhbhum	NAVEEN MURMU	8607	no	RADP	Rashtriya Deshaj Party
Jharkhand	Singhbhum	RUPSINGH HEMBROM	8895	no	IND	Independent
Jharkhand	Singhbhum	None of the Above	27037	no	NOTA	None of the Above
Jharkhand	Singhbhum	BUDHRAM LAGURI	8346	no	BMUP	Bahujan Mukti Party
Jharkhand	Singhbhum	MANOHAR SINKU	9303	no	BSP	Bahujan Samaj Party
Jharkhand	Singhbhum	SALKHAN MURMU	25547	no	JDP	Jharkhand Disom Party
Jharkhand	Singhbhum	CHITRASEN SINKU	111796	no	INC	Indian National Congress
Jharkhand	Singhbhum	DASHRATH GAGRAI	35681	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Singhbhum	JOHN MIRAN MUNDA	16952	no	AIFB	All India Forward Bloc
Jharkhand	Singhbhum	GEETA KORA	215607	no	JBSP	Jai Bharat Samanta Party
Jharkhand	Khunti	KARIA MUNDA	269185	yes	BJP	Bharatiya Janata Party
Jharkhand	Khunti	BUDAY MUNDA	4841	no	AKBJHP	Akhil Bhartiya Jharkhand Party
Jharkhand	Khunti	KALI CHARAN MUNDA	147017	no	INC	Indian National Congress
Jharkhand	Khunti	ANOSH EKKA	176937	no	JKP	Jharkhand Party
Jharkhand	Khunti	SUBODH PURTY	6407	no	BSP	Bahujan Samaj Party
Jharkhand	Khunti	NEIL TIRKEY	27158	no	AJSUP	AJSU Party
Jharkhand	Khunti	JITENDRA MANKI  ALIAS JITENDRA PRASAD MANKI	6596	no	IND	Independent
Jharkhand	Khunti	BASANT KUMAR LONGA	24514	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Khunti	JAITUN TUTI	11364	no	IND	Independent
Jharkhand	Khunti	DAYAMANI BARLA	11822	no	AAAP	Aam Aadmi Party
Jharkhand	Khunti	ASRITA TUTI	4349	no	IND	Independent
Jharkhand	Khunti	None of the Above	23816	no	NOTA	None of the Above
Jharkhand	Khunti	KALYAN NAG	4958	no	IND	Independent
Jharkhand	Khunti	NITIMA BODRA BARI	4425	no	SP	Samajwadi Party
Jharkhand	Khunti	MAHADEV RAVINATH PAHAN	13566	no	RADP	Rashtriya Deshaj Party
Jharkhand	Lohardaga	SUDARSHAN BHAGAT	226666	yes	BJP	Bharatiya Janata Party
Jharkhand	Lohardaga	None of the Above	16764	no	NOTA	None of the Above
Jharkhand	Lohardaga	RAMESHWAR ORAON	220177	no	INC	Indian National Congress
Jharkhand	Lohardaga	MAHENDRA  ORAON	6436	no	IND	Independent
Jharkhand	Lohardaga	RANJIT ORAON	6037	no	IND	Independent
Jharkhand	Lohardaga	JAYRAM INDWAR	11794	no	BSP	Bahujan Samaj Party
Jharkhand	Lohardaga	NAWAL KISHOR SINGH	5870	no	IND	Independent
Jharkhand	Lohardaga	LAL SAY KUMAR BHAGAT	13252	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Lohardaga	CHAMRA LINDA	118355	no	AITC	All India Trinamool Congress
Jharkhand	Lohardaga	BIRENDRA BHAGAT	26109	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Palamau	VISHNU DAYAL RAM	476513	yes	BJP	Bharatiya Janata Party
Jharkhand	Palamau	SHUSHMA MEHTA	8386	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Palamau	LALDEO RAM	4432	no	BMUP	Bahujan Mukti Party
Jharkhand	Palamau	RAMPATI RANJAN	20481	no	BSP	Bahujan Samaj Party
Jharkhand	Palamau	GHURAN RAM	156832	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Palamau	KAMESHWAR BAITHA	37043	no	AITC	All India Trinamool Congress
Jharkhand	Palamau	SHYAM LAL RAM	9592	no	IND	Independent
Jharkhand	Palamau	ASHOK  RAM	5648	no	IND	Independent
Jharkhand	Palamau	JORAWAR RAM	7538	no	JD(U)	Janata Dal  (United)
Jharkhand	Palamau	MANOJ KUMAR	212571	no	RJD	Rashtriya Janata Dal
Jharkhand	Palamau	RAM BADAN RAM	4976	no	SP	Samajwadi Party
Jharkhand	Palamau	VINOD RAM	5879	no	IND	Independent
Jharkhand	Palamau	RAVINDRA KUMAR RAVI	9145	no	IND	Independent
Jharkhand	Palamau	None of the Above	18287	no	NOTA	None of the Above
Jharkhand	Hazaribagh	JAYANT SINHA	406931	yes	BJP	Bharatiya Janata Party
Jharkhand	Hazaribagh	None of the Above	6827	no	NOTA	None of the Above
Jharkhand	Hazaribagh	BHUVNESHWAR PRASAD MEHTA	30326	no	CPI	Communist Party of India
Jharkhand	Hazaribagh	ARUN KUMAR MISHRA	30408	no	JVM	Jharkhand Vikas Morcha (Prajatantrik)
Jharkhand	Hazaribagh	YASHPAL YADAV	3540	no	IND	Independent
Jharkhand	Hazaribagh	SAURABH NARAIN SINGH	247803	no	INC	Indian National Congress
Jharkhand	Hazaribagh	TETRI @ SHEELA DEVI	8159	no	BSP	Bahujan Samaj Party
Jharkhand	Hazaribagh	MO. SHANUL HAQUE	3189	no	IUML	Indian Union Muslim League
Jharkhand	Hazaribagh	MITHILESH KUMAR DANGI	7140	no	AAAP	Aam Aadmi Party
Jharkhand	Hazaribagh	RAMAVTAR MAHTO	3365	no	IND	Independent
Jharkhand	Hazaribagh	PRAMOD KUMAR MAHTHA	3076	no	pms	Pragatisheel Magahi Samaj
Jharkhand	Hazaribagh	RAMESHWAR RAM KUSHWAHA	4423	no	AIFB	All India Forward Bloc
Jharkhand	Hazaribagh	JAVED ISLAM	8453	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Jharkhand	Hazaribagh	LOKNATH MAHTO	156186	no	AJSUP	AJSU Party
Jharkhand	Hazaribagh	MAHENDRA KISHOR MEHTA	3752	no	IND	Independent
Jharkhand	Hazaribagh	DR. DHIRENDRA KUMAR RAJ	4481	no	IND	Independent
Jharkhand	Hazaribagh	ABDUL RAHIM	2941	no	BMUP	Bahujan Mukti Party
Jharkhand	Hazaribagh	AJIT KUMAR SINGH	8360	no	AITC	All India Trinamool Congress
Jharkhand	Hazaribagh	PRAYAG PRASAD	8085	no	IND	Independent
Jharkhand	Hazaribagh	BADRI GOPE	14555	no	IND	Independent
Jharkhand	Hazaribagh	BALDEO GANJHU	5152	no	IND	Independent
Uttarakhand	Tehri Garhwal	MALA RAJYA LAXMI SHAH	446733	yes	BJP	Bharatiya Janata Party
Uttarakhand	Tehri Garhwal	None of the Above	10762	no	NOTA	None of the Above
Uttarakhand	Tehri Garhwal	JAI PRAKASH UPADHYAY	1619	no	IND	Independent
Uttarakhand	Tehri Garhwal	PREM SINGH YADAV	2007	no	IND	Independent
Uttarakhand	Tehri Garhwal	GAURAV PUNDIR	2302	no	IND	Independent
Uttarakhand	Tehri Garhwal	JAUHARI LAL SUMAN(MUNI)	877	no	BMNSP	Bharatiya Mool Niwasi Samaj Party
Uttarakhand	Tehri Garhwal	HAJI MOHD. YOUSUF	900	no	PECP	Peace Party
Uttarakhand	Tehri Garhwal	SOHAN PAL SINGH	6672	no	IND	Independent
Uttarakhand	Tehri Garhwal	RAJU MAURYA	1935	no	IND	Independent
Uttarakhand	Tehri Garhwal	SHISHPAL SINGH	11538	no	BSP	Bahujan Samaj Party
Uttarakhand	Tehri Garhwal	ANOOP NAUTIYAL	24043	no	AAAP	Aam Aadmi Party
Uttarakhand	Tehri Garhwal	NAVNEET SINGH GOSAIN	1629	no	raup	Rashtriya Uttarakhand Party
Uttarakhand	Tehri Garhwal	SAKET BAHUGUNA	254230	no	INC	Indian National Congress
Uttarakhand	Tehri Garhwal	SHIV PRASAD DEOLI	6577	no	CPM	Communist Party of India  (Marxist)
Uttarakhand	Tehri Garhwal	BARHM DEV JHA	1554	no	IND	Independent
Uttarakhand	Tehri Garhwal	JAPINDER SINGH	1958	no	RLD	Rashtriya Lok Dal
Uttarakhand	Tehri Garhwal	SATENDRA SHARMA	878	no	NADP	Naya Daur Party
Uttarakhand	Garhwal	(MAJ GEN (RETD.) ) BHUWAN CHANDRA KHANDURI (AVSM)	405690	yes	BJP	Bharatiya Janata Party
Uttarakhand	Garhwal	INDRESH MAIKHURI	7470	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttarakhand	Garhwal	(DR) RAKESH SINGH	6727	no	AAAP	Aam Aadmi Party
Uttarakhand	Garhwal	NARENDRA SINGH NEGI	6525	no	CPI	Communist Party of India
Uttarakhand	Garhwal	RENU AGARWAL	3051	no	IND	Independent
Uttarakhand	Garhwal	DHEER SINGH BISHT	9250	no	BSP	Bahujan Samaj Party
Uttarakhand	Garhwal	ABDUL SALAM	4510	no	IND	Independent
Uttarakhand	Garhwal	BHUPAL SINGH GUSAIN (UKD)	6699	no	IND	Independent
Uttarakhand	Garhwal	ANUP NEGI	2279	no	IND	Independent
Uttarakhand	Garhwal	(DR) HARAK SINGH RAWAT	221164	no	INC	Indian National Congress
Uttarakhand	Garhwal	None of the Above	8659	no	NOTA	None of the Above
Uttarakhand	Almora	AJAY TAMTA	348186	yes	BJP	Bharatiya Janata Party
Uttarakhand	Almora	None of the Above	15245	no	NOTA	None of the Above
Uttarakhand	Almora	BANSI RAM	3857	no	IND	Independent
Uttarakhand	Almora	AMAR PRAKASH	2045	no	UPP	UTTARAKHAND PARIVARTAN PARTY
Uttarakhand	Almora	SAJJAN LAL TAMTA	8387	no	IND	Independent
Uttarakhand	Almora	VIJAY KUMAR	2392	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttarakhand	Almora	HARISH CHANDRA ARYA	7916	no	AAAP	Aam Aadmi Party
Uttarakhand	Almora	BAHADUR RAM DHAUNI	14150	no	BSP	Bahujan Samaj Party
Uttarakhand	Almora	BHAGAWATI PRASAD TRIKOTI	1851	no	SP	Samajwadi Party
Uttarakhand	Almora	PRADEEP TAMTA	252496	no	INC	Indian National Congress
Uttarakhand	Nainital-udhamsingh Nagar	BHAGAT SINGH KOSHYARI	636769	yes	BJP	Bharatiya Janata Party
Uttarakhand	Nainital-udhamsingh Nagar	LAIK AHMED	59245	no	BSP	Bahujan Samaj Party
Uttarakhand	Nainital-udhamsingh Nagar	KHASHTI SUYAL	802	no	BCP	Bhartiya Chaitanya Party
Uttarakhand	Nainital-udhamsingh Nagar	ASHARAM KASHYAP	1037	no	RMGP	Rashtriya mahan Gantantra Party
Uttarakhand	Nainital-udhamsingh Nagar	BHUWAN CHANDRA JOSHI	3180	no	IND	Independent
Uttarakhand	Nainital-udhamsingh Nagar	SHEESHPALARYA	2706	no	ANC	Ambedkar National Congress
Uttarakhand	Nainital-udhamsingh Nagar	BALLI SINGH CHEEMA	13472	no	AAAP	Aam Aadmi Party
Uttarakhand	Nainital-udhamsingh Nagar	D.S.RAWAL	3392	no	BKLJP	Bharat Ki Lok Jimmedar Party
Uttarakhand	Nainital-udhamsingh Nagar	KAILASH PANDEY	1530	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Uttarakhand	Nainital-udhamsingh Nagar	KAILASH CHANDRA	1635	no	IND	Independent
Uttarakhand	Nainital-udhamsingh Nagar	AVTAR SINGH	9825	no	SP	Samajwadi Party
Uttarakhand	Nainital-udhamsingh Nagar	K.C.SINGH BABA	352052	no	INC	Indian National Congress
Uttarakhand	Nainital-udhamsingh Nagar	RAM SINGH	1064	no	PECP	Peace Party
Uttarakhand	Nainital-udhamsingh Nagar	SHAKIL AHMED	2529	no	IND	Independent
Uttarakhand	Nainital-udhamsingh Nagar	BHAGWAN SWAROOP	1869	no	IND	Independent
Uttarakhand	Nainital-udhamsingh Nagar	None of the Above	10328	no	NOTA	None of the Above
Uttarakhand	Hardwar	RAMESH POKHRIYAL NISHANK	592320	yes	BJP	Bharatiya Janata Party
Uttarakhand	Hardwar	None of the Above	3049	no	NOTA	None of the Above
Uttarakhand	Hardwar	RAZIA BEG	3741	no	IND	Independent
Uttarakhand	Hardwar	PRAMOD RANA	765	no	IND	Independent
Uttarakhand	Hardwar	ANAND KUMAR VERMA	710	no	HKRD	Hindustan Krantikari Dal
Uttarakhand	Hardwar	SANJAY GOYAL	915	no	NADP	Naya Daur Party
Uttarakhand	Hardwar	VISHAL CHAUDHARY	1043	no	SHS	Shivsena
Uttarakhand	Hardwar	D.D.SHARMA	2067	no	IND	Independent
Uttarakhand	Hardwar	V. K. KAMPANI	637	no	PHRC	Poorvanchal Rashtriya Congress
Uttarakhand	Hardwar	ABDUL ALEEM	1682	no	IND	Independent
Uttarakhand	Hardwar	ISHWAR CHAND	3740	no	IND	Independent
Uttarakhand	Hardwar	RAMNARESH YADAV	1117	no	kajp	Kalyankari Jantantrik Party
Uttarakhand	Hardwar	PERVEZ AQIL	454	no	AIMF	All India Minorities Front
Uttarakhand	Hardwar	SUNIL	820	no	IND	Independent
Uttarakhand	Hardwar	MUKESH SAINI	664	no	BMUP	Bahujan Mukti Party
Uttarakhand	Hardwar	RAKESH	1015	no	IND	Independent
Uttarakhand	Hardwar	BRIGADIER GOVIND PRASAD BARTHWAL VSM	1888	no	IND	Independent
Uttarakhand	Hardwar	ASRAF	2535	no	IND	Independent
Uttarakhand	Hardwar	ANITA SAINI	5765	no	SP	Samajwadi Party
Uttarakhand	Hardwar	JAI SINGH	2769	no	IND	Independent
Uttarakhand	Hardwar	MOHAR SINGH KATARIA	1051	no	IND	Independent
Uttarakhand	Hardwar	JAVED	614	no	ABML(S)	Akhil Bharatiya Muslim League (Secular)
Uttarakhand	Hardwar	KANCHAN CHOUDHRY	18170	no	AAAP	Aam Aadmi Party
Uttarakhand	Hardwar	RENUKA RAWAT	414498	no	INC	Indian National Congress
Uttarakhand	Hardwar	HAJI MOHD ISLAM	113663	no	BSP	Bahujan Samaj Party
Andaman & Nicobar Islands	Andaman & Nicobar Islands	BISHNU PADA RAY	90969	yes	BJP	Bharatiya Janata Party
Andaman & Nicobar Islands	Andaman & Nicobar Islands	None of the Above	1564	no	NOTA	None of the Above
Andaman & Nicobar Islands	Andaman & Nicobar Islands	PRAKASH MINJ	1587	no	IND	Independent
Andaman & Nicobar Islands	Andaman & Nicobar Islands	ANITA MONDAL	2283	no	AITC	All India Trinamool Congress
Andaman & Nicobar Islands	Andaman & Nicobar Islands	SARAVANAN	847	no	IND	Independent
Andaman & Nicobar Islands	Andaman & Nicobar Islands	N K P NAIR	436	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Andaman & Nicobar Islands	Andaman & Nicobar Islands	V V KHALID	429	no	IND	Independent
Andaman & Nicobar Islands	Andaman & Nicobar Islands	GOUR CHANDRA MAJUMDER	1139	no	BSP	Bahujan Samaj Party
Andaman & Nicobar Islands	Andaman & Nicobar Islands	R.S.UMA BHARATTHY	1151	no	NCP	Nationalist Congress Party
Andaman & Nicobar Islands	Andaman & Nicobar Islands	KULDEEP RAI SHARMA	83157	no	INC	Indian National Congress
Andaman & Nicobar Islands	Andaman & Nicobar Islands	SANJAY MESHACK	3737	no	AAAP	Aam Aadmi Party
Andaman & Nicobar Islands	Andaman & Nicobar Islands	RAGHUBIR SINGH	379	no	SP	Samajwadi Party
Andaman & Nicobar Islands	Andaman & Nicobar Islands	A PANDIAN	225	no	AIFB	All India Forward Bloc
Andaman & Nicobar Islands	Andaman & Nicobar Islands	N KANNAN	314	no	IND	Independent
Andaman & Nicobar Islands	Andaman & Nicobar Islands	C G SAJI KUMAR	334	no	IND	Independent
Andaman & Nicobar Islands	Andaman & Nicobar Islands	K.G.DAS	1777	no	CPM	Communist Party of India  (Marxist)
Chandigarh	CHANDIGARH	KHER KIRRON ANUPAM	191362	yes	BJP	Bharatiya Janata Party
Chandigarh	CHANDIGARH	RAMESH CHANDER	743	no	IND	Independent
Chandigarh	CHANDIGARH	REENA SHARMA	2643	no	IND	Independent
Chandigarh	CHANDIGARH	SURENDER KUMAR	550	no	BMUP	Bahujan Mukti Party
Chandigarh	CHANDIGARH	PAWAN KUMAR SHARMA	997	no	IND	Independent
Chandigarh	CHANDIGARH	SHAMSHER SINGH	2054	no	IND	Independent
Chandigarh	CHANDIGARH	SURINDER BHARDWAJ	434	no	JD(U)	Janata Dal  (United)
Chandigarh	CHANDIGARH	JASMER SINGH	389	no	IND	Independent
Chandigarh	CHANDIGARH	DEEPAK SHUKLA	613	no	IND	Independent
Chandigarh	CHANDIGARH	PAWAN KUMAR BANSAL	121720	no	INC	Indian National Congress
Chandigarh	CHANDIGARH	JANNAT JAHAN-UL-HAQ (NAINA)	15934	no	BSP	Bahujan Samaj Party
Chandigarh	CHANDIGARH	DHARMENDER SINGH	566	no	HEP	Hindustan Ekta Party
Chandigarh	CHANDIGARH	AJAY SINGLA	660	no	IND	Independent
Chandigarh	CHANDIGARH	KHEM LAL BANSAL	640	no	IVD	Inqalab Vikas Dal
Chandigarh	CHANDIGARH	AMARJIT KAUR	397	no	IND	Independent
Chandigarh	CHANDIGARH	GULKIRAT KAUR PANAG	108679	no	AAAP	Aam Aadmi Party
Chandigarh	CHANDIGARH	KAWALJIT SINGH	1968	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Chandigarh	CHANDIGARH	None of the Above	3106	no	NOTA	None of the Above
Dadra & Nagar Haveli	Dadar & Nagar Haveli	PATEL NATUBHAI GOMANBHAI	80790	yes	BJP	Bharatiya Janata Party
Dadra & Nagar Haveli	Dadar & Nagar Haveli	KAMUBEN KHALPABHAI PATEL	418	no	IND	Independent
Dadra & Nagar Haveli	Dadar & Nagar Haveli	PRAKASH VANSHA KHANVELKAR	1214	no	IND	Independent
Dadra & Nagar Haveli	Dadar & Nagar Haveli	BHURKUD DILIPBHAI NAVJIBHAI	1154	no	IND	Independent
Dadra & Nagar Haveli	Dadar & Nagar Haveli	KHULAT JANIYABHAI CHANDUBHAI	1611	no	NCP	Nationalist Congress Party
Dadra & Nagar Haveli	Dadar & Nagar Haveli	DELKAR MOHANBHAI SANJIBHAI	74576	no	INC	Indian National Congress
Dadra & Nagar Haveli	Dadar & Nagar Haveli	PATEL HEMANTBHAI KESHAVBHAI	982	no	BSP	Bahujan Samaj Party
Dadra & Nagar Haveli	Dadar & Nagar Haveli	KISHAN NATHU GHUTIYA	378	no	SHS	Shivsena
Dadra & Nagar Haveli	Dadar & Nagar Haveli	PATEL CHHOTUBHAI BABUBHAI	621	no	AAAP	Aam Aadmi Party
Dadra & Nagar Haveli	Dadar & Nagar Haveli	VIJAYBHAI NATHUBHAI BHOYA	232	no	JD(U)	Janata Dal  (United)
Dadra & Nagar Haveli	Dadar & Nagar Haveli	UMEDBHAI LALLUBHAI PATEL	348	no	BMUP	Bahujan Mukti Party
Dadra & Nagar Haveli	Dadar & Nagar Haveli	None of the Above	2962	no	NOTA	None of the Above
Daman & Diu	Daman & diu	PATEL LALUBHAI BABUBHAI	46960	yes	BJP	Bharatiya Janata Party
Daman & Diu	Daman & diu	None of the Above	1316	no	NOTA	None of the Above
Daman & Diu	Daman & diu	BHAVESH PATEL	490	no	BSP	Bahujan Samaj Party
Daman & Diu	Daman & diu	KESSUR GOAN	729	no	AAAP	Aam Aadmi Party
Daman & Diu	Daman & diu	KETAN DAHYABHAI PATEL	37738	no	INC	Indian National Congress
NCT OF Delhi	CHANDNI CHOWK	DR. HARSH VARDHAN	437938	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	CHANDNI CHOWK	MD. AFAQ	251	no	JMBP	Jai Maha Bharath Party
NCT OF Delhi	CHANDNI CHOWK	ABDUL AMIR AMIRO	1765	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	DILDAR HUSSAIN BEG	28605	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	VIJAY SHANKER CHAUBEY	1829	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	GAGAN RASTOGI	1424	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	SURENDER SINGH	789	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	BIR SINGH SONI	881	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	MUSARRAT JAHAN	592	no	RJ	Rashtriya Janmorcha
NCT OF Delhi	CHANDNI CHOWK	MOHD. MURSALEEM	538	no	PECP	Peace Party
NCT OF Delhi	CHANDNI CHOWK	ASHUTOSH	301618	no	AAAP	Aam Aadmi Party
NCT OF Delhi	CHANDNI CHOWK	HARIOM SHARMA	3531	no	AITC	All India Trinamool Congress
NCT OF Delhi	CHANDNI CHOWK	NARENDRA KUMAR PANDEY	7746	no	BSP	Bahujan Samaj Party
NCT OF Delhi	CHANDNI CHOWK	BALRAM BARI	602	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	JAGMOHAN SINGH BAKSHI	1553	no	ATBP	Atulya Bharat Party
NCT OF Delhi	CHANDNI CHOWK	DR. TARUN KUMAR	263	no	VSP	Vishva SHakti Party
NCT OF Delhi	CHANDNI CHOWK	ASHUTOSH	4504	no	IND	Independent
NCT OF Delhi	CHANDNI CHOWK	DHAN RAJ CHAUHAN	308	no	NADP	Naya Daur Party
NCT OF Delhi	CHANDNI CHOWK	ALTAF HUSAIN	272	no	bjdi	Bhartiya Janta Dal (Integrated)
NCT OF Delhi	CHANDNI CHOWK	AJAY KUMAR KHEMKA	1643	no	kajp	Kalyankari Jantantrik Party
NCT OF Delhi	CHANDNI CHOWK	KAPIL SIBAL	176206	no	INC	Indian National Congress
NCT OF Delhi	CHANDNI CHOWK	TARIQ MIRZA	253	no	RPI(A)	Republican Party of India (A)
NCT OF Delhi	CHANDNI CHOWK	JAGJEET SINGH	290	no	BGTD	Bharatiya Gaon Taj Dal
NCT OF Delhi	CHANDNI CHOWK	RASHMI KASHYAP	538	no	BPC	Bhartiya Pragatisheel Congress
NCT OF Delhi	CHANDNI CHOWK	SANJAY GANDHI	3390	no	BRAVP	Braj Vikas Party
NCT OF Delhi	CHANDNI CHOWK	None of the Above	4534	no	NOTA	None of the Above
NCT OF Delhi	NORTH EAST DELHI	MANOJ TIWARI	596125	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	NORTH EAST DELHI	ANAND KUMAR	452041	no	AAAP	Aam Aadmi Party
NCT OF Delhi	NORTH EAST DELHI	ABDUL SAMI SALMANI	28135	no	BSP	Bahujan Samaj Party
NCT OF Delhi	NORTH EAST DELHI	A.K. AGGARWAL	3191	no	AGRJP	Agar Jan Party
NCT OF Delhi	NORTH EAST DELHI	PROF. NARENDERA SHARMA	508	no	SUCI	SOCIALIST UNITY CENTRE OF INDIA (COMMUNIST)
NCT OF Delhi	NORTH EAST DELHI	DINESH PAL SINGH	1263	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	DAN BAHADUR YADAV	1921	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	JAI PARKASH AGARWAL	214792	no	INC	Indian National Congress
NCT OF Delhi	NORTH EAST DELHI	DHARAMVEER SINGH	601	no	REP	Rashtriya Ekta Party
NCT OF Delhi	NORTH EAST DELHI	MUKESH KUMAR ARORA	1669	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	ALI HUSAN	1695	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	MOHD. HASNAIN	879	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	VINAY	818	no	BSKP	Bharatiya Sarvodaya Kranti Party
NCT OF Delhi	NORTH EAST DELHI	HARSH KUMAR	663	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	RAJAN LAL	504	no	BRPP	Bharatiya Republican Paksha
NCT OF Delhi	NORTH EAST DELHI	RAMESH SINGH SIRAL	465	no	NADP	Naya Daur Party
NCT OF Delhi	NORTH EAST DELHI	OM DUTT SHARMA	658	no	SHS	Shivsena
NCT OF Delhi	NORTH EAST DELHI	MOHD. ARIF SIDDIQUE	784	no	AITC	All India Trinamool Congress
NCT OF Delhi	NORTH EAST DELHI	PRADESH KUMAR	489	no	ANC	Ambedkar National Congress
NCT OF Delhi	NORTH EAST DELHI	H.K. MAHENDRU	2419	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	ROSHAN ALI	745	no	NLP	National Loktantrik Party
NCT OF Delhi	NORTH EAST DELHI	AMIT KUMAR SHARMA	1892	no	IND	Independent
NCT OF Delhi	NORTH EAST DELHI	MOHD. HASNAN KHAN	1257	no	JKNPP	Jammu & Kashmir National Panthers Party
NCT OF Delhi	NORTH EAST DELHI	None of the Above	3824	no	NOTA	None of the Above
NCT OF Delhi	EAST DELHI	MAHEISH GIRRI	572202	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	EAST DELHI	None of the Above	4975	no	NOTA	None of the Above
NCT OF Delhi	EAST DELHI	RAJESH	422	no	ANC	Ambedkar National Congress
NCT OF Delhi	EAST DELHI	MOHAMMAD SHAKEEL SAIFI	18575	no	BSP	Bahujan Samaj Party
NCT OF Delhi	EAST DELHI	DEEPAK KUMAR	674	no	IND	Independent
NCT OF Delhi	EAST DELHI	DR. NABHIT KAPUR	210	no	NADP	Naya Daur Party
NCT OF Delhi	EAST DELHI	JAGANNATH TIWARI	1153	no	IND	Independent
NCT OF Delhi	EAST DELHI	VIRENDRA MAYER	395	no	SJP(R)	Samajwadi Janata Party (Rashtriya)
NCT OF Delhi	EAST DELHI	MOHD NASEER	339	no	PECP	Peace Party
NCT OF Delhi	EAST DELHI	ARUN THAKUR	1708	no	SPP	Samyak Parivartan Party
NCT OF Delhi	EAST DELHI	RAM BRIKSH MALL	527	no	HND	Hindusthan Nirman Dal
NCT OF Delhi	EAST DELHI	PRAVEEN KUMAR	325	no	BJM	Bhartiya Jan Manch
NCT OF Delhi	EAST DELHI	SHAKEEL AHMED	1515	no	JMBP	Jai Maha Bharath Party
NCT OF Delhi	EAST DELHI	MOHD SHAHID SIDDIQUI	3894	no	AITC	All India Trinamool Congress
NCT OF Delhi	EAST DELHI	SANDEEP DIKSHIT	203240	no	INC	Indian National Congress
NCT OF Delhi	EAST DELHI	OMPAL SINGH	349	no	kajp	Kalyankari Jantantrik Party
NCT OF Delhi	EAST DELHI	RAJMOHAN GANDHI	381739	no	AAAP	Aam Aadmi Party
NCT OF Delhi	EAST DELHI	PREM SINGH	761	no	SP(I)	Socialist Party (India)
NCT OF Delhi	EAST DELHI	MOHAMMED NAEEM	466	no	IND	Independent
NCT OF Delhi	EAST DELHI	MANJU CHHIBBER	402	no	RPI(A)	Republican Party of India (A)
NCT OF Delhi	EAST DELHI	PADAM CHAND	404	no	IND	Independent
NCT OF Delhi	EAST DELHI	KUMAR VIVEK GAUTAM	978	no	IND	Independent
NCT OF Delhi	EAST DELHI	MANJEET SINGH	415	no	IND	Independent
NCT OF Delhi	EAST DELHI	JAI RAM LAL	668	no	JKNPP	Jammu & Kashmir National Panthers Party
NCT OF Delhi	NEW DELHI	MEENAKASHI LEKHI	453350	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	NEW DELHI	ASHISH KHETAN	290642	no	AAAP	Aam Aadmi Party
NCT OF Delhi	NEW DELHI	SWADESH OHRI	582	no	ATBP	Atulya Bharat Party
NCT OF Delhi	NEW DELHI	NAVEEN CHANDRA	199	no	NADP	Naya Daur Party
NCT OF Delhi	NEW DELHI	VISHAL KHOSLA	298	no	IND	Independent
NCT OF Delhi	NEW DELHI	AJAY MAKAN	182893	no	INC	Indian National Congress
NCT OF Delhi	NEW DELHI	LUKMAN KHAN	203	no	IND	Independent
NCT OF Delhi	NEW DELHI	SOLOMON GEORGE	6088	no	BSP	Bahujan Samaj Party
NCT OF Delhi	NEW DELHI	NIKHIL SABLANIA	444	no	IND	Independent
NCT OF Delhi	NEW DELHI	K P SANKARAN MENON	499	no	IND	Independent
NCT OF Delhi	NEW DELHI	DAL CHAND	662	no	IND	Independent
NCT OF Delhi	NEW DELHI	SUJEET JHA	388	no	bjdi	Bhartiya Janta Dal (Integrated)
NCT OF Delhi	NEW DELHI	ANJUMAN AGNIHOTRI	887	no	IND	Independent
NCT OF Delhi	NEW DELHI	GHANSHYAM DASS	862	no	IND	Independent
NCT OF Delhi	NEW DELHI	PADMAJA KANDUKURI	213	no	IND	Independent
NCT OF Delhi	NEW DELHI	BISWAJIT RANJIT CHATTERJEE	909	no	AITC	All India Trinamool Congress
NCT OF Delhi	NEW DELHI	PRADEEP VARMA	161	no	BVLP	Bharat Vishal Party
NCT OF Delhi	NEW DELHI	LAKSHMI NARAYAN	366	no	BHBP	Bharatiya Bahujan Party
NCT OF Delhi	NEW DELHI	DEVI SINGH	217	no	RaJPa	Rashtriya Jankranti Party
NCT OF Delhi	NEW DELHI	HARKRISHAN DAS NIJHAWAN	429	no	IND	Independent
NCT OF Delhi	NEW DELHI	DHEERAJ PURI	347	no	SHS	Shivsena
NCT OF Delhi	NEW DELHI	MEENA SINGLA	205	no	PHRC	Poorvanchal Rashtriya Congress
NCT OF Delhi	NEW DELHI	SUNITA CHAUDHARY	528	no	JKNPP	Jammu & Kashmir National Panthers Party
NCT OF Delhi	NEW DELHI	NARENDRA SINGH RAWAT	687	no	IND	Independent
NCT OF Delhi	NEW DELHI	RAMANUJ PATEL	158	no	SPP	Samyak Parivartan Party
NCT OF Delhi	NEW DELHI	RATHEESH	532	no	IND	Independent
NCT OF Delhi	NEW DELHI	RUBINA KHAN	1260	no	IND	Independent
NCT OF Delhi	NEW DELHI	VED PRAKASH	186	no	IND	Independent
NCT OF Delhi	NEW DELHI	RAVI KUMAR GUPTA	20028	no	IND	Independent
NCT OF Delhi	NEW DELHI	None of the Above	5589	no	NOTA	None of the Above
NCT OF Delhi	NORTH WEST DELHI	UDIT RAJ	629860	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	NORTH WEST DELHI	VIJAY KUMAR	662	no	RPI(A)	Republican Party of India (A)
NCT OF Delhi	NORTH WEST DELHI	SUNIL CHHIKARA	1584	no	IND	Independent
NCT OF Delhi	NORTH WEST DELHI	RAJESH KUMAR	545	no	RBHP	Rashtriya Bahujan Hitay Party
NCT OF Delhi	NORTH WEST DELHI	RAM KARAN SAURAN	1621	no	IND	Independent
NCT OF Delhi	NORTH WEST DELHI	SHAILENDER KUMAR	3446	no	IND	Independent
NCT OF Delhi	NORTH WEST DELHI	DHARAMRAJ	1680	no	BPC	Bhartiya Pragatisheel Congress
NCT OF Delhi	NORTH WEST DELHI	None of the Above	8826	no	NOTA	None of the Above
NCT OF Delhi	NORTH WEST DELHI	RAKHI BIRLA	523058	no	AAAP	Aam Aadmi Party
NCT OF Delhi	NORTH WEST DELHI	BASANT PANWAR	21485	no	BSP	Bahujan Samaj Party
NCT OF Delhi	NORTH WEST DELHI	KAMINI KAUR	666	no	IND	Independent
NCT OF Delhi	NORTH WEST DELHI	KRISHNA	157468	no	INC	Indian National Congress
NCT OF Delhi	NORTH WEST DELHI	BHUP SINGH	1107	no	IND	Independent
NCT OF Delhi	NORTH WEST DELHI	JODHRAJ PAHARIA	2279	no	IND	Independent
NCT OF Delhi	NORTH WEST DELHI	INDER SINGH	1749	no	Aa S P	Asankhya Samaj Party
NCT OF Delhi	WEST DELHI	PARVESH SAHIB SINGH VERMA	651395	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	WEST DELHI	JARNAIL SINGH	84722	no	IND	Independent
NCT OF Delhi	WEST DELHI	JARNAIL SINGH	5960	no	IND	Independent
NCT OF Delhi	WEST DELHI	MAHABAL MISHRA	193266	no	INC	Indian National Congress
NCT OF Delhi	WEST DELHI	RAKESH KUMAR	458	no	bjdi	Bhartiya Janta Dal (Integrated)
NCT OF Delhi	WEST DELHI	BABU SINGH DUKHIYA	1654	no	IND	Independent
NCT OF Delhi	WEST DELHI	SUNIL SOURABH	3624	no	IND	Independent
NCT OF Delhi	WEST DELHI	ISTAK KHAN	988	no	IND	Independent
NCT OF Delhi	WEST DELHI	DEEPAK KUMAR	415	no	RJAP	Rashtriya Janadhikar Party
NCT OF Delhi	WEST DELHI	HAR GOBIND ARORA	703	no	SHS	Shivsena
NCT OF Delhi	WEST DELHI	VIRENDER MOHAN VATS	3078	no	IND	Independent
NCT OF Delhi	WEST DELHI	RAJ PAL SINGH	8707	no	BSP	Bahujan Samaj Party
NCT OF Delhi	WEST DELHI	CHARAN JEET SINGH KHALSA	451	no	AIFB	All India Forward Bloc
NCT OF Delhi	WEST DELHI	KARAM CHAND LATHWAL	353	no	BPC	Bhartiya Pragatisheel Congress
NCT OF Delhi	WEST DELHI	JARNAIL SINGH	382809	no	AAAP	Aam Aadmi Party
NCT OF Delhi	WEST DELHI	DINESH KUMAR KUSHWAHA	651	no	ABSP	Akhand Bharat Samaj Party
NCT OF Delhi	WEST DELHI	UDAY KUMAR SINGH KUSHWAHA	805	no	BMUP	Bahujan Mukti Party
NCT OF Delhi	WEST DELHI	None of the Above	7932	no	NOTA	None of the Above
NCT OF Delhi	SOUTH DELHI	RAMESH BIDHURI	497980	yes	BJP	Bharatiya Janata Party
NCT OF Delhi	SOUTH DELHI	None of the Above	4010	no	NOTA	None of the Above
NCT OF Delhi	SOUTH DELHI	SUMANT KUMAR	1640	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	SANJAY KUMAR RAI	10873	no	BSP	Bahujan Samaj Party
NCT OF Delhi	SOUTH DELHI	TEJ PAL SINGH	1215	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	NARESH KUMAR CHANDALIYA	913	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	MOHAN KANUGA	561	no	JKNPP	Jammu & Kashmir National Panthers Party
NCT OF Delhi	SOUTH DELHI	CHO. OMBIR SINGH NAUGHWAR	303	no	BHBP	Bharatiya Bahujan Party
NCT OF Delhi	SOUTH DELHI	SREERUPA MITRA CHAUDHURY	803	no	AITC	All India Trinamool Congress
NCT OF Delhi	SOUTH DELHI	RAKESH KUMAR	1071	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	RAMESH KUMAR	125213	no	INC	Indian National Congress
NCT OF Delhi	SOUTH DELHI	HAFIZ MUSTAQ	357	no	JSMP	Jan Samanta Party
NCT OF Delhi	SOUTH DELHI	COL DEVINDER SEHRAWAT	390980	no	AAAP	Aam Aadmi Party
NCT OF Delhi	SOUTH DELHI	D. K. CHOPRA	412	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	SHAHID	2841	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	RUBY YADAV	56749	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	SHRICHAND TANWAR	4475	no	CPI	Communist Party of India
NCT OF Delhi	SOUTH DELHI	DR. DUSMANTA KUMAR GIRI	560	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	SUNIL KUMAR	1140	no	IND	Independent
NCT OF Delhi	SOUTH DELHI	KIRAN PAL SINGH	314	no	IND	Independent
Lakshadweep	Lakshadweep	MOHAMMED FAIZAL P.P.	21665	yes	NCP	Nationalist Congress Party
Lakshadweep	Lakshadweep	M.P. SAYED MOHAMMED KOYA	187	no	BJP	Bharatiya Janata Party
Lakshadweep	Lakshadweep	KOMALAM KOYA	488	no	SP	Samajwadi Party
Lakshadweep	Lakshadweep	DR.ABDUL MUNEER	465	no	CPM	Communist Party of India  (Marxist)
Lakshadweep	Lakshadweep	NAJMUDHEEN C.T	181	no	CPI	Communist Party of India
Lakshadweep	Lakshadweep	HAMDULLAH SAYEED	20130	no	INC	Indian National Congress
Lakshadweep	Lakshadweep	None of the Above	123	no	NOTA	None of the Above
Puducherry	Puducherry	R. RADHAKRISHNAN	255826	yes	AINRC	All India N.R. Congress
Puducherry	Puducherry	G. PALANI	438	no	CPI(ML)(L)	Communist Party of India  (Marxist-Leninist)  (Liberation)
Puducherry	Puducherry	M.V. OMALINGAM	132657	no	ADMK	All India Anna Dravida Munnetra Kazhagam
Puducherry	Puducherry	PROFESSOR M. RAMADASS	2494	no	IND	Independent
Puducherry	Puducherry	S. KRISHNAMOORTHY	2060	no	BSP	Bahujan Samaj Party
Puducherry	Puducherry	G. SHANMUGAM	1726	no	IND	Independent
Puducherry	Puducherry	DR. V. RANGARAJAN	8307	no	AAAP	Aam Aadmi Party
Puducherry	Puducherry	S. EZHILARASAN	1777	no	IND	Independent
Puducherry	Puducherry	M. CHITTIBABU	1340	no	IND	Independent
Puducherry	Puducherry	I. SENTHIL KUMAR	477	no	IND	Independent
Puducherry	Puducherry	SANJIVGANTHI .S	5896	no	IND	Independent
Puducherry	Puducherry	K. MANJINI	767	no	IND	Independent
Puducherry	Puducherry	K. RAMADASS	1236	no	IND	Independent
Puducherry	Puducherry	R. VISWANATHAN	12709	no	CPI	Communist Party of India
Puducherry	Puducherry	RAMAMURTHI	1442	no	IND	Independent
Puducherry	Puducherry	P. BAKKIARAJ	610	no	IND	Independent
Puducherry	Puducherry	P. DHANARASU @ MATHIMAHARAJA	198	no	IND	Independent
Puducherry	Puducherry	None of the Above	22268	no	NOTA	None of the Above
Puducherry	Puducherry	P. ASARAVEL	3472	no	IND	Independent
Puducherry	Puducherry	R.K.R. ANANTHARAMAN	22754	no	PMK	Pattali Makkal Katchi
Puducherry	Puducherry	S. SUDARSANAN	1270	no	IND	Independent
Puducherry	Puducherry	V. NARAYANASAMY	194972	no	INC	Indian National Congress
Puducherry	Puducherry	A.M.H. NAZEEM	60580	no	DMK	Dravida Munnetra Kazhagam
Puducherry	Puducherry	MARIE UTHRIANATHAN	366	no	SAP	Samata Party
Puducherry	Puducherry	S. CHITRAKALA	309	no	JD(U)	Janata Dal  (United)
Puducherry	Puducherry	KALIAYAMURTHY	747	no	IND	Independent
Puducherry	Puducherry	V. VIJAYA	746	no	IND	Independent
Puducherry	Puducherry	R. VALAVAN	497	no	IND	Independent
Puducherry	Puducherry	J. DHANDAPANI	1144	no	IND	Independent
Puducherry	Puducherry	PUVALA NAGESWARA RAO	465	no	IND	Independent
Puducherry	Puducherry	G. SUTHA	467	no	IND	Independent


================================================
FILE: notebooks/Lesson 1 - Understanding the tools.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lesson Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this you will learn all about the following:\n",
      "\n",
      "- Platform: AWS\n",
      "- Programming language: Python\n",
      "- IDE: IPython Notebooks\n",
      "- Libraries: Numpy, Pandas\n",
      "- Administration and Monitoring with Ajenti"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Amazon Web Services"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "AWS is a cloud computing service offering a range of managed and unmanaged services. We will be working with EC2 which is commonly used for performing \"Machine Learning\" processing in production environment.\n",
      "\n",
      "AWS EC2 offers a FREE Trier which is what we will be working with. So if you don't have an AWS account yet, please create one.\n",
      "\n",
      "https://aws.amazon.com"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Alternative - Local Setup"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ubuntu (VM)\n",
      "Install:\n",
      "\n",
      "- python-dev\n",
      "- python-pip\n",
      "- python-ipython\n",
      "- python-numpy\n",
      "- python-scipy\n",
      "\n",
      "Start Server:\n",
      "\n",
      "ipython notebook"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be coding most of our work in python, so it important to be familiar with the language. One good I found was \"Learn Python the Hard Way\" which is available for free online.\n",
      "\n",
      "http://learnpythonthehardway.org/book/\n",
      "\n",
      "I'll go over some basics that will get you started."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 10\n",
      "y = 5\n",
      "z = x + y\n",
      "print z"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "word_1 = \"Hello\"\n",
      "word_2 = \"Python\"\n",
      "sentence = word_1 + \" \" + word_2\n",
      "print sentence"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's do some math\n",
      "print x + y\n",
      "print x - y\n",
      "print x * y\n",
      "print x / y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Formating String\n",
      "template = \"Hello %s\"\n",
      "print template % \"Python\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Using string formating to improve math results\n",
      "print \"Add Result: %s\" % (x + y)\n",
      "print \"Subtract Result: %s\" % (x - y)\n",
      "print \"Multiply Result: %s\" % (x * y)\n",
      "print \"Divide Result: %s\" % (x / y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Passing Multiple Parameters to a string\n",
      "template = \"%s %s %s = %s\"\n",
      "print template % (x, \"+\", y, x+y)\n",
      "print template % (x, \"-\", y, x-y)\n",
      "print template % (x, \"*\", y, x*y)\n",
      "print template % (x, \"/\", y, x/y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# List, Tupal and Dictionary\n",
      "numbers_list = [1,2,3,4,5]\n",
      "numbers_tupal = (1,2,3,4,5)\n",
      "numbers_dict = {1:1, 2:2, 3:3, 4:4, 5:5}\n",
      "\n",
      "# List\n",
      "print \"Full List: %s\" % numbers_list\n",
      "print \"List Length: %s\" % len(numbers_list)\n",
      "print \"First Item: %s\" % numbers_list[0]\n",
      "print \"Last Item: %s\" % numbers_list[-1]\n",
      "print \"\"\n",
      "\n",
      "# Tupal\n",
      "print \"Full Tupal: %s\" % str(numbers_tupal)\n",
      "print \"Tupal Length: %s\" % len(numbers_tupal)\n",
      "print \"First Item: %s\" % numbers_tupal[0]\n",
      "print \"Last Item: %s\" % numbers_tupal[-1]\n",
      "print \"\"\n",
      "\n",
      "# Dictionary\n",
      "print \"Full Dictionary: %s\" % str(numbers_dict)\n",
      "print \"Dictionary Length: %s\" % len(numbers_dict)\n",
      "print \"First Item: %s\" % numbers_dict.values()[0]\n",
      "print \"Last Item: %s\" % numbers_dict.values()[-1]\n",
      "print \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "IPython Notebooks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "IPython Notebook will be our Intergrated Development Environment (IDE). It is web-based and support interactive development. This comes in handy once we are working with data and we would develop out logic based on the data and results.\n",
      "\n",
      "In can write code and execute it on the spot and have others access your work to truely work as a team.\n",
      "\n",
      "It also support inline charts and LATEX to visualize your work and results.\n",
      "\n",
      "Let's test LATEX first to see can it can make our functions more readable\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Text code for function\n",
      "f(x) = (x ** 2 + 2 * x) / (1-epsilon)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "LATEX code for a function\n",
      "\n",
      "### $f(x) = \\frac{(x ^ 2 + 2x)}{(1-\\epsilon)}$"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Charts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A good resource for finding chart examples is:\n",
      "\n",
      "http://matplotlib.org/gallery.html"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "X = [1,2,3,4,5]\n",
      "Y = [3,2,5,0,1]\n",
      "plt.plot(X, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(X, Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(X, Y, c=Y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter( X, Y, c=Y, marker=\"*\", s=400)\n",
      "# Complete list of markers\n",
      "# http://matplotlib.org/api/markers_api.html"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "A Teaser"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.mplot3d import axes3d\n",
      "import matplotlib.pyplot as plt\n",
      "from matplotlib import cm\n",
      "\n",
      "fig = plt.figure(figsize=(10,10))\n",
      "ax = fig.gca(projection='3d')\n",
      "X, Y, Z = axes3d.get_test_data(0.05)\n",
      "ax.plot_surface(X, Y, Z, rstride=4, cstride=4, alpha=0.3)\n",
      "cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\n",
      "cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\n",
      "cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\n",
      "\n",
      "ax.set_xlabel('X')\n",
      "ax.set_xlim(-40, 40)\n",
      "ax.set_ylabel('Y')\n",
      "ax.set_ylim(-40, 40)\n",
      "ax.set_zlabel('Z')\n",
      "ax.set_zlim(-100, 100)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Numpy and Pandas"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Numpy**: A powerful library for dealing with large amount of numbers. It performs operation much faster than pure python.\n",
      "\n",
      "**Pandas**: Introduces DataSeries and DataFrame which are based on Numpy arrays. These objects provide many out-of-the-box tools to analize and perform complex operations on your data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "array = np.arange(100)\n",
      "array = array.reshape((10,10))\n",
      "df = pd.DataFrame(array)\n",
      "print df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Performs Operations on Complete List"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[1] - df[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[1] + df[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print df[1] * df[0] - df[3]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Read Data From CSV"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can read CSV file located on our system or on the internet."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Upload A File to the System with Ajenti"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can open Ajenti by opening this link:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "input_form = \"\"\"\n",
      "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
      "<p>User: root<br> Password: admin</p>\n",
      "\"\"\"\n",
      "\n",
      "javascript = \"\"\"\n",
      "<script type=\"text/Javascript\">\n",
      "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
      "</script>\n",
      "\"\"\"\n",
      "\n",
      "HTML(input_form + javascript)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
        "<p>User: root<br> Password: admin</p>\n",
        "\n",
        "<script type=\"text/Javascript\">\n",
        "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x7fc6e58037d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Read File from Local System"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data = pd.read_csv(\"data/yourfile.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### End of Lesson 1\n",
      "For questions please leave them on:\n",
      "\n",
      "- <a href=\"https://www.youtube.com/user/roshanRush\">YouTube</a>\n",
      "- <a href=\"https://twitter.com/twistedhardware\">Twitter</a>\n",
      "- <a href=\"https://plus.google.com/+Tcsaroshan/posts\">Google+</a>\n",
      "\n",
      "<a href=\"/notebooks/Lesson 1 - Intoroduction to Machine Learning.ipynb\">Next Lesson - Intoroduction to Machine Learning</a>\n",
      "\n",
      "In the next lesson:\n",
      "- kNN Classification"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/Lesson 2 - Intoduction to ML.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lesson Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this you will learn all about the following:\n",
      "\n",
      "- Library: scikit-learn\n",
      "- How to choose: Classification, Regression, Clustering\n",
      "- Data Sets\n",
      "- Classification Example"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "input_form = \"\"\"\n",
      "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
      "<p>User: root<br> Password: admin</p>\n",
      "\"\"\"\n",
      "\n",
      "javascript = \"\"\"\n",
      "<script type=\"text/Javascript\">\n",
      "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
      "</script>\n",
      "\"\"\"\n",
      "\n",
      "HTML(input_form + javascript)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
        "<p>User: root<br> Password: admin</p>\n",
        "\n",
        "<script type=\"text/Javascript\">\n",
        "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x7fd7d320a7d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Scikit-Learn"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://scikit-learn.org\n",
      "\n",
      "scikit-learn. Machine Learning in Python. Simple and efficient tools for data mining and data analysis."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Algorithms Types"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are three types of algorithms that we use according to the type of the problem.\n",
      "\n",
      "## Clustering\n",
      "<img src=\"http://scikit-learn.org/stable/_images/plot_dbscan_11.png\" />\n",
      "clustering is used to group data into groups according to their similarities.\n",
      "\n",
      "## Classification\n",
      "<img src=\"http://scikit-learn.org/stable/_images/plot_weighted_samples_11.png\" />\n",
      "Classification is used to classify data into pre-defined groups.\n",
      "\n",
      "## Regression\n",
      "<img src=\"http://scikit-learn.org/stable/_images/plot_gp_regression_11.png\" />\n",
      "Regression is used to find the next value in a series.\n",
      "\n",
      "<img src=\"http://scikit-learn.org/stable/_static/ml_map.png\" />"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data Sets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You need data to work with machine learning. This is some data sets to get you started.\n",
      "\n",
      "http://archive.ics.uci.edu/ml/index.html\n",
      "\n",
      "http://scikit-learn.org/stable/datasets/\n",
      "\n",
      "Inside these data sets you will find records of data. These records can have number, boolean, string, category, image ... etc.\n",
      "\n",
      "### Let's Examine some data\n",
      "\n",
      "This is a sample of a data set that is called Adults:\n",
      "\n",
      "http://archive.ics.uci.edu/ml/datasets/Adult"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Data Description"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **age**: continuous.\n",
      "- **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
      "- **fnlwgt**: continuous.\n",
      "- **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
      "- **education-num**: continuous.\n",
      "- **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
      "- **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
      "- **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
      "- **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
      "- **sex**: Female, Male.\n",
      "- **capital-gain**: continuous.\n",
      "- **capital-loss**: continuous.\n",
      "- **hours-per-week**: continuous.\n",
      "- **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Data Sample"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, <=50K\n",
      "38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, <=50K\n",
      "53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, <=50K\n",
      "28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, <=50K\n",
      "37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, <=50K\n",
      "49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, <=50K\n",
      "52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, >50K\n",
      "31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, >50K\n",
      "42, Private, 159449, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5178, 0, 40, United-States, >50K\n",
      "37, Private, 280464, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 80, United-States, >50K\n",
      "30, State-gov, 141297, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, >50K"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Classification Example"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be using the dataset above to make some prediction. Let's start by classification model to predict if someone makes more or less than 50K Annual."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Import Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Load Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\"\n",
      "path = \"data/adult.data\"\n",
      "columns = [\n",
      "    \"age\",\n",
      "    \"workclass\",\n",
      "    \"fnlwgt\",\n",
      "    \"education\",\n",
      "    \"education-num\",\n",
      "    \"marital-status\",\n",
      "    \"occupation\",\n",
      "    \"relationship\",\n",
      "    \"race\",\n",
      "    \"sex\",\n",
      "    \"capital-gain\",\n",
      "    \"capital-loss\",\n",
      "    \"hours-per-week\",\n",
      "    \"native-country\",\n",
      "    \"income\"\n",
      "    ]\n",
      "csv_data = pd.read_csv(url, sep=\", \", names=columns, nrows=20000).dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Statistical Analysis of Numerical Features"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Visual Inspection of Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import scatter_matrix\n",
      "scatter_matrix(csv_data, alpha=0.05, figsize=(18, 18), diagonal='kde')\n",
      "pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4bofR0WHq9Wksayu+38YwUnK5AhMTk+j6CJoWsn17hWp1eYKjfN7GNONlm804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bPP/gHgc8cd/2bg9dvtF/nCF77BvfcqOE7/jWR8fJxq9QkWFq7wgQ+8dWCfpZLBt771FNWqRrm8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jxmve6zc+f1F2Fk/D5wL/Cf2bJlC3v2CFHcxcUmsuwwODiBomgkCezYMcbIyGjXCcohji1A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\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DYlkWQbB/z1+lFMNhPPI8DNYlM6SU9HrxqAx0fc12qRiysCyHNE3xvOV1p6C0BdCcPz/P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\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7/o3+/gp/+ZfnkB7HA7TCMBw2n0KhMOL14ni9lWu6TqVbS0ahB9tIu1JuSrsaKTdrtXpX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\nX4jVuWeaS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wwNLaBp7nljcy62bs0yNfUUIyPyeWSLiqKgKC5xHL5o5eEvhKoqSJJDHIuq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\nuygWHRRFQ9dtwjCbC8CVCILVZ/SEYYjvq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aPra691tFw3VSiYNJstLOvCg/KKotBq5bWY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\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GsRiEDR05IuK6fbguyHKF1av78LwSnZ1tDA3lKZUUqtUw+XyJctkkFkti2wau6yEIYYrF\nY0pbLBZG1z3icfVZV+A9z1t0o7WEZxeqqhKLiUSjwikjzA0UWBfHUSiXzYay8XiUjg6Njg7tBO+7\np8K2RTKZFJ4nNTQ6BIbjGF1dEbLZxkYHXdfQdZ94XG5ocBkdHQVWAVvx/cbGiXK5TEDOvIFDh4Ya\nyu7aNUYisYWZmRRDQwvLTk5OcviwgKKsYfv2ow3LPHhwCEXZRji8lcnJyYay2Wyazk7o7k41HIuO\n4+C6wVpTqTTe5C3epi0ERNrrZr09A16dEJYlMzNjUSjIVCqBQWx62iQcbmN62sM057+3IAgkEhrh\nsIuiSHheCMPgaesYEJB9WpaEbcsL3m8Jf5rYuXMnsAk4HzgxbfkfiyBjm4jjqBjGiX3L8zzKZae2\nLjeeOxdDuWzi+xrlsndcGmZBEAiHJSQJNC2KYRiUyx6+r+G6wcYxFhNRFAXDsMjnHQYHPUyznYkJ\nC0FwyOfzJBKtjI0t7HFmGBauq2IYwjMah0tYQqUCjdSCJUPGiwPPi0fGvffey4YNG4hGoxSLRT7w\ngQ/w3ve+d5Y46+nANE0ee+wxBEFgYmKCO++8k3vuuYcrr7ySG2+8sSHB2FOhqjK+X0IQAvfdUChE\noVDBcQTS6SilkkA2G0VVXVpaNAzDR5YjGIaF5wmYZpVcbpLm5iql0kHWrs3S2RnD89IcOXIUx5kC\ngpjBdDqG68ax7VHGxx8lkTA5cOAg8XiFVEqloyNBqWSgqiLptIym2SxfrqGqRVpaRAYHDxGPSyhK\niZ6eMLGYAgQbRPBpbo6jKHH27h3AtvezalWaXG6Q3t5YjYAzcAWUJPC8Coah0NvbTCbTSrVaolwW\niMezCIJLMhlGlsN4nonrelSrJh0dKXRdZ3h4B4VCAUXJMzmZp1CYoVy2MM0cyaREIgGeB56Xo709\nSTweprtbZ2pqP9msjCh6aJoIBKRGktT4hCDwxHAQRb+Wj/x5t8U9q0gkwvT2NmFZAolElXz+IC0t\nKtmsiiAU0XUf1x1EVQ3icZdCYZDuboVQyKFQmCCZdIjHPRRFIxwO09MTJRyOUKk4OI6LbVuoahVB\n0HBdEU0zyGSypNMgSQbZrM7ExDSjo+M0NUXJZpPEYmG6uppQFJFQSMd1DUBE0yKIojibLs6yfKrV\nEpHIsWlGFMUTMo08Gwg4M8xaGzY+tV/Cs4tTnfFHEATK5TK2LdLU1Pi9hkIqLS0JBGFxV910OsTg\nYJlUqrHRIRwO092dxvMCt+jF6noy/T1IN7qXY5mqFkYymUSWD+K6Clu2LGsou25dlrGxw2SzNm1t\nbQvKJRIJdD2YX9avX5jHAuD889fx4IOHkSSbzs71DWUVxcWyBHzfbhhaIssy4TB4nkMk0ri/LN6m\nYwThOcP09/cDgedDOFxGlkFVwfNMJEkkFNJIp0NMT+dJpdRa6siF61h3dS+XLUTRQxSfft8OCHBN\nfB9k+U/XCL+EE9Hb28uuXUcJvNTmDy35YxBwO9j4vossn6hLiaKILINtGzW965lDVSUqFRNZ9k/Q\nwwKPZgAfRVGRZQvXNdF1+bjMK4oioaqQStnMzMyQSMiIoowoiszMjNPRsfBxuaJIVKsWknSMGHUJ\nS3g6OBmPDKOxg+YSXgB4Xsg+161bx+OPP87jjz/Om9/8Zi6//HJ+8pOfcNdddz3tsr7xjW9w2mmn\n8U//9E98/OMf58knn+Qf//EfeeSRR7juuuv48pdPTI1XJw/53ve+x9VX38jy5RGuu+5WVq1Ks2fP\nnlPxiEs4aYQIsmR0sXp1hEqlwuAg9PbKjIzAxo2tHDnicP75/ezcOclZZ61kdNRn1aoMiUTglhuL\nxdi7d4yLLnoJ09MGPT3N7N07xLJlzTiOTCIRYvfuQTIZnba2NopFi5Ure3AcH9OsMDw8RVdXlmLR\nIJHQMU2XaFSb3fg0Nx+fMtJ1XSzLRlEab3jmwvOC0z5ZlmbdOX/0ox9RLsPb3nbprNxcYptcLndS\nBIkvLEQIh6PE40mi0SSdnU20trbheWVM00eSwqRSKsuW9ZNOJ8lmU0xP56hULFpbMzQ3Z0ilkgiC\nSCikUiiUiUZ1kskYhUKZSESfzSAQCiloWghFUQiHw6iqgm07mKaBYViIYrSmuJnoenhBzx3P86hU\nqjVysPBx79QwDGZm8kSj+oIurk/FM+kfzwSmGRhqTpWhwLZtHMclFJrfW2Zu39y+fTvbt+/j3HM3\nsmLFioblvvKVr2R8HB577LaGcq95zWu4+eabARp6RNSJ6gJI+P7Cp3FveMMb+MlPfrJomcBxrsme\n5zV0VZ77XaNy/+M//mOWDLORXKVSITJHo2oku3PnTtauXXtSsnPrWSexmw/Ht2njMqenczQ1BfNS\nsVicJVle7P6maTb0nuvo6GB4eJpf/vJ6Lr744gXlbr75Zl7zmtfT3Z3m8OHDx93rqZnPtm17GcXi\nFDt3Pn5COaLYwlVXfZJPfvILTE0FvCQXX/xG3va215NKpfnd77azbdsaLqwxveVyOX75y1/xzW9+\nl/vv3862bb3ccsutJJPHG36q1Sp33HEf2WyczZs3Y5oWgnD8OK17p9TH2ejoKKWSwbJlnUtG1xc5\nrrjiCnbsGOGjH307W7duBY71zeuuu47LLrtsVna+cVYoBNmA6sS4phkYvU4m9erOnTsxTZ/Vq3sX\n5OmoVquUShUSiRiyLFMolJBlcXYcj46OUihUSKeTaJo6+/nc9aFYLDI9nWdqapIDBwZZs6YPSZK4\n4YZb6Opq5ZxztvGlL32F++7bzktfuo2uri6SyTTRqMz1199OU5PGe9/7TlpaWvj3f/8un//8P+O6\nZTKZDG9845u46qofs3Jllo997H0kEk2cddZmmpszmKbFAw88Qi43STQaYWIiR2dnK2eeuZ49ewZw\nnAqyHCYS0YjHY8TjcaLRCI7jUCpVAA/fF4hG9XnnIsuyKJUq6Lr2nHGlLQTLsnBdD00LnbKwmfn0\nUPjzJfv8wQ/g17+Ga6+d//v3vAf6+uC9C2d4X8KzjJPpm8+LISOIE3yUT3/603R0dHD55Zdz5pln\n8sgjjzytcmzb5rLLLuPHP/4x5513Hu9617soFAq84x3vYP/+/XzhC1/gmmuuOeG6esM0Nf0Fpvlu\nyuVPA38L3Ejg8jdAkAKyH3gQeAvwDeBDwD8DbwVuAFYQpMHUgBhBmrmLgP8GPg58qXbNlcCrCFLr\n9ROkvTwIbARuAj4IfAV4f+33JcBtwBaCVJlloBl4rFbO9cB7a3V6D/CfwEuBR4EOIAXcBVwI/A54\n95y6fBq4DLgDWE2QQm+cIK/53cDba7L/BHyi9hxX18rfThDHrQOHgJcB3wM+DHy99hzfqMk+XHvW\nwwTEVr21dvnInLb8IvAPwH8AFxCcoglAJ3AP8DfAj2vXfAlF+RC2/W0UZQuCsI9kchWS5OI4oyQS\nZ+E4d3PRRf8P999/NVu3/gO7d1/HhRe+gQcfvAFYj+MU6ex06OhYS3PzBJs3n82vf/0bWlrOY8+e\nmzj33EsYGnqEvr61VCozpNMxYrEkK1ZIxxkzZmZK+H5ggEmlTi7XeKFQxrYVwCaVCnPTTTdx9dVV\nQOeyy8pcemlgzJg7aDdtuoBHHnFq/cQkSKGaJOhzBkGcbYqgPwlAlCAdcIggW0muJhOuXZevXReq\nfZ4gSPWWq13r1T5vJkgBXAH6CFL0egQZT6yafF+tPGqflwjGQJ4gtVyEoN9qtXt6BC7fE0CEZDJE\nR0dbLa1rCNuO0tTk0dUVp7k5SmtrG8XiCIrSiq7bhMM+sVgbnpdHFCUEIYwsmzQ3J0ilIrS3JwiF\nPCDE0aMzRKNRFKVCNBpGkvSaB09o3o1csVhhYsLAdUVSKZGmphiCIOC6Lrt3D1EuR1CUMmvWtJyU\n0eCZ9I+nC8uyKBY9fB9isT8+bMN1XWZmDERRQVGceflG6n2zVCrx7nf/AEV5CZHI3Xz1qwuv8kGG\njS1AlFe84kZuu21hY4YgXAC8A/g1r371OL/61a8WkBOA/w28Avgavr8wWXQg++8E/fOji2z6zyGY\nV68DfrGgbFDmx4F24Av4/sKhGILQCXwMGKat7TsML5CwPijzXOprje//YZFn+hDBmP3UIs+0sfZM\ntwPXLvJMlxGsGV/G959Y5P7/QjA//PMi999G8E7/G7h5QdmzzjqLBx44i2BeOfk2Peecu7j33ntn\n6xUYMiSCtWUPwdr1Ko6t1RLBXJoh8Ny4H1hOsG4fAnaydeu5+H4ZXX8NkvQwP/jB35HNZvnxj+/k\nC1+4jx07HgS2AeO89a06X/7yJ44zZvzwh79ix45mYJK3vnUViUQ74BOPS/NunnK5HPffP44o6nR2\nGqxd29gwuIQXLn72s5/xznc+iiStYdmyG7nvvh8BTzWyvZUgNOzDJxhhS6USQ0PBZx0dMqqqUigE\n6cgXm+cHBwe55ZYpBEFh61aR9evXnCDjeR4HDowjCHEkqUAioTE5KeL7Fl1dOpZlcf/945TLLppm\n0d/fQVdXwG9RXx9ct8iBAwWOHi1x66334XnLicdn2LXrQSYnz8ZxdtHZOc1NN5UIxvMjdHWtJZuN\ncPDgQ5RKZyKKVV71KodVq2J84xt7qVQMAj3nXgLd+H3A/6BpAm95y3tYtWqSN7zhAh57bB8///kI\nhw8XKBaPEA530dMT4vTTXXx/JXv2HKapSSMSUenpSbJx43I6O3VyuSqVisrg4CCdnV2EQlWWLz8x\ntGdgYBTXjSMIBZYvzz5vHr8B15iFIEhomteQM+jpIJ8v47oqYJFMHuNQ+XM1ZHz72/Dww3D11fN/\n/5GPQCoFH/3oc1uvJRzDyfTN52WUxmIxPv/5z3Pttddy8cUX47ruSXEjPBU/+MEPeOMb3zj7fyKR\nmLVmFwqFhin8PvWpT2EYA5jm1QRp2oqATyscYwsAACAASURBVLCxh4Dx3CVQfkaBGYLN3QTHiJoU\ngo2bVbvOJdiwmbVrcgQby1zt83JNrkDAOl//bKL2WaX2eX1xs2r1KtXKFAmU12rtXlWCTWal9n+9\nXqXas9Q3vwcJNrpHa3IFgs1lgWAjW3/WUq08v1amUvu/Xv/6ZrdYu3exdn2eYHM8VLunVfu+UCur\nOuez6dozV+b8X08NKsxpH6fW5k6t3nk8bwwoIooxBMFFkoKTK9+v4nkOmuYCJqpqYVkGqmqjqnLN\ntdEGqjXFwSEUUpBlGVX1cZzAPVIQbFRVINh31t/BiZkGglzmT497IVBgfOp72kgkgm2bQGXB2H1N\nk2vtW/+pv3ej9lOd095GrU2d2k+9jSs1GacmY865rsyx/lYv0619V7+fXfvcrn3uzpG3aj/mHLl6\nP5xbP+cpcg5g4rplRNFBFF0EwcbzbMBFEFw8z0YUqX3nERxS1sOPQJJc6q9FEKgRh9UnvODdxGI6\nsZg+2/aN4c++n7mGB0kScd2gLieL4PJj7/rZQvCsx59AP1MEeeuPteNiUFUP286hKI1d44M5uACU\njktHOD8KBPNB49jt4Hllgv57Mu7ZM7VyTwYVgvVgMdSNhI05Ko59X6KlpWUR2Wit3JMJNwjadHG4\nHFsjFkN93jgZN+08QV0XQ5hgHmh8uhm0TX0uO5l7Uyt3PtTXqPpaXi/X5thaWeX4+apA8OwegmCT\nTut4Xh5J8mY9JEQRfN8l6HsmkMf3vXnnBscJ5rv6XLD4OHXwPJdQaMkb48UO3y9hWXlCoYU2nxWC\n/rnQouJRX8PmlHqS87yN7zfWpUUxCOGSJLFWX6+21tbr4+B5DqoqHve5IBzzVAvKsAiHJWzbqYWZ\nCnheAd+3URSfY4ceLoJgIIoesuwAVQShiiz7tXI1jo2/un4wCXiIoovrWiiKWFuXfCTJxfdNJMnH\n84LvIhEVQfARRauWQrVCqWRSLldm6x6kZm3sbSZJIp73QuHb8E96PT5ZBO3g1f5eIkctl5fIPv8U\n8Lx4ZIyOjnLdddexdetWzjvvPAYHB7nzzjt505ve9LTK+ehHP8r27dsRBIEHH3yQ973vfTz00EPc\ndNNNXHnllfT29s7LkVG38Nxxxx389Kc/RRRFbr31Vnp7e7nlllueRg1kAiXlJCjh/yiECTb5J6OM\nvjDQ3NzMxMRE7b/AAtzXt4oDB/aQTCYpFAqsXr2aqakpOjo6mZ7OEY1GCYVkSqUSmzdvZu/evZx+\n+ukcPHiQDRs2UCgUWLlyJaZp0tvbS7lcJhaL4XkexWKRTZs2USqV6OnpYefOnfT29uI4Dslkkl27\ndqHrOi0tLRiGwYoVK2qkaxajo6N0dXVRLpdrBgYbWZZRFAXXdU9IW+h53qzMyabI8n0f27aRJGn2\nmttvv51yucxf/dVfzcrNtT5WKhWy2R7K5fk2VjLHTheff8iyPNu+0WiUdDpNLBajra2NRCJBtRqE\nbmhaELLT3t5HIhEjk4ljWSbVapVMJkM6nZ51qVVVlUqlgqZpRKNRqtUqoVDgZmmaJrIsz5YXCgUh\nJo7jYNs2vu+j64ERw7btWojJ/JsE3/cxTRPXDYh2557CWJZFuVxG07RFUuodwzPpH88EdePvqUoX\n5zgOnuehKMq8Ss7cvrl//34OHTrE6aefXksHujDe8pa3MDMzw4033thQ7sorr+QjH/kIK1asYN++\nxjwR9fq97GUv44477lhQbm4qz8VCG042tMS27ePKabSE/su/fJGPfezDpNMZRkeHF3xXlmUdd9ra\nqMz77ruPc845B4Cf/vRX/M3fvHpB2acT2nGy4TJDQ6N0dgZ8G+Pj4yeE3j2TMm3bZuvWcxgeHuFr\nX/s3Lr30/15QFqC7u5tYLMaOHTuOu9dTQ0te//rXUyqV5vXu6e3t5/vf/w7ve9/7+MMfAg+Yq666\nhs7OFlauXMYTTzzBihUr2Lx5MxCclj/wwAPcdNNN3HLLLWzevJlvfetbJ4TWVKtVnnzySVKp1Ow6\ns1hax8nJSRzHIZPJLIWWvMjxne98h4MHD/LOd76Tjo4gC069b15zzTVcfvnls7LzjYlKJdDz6gcc\n9fXsZEjNBwYGME1z3jTWjuPMehoahoGu68hyoG+Jojh7v3pf1DQNVVVnP7csC8dxCIfDlMtlqtUq\n1WqVXbt20dPTQzQa5Ve/+hXLli3j9NNP57/+67/Yvn07W7ZsmR2vra2t/OQnPyEajXLZZZfR09PD\nDTfcwPvf/35GRkbYvHkzb3vb2/jSl75Ef38/73nPe4jH46xbt45YLIZpmuzZs4disYiqqkxPT5PN\nZlm5ciWjo6N4nocoilQqFtFoM6oq0taWnG1XURRxXXf22edro7rO8XyTyC+2Hj8TzKeHwp+vR8Zn\nPxsYKj73ufm//9d/hakpuPLK57ZeSziGF2RoieM4vPKVr+S3v/3tKS33/PPP5+677+bKK6/kl7/8\nJT09PXz3u9+dd7J6asNUqwaVil8jufRrC4eFJIXYsWMvnpfBMPbT1raWBx+8k2JxORMTD2Dbbfh+\nkWXL0shyE7ncAKHQGlz3YXp6zsc0d3D66S9lYmI3qtqF48zQ2pphbGyI669/HMdpIR7fxaZNl7Bn\nzy1ksy9jYOB/SKVezuTk7zDNTmAKXQ8RDjdTLu+hqek8VPVJzj77f7Fr153o+kamph4hkVhJqTSC\nokTwPIGdO/cRCnUDj7Nx48Xcc8/1JBKvJJ//LRs2/CUHDvyG0dFWfN9k82ad1tZV7N27nUhkHVNT\n99LX90oGB++kre1c9u37LZK0AtMcI5tNoetpYJx0up/9+39PNvsSBgZuZfnyC4F9XHTRy5iYOMrk\npIRpmjQ1ecRizTzxxJOEQv3AftatO5d8/iCdnSvZt+8P7N+vEgrZvPrV/WSzLezcOYjrxmhtNenu\n7kVRXOLxyOy7EgSPRCI45cvnDVwXHMdAUXQ0zScafXGm4pzbN8fGJvj2t3/OD34wQC5XJByusnLl\nSlauFBkbcxgbA9sewbIEDhyYqZGgiui6iSBEKRanCU5XPQJvog6CU6Ax4vEeQiGV5cvjnH12O7qe\nZmTERtMMens76e1tJpUKoao6pmlRKuUZGrKYnCzT3x+noyNGtapgGFWi0Sjt7UG2AVXVqFZLJBJp\nslmFZDJOoVBieLhMoVChqSmMLEsUChVEUSabjRKPa5RKgTEwHlcW3Zj7vk8uV8Z1pRf1u36x4Zko\nO5VKhSNHAkW5rU1ryKcwPj7NzAyoqkVPz8JuvZVKhV//+glMM0Zfn8eWLac3KHOcRx6ZRpJctm7t\nbOgV8s1v/oShoVay2THe856FN9KFQoE77tiL62qccUaE/v6Fs4wMDAyyf7+JJFls27bsOB6Mp0IQ\nzgb+ArgO31+Yq2nbtm089NBpgE5b240LhqsAXHLJP/Dww/1o2i7uvffTCxqdDMPg61+/mUolwxln\nWLz2tRc2KPMSfvGL04ASH/hAaF4eqjo2bfpbxsYuIBb7A7t2/eeCcqZpcv/9+3Eclb6+EMuXdy8o\naxgmpZKLKPokEse4jI43ZPwfYD9wMwcPDjE8bDM5OY4khWqnxT4tLc0kkwIjIzPcfvtRFEXg5S9v\n4bTT+hvOKdPTOSYmgtPlnp70kuFhCYvi+L75aoJQp6+f0s1jneDa9wXicfm4jXh9zAiCTzKpPW0D\nu+d55PNVXFckEjlGtlsfC4ri0N19bCzMzBQZGJjB8wRaW0Pouo7nSUiShesqQBDqKcsyExMTPPjg\nMBMTBv39cTZtWnZShwaNninQE10UBeLxBkfuSwD+fA0ZH/84RKPB7/lw1VWwbx98/evPbb2WcAwv\nyNASWQ4YiXO5k3FLPXncfffdAHz4wx/mnnvu4dprrz1pBcOyHCoVuzZRy4CKroeJxyOUyw75vEu5\n7GAYFlNTFQQhTrmskExmUZQUU1MihYLB6KiIqmYZH7cIhaJMTztAhLExE9MMMTXlcPDgEHv2HGRy\nMo5tt3H06P/P3ptH2XWW556/Pe+zz3yqTs1VUknWbA2WPGAGYwOBsEzn5joJcy5kgtw0pHt1shKy\nusO9dPqum4Qk0KRXh/QNBNtAEzBughNMwIAnRsmyZFmyBpemKtV45mHPQ/+xT5U1nSObK1s21LNW\nrVPDW9/e+9vf+H7v+zx1ajWBs2dtdH2QpSUJUSxSLkukUqN4XpFSKaDVipiZsTFNldlZi2QyT7UK\nihJfb36+zblzFWo1gaUli3pdIQwHaTR0RkY2UCqF+H6RuTmHZlNkaqpNvS5Rr2uUSk1MM2B62qNa\nlZmddclmx1lcdImiPOWyj6IM4fspWi0R0xSYnbVoNEQWFz3S6fUsLNio6gDVKnieQrMZ0WhIeF6C\nIPCwLItqFYIgxdmzDaLIYGnJQpKSNBoS+fwwmjZIu21SrTZotyVkOUOlYiNJCr4fN2TfDzBND8uK\nTyliiU0RiBm0LcvHti8MrVzWVreslw/9cBRFWJZNu2117aS27bOwYNNspmi1ktRqakeGt41lKQhC\nkWZTYWlJxLYTwDBhmKDVStFuG8S8GEViLgwFGCDmOAloNDSaTYNq1ePMmSaVSoQkpWm3IxYXG9Rq\nDmfOLGKaEUEQYJohliXhOAlsW6VeD7AskVpNwHHAsnw8TyIMJRwHRFHHceIQTc8LCEMZSBCrpMmk\nUmlyuQyCoHSk2yRAel5yqfF7jxVuPG9VXvXljPj0L8Q0Azyvd+Rao2FSrdpUq2ZPOT3P8wiCBIYx\nQLXaO+7TtuOx4mKJwMuhXhfJZCao10XsHlTlrutSqbhUKg7N5pWi5CSy2SKpVK7nZPwXf/EXxOkX\ng0BfzxLjSLcBoI+5uaWetvPzDpq2Dt/v70g/Xh61Wo1aLSIM85w713tu3rdvmjjKLsPRo0d72tZq\nMopyPa2WxtzcXFc7z/MwjByZTJErZZnGss4yYSj2GC/WArEKWqnUYHa2ztKSSRgqOE6IaTokk1lM\n06PVshGEPjStQBRd2TFqmi6OE9Fue6uyj6v4KbCWmJfl6sqvhmFIFEmIokwQXNgvlvtMFPXqM73L\nDgIBUZTx/ef+37Z9JEnH8+Ixs9WyOpGuAWGodFT9PKJIRJIUHMfvcNg8N9e3Wi6+L2KaOtWqg+s+\nP0niXs+USOjk84lVJ8YqeqLdXpVf/VnANTlKSCaTbN++nTe/+c0rYWuCIPCpT33qWtwOAJ7nk0oZ\nSJKLLMuk0xnabRNFActyWFxskM2KFApZLOtZtm3rI5GwUFWVdDqP74u86lUy5fIzTExsxvcrbNw4\niKaVedWrJqjXqyQSNtVqnqGhrezc+TBhWCWb3YggLLJrVz+qephf+7Vt1OuHue22PczOVtE0hdnZ\nCWo1h2QyhyCc5tZbJ5Gked74xutYXDzB5s1J6nUZVc2RzbZJpVLkcgquO8Vtt+1Bko7xznfewFNP\nPcntt4+Sz8/zxjdu4OjRJZJJjVtvHSeRCKlUDBznDHfcMUYqdZRf+qVtLC2d4s47r0dVl0gkcnhe\nGs9zkaS1WFaTzZvX0m7/gPe//1UsLJzkDW+YYGDAZWRklKNHl2i1WhjGILKsMzmpEoZzvP7161DV\nRV796klUtcKb37yFqalZVFVg7dpNhGHEpk0etl1h7dq1iKJNKvVcjnUURSubAkVRSCYDPM9H1zWC\nIFrJh1wOx7NtB8uKv5ck95qHDEK8cG+3QRBEJMm9LCN5JmOwZ88afvjDBxFFk74+l6GhKXbtuplG\nY4Hp6RkGB2WqVchma8zPHyCR6COfzxMEi9Tr88zOmsSEoE1iAtgU0E8qdY6BgTR9fYNs3jzG6GiS\ner2M5+VJJNKYZovJyWGCoEkqZTA+nkfTygwNuWzZMkixWODs2SUGB5NIEoyNpXEcG0GAwcEcUWST\nz8cqH7lciiCok8lAJpM6z8MqoKoSmqYSRfHGUVWvzBQeK9UoOI7bU4bx4jDzVVxdPJ/6VRQFVY3T\nNDStd7+LopAgiE/Le5GsZbNZrr8+yezsKfbs2XjF+4yZ9sUrOjJuv32Sffue5JZbRnqS6sUSnD6u\nGxFFvdvr6GgRWEJVpZ7RKO9617v4yEc+BzwMTPUs8/Wvfz2nTv0I0Ljttu5pJQDvec9r+fu//w4b\nNhgrCgqXQ6FQYOvWFKdPP8Mtt1waln4+3v72t/HJT8ZkoK997Wt72v76r9/Affd9hte+tp+BgYGu\ndqlUitHRFs1mjbVrR3qWmUhohKF1CfP+hXgEmGH37t2EoYggSAwOquRyLVw3RSKRxvcXmZwcJp9P\nEASHkWWdXbt29Lx2fH0VUTTRdWk1GmMVl0W3sXHXrl0cOPA4MMvVll9VVZVk0iYMg0sUNxIJjSiy\nkSQRRVFe8NwoyzKpVNBxOD43Nvb3Z1haqpNMarguCIKC49jkcgnC0MV1HQYH+wABz3NJp7M4joco\nPpdqNTpapFaro2kO4+N9PaPWLn4mcFbKuviZrhVR5ypeOTDN3o6MVApaz4eKahXXFNdkFr7rrru4\n6667LvjdtdxwtFoNpqZqKIrH4GAecJmZqeB5EbbtoKoS09NnOHUqgW0/yfDwq/H9FoXCOKrqoygq\n6bRBsZhl3bp+HnvscSqVBCMjTcbGhlhcPMnCgoNttyiVPAQBXvOaV5HNjnDs2EEkqY9mU0XX+wnD\nGdau3Yjv10ilBjHNKo5jI4oqqiqjaRlmZ08gCKMkElXGxrYhSS6ZzADttofnBQSBzGtfewOFwhiq\n2iaV6iORkNmwoYhtzzAwsJaZmePI8jCC4DI0NIiu5+jvn8Pz+hkby3HTTTcwN3eGkREDRTG5/vrr\ncByL+fkWjhNQq1WQpCz9/R7Z7BC2XUfTijhOGUVJ4PsWw8ODNBoqe/dOEYYphoYUJiYmyOdd1q1b\nT6k0T6MRoOsRe/bsIIoC6nULQZBYu3YUwzBotUwcJ8J1Y84BWZYwDBFRjDdHYRjiugGeFyHLIrqu\nEgRtKhUTTYtltqIopFJpIYoRmczLQ840JleMVSdE8fLdUNc11q0bZceOrWQyDYaHZbZtG2F0dAhZ\nTrN+/Rr27p3CNOv09w+TSGh4noTnge8nyOc3oChWJ1rDQ1VdGo148zU8rDM+nkcQ0jQaNcbHiwwP\nF6nXFarVNum0RhAEOI7D3FyZcrlNu91E11Mkk1UsKyQIJBRFJAwj9u+fot0O6OtTGR8fRlUFTp+u\nkEiIjI0VGRgoXPYZl/FCWblVVe3pkIpVPVxkWSCTMVYdGlcZrZaJbYckk3JPZ5IkSQwM5IgikOXe\n4cxhGEfGRZHbcxHqeR6zsxUqlZBSqdQzXUSW434WE9L1TlkqlSo0Ggazs7We7SXmXXFwXQ1B6H0i\nr6oqk5OjPW0g5nyICZOvPD7F6SEWkGJ8vHeUWblcI5UqEoYNTNPs6kyRZZlDh04wP6+xZs0UN920\nvWuZmuai6wNIknfFOt21aweieD0TE71PWeMTXxFJ0nHd3s5m27aZn2+iaSIjI90kjkeBFvv3f5dc\nziBWfcgwMpLh1Kkq5XKbfN5AURTm50vMzooMD/O8TqtlWURVFSRp1VG6igsRRRGNhonvR6TTl85R\nBw4cIFYGGntRrt9tLI7n8hBRDPG8Fq7LFcfui6HrGpdTJNX1BIIgIElRR+I5VlcZHh64yC7+vLi/\nCoJAsThAKhWniDzfPiWK4sq6wfM8Gg1ndb5fxQvClcg+Vx0ZrwxcE0fG+9///mtx2Uvgui5nz57l\n9OkFjh8vE4Y+X/vaQcIwwvdFyuUmxaLB7OxZ5udVCoUkp09XaLefRRBsms0j6LrE/PwUmpZgYGCY\nIBCYmVlEEBpUKg2y2UeoVJpMTbk4jokk+RiGweHDFpa1j4mJ6wjDRU6cWCCK9tJqLTI+HlKpzJLL\nTeA4TZaWniEMwTCKSNI05fI8jcYUjcYZbr5ZxjBkTp78OrqexHXXI8sOP/7xD4kin2JxDE0zUBQR\nxzmBqsocOrQPQZCQpDW4rsXTT09hmm1SqSF836dUMvnBD76HqqaQJI1EQmXfvqdQVYVWKySKQk6f\nfpZ222JgoMjAgI0sm2Qy8wSBhGlqWJaJ57nUai1aLZ8gqDMykiGZFHEcl8OHf4JlSTSbBmFoYpoO\niqLQbjdQFB3bXsSyTDKZAWzbJ5PRMQyZVCpFvb5AKpUiDFU8z6PV8gjDONrCdVsIgo4kRbiui6JI\n+H5IMqlfoDTSarVwXZdsNntJvqjrxhup5aiPIAhWSK6CILgs+dL5qjvPh5hLURQUJY5ZU9VuqiXx\niYNpLlKvT2PbFqZ5gnZ7mnw+z9TUHFNTDqdOHeHcuWeJSWcN4jSNBM8pwySImcCbxBEZk8hyiUrF\nJZVaT7ms4jgmnueQSEjYtkSpZDM7exjDGKJcdrBthXK5ThRVOXnyNIODOUZGhtB1h0JhmKeeOks2\nO8L09ALNpoWua2Szg7TbIblci0QisbKAiaJoJXRf13V838dxHBKJBJIkrfwsy/LKyfgy8dXF9RqG\nYWfxdCF5leP4SFICz3MJguAVd3J6tYk8ryZicl2HKJIJQ7vnYlhRFObmYr6H/v7uXBYArhtSLs+Q\nz6fxfb9rHzJNk4cfPkKl4uD7a1m/fn3XMlXVQBRNoujKbeDBB/dy+rTHmTNJ7rrrDT2jMo4dO8jC\nQo077/xAzzIB9u7du0L82A2f//zngcPESiSLPcuL7+uHgIbrvqGn7cGDVRqNJKYpUiqVukZFzMzM\ncPBgmVZLI51u8973di9Tlvuw7UcA0PXdPa//3e9+lx/84BgTE8O8/e1v6KrQZNs2Bw8eo1azuOWW\njWzc2D16pdGwOXlyHk2T6e9PXea93gA8RCwBDZmMRDIZ4Loex48f4+zZedLpCXxfZ3FxiSefPEm9\n3k+5XGbHjirDw4MrJMEXjyu2bVOrNWk2WxiGRhRd6qANwxDTNFfIiJ8Puo1vq3hlIQgC5uYquK7P\n6GiBvr7Lvc85YiW5q49u84br+oiijue5OI5LMpnDNJsvyJFxOViWhywb+L5DKhWnlyiKQhiGOI5D\nFEXoun6BY7rZbBIEAel0GkmScF2fIBBZWDhFMjmM68osLCyQzWbJZrOXdWpf/Jy27REEEr4fy8bG\njhXpkrXdKlZxPq4UkZFOQ7P50t3PKn46XJPV/eTkpeRogiBw8uSLM7h3wze/+SNKpQEefPBBSqUt\nnDr1FRYXb8KyThFrzG8Bfogk/RpB8GkMYzOm+RQzMzdQr3+X4eFbaTbP0GoZQAJB+BdyuV+gWn2A\nTObdNBr/yunTOzh48D5E8c1Y1gEMY5AgCGi15tG0mxHF/4c9ez7It771CMnkr1Kvf4c1a4ZoNPYx\nMFDFNGepVLKIYoZm81uMj/87pqd/yMjIRmq1x5ie3saxY59H138Bz3uGTOYMgpCgUmmgKHtotT7H\njh0fZGrqH9iy5Xc4ePAfSSTeiu8/ychICc9zOp7sIaLoMTZseCvHj/8za9f+B+bmvsDrX/9+pqf/\nhcHB3XjeEhBhWQFPPXUSWd6O532FV7/6fUxPf4NXveo/cOTI1xgcvIVK5QCeN4ZpzlMu1zGMDczP\nP4MgjPHII/9CPv8LHD36JcbH34hlHeHGG2+l3S5TqwUEgUitdpZs9gYajS+wffudNJuPctNNb+bs\n2e+SSGwgik7x6ldHKIrC7GyZINCYmztDOr2OZvNpJiY2IYp1FGWyw64dIsuxc8J1XY4eXcLzEgwN\nzV9wYhrzVoDrthEEiSiKKJebKEoSWa6RzxdRFPOC3EvHcWg2Q3w/5u1QFJVUyrlsusgyTNNkbi6W\nnRXF1mVPSuv1Jl/84vf42tfm8LzlCbnGv/zLaXK5Z6nXfYKgRJxbf0Pn78s/K8R57AvEjox+npPD\ndahU8kCKpSWVU6dq7N//NLouIooilqWjqhbJ5ACKcpy+vnHCsE29XsE0dUTRIZdLkU7Psm5dCkE4\nRhAUaLcPk8328fTTLUZGXPr6bPr6XIrFLI7jkMvFpzGNRpulJZcwjCgWXep1F8dRSKVqDA5mKZfb\n1GoBuu4wOBif5NdqDiCRTF64cW42LXxfRhAs8vnkioNJ1xWaTQtVFV5xi5nl9gQR2ezL05lRKtWw\nLI1cLmBgoLvM9dGjR7n//kWiSMH3D7Br166utt/85uMcOTJCIvE0e/aso7+//7J2c3NzfPGL+zDN\njZw8+U+8851v7lrmsWOHefTRBqLosmlTrLLSDV/60sNUq79IIvEQf//3f9jV7tvf/jb/8A+niaJJ\n6vX/jQceuKer7b/+63d49FEdQTjMhz+cWFExuBi//uu/DuwB3gnc3bU8gI997GPAB4AMX/nKXwGf\n7mp77NjjHD16K6o61ZP3Y3FxkUcffQbX3c7i4reA/6mr7X/5L38E/D7Q5sMf/jAf+tCHutr+7d/+\nAPglnnjicU6ePNm1/mdmZrj77oO47hCW9RgbN67tWubhw0f43vdcZLnBunWZTjTL+XgS+M/AUeAA\njz02i6KEPPXUGY4cadBqtRkZqfC6140wPDzM00/PcfjwaYaGRGZn12IYaWRZxHUlBMEmn49PeH3f\nZ3q6wb59pzh3LqBYlBgezl0i814q1ahWRUTRZM0a8YrOCd/3u45vq3hlodVq8ZOfnMV1U2zbVufW\nWy833u0ANhO30asHz/Oo1z3i6KPwAkespsUpH5omIEkqrtsimfzvd5oZhkqzaaKqIq2WTbkcIIoe\nqZRJswlRJFIs+uTzsRJZrdbg5MkGjgNr1gQMDeVxXYfvfGc/c3M6J09OkcuFlEp9qOo53vKWrRSL\nF0apxSSfF8+PIfW63ZFL1gD9kjXBKlZxMUxzNSLjZwHXxJGxd+/ele9t2+a+++6jXC6/5PcRExWl\nkGWdNWvGOXvWQJIMRHGYMKwTbwB1VHUEy0oxMLCT06eHSSQGabf7SSaHsKwlBEEnigyiSCOTGaNa\nHSKb3YppfodUaoAoytPfn6ZWy5DL09/MQgAAIABJREFU9eG6AZZVR9clHCdPoTCBLGfJ57dRrw+Q\nSBSxrD6Gh9dRrXrYdgFFyeJ5WdLpMXR9mKGhbbRa3ySfHyMMtU7kQYr+/ixR5FCpaCSTBSwrTT4/\nShhqJJOD+L5AMtlHvZ6hWBzCNOs0GhaaVsT3dQYHJzlxIkUqNUgYJhgcHGVxMUU63U+1apNKgSDY\nRJFKImHg+yIDAxM8+6yCoqRRlAwjI6M4zhS+n0WWLSTJYHh4HYbRplAo4DgqipIBDNasWcfc3DyJ\nRLLjPPARRQnX1UgkkszNxaeqtg2SpGPbIZLkEQQxL4am6QiChCBEeF4cmgwihUKOdnt54S6SyyUR\nRQFRFPF9H98HRVHxvAtHqTCMEAQREBAEkSDwiaVvhQ7/hsjFnH3xzwJRFH8f2/Rm2Y1DmEWgO/lW\nLAEpo+sFPE/s3EeEICQIghBZXiYw1Im7skgsx2oQn0YKgE0csl4gjtQIiCMzVGISUK3zrAk0TSeK\nAhKJNGGoEAQGuVyObLaIYRRIJJI0Gil8v4GuS6TTBVIpjShSyOWu66QaFJFlA8OwGBsbRFXbnYWE\ncF6Ey/KzR4RhRBQJCIJEEPgd8tYIUZSIovC8ehRWUoku974urm5FUSgUXn4OgOeD5fYUf//yZBI3\njCSJRApJavS0syyLet1EEBRMs7dDyfNkisVRPK832WSpVMIw+kgk1qMoZ3vattsBhpFBFJ0rEjOK\nYppMZj2CsJezZ89eZoMco9lsIst9CEI/zWbvPOx63UGWM/i+hnVF1rBlMt7uDtDnUAQyV7QyjAFG\nR3cTRW5Psk2AdHoUVb0JSepN4BljkngcuRJUYD1xBEl3mKaJphXJ5UZptXpL73oeDA8XCQK1SwpS\nBkizXD/NpocomliWjW1LSJJBJtNPJpNCFFUKhSGuv94gmXSQJJ0oWiYSVDpjenTe2CMCMrlcimRS\nvOwmKQxDJEklivwXQKx4+fFtFa8sCIKAYaRJp/PESmGXQ4a4/6ZfjDtAEOJ0z/MhyzKFQvcop58W\n58+zpVK9Q+QJYRggijEh7/nkoHH7ljoysPH8LooimqaTTmcJwwbtto0o6vh+85LngOX+KF4QYSuK\nEoVCmigKCEMXSVpeL61iFd1xJbLPdHrVkfFKwEsuv9oNu3fvZv/+/S/JtZbJBiuVCseOzZDNyvzo\nR08xMdHPPfc8AEBfn8jBg7OsWWPwzDMl3vSmnRw+3GLHjixzcxabNw9RLnv09SkcPHic6ek2e/YM\n02yKZLMShw6ZvOENa9D1LLmcztNPz7NmTQ7Pi4giaDRqHD++xG23bcF1VRxngUcffZZt2/JYVoKB\ngURHwjLJ008/zcxMi92713HmTJXx8SwHDy5w002TVCoBhYLGkSMn2LRphGx2GIByeZpjx+a59dat\nPPtsiw0b0hw7VmfdOoMDB06xZk2OsbGNBIHP1NQpFhYsdu1ah+NIZDI+J07U2LNnFNApFtMsLTXJ\nZjWiSMW2bX7yk8PMzzvcfPMwkpRhcjLH3JzJxESWWs0mmZSoVOKFeyolMTNTZsuWLYBKozHP9HSV\nDRv6abV8BgezCIKKJIUsLrZotWIZUMsKyeVymKbCwIBCImEgigELCw6plMz69TEpXKlk4nkRimIT\nBBGFQgbPi0inEwiCiCgKnc2xsHJKUalUsCyXYrFwQQhinPbgIEnPLQZ838N1fZLJBEEQoWnKBaf8\nURRhmtaKoyQMI3Rdu+JJQK0WbwLjBbV4QduEeNI/fvwEn/jEP3LkyAksyyWR0Ni0aYL+/kHOnSuz\nsHCGo0fPMj09A9SBCFXNAzau6xGnlJw/Eg8AGfr6FKLIQpZTbN26g5GRIoODKQQhlsz1vABRVNi0\naYxcro9UKoHr2pw4cRZFkSkU8iQSKsPDoxiGTK3WYnCwj3bbQxBCxseH8TzI51OdENLnTibDMKTV\nanfaRhLXdTFNm1TKQFXVzs8WiqJgGHH+reu6BEF4Sb3Gub8eqtotV/6VhyiKcJyYU6BXVM9LjfPb\nZsxQb5PJGD1PnBuNBg88cABRFHn9669jZKQ7U3+pVOLhh3/E5OQwe/bs6XkvX/jCF3jiiWf5wAfe\nwebN3ckpq9UaDz20j1RK4Y1vvLXnvX72s/fw3/7b9/nlX97MH/3R/9y1/3qex0c+8p945plFPvGJ\nj7BpU/eUEcuyeOihxxgaynPTTTd1tXMcp5OGMAbM9HRgVSoV+vpiZZPXve52Hn20u5T5/v37+au/\n+iy33rqJD3/4w13tAD7xiU/w+ONH+JM/+SA33nhjV7u/+Iu/5CMf+eOV++5Vpx//+Mf567/+Ju9+\n907+5m/+puf1H3roIWZmqvzqr761JzGq4zjs3fsUhUKSrVu3rvz+QonLGPfccy9bt96EomjMzc1S\nLlcJgjYbNqxj06brCMOApaUyZ87MUyik2LRpI4YRh8I7jnsJoWir1aJWa1AqVSkU0pd1dvm+T6PR\nQlXlns9xPrqNb6t45eHs2bM0Ghbr1o1fQGYfRREPP/wwd9xxx4rt1V5+O47zvNcfVxtBENBstpEk\nkURCxzQtwjAinU6urJfCMKRSqREEPoVCfoWkc35+gZMnp1mzZphMJsPx49MUixnGxkYuiahcXqOd\nv547f90mSdLP3JrgxcbPq/zqDTfAZz4Du7tkSJ47BzfdBD0UzlfxIuP5tM1r4sh44oknVgbZMAzZ\nt28ff/d3f8fBgwdfkutfXDFxqJqPJEVks8bKpvI54iaBMFz2ElvIcszILAgyQeBRr9sIgsToaArD\nMJiZWcQ0RTIZGBrqx7Yd2u2AIHARxfM91gph6CCKOvV6mTBM4Dg1NC2HLAesWVPshLPW8P2QMLSR\n5RSK4nSUUiwkKYGiRGQyBkEQ0Gg4RBFks9oFg3hMmknHNtkhR3LxPJdGw+pI2ZkoSoa+PplCoXu4\nuOu6HDo0je/LrFljMDRUvGKdR1FEvR7XZRDEdbh8L8uIF4Dx/WcyKoqiUK+38TwBXYdUylipS0EI\nyeUMwjDk7NkyQSAyNKSTTqcvedZeaDTi8jWNK8rudcNyXQoCZLMvXKP9fFyu0zYabSwrxLZtkkmD\nZFKkUjGZn2+iKCBJMDVVptFok0hIDAzkmZwscvr0OZ58skQYmuzcOUmz2WRuziaZVEmnQ1S1iKa1\nGBqaQBQd8vk8iiJgGAqlUhvT9MnlVPr7s9i2w/x8E8uyMQydfN4gkzFW+oeuCy+YsHMVryz8NIsd\n27aZno4ddsvjYzdYlo1phshydNUI20zT5Ny5FqII4+O5npvu+fkSjQYYRsjYWHeFjTAMqdVMokgk\nlZJ6cmnEaUI+ohiRyxldIghivPGNv8fc3Fq2bj3Lfff9X13tlpaWePDBY/i+wJveNN41cuSFIAxD\nZmZKOI54xfG/VCqxf/8iohhxyy0TpNPdT5ZfyFj834vl9nnHHW/h4Yc3A9Pcd997uOOOX8T3Q6LI\nQ5Z1DEO8IH0jCALqdfuCeWcVq7iaWG6bX/7yl3nHO+4B1rN581M880x3J+QqVvFS4efVkbFxIzzw\nAGzadPm/NxowNhZ/ruLa4Pm0zWvirvyDP/iDlUWqLMusXbuWL3/5yy/5fSyfnHiej2mKCEJAvV5C\nlmVKpRK1mk06naHRcEmlNIrFIZaW5llaapBIiBSLI0BAFEVIkky16lOp1DFND88TsSyBc+fmSacN\nIOikTcQL+WbzHGEokskYlMtzLC01qNclgqDBmjUyui4xO7tAGHqUyz6WFbC0NIOiZEgmA8bGRFIp\nmf5+Ddd1efzxvei6QDo9jCAIOI6IKMqk0waO41GrtajVPJJJgZmZWXRdRhDSBIGP6zoEgYhpNvE8\nE0hSqdTo788RhqAoEu22japKK8SXw8N9eF6EKMYedlWVcV0fVZUplWqkUvpKjnur1cL3Q3RdxfcD\n6vUIy3KQJA/Pc9F1FUmSEUUBWQ4IggBJijfFmYzRCdWNnQOqqtBum8iyhOd5eJ5HOp3upCa4nfBh\nH1VN43ntFanWRqOFLIsXnJBFUdSJ5DBw3fbK7xzHRRSFlU2P68Z8DpqmXnZz5fsBEJ8sxPd+dTgZ\n4vtuMj+/QLvdptVqI0kSa9euIQwFRDHkySf3cuLESRYWbDRtAFGUSCY1du0axDQdfF/j1KlTRFGV\ncrlFqxWSTsf8EanUCMPDKrmcgmX5zM2VaDQaXHfdEOl0FllWmZ+v4Lom7bZNrQZhKCHLIu22S7NZ\nx3UhnS6wtFShVquRShmXVZFwHAeIB6VWy8Qw9OdNhLcMz/NwnJjIS9e1Vxz3xc8SfN/H8/yOekP3\n96DrOpOT8vMiMmy3HZrNAFkOSaV6OwSnpqaYm6uwY8cmMpnuKRYxcXPcn6+EdttkerrC0FCa0dFi\nV0eKKIrMzp6m2XTZvXtbzzJN02FmpoIsR6TTY10dGY1Ggx/+8Gks62mq1d71JEkS3/ve12m327zl\nLX/a07ZWq7Fv39OMjfX3jFzxfZ/jx09Rrfps2jTY05HhOD7f+MY3MYwEu3e/o+f16/U2jYaHYUik\nUomrIol4uTH6fDz88GPAccDhmWdu5C1veQuiGOfOL5/SWpa9Em0RBAFRJAMCvh9c0ZGxfPrbW/71\nxcGV5qJVvLzRbreB/cDTHD36s33M6/s+zWYbTVNWHNiu63bWS6Ao8kr/CcNwZY2WSCRwXQ9FufoR\nFUEQk/5ead5axc8HrkT2mUzG6SdxyvhLd1+reGG4Jo6Mhx9++Fpc9hLMzlZw3RSLi+dQlByVyiy2\nnaVSOcORI0vo+gi12qNs2PA6guAQr3tdH488coAo2ka1eoTt2yGRiAdiUdQQBIuBgTWcPPkMg4Ob\nmZ8/zPr1e6jXTzIyMoYkRfT1NbFtmyNHHMJQYX7+CSYmdvHwww9RKNxCvX4CTRuh0ThHsTiKbbep\nVpt4Hhw7Nk0utwXfP4YgjJFOlxgbK/L97z/BT36SpFY7zfXXm+TzfciyRy43gm1PMT5+HT/+8TMY\nxgaeffb7jI3dTKt1lnXrBpGkWIFAEBI8++wp+vu38OMf/4gbbvgF9u8/yO7de5idPd2RiJ1DklQM\nI41hzJPJ9HP2bJVcbpS5uWcZHr6OY8cOIYojSFKFm2+OWdvPnbOJIhlJKlMoFJmdPYsg9NFonGNi\nYgOWVaZYLOA4Ns1mgKJoCEKLXC6zwj69jFKpRq0m4zhNCoV4cyRJLoIg4XkRQSASRQFB0CaZjNVF\nqtU6S0sAPuPj5gXhnsmkgmW1SaXiBbFp2ti2SBT55HLxorvZ9AGJILAvG3WgaSqua3X0zK9eVEK7\nbXL48BKHDi1y6tQSlYpPJqNy000i+Tw880yVf/qnk8zPCzSbTbJZB1G0GRrawIkTJ1i7tp/9+48T\nBBJ7957FskJcV0BVA6LIRxRttm7NsrAQoaopZmbOIcsFpqZ8du50EASJajVElpudvPGAYjF2IFQq\nLpaVxrYrGIaF4/jYtkEqVWX7dpVE4rl6iKVQQ0CgXJ5HkopUKjXWrRt43hubONrIpV53kCSJdDp8\nUXJ+V3FlxNFVNoKg4TgWuVzv9/B8F6PNZotKRUZRHIaHu2+kl5aW+Nd/PY0sj7Gw8AS/8it3dLVt\nNFpMT7uIYsjgoNnTmfL97x+l2VzDqVNTbNgw3tV2ZmaGb32rhiRlqdf38ou/+NquZU5Pz3PqlEAU\n2YyNtS8hhlzGwYMHsawIeDfz85/pWh7AJz/5Sf75n/sIwzX8yZ/8Offc88mutg888EPm5sb5yU9O\n8nu/N9T1+rZts3fvAp43jGkeYceO7ukyn/nM/8u//VsKUbTZsOGf+Y3f+I2utufOlWg2syhKibGx\ny5O3vlBYloNlCStj9KXtSwb+R+AEf/qnH+Otb30H6XQeRYnI5yNE0cP3FaLIJZ+PnRGqanUcBFce\nv5fnCPDI5cSXbEMUH7z4CIJEGDoYxiop6CsNv/mbvwn8JjGR/Meu8d28uFhcrNFu60CLtWvjdXKj\n4dNs2qiqjK6H5PMSoiiurOuiyCaTsdG0LJZlX3WyznrdAjRs2yKfX10//LzjSmSfkhTLBl/JbhXX\nFtfEkVGr1fjYxz7Go48+CsDtt9/ORz/60cue5L7YCEMfSRJJpRI4joZtxyEssgxB4JJICKRSEhCH\n0qfTOrYtYRhJCoU8sTymTRQFiGJ8Ip9M6iSTOrquAAGSJHb4EyCTSaKqEqJYJ4pA0yTAJ5XSKBQy\nCEIGw9AIAr3zvyH9/WlEUWdpqUw+n8ZxEqRS+op8J8Q8DqoKyaROOp3Atj3CMAACwtDHMLSOhGnc\nG1VVIJPREYSYWTqKNFIpA01TkGWZIPBXyMdim+ciDWJCSJ102qDdbgIhsiwBIaIY1yn4Fy0ww45H\nM87f1DQDyxKpVBqEoUWxuOzxDDvldN/gxiRPcS60IAikUgayLFOrWURRfFp1fhizKIoIQngBOdQy\nYm107Tzb5Tzr+Ofnwpq6hzaJokg2++KMcpIkEEVxvcbvMiQM4+iWVEpHln1iXgyFdDpDMql3wvKr\naJrG6GiBSiWgUrHQdRVVFVHVZRLRJOl0GlGMn1NVFVQ1ZgBXFIlEIollOfi+jaKoZLNJxsdz9PUl\nWFxsYFlhJ81Eo1w2sawAQYguWXg893PUqd/l53n+WC5j+V2sesevLZb78tVcZGqaRjqtIkm9y9Q0\nDdt28f0Ww8NXIvAUSac14oi43uXquoznKSjKldn8ZTnC9+NxuxcMQ0XXBcDraRejCOSISaa7o1Ao\nIMstwKFYvPIm2vc9ZPnKJJLZrIHn6aRSvUkICwWFZFLG8+Se0TBAh3QvnhuvFuL5qNcY0OT8OgzD\ngHq9iaZJ5HLZFTLm88f4OGryheKlD8W+8rOv4uWMRCKBZenExNsv/Xr3pUTcVp+b688ffy9uw7Gi\nXGx78Rrsat9TTBB+9ctexSsPVyL7hOeUS1YdGS9fXBOOjLvuuovt27fzvve9jyiKuPfee3nqqae4\n//77X5LrL29Oy+Uy+/btY9euXXzqU5/ihhtu4Gtf+xqDg7GO/MLCAjfffDOHDh3igx/8IF/4whd4\n5zvfyec//3luvvlm5ubmKBQK6HqBRqPFnj1bmJ2d4cYbb2Tv3r3cdNNNPP7442zatAnHcRgaGqLZ\nESVWFIWFhQUmJyeZm5tjaGiIAwcOsHPnTp555hkmJyeZmZkhk8mgqirNZhNN0zh27Bh33nkn9Xqd\noaEhjh07Ri6X45FHHiGXy7Fhw4YLrlUsFpmZmSGbzXLixAk2b97M2bNnGRwcpFqtkkzGfBmtVoti\nscj09DRbtmzhzJkzbNy4kWq1Si6XY35+fuWU3bIsxsbGaLVaGIbByZMnGR4eZmlpibGxMRqNBqlU\nCtu20TRtRfLUMAyq1SpDQ0OcO3eORCLNwkKdVMogmRRIJBJUq1Xa7TbXXXfdyj0ts/3Pz8+Ty+WQ\n5TjksFKpADA0NEQYhh1Z2xZ9fX34vo+qqlQqFQzDoFKpoKoqfX19K/lWvu+jKAqWZZFIJFaI4oIg\nuCAvK1ZI8Ugmk0RR9KISSF2cDzY7O8uXv/xljh07xuTkJKqqUigU0DSNSqXC7Owsx44dY2FhAV3X\nGRwcxDRtWi2LwcFBarUlyuUa7XYbx7FYu3YNmqbRbrdJJpNs2LCBgYEBdF3HMAwWFhYYHBxkeHiY\nbDaL67pIUpxSZBgGQ0NDpFIpWq0WrVYLXddJpVJ4nke73SaTyZBOp1EUZcXRtPz+BUFAURSq1SqJ\nRKJzEqoSRdEFbP2yLHdJ4fFXtOkN4zm+Ad/3V64VhmHP97Nse6VT1Djc/MV91680nN82l9+Frl+Z\nE6Zer+N5Xlc51WWEYcjRo0fp7+9nYKA7R0UQBDz44Pc4c2aaX/u1f8fAQKFnuY899hjpdLqn9CvA\n4cOHueeee3jnO9/JDTfc0NP27rvv5vDhw/zZn/1ZT44M3/d58MEHGR8fv+L1L17oPx/bK9nZts1f\n//Vfs2vXLu68886etg899BCHDx/md3/3d3s+E8Dg4CD9/f0cPny4p92RI8+yf/9T7Ny5nW3b1vd0\nUJdKJUqlUs8UGIifeXp6Gk3TGBwcXPn9cvt8//t/i7vv/iwAx48fxzCSlMsO5XKJgQFj5X90XSfZ\nWZ26rosoiiiKgm3bSJK0Mk6c/16W5xnLslBVtWt6nO/7iKJ4VVJpzofnLUt8K6upJS9TxKpo/gVt\nY7ltmqa50ube/vb38E//9PlrdZsvCpYPWmRZxrZtFhYWSKfTFArxGF0qlWi32/T396/M/xDXWavV\nQlVVNE3DNM2OAszleYWe7zx+MZZToxVFuep985WMn0eODN8HTYs/ew2l69fDv/0bXNc9SHEVLyJe\ntmSfO3fuvITY83K/e7GwXDF/+Id/zsGD4zz00F8CbwX2AcOACdSAjcBDwG8D/zfwe8A96Po7sO2H\ngJ0oShXDcEkkNpDJnGB8/E5mZv6ZwcFf5vjxuxkYeBft9nfZvfuNmOazVCqxMsTwcIu+vl1I0hTr\n19/O3r33k0zexqlTXyebvZ2FhUdIp7cShtXOyXuWmZlD5PO3MjJyjN/6rd/la1/7EtPT13Hq1NdJ\np28mDBdZt26A4eFRCgWXfH6Mw4efxDC2cvToA/T1vRHL+gk7d/4C09NP0GrlaberRFFEIjFEGB5j\nfPw2lpZ+xOTkbTQah9iy5dVUKgeBCWy7AsjougFUGRjYyI9+9BCieD2Li48zOXkHun6OO++8jcXF\naQ4etDDNNmNjMrncCE8//SS6vol6fT9jY6/izJkfMzi4g2ZziomJzVQqs5w6VUNRDNLpJuPjN6Bp\nc0xO7uS73/069fomEokZfvu37+TcubM89NASnmfzmtf0MTq6nlOnTiJJg0jSPGvWbGFq6hCmWaTR\nOE0uN0oiIbJxY4FkMk21WkOWU8zPn0GWB1CUOmvXTiJJAblcEsuymJ5u4fs+YeiiKElU1SWTKaDr\n0U9NDPp82ybERIW/8zv/B1/84jyxbOoSijKA5y1LA0OsSGIBfcRSqw6xUkm+8327YxsQy71ViKVZ\nl2Vl5c6X1+EpKSCKbVKpBIVChmxWxPPixUU2K7Jt2ySFgkalYhNFEalUgsHBVCe9JiCTSZDPG4yM\nZMnlMshyLHcbBCKplIrv2zSbArValb6+AqkUqKpOq+V0Fhgq+bxONnsp2WNMsmgRhgLJZEzYF6et\n+IRhQBSFndQn4QIyv2XEpL7x6XAvUlbf96nVHARBIJNRVsn/Olhum88R94poWtTzNHtubo5HHplF\nlhVe85oiw8PDXW2/+c3HOXxYIZ2u8L73vaHrZvrMmTP8x//4jzjOGnbvLvPxj/9h1zIfeeQR7rmn\nhKJYfOhDu7j++uu72q5deyel0m1ksz/h3LmvdrW79957+dCHHsfzitx++zTf+MbdXW0/85kvcf/9\nDslknf/8n990gcrGxRCE24nnoc8TRYd62AnA+4ilWj/Rc5L/9//+w+zfvwldP8Kjj/6nCzb+56NU\nKnHnnf87rdb13HjjSe6++8+7ljkwMMDS0nuBgN27H+eJJ57oavue93yUY8fWMDJygq9/vXuZpVKJ\nv/zLh7AsgzvuULjrrrd2tT148Ag//nELXXd529u2rmySLlQt+UNgCvj/+PSnv86zz1Y4erSKovhc\nf/0YGzeOsWXLGBMTWURRpFz2UJSIRCKi2ZRptxsMDfWRSDwXbReGIfW6RanUwLYjkkmZiYnCJc7O\nmLQ26hBSXx1ekFW8MuD7PmfPVggCmb4+cYVr5sK2+XZgLfCXP1Obx+X+EYYiguBw4sQCZ8449PcL\n3HrrdViWxde//gzNJuzYobF9+2by+fhwKCZPljAMAc8LmJoqdQjctUuIl5fn/CiKyOW01cOGq4Cf\nR0dGpRI7KarV3na7dsHnPhd/ruKlx/Npm9dkhk0kEjz22GMrPz/++OM92exfLJw65VMo3AwMAjuJ\nN4PDwFDnc1vn80biiecWYADb3rVi53kTeF4/qjrK0lKSMJykXNYpFG6h0chQKNxKq5VBkoaoVESC\nYIQoGqFcllGUccrlgHx+HbOzEf39O1hcDBDF9ZTLORRlAMvK02ymgPhT07YxPe1hGAXOnXPIZjdS\nqymo6kZMM4vjSHheklotArKUywKFwkbm5kJSqY0sLIDrZlhYiHDdDI6TwDQ1JClPuSyh6yMsLMT3\nVCq5GMYACwsiYZii3RZwHBXPMyiXA1otkdlZkb6+LczMtJDlARYWXBwHSiUL388QBAa2LWJZEuWy\nRCo1zvy8Qza7hkolJJMZw3WThKHWKb9AGOZYXBSAAouLbQyjwPS0T1/fFkxTw3FiEqkoygODnbxH\nlXZbQNf7WFz00LQUpZKHLPfTbsuIYgLfT2BZHiDieSKgUan4GEaBRsNHkhTCMO40scdfJ4pkfB8k\nKYFth8iyiuddOUz7aqDdbjM7axNLMq4FBvC8EWIJ1SFgtPM1Qux0GyXe3KzpfI11/jbc+bqu8/No\n5+8jwHjnbwMEQR7PG8VxRnHdIqaZpdVKYJp9eN4ArZZCrQblcoDvpwmCFLadwLJkHEfB8xI4jkoQ\n6DiOgOcJhKGI5wFoRJGAZfmduhQAFdcNCQKBMJQJAgmQCYLLnzSHYUgUiYiisqJNHwSxLn0QCAQB\nCIJ8gW79+QiCOKUmVujp/g6Xte7jcl+ad/1KQkxqC7Ksdq3rZXieB6SJohT1utnTtlRqk0qN02pp\nNHrQhMd/GyWbvYGlJbdnmbOzNQxjHM8bolwu97St19No2mtpNDKcOXOmq92JEyfwvHXI8q3MzvYW\nmZ+erpBMbsbzij2fKT6lzQE3Aet7lhljG3H/7o2ZGZdUaie+P8ZTTz3V1e7o0aPY9gSp1Hamp+2e\nZS4tVTr3uJHp6emetqWSz+DgrbTbGao9VoylUgnLSiEI40xP915Z1mommjaCbSdptS5X/wPEHARx\n/dTrEmFoIEkjuG6eVkvDtiWLd9Q+AAAgAElEQVR8X8K2fRwnHpOCICYxFgQd1wUQ8f3nxqI4GkPA\n80SCQMXzYiftxfD95XFG6DnOrOJnDzEJsogg6Nh2t7S3QWAT0N2p+krEcv8QBBnbjgnqZbmAbcs4\njoNlWURRGkXpo9WKiCKxM6dHK/O654W4boDvy0hSCtsOLrnO8pwP0mr/WsVPjWYTrpAZCcSpJZ1A\n+lW8THFNIjIOHDjA+973Pmq1GhDn/H7uc59j586dL8n1lz08jz/+OF/60g/R9QXuvvsQoniOcjnO\n0fb9KpK0Hl0/gyBMMDQ0y8zMGBs31llc7COTaZPLjSHLJuPj64GI4eEUjYbGunUCJ09GDA1ZHD7s\nsmNHAsNYRy4ncPp0vJjeuHGYej1k+/ZBKpUITTM5cqTCwEDIoUM1BgYkwjCBIAgUiwaOA/X6EnNz\nHu94x6vo6xtlaWmahx8+TiYTMj1tMjqaYHIyjn+amOin3Q5RFJtnn62RTLocP95mfFxG0/rIZhXO\nni11FCAiGg2XoaEcjUbIddflmJ21GR9PYtsKqZRMtdrCMNTOphTSaY12O8DzKpw4USOZjFhc1Fmz\nRuXGG69HVUUOHDiOIAhMTBTx/ZgbZXGxzehoglotYnjYYH7eIpfTkCQVQfA5fjxm8l6/fphq1ea6\n64r4vky7XeaJJ86yc+cIW7dux/ddHn44Pgm85ZZtRJGE57WpVByGhtKEoYwgeJw8uUQmoyDLGrIM\nw8ODSFI8sdq2jyj6NBoufX1JVDWBqsZyimEYUirF7VMUwXVjuVffjzCMF+8U4GLv4ze+8S3++I//\nT44dm2XjxgFyuQKOYwISjmMjSSKLiwvMzjaJI4mWoy884qiLmGTzuRx9pfMVABL5fB+FQpZ0Ok2x\nmKDVioCQgYFBCoU+stkEqhoRRQrptMaaNZtIpXQsq46mqSQSOvl8BkmScF2PREInkdDp78+hqkpH\nbjjC83x0XUMUoVptE/O/KGQyBlEUn7LEzNACyaTelWjRsmx8PySZ1BFFsROua3fqDoIgfj+Xi7YI\nw5B22+4wo3cnyju/TMPQV0O4Ozi/bbqui+P4JBJqz77geR4HDhwjiiJuuGFzz+iWmZkZfvzjE4yP\n57n55t7HH3/3d/dy5MgCH/jAW9i+fXtXu8XFRe6777sAvO99/8NKWPfl8Ed/9L/yla8c4c1vHuXT\nn/7bnu/9ttt+lZkZl89+9n/h9ttv7/lMn/jEfUxOZvnQh7qTYkZR1Dm53woc6XkC8ZrXvIYf/CB2\ntNxyywQ/+tEPutp++9vf5r/+1/u5/voBPvWp3uSCf/qnH+fgwQU+8IHbedvb3tbV7v777+dXfuXD\ngMBXv/op7rrrrq623/nOd7j33u/zpjet473vfW9XO9/3uffeB5ifN3nXu17H2rXdJWVbrRaPPfYU\nxWKKG2/csfL7C0+9dwBP8cAD32F8fIBSqcH+/c+gaSIbN65naKifYrGfYjFHFEVUqy10XSGR0CiX\nm8gyJBIGmiZfMBbF8t8WpumQTGqXVXcJggDTdK44zqziZxOVSg3b9unvz6y0neW2WSqVKBaLwHbg\n0M/cKbhtO3hegKbJVCp1Tp6cY3g4x+Rk3J/37XuKcrnFjh3Xkc9nV/jJlv/PMOK1V7lcw7Y9BgZy\nlxxyrs7PVx8/jxEZhw7Bu98df/bCW98Kv//78ecqXnq8bFNLbNvmq1/9KlNTU9RqNbLZLIIg8NGP\nfvQluf5yxcQhoD623WR21sT3W5w71yaKXAxDR1UzrF2boFAosrAwT7OpIIr/P3tvHiTHdd95fvKs\nrKqso7urb3SjuwHiIgCKB3iIp0hapmRZt+T1aB2W7B07VpZjx3ZYofDOzkheH2MrpLDHZ2issT22\n5YNaS0PbI9HWTfEST4AgcQONvrvrrsrK+9g/sqoJEKhC8wRF9jeioxCoX756+fK9l+/93u/3/daB\nNLousmPHJIIgUK+bhGG8AQQJQfCJIhnLamJZAum0zNjY8/nhsaymie9HGEYT1xVIpwUSiTSeZ9Js\nhihKiK7rBIHPwkKZKBLZtWsYXdc5c2aRUslmeFhjcnIcz/NoNBxkWSCTiZ0fnfKz2QSKotBotPA8\nCd83kSSFIHBpNl0kSWBiooAsy22biHgDLGPbLUwzQpYDstkcohgTwp0bKmsYJo4TUq1WCMMsUdSg\nvz+PJEEUCYiiQDabRJKkC8rXNOmiKiDnYmWlRLPpMzCgXXTReG5bdu61VKpQrbpkMjIjI68MUz5A\ns2niuiGplPyqLVBfOGht22F5uUqxWMX3Q5LJBIODeTIZFcPwKBYrtFounudSLNZwXYG+vthZkUgo\nzM3N8oMflDCMCrt2baVQSJJKJRkYyHHddTOYps/8fLnNKSIiSQrJpEo+n0ZRNBIJFV2PTy7r9Rat\nlo/vewwM6GSzSXQ9hWXZrKzUEUWB4eHceeSpm3jj4KUsdmK1GYsoglwu+bqWvHv22RMsLLgMDkpc\nc013noYgCDhyJFYB2rGj0JOk+ty5OSbh7b7ovuWW/51Tp/LcfLPPl7/8p13tDh8+zMc+9heEocR/\n+k9v5T3vec/GbvAS6MzP6bTScwzXajWeeGIOSYLrrps5T9L6pSIIAup1mzAU23xJ3efXF77vOu+j\nTv+87robeeKJLUCJ++//Nd7+9rfTalm0Wm6bqylJNqtthqRv4jVDp2/ef//93HPPbwMDXHPNPE88\n8YPLXbXXDHHqiUkUsTn+Xmd4MzoyHnwQPvnJ+LMXPvQh+PCH489NvPZ43aaWvOc97+G+++5D0zTG\nx8fRdb3nSdmrBcvyUZQ08/N1qlWRw4dXse0CpjmEZYXo+hC1WoCipGk2Q/r7CywttUgkxqnVJFqt\nFrZtE4YKQSBjmiGSlKRWc1FVnbU1k2RyiGYzxDTNdtihh2VZ2HaE40SUSg7J5BArKwaqqrO8bCAI\nGarVgFrNYmWlimWlEMVhFheL1Ot11tZMcrltrK7GXmnH8RFFrR0qa6+XHwQSth2fxGcyKbJZCVVV\nCEOZatVscxXEjqVYX1tAkpLU6w6qqrO6aqKqAzQaIb4vEgQyQfB8qF8YhjhOLN2Zy+XYskVlaCiP\noqRptQKCQCYM1fU6eZ6ALKeo1WxUNQ4bfGEH9TyvHY7e0SEPSadHqFYvHu4cE2tJiKKG48ShnNWq\nSzo9Qr3uv2Khh517VZQ0ltVbKeGVhGV5uK6MaWZpNhNYloZti5RKLSwLKhWBVktjft7h9OkI1x3l\n9OkW1WqSuTmTkyctYIpWa5BKJcHcXIBljeF5BU6dWqFS8TCMNOVyyNqaQL2eoNEQcRwJVYVcTkUU\nRXxfQlFSRFGEqqYQhCSOE5N0Nps2gpAhCJKYZu9Q/028uRBzzKhAAtfdiHLH5UOrFTE8PIXjnD/P\nvRCGYVCtivh+ltXVes8ybdtrz8PRRdMQOnjyySc5cWKAkZH/i4cf7p2C8/jjjxME+xGE23jsseO9\nb2qDCIKAVsvDtsNLjuFSqUapJLG0xHpU5cuFJEnkchq53KWdxOe+7y72nJ54IgTeD9zE5z//eaIo\nwrYDRFGj0YglqD3vtZvDN7GJDj73uc8BNwLv48kn31wbx/hdoCAIGo7z+n4XbOKNj0ZjY6klmcxm\nasnrHZfFJbq4uMj9999/OX76PKRSCq1WC9+vc+SIRxS1SKfPksmkGB3tQxTrbNmSxfdb5PMK1WqZ\nsbEUrrtIOh3hukp7geQiyxKKIhGGFv39Gq5rMDqawTDWUFUf0xSp15sIggxE7XxpjUwmwrLWGB3N\n4LoGfX0qa2tFXLeGZcUnfYpSxfMMbFthZSVCURzq9VOMjcXOH01TcF2bKPJptRSCIKReryOK2vrJ\nWkcxQhAsarUWptmkWIyQZaGdPyyRSIDnWQwMJHFdg4mJDI1GmYEBBUUJkaQIWT5XqlRE00Rs2yCT\niX9LUTyaTQtdl4ginyBwaLWkdhqATRCEFAopXNcgmTyfEd7zPOp1DxDIZMI2waRMvb7CwMDFF7ex\ngolLEDzPEj4woFEur9DXp75iRGvxvQo4Tot0+rUjf0ynVVIpE11vkkpFaJqApiUIAgHDcFAUA9f1\ncd0mktTAMBqMjuZYXZ3FthXAwjAO4fstmk2T/v5+fP8sophnYGCa1dUa5XIJz3PI5XQEIV7g+75G\nGMbqMLF6h0u12kBRFILAQhSVdqqISCajYVkNFEVA19/YknKbeHGI2eHjjbmq9o6+utzIZCQWFs4y\nOCj3jBxRVZVabQ3blhgf766uArFUdb3uIMth1zkM4MorrySXW+DEid9n587evBv79u1DFP8Hvi9z\n8813976pDSKKIorFGpYlMzws0t/fXYLVdU2ee24W8Lnppt5SrS8GkiRtKGKn876TZeG891EHAwOr\nlMtfBtZ45zs/jCAIJJMStZpBGDo0myK53MuPItnEJl4s3vGOd/Bv//YV4CT5/PLlrs5rinitZhEE\nHonEZsrVJi4vNurIyOXgFfLXb+JVwmVxZLz1rW/l0KFD7N+//9LGryIkSUSWBRKJJNPTg9h2in37\nsvT1xSkMYQj9/c/nOQ4OJoB++vrS2LaDaQoIQoQsi0iStJ633+FfyGb7GB1VaDQMqlUbxzERRYUo\nglQqRSqlk0ikSaeT1Ot15ueLpFIi27aNUamoBEESQYiYmBhFlmXOnKkhCDIjIyOMjRXw/TjcX1Ul\n+vt1XNel0QiBCF1Pk0zGqSr1eotUSm3npwsoikQikWJyMoMggGU5CIK5Xn/XdbEsj2Qyy+Cgus4t\nIIoXhkWn08nz9JUVRaG/X1m/RhBCgkBCEMT2PffmJgABQRDpBGoMDfUzNBQreMzNrZHNauTzz88+\ngiCss8p30N+fp7+3IiOWZeO6Aen0xvkudD3FKxBF/aKgqioTE0NMTMQbJtO0cN0AzwvRdY1EIqDR\ncBFFla1bJwiCBsPDA4yOuiws2KhqxLXX7qNcDvA8k1RKZO/eEXbunKBeD0gmTaamxoCwHXYtk80m\nyGTSbW33OOdc1zWq1RaimGgrmjy/gUkmNbZufWkLkyiKaLXi1INO/9vEGweiKNLXd/k2jefOXZfK\npx4ZGWZ4OEEUOedwLVwIQRDYsmWEKFJQlN4bb0mSGRhIEIaXjgB429tuYW2tj337rJ52Y2NjfOhD\ndxAEItu27ehpa9s2a2sNdF29aGreuUgm43eSKPZ2pASBQC6XRRAuz6mqLMv093fvU/v2vZXvfncr\nkGZmZgaAVCqJLEuoavyuiFNZ4nfnJo/FJl4rxP1xKzDEvn1v7Hed53mYpoumySQS8aHHC9dqm9jE\n5cJGHRn9/ZdWNtnE5cVr6sjokLIFQcCf//mfMz09vS6xJwhCT1b1VwOG4XD69BLZbB/f/vbXmJgY\n4V/+pYbjeOzefQW1msmNN05TLK5w8803873vfZ/bbruFL3zhC7ztbW8jndZRFJl6XcKyGoyN5ZAk\nEceBSsWgvz+NJJkEARhGE1GMaDZbyLJMOq1RrdaYnp4iCAJOnCjhullarRq6XmdkJEOz2UJVYweE\n53kUChKl0jJDQ9Pt9JGAatVC02QMo9nOVTaRpKhNyNgAElgWhKFDOh0vzgUhdnS0WkVAIAzHqNc9\ngiAmhwwCGUFI0Gw65HIitu3SbAZIkkAYGiiKcp5GumVZJJNJKpUK/f392LaN5/lYlkgUyaiqgyhK\naJpOEARtYkgXVVVxXRdZltc/U6mAIPCQZW3dxjAMikWjrbDSQtdTyLK8njYSqygEiKKIbdvn5Wxb\nloWqqusnfWEY4nkerVaIJCVotRySyRBVVdfL6GxiOhuZjuTk5dhk+77PmTNnWFhYYGxsjCBIkEpl\nsKwyrutQqwUsLjYwzSJRJCNJGSoVkyBYYXU1xPclTp9eIIpCTp1aYHz8LZimgeNUsG2BVisgl8tz\n4sQxhoZmWF1tkM/LZLNNhoaS7bQmkURCQxRlHMdG15V2qpCHqsaOro4uuyRJ61rynTbsPPNO+3WY\nygVBwPM8DCNuf8dxe24qOs8nDENEUTzv+YRh+LL5F86t8yZYf24vF24sAdGVwPVcvHC8doNhGKys\nrLD9EuLujuOyuFghmUwyOir1rEM2m+Sppw7ylrfs79kHJEnCcarMz6+wffvNPX8/ldKoVBbJZHRk\nubsyVxiGJBJZvvrV32Pbtl/oWWa1WuVTn/otQOXmmz/Hjh3dVU7W1hq0WglM00bX3a73L8syiYTN\n008/zjvf+faev5/J6Cwufp0oEsjlLq1Jd/LkyUs+J4j7WxAEG+onvu8jiuJF52TH0Yii3wFgefk2\nwjDE9/32YUM8/ztOhCSlMU0HSXLXndmv1By/OZds4mKIU6H+GgDL+neXtzKvMppNB0hQr5sMDqrr\nawHP85DlOOqt2/h4Oe+ezbG3iY2g0YjTRi6F/n547rlXvz6beOl4Tck+Z2dne34/NTX1mtSjQx7y\nW7/1Zxw6lOCBB/6U5eV9RNHjxNKUIrAK7CCff4axsZ+gVvsKIyMf4skn/xD4cVT1KT7ykZ8klXIo\nFj1kOc9VV0lMTl7F8eNPIEnb8f3jXHnlW1lYeJpiMU+t9hyOk8d1HYrFRXK57XzgAwXuvPMe7rvv\nX1lezhEEJ7niimuIogq53DCOY2JZFqCxvDyHIAwyPGxwxRVv4eTJpyiV+imXn8GyBgmCEtPTk6hq\nAlW1SadHgBK53AyJRJ2xsWmKxXnqdY25uWd59lmXKDKZmkqTyYwhyzVSqS0UCg5bt+5EFE3y+QGW\nl+c4cqSF4zTI5zOoaoprrhmiUChw5MgZmk2JkycPY9sFwnCOyck9+H6VXG4AQRDxfZtEQmdoSCGb\nHaDRqOD7Gq5bR5YzmGaJ1dUIsBgd7SeRSFKrVYEklcoCsjzM2toxEomt5HIOd999LWEY0mg4uK7L\n2bNrhKFEvb6GKBbYulVk587trKwUmZ01SCZh795JgiBgfr5CEHTIVXWq1RKQQpIscrkBwtBBEBRE\nEfL5eOMRk7kKZDLKhhbZr0TfhHhT90d/9A/87u/eT6UikE5X2bt3F9msQrPpUS6bVCp1Wi2fKAqQ\nJHDdANeV2mXUiCUdO7KPCWKi1RSZjIAoJkgk0ohiDVkeQZaLJBIjBEEDQdCRZZ90OkOhoHPVVUNM\nTc3geT75vEa5XCcIFKLIZnh4mPHxLFNTYwwPx/woMalXSBB4hKFEJpNgbKyPMAypVEwMwyGb1TCM\nJqWSTyYjsX37SFdVi5iYN8TzLCQpgarG5IkAjYaJ50E6/dJPV2Nteg9BiMjnU2/6yJCY+FEgkYgj\nkeClEYIZhsHRo0UAdu0a7EkMubi4wuKiSyoVsHv3ZNeFrGEYfOYzf8XycpK7707z0Y92Z+F68MEH\n+cIXjqOqAZ/85O1cccUVXW1/7dc+z3PP9bFlS5E//MNPdrV7/PHHef/7/wTX7ePnfz7NZz7TXQ3k\noYce4t57F0ilTH7pl95FodCdfFgQ9gE/AvwtUdQ97Dx2Fn6YeDz/Zc9ncvjwMQ4fNsnnfe666y1d\nx1epVOLDH/4NyuUt3HVXi89//j93LfMTn/gEf/RHLqLo8Qd/cICPf/zjXW0///n/waFDCuPjVX7z\nN7vbua7Ls8/O43ki09N6W9nh4jAMg+VlE1EM2bq1sO6EOF+15KeBk8CDHD58BsuKyOVUVFXCdUXA\nJZPJ4vsOipLAth10PUU2q/ZU1tkIYmUTH1GMLiDH3sSbE+f3zVuI5Yt7j90fdiwvl1haMpHlkO3b\nhxFFOHjwLGtrFmNjaWZmRujry1zgcIgJ5EFRIrLZFxfBYdsOhuEjyzEx/aYzY2N4M5J9/uf/HCvd\nffrTve3+9m/hvvviz0289njdkX1OTU31/HutcfRoA7iCpaUkUXQAmCKWbdsCDAK3UasNomlvZ2mp\nH0m6HZgAbsV1r+DsWZvlZY1GI0MYjvPcc2WKRZGTJ1tks1OcOdNAEIY4dKiCIIyxtCTTaOSp1TSq\n1Ryi+Ba+973T+L5AGGpMTU1RrysEQT9nzxpUKj7FosvqqkC1qnDqlIUsT/Pss2sEQZqDB9fw/Qwn\nT9Zx3S2UShmWlgJqNZXjx02KRYmFhRaalmVxsUat5rK01CSTGWB11aLZ7KdSyVGvS6jqEIuLAYnE\nBOWyw8BAFlCQpASViocoZmi1QppNAcdJUyxWaTSaNBoRmcw4x47V0LQZzpxx8P0czWYKWU6hKAlW\nV1sUiw7z8xUMw2VxsYZti8zPVzBNOHJkhTNn6pw8WcMwfBxHYnm5Sa0WceRIiWZTZnXVZmCggCwn\nOXlylpWVVYrFGktLaywvt5ifN5iba5HJTLK8HOfkr601EcU+6vWQWq2GYRgEQQpRjFNqEokQzxMQ\nhBTlsoUsa7RaAYKgtlVnLEzTpNl0aDQcLOuVJbKMJSydrt83myYHDy5Rr08TBNfSaAyytpZmdTXH\n0lKGpaUMa2sFWq1BTHOSZnMYx8kQRduAK4ARYDvxomkHsAsoAAM0myr1+hiNxjgrK0kcZ5LV1TSW\nNUa12o9hjNFojFAqTVAq9XP8eIO5uTr1eoJyWWRlRaRW0zl1ymNursXqqoXnyViWg+tGNJsBpilg\nGBFhmMZ1xfZpTEAQSARBEteNMIyAbLYfRemdH2/bPrKsYZohoqjieQKu62KaFo4TtSN4uhM0XvpZ\nBIhigijqTfT4ZkAURXgeKEoS1704Wa7v+1iWfUky3ThyTMWyNOr13ikLS0t1PC9FpeKtR3FcDCsr\nK8zOplDVa3n66dWeZcYS09vwvHGWlpZ62h46VMP393PyJG0Oo4vj4YcfxrJ2E0W38q1vrfQs89FH\nz9BqjbG0lGZubq6r3W//9m8TS6++nXjMdkfc5vuAm3raAVhWfLLpefQkGz169CgrKwVU9U4efrjS\ns8xHHz0D7CAMt/HUU0/1tH388RXCcCfHjzusrHRvK9d1sW0NWR6kWu2dWtNsOnheAssSu/STEeLN\n4vUALCzUsW2JWs2jVnNwHBHT9EmlQNM0JCnRdm4oeJ7floLsnjYTk4d2t/G8eC4JAukVI5t+KQjD\nENt2ej73TVwOXAfcTNxP38gQcByHMBRxHL+9ntIIwwEMQ8BxwouOD8vy8H0By/IvuoGJogjHcS46\n9l03QJY1gkB807/HN9EbzebGU0sqvV+Jm7jMeFMfFYyOuszOfoNMZgX4GvAM8AhwEDiMKP4Tw8MV\narX/xrZtVVZX/x5YBO4FnmXHDoWJCY/BwSaCEGvUnz17hmzWZnn5OXbuzFMsnuDqq0cQxZNccYWA\nri+Ry1WZnq4hCN/jjjv20mxaDA0laDQWmZlJUa+fpr9fIIpaqKqLphnIcpnxcZ96/Um2bctTLK6Q\nSnmcPTtHoZBAFI8wMlIllzMIw1UkyaRaXUUUHcrlIr5vsbRkEoY+nldi27YBFGWWvr4yV16ZZWSk\nyZ49eUzzGLt3DyMIDkNDOlFkMTSk4boVdF0inXYQhBKqmsbzVPr7BWx7lttvH6fZfJIDBwroepVd\nu5KMjmokEiaVis38fIXV1SKlkk29XqVYrGPbLWq1JtXqGidPrrK4WEEQamiaheuGLC9XMIwSs7NL\nCIJFo1FiYeEsp04pPPTQPM89t8aZMw1On56jWrUpFAIc5zh7944BMDio02otoSgWvq/h+wkSiRaS\nVEcQFFxXwfedNqFpkjA0KRSSyLKHINiYpkCt5rGyUqNcdmi1eqsJvBjEfCYBzWbU1ZmRy+m85S1D\n9PUdBf4VXS8yMtJkenoFTVsiCM4QnzoeB54C1oCzwJPtvyXgW8DT7b79XWAeKAEGgvAMkvQU/f01\nXPcZhoYsUqljDA4uMTx8mrGx04yOPoumnUHTBFw3wvMWUNU6Y2MhQXAKQajSbBq4boNUyiWfz+K6\nFomEiCxbjI2lSKVM8nmxnZKkkkqBrjvoukQyKWOadZJJqefJpa4n2s9HQxRdEomIVsvHcWR83yaK\nLFKplx4tk0yqiKJDIhG+6WXhBEEglZIIgha6fmGbRlFEvW5jmiKNRu8xkUwmEYQm0Ljk6Vo6LVEu\nLxNFds9T8cnJSUZHK9Tr/8b114/1LHPv3u2E4TNo2pme0RgAQ0MeZ8/+C5q21jPy6sYbb0TTfkAY\n3sddd/V2OoyNZWk0DiGKcz2jMe6++27i8fnXwEbiWL8D/P0lrYrFErOzTc6eLfc81bjmmmsoFBao\nVL7IXXf13mDdccce4jnlKa677rqetjMzMnNzD5DJGPT3IC7SdZ2hoQBVLTI21pvgKJVS8LwmgmB3\nGasrwP9HPPeBabqsra0gSRGaFlAqFXFdiVbLJZNREUWXfF5Elj2CIKTVgkbD67oRMk2bVgvqdfei\nNsmkiiA4JJNc1rnEMCxMU6Bedy6rQ2UTL8R3gK8Q99M3Lur1OpVKxOJiBUkKyeVyjI5G9PdXGBtT\nyWQunkIYhgGGYeP7F3cUOo5LsxnRaAQXOBNTKZUostC0yzv2NvH6x4vhyNh0ZLy+8aYe6bt376VQ\nGGNiwqJQuIPl5ceR5TFEUWFoqM74+DVo2jx79tzIt7/9vxgdvZ2HH04xNnYXnneaj3zkNiQppNkM\n8H2BM2dmyeWmsawEe/bcRK12km3b9lCrjZBOD2AYJVqtWLUklQrJZgusrJzm7Nk18vksO3fupFzu\no69vHMMooWlZPM/CMCwkSUOWp8nnRyiX5xkY2EqjMcOePTuwrCz79+8AXARBxfM8jh6dR5bzpNMZ\ndu+e5vRpm1yuH9e12blzitHRFFu37kUQYO/eHJlMpn0SJqOqAZnM8/ncUTTINdcM4nkW+byGoij4\nfuwNHxsbIZ1OUiz2s3VrRCIRMD5eWA/py2ZVFhclgkAikaiRyaRwnAEKhUHKZZOBgUEqlVFEMYck\nuWzZMsLg4CD1eojva8hyjaGh7TQaHnv3znD0aOyMiaKQdDomQ52cHCGdHmV8PM+WLVvWN1+ZjM7+\n/Tlsu4UgCIgijI0VkOSFo5YAACAASURBVCSJatUiiiLy+SzpdB7fN4giEASRXC657mgASCYTqGoS\nWX41FoMxwenFoCgKt956E5q2m7Nnl8nlcmzf3kehEPDIIyUeeWSRWq2JLAdYlkkiMYosV8jlcti2\ni20HlEor7TQeC1XVaDSayPIA2WzAzp39DA0N4Hk+USSjKD7Dw/0MDaW44ootqKqA47g0Gh7LyzXS\n6Szj4xqFgoYgyKytVVlbc1CULFNTEiMjA8iyTF+f3pZtdS9K9NjXl6GvL9Pm0hDp74/7XC8oikJf\n3/Ob2zAMqdXiZ6jryRcdgvpCxPKPm0RkHSSTGskeIiOdiN1Lhe6qqsrOnVvb/+7tN8/n8+zaNYQk\nXVxq+Vy8//0/iuclGRvrPSZzuTzvfe9tiKK/zsfUDTt3Xkkut5Vsdran3cjICJ/4xM/hukluvbX3\nPc3MTPPe9+5Elm1Sqe4cGTHeCvwYsTOyO97//vfzj/+4C9C49trukSMAmpZkYCCJolw6xPqXfun/\nwPf7GRnpXeYNN9zAu97lAgI7d472tL322uuYnt6ConSPRulg27bJS9pA3Kfid0zYxflZIJa4nAMO\nMjY2SDqt0tenk0gEiKJPFKlIUkAikTivX7RacTTIRsKsu3V9WZbJ5y//0ur5dIbLXZNNnI9d7b/H\nLndFXlWoqkqhoCNJ6jrv0e7d0+ze3fu6REJFllUE4VIRsBeO0c76YxObuBSqVcj35r8GYkdGufzq\n12cTLx2vKUfG6wWdF/zs7BILC0XSaZX5+RMMDw/TbAsGT09P02w2GR4exnEcNE3j7NmzaJrOmTPL\nDA72s3fvNhRFwbbtdfKxer3O4OAgjuOQTqfxfR9Zltc/bTtepGuahmmaPPtsGV0fxfcX2b17fJ0A\nU1VVfN/H932azYgoAl0XkGVx3QZgeTne4Kqqun6K6Ps+8/NlTNNhamqIZFJFlmVarRbpdHpd5q4T\nPt05KfN9f51s7dwNShiGmKZ5HsGiKIr4vk8qFechlkoNokglilwKBR1RfD60r1qt4vs+uq4ThuF5\n99j5nJ2dJZFIMD09DcR50B3iv2azST6fJwzj0/JKpYKmPa9wESsTtNC0PpLJDEHQoq9PJ4oiXNdd\nJ6DsSNCee6+SJBEEQVvKNNN21iTWCUk7tp36v5L5zhcjQXxhPpjruszNzWEYMcnq4OAg2WyWpaUl\nZmdn14mzPM9bJyX1fa19T01mZ1dxXQ1d18lkrHVeElmG3bun6e/vp1qt43kKjiNSKKTI5TQGB/vW\nn3EQBOtRI8lkElGUsO34N4OghqIoDA0NrRPABkGA7/vrBKC90Cl/I7av5LWbePE4t2++mGccc/zE\nfacXfN/HNE1UVT2PTPiFiLluygQB9PVp9Pd3P1YplWqcPFlDFGHPnkJPjo4nnjjG0lKT/n6Nm27a\n0/W+PM/jySePUK+3uOaaXRQKfV3L9DyPubk5UqkUo6PdN/1RFPHzP/8J/u3f7uenfuqn+PVf785R\n4bout9/+dkDgW9/6Wk9emGKxxuJiiWRSZfv28Z7pW6VSiXq9zuTkZM+IGMuy+epX/5UoCvjAB97Z\n00HUbDaZnZ1lbGyMgYGBrnYvBp15XRTF8+rZ6Z/T0zcwO/sc4PPtb3+Nq6++GkVRkCQJWZYxzTiK\nKJ1OX/CMu5V9MZtOea9XdIitX+/1fDOg0zePHj3K7t37AYWJiT3Mzb1xnRm+71OtVtE0jcxGWBXb\n6PTbDiHoxeC67nnruU28PLwZOTLuuCPmyXjb23rbVaswPb0pwXq5sJG++aZ+uw0N9ZHJ5CgWVxkc\nfAueZzA8HDsTRkd1ZmZmqNVaqGqecnmNfP4KKpUFxsZ2Isseuh5vli0rQpI0Mhm5Z/gwcN6pXDKZ\nJJFYpVxeZOvWBNl2nNO5i/j41NoEBDKZ54nINE2j1bIoFCZxXZMgSOC6kMtpeJ5HsWjjujJDQybD\nw4ULfhu4INQ31vm+sEuIooiu6225V4cwjIgiB1GMw/GTSY1USsGyAhQlThHwPI9GwwMi8vn8BeV2\n7lHTYnWSQmESQYjWnQu6rq9vOnK5XNc27KBQKLQJCk1SqefJ37otss+911gBJcI0TRSF9Zdnx8Hw\nahF8bqRcVVXJ5foJgj4UJWBgYABJkpiZmWFmZgbP86jXY4dIIhHRbPqcPLkEKNj2PCMjBaDF9u1j\n7NgxjmnaPPPMCs2mRaORIpVKsGXLFYRhhCjGajbZbAJVVajVLEAlmxVJJrU2kVasKqMoEY7jk04P\nkUqJ5/XZjqNsI+jW517tazfx8rDRZxw7J0JAQJa9ngtPWZbX58BeiCVd0/g+62O9e5nxH3Q7vX8e\nExODZLN9pNMXV8PoIAgCZDlDNpsjDC8dSbRtW3dVkQ4EQWBk5BruvPMeNK0370e1WuXnfu43iCKF\n5eWldYnRiyGTSbbluy+tyFEoFC75/gKIopADB24CNhKhJjE6uv0VjWbrNa8D3HHH9Xz1q3chy0uY\nJgiCTCqVakdxmUSRjKZdvD0uVfZGbV4PiNWmXv/1fDNhbW2N/v6fwvOGuOaaxctdnVcVsiz3JO3t\nho3021ebdH0Tb3yUSrCB1x25HBgG+H5nLbGJ1xt+6B/Lo48+yi//8i8jiiIHDhzg85//PJ/97Ge5\n77772Lp1K3/xF3/RdbOTSKgEgY3nBbhuCsPw0HURQdDwfb8t6ykgSQr1ukM6nadSMUkmNTIZ1iMO\nTNNDFEWSSYFz59cgCLBtF0W5UPYvJgvz2bZtsh3Wf/EFsSiKaJpEFF2Y8+d5IaKYwHUjFEVel730\nfZ8wlBAElUqlSTpdIZ/XX/bkH8tmSu0cRgtVFVEUkWQSUqkkmvb8ZiEIQkBab4eLPYNO+7iui2XF\nobqKYqIoCoIAQRAhyyK+H5JIKJfctGaz6XVpzheLZFIjkXhp177asO0AWdbx/dZ5USS27RKGPkEQ\nS+QWi0UsSyYIBBIJhSBQ2bJlBss6zdBQilptleVlB9MMEEWdet0nkXBJp0Oy2QQjI9l2lEXY3oC6\nCIKMqkYkk/EzFUWF2KkWb/ZkOYXvW4RhiGU5SJKIpp2/CImiCNO0L/rdJt7YCIIAy4rJBlOp7qfc\nHdtu8+W5EASBZFLBcTwUpfeckEhoTE2NrEdz9UJ/f5ZEwiKV6q18E5eVQJIULkU94Ps+lUoDVZXJ\n57s7aUzTpFYTSKW2sLraXbEEwHEcTFNpR6v1TsPRtNgpuZF5zXVdPC9A03rL33qez+nTs0DE5GT3\naJTYNkSWk20em4jXQkVgdTXCNGUEIU29blCvm2haHJERRSKiGHMjbWITrzWq1SqNhoLvKxSLb64T\n8E1s4vWEchk2EiQoirHd2hqM9abk2sRlwutv1/YiMTU1xbe//W0eeOAB1tbW+N73vsd3vvMdHnjg\nAfbv389Xv/rVrtc2GhYPPXSQRELj4MFvsWVLmqEhmXw+TiN48MEH0XWZM2eeZXBQ5+jRpxEEn2PH\nFqnXfZaWloiiiDCM8P2AKIqoVqsAtFotmk2LWs2j2fTWU0ogXtwaRkC97iGKAVHUIpPRqLVjlzqh\n2BCHJpsmlMvWeUzpjuOg6wl8v8HgYBpBsEin41A7WY4dI65rUK97VCoyi4uV9WsvRlDWqV8nhOeF\nto7joCgKyWSEpoUoirp+z520g86n67okEiqaFqJp4Xp6yLnlxm1gU6/7NBqxeodlWbRa0GyGlEom\n5bLN8nKdSsWh2bTX2+Xctuyg89sdUrNzf6cbOt91PjvXXowYrdMOnc9OO70WJGqZjEKtdhJFMdr9\nLWRpaZVi0aRWc1henuP06VM89tgCJ06UKRaXiCILXTcpFg+ytLTI009X+Id/OMj8vEelchZVXSGf\n99C0EMcxqdXqNJtNikWDSsWhXK7RbLaoVKptgliv7TRpIstxWLWmCYBFKiVjGCaWJdBoeOtOwNjx\nFbWlU8Ewwp5qFL3Cx7p91/n/N1tY5OsBG23zIAgJw0vbGobdJsD1eo6rIAhoNn3KZZtWq/dGvjMP\ndebGXmg2bc6eLdFoOD3vTVVVUimBhx76Bslkb8dcpdLg7NkWq6vuReet5+uZYP/+DH/4hx/g+ut7\n807EvA4WkmRc0ukS1+HSTGVxm3osLtYwjN5teubMPKdOJTh2TObs2d58HplMglptmUxG2ZATY6MK\nG73sNC3AdX8DQShjWQ1kOUmlEqeTJJMCsuyeRwzcScl7IV7KfPRysNEyN+e61z+6EcVKkoQgNIDf\nIJl846vJBEFwQX/ttgY4d84/95pu/+6Glzs+NsfXGx9RtHFHBsCWLbD4xg6g+qHGG4oj42Mf+xjX\nX389hmHwq7/6qzz55JP8zd/8DZ/73OfOs+vk3KjqVjzvWuCfgB8HjgIDgAU0gSuBxxCE24mivwfe\nD3wXQbiHKHqGoaErSKUEstkUkpRD15eIot1MTCyRzV6PKJ7B97eRy9XYvfst2PYyx49X8LwQz7MQ\nhEE0bRVJ2sLa2iMsL4/R3z/Pzp1vI5WqMzg4QatV5+mn5xGEJAcO5Bga2oFhzNNopPH9eRxniGSy\nQS43ie+vsrzsYtsRV101TjY7xpEjP8A0C+RyRUZHd5NO2xQKWwhDC0FQMU2DJ588ThAk+NEf3cH4\n+FaOHj3O8rJHOu1QKIzTaKxw7FgDRXHRdR1F0RgZ0VGUHCdOPEWlomGapzGMAvl8jYGBnWQycMst\n+ymXS/zJn/wvGo2Iq6/WmZjYTyrVQlEKrKwc49gx0PUWN910PbZt8NBDR7GsgEplFs8bw3UPE0W7\nGRhYoq9vH6nUKoIwSRRVMAyZRALyeQlB6GdiQkLXRwiCCoaRJJ02keUBBMEgDDWCwGF1tU4UyQwN\nSajqAIrSwvPSCEKdKMqRSgUMDY0SRQ7NpksQRG1nk4okWQRBkkJBJZvtw/NMms0QRQnRdR1JEsnl\nUhs+/VxcrBCGMDHRv34C3embQRBQrRp86Uv/xCc/+ec4jg8E7No1gqYlWFiwMAwD2+701RSxGskA\ncch3Z7EgACagABpgA3mgCowD9fb/J1FVC01LIwgiQeAhSRAESfr7fVKpPJWKiyC4DA72MT6eZXp6\nhkIh5stwHI+ZmTHy+QzT0wVyuQwQl1OtttC0VDvqo4/+fv2C0/HY2eGTSIjo+vmpQ67r0my6yHI8\n1jobombTxHVDVBVclwu+38Qri3NzFQ3DxHFCUim5J0fD/Pw8f/mXD+J5Aj/7szcxOdmd0PE733mY\nhx4qMzYm8dM/fU/X51ir1fiVX/ljlpYUPvrRKX7iJz7UtczDhw/zhS88jCyHfOpT72NoaKir7d13\n/288/niaiYkizzxzX1e7+++/n3vu+W2gn/37T3Hw4MGutn/1V3/D7/3es+TzLl/60q8yPDzc1VYQ\nRoklVb/SczEdt8stxBLhvW0/+9nP8ld/VWJszOTrX/+DrnZhGPIjP/ILLC2lede7+vnsZ3+tq+3k\n5CTz89cBRT74wRHuvfferrb/8T9+lm98o8GVV3p88Yv/paud7/s89tgxDCPg6qvHeqa4lEolnnpq\nCV2XOHBg5/pc8jy5pQC8B6gB3+XjH/89kkmdffsK7Nu3l2xWIYpU+vo0UqkEy8tVFhcr9PfrbN8+\nsp7u2Gy6KIpAJnP+nNJtPnqpiKKIRsPE9yOy2URPbo6N2G3i8iEIAo4cmcOyQrZt61tP3z2/b94O\n9HOpsfvDjnK5wuHDSyQSIvv2TZFIqDz22HMcO1bhiivyXH/9lSiKQhRFVCp1KhWbfD5BOq1hWQGq\nKhBF4HkRmUzM29ZsOkiSQCaTvCi/zcsdH73WIa82giCg0bCIIsjlkhtOz30l8GbjyGg0YHw8lmDd\nCN7zHvjoR+F973tVq7WJi2AjffOHPiKjg0OHDlEsFsnn8+t51tlsdj3K4YX49Kc/jeelgO3AW4id\nFPuAu4idGlcC7wBuIp3+PeA2Bgf/GthNFH0M2IXvv51abT+VSh+Os5ujR0fI5z/CAw/4XHHFz/Lg\ngzW2b38/hw+7eN4kBw+2WF3dweLiJLOzIqnU1Tz2mMPw8I/zwAMtdu78v3niCQVFuYnnntMol9Ms\nL0c4zjYU5Toef7yEpu3g4YeLTEy8g+99r8TIyD0cPOhhmgOcPu2xsrIFQbiKYrHGwECGIBhg166b\nOXLEZHj4Bo4caWHb6fYJoc7cnNFWDbmaJ5+cJYoUTpxoMjx8A4cP15DlEQ4eLOH7Ozh1SqJUShEE\nWyiVWihKglOnBCYm3sG3vrXM6Oi7efRRkyCYYnU1RaXS4tSpeRYWtuF5N/PAAxVSqV08+ugaAwP7\n+cEPigwN3UaplEcQFAzDxnW30moNMT+fR9dv5uBBkT17/j3f/36d0dF3893vGsBejh8XKRYHWV4e\n58SJgCjazsGDdfL5PTzyyCrDwzfw2GMlEolpZmd9TDPH8jJUKllse5Rjxyw0bYYnnywyMLCfp54q\nk8/vYW7Ox7ZlKhUXw0jRaKisrYUIQoHjx+vo+hSnT1dRVZ21NQtF6afZBNcVCENlwyeKtm3jeTpR\nlMUwLpSw7PCRfPObp3Gcq4APAXeyuDjM2bNbMc192PbVxOz8twH3EG9u7iBeKF3X/nxn+//e2ba5\nCbi7fc3twLb2/92O615No3ENhnEdhnEl9fp1GMbtlEqTLC5uxzBupNHYR7G4g4WFQYrFCY4eDSgW\nRzGMHayuBgTBIKWSQKUClpWgUoEwHEKSkkSRhKLouO6FbWSaHqqq4zjRBafxtu0jSUl8XzwvMsZ1\nQVHSVKtm+3tpw+2/iZeOMAxxnAhV1dfTRrpheXmZKNpDMnkVx4/3Vq44erTC+PhbqVQSlEqlrnZL\nS0ssLQ0zMvIhvvnN0z3L/MEPThFFN2Caezhy5EhP24MHFaan/5TFxWnm5+e72n3qU58C3g78PIcO\n9V5sfvObpxge/nc4ztU88sgjXe1iItS7gP8GvKtnmTF+AvjAJa3uu2+NwcFfYH5+G9///ve72j30\n0EOUSjOMjPwCjzzSO4Ijbpt3AR/jy1/+ck/bb3xjmR07/l+efTbLyZMnu9rVajWaTR1Nm2FurjdF\n/OJiDVmepNnUu7zf9wM/TTzvKczNpZHl/Zw+HeB5aRYWmihKP4YR0mhYmKaI5w1gmhqmGUejWJaH\nLKfwPPGC0/WLzUcvBzGZtIQkJdsO6952oqj1tNvE5YNlWTSbKqo6xuqq0cXqFuCjwNWvXcVeI8Tv\nZZcoilhZaQCjGEaWSqWJYRgsLkZo2l6WloJ10t04GixAVQdoNuMxqao6punjOCDLKSzLw3V9BEHD\n86SLjrtzx5FtX1y69VLotQ55teF5PmGoEkUqnrc5vl9NlEobj8YAmJiAHkuCTVxm/NBzZEAcOvuL\nv/iL3HvvvTz++OMsLCwA0Gg0yHfR1/n0pz/Nf/2v91KtngSeBv4ReAg41bbouOoeoNX6D8DDFIs/\nBZwgkfjvBMFRCoUcolghnVYIQ59du1awrL/gjjs0Tpz4InfeOcTKyn3cfHOKZHKePXs0nn32WZLJ\niMnJCEU5yIEDEadOfYUbbkhw7NhvctNNAlH0IHv22AwMtMjlZMJwBccps3//CLZ9nJtuGmR+/mvc\nc88wKytf5/rrNdLpMjMzCsvLC4jiEtdfv4eBAZndu9MUi09z552TrK4+ys6dKWy7jK5HJJMNJid1\nGo0zWFaFa6/diSB47NmTZW7uUQ4c6Mf3V7j66mGOHDnOtm0Bum6STC6ybdsgmiZw5ZVquy6TLC/f\nxw03pFDVWQYHYWAgja7vYWLi76lWT3DHHQWWlw9x3XV9lMuHuPPOCY4efZjduwXGxzPousvRo08R\nhhG7d9uE4cPcfXeO06f/jLvuKrC8fB+3364jy8+wY0eI664iCCL5vISinOTaa3PUas9x443DrK4+\nyoEDBRznDFNTMmFYJ5mEZNLEtk0mJ5PY9mmuuWaQcvkQ1103TK32HDMzMqmUTyKh0myabU+gCJTY\nsSOHYcwyM9OH6xqMjuo0GhWyWQFVjZCkePG7EaRSKRQl3qzpev8F38uyTC6X4Md+bCff+MYXMM3H\ngIDJyTHS6RZzc3XCML6X+OQxB6wBOrEsWceDGRFHZ4jEw90FssRRGiNIUgOYB5Ikky6JRBpBkAC/\nLXObolCISCTqlMsOkuS3IzIyjI7mGBrSaDaXEMWImZlhVLVCMqmTywUoiksyKWCaNTIZnYGBFFFk\no6oXnuCnUgqmaaBp0gUnLcmkQrNpoSjC+imFJEkkEuA4Lfr7U7iuhSwLyPImB8erjZiMTcBxDNLp\n3qdeO3bs4MEHvw3A3r039bTdt6+f7373IaampJ4n8rt27WJ8/CssLv4DH/1od6JLgOuv38bTTz9M\nPh+we/f7e9oeOACPPPJ/snOnxcTERFe7X//1X+fd7/4c8Dh79vSWCHzf+67iD/7g7xgehttu+5Wu\ndq1WC0maBP498M89y4zx9xuwgY98ZBt//Md/xPbtIbfc8h+62t1yyy1MTf1Pzpz5Uz7+8e09y7z1\n1lt54IE/B+IoyF64++5RvvGN/4err5bYvr17ufl8nnx+lWazzu7dvRORt24tUCrNkclI5PPjF7E4\nBPwl8bzoceWVNqp6lJ07+1FVk5GRHL5fIZfTSKc1TNNBUcptgul4Lo7nHBNFES6IHrvYfPRyEJMW\nuwRBb4ngc+16qfps4vIhmUySzxdptZaYmuo2h/1P4DngqdewZq8+wjCkXrcJQ4lk0mZsLE+ptICu\niwwMTKOqClu2iBw7dpidO/tJp2O5c0mSyGQkarUy2WwnIsNor6sjfN9E11VEUcRx7PaYvHCcxCnV\nLr7vk06/tHVAr3XIqw1FkRFFq/3v3gpfm3h5KJc3RvTZwZYt0N5WbuJ1iB/61BLf93n3u9/NZz7z\nGQ4cOMDa2ho/8zM/wz//8z/zu7/7u8zMzPDBD37wvGs6oSqnTp3i4YdPMjqaYfv2Pdi2QanUbE8o\nDqKoMDlZQFE0FEXg0KHn2L9/D54XoaoSyaSG4zisrLSIIgFdj1BVjXQ6gW3bSJLE2loDTRPxffA8\nB8OIm1vXYxJRSVJIJHQkySWKPDKZDK2W0ybR1NaJEsMwIp1+XnLUceI87rW1Btms1pNIDmirW7SA\nCEFIAz75fAJRFNfLF0Xw/Yh0OkEURS8qNK9erzM/X6FQ0BkZOZ+pem2tguv6BAGEoUo2K5DLxS+x\nVstBlgXCEDzPpdEIAPG8trQsC1EUKZWMDd1r53479Y/lWW06EcBRxHlt2ZHGvRRs28FxfBRFwHVD\nkknlFWeFf2EYVRiGrKyUqdcbZDIpHCdAliU0TeLUqQrz84vMzp5ledlmeHiI22+fJpfLUKtZbflU\nkZWVZRRFw3FMpqa2MjLSh2k2aTQMNC3FyEiefD6Labokkwqu61/QRoZhIYoCmqZiWQ6CIKCqMqbp\nIkkQhgKe5yIIKaIoIJ9X2/nA54ddW5aN6wak04kL2vy1IgPcxEvDSwk/7TZ/XQz1eosgiCWc+/ou\nDB3uIE67shFFFUXxyGRemRDger3OI488w4EDey5QdDoXhmHwd3/3KEGgcOONBa66ak9XW8uymJ8v\nomkSk5MX23THCMOQL37x7/i5n/tFvvSlP+Mnf7J7DGupVOJ3fudLmGbAL/3Su9m+/dKqKBtBpVLD\nMFyGhrI9N8qGYfD97z9DMqlw0037exKzdt4Lw8P6S1IwuBiCIMA0HURRIJ1+fsHf6Z/vec+Hue++\nCrDA8eP/xJYtE22Fpd5pGxude4IgOO8dvYlNXAqdvrmwsMDExLuBAh/84Bbuvfe/X+6qvWIIgoBa\nzQFkVNUnlUrQasUk3+eO027Xnjumu43HMAxpNk0sy0HXk6TTyVd8zfBGXof4vk+r5ZBIyOcRr7/Z\nUku+9jX4/d+Hr399Y/Z//dfwL/8Cf/u33W08D975Tkgm4atfjUlCN/Hy8aZILelEYXzyk5/kbW97\nG6dPn+a2227j1ltv5dChQ7z3ve/teu03v3kc09zPY49VMM0WlmXhuklWVmxOnjQpFvMcObJEFGms\nrDTZvfsaVlaaBIGKaYYEQYAgCO0NW4RpRgSBSqvlkE6nWV6u4ftZTp4s02yqFIsujUYsS1oqedi2\nju+7JBI+uq6Rz+dptRw8T8YwAnw/JllstSJME2z7+dO/RCLB8nKNMMyzumpj23bPh91sOihKti09\nF5DJxBrdvu9jWQK2LVIuW0SRRqvlvOiTptOnS8A4c3PGeWROpmlSrYJpJmk0WqiqTCzFKNNqOfi+\nQrlsrdchDMPz2tI0HXRdZ3W1QRjmKRbtnukDnXDATv3DMMS2HVxXptHwqdc9bFvEcZ6v40Y877Ez\nxG/3hQaCkMIw/Fd98m80DKpVgWpV58yZOsWiQLkss7BQo1oNOXbM5+zZHHNzWUqlFEePljl+3KLR\nGOTEiQqVik+1mubYMZtWa4SlJZNarUytluTYsZDlZYX5+RbVagtIUqm0cBwJ15X/f/bePMau6zrz\n/Z15uvO9NXMURZEiZUryqHjupG23bbmd0Wmn8x6SF8BwgAgdJJ2gk26jEfcI5+WhE3QncV6cIMgA\nJ3YbiePhyZYlW3Is0ZYlSqJEcRLJYs237nzm8f1xbpVYIusWKdGyJNYHECwW191nn3P3sPY6a33f\n+nhzHI84VgkCEd/PZX2DQKLdHgAGzaZLHCtkmYKmxRSLErIsX+YM5M5Kuj7GXohRzsMaeeg2Xl0I\nwxDfF4kimTAcne5bKOhoWkyppI6ck5IkUSop6HqCZV2/g+Tp06tMTb2Rs2c7I0sGBoMBSaIDlS1L\nay5ebNLtFpmby+j1epvaOY7Dvfe2+fmff4B//Mezm9pBXgby5JMzXLx4mK9+9Vsjba8WYRjSasWk\naYWVlf5I22effY7FxQanT1vMzo4uFzp9epU0neLcOXsk0e+1wPdDokjB854ndb4UX/ziLPCvgA/x\n7/7dv8Nx8jXHuhL8rwAAIABJREFUdTe//rUcXFw3GO7z6XYp2zauCZ/61KfIS0t+hs9//tQPuzvX\nFXlmhYxhpFiWju+HxLG66Ty9FC+c05vNxyAIse2UwUCl34+3bPfF4LUaxICc0Dr3v+Ib2p+an885\nMq4Wt9wCW1Sm8vnP5zKtFy7A17720vq3jWvDq7605KMf/Sgf/ehHN/zurrvu4jd/8zev4tMDTpz4\nPrVak1LJJIqEYY2siChmQJdaTSWOAwxDIo4DLEsmTSMkKZfqzLIMw5BJEpE0jUiSEF3PnXBBSFhZ\naZKmAeAjSQlrfo8oQpa5FIvGhmh1kkS0Wg6KklKr6SRJwuzsEmkqcsstlQ0s9Zal0un0EYSQwSDC\ndWMqlSuTTaqqSBAEqOrG6LgoiohiSJqCpgnEcUCS+MSxgKZx1YRHpZJCq9XENDdqfOfpsDFJklAu\n62haLie7dq+rqy6C4GMYGrKcUShogHDZszQMmcHARtOuHHhYI3qKIsiyAFHUSdMAUVRJ0xBBUBGE\nhMHARRRDTDO/r5wwki3vVRAEZBniOMQwZOI4QJZ/8JuepimIoo0sBxiGSBT5CEI0fGsQUipF2LZH\no+EOSTSh0+kiih0MQySObRynRxAMaLcDDKMMGCwtLdBszgEe1WoVQSiwutpFknJnv9+PgBBN05Ak\nkTT1KJUMZFler8/WdYUkCTFNiTSNkGWwrM3J70RRRJKy4Xy6+hjqpd+tZUnbb0FfRcgDigFpCpI0\nWv5ZkqQt39ytQVVVXqKa9BWuH7O8PEexODqQWywWMc2csNmyRmeH6bpEu72CYURo2ua5rMViEdc9\nx7lzsH9/Z2SblmXR73+HJFGYnt4/+qauEnladkoQ9KlWt5K51pmfP40kZajq60baalrC4uIcpVLw\nkuW/n++riO9HiGJ2xb1AURaJoseAWQ4ffj2SlA73kutDnifLeSBcFF+Zct3beOUiJxt+BEiwrIUf\ndneuOy5dl7eap5dCkkSyLLcdtfbKsoQgJECMJI0OeG/jcqiqhO/nWbSv5YDNVpibu7ZAxm23walT\nOan8ZtvYZz8Lv/IruUzr5z4H/+JfXJ++bmNrvOoDGS8FN9+8D9tepFKp853v3Mfk5OQ6ediBAwfo\n9XpUKtMsL1+kWi2ytNSiWi2yurpKpVLANHNJOcuSSFOBNBXwfR9ZLuL7wfCtdI+xsQqTkxpp2qDV\nysnEymUJQQDT1PH9AMiGEeyUWq1ElsWcOXOGJEnQ9QZZJuK6IboeIEkiSZJSrRZRVZc0VRAEgzSN\n8TwPRVGGxEd5yUQYxui6iu+7KEp+PUkSURQFSZKGB9EUWS4QBCH9fkYQRPh+NLST18sA1oicXlhS\nsW/fLiYmbHQ9L7cRxbx9VVWp11XiOMYwSniejyxb+H5+uLEsDUGQEQQfTVMJw7zGUdM0HKeHZVXo\ndvvUaiV03UNVdRYWFtB1fT39WdM0oijC82IEQWVxsYUkGaSpT6lUQ5JSKpUEVZWQ5Rog4HkuYRgP\nAzZFwtAZOVYEIWeoT5IEWW4Qx/HLwiqdy5xGiOKaYoxCr9djdnaALEccOVJg796U+XmHJFmi2zUo\nlUSiqItl1fD9gFIpptvtsrBwCkHYTRAsUy6Pc8cdBzCMDEWRaLc79Hohvu8yOVlBFAu02zHgMzU1\nThT1abd7uK5OsVhAlhVU1WBlpYkk5Qzfuq5fcXPMx2KCqiqUy2vP8OqXnjRNiWNhOHZdjO3y0VcN\nZFnGNKNrLlXbCq6bz99CwdxyLM3NzSHLMpOTkyPtpqbG0HWfYrEyMr1Y0zREsYltt6jVPjSyzWKx\nSKMxQFVHs9CHYYiqJpw9ey9vfvMbRrZ5+PBhXPf/wbYdbr/9X420TdOUMIyQZWnkcxJFkVJJZmWl\nTam0Y2SbjcY4Y2NPoarqphxUaxgbaxDHXcrlKmk6+uC/urqK7/tMTk6O7Kumachyng15pfbq9SmW\nlr4ApNx++y9iGDLtdpcsk7Btm0LBGpKrXjvW3haXywpJkhBF0TWXF2ZZRhiGw/K86xyN28YrGm9/\n+9uBPwAeRdev4ST1KoSqqhiGhyTJ62tfp9MhjmNqtdqG9VDXNRRl8zm9BkVRmJgorfsQ24GMa4Nl\nGWhafMWy3xsJc3Pw5jdfvb1pwu7d8OyzcOTI5f8fhvDNb8Kf/ik0m/C//td16+o2rgI3dCDj2LGT\nnD07xdNPf57p6R9ncfEBFKWOZZXZvfs+br75rbjuw+zf/xaOH/86Bw78KKdO3c+BA28lDGd5xzsM\nFEUCcpmodruHopRoNucZH5/m2LGz+P4korhAoVAmDH2aTY80Tej3RUyzRqu1zNjYBLOz85jmOFnW\np9GQuHjxIidPKoRhwMzMLOVyHd/Xse0Mx+lQKFSw7TbFYpUk8ZEkF0hxXZUgcOh2XWTZotu9QK22\nk2bzDGNje1hYWGZqahJBiKlW81IO205JUwjDAaZZZGFhDkGokGVdDKOE6wZUqyJxHA85LAQguMyB\nKxQKOI6H74tARKUiEkURy8s5P4Ztz1OrTbO0tMDU1Ay9noMgGAwGbTStxmDQxLZTskzGdReo12c4\nefJpdu48iOfNsWPHDo4de5Z+v0oQzDE1VaJQKGCaLpZVZDAYIIomZ85cBHbT653h8OFDCEKALE8M\nnfmQJElZXQ0QRQtJ6lOriVuSFsJaVkY+Za7lIP5ikWUZJ0/OceqUT6fTp1QaMDlZ5MSJC5w759Pp\nDND1lNnZPvPzAUGwiqJUqFZtLMtDFB3Aod/3WF7Os3ZU9RT79tW47baE178+wDAsXFfHtjssLDhI\nksHu3R6NhkkQ5KSfWZayutqn3zfIsg4331xmz54pVlfPs7KiEUUhBw+K7NihXnZYy7KMXs8HVILA\no1y2rvnZSZKEYQgEgTvM2NnGqwVhGOI4IAgiknT5mvFiEMcx8/P2sFygy/T05pkO58/PceJEAgS8\n6U2rI0lEez0b21ZJU5vp6dqmjt6jjz7KX/5lB0maYnX1c3zyk/ds2ubs7CIXLgjIss3OnTbVavWK\ndufOneMf/uEMcDd//Mef54/+aNMm+a3f+i2efPINgMGHP/xLPP30Nza1dRyfKJLJsnwN38zx932f\nRx9dQpJquO4FXv/6A5u2+eSTx3j8cQNBCLn11me46667NrWdm1um0ynguh12795cerbb7fLYY22y\nzKTXO8/hw6MJR0cFhZaWzgC/DTzNT//0z/Lkkyfp9w3m5s4xM7OTSsXnllvGrzmIkMsjhoCMIDik\n6dpYvrZx7fsBjgOQUKmIL8teso1XBt7//vcDPwccptX6rz/s7vxA4fsBnieRZQmSFOO6LidPDgAN\n319h586pDfZX+2JIkqSXVZr0tYbt9SYPZPzkaO7vy/CmN8F3vnPlQMZ3vpOXn+QiBzmZ6PIyjFBb\n38Z1xA09oh966Js89BC0Wg+Sqzs8BTSGzNNdSqUTVKtt6vUVWq1v8aUvLSOK3+Opp2wUZYVTp05h\nmgK6nkuxqWqIJI2zcyfYtki/3yVJisiyhyRJ60RFcRwRxxJRpCBJNopiYdsDwtAkCJZYWFil01nC\ncaYIQxfTLFEqWciygCAILC0tMxi0MM2QiQkFUQwplUx83+fYsTMEgU+73SYMLWq1GM9T6feb+L5B\nHC/TbLqoakSpVAQSdN0CFGy7A9j4vouuV+h0Wnz72y7VqsShQ3uBlCxTEYSc/NF1I6LIx3FixsYK\nFAqF9WcbRRGzsz183+fs2Ta+nzExoZKmKefOzfLYY/PU6wkHD95GGIY0m8vYdpe5uTZZJqNpMUGg\nsrJyhhMnbCYn88yT5eUmvV6K562wvLyIaVrceus4QQBpGqOquXReHHvDkp48S+b48ecwDJnp6RJp\nCv1+gChmTExIVKsFwjCk07FJ04h+PydDmpysbXD8V1fbDAbhZfeaJAm2nZOJFgqbkxReinws5AzV\nV9JEh5zQ9dixk3z960+ztNSlUpEQRWi1PNLUoNVy6fcXaDZDXDcjTQcoSplCoYAoDsgygX7fHpYz\nJcMx7nH6tMrFi7txnH2srDikaZGJiQL9fkSS6CwvZ5TLJiBjGBKuW+f8+SWeeqqPrscMBjexvLzK\nysoS3a6JZclUKlNIUobnBUNuDYFqtUilYtHp5KSighCQphnFoo4sy8RxzGDgoyjilqRdpmlgvryy\n7tu4DojjmLm5JgD79zdGHviiKMK2A1R16xKTixcX6fczDhwojgxk9Ps9vvvdsyhKwh13bH7gBnjo\noe9x9GiXO+7QufXWXZvaLS8v841v3Esc1zCM0Zlc3W6Xp59uYpoJb33raDWOXDHrWZ5Xzroyvvzl\nL5PLg1s888z9I20vXpzloYcuMjWlc/fdbxt99bMXcZwl7rhj9LN/+OGH+fSnjyKKIbfd9sGRgYzV\n1SbHjy+xY0fMXXcd3NROkiQGgwFZlhHHo3mH1tYNWRYpFK60brSAR8lVSxLOn79As2nR661iGCa2\nnTI2pjE+3rjss5euSZuVGsZxjG3bSFIytLlx32xu49rg+z7wNOCTj9PXLuI4pt32kWUolxXSNGVl\npU0USTQaxZelD47jEYYJhcLmRL/buPHw3HOwZ8+1feZDH4LPfAY+/vHL/+/ee+F978t/FkW46y54\n5BH48Idfcle3cRW4ofOyjh+Hqal7gJtQ1ZuAHZRKt2FZhxCEXVjWO1lYMNix482cPy+zf/+HWV4u\nMT5+BysrMq67m4sXS8zOivj+TpaXM+r1/SwuhtTrY1QqdQ4cqHDTTdMUiwKlksb4eJ3x8TqNRonx\ncYNSqUSxaNJoVJmeNuh0ImA3krSTm2+OeeMbK0xPT1MqWVQqBoaRkKYqY2O76fUSikWDNBXIMo1W\nyybL6nheiU5HpV6/GddNmZyso6oFpqfrBIGEqtZpNgN6PQPbtoZlHzlZY6lUR1EMpqYMbDujVLqJ\nZvN5kkZNi9G0GNBJEoX5eRtFGV/XTDdNnUIBBCHE9036fYEsU5mYmKFWM2g0YmxbZGLiDpaWEnRd\nQddNKpUylmVRLNap1aYYHy+zY0eBICixc+etzM9HlEoNisUGN92kMz6uUirtolCYJMsSdF1GUXSK\nRYO9e6c5fLjMvn17mJw00TQF05zA91VarYw0raAoArWash6QcJyQNFVZWrKJ4yK2LQ+djhxxHNNq\nxSjKOM3mRn34MIwIQwnfF66a/C2KIuJYJo6VDcSjl2JlpYMkjRNFuzDNg/T7BTqdHWjaGxAEn1Kp\nTBzvpVC4FVluUKu9AVkuoiglVPUQaVpDkm4GjgDTwH7gHYjimwnDSS5cSInjtxGGE4DEzMxOJicr\nFAo7EIQZbLuApu1gdVUkioqY5iTF4t4heasJTFIulymXDTStTKeTsLoqMj+f0umo2LZMszmgXm+Q\nph6KYiAIxjrpnueFCIKB7wsjyRW38epFXt5mIctFgmA0MZttB4iiie9nI8dDFEVIksXk5A6iaPQh\nUhRlZmZuotHYuSUx3IkTXXbu/ACnTrFh7r8Qp06dQlUPYllv4Phxe1M7gDSV2LlzL9XqzMi1Ie/b\nIeAWYM/INvO+vQf4lyPtAJ59dplC4QgXLwrrZZNXQhzHjI/XufnmPQjC6OyCL33pu0jSTwH/nIcf\nfnikba8nsGfPPqLIIAguJ/hdg2EY3HbbOLfearBr1+iAj+9HgE4Yips80wngLeTPE6DGxITFwYM7\nMc2Q3btvwravvFZfuia98P8lSaJc1hDFkFptEk2TMc3Lyyy3gq5rFIt5ecr229EbEW8EfpR8nL6W\nIVIsGhhGPj8URWFqqsLOnVUM4wf/ViJJEjwvQxTNkUS/27ixEEUwOwv7rlHs6/3vh+9+NyfzfCEu\nDWQA3HEHPPHES+vnNq4eN/Qu+oY3SHz3u59lbKyPICwSBAvoeglRTKnXByjKsxw6lOH7T3H77Qq9\n3gPcfruKosxx8KBKsbiKpvUxTY1Coc30tIUkNTlwYIwoGrBv3xiiqGEYGpZlDVmd+6SpQqEgo6oG\nWZaTF+URapnp6QLnzp3DMAIOHDiIJOWxJkkCw8ijyvW6Qbu9zM6dRSQppVRSgYhqtcDi4jKVSowg\nSEjSKocPTyDLIXv2VBHFkEZDZTDoYFmgqg6SBJVKBVWVqdU0ksRlerqMrsvs21dneXmBRkNEVQXS\n1CcIJARBHBJqKpTLEo6zSrWap+kKgoCmaZimzuzsKkkSUK+DJHlYlkmWGYyPKzSbJ9m3r4CuSzQa\nFoNBiiBodLs2ghAwPV2kWjWZmlJZWTnLrl0F0nTAzp1FRLFAsQjNZkgcB4higSBIMAyQpJSZmSph\nqNJoNCgWdUSxxPKyhyQlmGaKIDhomjTMVMiZm5MkotNZI9K0UZQMVX0+60KWZSwLPO/5e11DlqVD\naduUcnl0zfil7QmCBwgoypUd4Wq1QKMhsHPngFbLxTQVbLtPlgUUixMsLg7odPqEYUi16pJl0rDW\n3gNCCgWH1dXWsBbbQxAUPG+JUklm9+49HDhQYHb2SWRZoVwuUKsxDOyIeJ5LpRJTrYaYpoCmGfT7\nc9TrVQ4fHkdRgmGtpcLYWJlSKU/17HbbpGlAkuTkfuWyTpIEVKsFkgSSxMcw8mVH02QGAx9JyhDF\n7ZKR1yJ0XccwXEDENLcixpRxXQ9ZHq0klPPuSLjugPHx0W/26vUS9fo8kFAul0fa3nSTwbFjD3Dz\nzck6GfCV8M53vpNK5Q8Igj4/+ZO3jmxz375xWq0mmhaPvP5tt90GLAAngdGqIYcOHeLo0UcBjV27\nNs8cAWg0DB5//AS1Wq6ktRl0XWd62mAw8Nm9e3PpWYAPf/hHOHbsIaDNj/3YB0faTkzonD49x8RE\nOvKZiqLI+HiVNBW2LPNTVWnIw5RtQiC7DDzOWmZLoyHR62WYpsbUVBUIMM0rp6erqoRt52+Rr/T/\nsixTLJo4ToBhyC+Ka2Ntj9zGjYonAJt8nL52oWm5UpUo5sp+mqZRr+skiUilsvladL2Q87RlxLGH\naW6Xomwjx/nzMD2dE/xfCwoF+NjH4L//dzaUfi4uwrlzeRbGGo4cgS984bp0dxtXASG7kcSDh1jT\npe10bJ555jluuWUvx48/BojMzhokSczOnSmaZrFz5xRRlFCrmbRaKzQa43S7ufqHIOTSoEEQ4nke\nU1MTJEnuBK8RbcZxjCiKeF5OsKmquZMWBOGQADEn4zTNnCjRtn0cJ0UQctWStXS4LNvI/JwfWNV1\nArVctvT5N9tZlhFF0WV98bwQ1wVIKBalIXGduN7+mm2apmRZRrfbp1jMeQ2iKGIwyEnwDCNF17UN\nn7kUnpfzgQA0GhqCIOA4KZKkIYoBiiJgmuaGfsdxTLeby0KVyzKqqjAYhPh+iq4LlEr6hmeapukw\nHT1DECQMI8UwtKFUqodp6uukRvlb3Od/7vdjRFEhy1wMQ8N1AyTJIstCikVlUyKpK92r63q0WgFZ\nljE5aV117fXa1Ls0vfmFmslRFBFFEWEYoigKvu/jeQFJknDyZJMkKaFpA6anSyws9Gm3Q6LIxzBU\n4ljmwoVFkiTjllsm0LQMyKhUJMbHJ6nXy5w5M0cUWaSpwNSURa2mIUkCQRCtl3sMBgFZpuL7A+p1\na4MTnpPEyuv302o5iKJGmnrU68X1kqq1MfbCcbz2/a89gyAIiOP8e9wm8npl4cVqza/JvF1tydXV\n2CVJQpIkW861NE1ZWFhC0zTGxuojbdttm3bboVq1qNWskaVOZ86c5cKFWf7ZP3vXlv11XRdFUUam\nNvu+z+/+7j9y9OgKb3lLjU984qOb2j7zzDP80i/9JZ3OLJ/+9D28612bl3b0eg79foRpqlQq2sja\nctu2abf7TE42Rj5X23b41rceJ8tS3vveu0barqx06fdDCgWViYnylhLLL1wfNsML1w14fnx+8IP3\n8JWvfBGw+cpX/op3v/vd9Pv5uDLNjDhO0DRlnSz6atp+MTbb2MYa1sbmV7/6VT7wgZ8FJnnPe36U\nr33tj3/YXfuB4oXzJE3TDT7Dy9WHbV9iNF7s3v5qxBe+AH/2Z/ClL137Z1dX4dZbc2LPw4fz3/35\nn8NXvwp/93fP2x0/Dj/1U3Dy5HXp8g2NqxmbN3RGRrGoMz1dRRQTDhy4kygKEIRFskxhZqaBplmY\nZoqimIRhTKOxeyhDKQ/loop0u30cJ0VRKjiOR6WSv3W8lBTScTyCQCJNYyqV3JHMte1FOp0BhUIZ\n2/YplUwsSyPLvKGM1fMO4qUbwZoiyKW/X1uoL90gXkhMmTtyGhAgy8oGGcu1di61zbMMLAaDiEpF\nGrJQ+2RZimEYl33mhUz/aZr/ey0aLwgBQRBgWdqG66y1kWesZKSpuN63QkFBlvPSlyvdT/6zTxwn\n65/p931Ap98PqFbNy9jhNU2jUMgDPb4v4PsSWQayHKGq8kjH/EobcJZlJEmGIHBNm8HVOMGKoiCK\n4nqfHCcjDAUcx6NWqxMENvv27cAwVMJQpt9v0WhUMYyQNE0wjN0kicf+/Q0GgxhBKNPtLlOv1zh/\nvsn09G5WVppUqxrlso5pakPpVYsoiimV8kwiz4uGmTvPP5ssy5Blef2eRVGkUtHx/RjDsNYPTWvf\n8ZUc/0sdjLw+PUEUZSC4ainObbyy8YNwIiVJuqp2u90+rlvAcWIMw97AbfNCFIsacRxSLKoj52Yc\nx1QqUwhCAc/bepzqur5lXxVFYc8eg5WVAocOjQ64VKtVPvKRdxDHCRMTo7MnclWoPNNgK9WUxUUX\nKLO01GXXrvFNbft9G8vaNeT5GVCvj+pvhqLoZNnWJXfXEhQY9TwnJnTgTUxN1alUKmiahmX5QEaW\nQZJo2HaMLF8elN6q7Wux2cY2XohiscjY2EdoNgeMjb04xZpRikqvJKwFJS/1iURxc8Lh63lduNw3\n3sY2AI4evTbFkkvRaMAnPgH/5t/A178OggBf/jLcffdGuwMH8vIVz2NbZe9lwA0dyPj3//5T3Hef\nzcTEPHfe+UFU1eXgwb2kKXz724+TJBZvfWudvXsPMRi0SRKLZvM0Z85E6PqAmZl9CEJEsVhBlj2C\nIKDV8pma2kgGKYoCaZogCNl6dGmNhFFVE9I0IY4D2u08Da7fT1CUlEKhgCDkkyVN8zdmUSRSKknI\nsoEoJqTpmq62RJJEDAZ5bXdeUqFQKuXlKO12l1bLp1iUmZx8nhxvjXQySbL1jIc1xHFIq+Wh6xnV\nqjGUjUvIMjCMdINj7Hk+rhujaTlJWi7Jqa+37zgRgpCSpiJxfGX5zSRJWFnpEccpghCTZSpTUwVK\npcsPH57n8eyzC2RZxvR0BVXVmZ9fxvcF0tRDEEooSv4sBCEhy0RysstcQaFU0jEMnaWlWbrdHmNj\nEuPjV1cW8kIoikyhIABX9zbxahFFEd/+9mM88MCzGIbGO95xE66rYNs+ExNFyuUaCwsdHn30JLbd\nQxA0giCm1wuRpJRqNePUqQGrqy1KJZVez6ZQGGNyUuOWW1bIsiaKMk69LnP48E6CQEDTepw712Ew\niNi9u8jERANVVbAsFdsOkaSIYtEYBh1CgsAjCBIURWZ8vHRZAO5qkCQJvV6u5pNlImkqIIqvfEdt\nG1sjDEPm59ukKezcWRs5NrrdPs2mi2XJIwk8syxjMHCJooxicfR4i+OQ8+dbyHLKzMzukX391reO\n8r3vrXLwYJGf+IkfG9HPLr/wC/83vZ7IPffcyUc+8jOb2rquy+JiH0UR2bGjsen6IEkS/+N//AUX\nLkxw5ozNT/3Uezdt88SJE/y3//ZZskzFMO7g4MFbNrXNs9YyIEbXN8+lzbMGXVw3ZGpq9Nxz3QHf\n+MaTSFLGgQOjCVR1XUUUpfUSyc2Q74kucZyt71kvFn/+578P3M3i4kn+6q/+jnJ5CsMwmJ6ukKbZ\nhr14G9t4OfGtb32LZvMMMMnf/M3/y1//9R9c0+fX/Cxd35oQ+YcJx/FotwesrvYRRZFdu8aoVAov\nec6t+QqCAOWyuWE9XVtDBgMPWZYoFLRNCXu3cePi6FH4rd968Z//5V+GP/kT+Pu/h3e+E+67L//3\npVAU2L8fnnkG3jBaTX0b1wE3dKjywQe7TE39Ok8+WaLXq7O6WqDXk+n3RTqdKopyiKef7mIY47Ra\nEWNjUzz5ZItK5Uc4f15HEEoUCtNUKhpjYwZZVkTTGrTb7obrGIZOuSxTLuepvVmWYVkm1WqJarVI\nuSwhSQqqWmBx0UZV6/T7EIYCYSgQBAJRJNLrZahqncXFAapaoN32UBSLwSAmTRVsO8L3dcLQpNsN\nkCRjSIwGrZaLZU0yGKQbSMziOCaOZQRBv4yMTxQVarUiuq6vl6qkqQpohOFGW8+LURSLIMhI03TI\nj6BQLEqATpoq9PsRimLheVcm3fN9H99XcRyJTife8CzzIEq4Xk7SanUJwyq+X6TVGuC6Ca1WjGGM\nMxgk1OtFkkREkgwcJ8X3M1w3IQyFYf/zEpYkkRkfnySKXvxUyO9VplJRrysztuN4nDrlEASH6HZ3\nc+xYiywrUixOo+s6uu4yGKj0etPMzzfw/WnCUCMI6iTJYc6dEwjD3XQ6+1hYmKDV2oPv78FxSiSJ\niuc1gEkcp8j8fIAsV1hcdJGkMro+jueJ+L5Imio4jocg6EOC0hjPi5Akg34/JUlMokjd9HvdCvlY\n0hAEA8uSKJflDdlC23j1wvd9oqhAlpWwbXekbafjYhjjOE4eANkMaZoShgKybG455kRRpdGoUqtd\nWfb0Unz/+yuMj/9zjh93R5J9/tM//RPd7hGKxZ/nK185MbLNbtdFkmr4vjqyzccee4yFhRn27v1t\nnn569Fr04IMPkiTvRlE+yIMPHh9p67oRsmwShqMJdQVBYGyswu7dFYrF0Vwm3a7L9PRBxsYO0Ov1\nRtoWCjlBdbGob5nlEsfShj3rxeM2conLt/N3f/dZXNcgTa0h94WEZUGlom9LOG7jZcef/MmfAG8H\nfgIYza/pXW/OAAAgAElEQVRzJbhujKoW8LzkFVsKkGUZvp/i+zKDgUYUlbHt6KqJ0EdhzVdIU/Uy\n8ua1NSSKFNJUW/dFt7GNNbguPPYYvOUtL74NRYE/+AO45x74xV+En/s5qF0hMfJ1r4Onnnrx19nG\n1eOGDmS8//1TLC7+HocOddH1JQqFATMzsGuXQq3WJwyPc/hwDc9bYd++KlHU5m1v20m3+zD796eM\nj4uUSgHj42UqFYNSCYJglVrt8iiwomwsjdB1AUmKMc387ZNpKoShzdRUgTBsUSqBqmaoaoamZShK\nSrksEIYtpqaKhKFNrWYQRQ7FoowoRhQKCrruo6oulUrOU6Dr+cG6XjdxnCWKRfGy8hNZjskyH03b\neAg3TQVBiDCMPC05L3MIgWCd62MNhiETRQ6aJqxHyRUlr0NWlARRjCiVFKLIwTSvfNiXJAnH6REE\nDoVCuuFZ2rZHv5+yuNim240ABUlaRVG6CIKI54WYZoznrTAzUwJC6nWTJPGQ5QjH8QmCAEmKhv3P\nOTDqdZ0oatNovLS3G5d+v9cLlmVw4EARXX8GXT/N7t0VoEMQNBFFAUEoUCikKMpparVVLGuJQ4cK\n7N3rUC6f4447LKamVti1a5Zq9SKVygJwnPFxME2bffsUisUmk5MxO3boxHGXmZkChUKEpnVpNDR0\nPUUUc76MLPPX07ENQyFJvGEQzkVRQgzjxQVx8rEUIIrhcLxsy6S9VmCaJopiI0n9Ld+O1esmnrcy\nJCLePMtCkiRUNSOO3S3HnKIIJIlHkgRbHlx37FA4fvzLNBr+pvwJkJN9Tkw8Qxj+DT/xE7ePbLNS\nMUmSNroejmzz9ttvZ9euNufO/RcOHRrt8L/vfe/Dsr6JKH6Z973vTSNtTVMhjl1UNRt5/5IkUSio\nqCpY1uiMqptu2oFlrVCtNrckG7Vtb3ig8UcevPJ9KNmwZ71YmOYZ4G+Ab/Oxj30c0/QQRQdZFuj1\nYlw3e8UeArfx2sZv/MZvAN8G/pZS6fw1f940ZcLQxjCkV2xGkSAI6LqIrscUiwGK0huWCL90/+hS\nX+GFfsLaGqKqEaIYbPBFt7ENgPvvh9e/HiovLvl6HT/6o/B7vwc335yTf14JR47Ak0++tOts4+pw\nQ5eWvO1tRzh79mF27TpCpWKh6wbPPHMCXTdIEg+IeeCBh/nrv36EO+7QmJvTedObquzfv5fp6QJj\nY2UkSRjyTggYRoU4zksu+n0XQUjo9QIKBZVKpUSSJMzOLgGwa9ckkiTh+wGe56IoArIsUCyWGRt7\nnmgyyzJc1ydNMwyjQhSlqKpIFGWoqkoYpmiajq5r6wSdaZpSr5cRRXG9fV1XGRuTEYSEU6dm0TQR\nRdGRZXGdWDEIQlw3RJYhjnMVgUtJ70RRpFrNyzza7S6+H6Oq4PusE2RmWcLCwiqatsaLISBJwjpz\ndRSlBEFAszmgUJARRRVJyktnwjDANBXSVMA0MyRJpN1u88wz82SZzdISiKKNYVQxDMiyEElS8H0Y\nDGJuuaVBuVzE931aLYdq1cCyLLrdmBMn5hDFlPFxA1nWkKQKopg/jzD0yTKFft9FlgWSJEOSBLIs\nf/5BEBAECY1G6ZrLJjbD2lhI04Q9e2Y2ZcjfvXuCev007XaPp58+g+O4HDy4G4h55plzQIwoxliW\nhG0vcvToeXRdRNNkzpwJcd0+mpaiKBJ79tRZXW2xvNxE00J27LgF01Tp95scP96j0bC4eLFPtarR\naFRZWWkTBAFpmh8IV1d72LZDsWgxPT1JtWqiKCaVioiua9fkWGVZNszyEDBNnVptc+6Cbbzy0O/3\n6fdDajVzpBqFJEmMj5fJsmzLAJWqqmiaj2luPcfuu+9Bzp4d8KEPHeHgwYOb2omihGHka8xW4zM/\nJDgUi9WRBHHVapV//a/fxtxcjzvvvHNkm2ma0uv1KJW2Jq+dns544omLTE2Nlh8NgoCLF58ATBYX\n9460DcOQ5eUW5bJGqTRaKUBRJNJ0dMAD8hLHJ598BFXV+eAHXz/S9tSpMxw71uTQoTI/8iNv3NRO\nEAQuXpyj0/E4cmTvSIWXnMzZH+69lwegXXcAnAOOceedv0a1aiEIeW1+rriVrhOLuq4/zJA01kmn\nXTfA9106HRfLUpmZmRx5jy/EWhv53np9M8tcNy8DtayteVe28crD3XffzT333APcSb/fuebPG4aO\nrr/yy6Isy0CWRTzPR1EkqtXL1aXW5h+wTnZ/NZCkfF1znNxHkyRxff6WyxblsvWq4RHZxsuL//2/\n4cMfvj5t/ezP5n82w+23w6c+dX2utY3RuKF3ws985lGy7CN88YvLnD1rcf/95/je96p84xsB3/xm\nn6eesvjsZ5fodv8ln/rU9wmCH+d//s8n6HYnePTRLgsLAc1mQqvl4XnQbvskicbS0oA4Vjh9eoU4\nLtFs+sRxTLvdZmVFZWVFpd1uDx2ymDTVWFzskWU6g0G4YQGOogjPE/B9gU7HJ001lpcHgMHiYp8s\n07HtmCRJsG2bblem31fp9+0N7S8t9QGDEycWGQyqnDjRo9mMWVlJaLVcHCej1fKIY4Xl5cEV+7KG\nPFCQ4jg6zz7bJI5LPPfcKlmmc+FCG8fRWVz06fUiBoOEXi/E90VWV12yTOfMmVXStMKZM22iSKbd\n9vE8gcEgwXUzfD9jYSHA80wefvgiQTDDvfeeIIp28MQTTbpdk8cfX+bsWYVnn4UTJ5YJAot2O6/H\nXFzskyRlFhdtsixjdbWD7xeYmws4c8an3da4cGGVIJB44oklsmyaJ56YJ001VlcdgkCi0wmGfY+Y\nn/dxXZPV1dGyiNeCtbGwumrQbLavaON5Hv/0T2d48slxHnlE4r77Wpw9O8O3v73AmTMDTp0KeeSR\nLo89pnH0qMC993Y4erTO17+u8uUvJ3zrWzpf+5rKgw+WOXaswgMP+Jw6Vefpp0s8/rjK/fe3eOQR\nmwceCHjiCZO//dtnOHu2zv33Dzh6tM2JExHHjnW5cCHiscdWeeIJn6eeUvj+9x3m5nzOnl0limRc\nN1k/GFxtKmcQhASBhO+LI8sItvHKQxzHLC/7xHGJ5eXRcyIM87kfBBJBMPp7Xlnpk6YVlpf9kWNi\ndnaWhx4K6XTewBe+8OgW149IU4UwlLZMbb733vMIwl1885tLeJ63qd1jjz3G/fcLzM4e5r77vj+y\nzVOn5mm365w+nbC6ujrynr72NQ1Z/rd87nPNkW3+6q/+KvB/Av8Xn/jEH460PX++ie/XmZ8PR95T\nkiQ4TkIcKzhOMLLNz3/+/+PkyTdy9Ogu7rvvvpG2Dz98kSi6laNH2wwGg03tVldXefbZhGZzkief\nPDeyTc8LiCIF1+Wy9PIcrwPuAX6bn/mZD3P8+BKOk5e+mWZGoZCTZV86Nn0/v2fXDYhjlZMnm3S7\npS2f25Ww1obrZtclnX4NURQN71nB80Z/R9t4ZWLv3r3kY/NXyEugrh2vlgP6wsIqvV6ZpSWuWIK2\nNv+uxQdw3QDHgW5XYmXFxXVzQsUXrgOvlme0jZcPg0HOa/FzP/fyXO/OO+Hxx2E7+e8Hjxs6kNFo\nhMzPf5tKxaFU8tixQyJJlkjTJUolnzQdYBgdlpYepFIZMDd3P/V6iG0vI4p9DCMvD9E0CUFIURRI\n04gkCVhd7SFJCVFkoyisK08Igo0g2MOfBSQJkiRE1yWSJESWNyo75JKhCYKQIst5+4YhE8cBhrH2\nmUvb98kyH1WVEQQBWc7bNwyZJAkplxXCsEOhkCHLKaIY4jgu/b6DKMZkWU4kFce5ROqVkEuThqSp\nT7EoEUU2pZJCkoRYlkya+qhqiiSBKKYoioAgJKhq3pdCQSII+pRKElmWZ3WIYoqqChQKMpYlY5qQ\npj6lkojvLzMzYwEtpqdNNC1gfFxBEFxEcYCiZLhuD0XJh3Mc+ywvLxNFPqIoUiwayHKIaSbEsUOv\n10ZRMiChUlFw3Sblct5/VRXJsnj4TPN7UNWUJHGv6k3x1SJXfbHJMhvDuHK7iqJQLILrniHLFtH1\nPmk6S7kMmpagaS6SZBPHC6TpAoYxQBRnMc0lisUOkrSMpi2jqksYxipjYyGFQh/D6FKtuhSLMaVS\nSKFgI0mrTE9LpOkyhYIDuMRxF10P0bQIy8rQtABZ7mIYIVnmkaYxvV4fUcwDGJ2OQ7frbXK42Ihc\n9ScG4u1a9VcZZFlG0yCKbAxjdFJf/t3m68pWhI+6LhNFNpp2ZXWgNTQaDUyzw2DwNHv3js4yEEUI\nAo8wdLd0bmdmFJLkLFNTKpa1ebu1Wg1JWqHbPUm9PrpcplaziKI2quqPVEzZu3cvqjqH799PsTia\nS2RmZgZ4DHiCgwdH81lYlkoc91CUaGQ2WS6zPKDd7hNFow8V+/dP0W4fI4pOU62O5h6ZnraIoouM\njUGptHlfTdNEVV3CcIWxscvf3l4KSRJJ0whR3Cxr5ingKPAMUKJYVMmyXJI6f6OtDdt5fmzKcr4G\nybJIkoRUqwpR1EUURz+3K2GtDUFIrmvWRK74kJKm0ZZzaRuvTOQKP2eA7wOj+W1e7cgl4HsoSnjF\nOZTPjXz+Xe08kWVx6A/npSP5HNuWV93G1vjc53JyzomJl+d6ExNgWXD+/MtzvRsZQnYDFouuKYc8\n/vgp7r//EfbunSJNe4yNjaNpNZIkwnFcms0mqlpkcXGZvXt3kmUJ09MTVCo1VFUaHiY1HMcBcgc7\nz6DIkCSdJHFRVdZro0VRxHVzJ9U0zXWN7SiK1gk1RVEkiqIhi7yHKIpDUsqEcrlMmualK0mSrP8t\nCAKO4wxrBOX1e0zTFEVR1nW74zhGEASWl5cxTRNZlgnDEM+TAAFFCZFlEdM0ieMEVVXWa4n7/T6q\nqq4fOnVdJ47jYXlLuP5vSZIIgmAoO+tssFUUhSiKkGUZz/OxLHNdkjMIgg3PR5ZlfN+nWCzS6/Up\nlYosLS1Rq9VoNpuUSiXiOCaKIpJEx/dDxsYsdF1hMAhIEgVRjFHVFNM06ffzN8dRpJEkKePjFrqe\nE/AtLTVpNPJ0csMw1iVP1+49DEPCMKRUKm04DK0RkF4qh3stWHvTZ1yiz3SpZnKWZZw+/RyPPvrc\n8BloCELCkSN34Dh9ut0erpvhec4wlTLGdQeoaoooFhgMBkSRh+8HaJrEkSO3sri4iOcFHDx4Czt3\n7hymKsfrY3AwGGAYFobRQJJEqtW8ZMh1vSHbv4AoSoRhgq5XGQy6jI1ZyLJMqxWQJCmqGq8HlfJA\nnIBhGJcdTtfIB7cDGT9Y5OSYEbIsvaQ65UvHZt7maN6HNVzt97wm52kYxpaHx6WlJZaXlzly5MjI\nAIXn+fR6CVmWUq9rI9ttNps8/PCjvPGNtzM9vXl5RxRFfOYzn2Vubo6Pf/wX2LFjalPbLMtYWVnB\nNE2Kxc0P6EmS8Gu/9l/4whe+xi/8wo/zn/7Tv93UdnZ2lre//f9AEGS+/OU/5LbbDmxqm6vGzFMu\nl6ldiZHskutfvLiK78fUaibj45sHKNrtLp/61GdI04T/8B8+PjJAEYYhTz31DPv27aGyRWFyr9fD\ntm2mp6e3DDrFcXyZlOPa+HzDGz7MY499EYAvfelLvPe9713fWz3PW+duWrvvJElI0wxVzdf9tbZd\n131RKkyb9e96IE3T9f18G68erI3NRx99lDe9Kee1ufPOD62P09cqbNseOYdejA8Qx/F6BmgURWia\ntj0fXiIu3dtfq3jHO+DXfx1+/Mdfvmt+6EM5IehP/uTLd83XGq5mbN7QgYw//dN/4MKFKZ577uvs\n3/9eBGGV97xnF5IkcezYKlEk0WyeYnLyduAUb3zjO1GUAbt3T2HbA1zXoN1eYn4+xDAKTE1F7N69\nhzR1kGWVKAqR5QKO08cwCkSRjyDkC3aWJciyhu87WFYJSYooly1s2yUIJLrdNmGo4nkOggCmWWJi\nQqRSudxpbLe7NJsAMbt3F5BlmV4vBCR0Pd0g07Wy0qbblfH9Ho1GGciwbZcsE3BdD8uqE8dtJiam\ncZwullVmcXGOXs/A97tUqwUsq8DUlDzSge33+ywuxiRJjGmCaRaIIg/DKDIYdCgUqohiSLVawPN8\nHAdc16bfTwAB3x9QLDaAHo3GJAsLcxjGOBcvPoeuTxAEHUolE0mSsO0BgmAgii6Nxk663Tmq1XHm\n52dR1WkcZ5lGo4EkZQhChKZZTE2Z6LrG44+fJ0mqtNun2b//MILgsnt3bX1jjON402fpOB6+n79V\nuF4s+JdO2sHA4bvfPc/jj8+xuNgly0RuummSSsVB06Z47rmloWMuIggB3a6C6w5IkmwYTItptz1s\nW2RszKRScXFdC98vcOSIxOHD0whCEd8fIAgStp0hSSkzMxbT0yWmpiqUywU6nQGrq3lWkGEkQy4Y\nZ1gqoGFZCqYpsrho0267ZFlEFMlEUYSqypRKFvW6ythYcTto8UNAr+cQxwqCEA75Al5c2u0P2tnp\n9/N+QkSlYmx6CMyzfzxARlUTisXNsyLykjsfQYBi0Rx57//4j4/geTMoygJ33/36TYOTDz30EJ/8\n5FNEUYN3vavF7/zOL2/a5vNrREK5rG7qcM/Pz3PHHb9JFN1NqfS3zM7+/aZtfvrTn+YTn2gDFj/9\n00v84R/+101tT52apdPJs7/uuGNm0wNFGIZ85ztniWODqamEw4f3bdrmX/zFX/BnfyaSJD6//dsz\nfOADH9jU9oknzuL7VSSpw5137tl0/o9aZ68Wa+NTEKaA3wCeZvfub3D+/HnSNGV5uUe/L6CqCdPT\nBTRNI8syOh2HLFORpJBKZZurZxvXH2tjc9euXVy8+B7gMPC7ZNniD7trr0pcOm9lOfedt/Hi8VoP\nZDz7LLzrXTA3l6uOvFz4j/8RkgT+83++OvvPfQ4++Un4nd/ZDn6s4WrG5g0dxsyfTUIc+7RabXTd\nRpahVLIoFBzCUCaKSkxO1lleluj3HYrFy9Nu19JS12AYOoWCSbPZodu1iWMfXR+90AZBQKeTEcch\novi8bZ4ZkPMObMU/kKbpsEREYjAISRKJsbHccc3JkUI8zwcKG+q2ct/+yg7+lRx/Qbg+b5nW+pAk\nCf2+j++7ZJm64ZqX3nOaQpLEDAYOUeQA2VABRsWyiqyu9un3bQRBpFIpsLQk0u87hKE/vJ5AsWhh\nWQUkafRhLn877BHHMWkqIYqbH8B/kOu/oihUKhXCUMB1Y0zTwLJSZFkly4ThWDNotVbw/YAoSvA8\nh9VVB0FI8P2UKDIIQxnPC4kihTAMaDY9FhdVGg0Vz/NJEpEsUxHFDM8LEEVhmN3i0u3apKnGpWeQ\nXGlHJQxlIM/0KZUs4ljAtgdcRXXJNl5mvIb9lE0hSdJ1d3LzQ3eLKFLodltb2na7IbKcUSpdjQeV\nsFWcSRRzMl+QLlOPeikwTR1RLKEo9kg713VpNvsIgk+zef3K7a4W+X7hIcsihYJxhT0qAvpAiOfF\nhGG4IVNxzT7L8iB+t2tjWSW2s9O38YNGEARACPSAbZ6TbWzjesLzwLZhbGzj73//9+HjH395gxiQ\nK6T80R9dne3Fi/DLv5yroXzsY3kZTKPxg+3fawU3dEbG44+f5vTpJkGQv+2uVIq8+92vQ9d1Wq0W\nrutiGAadzgDHydC0Bp7XZNeuHUCCYeRM/P1+nyiKmJ6e3lDO0e06JIlKmgYUCtI6L8ZaH9aY8ZMk\nxbYDVLVIGDroen5QXCu3gPxgnZd8xJelc6dpim3bJElCHOtEUUwQ+BiGiWHkDOf9vg9oxLGHqqbr\nZSJRFNHtxoCApsVDJvjnS0vSNEMQoNPpDIMG+bVH1XuvwbZzh1jTtA33Kkn534oirzNXOw5kWYqi\nBMMafG39XsMwQlUVXNfH9wP6/ZgoCtE0HVVVMIyENE3xfQHfTykWJUxTJQhimk0bVRUplfJ0Yl3X\nSdN0/e8oiuj1bCxLx3E8SqUCpmkSBAH9fkqWgWHkz+LS7w9eemnJlfDC0pJ+v8/KSpMkSQjDgCyD\nPXt2s7y8SqvlkKY5X4ZtK1y82ME0Ay5eXMZxGjjOKrKc1+YbhsPrXnc7CwuzdDoOMzOHGBvLDy2l\nUoMoigEHyzJJEh1dNzCMBEEokP7/7J15mBTV9b/frqX3dTY2QRAURBgFEcWNVcQFNEbECAhRUdEY\nIkZjTFRcEpdgNPGrBlcQffiJUcQkRiMQxUQlMCqbJjEKIg7LMFtPr9W1/P6omYaBmZ61p2eg3ufh\nYaam+txTVbdv3XvuvZ+jG6RSVfh8HtxuN/F4AsPYXx/NbAC22qCPjq6b98Vm27+1xEqrmjuysbUk\nG+i6TiwWx+GwN1lXVNUUOD74O9kQdd/Rpq69oqKCbdt20q/fURm3YezatYvHHnuXWExj0qQBXHDB\nWY2eGw5HqagwM2x07+5r9Lo0TeOhh/7A229vYubMCcyZM7VRm5FIhFtueZBEIsmjj/6CvLzGt2wo\nikJ5eTlerzfj1hbT1zAVFdX07Nkt43aKf/xjPX/4w0Y0LcnPfnYGJ510UsbyS0v3UFiYl1F3BMxB\nnqqquN2ZV86YKV0FDMPUOKq7p3X1c/Dg8/niiwrA4MUXf87EiWdTVBSqzT6l4HDYcTqdtW2/Cgio\nag35+QFkWc7athCLI5e6uvnXv/6V889fADjp00fmm28yi+VaNI6qqul+6oHf1bp+iCzLluhnM8n1\nigzDgF/+cn/A4Te/ocmA/sFs3gznnmsGMm66yVwFYbPBnj0waBB88QV0b1kCqjZTUQF9+0JZGTgc\nmc+9/XYzA+Rjj8GcOdC7N9x1V4e42amxVmQ0QTIZxzB8pFL7cDp7ARqSJGG32+nRoweGYVBdHcPh\nCLFz5w7i8SiJRIwdOypwOjWGDj26Nj2nDYfDvNkOh6N2KbeApqmIogToJJN2Uim1wSXTsgyqqpFI\nxNC0JImEC0VJEQx66wUyvvlmL4oiUViYqNd5FQQBv99PIpFg584wmmYO5iXJQFFSqKpIVVUlmubC\n5dIpKiqq99lkMoqm2QADp9NHZWUEu92Drqfw+z0oioIs+7HZDFyu5m+haCjYUdePl2UzlVwsZqDr\nqXTwxzAcaJo54HK73el7aRgqwaC/1pcUup7CZhNrs8GkAImyst3IcoiKir0UFBxFZeVudN2Lqlai\nqoUIQhyfT8PhcBIOVyFJTjwege7dC9m7t4KaGolEIkKfPs7atImmyr7P58PRQCtks9kaPN5e2Gw2\nFEXDMELY7QqJRIzych1F2VObblXC6RQIBJxEIgkEQUXTvNjtMaqrYwhCkkTCgSCIeL1B/vvfGqJR\nkby8Y4hEKigs9NGjR3cMw8auXSp+f4jCQh+7d9ewe3cVwaCdusyaougmlTL1XL75ppxIxKCwMMLR\nRxfVpu01t6UEg3VbF6ylnp0FQRDSAoedmXg8STIpoKoKgYCUsRN6oB5QJiKRCLt2JQCd3r39GTU9\n9u2LoGn5lJZWZwxk+Hw+TjyxD4mEnWOPLWz0PIBEIs7u3VEcDo1u3RoP/oqiiKI4GDRoEuFwNKPN\n//73v1RV9cIwJDZv3sjo0aMbPTeV0pDlAKqqZ0xJqOs65eUxFMVFVVWEoqLGr18QUqiqqTvS1LvA\nFA/1UF4ezRjIMINYKppmQxSVjPVV11UqKxO17c2hfiYSu4BRwNds2rSR4uKT0bQUDocfSRLTdUAU\nRUQxiaIoGIZIdbWC3Z5AUUQEQScYdFvBDIt2ZevWrUAecDTJ5Pu5dqdL09A7IB5PsGdPBFU1yM93\nNpj21aLzsXw5rFhhZvm47DJ4+mm47rrmf15V4fLL4YEH4MIL4bzzoLwcnngC5s+Hq6/u+CAGQF4e\nDB4M//wnjBvX+HnRKDz3HKxbZ/4+dy5MnQp33tnygM6RyBEdyDAMAZfLTjTqoKAgH6dTPWgrg048\nnkLXJTweH0cdVcCOHSqC4EEQEkQi0dpokZxebWAYBqpqIEkOdF3B4QBVlTAMGV03ZxEP7BylUikU\nRcXlcuB224hEbGiaHU1T0qsYwIwyx2I6hiESDic4sJ9tGEbttgJztskwDIJBc7VCdbUNUbSj6wJu\ntwNJ0g7p0Pr9fnTdIJmMIwgyiYSG2+0glYrV+qhhs8kYhkYsFq9dmeFodbRbVVWSyRThcIQ9e2K4\nXDaCQXNPtCj6EASBbdu2E4vpFBbm4/Plp30x77MpXunzeYjH41RXG4BEIqFjt4uEwwq9evmJxb6j\nZ898KioUbDZHrTiogd0uEI0qOBwyoqjhcjmJRhVkOY9Uqip934NBf+29Euo9K0kSUFUdu13K+iqD\naFQhErERjVYSjabQND+qWoMsewkECkgkIpSXRxFFMzAEBi5XiJ49VRIJmXjcjWGoxOMpBEEjGpXp\n1687uv4dfr+D6uoIggCplI1IRCWRSOL1etB1D3Y7OBw6lZU1uN1BdN1Mk5ZKSQiCm2QyWruqRUcU\nzfqu63rtjIiBw2G3BgIWzcasR05UNZEWNW6MSCRCLKaQl+fPGNCIx5Ps2VONYRj06JE5w0hFRRxB\n6E5Nze60mHJDOBwOiov7EIupFBYWNXhOHapqZirQtGRaHLkhEgkzBbXN5qOsLPN2lZqaGkSxF06n\ni507G07dXIeiqCQSIIpaxntqzm5KOBx+EomqjDZ1XaKgwIEgBOoJFTdEZWWMeNxFLBajZ0+10We1\n/10rIst6xkCGzWZut2xslqa01A24gQJKS/egKDa++WYvAwYEUFUj/f4z23gPyWSSSMR8tyQSEWTZ\nha6n6r1/W4K5OtAUzm6NUKjF4UtVVRXgBwLs2ZM545BFw9T1d82VnvXbCVXV0XUJm01EUfRWf4ct\nOo5EAn72M1i8GI49FpYtM4U5J06Efv2aZ+OZZ8wsIVdeaQ78V682RT2LikybixZl9RIyMmkSrFyZ\nOZDx4otw5plwzDHm78OGgSjChg1Qqw1skYEj+hueSikkEgoul4zLFScYFOrN2NVt/1AUFa/XFAMr\nKvN1oPMAACAASURBVPIgCCkMI0kyKZFMikiSgiyrOBzmMmefzw7EsdkMUik7qRSIYhK3m3oDX1PT\nQiGZlGpn1AU8HgeiqODx1F8KLYoidruBYSTxePZ3juq2ZsTjApGITjKpoOsGgmBDksxUpjZbArdb\nrm3ktXr3wFz5AG435Oe7kKQU3br5gHjtdYDL5UCWUwiCec3RKLVCj60jHE6gKDJffVVKZaWd//xn\nD3v36lRWChhGlFisnI0bY3z5pYf//ncbhhHD5zO3p1RUhAmH7ezenUJRFDweD6EQuFwx3G4H8bhC\n9+4+oILi4l643QmOOSZEIKBTWCiSn+9CllVkWUJVdcDsDPfoEUQQqigsdKZX5TidOi6XOSA/8FmV\nllaiKDLhcDLry/FkGWpqIqRSUFBgJ5ncQ1GRh+7dvfj9USCBJPnZvHk71dUeSkuricercDgciKIN\nny+Jx5MiEACnU6NPHyd2+3f06FHAt9/WsHNnih07klRUhInFkthsEsGgg1BIw++3UV2dAPIoKyvH\n6TTw+Xz06GEnEAjTo4e5VL6uzrrdZqaXmhqNeFwgHrf2AFs0H6/XgSAk8XqljEEMMxNHjHDYyd69\nmQfdsVicqqoU4XCSRCKR8dzu3b0kk3spLGxcaBTMLRBlZSrxuJvy8uqMNgsK/Hg8CYqKRNzuxgMp\nZkA8RVnZdzidmbWQBg0axIABFeTn72DEiBMynmsGFnVUVct4nt1uJz9fQpLCFBVlHmBFozFqajxU\nVwtUVlY2Ub6paaTrSpMDCk3Ta7NwZTyNaDROLGYjHE41mOo5P18DIkCYvn17UVZWiSSFKCvbW/s+\n3F9A3ao6c5uoSijkq03VbWv1NixzC6RAOKyiqmqrbFgcnnTr1g2oAnaTl3fkvB/bs59krgC1EYmY\nW1gPxO12EAza8HgUAgGnFcToAvzud+bAfcwY8/dBg+DWW83tFXXVxjDg7bfh+efN7RoHUlUFCxbA\no4/uX73g88GqVfCvf8E//gHN2AmfNX74Q3jpJahupKugaabvN9+8/5jNZq4wWbasY3zs6hzRKzJ8\nPg+JhINksgq73UzLWVFRU9uBMTAMG8lkAlU1lZFl2Yks2+nRw42iODAHwQaqanbAUqkaDEPE57MT\nCHioqYmRTGpIkoDf76lV2zd1I/x+V622gIGua8iy+Q0URRG/3127ZSKCrqtEIglSKR2Px0ko5MHl\nMr/diUSSaDSFosSIRGykUvEDsqKY5zidjnTUWlUlNC1GRUUEWRbSKv5NKcQLgnmuuac4Bdha9YKo\nqYmhKDqapiCKMpJkIxKJoesKVVUR7HbQNBu6bsMwYhiGu1ZPA/btqyQS0YnHq0gmvQhCCpvNl97O\nIwgSDkcYUZTw+WT69CmqXTKsYLNpiKKAKO6/Vl0X0HUzOGTeJyd9+tQPYnm99Qcedc9KkgR0XWuT\nOJy5dcVcZeL3uxoduImija+//obS0nKGD+9Nnz6FyLKTioowVVUJdu8uR5Yj7NtXSXV1DK9XJZGQ\nqK7ejaom6dOnBw6HQDKpI0k2evfuRt++HlRVorJyD4mEgc9nx+8PkJfnw26XcLmcuFzmvYhGk0Sj\nGj6fk2RSI5WKUlAQolu3/SpEkiTh95tNiTkQMWrvj602sBEjlTLLsWYojyyaW8+hfj3KhCAI7N1b\nRiRSycCBmdsut9tBfr4N0JpcPWUYdXo+DYscH1i+rqdIJmNIUuby7XY7PXs2T7Hrm2++YdeufPLz\nM4ttmu8ac5WfKGZuhBwOGcMQEITMW3UA8vKCZNhRk8YMcCuA2ox7qpJI2HC5MgdSbDYbbrcDEJsU\nZDPbKB1BoMFggyCEa/0z01t37x7EbpcJhaRGtwge+A5s6yq7ugkQm63xrTwWRybmwNvMzOZ0Zt5C\ndrgQjyeIxVScTrFV2YgOxuxXaICR7u/WIYoieXmBNpdh0TGUlZl6GB99VP/4/Pnwxz/Cr38N48eb\nv8fj5uqKu+82/3bqqea5d91lrr448cT6Nmw26N948q0O4+ijYdo089+CBWbQ5sDX0GuvQX6+uQrl\nQC6/3FyVsnAhlhB1ExzRgYyjjupOKKRSWdkNuz1IIlFDLKYjSWYnyW53YBgCPp+TmpokHo8D0PD5\nbEhSIC0WGY+70DSdWCyK1+skHk9ht9vxeJwIQhy73dyGkUqp6Lo5kEulVJxOB4GAC03TkOX6HSxF\nUQEH0WiScBicTh82WwK/f7+wZDyeQpY9RCJJvF4HqZSAzSbWDhbrd6B8PhfxeJxkUkTTZBIJFbdb\na9Gsk5lBwxycNqezl0qlsNnMmS1TrBJk2QPo+Hw2+vfvTWVlAlU9Cq/XTzxupp61212cfLKBYWj4\nfAOw271s376bvLzeJJMxfD4bDocHQRBql0SLgITP58HhcOHxmC+3aDRZm+0jgdPpQpIEEokEsizj\n9cqkUqkGl0anUikEQag36DKzcjhJJpMEg3kkk0kcDmerO6qpVApdt9fqYKRwuRoe4CmKhiTl0atX\nX6LRPfTtK+Fyudm1K0kyGULXk2hagiFDhlJREUGSEgiCm/LyGiSpkGDQQ1XVHnr2PI5YrILu3QO4\n3R4gwXHH9cBmsxEKSWnxV/O+RZEkU4g1P9+L328GjRTFjq6bdbfxwItIIOBI1xFzO4+AJDmIx+Pp\nQIammUvdJanpAZZF16W59Ryo3ZZn1q1MgVJd1/H7/QQCTmy2zDPehYWF2O3ViKLYpECxpgkUFRWS\nTFZkXJIsyzJ9+oTQNIP8/FBGm82luroaSerN0KGnAx9kPHfv3r1Eo/nYbDLbt+9mwIABjZ7rcjlQ\n1ZraYG9TKyKa950sKurJ8OExZFmioAlZdVl2c8wxQVQ1nPGeHtxuZCIvL4goViOKDQdGvd7jgf6A\nE1WNM2hQPoZh4HQ608JhdYKe2UgH7fG4sNtTCILc7vZVVW32+9ei8xGPx4FBwNH4/TUt/nxz28jO\nQiqVIhxO4HD4qKkJ43a3vs9Uh91uJxDY37e06JoYBvzkJzBzphmgOBBJgv/3/+Cqq2DpUvO8a681\nB/R/+hNMnmyu5AAzqLF5c8f73xIee8zU77juOvjySxg1CmbPhpEj4ZZbzK0lB38tBg82Axz/+IeZ\nwcSicY7oVsBcyixis7nYs6caXU8Qi0nIsjkzaLOZ0fOamgSKEmPfvmq8XnA4zIivKRYmkkzG0HUN\nVVWorKyhoMDsZESjCZJJkWQySShk2hUEc5ZIll31bByM3S6RSCSw2w2SyQjJZJxu3fLqddxcLplo\nNIrPZz5Gh0PGDGDo2O31AyORSBxFEQmHq0il7DgcBvn5LY+ON/fFoSgK4bA5yAgEzI6X3Q6KEsXj\ncWC325GkOCBjtxu43SKybGfnzjDxeJRu3dz4fCFUNYqiRPD5JPbu3UUyWYmmFSDLCj17BmrtKKiq\njihCIqHjdpstQjQapbRURdOqCQRCiCLouhtRNNC0JHa7G0jWmyWIxxNEowY2m04wuF/Y1MwgkkDX\nJaqqKnE4fChKglCodWvWJMmsC4YBdnvjz6GwMET37qXs2vUNRUV+JMlFKhWjqEhm48avqKzUCARs\nFBbGEQSxNuizi6oqHUEop7LShqra2bHjM4YO7YGmKcRi5gxtJBLF73fg85lbROLxON9+G6GyMoos\nmzPPgYCXvDwHHo85KID9dTfTtdUhiiKynCSViqW3KpnbdBJomojTmTpk5YvF4UNz6zmYWwYSCQFB\nSBIKeRrt8JoZJdTadqJpUdlAoHkzdHl5LsrLKwiFMmu7mPU3iaqKhEJRfL62119TgHkHn38eY8SI\nzNv2NE3jm28+J5WSUNWBGc8tL6+mslJAlmvo00fOqFHR/O+kSiymIIpNL40vLPRSXh4hFLI3+e5o\n7rslHk+QStlRVR2XqyEtk2+B7sDX9O8/7JAtPc2tZ20hG4EGTdOork5iGAIej5ZeNWfRdTj66KMx\nA5UaorirxZ8Ph2OkUqZIbWv7Hh1FXV8qkVCoqtqDy+UiFku0y6oMK5DXNamoMLOH7NkDr78O//mP\nqW/REMccA++9d+jxyZPNbSY//KEZDHnzTXPA35mx282VJHffDbEYvPWWqQkydy7cey+MHdvw5+q2\nl1iBjMwc0YEMl8uJ02ngdIq43TYUJV675UFClhUMwxTCFAQH4bBGIBDAMExdBMMwiEYTCIKNYNBT\nO9skAxK6HiMcjpFMJpEkH7puCmyKonjIy2f37jLC4Tg9e+bVmzGUJIm8PC+plANZ9mCzCeltEIqi\nkEiouFwyeXkeDMMgEokjy/ZGOzeaZiCKEoIgk5/vB7Qm9y2qqko8riDLYouzHphpW8X0vQLw+dz1\nhEYNw4bdLiFJAqGQB01zIsu+2k51FZFIkkDA3KYjCJCfb2fHDoXdu2twu21pW4GAB03TAAGbzU4y\nWcXOnfuIxxOEQoXE4xAK+TAMnUgkjiiqSBI4HCLhcCWVlVHy8ty43e5afRERXTeIRGK1GiLO2uuw\nIYoS0WgCVXVgt6sZMwFkoqG60BAOh4PTTju+dq+7DUFwEo9X4fO5GTxYpXt3D/F4FYMH+xFFH6Wl\ne6ipSSBJArFYJRUVcSTJT2GhiMfjZs+efXg8brxeP8GgH693f/pKTdPRdRHDkGqFawVAqK07jfsb\njydQVR2320EkEiUSUcjP96ZnQP1+T1qgq6YmhsMhoWkgCGLtc7M4XGluPQezjRIEEcM4VJD4QGw2\nG6Jobu84eOVZW/D5PAiChNudefuTYRh4vT4EwYHNdqhGQ2tQFIWTTx5J794B+vbNPFPrcDgYNuwU\nJEkmQxIWACKRBPG4nUQiWU/I+mAMw2Dbtp1UVaXo3z+UMZAhCHaOOeboQ4SrG0IUbShKAru9aWHD\nA9uRTCsZ9rfRNChgGgr1BfxI0lGEQoempm1uPWsMTdOIxZJIklDvfdtc/1uLmdVLqL12q93sing8\nHiSpB6rqIxTq1eLP1/XjdL31fY+Oou576nS6sdtVHA5PbZtt0pb+pUXXIhYzt4k88QQMHAg9ekBx\nMTz5JGSQjmqU4cNh48b297MjcLvh0kvNf01x+eXmqo3HHms6feuRzGEbyLj55pspKSlh+PDhPPbY\nY42eZ7PZcLkcQBKv14wUp1IpkkmpNqgRw27X8HqDtarzjtqUq2b2BjNftbmVxOeTUBSVeFxAVWUg\nhd1uphZtqMOnKAo7dkRwOLrz5ZelDBvWULpSGa9Xr10a66jVHFAQRRc1NXHy8mRiMVPHw8yokWow\nWm1qHKTo0SOAoqjIstjkDFg0mkTXHbUpTxtX8W8IM3tKMn0NdcuKD37xSpJYu1JCrxWOTKKqOori\nxAxM2GoHw2Y0XxDAZvOQSCTTQmp1L3SfTyaVUtm3T8FuL0RVEwQCUbp18yPLMoqioGmmAJTbrSMI\nCcJhFVnOY9euKvr3d+NyOdD1OJqmoqpOVBUkScHhcOD321GUFD6fG10XOmRpp1nPzPvncmloWgJR\ntJNMmntBk8l9FBT4EUU3EMcw7IiiD1WtxOFwcdRRXhKJBMccEySZNEgkXCQSGg5HikDAhssl1O75\n13G7XXTrphII2NPZb2TZDOQcSF1gymaz1WbTMbDZZMLhKPv2JZDlILt2VdCv3/58V5qmEY0aCIKM\nzabi98u126usWUULE6/XSSKhNNpe1qEoCpWVKQwjwO7dVfX0WhriwPqaiZqaJLouU1OjkJcnN3q+\n0+mkWzcFRUmRl9c+mQcEQaB79wCqaiMUymyzb9++DBiwB1HUGDhwcMZzfT4XiUS8VpC38fY7Ho+z\ne7eKIBTw7bdlHH1044Msr9dNXl44HejPxJdf7kMUu/O//+3l5JMDGbOm1LUjsVgy4yqXujZaksQG\n33VebwgIo6pJPB7PIQO+5tazxojFkqiqnWTSFPmu2zoZjeqIop1IJEEg0P7pp+v6Apqm1fZZLLoa\ndru9VsDQwGZr+QjO3N6aSm9X7szU9avdbhmbzdxmemDgry39S4uugWGYGTtuvhlOOw22boWePXPt\nVdehXz8za8nixS1LR3ukcVgGMj755BOi0Shr167lhhtuYMOGDYwYMaLR881sIfuXu9ntcm0KwBRe\nrzMdLXY4TIHNcFhB1xVsNjs2GwiCVPs5c89uVVUZsZiCzwceT+PLmkVRxOGAZLKC/PzGl8odHK2W\nJBuplILdXicQas5OCoLRaMesLud2MplEUUDXNRyOzBF9U1MihSC0PIWVGSByous6VVVRdN2G32+v\n1/E0hejMTmw4nMRmSxIMunE4zEwvui5gt5vlKkoKRTH3zoH54pMkKS0mqOtmIMPjceF2y0SjEUTR\nwOHwYBjmPTQzlSRryzRnE2OxGpJJjbw8c7ZSVVUUxVydAErtMnZzhlaW5dqgjEEyaUOWbVnvTIii\ngGGo2GxGbSYSkfLy3ZSVVeDxGAQCPqqrUxhGuHZViYjXC5GIjKrG6dWrCL/fgccjsm1bJZWVVTgc\nZuDD5/MQDidqM5OALEsEgz5CocbrrKqq6fPrVMEFQUfTUrhcErIMqVQkvd2pjrrzdD2FJIm135Xs\n3TeLrocoNk8Mzm6343DY0HUFvz/z+alUipoaBTAIBDKLjVZUVFJerhMIQH5+5lUkwWD7pk6UJIl4\nPE4q5UDTMut+2Gw2Bg7sB4hNDmgTiQSRSApVTXDUUY2vv3U6nbjdpphvKJT5nnq9bvr27Q7ozdgu\nolFWVorPp2S892aQu659yPyuqWujVVXF4Tj03dStm4Tf70OSEqiqQGVlDL/fkfa1ufWs8WsSSCaV\neu9FM52r6b/Tmb0BmTVz3bXxer34fAKKIhAMtnw1V10/ritwcL/64BnltvQvLToHqRT85S+mjsWG\nDaYgp99vpkLt1g3+9z8zmPHss6Zop0XLufNOuOwymDqVZolxH4kclq3HunXrmDhxIgATJkzgo4Ml\ncZtAFEWCQRcbN350SMchmVSRJBc2mwOfTyIYdNZ7sZgrJ1wUFAQO0amo473ajV+iKDJkSB8GDw7Q\nv3+fZvvn97sJBuX0rJXT6SAYtPPZZ+uajGonkxqi6ERVhSaX9Xs8LgIBmU8//VerB+yapqFpEoLg\nqBUw3c/69esIBGTcbjuC4MQw5LQIWzDoJhjcv1UmkVCRZTdud4jBg4s4/vjeiKJYK1BXZ1/jvffe\no2fPAo4+2k1hYRBJcqWvVZLM5+X1ithsDkTRid/v55hjCsjLM0X7FEVDEByIogu/XyYUciLLcvqZ\nAXi9LoJBGb+/7Xvj32toE+ABfzOFrezpemaz2ZBliby8Qmw2D4LgwOVy4/EEsdns9OuXz6BB3Tjl\nlCGMHFnMiBE9OfHEowgEQgwefDzx+DcMGzaA7t0LaoXjZBTF7FjV3f9MmGkc7YA9vbQ8EDC/Kx6P\nmz59Cujd20337vVnyc1n6iIYtLeoM57p/rSWbNjsanaz5WtHlCeKIsXFvSkv30S/fpmXZ9fVV8OQ\nm7GNSaJbtx6sX1+ScRtGa2jq+lVVpWfPoznttGEUFHTLeK4syxx1VCHffvs5DkfmFU2GIVJQ0A1Z\n9ma8fkmSOO2040gmv+CEExoXDwVwu90cfbSfo48ONrmiqlevbpxwQnd27dqe8Z4e2D40pf1Q10av\nXfthg9c0dOipjB49nCFDCnA6vYCdVKr90qC6XE6CQTvBoDs9AKt7ZwUCctr/jv6OHUwuyz+Srz1T\n+cFgkNNOG84JJzgYMWJcxzrVBB19z+r6l4GAu8H+Za6fYUMc7j4119bmzfDTn0Lv3vDII3DhhbBm\njXn8rbfgwQfhiivMrSQbN7YsiJHre5zr8g/2YdSo/RlM1qwxt+k0hqrCu++aW3bWr9+fvrYt5eeK\nlvhwWAYyqqqq8Pl8gCn0VlVV1WIbgiDwwQeHqse73XZ0PYbTSXqG/EDMVG4Spohkw1POBz6g5qjp\nH0ydWvOBjb8kSaxdu7bJz7rddgwjjsNhNCuy31y7mT5vt2tAAqez/v1477330hoUkECW96fzEwSh\nnn8ejx1VjZKf78Tnk/F6pfTshN2u19o3Aw6CIOB0OvF6neh6rN61iqKI0+nE6QTDiFNY6EWSNLxe\nc3DtdMpAArtdr/d8D3xmDd3/1tJUIAPMe3hgPcvP9yKKEUIhgUDAjs8HdruKx2NmH+nRowCPJ0VB\ngUx+fj4ej4dQyIHdHmXHjq24XAI+nx2Hw4Esq7hcBi6XUe/+N4bdLiNJqXpbmA78rtTd+4Y4+Jk2\nh640iO9KdrtyIAPMVRnr1q1rxnkygpBsVt0uKPCgaRVs2fKvdp8hbOr6JUni2GN9QCmDBxc1ea7H\nI/Cvf32A3d7UNXmx2arIz5eaTH3sdDrZsGFDxnPqqFt92BR+vxOn08aGDR81eU+b2z7UtdEff/x+\ng+eff/5wBgyoRJb/w5AhxyEIySbvU0sxRWzrX8/B/ue6M2gFMjpf+QMGDGDUKCe6vp4pUxpfJZwL\ncnHPGvoe1ZHrZ9gQh7tPDdkyDPj6a1iyBObMMTUuzj8fZBnefx8++MAU3uzf31yFcdxxZirRSy81\n/2/pqzTX9zjX5Tfkw8MPm1lbfvYzKCgwU8/ecQesWgXffAN/+5spHNqzJ/ziF/DZZzBjBpxwAvzf\n/0E43Lbyc0FLfOgaa9RaSCAQIFz75KqrqwkGDxX8WrBgQfrnMWPGMGbMmGbZlmWZUChzp8jlctJA\nVs9OgSRJHap2XSf2mInmCALa7Xby8hruODe2nzrTs2pMzK6j709rcLlc9Ou3v4IFAvX9dTgc9O1b\nf1Y3GPQRDPrwej3k5/vSx5t6NgdjrsBo//3fFhbZwFxd17zvczBoCuDmKhvEgAF9yZBJNY0gCPj9\nHpxOe5Mr8NxuN/365S4rkMPhqA2Ytl9Xo66Ndjga1jEZMmQIv/3tEBYs2EXPnpmDQhYWHYnX6+Wu\nu65B13dy0kkn5dodC4tD0DRzZcUHH5ipPz/4wEwNeuaZZmDiRz+CoUNbHqCwaD02mxnIuPZaSCTg\n44/NIMbdd8O330KfPnDBBebxY44xP2MYsHatuSrmrrtg2jQYPdoMOBUWmplevF7Tdk0NfPWVuRXo\nf/8ztwtJEgwaZD7r/v3rtvVnF0Ux61VLyzosAxmjRo1i0aJFTJ06ldWrV/PDH/7wkHMODGRYWFhY\nWFhYWFhYWFgcqRx/PIiiGbi44AJzm0jfvuaA1yL3OJ0wZoz5LxM2mxm4GD0aSkvNFTWvvWYGLMrL\nYd8+apMZmEGPY46BY4+FAQPMIEckAi+9BFu2mJ/v3t3U6Dj4X34+BINmnakrt66uHPzzgccUxfTl\nv//dH0CpqYHbboNf/apl98RmNJWDs4vyk5/8hE8++YRhw4bxu9/9rt7fxowZw/vvv58jzywsGicQ\nCFBdXZ1rNywsDsGqmxadGat+WnRWrLpp0Zmx6qdFZ2X06NFNbjM5bAMZFhYWFhYWFhYWFhYWFhYW\nhx/WLicLCwsLCwsLCwsLCwsLC4sugxXIsLCwsLCwsLCwsLCwsLCw6DJYgQwLCwsLCwsLCwsLCwsL\nC4suw2GZtcTCoquxYcMGPvroI6qqqggGg4waNYoRIzpXnnkLi+ayZcsWJEli0KBB6WMff/wxp512\nWruV8cQTT3DjjTe2+vOlpaX07NkTXddZuXIlX3zxBccccwyXXnopUitzjSmKwttvv01BQQGjRo3i\npZdeIhwOM3369AbTgFu0D1b7aZFrampqqKqqIhQK4fXWT/ls1U+LzoZVJy0OFyyxz1pUVeWNN944\n5It98cUXt7pTmw2blt3Dz+7atWtRVZUJEyak1aNXr16NJEmHZNxpDZk6WJ3JZleym0ql+Pe//522\nOWjQoDbVja5qtyHmz5/P3r17kWWZsrIynn/+eYqKihg7dix///vfW2XzrLPOwmazceDrauvWrQwZ\nMoS1a9e2yua4ceNYs2YNP/7xj3G73YwbN45PP/2UkpISli9f3iqbF198MSNHjqSqqoqSkhLOP/98\n8vPzWbZsGe+8806rbEJ22iRd13nrrbeQJImJEyciCOYCzTfeeIOLL7641b4ezF133cW9997b6s9/\n8sknDB8+nFgsxqJFi9IBp+uvv55gMMhPfvITFEXJWvvZFJs3b+bOO++kqqoKwzAQBAG/3899991H\ncXFx1su3yC2rV6/m/vvvx+fzEQgECIfDhMNhfvGLXzBhwoSc18+D+e6773jggQfYunUrmqYhiiIn\nnHACt99+O0cddVSH+2P51PF0hjqZrX52V/Ih1+V3Bh9qampYtGjRIeVfd911+Hy+ZtmwAhm1zJgx\ng+LiYiZMmIDf7yccDrNq1So2bdrESy+91GlsWnYPP7sLFy5k7969h5x/9tlnt3qQBk13sDqLza5m\nd+nSpTz77LOceOKJaZufffYZV199NVdeeWWrfe1qdhvjrLPO4oMPPgBg06ZN3HTTTSxcuJDbbrut\n1YGMRx99lI0bNzJr1izGjh0LwHnnncdf//rXVvs5YcIEVq1alf6/jrYEXA787JAhQ9iyZUubbUJ2\n2qTp06fTr18/ZFnm3Xff5dlnn2XQoEFt8rVPnz706dMHW13SeNov4HTllVdy+umnpwNOS5Ys4a23\n3mq0nWxr+9lczjzzTJYvX07Pnj3Tx0pLS5k2bVr6e5BNch1IOdLLP+OMM/jb3/6Gx+NJH4tGo5xz\nzjl8+OGHOa+fBzNu3DgefPBBRo4cmT72r3/9i5///OesXr26w/2xfGoe7RlY6Qx1Mlv97K7kQ67L\n7ww+TJ48mZkzZx5S/tKlS/nTn/7UPCOGhWEYhnHmmWe26HiubFp2Dz+7PXv2NObMmWO8+uqrxttv\nv20sX77cuO6664x58+a1qbzTTz/diEQi9Y5FIhFj1KhRncpmV7N7xhlnGJqm1Tumqqpx+umnt9pm\nV7TbGKeffrqRTCbTv5eXlxvnnXeeUVhY2Ca7iUTCeOKJJ4zLLrvMeOONN4xzzz23TfaWLFliG4oH\nqQAAIABJREFUXH311cbs2bON6dOnG4sWLTJuvPFG46c//WmrbU6ZMsW47777jFtuucUYO3assXDh\nQuO5554zzjnnnDb5mo026eyzz07//N133xnnnHOO8cYbbxhjxoxptc3XXnvN+MEPfmA8//zzhqIo\nhmEYxqRJk1ptzzAMY9y4cYamacbEiRMNXdfTx+v8/8lPfpKV9rO5nHHGGcbOnTvrHdu5c6dxxhln\ndFj53333Xb1j3333XZvfV1b5zWPs2LHGhx9+WO/YRx99ZIwbN84wjNzXz4MZNWpUVt61bcHyqWnG\njh1rrFu3rt6xdevWpetZS+gMdTJb/eyu5EOuy+8MPpx++umH9E81TWtR/9TSyKhlypQpXHDBBYwZ\nMyYdFXr//feZPHlyp7Jp2e04u2PHjsXn82Xd7rx585gwYQLr1q3jyy+/JBAIcN111zFs2LA2ledw\nONi0aROjRo1KH9u8eTMul6tT2exqdkOhEK+88grnnHMOfr8/vSwzLy+vTb52NbuN8dvf/pbKykq6\ndesGQF5eHm+++Savvvpqm+w6HA5uuOEG5syZw9KlSznppJPaZO/KK69k/PjxvPPOO+zZswdN05gz\nZw4nnnhiq20uX748rZHx8MMPs3TpUsLhMK+88kqbfM1GW2cYBjU1Nfh8Pnr27Mmf/vQnrr32WkpK\nSlpt85JLLuGSSy7hrbfeYubMmYwaNQpFUVptD+D2229n2rRpBINBRo8ezZlnnskXX3zB9773PcBc\nrfPJJ5+0e/vZXP7whz9w0003UVlZia7r2Gw28vLyeOqppzqkfKDelqu63w8+ZpWfHV566SUefPBB\nfvGLX6BpGoIgUFxczIsvvgjkvn4ezP3338/kyZNxuVzptiQej3PfffflxB/Lp+aRSCQ44YQT6h07\n4YQTiMfjLbbVGepktvrvXcmHXJd/oA/tPd5pLjfccANjxoxh6NCh6XuwZcsW5s6d22wb1taSA1i7\ndi1bt24lGAwSCAQYMWIEX3/9dZsE6tavX8/69esJhUJ4PB6qqqqYPn06oii2ydeysjI2bNhAVVUV\ngUCADRs2cNddd7XJJsDevXspKSlhw4YN9O/fnwEDBtRbWtcaSktLkSSJDRs2UF1dzbZt2+jduzeX\nX345siy32u7KlSsZPnw4W7ZsSd+HESNGUFRU1CZ/FUVh2bJl7NixgwEDBpBKpdi+fTs//vGP2yzY\nt3bt2rS/wWCQCy+8kD59+rTJZmOUlpby4IMPsmXLlnodrNtuu41evXp1GptdzW5NTQ3PPPMM69at\nS9e7UaNGcc011zR7T9/hYNeiY9m7d2+6Da1r67Zv397q9nnbtm2EQqFD2rS1a9dy9tlnt4fLrFmz\nhi1btjBq1ChOOeWUVtuJx+N89NFH7Nmzh2AwyIgRI9i2bVub302HA1u2bOGuu+46JJByzz33MHTo\nUKt8iwaJx+Pp94Hb7c61O4DlUybWrFnD/ffff0hg5Re/+AXjx4/PmV9tIRtjrpaSrbFUc8jW2Kgl\nZHO801xSqRT/+9//0s/guOOOa5E+hxXIqCUbAnVz584lmUwSj8dxOBz4/X78fj87d+5k8eLFrfY1\nG8J3AJMmTeLtt9/mscceY9WqVVx44YX885//5KijjuKBBx5otd1siOoB9OzZkz59+tCtWzcuueQS\npkyZQigUarW9OuoE+yorKykpKeGCCy5oF8G+n/3sZ8TjcU466STWrFmD0+lEFEXOOOOMrGgVWGSX\nOgHRYDDYrgGBrmbXIvvoug7sn3mua/8nTZrEu+++2yabB5Itm+eee249HZLW2GzPa29PDmdRQIum\naUijIxAIcO+993ZKsdf2ENezfMrd+7OzBFbaSjbGXC0lW2Op5pKtsVFLyNZ4p7m0h9iotbWklvXr\n19cTqJs6dSoLFy5sk82tW7emvwxDhw5l8+bNAIwePbpNdi+55JJ2F74DSCaTALz++uv8/e9/RxRF\nrr/+es4444w22a1Tw//888/TndmJEyemfW8tAwcO5O9//ztff/01r7/+Ot/73vew2+1cfPHF3HDD\nDa22W11dzR133AGYgn233HILAEuWLGmTv+vXr2fNmjUAXHXVVWmRwfHjx2clkJGNDla2Om1dyW5X\nEibNpl2LjsPj8TQ4S7Vx40bLZo6ZOXNmg6KAs2bN6hBRwFwHUo708ufOnZtTsdeWcsUVVzBz5kyu\nuuqqeuJ6V1xxRfPF9SyfOtyfzhpYaS3ZGHO1lGyNpZpLtsZGLSFb453mMnv2bIqLi5k+fXq979ns\n2bObLzbaKnWOw5BsCNQdKFaycuXK9M+jR49utc062lv4zjAMo6ioyJgxY4bRq1cvIxaLpY+ffPLJ\nbbKbDVE9wzAaFKXbtWuXsWjRojbZzZZg37Rp04wHH3zQ+Mtf/mLcdtttxk033WQYRsPX0R5kQwQt\nW8JqXcluVxImzaZdi45j2LBhRmVl5SHHx48fb9nMMbkWBWxPEUCr/JaTa7HXltIe4nrtjeVT01x4\n4YXGK6+8YpSXlxupVMooLy83XnnlFePCCy/MiT9tJVui4C0lG2Op5pKtsVFLyNZ4p7m0h9iotSKj\nlmwI1D399NOoqookSUyZMgUw9yPNnz+/zf62t/AdwLp169I/12l4RCKRNosbZUNUD0wRuIPp3r07\n1157bZvsZkuw76WXXmLFihVs2bKF008/PS2m8/LLL7fJbiaMLIigZcNmV7LblYRJs2nXouP4y1/+\n0uDzevvtty2bOSbXooDtKQJold9yOoPYa0toD3E9y6eOp6KigksvvTQ9i5+Xl8ell17K7373u5z4\n01ayJQreUrIxlmou2RobtYRsjXeaS3sInloaGRYWhynZEEHLlrBaV7LblYRJs2nXwsJiP7nau55r\nEcAjvfyuSFvF9SyfOp6XX36ZRYsWHRJYmTNnDjNmzMiJTxYW7UFbhcytQIaFhYWFhYWFRSvoLHvX\ncy0CeKSWn2uNjpbSHuJ6lk+58akzBVYsLNqD9hDztgIZFhaHKdnoYGWr09aV7HYlYdJs2rWwsIDJ\nkyczc+ZMJkyYUE+sbOnSpR0iCpjrQMqRXv64ceMaFHv9+c9/3iFiry1lxowZFBcXH1JfN23a1Hxx\nPcunDvenMwZWLCzaisvlalTMu6KionlGWqzMYWFh0SXIhghatoTVupLdriRMmk27FhYWuRcFzLUI\n4JFefq7FXltKe4jrtTeWT00zffp046GHHjJKSkqML7/80igpKTEeeughY/r06Tnxx8KiPWgPMW8r\njGdhcZiSDRG0bAmrdTW7RhcRJs223cOJxYsXU1JSwuOPP95uNleuXMlxxx3H8ccfD8Ddd9/N2Wef\nbe3dP4zItShgrkUAj/Tycy322lLaQ1zP8qnj+eabbw5ZCTJ8+HDOOuusnPhjYdEetIeYtxXIsLA4\nTMlGBytbnbauZDdbKvVdza5F06xYsYLJkyenAxn33HNPjj2yaG+mT5/OZZddlrO967kOpBzp5Y8b\nN45x48blXCOkudx6663MmjUrLa7Xu3dvZs2axfbt23Pq06mnnsrWrVvx+/1pn77++uuc+jRmzBjW\nr1+P3++nR48edOvWjenTp+fEn84WWLGwaA969OjR4PGWvD8tjQyLJvne977Ht99+SyKRYN68ecyZ\nM4fnnnuOhx9+mGAwSHFxMU6nk8cff5yysjLmzp3Ljh07AHjsscc4/fTTc3wFRzbZ6GBlq9PW1exa\ndB1eeuklHn/8cRRF4dRTT+XJJ59kyZIlPPjggwSDQU488UQcDgePP/44s2fPZvLkyXz/+98HwOv1\nEolEAHjooYd4+eWXEQSB888/n1//+tc888wzPPPMMyiKwoABA1i6dCmffvopkydPJhAIEAwG+eMf\n/8i9996btrt69WpuvfVWVFXllFNO4amnnsJut9O3b19mz57Nn/70J1KpFK+++ioDBw485HoaO2/B\nggX4fD5uueUWAIYMGcJbb72FrutMmjSJUaNG8eGHHzJixAhmzZrFPffcQ1lZGS+//DKnnHJKxz2Q\nw4TOsHc91yKAR3L5udboaCntIa7X3syfP5+9e/ciyzJlZWU8//zzFBUVMXbsWP7+97/nxKe5c+eS\nTCaJx+M4HA78fj9+v5+dO3eyePHinPhUVlbGhg0b0vV8w4YN3HXXXTnxxcKis2CtyLBokueff55Q\nKEQ8HmfkyJFccMEF3H///Xz66ad4vV7GjRuXzr08b948br75Zs444wx27NjBpEmT+Pzzz3N8BUcm\n2ehgZavT1pXsdiVh0mza7Up88cUXLF++nA8//BBRFLnxxhtZunQpCxYs4JNPPsHv9zN27FiGDx8O\nmJ37A6n7/a9//Stvvvkm//rXv3A6nVRWVgLw/e9/nzlz5gBw55138txzz/GjH/2IKVOmMHnyZC65\n5JK0HZvNRiKR4Ic//CFr1qxhwIABzJo1i6eeeop58+Zhs9koLCykpKSEp556ioULF/LMM88cck2N\nndeY7wBfffUVr732GoMHD+aUU07hlVde4Z///Cdvvvkmv/71r1mxYkU73fEjh9mzZ1NcXMz06dPr\niQLOnj27Q0QBVVVl5cqVOQukHOnlX3HFFcycOZOrrrqq3vO/4oorOkTstaV4PJ5GxfVyxfr16/ng\ngw8A2LRpE1OnTmXhwoU58wdg69atrF27FoChQ4eyefNmAEaPHp0Tf84666x00OlAH1etWpX208Li\nSMQKZFg0ye9+9zveeOMNAL799luWLl3KmDFjCAaDAEydOpX//ve/AKxatYovvvgi/dmamhpisZg1\nC54DstHBylanrSvZnTlzZoMq9bNmzWqTSn1Xs9uVWL16NSUlJYwYMQIwV+h8+OGHjB07lvz8fACm\nTZuWbscaY9WqVVx11VU4nU4AQqEQYGaG+eUvf0l1dTWRSIRJkyalP9OQPsl//vMf+vXrx4ABAwCY\nNWsWTzzxBPPmzQNIBz6GDx/O66+/3qg/zT2vjn79+qU1Y0444QQmTJgAmKs2crm0vCuT673ruQ6k\nHOnl51qjo6Ucf/zxrFixIt1/q6OuLcgFuq6jKAp2u53i4mJWrFjBjBkz2Lp1a8580jQt/fOvfvWr\n9M8HB4o7iksuuYSNGzcya9Ysxo4dC8B5553HX//615z4Y2HRWbACGRYZee+991i9ejUff/wxTqeT\nsWPHMmjQoHrBCsMw0o27YRisW7cOu92eK5ctaslGBytbnbauZLerCZNmy25XY9asWfz6179O/75y\n5cp6g/8DAw6SJKWXYNd1soFDZsTqmD17Nm+++SZDhw5lyZIlvPfee+m/NdTxPfjYgW0ogMPhAEAU\nRVRVBeDcc89l7969nHLKKTz99NONnneg72A+/4PtAgiCkG6nBUFIf96iZeR673quAylHevm51uho\nKe0hrtfe/Pa3v6WyspJu3boB5nv6zTff5NVXX82ZT08//TSqqiJJElOmTAFAURTmz5+fE39uvvlm\nkskkzz33HH/4wx+44oorjmjB7u3btzN58uT0ShmL/Ry8vfRwxwpkWGQkHA4TCoVwOp38+9//5uOP\nPyYajfL+++9TVVWF1+vltdde48QTTwRg4sSJ/P73v+enP/0pAJ999ll624lFx5KNDla2Om1dyW5X\nEibNpt2uxPjx47nooou4+eabKSwspKKigpNOOol58+ZRUVGBz+fj1VdfZdiwYYCpP1FSUsLUqVN5\n8803SaVSAJxzzjnce++9TJ8+HZfLRWVlJaFQiEgkQvfu3UmlUrz00kv07t0bAJ/PRzgcrueLzWZj\n4MCBbN++na+++or+/fuzdOnSJpcsv/POO8261r59+/LnP/8ZgE8++YRt27a16F5ZtIxcCxXmOpDS\nWcofO3Zs+vvWkeXnWuy1pbSHuF57c+qppx5yTJIkfvCDH+TAG5ODg/8Adrs9HdTIBQ6HgxtuuIE5\nc+awdOlSq2/dztQFrrKNruvpybVskKtVQ7mic7a0Fp2GSZMm8Yc//IHBgwczcOBARo0axVFHHcUd\nd9zByJEjycvLY9CgQfj9fgB+//vfc+ONN3LiiSeiqiqjR4/mySefzPFVHJlko4OVrU5bV7KbLZX6\nrma3K3H88cdz//33M3HiRHRdR5ZlnnjiCRYsWMCoUaMIBoPpIAbAnDlzuOiiizjppJOYNGkSXq8X\nMFdFfPbZZ4wYMQK73Z7WC7rvvvs49dRTKSws5NRTT00Lg15++eXMmTOHxx9/vN7sosPh4IUXXmDq\n1KmoqsrIkSO5/vrrgfqdkDpNjYZo7Lzvf//7vPjiiwwZMoRTTz21nlBoJv2MI63z0140JlQ4bdq0\nDhEqvPXWW5k9e3ZaBLB3797E43Fuu+22rJcNZht7YBaM8vJypk2bxuWXX94h5c+bN4+ioiJ27NhB\nr169cLlcjBw5kmuvvbZDys+1RofFkYcsy1x11VW5diPnaJrGtddey4cffkivXr1YuXIl//73v7n+\n+uuJx+P079+f559/nmAwyJgxY3jkkUc4+eST2bdvH6eccgrbtm1j8eLFvP7660SjUXRdZ9myZVx2\n2WXU1NSgqipPPfUUZ555Zr1yFy9ezIoVKwiHw3z33XfMmDEjLbrakKi4IAh4vV6uv/56Vq1axZNP\nPplOgrB+/XoefPBBXnvtNVauXMkPfvADwuEwqqpywgkn8NVXX/HVV1/xox/9iLKyMtxuN8888wwD\nBw7MmFyh7n3+zDPPsGLFCl5//fX0ltjDDStriUWriEajeDweVFXlkksu4eqrr+aiiy7KtVsWB5AN\nNf1sKfR3JbtdSZg0m3YtLCxMEb4DhQpvuukmFi5cyG233dYhgYzGRACHDBnSISKA48aNY82aNfz4\nxz/G7XYzbtw4Pv30U0pKSli+fHnWy7/44osZOXIklZWVlJSUcMEFF5Cfn8+yZcuavYqpLcyYMYPi\n4mImTJhQT6Nj06ZNHaLRYWFxJLJ9+3aOPfZYSkpKKC4uZtq0aUyZMoWHH36Y//u//+Oss87i7rvv\nJhwO8+ijjzJ27FgeeeQRhg8ffkgg484772Tz5s0Eg0EeeeQRkskkd9xxB4ZhEI1G0xMZdSxevJg7\n7riDrVu34nK5OOWUU1i8eDFut5uf/exnrFixAlEUueGGGxg1ahQzZ85EEASWL1/OpZdeWs+WqqoM\nHDiQr776ip/+9Kd88MEHPProo6RSKZ5++mlefvllxo8fz6JFixgwYADr1q3jjjvuYPXq1VxxxRXc\neOONhyRXuOeee/B6vTgcDlavXs3y5cuRZbkjH0+HYoWLLVrFggULWLVqFYlEgnPPPdcKYnRCsiGC\nli1hta5ktysJk2bTroWFRe6FCnMtAli3RPrzzz9n1apVgLnFtM6XbFNdXc0dd9wBmKK1dfvClyxZ\n0iHl51qjw8LiSKVfv34UFxcDcPLJJ/PVV19RVVWV/u7NmjWLqVOnNmln4sSJafHbkSNHctVVV5FK\npbj44ovT2+Yb+kyd2Pcll1zCP/7xD0RRPERUvHv37oCpY1WXzv1AJEmif//+/Pvf/2b9+vXMnz+f\ntWvXomkaZ511FtFolA8//LDeddRpdjWUXCEajWIYBi+++CK9e/dm5cqViKLY5D3oyliBDItW8Zvf\n/CbXLlg0QTY6WNnqtHUlu11JmDSbdi0sLHIvVJhrEcArr7ySa665ht69ezNjxgzOPvtsNm3alO7M\nZxu/38/9999PVVUVRUVFPPLII4RCoQ6bgcy1RoiFxZHKgeLVoihSVVVV7++NCXgfKIAN1NtqW7fC\n7s9//jOzZ89m/vz5+Hw+7rnnHgCeffbZjGLdB4uK1+F0OtPnTJo0iT179qSFu88++2zeeustZFlm\n/PjxzJo1C13XWbhwIZqmEQqF+PTTTw+x2VhyBZvNxtChQ9m4cSPffvstffv2bfgGHiZYgQwLi8OU\nbHSwstVpy7bd9hSC60rCpNm0a2Fh0TmECnMpAnjllVcyfvx43nnnHfbs2YOmacyZM6fRmcz2Zvny\n5bz99tsUFBTw8MMPs3TpUsLhMK+88kqHlH/rrbfW0wipE3u10hm3jNLSUubNm8err77Kxo0bKS0t\n5bzzzsv4mffee49HHnmkwZWFB2oiWBwZBAIB8vLy+Mc//sGZZ57J0qVLGTNmDGCKYG/YsIERI0bw\nxz/+sVEbdVo711xzDclkkk8//ZTf/va3XHzxxelzNm/ezLvvvktlZSVOp5OVK1fywgsv4HK5DhEV\nj0Qi9OnTp14ZB2cIOuuss5g5cyazZ8+moKCA8vJyysrK0oKz/fr1449//COXXnophmGwefNmiouL\nD0musHHjxnS7O2zYMObOncuUKVN45513GhX5PRywNDIsLA5j1q5dy9atWwkGgwQCAUaMGMHXX3/N\naaed1mqbZWVlaWG5QCDAhg0b0kJHraW0tBRJktKdwW3bttG7d28uv/zyNs2sKYrCsmXL2LFjBwMG\nDCCVSrF9+3Z+/OMfp5cStoZUKpUVlfo6u5WVlQSDQY499th2mVlMpVJ8+eWXaY2Mzqyqb2FhYdFV\nqJvlretK1+mVTJo0iXfffTeXrnVZFi9eTElJCY8//njG8zIFMg7URLA4/Ni+fTtTpkxh06ZNADzy\nyCNEo1Euuugirr/+emKxGP379+eFF14gEAjwn//8h8suuwxRFLngggt4+eWX+frrr1myZAklJSX8\n/ve/B+DFF1/kN7/5DbIs4/P5ePHFFzn66KPrlb1kyRLeeOMNqqur2blzJzNnzuTOO+8EzMDqAw88\nkBYVf/LJJxk5cmR6Eqkh4vE4oVCIP//5z0yYMIHrrruOPXv28MYbb6Svde7cuezatYtUKsUPfvAD\nfvnLX1JeXs6NN97IF198US+5wj333IPP52P+/Pn87W9/4/bbb2fVqlXk5eVl63HkFCuQYWFxmNKY\nmv7YsWNbLUKXLWG5bAnGZUMILlvCpLqu89ZbbyFJEhMnTkxvBVm5cmW7a9Dcdddd3Hvvve1q08LC\nwuJIw+VyNTgxsHHjRioqKnLgUW548cUXeeSRR7DZbBQXF3PZZZdx//33oygK+fn5vPzyyxQVFbFg\nwYJ0JoZ9+/Zx2223cc0117B9+3YmT57MJ598Qv/+/UkkEvTq1Yuf//zn9OvXj3nz5pFIJHC5XLzw\nwgscd9xxzQ5kLFu2jAceeADDMLjgggt48MEH0TSNq6++mpKSEmw2G1dffTXz5s3j97//PYsWLUKS\nJAYPHsyyZctycDctOjPNDbRZdAzWlJyFxWHK+vXr66npT506lYULF7bJZraE5bIlGJcNIbhsCZPO\nnDmTfv36Icsy999/P88++yyDBg3isccea1Mgo0+fPvTp06fevs6tW7fy3nvvdUhWAwsLC4vDleOP\nP54VK1YcssJvwoQJOfKo49m6dSu/+tWv+Oijj8jLy6OyshKbzcbHH38MmLoCDz/8cLr/sWXLFj7+\n+GMikQjDhg3jwgsvTNuSZZn77ruv3ix5TU0NH3zwAaIosmrVKu64446M2wMOpLS0lNtvv51PPvmE\nYDDIxIkTWblyJb1796a0tJTNmzcDpGfLH3roIbZv344sy43OoFsc2WRKi27R8ViBDAuLw5RsqOln\nS1guW4Jx2RCCy5Yw6c6dO3n55ZcBmDNnDrNnz+bGG29sk034/+3dX0zV9R/H8eeJHcgCEtQiWKvR\nhhMGcgiaSXg4OMNAU3SypfFn1IjFOFBubuQgGLZZemG6lSgFWDlR5+bI/o0Ri61QwgaUlnNM2UJq\ngcSfRJFOF/zO+YGiQgeUo6/HFRy+5/39nHNx+PLm8329R2aLHz58mGXLlvHSSy9hNBpv61QDEZG7\n1bFjx5g1a9Z1j197H/zdrLa2luTkZMfWdR8fH1pbW0lOTqazs5MrV64QGBgIjPwRuGrVKjw8PPDw\n8MBisXD8+PExmSo2m23MdUVPTw+pqamcPXsWg8HA0NDQhNZls9lobGwkNjaWOXPmALBhwwa+/fZb\nCgoKaGtrw2q1kpiYyHPPPQdAWFgY69evZ/Xq1WNyEUTs0tLSSEtLu9PLkP+5704vQESmhz1N386e\npu/stAp7sNwnn3xCV1fXlATLpaamUlxcTExMDCEhIY7AOGen4xw8eJDQ0FCSkpKoqalh7ty5DAwM\nOBUEZw8Q3bZtG6WlpWzbto0VK1Y4HUxqs9no6+sDwN/fn+rqao4cOUJTU5NTddesWcP+/ft55JFH\nSElJ4b333nOM7xIRkf/u0UcfHTM9we5eyiC69nZTgJycHKxWKy0tLZSWlnLp0qUbPt++I/NGCgoK\nWLp0Ka2trVRXV183dQIgPj4ek8lEZmbmdWsbzb7O2bNn09zcTGxsLLt37+aVV14BRhpT2dnZnDx5\nkqioKIaHh2+6NhG5s+6dT1qRe8x0p+kbjUYyMjKmpBZAQEDAlNaDkabL6NsypqKLPl0p9ZWVlWMu\nmjw8PKisrOTll192csUjEhISSEhIoLa2FoPBQGNjI1FRUVNSW0RE7k1xcXEkJSXxxhtv4OvrS3d3\nN729vfj7+wMjmQJ2NpuNo0ePkp+fT39/P3V1dbzzzjtjmhPe3t6Opj4wplZ5efm4axgv88pgMPD0\n009jtVrp6upi9uzZHDhwwPG90WhkzZo1BAUFkZKSgs1mo729ndjYWKKjozlw4AADAwN4e3tPxdsk\nItNAjQwRkUn4559/mDt3LvHx8cD//xu1YcMGp1Lq7cnY9hR8GLnoKykpcaru6HowMpbObDYTHx/v\nyCMRERH5L4KDg9m8eTNmsxk3NzdMJhNFRUWsW7cOHx8f4uLiOH/+PIAjDNRisfDnn39SWFiIn58f\n586dc+yesFgsbN26FZPJRH5+Pps2bSItLY0tW7aQmJg4ZpfFrbIK/Pz82Lp1KxaLBZvN5tg92dzc\nTEZGhuP3oz0ANCUlhb/++gubzUZubq6aGCIznKaWiIhMwnSl1LtaXRERkckoLi7G09NVnLZ1AAAG\nb0lEQVTTEbwtIuIM7cgQEZmE6Uqpd7W6IuI6Ojo6yM3N5dChQzQ3N9PR0cHzzz9/0+fcbLzlZDU1\nNbFv3z6nM5rE9Wnig4hMFe3IEBGZhAsXLuDr63tdwNvVq1edCnhztboi4poqKipoampi165dNz1u\nKhsZIiIiU01TS0REJmG6Uupdra6I3D779u1j4cKFhIeHk5qaymeffcaiRYuIiIhg2bJl/PHHHwAU\nFRWRkpLC4sWLCQoKoqysDIBz584RGhrK0NAQhYWFVFVVYTKZOHjwII2NjSxevJiIiAiio6M5c+bM\nLdfz+eefs2DBAiIjI7FarY6pTSdOnBi3Vl1dneOYoqIiMjIysFgsPPnkk7dsqIiIiIxHV7IiIiIi\nM9TPP//M22+/zffff4+vry8XL17EYDDQ0NAAQFlZGe+++y7bt28H4KeffqKhoYH+/n5MJhMrVqxw\n1DIajZSUlNDU1MTOnTsB6Ovro76+Hjc3N2pqanjzzTc5fPjwDdczODhIVlYW9fX1PP7446xfv95x\nu8CCBQsmVOvMmTN888039Pb2Mn/+fF577TXc3Nym7D0TEZG7nxoZIiIiIjNUbW0tycnJ+Pr6AuDj\n40NrayvJycl0dnZy5coVAgMDgZH8gVWrVuHh4YGHhwcWi4Xjx4+zcOFCRz2bzcbou4p7enpITU3l\n7NmzGAwGhoaGbrqeX375hcDAQMekpRdffJE9e/ZMuJbBYCAxMRGj0cicOXN4+OGH+f333x0jNkVE\nRCZCt5aIiIiIzFD2Ec+j5eTkYLVaaWlpobS0lEuXLt3w+ffdd/NLvYKCApYuXUprayvV1dUMDg5e\nd0x8fDwmk4nMzMzrwhpHr20itQDc3d0dX7u5uXH16tWbrlFERORaamSIyIy2c+dOgoODSUlJueEx\nnp6eTp+nsrKSCxcuOF1HRGQqxcXFcejQIce45O7ubnp7ex07GCoqKhzH2mw2jh49yuXLl+nq6qKu\nro6oqKgx9by9venr63N8P7pWeXn5uGv46quv+PHHH9mzZw9BQUG0tbVx/vx5AKqqqhzNjYnUUsa8\niIhMBTUyRGRG++CDD6ipqeHjjz++4TFTMc6toqKCjo4Op+uIiEyl4OBgNm/ejNlsJjw8nI0bN1JU\nVMS6deuIjIxk3rx5js9Ag8FAWFgYFouFZ555hsLCQvz8/Bw/A7BYLJw6dcoR9rlp0yby8/OJiIhg\neHh4zOfpeJ+ts2bN4v3332f58uVERkbi7e2Nt7c3wIRqGQwGjeAUERGnafyqiMxYWVlZlJeXM3/+\nfNrb21m7di1tbW20t7eTl5dHTk4OAF5eXvT19ZGdnc3y5ctZuXIlSUlJ+Pr68uGHH/LRRx/R1tbG\nli1bKCkp4dNPP2XevHk89thjPPXUUzzxxBOkp6cTEBDAAw88wHfffcf9999/h1+9iMjkFBcX4+np\nycaNG6f1PAMDAzz44IMAZGdnExQURG5u7rSeU0REZDTtyBCRGWv37t34+/tTV1fH66+/zq+//srX\nX3/NiRMnKC4uZnh4eMzxS5Ysob6+HoDffvuN06dPA1BfX4/ZbKaxsZEjR47Q0tLCF198wQ8//IDB\nYGDt2rVERkayf/9+Tp48qSaGiLis27HbYe/evZhMJkJCQujt7eXVV1+d9nOKiIiMpqklIjLj2TeO\n3Srp/tlnn2XHjh2cPn2akJAQenp66OzspKGhgV27dlFWVsbq1atxd3fH3d2dlStXjnseERFX9NZb\nb92W8+Tl5ZGXl3dbziUiIjIeNTJExGXcKuk+ICCAnp4evvzyS5YsWUJ3dzdVVVV4eXnh6el5Xfr/\ntY0L3bctIiIiIjLz6dYSEbmrLFq0iB07dmA2m4mJiWH79u3ExMQAEB0dTXV1NZcvX6a/v59jx445\nnufl5UVvb++dWraIiIiIiEyQGhkiMqPdKkH/2sdjYmIYHh4mMDAQk8nExYsXHY2MyMhIXnjhBcLC\nwkhISCA0NJSHHnoIgPT0dLKysoiIiGBwcHAaX5GIiIiIiDhDU0tE5J5iT9v/+++/MZvN7N27l/Dw\n8Du9LBERERERmSBlZIjIPSUzM5NTp04xODhIenq6mhgiIiIiIi5GOzJERERERERExGUoI0NERERE\nREREXIYaGSIiIiIiIiLiMtTIEBERERERERGXoUaGiIiIiIiIiLgMNTJERERERERExGX8C2KbJYEU\ndnqOAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f845b60e310>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Visualizing Some Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We want to visualize the relationship between the number of years of education and age and income."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(\n",
      "    csv_data[csv_data['income']=='>50K']['education-num'],\n",
      "    csv_data[csv_data['income']=='>50K']['age'],\n",
      "    alpha=0.02,\n",
      "    label='>50K',\n",
      "    color='r'\n",
      "    )\n",
      "\n",
      "plt.scatter(\n",
      "    csv_data[csv_data['income']=='<=50K']['education-num'],\n",
      "    csv_data[csv_data['income']=='<=50K']['age'],\n",
      "    alpha=0.02,\n",
      "    label='<=50K',\n",
      "    color='b'\n",
      "    )\n",
      "\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data['education-num_rnd'] = csv_data['education-num'] + np.random.uniform(-0.5,0.5, len(csv_data))\n",
      "plt.figure(figsize=(12,8))\n",
      "plt.scatter(\n",
      "    csv_data[csv_data['income']=='>50K']['education-num_rnd'],\n",
      "    csv_data[csv_data['income']=='>50K']['age'],\n",
      "    alpha=0.1,\n",
      "    label='>50K',\n",
      "    color='r'\n",
      "    )\n",
      "\n",
      "plt.scatter(\n",
      "    csv_data[csv_data['income']=='<=50K']['education-num_rnd'],\n",
      "    csv_data[csv_data['income']=='<=50K']['age'],\n",
      "    alpha=0.1,\n",
      "    label='<=50K',\n",
      "    color='b'\n",
      "    )\n",
      "\n",
      "plt.xlabel('education-num')\n",
      "plt.xticks(arange(17))\n",
      "plt.xlim(0,17)\n",
      "plt.ylabel('age')\n",
      "\n",
      "#plt.legend()\n",
      "plt.grid()\n",
      "plt.title(u'From Census Data of 1994 (education-num \\u00B10.5)')\n",
      "plt.show()\n",
      "csv_data = csv_data.drop('education-num_rnd', axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Processing Text Feature"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have a lot of data that is not numerical. Most of machine learning algorithms use numerical data as features. We process text data according to what does the text represents. Commonly you will find:\n",
      "\n",
      "1. **None Organizable Categorical Text (City, Country, ...etc)**\n",
      "<br/>Process it using **Vectorizer**. This finds each value and makes it into a separate feature. For example if you have three values for city where users live (London, New York, Toronto) it will make three features each indicating if the user lives in that city with with 0=False and 1=True.\n",
      "\n",
      "2. **Organizable Categorical Text (Rating, Income Brackets, ...etc)**\n",
      "<br/>**Convert to a series of numbers** representing the value. For example (Excellence, Good, Average, Bad, Terrible) can be converted to (5,4,3,2,1).\n",
      "\n",
      "4. **Text Document**\n",
      "<br />Process it using **CountVectorizer**.\n",
      "\n",
      "5. **Special Values**\n",
      "<br /> Email, URL, Phone number, Usernames process using **Special Treatment**."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.feature_extraction import DictVectorizer\n",
      "vec = DictVectorizer()\n",
      "features = vec.fit_transform(csv_data\\\n",
      "                             .drop(\"capital-gain\", axis=1)\\\n",
      "                             .drop(\"capital-loss\", axis=1)\\\n",
      "                             .drop(\"income\", axis=1)\\\n",
      "                             .drop(\"fnlwgt\", axis=1).to_dict(outtype=\"records\")).toarray()\n",
      "results = csv_data[\"income\"] == \">50K\"\n",
      "\n",
      "# Wenow split the data set into a traing and testing\n",
      "X_train = features[1000:]\n",
      "Y_train = results[1000:]\n",
      "X_test = features[:1000]\n",
      "Y_test = results[:1000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data.to_dict(outtype=\"records\")[8]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Train Classification Model using kNN Algorithm"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from  sklearn.neighbors import KNeighborsClassifier\n",
      "from sklearn.metrics import classification_report\n",
      "\n",
      "knn = KNeighborsClassifier().fit(X_train, Y_train)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Prediction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predictions = knn.predict(X_test)\n",
      "accuracy = predictions == Y_test\n",
      "print \"Accuracy: %s%%\" % (round(np.average(accuracy)* 100))\n",
      "\n",
      "print \"===============\\nSamsple Data\"\n",
      "for counter in range(10):\n",
      "    print \"Prediction: %s is %s\" % (predictions[counter], predictions[counter]==Y_test[counter])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Improving Accuracy"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "target_names = ['<=50K', '>50K']\n",
      "for kn in range(1,15):\n",
      "    for method in ['uniform', 'distance']:\n",
      "        knn = KNeighborsClassifier(n_neighbors=kn, weights=method).fit(X_train, Y_train)\n",
      "        predictions = knn.predict(X_test)\n",
      "        accuracy = predictions == Y_test\n",
      "        print \"Accuracy: %s%%, Neighbors: %s, Weights: %s\" % (round(np.average(accuracy)* 100), kn, method)\n",
      "        actual = pd.Series(Y_test).map({True: 1, False:0})\n",
      "        predictions = pd.Series(predictions).map({True: 1, False:0})\n",
      "        print(classification_report(actual.values, predictions.values, target_names=target_names))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Results Inspection"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "knn = KNeighborsClassifier(n_neighbors=11, weights='uniform').fit(X_train, Y_train)\n",
      "\n",
      "predictions = knn.predict(X_test)\n",
      "\n",
      "actual = pd.Series(Y_test).map({True: 1, False:0})\n",
      "predictions = pd.Series(predictions).map({True: 1, False:0})\n",
      "target_names = ['<=50K', '>50K']\n",
      "print(classification_report(actual.values, predictions.values, target_names=target_names))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data[\"prediction\"] = pd.Series(predictions)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Quick Prediction"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Select values for a person to predict if They make more or less than 50K\n",
      "person_features = {\n",
      " 'age': 35,\n",
      " 'education': 'Bachelors',\n",
      " 'education-num': 14,\n",
      " 'hours-per-week': 55,\n",
      " 'marital-status': 'Never-married',\n",
      " 'native-country': 'United-States',\n",
      " 'occupation': 'Sales',\n",
      " 'race': 'Black',\n",
      " 'relationship': 'Not-in-family',\n",
      " 'sex': 'Female',\n",
      " 'workclass': 'Private'\n",
      "}\n",
      "person_features = vec.transform(person_features).toarray()\n",
      "\n",
      "prediction = knn.predict(person_features)[0]\n",
      "\n",
      "labels={True: '>50K', False: '<=50K'}\n",
      "\n",
      "print labels[prediction]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- **age**: continuous.\n",
      "- **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
      "- **fnlwgt**: continuous.\n",
      "- **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
      "- **education-num**: continuous.\n",
      "- **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
      "- **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
      "- **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
      "- **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
      "- **sex**: Female, Male.\n",
      "- **capital-gain**: continuous.\n",
      "- **capital-loss**: continuous.\n",
      "- **hours-per-week**: continuous.\n",
      "- **native-country**: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Chart of Education Education in the United States\n",
      "From Wikipedia\n",
      "\n",
      "<img src=\"http://upload.wikimedia.org/wikipedia/commons/thumb/8/81/Education_in_the_United_States.svg/497px-Education_in_the_United_States.svg.png\" />"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### End of Lesson 2\n",
      "For questions please leave them on:\n",
      "\n",
      "- <a href=\"https://www.youtube.com/user/roshanRush\">YouTube</a>\n",
      "- <a href=\"https://twitter.com/twistedhardware\">Twitter</a>\n",
      "- <a href=\"https://plus.google.com/+Tcsaroshan/posts\">Google+</a>\n",
      "\n",
      "<a href=\"/notebooks/Lesson 1 - Understanding the tools.ipynb\">Previous Lesson - Introduction to Machine Learning</a>\n",
      "<br />\n",
      "<a href=\"/notebooks/Lesson 3 - Regression (1-3).ipynb\">Next Lesson - Regression (2/3)</a>\n",
      "\n",
      "In the next lesson:\n",
      "- Linear Regression"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/Lesson 3 - Regression.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lesson Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this you will learn about the following:\n",
      "\n",
      "- <a href=\"#Why-do-we-use-regression?\">Why do we use regression?</a>\n",
      "- <a href=\"#Temperature-Data\">Temperature Data</a>\n",
      "- <a href=\"#Loading/Analyzing-Data\">Loading/Analyzing Data</a>\n",
      "- <a href=\"#Linear-Regression\">Linear Regression</a>\n",
      "- <a href=\"#SVM-Regression\">SVM (Support Vector Machine)</a>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "input_form = \"\"\"\n",
      "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
      "<p>User: root<br> Password: admin</p>\n",
      "\"\"\"\n",
      "\n",
      "javascript = \"\"\"\n",
      "<script type=\"text/Javascript\">\n",
      "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
      "</script>\n",
      "\"\"\"\n",
      "\n",
      "HTML(input_form + javascript)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
        "<p>User: root<br> Password: admin</p>\n",
        "\n",
        "<script type=\"text/Javascript\">\n",
        "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x7f173abf6850>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Why do we use regression?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In classification, we train a predictive model to produce a class or a category. Regression is used when you need a predictive model that produces numeric values instead of classes. For example you would use a classification algorithm to predict the probability of rain but use a regression algorithm to predict the temperature."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Temperature Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be using data from the Climatic Research Unit (CRU) of the University of East Anglia (UEA).\n",
      "<br />\n",
      "<br />\n",
      "<a href=\"http://www.uea.ac.uk/\">\n",
      "  <img src=\"http://www.cru.uea.ac.uk/UEALR04-theme/images/UEALR/logo.jpg\" />\n",
      "</a><br />\n",
      "<a href=\"http://www.cru.uea.ac.uk/\">\n",
      "  <img src=\"http://www.cru.uea.ac.uk/documents/421974/487107/cru+logo.gif/74cdd7b1-07f8-4abf-bd79-008ba17f33bd?t=1341910824330\" />\n",
      "</a>\n",
      "\n",
      "You can find the dataset of their website:<br />\n",
      "http://www.cru.uea.ac.uk/cru/data/temperature/<br />\n",
      "http://www.cru.uea.ac.uk/cru/data/temperature/CRUTEM4-gl.dat"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Data Sample"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      " 1851  0.823  0.357 -0.564  0.510  0.062  0.062  0.367  0.279  0.172  0.672 -0.132 -0.240  0.197\n",
      " 1851      3      3      3      3      3      3      3      3      3      3      3      3\n",
      " 1852  0.132  0.185 -0.016 -0.889  0.427  0.485  0.539  0.064  0.147 -0.252  0.126  1.834  0.232\n",
      " 1852      3      3      3      3      3      3      3      3      3      3      3      3\n",
      " 1853  0.711 -0.656 -1.348 -0.478 -0.213 -0.196  0.418  0.325 -0.333  0.082 -0.527 -1.230 -0.287\n",
      " 1853      3      3      3      3      3      3      3      3      3      3      3      3\n",
      " 1854 -0.444  0.256  0.200 -0.355  0.428  0.107  0.616  0.399  0.171  0.702 -0.501  0.346  0.161\n",
      " 1854      3      3      3      3      3      3      3      3      3      3      3      3\n",
      " 1855  0.059 -1.359 -0.521  0.444  0.243  0.197  0.277  0.127 -0.303  0.346 -0.431 -1.622 -0.212\n",
      " 1855      3      3      3      3      3      3      3      3      3      3      3      3\n",
      " 1856 -0.303 -0.751 -1.241 -0.022 -0.592  0.232 -0.413 -0.293 -0.601 -0.599 -1.486 -0.141 -0.518\n",
      " 1856      4      4      3      3      3      3      3      3      3      3      3      3\n",
      " 1857 -1.157  0.283 -0.708 -1.460 -1.028 -0.316 -0.007  0.147 -0.325 -0.412 -0.726  0.647 -0.422\n",
      " 1857      3      4      3      4      4      4      4      4      4      4      4      4\n",
      " 1858  0.054 -1.353 -0.734 -0.397 -0.365  0.146 -0.149 -0.344 -0.361 -0.059 -1.819 -0.382 -0.480\n",
      " 1858      4      4      4      4      4      4      4      4      4      4      4      4\n",
      " 1859 -0.167  0.153 -0.024 -0.149  0.021 -0.159  0.049  0.044 -0.551 -0.152 -0.310 -1.072 -0.193\n",
      " 1859      4      4      4      4      4      4      4      4      4      4      4      4"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Understanding the data structure"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Columns:\n",
      "\n",
      "- Year\n",
      "- Jan\n",
      "- Feb\n",
      "- Mar\n",
      "- Apr\n",
      "- May\n",
      "- Jun\n",
      "- Jul\n",
      "- Aug\n",
      "- Sep\n",
      "- Oct\n",
      "- Nov\n",
      "- Dec\n",
      "\n",
      "### Rows\n",
      "\n",
      "- Odd rows: Change from previous month\n",
      "- Even rows: Percentage coverage of globe"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Processing data with Calc/Excel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This data was processed using OpenOffice Calc (Similar to Excel).\n",
      "\n",
      "### Download and Open file using Excel/Calc\n",
      "This file has fixed width columns. So we using fixed width columns when Opening/Importing this data.\n",
      "\n",
      "### Adding column names\n",
      "Column names are added to row number 1\n",
      "\n",
      "We add two new columns:\n",
      "\n",
      "- Average\n",
      "- Odd/Even\n",
      "\n",
      "#### Average\n",
      "This was calculated using this formula:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "=AVERAGE(B2:M2)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Odd/Even\n",
      "This was calculated using this formula:"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "=MOD(ROW(),2)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Filter out odd rows\n",
      "You should filter out all columns with value of 1. This is what you end up with"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Year\tJan\tFeb\tMar\tApr\tMay\tJun\tJul\tAug\tSep\tOct\tNov\tDec\tAverage\tOdd/Even\n",
      "1851\t0.823\t0.357\t-0.564\t0.51\t0.062\t0.062\t0.367\t0.279\t0.172\t0.672\t-0.132\t-0.24\t0.1973333333\t0\n",
      "1852\t0.132\t0.185\t-0.016\t-0.889\t0.427\t0.485\t0.539\t0.064\t0.147\t-0.252\t0.126\t1.834\t0.2318333333\t0\n",
      "1853\t0.711\t-0.656\t-1.348\t-0.478\t-0.213\t-0.196\t0.418\t0.325\t-0.333\t0.082\t-0.527\t-1.23\t-0.2870833333\t0\n",
      "1854\t-0.444\t0.256\t0.2\t-0.355\t0.428\t0.107\t0.616\t0.399\t0.171\t0.702\t-0.501\t0.346\t0.1604166667\t0\n",
      "1855\t0.059\t-1.359\t-0.521\t0.444\t0.243\t0.197\t0.277\t0.127\t-0.303\t0.346\t-0.431\t-1.622\t-0.2119166667\t0\n",
      "1856\t-0.303\t-0.751\t-1.241\t-0.022\t-0.592\t0.232\t-0.413\t-0.293\t-0.601\t-0.599\t-1.486\t-0.141\t-0.5175\t0\n",
      "1857\t-1.157\t0.283\t-0.708\t-1.46\t-1.028\t-0.316\t-0.007\t0.147\t-0.325\t-0.412\t-0.726\t0.647\t-0.4218333333\t0"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Export as CSV\n",
      "Copy your data and past it in a new file. Then, remove Odd/Even column and save you file as a CSV file"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Average\n",
      "1851,0.823,0.357,-0.564,0.51,0.062,0.062,0.367,0.279,0.172,0.672,-0.132,-0.24,0.1973333333\n",
      "1852,0.132,0.185,-0.016,-0.889,0.427,0.485,0.539,0.064,0.147,-0.252,0.126,1.834,0.2318333333\n",
      "1853,0.711,-0.656,-1.348,-0.478,-0.213,-0.196,0.418,0.325,-0.333,0.082,-0.527,-1.23,-0.2870833333\n",
      "1854,-0.444,0.256,0.2,-0.355,0.428,0.107,0.616,0.399,0.171,0.702,-0.501,0.346,0.1604166667\n",
      "1855,0.059,-1.359,-0.521,0.444,0.243,0.197,0.277,0.127,-0.303,0.346,-0.431,-1.622,-0.2119166667\n",
      "1856,-0.303,-0.751,-1.241,-0.022,-0.592,0.232,-0.413,-0.293,-0.601,-0.599,-1.486,-0.141,-0.5175\n",
      ".\n",
      ".\n",
      ".\n",
      "2009,0.779,0.792,0.633,0.829,0.581,0.659,0.587,0.876,0.976,0.724,0.845,0.492,0.7310833333\n",
      "2010,0.778,0.826,1.111,1.062,0.936,0.996,0.974,0.863,0.732,0.888,1.195,0.448,0.90075\n",
      "2011,0.467,0.382,0.585,0.83,0.523,0.778,0.775,0.766,0.852,0.941,0.609,0.827,0.6945833333\n",
      "2012,0.557,0.323,0.645,1.03,0.963,0.947,0.792,0.891,0.9,0.847,0.849,0.261,0.7504166667\n",
      "2013,0.935,0.949,0.669,0.581,0.773,0.827,0.671,0.706,0.776,0.821,0.984,0.795,0.7905833333\n",
      "2014,0.95,0.408,0.929,1.048,,,,,,,,,0.83375"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The final file is available on your system in this relative path \"data/temp_data.csv\""
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Loading/Analyzing Data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "import the required libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import sklearn\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Load CSV data from file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data = pd.read_csv(\"data/temp_data_features.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Visualize data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20,5))\n",
      "plt.scatter(x=csv_data[\"Year\"], y=csv_data[\"Average\"], marker=\"o\", s=50, c=csv_data[\"Average\"])\n",
      "plt.plot(csv_data[\"Year\"], csv_data[\"Average\"], label=\"Annual Global Average Anomaly\", alpha=0.4, linewidth=2, c=\"grey\")\n",
      "plt.hlines(0,min(csv_data[\"Year\"])-3,max(csv_data[\"Year\"])+5)\n",
      "\n",
      "plt.legend(loc=\"best\")\n",
      "plt.xlim(min(csv_data[\"Year\"])-3, max(csv_data[\"Year\"])+5)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.colorbar()\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kPtonMao+rqSCh4cHOp2OvLw8rFZrsRNyl5Li07FjR5SAAL4xm2kSHAxA+N69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mLQ3o1Df/shco1tfJWarEiMFuIPnqF27VpuPZ9Go6F8+fL06NGDTp06ERwcnOtx/ebNm5w+dZrS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7csstt3i6idLGKioqOHBgD7AWh+NfI4Cqq32BKXz44a8xxlh6dJCIiIiIR1yiRkJbCg4OZsuW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\nIiIi0irqVBARERERERGRVvn/sEc9qPB1GfQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1730f20a10>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(\n",
      "    csv_data[\"Year\"],\n",
      "    csv_data[\"Average\"],\n",
      "    width=0.7,\n",
      "    edgecolor=\"none\",\n",
      "    color=(csv_data[\"Average\"]>0).map({True: 'r', False: 'b'}),\n",
      "    label=\"Annual Average Global Anomaly\",\n",
      "    )\n",
      "plt.hlines(0,min(csv_data[\"Year\"])-3,max(csv_data[\"Year\"])+5)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.xlim(min(csv_data[\"Year\"])-3, max(csv_data[\"Year\"])+5)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f1730b87650>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Final Record is not complete so average of the last year is not reliable\n",
      "csv_data[-1:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Year</th>\n",
        "      <th>Jan</th>\n",
        "      <th>Feb</th>\n",
        "      <th>Mar</th>\n",
        "      <th>Apr</th>\n",
        "      <th>May</th>\n",
        "      <th>Jun</th>\n",
        "      <th>Jul</th>\n",
        "      <th>Aug</th>\n",
        "      <th>Sep</th>\n",
        "      <th>Oct</th>\n",
        "      <th>Nov</th>\n",
        "      <th>Dec</th>\n",
        "      <th>Average</th>\n",
        "      <th>TSI</th>\n",
        "      <th>CO2</th>\n",
        "      <th>CH4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>163</th>\n",
        "      <td> 2014</td>\n",
        "      <td> 0.95</td>\n",
        "      <td> 0.408</td>\n",
        "      <td> 0.929</td>\n",
        "      <td> 1.048</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 0.83375</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>1 rows \u00d7 17 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "     Year   Jan    Feb    Mar    Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec  \\\n",
        "163  2014  0.95  0.408  0.929  1.048  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
        "\n",
        "     Average  TSI  CO2  CH4  \n",
        "163  0.83375  NaN  NaN  NaN  \n",
        "\n",
        "[1 rows x 17 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Visualize monthly temperature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Prepare monthly data\n",
      "monthly_temp = csv_data.drop(\"Year\", 1).drop(\"Average\", 1).drop(\"TSI\", 1).drop(\"CO2\", 1).drop(\"CH4\", 1)\n",
      "monthly_temp = pd.Series(np.ravel(monthly_temp)).dropna()\n",
      "month_index = list((monthly_temp.index/12.) + 1851)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(15,8))\n",
      "plt.scatter(\n",
      "    x=month_index,\n",
      "    y=monthly_temp,\n",
      "    marker=\"o\",\n",
      "    label=\"Monthly Average Global Anomaly\",\n",
      "    c=monthly_temp,\n",
      "    alpha=0.6\n",
      "    )\n",
      "plt.colorbar()\n",
      "plt.legend(loc=\"lower right\")\n",
      "plt.xlim(min(month_index)-3,max(month_index)+5)\n",
      "plt.ylim(min(monthly_temp),max(monthly_temp))\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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afU5BVgahvjU7pSmKQpiLHXm5OZcck5V0nl4+Lhc/v7mXM7uTEv9yrrS0NDZ+9zmvt/bD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GAAAg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yc7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CDgoamzeO3jtwh4qi/NFQ04WKsImuZNVvql7gBm8MBBKG3tOJ99ASu5L6MXjOqRONBqtRw4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\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6tYbyfafp8fz0v+HK/x0UMjlVWXl1xxVZuTjLHk5EwWMe83dziYp7n/QIeZT2/v/Egflf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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1730e07e90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(month_index, monthly_temp,  width=0.1, edgecolor=\"none\", color=(monthly_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Monthly Average Global Anomaly\")\n",
      "plt.hlines(0,min(month_index)-1,max(month_index)+1)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.xlim(min(month_index)-1, max(month_index)+1)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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USy3heCRDks5DeSxJii3tOJYw8DzNLckCTXMrv00xKdPc/Nz6HNfUFK+3mYYx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\n0nRs3a7fXuuylP9dvo5d+37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/B/B3VfAKClOhtjpAqQil4KwocWy6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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f17309e5cd0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Linear Regression"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Predicting Annual Temperature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "annual_temp = csv_data[\"Average\"]\n",
      "annual_index = list(csv_data[\"Year\"].values)\n",
      "annual_index_feature = list(csv_data[[\"Year\"]].values)\n",
      "prediction_annual_index = [[item] for item in range(min(annual_index_feature),max(annual_index_feature)+10)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Code source: Jaques Grobler\n",
      "# License: BSD 3 clause\n",
      "\n",
      "from sklearn import linear_model\n",
      "\n",
      "\n",
      "# Create linear regression object\n",
      "regr = linear_model.LinearRegression()\n",
      "\n",
      "# Train the model using the training sets\n",
      "regr.fit(annual_index_feature, annual_temp)\n",
      "\n",
      "# The coefficients\n",
      "print 'Coefficients:', regr.coef_\n",
      "# The mean square error\n",
      "print(\"Residual sum of squares: %.2f\"\n",
      "      % np.mean((regr.predict(annual_index_feature) - annual_temp) ** 2))\n",
      "# Explained variance score: 1 is perfect prediction\n",
      "print('Variance score: %.2f' % regr.score(annual_index_feature, annual_temp))\n",
      "\n",
      "# Plot outputs\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "\n",
      "plt.plot(prediction_annual_index[:], regr.predict(prediction_annual_index[:]), color='green',\n",
      "        linewidth=3, alpha=1.0, label=\"Linear Regression\")\n",
      "\n",
      "plt.grid()\n",
      "plt.xlim(np.min(annual_index_feature), np.max(annual_index_feature)+5)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.legend(loc=\"best\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Coefficients: [ 0.00651672]\n",
        "Residual sum of squares: 0.06\n",
        "Variance score: 0.60\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A8fHx+OMf/4iHH34YGzduNLlv7ty5ePLJJzF58mR4e3vjtttuM5k2JJfL8dFHH6F3795Q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\nHKUnXhygvQptvDsAKzIBKzIBKzIBKzIBKzIBKzIBb36LSZs2berw/u3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       "text": [
        "<matplotlib.figure.Figure at 0x7f172867dad0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Predicting Monthly Temperature"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "month_index_feature = [[item] for item in month_index]\n",
      "prediction_month_index = [[item[0] + 5] for item in month_index_feature]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Code source: Jaques Grobler\n",
      "# License: BSD 3 clause\n",
      "\n",
      "from sklearn import linear_model\n",
      "\n",
      "regr = linear_model.LinearRegression()\n",
      "\n",
      "# Train the model using the training sets\n",
      "regr.fit(month_index_feature, monthly_temp)\n",
      "\n",
      "# The coefficients\n",
      "print 'Coefficients:', regr.coef_\n",
      "# The mean square error\n",
      "print(\"Residual sum of squares: %.2f\"\n",
      "      % np.mean((regr.predict(month_index_feature) - monthly_temp) ** 2))\n",
      "# Explained variance score: 1 is perfect prediction\n",
      "print('Variance score: %.2f' % regr.score(month_index_feature, monthly_temp))\n",
      "\n",
      "# Plot outputs\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(month_index, monthly_temp,  width=0.1, edgecolor=\"none\", color=(monthly_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Monthly Average Global Anomaly\", alpha=0.1)\n",
      "\n",
      "plt.plot(month_index, regr.predict(month_index_feature), color='black',\n",
      "        linewidth=3, alpha=0.5, label=\"Linear Regression\")\n",
      "plt.plot(prediction_month_index[-5*12:], regr.predict(prediction_month_index[-5*12:]), color='green',\n",
      "        linewidth=3, alpha=1.0, label=\"Linear Regression Prediction\")\n",
      "\n",
      "plt.grid()\n",
      "plt.xlim(np.min(month_index_feature), np.max(month_index_feature)+5)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.legend(loc=\"best\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Coefficients: [ 0.00645693]\n",
        "Residual sum of squares: 0.16\n",
        "Variance score: 0.37\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+ydFRrHG75e+7+v2cHBmBDsDGpqaU179n//60n4f0fqX4qvT3/amnnsKpU6ew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\n6IislMYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f172890e850>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "SVM Regression"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVR\n",
      "\n",
      "# Create linear regression object\n",
      "regr_linear = SVR(kernel=\"linear\")\n",
      "regr_rbf_1 = SVR(kernel=\"rbf\", C=100.0, gamma=0.004, epsilon=0.01)\n",
      "regr_rbf_2 = SVR(kernel=\"rbf\", C=10.0, gamma=0.0001, epsilon=0.01)\n",
      "regr_rbf_3 = SVR(kernel=\"rbf\", C=1.0, gamma=0.0002, epsilon=0.1)\n",
      "\n",
      "# Train the model using the training sets\n",
      "regr_linear.fit(annual_index_feature, annual_temp)\n",
      "regr_rbf_1.fit(annual_index_feature, annual_temp)\n",
      "regr_rbf_2.fit(annual_index_feature, annual_temp)\n",
      "regr_rbf_3.fit(annual_index_feature, annual_temp)\n",
      "\n",
      "\n",
      "# The coefficients\n",
      "#print 'Coefficients:', regr.coef_\n",
      "# The mean square error\n",
      "print(\"Residual sum of squares: %.2f\"\n",
      "      % np.mean((regr_rbf_1.predict(annual_index_feature) - annual_temp) ** 2))\n",
      "# Explained variance score: 1 is perfect prediction\n",
      "print('score1: %.2f' % regr_rbf_1.score(annual_index_feature, annual_temp))\n",
      "print('score2: %.2f' % regr_rbf_2.score(annual_index_feature, annual_temp))\n",
      "print('score3: %.2f' % regr_rbf_3.score(annual_index_feature, annual_temp))\n",
      "\n",
      "# Plot outputs\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "\n",
      "plt.plot(prediction_annual_index[:], regr_linear.predict(prediction_annual_index[:]), color='green',\n",
      "        linewidth=3, alpha=0.5, label=\"Linear Prediction\")\n",
      "plt.plot(prediction_annual_index[:], regr_rbf_1.predict(prediction_annual_index[:]), color='blue',\n",
      "        linewidth=3, alpha=0.5, label=\"RBF1 Prediction\")\n",
      "plt.plot(prediction_annual_index[:], regr_rbf_2.predict(prediction_annual_index[:]), color='orange',\n",
      "        linewidth=3, alpha=0.5, label=\"RBF2 Prediction\")\n",
      "plt.plot(prediction_annual_index[:], regr_rbf_3.predict(prediction_annual_index[:]), color='red',\n",
      "        linewidth=3, alpha=0.5, label=\"RBF3 Prediction\")\n",
      "\n",
      "plt.grid()\n",
      "plt.xlim(np.min(annual_index_feature), np.max(annual_index_feature)+10)\n",
      "plt.xticks(np.arange(np.min(annual_index_feature), np.max(annual_index_feature)+10, 10))\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.legend(loc=\"best\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Residual sum of squares: 0.02\n",
        "score1: 0.87\n",
        "score2: 0.82\n",
        "score3: 0.82\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Vq6ZuJyQk0LdvX6NzLSwsSE1N5eLFizRo0EDdb2VlhYODQ5HPS0pKwsvLq9g2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nkRzYhwqZTXpqbg2+luAgGFTKbcpnOxbe+zmhbnlVdmlysKJWFpblugWvstEVzLE3pcnZgwCulL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\nYAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f171c64ed50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVR\n",
      "from sklearn.grid_search import GridSearchCV\n",
      "\n",
      "regr_rbf = SVR(kernel=\"rbf\")\n",
      "C = [100, 10, 1]\n",
      "gamma = [0.005, 0.004, 0.003, 0.002, 0.001]\n",
      "epsilon=[0.01]\n",
      "parameters = {\"C\":C, \"gamma\":gamma, \"epsilon\":epsilon}\n",
      "\n",
      "gs = GridSearchCV(regr_rbf, parameters, scoring=\"r2\")\n",
      "\n",
      "gs.fit(annual_index_feature, annual_temp)\n",
      "\n",
      "print \"Best Estimator:\\n%s\"  % gs.best_estimator_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Best Estimator:\n",
        "SVR(C=1, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.001,\n",
        "  kernel=rbf, max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.grid_search import GridSearchCV\n",
      "\n",
      "regr_rbf = SVR(kernel=\"rbf\")\n",
      "C = np.arange(gs.best_estimator_.C * 0.9, gs.best_estimator_.C * 1.1, gs.best_estimator_.C * 0.01)\n",
      "gamma = np.arange(gs.best_estimator_.gamma * 0.9, gs.best_estimator_.gamma * 1.1, gs.best_estimator_.gamma * 0.01)\n",
      "parameters = {\"C\":C, \"gamma\":gamma}\n",
      "\n",
      "gs = GridSearchCV(regr_rbf, parameters, scoring=\"r2\")\n",
      "\n",
      "gs.fit(annual_index_feature, annual_temp)\n",
      "\n",
      "print \"Best Estimator:\\n%s\"  % gs.best_estimator_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Best Estimator:\n",
        "SVR(C=0.93, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.0009,\n",
        "  kernel=rbf, max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.svm import SVR\n",
      "\n",
      "# Create linear regression object\n",
      "regr_rbf = gs.best_estimator_\n",
      "\n",
      "\n",
      "# The coefficients\n",
      "#print 'Coefficients:', regr.coef_\n",
      "# The mean square error\n",
      "print(\"Residual sum of squares: %.2f\"\n",
      "      % np.mean((regr_rbf.predict(annual_index_feature) - annual_temp) ** 2))\n",
      "# Explained variance score: 1 is perfect prediction\n",
      "print('score: %.2f' % regr_rbf.score(annual_index_feature, annual_temp))\n",
      "\n",
      "# Plot outputs\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "\n",
      "\n",
      "plt.plot(prediction_annual_index[:], regr_rbf.predict(prediction_annual_index[:]), color='black',\n",
      "        linewidth=3, alpha=0.7, label=\"Best RBF Prediction\")\n",
      "\n",
      "plt.grid()\n",
      "plt.title(regr_rbf)\n",
      "plt.xlim(np.min(annual_index_feature)+1, np.max(annual_index_feature)+10)\n",
      "plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature)+10, 10))\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "plt.legend(loc=\"best\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Residual sum of squares: 0.02\n",
        "score: 0.84\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pM3PmTDg7O0MikaBdu3b4/fff1aYrLi7G2LFjYW1tDalUCi8vL2RmZgJ4vGvQ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\nlPRNNKIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1730da5110>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Examining Extra Features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are three features that we can add to the tempreture data to build a better predective model. We will look at greenhouse gases (CO2 and CH4) and solar activity. So we will use the following extra features:\n",
      "\n",
      "- CO2 Level\n",
      "- CH4 Level\n",
      "- Total Solar Irradiance (TSI)\n",
      "\n",
      "This data is from the following sources:\n",
      "\n",
      "###CO2 and CH4 Data:\n",
      "Dr. Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/) and Dr. Ralph Keeling, Scripps Institution of Oceanography (scrippsco2.ucsd.edu/).\n",
      "\n",
      "- <a href=\"ftp://aftp.cmdl.noaa.gov/data/greenhouse_gases/co2/in-situ/surface/spo/co2_spo_surface-insitu_1_ccgg_month.txt\">CO2 Dataset</a>\n",
      "- <a href=\"ftp://aftp.cmdl.noaa.gov/data/greenhouse_gases/ch4/flask/surface/ch4_brw_surface-flask_1_ccgg_month.txt\">CH4 Dataset</a>\n",
      "\n",
      "<a href=\"http://www.esrl.noaa.gov/gmd/ccgg/trends/\"><img src=\"http://www.esrl.noaa.gov/gmd/images/noaalogo2.png\" /></a>\n",
      "\n",
      "###Total Solar Irradiance:\n",
      "The Solar Radiation and Climate Experiment (SORCE) is a NASA-sponsored satellite mission that is providing state-of-the-art measurements of incoming x-ray, ultraviolet, visible, near-infrared, and total solar radiation. The measurements provided by SORCE specifically address long-term climate change, natural variability and enhanced climate prediction, and atmospheric ozone and UV-B radiation. These measurements are critical to studies of the Sun; its effect on our Earth system; and its influence on humankind.\n",
      "\n",
      "- <a href=\"http://lasp.colorado.edu/data/sorce/tsi_data/TSI_TIM_Reconstruction.txt\">TSI Dataset</a>\n",
      "\n",
      "<a href=\"http://www.colorado.edu/\"><img src=\"http://lasp.colorado.edu/home/sorce/files/2011/09/CU_logo.png\" /></a><a href=\"http://lasp.colorado.edu/home/sorce/\"><img src=\"http://lasp.colorado.edu/home/sorce/files/2013/01/SORCE10Years.jpg\" /></a>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "\n",
      "def average(x):\n",
      "    assert len(x) > 0\n",
      "    return float(sum(x)) / len(x)\n",
      "\n",
      "def pearson_def(x, y):\n",
      "    assert len(x) == len(y)\n",
      "    n = len(x)\n",
      "    assert n > 0\n",
      "    avg_x = average(x)\n",
      "    avg_y = average(y)\n",
      "    diffprod = 0\n",
      "    xdiff2 = 0\n",
      "    ydiff2 = 0\n",
      "    for idx in range(n):\n",
      "        xdiff = x[idx] - avg_x\n",
      "        ydiff = y[idx] - avg_y\n",
      "        diffprod += xdiff * ydiff\n",
      "        xdiff2 += xdiff * xdiff\n",
      "        ydiff2 += ydiff * ydiff\n",
      "\n",
      "    return diffprod / math.sqrt(xdiff2 * ydiff2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy as sp\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.2)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "\n",
      "tsi_ax = plt.twinx()\n",
      "tsi_ax.plot(csv_data[\"Year\"], csv_data[\"TSI\"], linewidth=3, c=\"orange\", alpha=1.0)\n",
      "plt.ylabel(u\"TSI Reconstruction from IPCC AR5\")\n",
      "\n",
      "plt.legend(loc=\"best\")\n",
      "plt.grid()\n",
      "plt.xlim(np.min(annual_index_feature)+1, np.max(annual_index_feature)+10)\n",
      "\n",
      "plt.show()\n",
      "print \"Correlation between TSI and Temperature: %s%%\" % (round(1000*pearson_def(\n",
      "                                        csv_data[[\"Average\",\"TSI\"]].dropna()[\"Average\"].values,\n",
      "                                        csv_data[[\"Average\",\"TSI\"]].dropna()[\"TSI\"].values))/10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/pymodules/python2.7/matplotlib/axes.py:4747: UserWarning: No labeled objects found. Use label='...' kwarg on individual plots.\n",
        "  warnings.warn(\"No labeled objects found. \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KbyWuLrftG/veA9FpXvuvtbhdU9m1a42JRJr777+fRYsWNTs/evRo5syZw/Ll\ny33qx2tQq6amht/97nftn6GIiIiICHguPUw+ARIHQdppULDKrCWU9zaMuCV08wsEf2dqgVksviGo\nVbgRjr/cP/1KaPk7UyuCMpI8lgsCOOtwHnMLCMf1h+RhkDQckodD0jC+2lbB2NSJPvVfZ+ntanTx\nAvoikaasrIzBgwc3Oz948GAKCgp87sdrUOvKK6/kxRdf5KKLLiI2NrbxfK9evXweRERERES6Mfed\nD1NOMP8+/gozqAXmEsSuFNSqK4W6+q3KLbFmDTF/6OW2FEPF4ruGqnyozjePoxI8M67aI7GLLD+s\nO4iBwzxOGASz9jW7pObbZb53Z3HbhKL6MDjsYInq7CxFxA+Ki4tbfayqqsrnfrxWl4yLi+Pee+/l\n1FNPJSsri6ysLCZPnuzzACIiIiLSzTXN1AI47odAfUmLIyug6lDQpxUwHllax5lLB/3Bo1j8BnA6\n/dOvhE7JVtdxj3EdL/6feJzr3qqDYK/u/NxCoXa/6zhpWKe7cxoxENtQON4ONUc63aeI+MdZZ53F\nz3/+c5xu/5c5HA5+8YtftKtYvNdMrWeeeYZdu3aRluZ9zbKIiIiISDNlLWRqxQ+AvtPgSDbghH1L\n4MQ7QzE7/wvE0kOAxMFg7WFmgdUWmeMkDfZf/xJ8/tj5EMBiNTObGt575bmQOrJTUwuJOregVnLn\ng1qAuclCTf1SpqqD5vceEQm5Z555hhtvvJFhw4YxYcIEAHJycpg8eTJ//etffe7Ha1BrxIgRxMfH\nd3ymIiIiItK9lbaQqQVmTagj2ebxvtcV1PKmoVh8/qdmu2ijglqRzh/1tBokDXW99yr2RH5Qyw+Z\nWgDED4TiHPO48oDnMt42tFTEPjolhcwpU/wzL5FuLikpiddee43du3fz9ddfA2aR+GHD2ve17zWo\nlZCQwIQJE5gxY0ZjTS3DMFi4cGEHpi0iIiIi3UptEdQcNY+j4iAhw/XYoNmw/g5zWdDRL6Aizywi\nH+ncg1r+2PnQnXtQq3AjDLrUv/1LcPlj58MGiUOA+t3CfKyr1TRwE/Kgjfvyw+Th/ukzYaDruB3F\n4psVsQc2tKN4tYi0LT8/nyeeeIKdO3cyfvx4HnzwQVJSUtrdj9eg1qxZs5g1axZGfS0Ap9PZeCwi\nIiIi0qbSHa7j5BGeNYPi+kC/s+DwR2Z73xsw6p7gzi8QApWpBeYOiA0KVSw+ojlsUPK1q91jXOf6\ncy8yX+5egdqlAAAgAElEQVTbDohNAzchD9rUHXAd+y1TK911XHmg9etEJKiuvfZaJk+ezJ133skH\nH3zA/Pnzefnll9vdj9eg1vXXX09NTQ3ffWemjY8cORKr1drugURERESkGyr71nXsvvSwwfGXu4Ja\ne19XUMsb92LxRfXF4vUL58hU9h04as3jhEEQ07Pt673Ye8hGw7utaPca9tWsjqylck5HgIJaHcvU\nEpHAOnz4ML/+9a8BOPfcc5k4cWKH+vG6vUZ2djYnnHAC8+bNY968eYwYMYIVK1Z0aDARERER6WZa\nq6fVYNAlZpFrgMIvoWxXcOYVSIEMaiWPgOgk87j6SNfaNbK78VeR+HpVNa5lOz2No83qQYU921Fw\n1pjHsb0hJtU//Sa4ZWopqCUSNpxOJ4WFhRQWFnLs2DHsdntju7Cw0Od+vGZq3X333Xz00UeceOKJ\nAHz33XdcccUVbNyodGcRERER8aKlnQ/dxfSE/ufAwQ/M9p6/w/jHgjO3QLBXQ/Vh89iweH6g9gfD\nAj0nwNGVZrtoo2fNIIkc/iwSD9REue3qV3fAzOKLJIEoEg9NMrUie/mh4ayDzQ8wsGIP9JoHlsRQ\nT0mkw0pLS8nKcm3c4HQ6Pdp79vi2jNprUMtmszUGtABOOOEEbDZbe+YqIiLSvVUfgdXXQXQCTPkH\nROuHUOlGyrxkagEMvtIV1Nr+DAy/xf/BoGCpyHMdx6e7stD8qeckV1CrcCOkX+j/MSTw/BzUshk9\nwIgDZzU4Kohylne6z6AKSlArsjO1+la/C9v+ygCA/d9Bxu/BEh/qaYl0SG5url/68RrUysrK4sYb\nb+Tqq6/G6XTy6quvMnnyZL8MLiIi0i1s+y0c+tA87jsDTrwjtPMRCRan0/vyQ4DjLoNtT0LxVrBX\nwuYH4bR/BGeO/lYZwKWHDTzqamn1RMTy586HYNZWsw6EWnPnw1jH4c73GUy1AQpqxfUFI8rcZbXm\nGNhrICrWf/0HUY/aVa5G1WY4cDekLwBLXOgmJdJB3lb/TZo0qc3HG3gNar3wwgv84Q9/YOHChQCc\nfvrp3H777T51LiIiIsCRz1zHxZtDNw+RYKs6aAapwFxmGNu75ess0TDpWfj0LLOd+0844XZIOzU4\n8/SnQNbTatBLOyBGvNpiqKzP6rPEtB7wbS9remNQK8YeYfXW3DO1kv0Y1DIsED8AKuv7rzoISUP8\n13+w2EtJtH3jea7ySzh4Hwx8uvX7nHY4/D8S67YBZwR0iiLtcffdd2O0sdHJ8uXLferHa1ArLi6O\ne+65h3vu6QI70YiIiASbrQqKNrnaJdtCNxeRYPNYenhi27v09T8TBl0KeW+b7Q0/gZmrzQ+kkSQY\nQa2UURAVZ9bvqsyD6qMQ1ycwY0lgFG91HaeOMQO7/mB1LduNdeT7p89gCcTOhw3iB0Z+UKtyHQYO\nAOxGAlHO+l8YVKyCQw9AzH1guL2PnE4oX8Hokt/Dp3sZCZDyB0g8JehTF2lJdna2X/rx+lPCypUr\nOfvssxkxYgRDhgxhyJAhDB061C+Di4iIdHmFG8DpVouyZHvkFe8V6ahSL0Xim5r4f2CpXxZ0bB3s\neSUw8wqkYAS1LNHQI9PVdg+cS2Twcz2tRlZX/ahYu5YfNop3q9FXGaHF4itcSw/z4y6B3je5Hiv/\njCHlvzGzspxOqFgL+66Dgz8j3u72PalyfRAnLBIcXn8lMHfuXJ599lkmTZpEVFRUMOYkIiLSdRSs\n9mzXFZs7o8UPaPl6ka7ElyLx7pKGwsi7zfpaADkPmNlb1qTAzC8Q3INaCQEKaoG5BPHYWvO4cCMM\nmBm4scT/ghDUinFE0PJDeyk4SgFwEIvF3/9HRnqxeKcTKlw/T5RasxjY+3Rw1kLh3wHoVfs5Rrkd\nqmtbD17ZjgRjtiJB5TWo1aNHD84777xgzEVERKTraRrUAnMJooJa0h20N1MLYMyDsOdlqDpk/tn2\nJGT+OiDTC4hgZGqBuQNiAxWLjzxFfi4S3yAmo/Ew1h4+yw9zVq/GVlra2I5OSSFzyhTXBW71tGqi\n+hPf1lLljnDfTTUSg1o1O8F21DyO6UlFdP1y7rQ7zMBW0WIAetauglq3+4wYSqLHklpX/z2ioQ+R\nLsRrUGvGjBnce++9XHrppcTGunaJ8LUSvYiISLfldELBqubnS7ZD/7OCPx+RYGtvphaANRkyn4I1\n15nt7c/AsLlmFle4c9hddXsAEo8L3FgqFh+5nA4ocaupFbBMrXzzPWkJ/WobW2kpWWlpje0NBQWe\nF7gtPayxDCQeHwJh7eGeqRWJyw/dlh7SfyZU1P+bGgb0uRucdVC8xO2GKEi9GHrfyIFje0ktqd/o\nTUEtCVM5OTnk5uZis5klOwzD4NJLL/XpXq9BrTVr1mAYBuvXe6Yw+lqJXkREpNuqyIXqFn5TXqpi\n8dINOOqgfLernTzc93uHXA07/mDW1XLUwKZ74fS3/D9Hf6s66KqhF9sHohMCN1bqGLBY61/nXeZu\nejE9Ajee+E/5HrBVmMdx/SCur//6tiRAVE+wF2HBZr4nEwf5r/9A8cjUMjOZvQbC2iPSlx+6B7UG\nngs73B4zDOh7H4erDXrXfII16RRIuxlizKB6raXcda2WH0oYmjNnDlu3bmXMmDFYLK6y734LarVU\nkf7w4fArOrhh2bLG405F8UVERPzFfelhVDzYq8xj7YAo3UF5rivAk5AB0Ym+32tYIOs5+Kj+57m8\nt+Hwp+YOieEsWEsPAaJiIXWsq0h80WboNz2wY4p/uGcwpo72f//WgWAvMo/Ld0dsUMuvInn5oaMC\nqja72gPOgR1bPK8xLBxInMuBxLkegUAAu5FibsDhqDH7clSawU+RDrrhhhv4z3/+Q9++fdm6dWuz\nx7Ozs/nBD37QuMHg7Nmzefjhh1vtb+3atXz99dcYHVx27PMeycXFxfz1r3/lrLPOCsulh1lpaY1/\n3NNURUREQsY9qHXcZa7j0u3Bn4tIsHVk6aG7tFNh8NWu9sa7w3/n0GAGtcBzCWLR5tavk/DisUQ1\nAO8Tq1sAp2KP//sPBI/lhwEIanlkah0I2PeSnNWr2bBsmcefnNUt1NZsj4ovATsAlVHD2l+T0zA8\nn7+ytaST5syZw4cfftjmNdOmTWPTpk1s2rSpzYAWwEknncS2bR3/hW+bmVqVlZW8++67LF68mM2b\nN1NaWsq///1vTj/99A4PKCIi0m24B7WOvwL2vWlma1UfgeoCiEtr/V6RSNfZoBbAhKcgbwnYq6E4\nxywcnzDQ+32hUhnkoFbKSNdx+a7Ajyf+4V7TKT699es6yj2oVR4hQS2PTK0AfI1bU10Z07YKsJWB\nNcXvwzRdMgmdXDYJHksPS6xZdCjHKmGgK8BpK4CYwZ2bk3Rrp59+Orm5uW1e42xH4HjOnDlMmTKF\n/v37N9ZxNwyDLVu2eLnT1GpQ68c//jFr165l5syZ3HXXXUybNo3hw4czffp0nycnIiLSbdkqPDMn\n0qaYH0AblgqVboc4/ZJIurCO7HzYVEJ6/ddN/ddS5f7wDmoFO1PLvXi+e/0yCW9VbplaCRmtX9dR\nbsXiI+J94ah2yx6KosbixxpjDQzDDCCW7zTblQcg1f9BLb9zOqHC9Quy0piT6FAem3vwtE6ZWhJY\nhmGwatUqMjMzSU9P5+mnn2b06NaXWs+dO5dXXnmFsWPHetTU8lWrQa3t27fTt29fRo0axahRo4iK\nCv2uGW3JXr+e6ZMnA7A+J4ey2NjGAFxDXTC11VZbbbXVDlr72HqyvzaXC0yfMhpiepC9sxfkw/TR\nQOl2srfZw2e+aqvt7/bna6Go/v2efELH+4vPgKLNZG8Doj9k+o9ODo/n11J70yam98NsbymFQ9mB\nHa+sELMF2au3AgEeT23/tCsPmO9nYPoZ6X7r/9ucHLLOOgus6a7+++xp8/5k8zKy6zcFSx48OCDP\nf31ODmWpqc0+ryUD1B10zTezPxjRns+nfn7flpSQdc45HvNtsb/Wns+OBKbXR4Sy/7cUeuX79nq2\n0F+bz6eF+XX0+aQ59oPtkPn6WOJIPm1Ui+M39Dc5M7Pl8b92QF7992PbkeavTztfz9bGb+35hMvr\n6c/nA2Hy/SQA7WeffZbNmzczuP71aa9JkyaRl5dHQkIC//3vf5k1axbfffddq9f37duXiy++uENj\nARjONvLCtm/fzuLFi3njjTfo06cP27dv56uvvqJ///4dHjAQDMPA6bY744aCgsY3oIiISEh8/RTk\nPGgeD7sRTvkLfPVr2FJfV+DEn0DWs6Gbn0ig/XuQq3bQhd9ByoiO9bPuNtj5J/M46zk4cb5/5hcI\nH4x21cw7dyP0mhjY8WpLYEn9jodRcfCjCrPIvoS3/4yDkq/MYz++TzYsW2YufavdD3tmmSfjB8Al\nB73f09AO0Oeo1sbZsGwZWXHb4MDd5gMJJ7Mh/jHXY63d08ISv7buAeCLK2HvYvN4yj9gyDXtmrMv\n4/j7nrx/38KgyhfNk0kz2BB7f5v3AC33ddxXsOln5okel0O/e73fE8TnGYn3dCeGYTRbTpibm8tF\nF13UYqH4poYMGcKGDRvo1atXi4/ffvvtFBcXc9FFFxETE9M4pl92Pxw1ahSPPfYYjz32GOvXr2fx\n4sWcfPLJZGRksGrVKp8GEBER6Zbc62ml1e/gljrKda5ExeKlC7NVuAJaRjQkDe54X+67lrnXIgo3\nTmfwlx/GpEJsb6g5ZtYdqzoc3sszxeReKD4hEDW1+gNRgN2sQ2erguh4/4/jL25F4rEGYDlmA/di\n6eH8vcRNSp0rcYPE08DWwY48CsUf7dScRLzJz8+nb9++GIbBunXrcDqdrQa0wKzlHhMTw0cffeRx\n3i9BLXeTJ09m8uTJ/N///R+ff/65r7eJiIh0P04nFLj98qchqJXiVk+gtOO7vIiEvbKdruOkoWCx\ndrwv95pD7sGAcFNzDOyV5nF0EsT0DM64iUPNscGsn6SgVnizVUBdsXlsiYHYAGwYYkSDtR/U1Wdo\nVeyF1JFt3xNKbkXiickAR4DG8dgBsfXstbBhqyS5zi0LJnEKlHSwL/fgqYJa0kk//vGPWbFiBQUF\nBQwaNIhHH32Uuro6AG655RaWLFnCCy+8QHR0NAkJCbz22mtt9vfyyy93aj4+B7UaWCwWpk2b1qlB\nRUREurTyXVBTv9uRtQeknGgeJw8zP9w76swP53WlAdl9SSTk/LHzYQP3oFZVGAe1mu58aBjBGTdp\nKBR+aR6X74a+U4MzrnSMx86HAwO3XNSa7gpqle+OnKCWNQNqAjSOe2DHLaiVs3o1ttLSxnZ0SgqZ\nU6YEaBLtkJ+NBTNQQMzQ+gy8Du6k6F4o3qZC8dI5ixcvbvPxefPmMW/ePJ/7y8vLY/78+axcuRKA\nM844g+eee46MDN8yN9sd1BIREREvPJYenur60GKxQvIIKKnP0ir5BtJODv78JKTC9gOUP5V+6zru\n6M6HDeIjJFMr2EsPG2gHxMhS5RbUCsTOhw3cd0Cs2BO4cfyh6fLDQAW1Wll+aCstbVYzKSwc+tB1\nnHha5/ryWH5YAE6H6u9J2JgzZw5XXXUVb7zxBgCvvvoqc+bM4eOPP/bp/m4b1OoWP1CKiEhoeAS1\nmvwgmjLaFdQq3aagVjcUth+g/KnUn5la7jW19pvLe4OVBdUeCmqJLzzqaQUpqBXO7wun3ZVRBhCT\nDlQFZqxIW37oEdTq5OfU6HhsRjLRzjLADvYiiO7duT5F/OTo0aPMmTOnsX399dezYMECn+/3Gp49\nfPgwc+fO5dxzzwVg27ZtvPTSSx2Yanhp+IGy4Y97gEtERKRT3INafZr8IJrqVlerRHW1pItyX37Y\n2UwtazJYU81jR61raW+4CVlQa4jbHMI4eCEmj+WHASgS38Dq1nd5+GZqxTgKaKx+HtUbLImBG6xp\nUMsZqOJdnRdjPwhlO8yGEQfxnd8hs87iFsRSXS0JI7179+af//wndrsdm83GK6+8Qlqa7/UGvQa1\nrr/+embOnMnBg2Y0e8SIEe2KmomIiHQrdWVQvKW+YUDvJplYKdoBUbo4p9Nz+WHyiZ3v06OuVpju\nWuYe1EpQppa0ImiZWu5BrfB9X8Q63DKmrAEM8oG5A2TDBg5OW/gGyIFU910PE04yNxXopFqPoJbq\nakn4WLRoEW+88Qb9+/dnwIABvPnmmyxatMjn+70uPywoKODyyy/nqaeeAsBqtRId3W1XLYqIiLTt\n2Jeu3/72GNu8EHyqdkCUJpx22PJLKPkaJi2AxONCPaPOqTnm2t0tOhHiB3S+z/h08/UBMyjQc0Ln\n+/S3UGVqJQwCI8p8H1UdAlslRCcEb/wIFpJyJO6bHSS0P4jTdM7QyrzdA0QVe8J22W6s/ZCrERPA\nIF+D+HSoLTKPqw5CXN/Aj9kBKbUbXI3OLj2sV6egloQhm83GQw89xPvvv9/hPrxGp5KSkjh27Fhj\ne82aNaSmpnZ4QBERkS6trXpaYC7FMixm4Kt8D9iqzN8eS7fVp2YpfPUHs2Gvhun/Ce2EOqvpzof+\n+CCd4N9i8T4HBtojVEEtS7Q5XkM2TkWuZ/C8q3A6za8PP36/DEl9O4/lh+0P4jSdM7Qy76ie2Ikl\nihpzp93aIojt1e7xAs0jqGUNRlBrIJR8ZR5XHgjPALmzjuS6za52Z4vE16u1uL1vbOGbpSbdS3R0\nNHv37qWmpobY2NiO9eHtgmeeeYaLLrqI3bt3c9ppp3H06FGWLFnSocFERES6PI+gVgsfkKPiIHEo\nlO8EnFD2bXj+UC3BYS9jQOUrrvahj6C2GGJ6hG5OndVQBwbM3T79wc9BLZ8DA76qK4faQvPYEgPx\n/Tsxuw5IGuoKapXv7npBLXstZJ8H+cth/K9g7M9DPaOOq+xcppbPDIPaqAHE23PNdvnu8AxqOYIc\n1EqIgGLxVVvNYCRQYxlArJ8y2JSpJeFqyJAhTJ06lYsvvpiEBDPT2DAM7r77bp/ubzOoZbfb+eyz\nz/jss8/45ptvcDqdnHjiicTEdH5Nr4iISJfjdHoPaoH5gbN8p3lcsk1Bre7s2N+wOktcbacNDnwA\nQ64OyXT8shzLPWPJvd5TZ/g5qOV3HvW0BpnZmMHU1etq7X4J8j81j7c8DCkj4bjZoZ1TRzjqoDq/\nvmH4Z2luG2os/VxBrYo90HtyQMfrSAZkSJYfNgjXoFbll42HpdYJ9PFTtx5BrToVipfwMXz4cIYN\nG4bD4aC8vLzd97cZ1IqKiuJf//oXP/3pTxk7dmyHJykiItItlH3nytaI7d16lkrqaDjwnnmsHRC7\nHqfDXO7jLduq9gAUv9b8/P53QhbU8styrMo813HCID/MisgKagVz6WEDj6BW+O501yG2SvjqV57n\n1syBHuM6v7NmsFUdApzmcVw/sFgDOlxNVH+oq28EIdjZ7gxIpzP4mVruOyBWhummE25BrTI/BrVU\nKF7CzTXXXMM///lPUlNTueuuuzrcj9dfI02dOpU77riDzz//nI0bN7JhwwY2btzY4QFFRES6LPcs\nrd6ntl5LyH0HxFLtgNil1JbAf0bDW31g22/avrbgeXDWf+JMGu46f/C/5gf5SFWxz3Xsr6L34b77\nYWU4BbW6WKbWd3+oDwa5sZXByh9G3teJexAlkDsf1qu1uGWC5f4L9r9rZouFi5oCopz1/4ZGAkT1\nDPyY8WG+/NBRBVVbG5tl1ky/dV2nmloSZjZs2MDBgwf529/+RmFhYbM/vvJaU2vTpk0YhsEvf/lL\nj/PLly9v/6xFRES6MvegVp82Cru617tRplbXsnsRlH5rHm9+ACxxMPInza+ryoGyj13tKf+AdTea\n7wd7FRxaBoMuCc6c/S3gmVp54beTW1hlanWhoFZtCWx7ytUecTvsegkcNVC8Fb68DU59ObzeC23x\n2Pkw8EGt6ii3MYq3wGezzAyxIdfC0DmQOqr1m4OhfJfrOCYjoP+ODUsjE2z7aHjWVQXfEXbbtFRt\nAuzmcY9x2Cz+q69oM1KBKLN/Rwk4qv3Wt0hH3HrrrZx11lns3r2brKwsj8cMw2D3bt/+P/Ma1MrO\nzu7QBEVERLodX+ppgVkPpkHZDrMIcpTqVXYJe/7u2d54F1iTYdgNrnNOJxxZ0NgsjDmDXn2mQMal\nriBn3tuRGdRyOqEyAJla1h4QFW8G/GwV9cs7w2g37nALaoVb0K+pL++AvCUw9hdwwrzWr/tmgWtJ\nd9JQmLQAek2CtTea5/b8A/p8D4bfHPg5+4P70tn4ABaJr1dqnQAZl5hLmhtU58P2/zP/pE0hpfY8\n4PyAz6VFZW5BLWtgX4/GpZF1DqgvYxgdjnWlKlxLD+l3FvgzocqwQHQa2OrrutkKgDg/DiDSPvPn\nz2f+/Pnceuut/OlPf+pwP16DWo8++iiGYeB0OjHc/nNsmrklIiLSrZXvgeL6bcINC/Q6qfVrrUmQ\ncJz54d9pN4vGd7Xdyrqjohwo2tz8/LqbzMDWcZcB0LN2BVQ3vFesHEi4gV4Agy6Frx83zx94PzKD\nnXXFZtAJIDrRDEb5g2GYmS0NOytW7ldQy11MT/O1risGeyVUH4H4fsGfhy+KtsCOP5jH6++AqAQY\nNqf5ddUF8M0zrva4R82vh2Fz4egXZlYkwPo7oVeW+SdMNWQJpVd8QeO+mEHI1MKIhjPehtId5uu1\n52XPpZwFqxnOWrCdbAY7gs09UysY9bQAonthVuBxYHUWh9dyTPCop0W/M/0b1AKI7usW1DoC+OkX\nDyKd0JmAFvhQUysxMZHExESSkpKwWCwsXbqU3NzcTg0qIiLS5Xy7kMYCwP3OMgNXbdESxK5nt1uW\n1sALoedE89jpgFVXwcEPwVZFeuXfXNf1/DG1UfUfc3tOgMTB5nFdCeRHYKkH93paCcf5N1sonIvF\nhzqoBZA0xHUczksQG3YxbLDuJrOOXFPbngJb/S5YqaPh+B+7Hpv8B+gx3jx21MLnP4Qa3+uvBFtD\nllB/q9vOgAmBz9RqlDICJjwBP9gH0z4wA+iGmdtg4ICaXV46CJCmyw+DwYiGqF6udtN6baFkL4Ea\nc/m6Ewv0m+b/MaLdys6rWLx0EV6DWj/72c+45557uOeee3j44YdZsWIFu3aF6BufiIhIOKothl1/\ndbVH3u39HgW1uhZHHex91dU+4Q6Y8SGknOh6/PNLYe1cYh31HySiekAvt2WJhmF+2Gyw/+3Az9vf\nAlFPq0G8e7H4MApq2WvdPhgbnvMMpkipq9U0WOu0w8rL4Nh617nK/fDd8672+MfBEuVqR8fD6W+B\nNcVsV+TC6mvDL+umKZvbcrdgZGo1ZYmG9AvM1+64H7nNKz/4cwEo2+k6DlamFngGdsKpWHzlehp+\nOVYRfYLr/e1P0X1dxyoWL12E16BWUxUVFRw4EIa7zoiIiITKrr96ZhQMOMf7PdoBsWs5+KG55AvM\n3bX6fx/i+sKZn7gyd+xVsHex657et0BUk4w+j6DWv8FhD+y8/S0QOx828MjUCqOfRSvzaMzSjB8Y\nuiWjkRDUctjhyApXO7Y+uGCrgBUXuOb91eNmMXiAXpMhY1bzvpKHm0XiGxz8D3w6E6rDsE5SA/fM\nmCDU1GqT+9dTXYgydjwytfwcBG+LNZyDWqYy64TAjOG+zFSZWtJFeK2pNW7cuMZjh8PBkSNHVE9L\nRESkgaMOvn3O1R55t29LrpSp1bW4F4gfco0rqyQhwwxsfXw6VB92XRMzBHq0UAg+bQrE9TevrT4C\nBaug7+mBnbs/uReJ93emVrguP+zA0sOGGkvuolNSyJzSxgYT3rgHtSrCNKhVvNlcWgsQPwDO/B98\n/D2oLTLf78vPNXcC3fWS657MJ1r/njroEhh1H2z/rdk+kg0fToYz3jELyocTp6NJplaog1puX58h\nyNSyOKvMovUAREN0EGvARbkFtSoPACcEb+y2VK5rPCyzTmBAIMbwyNQ6ChFWtjEg7DWk1H4JnBfq\nmXRbb731Fg888AD5+fk4neYviQzDoLTJ/5Ot8RrU+uCDDxo7jo6Opl+/flit1k5MWUREpAvZt8T1\nATuuLwy+yrf73LdSL/3WzGBwX14jkaPmmFnYvcGQ6zwfTx4OZ34En0wzP7wD9PlJY00bD4bFzErZ\nWV80Ne/tyApqVbgtPwxoplZkB7Uad2Jzs6Gg40uBclavJr6gsPGjefnBzXip6hca7ksP+84wvw9O\nex8+/T7Yq82NAD4+HZy2+mumm1mPbZnwpLlMa8svgPrdNz/+Hpz8Vxji4/fjYLAXg9NcHmkzkoiO\nTgztfBJDG9SKtbvVsrIOBCOI//81W34YBkGtuiNQW/+9xIihPHpU29d3lNU9qHWk2we1DGcdrLyM\nEWXvQ3El9Jgd6il1S/fddx8ffPABo0Z17H3vdfnhww8/zODBgxk8eDAZGRlYrVauueaaDg0mIiLS\npTid8M3vXO0R8yDKx+2xY3qaGTlgLrOp2OP/+Ulw7H3NLFYN0Ptkz4Blgx7j4PsrYPBV7EucB0lT\nW+/PfQli3tvm+yxSBDRTyy2zJZxqaoVBkXhbaSknpLmyP2Nqw+j1cece1Oo3w/y7z/fgtH8B9dlY\nDQEtgMxfe898NSww9ucw7T1XDSJ7Nay+GjbeAw5b2/cHi9tSrzqL950Gc1avZsOyZY1/clav9u98\nPJYfhjioFawi8Q2sTTO1woD7rofxmTiN2MCM41EovpvX1HLaGFL+pOuXUvlPQtXW0M6pm+rfv3+H\nA1rgQ1Drq6++8mjbbDY2bNjQ4QHdffjhh4wcOZIRI0bwm9/8psVr5s+fz4gRI8jMzGTTpk1+GVdE\nRMQvjn4OhfU1MKLiYMRt7btfSxC7BvddD5tmabnrMQ5Oe4WjcRe13V+/6WDtYR5X7oOijZ2eYtB4\nFIr3c6ZWfJhmalUGsI5Ye1j70/CjvdVxDOw1zQIjAQmO+MphgyOfu9oNQS0wlxFmLfS8fuCF0Oc0\n38RaswUAACAASURBVPtPvxDOWQcpI13nvvmduaQxHHZGdAtq1foQ1GrI5mv403S5aqd5LD8Mfm2l\nGIfbmNaBwR08HAvFuwe1EiYHbhyPoNbRyPqliT85bXDoYXrWrnKd63UdxI0N3Zy6scmTJ3P55Zez\nePFi3nrrLd566y3eftv3zXJaXX74xBNP8OSTT1JVVUVycnLjeavVys0339y5WQN2u5077riDTz75\nhPT0dE466SQuvvhijwjd0qVL2blzJzt27GDt2rXcdtttrFmzptNji4iI+IV7ltaQayGuT+vXtiR1\ntGt7+9LtwMV+m5oESck2KKz/MGKJgeOv6HyfFitkXAx7/mG2896GXlmd7zfQHHbPYJO/d3eL62O+\nNo46cxmnrQJCvYQLPDO1EkKTqQWAYTXrEtkOYeCEir1tLnNsWter0zW9vCncALYy8zhhkGcNMIAT\n7zBrLH39OEQnm8sK2yvlRDhnLay6Bg68Z57L/x+svxO+92rb9wZanXumVu8QTqRebJr5PctRC44y\ncFSCJSFow8c43OqLRfcP2rjmeG5L8KoOQqgr6zidTYJaJ0NFgMayJIAlERwV4KwlylmK3UgN0GBh\nymmHQ/8Pyj5pPJUfdyn90u7wrSaq+F1JSQnx8fF89NFHHucvvfTSVu7w1GpQ66GHHuKhhx7igQce\n4KmnnurcLFuwbt06hg8fzuDBgwG44oorePfddz2CWu+99x7XXWf+xvOUU06huLiY/Px8+vULYiFB\nERGRlpTugP3vudon3tX+Ptx3QFSmVmRyz9JKvxhie/mn30GXega1Mn/tn34Dqfqw+WEBzF3touP9\n279hMXeMq8g125UHICUMauH4eflhp4rIx6SDrX5ZV/luGpf0taBpwKszNb184lFPa3rLHx4zf2UG\nhmN6QkIHs3esKWah+K2PwVePmuf2vwP22tDtTAntztQKOMNiBp4bdpysOwKxg4M2vGemVrCDWm6v\nf9WBkAe1Yh0HXXXNLIkQNwoqigM3YHRfqDVLHsQ4jlFl6UZBLacdDj8KZctc506Yz/6C8+ingFbI\nvPzyy52632uh+KeeeoqioiJ27NhBdXV14/kzzjijUwMfOHCAQYNcaa8ZGRmsXbvW6zX79+9XUEtE\n/KNiL5TtNH+4VoFuaa9vnwXq0/YHnt9yHSVvtPwwsjnskPuKqz20jaWH7dV/JkQlgL0SSr+Bku0d\ne4+1ICA77wFU+L4Mr8NZQgkZrqBWVRgEtZwOzyWXfghqdaqIvDUdqF8SXb4bGNbp+fhNS/W0WtJj\nTOfHMiww/hEzMFyxB+xV5lLx9ixn9Ld21tQKCveglu1wkINa7plaQf5sZ0kFIwactVBXau7EGELJ\ndZtdjfhJLW8i4k/RfRqDWlbHMaoY6uWGLsLp4PiK56DGlQ10JPZC+mY9C00yhCS48vLymD9/PitX\nrgTMWNNzzz1HRoZvGd9ev2L+8pe/sHDhQvLy8pg4cSJr1qxhypQpfPrpp52auOFjJNTZZJ1va/fd\nvGBB4/HJU6aQdc45rF6dQ2mpZ3HIlJRopkzJJDolxeMHhOgUs7BkW/c0fay187on+PcAPvfVkXvC\n5XnqHv/d0zuhiMmHZoGtgl3xN7Az4fawmZvuCZ97oJXvE5MGYd/5NxpCoV+WX0Thsg3t/t7SK9HJ\nSfXH9sKt/O/DtTjrf5gN99dG90Qz5fhDjfVYaozerNjah+R9OV7H8fVnkMyoKfS3/w+Afav+wHbL\nHL88n7Z05uejfjUrmNDwYMKgNu9pjbdxBpckNG5zvyNnFSNmzujQa9D0ebb1XNt6rXsnFDK5fpOA\nWiOV5Z9+6/WelJRoEtoYv7W5+fKe6l/bk8Zy+uW7iU6Z6PM4gfxZuKykmjMLP3N98OjXsX+3dt/T\nbxrsNj+8f7fyVfYkxLb4ugX6NYhOSaF0/wHqy9hjTzrO6zitza2t9267n49bXa3cot0cqxreqdeg\nPXMb7zzWeLy1LIbaSu/PtTOvQdPzY41exDoPAxCXUBuwcXy5J5WvG8/lOUdzpKCgU+N4G3+wPYWG\nBbBxsRVUJgfneYb0nuQkWHcraW4BraOx53FowAP0NYwOjSP+M2fOHK666ireeOMNAF599VXmzJnD\nxx9/7NP9XoNazz33HF9++SVTpkxh+fLlfPPNNzz44IOdmzWQnp5OXp7rN1t5eXnNInFNr9m/fz/p\n6em05MVXXml2rrTURlqaZw2KggKzyH1rvw1s656mj7V2XveE5h5f++rIPZ2Zc1vC+fXs6vfE7v+F\nWY8FGFyzhJKM3+OsryUR6rnpnvC6p8XzO/9ElNPMXq6MzcTS/xbSDKNj31sSj4eKvURRw6Akg8r4\nrIh5bbr9PXtcSw+Le1xH7z6n+DSOrxlR+TEz6F9rBrVSS/9H2vDn/fJ82hq/Mz8fpRUuh/L6BxOO\ng7rWn1tHxzESx0Gt+aHEWXnI57k1Pd/Wa9Ce90HdoUWNx7aYoY2Pe/v3mXJO+/8Nli3b4P09lVsI\nq142j8t3k3lGYP6tvZ1v+tjxSXb+P3vnHeZGde7/j9qqbPeuu417w7ixBmMHsDHFYGIHCCaQhBJK\naNcklwuXJL+bxCQQSLtxwIQACZhLKAklYIppxmt6cQEbGxu3dVnb6+2rXXVpfn8cSTPaXe1qtRrN\naD2f5/Hjc6SZOUdaaTTnO+/7fa0N0XOmeQiugpG0tCR/PRn7/o6bB3tWAjDQtAt3u79Ptt6DabNn\nwyve+Hdi9IlnpbZPJ6Tz/U2Kwvdu5OhiRk5ZEO+rem4JB+CfMVHLxJRzLxN+eV3tk8LjPdrnrTFQ\nK0StSRMGiAIdaozT3XNShJLQF/Hu8Hk3M7xkSq/G6Xb8z9fBNuEnNXywg+FTsvA6td5n+59h4yNy\nf/TV9J/1CP1N5rTHMcgctbW1/OAH8o27q666ij8pgpa6o1tRy+Fw4HQKTwSfz8fEiRPZsWNHGlNN\nZObMmezcuZOqqiqGDBnCP//5T55++umEbRYvXsyKFSu49NJL+fjjjykpKTFSDw1yju7uihtoQ3FI\nvitmibRQ6n6OhuIrNJyRQa5gkgKw4/54v6b01t4Zi/Y/Le7JU+B9D4/z5N5O0SBNioqsCTckYpF3\nnWGNuOHAv+P9+qLepx52+L0IjoPnfwWRAMXh7Th9m/A6ZvR6HLWwBZVpeMOZPSnzv39Bq7wId4Sz\nX7GtPc7I4Xjbb9PQJD6G0nw9llamAwo9cuphg62CrNmRD5gbbxZ4PwApKAz1tcBTLbczXUShG5Je\ni379vtzOZkVRbzXx9H3nkE4FLdVxKjzbtKyA2LQF/FGBz94fijOQftsdLkWQiPJz2VeRJNj5gNwf\neTmc/LBIUzbQBWVlZTzxxBN897vfRZIknnnmGcrLU0/T7lbUGjZsGI2NjVxwwQWcffbZlJaWxs3d\ne4PVamXFihUsWLCAcDjMNddcw6RJk3jooYcAuP7661m4cCGvvfYaY8eOJT8/n8cee6yboxoYGBik\nRknoy4R+efPfDFHLICUGBdYIQ2wgYBlMY1Evq931PzXuy1TgfZ+j/Fdvp2iQJj25CTEo8BZE/AB4\n7DPwOqZmfkK2IhhyvjC5Bo6ruYkdx32g2wvxvJDCU8vVtadWugRsClErUqPKGD1BKWoF9ChqSZIu\nqnklilozyZqkUzASr3kwzshhLFIb+b4NtDlPydboMsEWufKjxSGM8PWAUlxTesOpjdJ/T5ECmVWc\nOhF2jqyR2wPnZ+f8rnztWgp62aJhA7h3ira1EE5+yPDS1RmPPvooS5cu5dZbbwVgzpw5PdJ+uhW1\nXnzxRQCWLVvGvHnzaGlp4dxzz01zuomcd955nHfeeQmPXX/99Qn9FStWYGBgYJBJzJFWCsK7Ex4r\n9L6HPbADf94EjWZlkCuUBjfF23Ul1yOZellNa8Bp8WaB933dLEINumZgQF6k1xdn0CC+PdPvgUOv\nQiRAge9jypofpb7kWvXG6wV5ykgtlRaqykgte0T7SC1H+Ei8HbDqQNSyl4O1AEKtQkTx14NDW1Ny\nkxQQUVJRGqwzszp+g+1EhvpfBaDAs04bUUsZBeUcpp9zvPJ7ms1IrYTiCuoI4N2il0itGoWoNWh+\ndsZMeO3HQKRW1VNye/hFma/Ma9BrRo4cycsvv5z2/l2KWqFQiBNOOIHt27cDMG/evLQHMjAwMNAL\nLt96TEQ6PF7e9HeqB/xOgxkZ5BKuiHzh73FUdLFlihRNJGAqJk9qxhauE+KqfWLvj2ugKs6wvBBo\ncZ2p3kBFE2DSf8PWuwAYVnsHTQUXqDdeL0iI1FJpoRpQph8qq6dphO4itUwmEa3VtFn0W/doLmqV\nhL7EHPUg9NnG4rdk10qk0SqLWoXeSmq4I6vjA+1SDzv3B9aENCO1epKq3Ske9c8V3eLSQbRSJAhH\n35X7A1X8LVFyLKUfRsKw/xm5P/K72s3FoAO//e1vueOOO1i6dGmH50wmE/fdd19Kx+nyDGS1Wpkw\nYQL79u1jxAgd/FAbGBgYZIB87yfxtsc8FFdE/KCXtTzOof53aTUtgxzBFZYvxn1543p/QJOZJus0\nBgTFhW2h973cFrX89fQLfoopcgKS2a71bNRBkhKihJTRQzHaL/pij6XF5J/h2/EYjlA11kgD5Qev\nwTvkV+kdSyVMES+2sBCZIlgwOwapMk7QOggJMyYi2KV6YTitIQ69iVrQUdQq19anr19wfbztdp2R\n9fEbbPLNhwLP+yCFuthaJZRRUFn20+oSe38w50EkAMFmCLrBVtjtbr32i9VF+qEOopXqPxNRlSCK\nxijTh9XEMVCkOUoR8NdC2A+WPvp7fXQdeKPnaccAkeJpoBuOP/54ACoqKjApIlglSUrod0e3V1cN\nDQ1MnjyZk08+mfz8fECoZqtWrerpnA0MDAx0Qb5PFrX2Oi9nnP8J8kLV2MJHKW59hVp0sjAx0B2m\niC/u4yNhJmAblZHjNtpmxEWtAu971JVcl5HjZhuz5IPVMzjJc4Ba00b2D3qk+51yEEukGSsi8iSE\ng7C5uMM2GS0SYnXi+MZDsO6bAAzzr2LY2P8GuhbPeh1N0QPyQvKi3W8egFMtvxKTlaB1MHmh6CJU\ny7QhScKpSD/06yH9EBIXxm17tZtHlA6iVpZ1SK95KAHrMPJCB7FIrbh8G4Es++l4OzeJz6j4nQ4m\nk5hPrKiA5yAUT1J/bsqoMJX897pFKWp5NDqPJERpzc9eWqrZCo5B8vnTd0SIan2RfYrUw+O+I167\ngW5YtGgRAC6Xi0suuSThuX/9618pH6fbv+qvf/3rDo/1RDUzMDAw0BtKUavJOo16h5XB9SJCq7z5\nb+xydDzvGRgA2IO7MUUrNnnNg3vvpxWl0To93i7wvt/FlvqmOPRlfLFS1vIkBwas6JPRWraQvED1\nmwdkZyEy9HwYdmHcNJ7PboRzN3QpnmWz+m5eUBHBaB6Imo4lQetQWdTyHIQuRlNT2LOGj2DFI+Zk\nyidsKcvYsXuFnioghryUhLbEu27XPAhkWUAwmXC75lHWIgpyFHrXAelHa6T1mUrw1JJTv3RRIds1\nvFNRS9W5KSO18vUQqXVIGz/LZrkSN2VZjqh0DpVFLU913xS1wn7Y/5zcN1IPdcs999zTQdTq7LFk\ndHsWnjdvHlVVVezatYuzzjoLj8dDKKRB2K6BgYFBBrAFD5IXiv6IW/NptYymrmhWXNQqansdh+1m\nDWdooGfsgZ3xtseSubvLLdaJRExOzJIXe3AvtmBuelwUhuT3xyx5yfd9QqvrdA1npA6JolYW/Yoq\nlsPhNyDsEWXgd9wPk27N3vhdkBeSIy+85kGoWdstYB1GPp+KjucgkDwNWM2FudO/Ld5us4zWj/m3\nnkStuo8wEwTAmzeRkHUwkL6olW70kNs5Ny5qFXjWgSN9USutz5Sn80gtXaBFBUQ9RGrZCsBWLNIu\nI36RouYa0v1+maTlK7ldNCm7Y7uGQEO03VfN4g+tFn9fgPxRUDZL2/kYdGD16tW89tprVFdXc8st\ntyBJ4sax2+3GZrOlfJxufwUefvhhHnnkERoaGti9ezcHDx7kxhtvZM2aNd3tamBgYKA7lFFa9JsJ\nYQuBvFG0uM6iyPM2JiSG+l8Gztdsjgb6xRGURZs2S+buLksmG22OWRR6K4FYtNbYjB1fLdovMGeY\ndyU8X+hZ2ydFrTyFqOUzD8jewPnHwZRl8LlIPWTLL2HEJbpYJLeP1FITpVk83q5FLTVxBGRRq9XS\neSqyJullehK1auQqoZnw00pXpGx1zY23C73vgT3c67n0CG/nkVq6INsVEIMtstBgcYiKnVpRMhVq\n3xPt+k/AdWH2xpYi0LJd7hdnWdRyHgNm8VVPyu2R39XPjQeDOEOGDKGiooKXXnqJioqKuJdWYWEh\nf/rTn1I+Tre/qg888ACffvopp5wiyt+OHz+eo0e1L6FsYGBgkA5Kk3jKZkH0dFZXfC1FnrcBGOp/\nCSIPaDA7A72TEKllzmzKhNt1mkLUeg8s+he1OiwwVyfe5S/0vMNhfpnFGcmomXZmC8mRJn5z/4wd\nNyUm/hj2roTmbcJgeMN/wmnPZncO7SgqshI5tEl+IF/daIegTRlZot1iTBmp1Wrp3OA5k5FiKQtk\nyjQiz35RYc2c+h3vjHJUFrVanfO0mQPgt40lYB1CXugQlkgLReGvgSymexmRWjJtyiit4doKDeWz\nZVGr7iMYnkVRy3MAQm2inddPmPZnk4Tqj31Q1Aq2QPXLcn/k97Sbi0FSpk2bxrRp07jooovIz8/H\nYhF+h+FwGL/fn/Jxur3Cs9vt2O2yH0YoFDI8tQwMDHKWhEitclnUaiq4gJC5H9ZIA87IEahZA+jE\nH8VANzgS0g8zK2q1Ok+Ntwu870PBDzJ6fNWJhBL9QYB870eYIh4ksyvr01Ez7cymVaQWCHHipAfh\n7WjkyYHn4NAbMGRBduehYPbsafCOB6Ke6aMnz1Z1vIRIrWxEliTBEZA/721JIrUyScqfaYsj6pdT\nLaJB2vZD4Rh1J9fZNCQv1H8a77td87I+hzgmE63OufRzPw1AaXAD8P3sjB32iQpzACaLqDynJ7Id\nqeVRVj7UKPUwRrniXFX3YXbHblakHhZPyr64lxCppWHBDbU48G+RVgpQOj37kXAGPeKcc87h7bff\npqCgAACPx8OCBQv48MPUvpfm7jaYO3cud999Nx6Ph7feeoslS5bEXeoNDAwMcgophMsnV2FS5tZL\nZjv1xVfIz+3+WxYnZpAr2IPqeGoBtDlnI0V/lp3+zVgj7oweX3VadsgXkFHMBCnwfqDakLHIFeW/\nbFQOy0vw1Mry3XWAAafDqCvl/o7UQ/RVQxnhka/uQjWoB1FLknD6ZVErWaSWZuggBbEk+IWIEgO8\neZMJWbMsALdDKar1C25IvmGmUVbodA4GtSqDpkvWI7V0YBIfQylq1a+HcBZLcyb4aR2fvXFjJBjl\n98FIrSpF1cMRhkG83vH5fHFBC6CwsBCPx5Py/t1e+d177738/e9/Z8qUKTz00EMsXLiQa6+9Nr3Z\nGhgYGGiI078ViyROkD7zAByuocRDC4C64msY2LhcdA6+iK34hxrM0kALkqX1tLTIhVFMEY8sZpgs\neM1DyGT8UcRciMc+nXz/RkxIlIQ2A/N6PGfNaPqi04cLPe/gzj9blSG1qhymNPL3mQegSWLX1Dth\n7+OiXbMWgm6wFWoxE1E1TBl9obKo1d5Tq2hg9r8L1nAt1kjUZdmaj888kIKud8kuBaPltCqNRK1+\nIflvctQ8Of430uo85Vb4apWGPodIODsCkzL1UG9+WqBBpJYOTOJjOAeK70rrHnFTpvFzKM9SWmr7\nSK1s4+rDnlreGqh5W+6PuFS7uRikRH5+Phs2bKCiogKA9evX43SmXke5218Vi8XClVdeyaxZszCZ\nTEycONFIPzQwMMhJlKmHzdbJONo977OfQKvjFAp8H0MkyBD/q7RxVnYnaaAJycSRN96QF2WOgMIE\nPX8UkinzC7NW12nk+zcC0UVXF+iiFLySRlnUcltGUxgWC+kizzu9qHWmTxIjtTQStfJHQMk0ISZG\nAnDk7ez6wSgJNsneMNZ8sJWoO5xVGWFwmNmzJoM5u0KJ0iSeoklg6jb5IbvoIFKrKCS/RyNmXsyI\n4yo0mUcMv208QctAbOEabJIbmjZDvxnqD6wUivTmpwXCqN1sF6JOsFl9gVwZqeXSOFILRLRW7DtS\n91H2RC0tKx9CosDqrRY3J/rKGn//v0TqNYjIZq0jAg26Zfny5VxyySUMHjwYgMOHD/PPf/4z5f27\n/QV+9dVXGTt2LLfccgtLly5lzJgxvPbaa+nP2MDAwEAjlKJWk/WETrepK5YjUYf5XxI/8gYGJKYe\nUqhOtTWlr1ZJsGtRS3coIrX2O76DhLg4dvnWYw439+rQ7dMMNY1Ik4JYwzXRjgl7yUDt5jb0m3K7\n+pXsjduetnYeOb1YGKWSUiqZ7QQt0bRPKQy+mk6OpC5Kk3iKNUgd6g4diFqFIcU5s2S6JnNIwGRK\n9PU6ui4743p1bBIP4vvqymJKbxZTlVNCK1+tFo0jtWxF4iYEQNgrV6TsCxiphznHSSedxFdffcWD\nDz7IX//6V7Zv387MmTNT3r/bK69bb72VtWvXMnasqMK0e/duFi5cyMKFC9OftYGBgYEGKCsfNicR\ntRqLvsOwmluw4qEgvBeXfyMeh7Z3lw20Q5niF6tMKDrjoD7z4ylFreLQVginXvlFcxSRWg22Cjz2\nE8n3b8BEhELvuxwl/Yp4eopKs4WOYCIqdjsGcMqc1C+6Ms7QRbD1btE+9Kq4M61SxFCX6a6edtXM\nekGqf+ugdRi2cNR821OdmEqTBRIitYqPh5asDt89Gota1tAR7JKcnqmFUX1nuJ1z6eeO3v0/uk5U\nE1UbpUikx/RDEN/b1t2i7Tmgrsji0WGkVoy6j7Izpq8W/NGLCItLm/fBZBK+Wu6o+Oyphjx1o2yz\nQuseqP9YtE1WOO5ibedjkBKPP/44JpMJKRpMsHGjyFq44oorutotTreiVlFRUVzQAhg9ejRFRUXp\nzNXAwMBAMyyR1vgiRMJMi7XzC7aIuYCjeaczJPA6AEVtbxqi1jFMwgL7kwches2vlqgVsg7CZxuL\nI7gLCwFoWA8dEmV1iO8o+KL+dBYnHvNw3K4zyPdHBUHPO2DOUqUxlVGmHiYY7WpB2UmiDLy/VkQr\n1a9XLXWmS7GpLXt+WjEC1mG4/JtEx3sQyFLKUJTE9EM9RmopRKSmzdC8LasRZS6/wmOveEqPxdb2\nImqmIiBbEyK13lVVCI7j0XmkFmQvUkuKtEvH1IGoVTJVCEthjxD0siGSJ6QeTtQufdk5VBa1vNVQ\nMlmbeWSSqqfl9uBzwW5UMs8FPvvss7jFlc/nY82aNZx44omZE7UqKipYuHAhl1xyCQDPPvssM2fO\n5IUXXgDgoosuSnfuBgYGBlmjOPxVPLrCa59C2JTc4rs+75QEUetI2U+zMkcDneNWP/0QoNV5Go5g\n1L/r6HuAOibrGUURpUXJFMCC2zmfQY1/AKDQsxYKei5qWSQPbL1XXPQPvyBDk+0dtpCOTJ9NZhh6\nPuxZKfqHXsmeH4wSDSIvAjZtKyB2TD9syvocusQ5EPqfJszipRB8ch2c/V7WFs9O/2a5U9rzSEu1\nojN9eRMJWgZgCx+FQAM0fQmlU1UZK06CiKPjSK0YalZA9B0VHoAAeaVg00F5BbMVyk6Go5WiX/eR\n+tE9zcrzhwaphzH6mlm8JEHVk3J/pJF6mCusWLEiod/U1MR3vvOdlPfv9pfN5/MxYMAA1q1bx7p1\n6+jfvz8+n4+XX36Zl19+ueczNjAwMNCA4tCX8XabY1aX29bb5OfzvR9gjrSpNi+DHEIpahWpKGq5\n5BREat9XbZyMoqx8WCIWo62uU5Gi985c/i+wRXq+6J/Y9gf44qfw3oWiKpUOsIUUtvd6WKAOUfpq\naXRd1pZ9j5ygVTtRyxKuxxb1VQtjh/yRWR0/ZU76i0i/AeEVtOvhrA3t9CnPCSqLRj3BZKLVebrc\njwkZauLVuVE8ZC9SS0+VD5UoUxBrs+CrlVD5UMNIzwSz+D5Q0sW9S46Cs+bDsMXazscgbVwuF3v3\n7k15+24jtVauXNmb+RgYGBjoguKgQtRyzoJg8m395v5QfAI0f4mZIAWedbQUGD6CxzTBVvAeFm2z\nLXox3qjKUK3O0+RO7QdQGFFlnIyijNQqnQZNEDEX0uY4mQKfWCD0C64Hzkz5kJZQHUP8q+UHjrwN\npdqbTefpKVILYPA54jMZCQrhz3Mw+wtnLSK1uhO1pAg0bYGiCWDJbAqvwy8vSNssIygyWzJ6/IxR\ncgIcf4fsu/b5HTB0MbgynzbbPl1wvOdj+ck0IrXUxO2aR2nrc6JzdB1MuEW9waSw/NsB2qcsJyNb\nkVoapCqnRLZ9tbSufBhD+Xn09oFIrXrFeaf/6bIRvoHuWbRoUbwdiUTYtm1bPFMwFboVtfbs2cP9\n999PVVUVoVAIAJPJxKpVq9KYroGBgYEGSBIl7SO1gr6u9xl0NjSLfYo8bxmi1rFO6y65nT9KpCuo\nhN82Vk6PCTZREN4NnKTaeBmhk0gtAHf+/HaiVuqUt6zErFSfGzYk3ziLJKQf6iFSy1YIA+bBkbdE\nv/oVGHdDduegQfRF0KpMm2knagXdUHm+SL3rfxqcVdmjtLvu/JycCj+tVstodO00e8L/iPL27p0Q\nbIENS+G05zM+TEK6YNgP/9on9/UUqQW4XXPljsq+WnapQQhbAPbyjAusGSNrkVo6M4mPoRS1GjeK\nz7DFrt54ehG1+lr6YZ1cEIryrrMyDPTFbbfdFjeJt1qtjBgxguHDUz9HdHtVfsEFF3DttdeyaNEi\nzGZxwjf1olSzgYGBQdbxHMAuCVfvsLkQX95EoJtUpsHnwI4/AcJXy+AYJ0t+WkA0PeY0SlvFZRf2\nkgAAIABJREFUwrM09DleLlV3zN4Q9iemUpROBcT71eKaz+D6uwDoF/wMSNEAWopQ3vRQ4mP1PRPF\n1EJXnloxhn5TO1ErEm7nGZSdKLEETy1lhEHIA+sWCUELxP81a2FQ6lGC3fk5KU3i2yyjUj6uJlgc\ncPLDsOYM0T/wAhx4UV2PuuZtwscLRBVGW6F6Y6WBL+94AqYS8qQm8NdB89aoF2DmsUeOyh29nC86\nI2uRWtlPVU4JR7n4bXfvFJ5fDRuh/+zu90uHoFs+Z5qs2lYGTUg/7AOiVr1C1CozRK1cIRQK8ctf\n/pLKysq0j9GtqOVwOLjlFhXDcg0MDAzURvEj1+Y4CUwppIoMOJ0INswEcQa2YQv2gR97g/TJpqgF\ntLoUolZwE17VR+wFLV/JC9j8UWCT41baHLOJmOyYJT8FkX3gqU7JALrQs1Y2y4/RugsCTZqUHFcK\ncRN9u+Un9BCpBULU2vAj0a5ZI4Qda/JiGBnFd0QRidIfrM6sDNshUkuSxGL03QtFSpmS3X/rkajV\nHUqT+FbL6IwdVzUGzoPRV8OeR0V//X/AoPkJ39WM0qQwiddZlBYAJjMNtgoGBdaI/oEXVBO1HGGF\nqKVXPy0QFeIsDgj7RERfsEWdz4deI7VARGvFfuvrPlRP1GrZLrcLx4n0ca1Q/oa17c9ONVC1CPsS\no8bLNCiacgxx9dVX8+qrrzJgwAC2bNnS6Ta33HILq1evxuVysXLlSmbMmNHpdlarFYvFQlNTEyUl\n6V3jdfupXbp0KcuWLeOjjz5i48aN8X8GBgYGOUOdUtRK8c6N1UWjVfbvKfK8lelZ9YrYIjv2L1Pl\nzg2SkG1RyymbxZeGPhcLdr3S3k9LgWR20Or8hvxAzdqUDtm/6a+dP9GgzfXH7NnTWLCgggXnnEi+\nuV5+Qi+iVsFo2Ww47IMja7I3dps2i9SIuYCgKRoBFAkI36L3L4EjnUTWHngB/PUdH08TZaRWq1Xn\nkVoxZvweHANE21sNn/9MvbEaO09H1hNH8hRVZff+Q7VzrCOSI6KWyQTOLKQg6tUoHrLnq5VgEq9h\n6iEITy1r9Dzqr4X9z2k7n97QsEl4S4K4TrP303Y+fZwf/OAHvP7660mff+2119i1axc7d+7k4Ycf\n5sYbb+zyePn5+UyZMoVrrrmGpUuXsnTp0h4FVnW7Ctq6dStPPPEEa9e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tSrabQW/49IfC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\niIhI2FFQS0REREREREREws7/A6bCXyE34A49AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1730da5610>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Correlation between TSI and Temperature: 35.2%\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(csv_data[[\"Year\", \"Average\",\"CO2\"]].dropna()[\"Year\"],\n",
      "        csv_data[[\"Year\", \"Average\",\"CO2\"]].dropna()[\"Average\"],\n",
      "        width=0.7, edgecolor=\"none\",\n",
      "        color=(csv_data[[\"Year\", \"Average\",\"CO2\"]].dropna()[\"Average\"]>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.2)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "\n",
      "\n",
      "co2_ax = plt.twinx()\n",
      "co2_ax.plot(csv_data[\"Year\"], csv_data[\"CO2\"], linewidth=3, c=\"g\", alpha=.8)\n",
      "plt.ylabel(u\"CO2 CCGG (In Situ) ppm\")\n",
      "\n",
      "plt.legend(loc=\"best\")\n",
      "plt.grid()\n",
      "plt.xlim(np.min(csv_data[[\"Year\", \"Average\",\"CO2\"]].dropna()[\"Year\"]),\n",
      "         np.max(csv_data[[\"Year\", \"Average\",\"CO2\"]].dropna()[\"Year\"]))\n",
      "\n",
      "plt.show()\n",
      "print \"Correlation between TSI and Temperature: %s%%\" % (round(1000*pearson_def(\n",
      "                                        csv_data[[\"Average\",\"CO2\"]].dropna()[\"Average\"].values,\n",
      "                                        csv_data[[\"Average\",\"CO2\"]].dropna()[\"CO2\"].values))/10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yD6wIWIFVM1dhissUZJ86xXCLaIwzGHA1NTX1uVOiIAhDsoOiUqmEr6+vvu3j\n44OMjIwefYqKitDV1YUlS5agqakJTz75JDZu3DjoaxMREREREdHoUtNRj+O30nC0+ivcbKvqdVwi\nskKYUxAeS3gSi3wXwUpsNQxVEtFwMRhw7d6926wXHkhI1tXVhfPnz+PMmTNobW1FVFQUIiMjERAQ\nYNbaiIiIiIiIaPh16TQ4W3cBR6u/wjd1udBB6NVnuswHqzwXY/nERbiu7kLolJhhqJSIhpvRNbja\n2trw97//HXl5eWhra9MHUwcPHhzUheVyOcrKyvTtsrIy/a2It/n6+sLDwwN2dnaws7PD4sWLkZub\n22fA9f1ALi4uDnFxcYOqj4iIiIiIiIZHcUsZPqn+CsdvfY2GrqZexx2s7JAwMQqrPBcjyGGa/vvU\n6+C6zUSjmUajwfHjx1FaWgqNRgNg4MtkGQ24Nm7ciMDAQJw8eRIvvPAC/vd//7fHOlmmCgsLQ1FR\nEUpLS+Ht7Y0jR47g8OHDPfqsXr0aO3bsgFarRUdHBzIyMgy+KHPPOCMiIiIiooHJPXcOGrX6js+T\nODlhXlSUGSqi0UCpVuLLG1/i3eKDqO6q6LNPuEsQVnkuxhL3MNha2Vi4QiIyt5UrV8LOzg7BwcEQ\ni8V3dK7RgKu4uBj//ve/kZycjE2bNiExMRHR0dEmF6u/sESCN998EwkJCdBqtdiyZQsCAwPx1ltv\nAQC2bduGWbNm4d5778XcuXMhFovx2GOPISgoaNDXJiIiIiIi89Go1Qg1YYOobO6aPq4IgoACVQFS\nS1OReiMVRbVFAIDWNjVkku++VfWyccdKzxjcPzEGcruJw1UuEVmAUqnExYsXTTrXaMBlbW0NAHB2\ndsalS5fg5eWFmpoaky72Q8uXL8fy5ct7PLdt27Ye7V27dmHXrl1Dcj0iIiIiIiIaPlpBi5zGQqTW\nZiO58hxab2r67GctlmKJeyhWecYizCUQViIuGE80HsTHx+PUqVNISEi443ONBlyPPfYY6urq8Lvf\n/Q6rVq1Cc3MzXn75ZZMKJSIiIiIiovGlXduBjIbLSK3Nxtm6HP2aWq0aDWR2Tvp+1lbWiJBHwKfe\nGVunLoGz1GG4SiaiYbJo0SKsWbMGOp0OUqkUQPcaXOoB3PY+oIALAGJjY3H9+vVBlkpERERERERj\nXWNXM9LqcpBSm4X0+kto13X22c/RxhExk2MQ5xeHSJ9IyKQyZJ86xXCLaJx6+umnkZ6ejjlz5gz9\nGlz19fX9CdBeAAAgAElEQVT45z//2WsF+3379plWLREREREREY05Ve0qpNZmI7U2GxfUBdAKuj77\nTbR2RZx7KDwl/vjJmp9BIjb6bSkRjROTJ0/G7Nmz7zjcAgYQcN13332IiorC3Llz9Vuv3v6diIiI\niIiIzMPU3SgBy+xIqRN0KKkrwce3PsWfywpxtbnUYN9pMjni3EMR6x6CIIdpEIlEyFapGG4RUQ9T\np07FkiVLsHz5cv2a8CKRCE8//bTRc43+bdLR0YE//elPg6+SiIiIiIiIBszU3SgB8+xIqdVpUVBb\ngAuVF3C+8jxyqnPQ2N6IVnXPXQ9vm+vk3x1quYViimzSkNdDRJbX3t6O2NhYdHR0oLOzE6tXr8ae\nPXvw8MMPo7CwEADQ0NAAFxcXXLhwAQCwZ88eHDx4EFZWVti3bx/i4+MNjj916lRMnToVnZ2d6Ozs\nhCAIA55kZTTgSkxMxIEDB7By5UrY2Njon3dzcxvQBYiIiIiIiGj06dB0IK8mDxeqLuBC5QXkVuei\ntavVYH+JyAoLXWYjzj0Ui91D4GHtYsFqicgSbG1tkZKSAplMBo1Gg+joaKSlpeHIkSP6Prt27YKL\nS/fnPy8vD0eOHEFeXh6USiWWLVuGwsJCg7cg7t69GwDQ2NgIkUgEJyenPvv1xWjAZWtri2eeeQa/\n//3v9QWIRCJcu3ZtwBchIiIiIiKika21qxWXqi/hfOV5XKi6gMu3LqNT2/fi8Le52rlirsgf63xj\ncJfrPNhL7CxULRENF5lMBgDo7OyEVqvtMQFKEAR88MEHSElJAQAkJydj/fr1kEql8PPzg7+/PxQK\nBSIjI/scOzMzE5s3b9bvmuji4oK///3vCAsLM1qX0YDrtddeQ0lJCTxMnBpLREREREREI09jeyNy\nq3P1gdZV1VVoddp+z/F08ESIVwgWTFqAkEkhmOI8Bec/+8zkWymJaPTR6XQICQlBSUkJtm/fjqCg\nIP2xs2fPwtPTE9OnTwcAVFRU9AizfHx8oFQqDY69efNm7N+/HzExMQCAtLQ0bN68GRcvXjRal9GA\nKyAgAHZ2TOGJiIiIiIhGK0EQUNVchUu3LuFC5QVcqLqA4rpio+dNcZmCBV4Lun9NWoBJDpO46RjR\nOCcWi5GTk4PGxkYkJCQgNTUVcXFxAIDDhw8jMTGx3/P7+ztEIpHowy0AiI6OhqSPNf76PNdYB5lM\nhvnz52PJkiX6NbhEIhH27ds3oAtYyve/oKmpqQDANttss80222yzzTbbbA9DOys3F03Ozoj79paS\n1Kys7uNG2o5+fiOiflPbjsAdvd7b7azcXDTZ2AzZeJ9mfINvVNdQ6tuMoroinP3yLJRNSkimdX/7\n11TQ1D3+TMcebadZTghwC4BThRMC3APwyAOPwF3m3l1PBeA9w7vP1z/U77ep4+nbfbw/Bbm5CF26\n1KTxhvr9GerxUrOyUNDYiNCEhL5fvwnjAUP//vD9HprxzP1+v/7668jJyYHft+3+ODs7Y8WKFcjK\nykJcXBw0Gg0++ugjnD9/Xt9HLpejrKxM3y4vL4dcLjc4ZmxsLLZt24b169cDAI4cOYLY2Fj9mCEh\nIQbPFQmCIPRX8DvvvNPd8duE7fYK9ps2bTLyUi1HJBLByMsgIiIiIiILyT51yqRb1rJVKv03baPR\nUL9uY+NpBS3K2qpR1HITxS3lKG4tQ1HLTVS0q9Cq0UBmZHFmK7EVAj0CETIpBAu8FmCe1zw42Qx8\nQeeB1mnwPBNf92gfczTUaI4xR0ON5hhzNNTY35g/zFtUKhUkEglcXFzQ1taGhIQEvPDCC1i6dClO\nnjyJP/7xj/r1t4DuReYTExOhUCj0i8wXFxcbnMUVFxfX49gPd1H8/tg/ZHQG1yOPPIKOjg79do+z\nZs2CVCo1dhoRERERERENkdrOBpS0lKOopTvEKm4tx7VWJTp1XQMew8nGCTPcZ2C+13yETArBnIlz\nIJPKzFg1EY01lZWV2LRpE3Q6HXQ6HTZu3Iil386WO3LkiH7m1W1BQUFYu3YtgoKCIJFIsH///n5v\nUbw9I80URgOu1NRUbNq0CVOmTAEA3Lx5E//4xz8QGxtr8kWJiIiIiIiGU+65c9B8u0vXnZI4OWFe\nVNQQV/SdhvYGnK88j2OV/8HBynoUtdxEXdfAa5WIrOBjOxER/jHwd/NHgHsA/N38MUE2getnEdGg\nBAcH97gF8fsOHTrU5/NJSUlISkoa0PgqlQovvvgi0tLSIBKJEBMTg9/+9rdwd3c3eq7RgOvpp5/G\nZ599hpkzZwIACgsLsW7dOoMviIiIiIiIaKTTqNWDupVnKDVrWpByPQVZFVnIrszWL/7eqlZDZmRx\n5YnWrvC390WAvS/87X3hL/OFn8wbF+saEHr36L3dk4jGp3Xr1iE2Nhb/+c9/IAgC3nvvPTz88MM4\nffq00XONBlwajUYfbgHAjBkzoNFoBlcxERERERHROKXuasYFdQGyGvKR3ZiP3MZrsFP2v/aVrdi6\nO8Cy90GA/WT4y7oDLWepg4WqJiIyv6qqKvzmN7/Rt3/961/jyJEjAzrXaMAVGhqKrVu3YsOGDRAE\nAe+++y7Cvl1tn4iIiIiIiPrXrGnF+caryG7MR3bjVRQ034CA7xZt/uF2WRKxBEETguBhY4d7vYIR\nYO8Lue1EiEViyxZORGRh8fHxOHz4MB5++GEAwIcffoj4+PgBnWs04Prb3/6Gv/71r9i3bx8AICYm\nBj/72c8GUS4REREREdHY1aJpwwV1AbIb8pHVmIeC5hvQ9YqxviOGGHMmzkHopFCEeYdhntc8yKSy\nQe2IRkQ0Gh04cACvv/46Nm7cCADQ6XSwt7fHgQMHIBKJoO5n7USjAZetrS1+8Ytf4Be/+MXQVUxE\nRERENAgjeYFwGn+0Oi0yKzLxf1XJ2Fd+E/lN140EWiIEOk5FqHMgwpwDoetyR/R9qy1YMRHRyNTc\n3GzyuUYDrrS0NLz44osoLS3Vr70lEolw7do1ky9KRERERDQYI2mBcBq/WjpbcLTgKI5cOYJydbnB\nReHFEGGmw5TuQMslCPOdZsBBItMf559JIqLBMxpwbdmyBa+//jpCQkJgZWVliZqIiIiIiIhGrJrO\nWvz53J+RXJCM5s7esw1E+kBrFkKdA7HAeSYcJfbDUGlvnP1IRGOV0YDLxcUFy5cvt0QtRERERERE\nI5IgCLjYVIT3lCdxrCoddpWOPY472TjhLvf5eFC+EPOdZo7Y3Q05+5GIxiqjAdeSJUvwzDPP4MEH\nH4SNjY3++ZCQELMWRkRERERENNw0Og3OqDJxuOIULjeVAECPHRD9XPywfs563BdwH/K++Aqh7lwU\nnohoMFpaWlBWVgaRSAQfHx/Y2w9sBqzRgCs9PR0ikQhZWVk9nk9JSTGtUiIiIiIiohFO3dWMj6pS\n8UHl56juqOt1PEIegcTgRET5RkEsEg9DhUREY0dTUxPefvttvP/++1CpVPD09IQgCKiuroa7uzt+\n8pOf4LHHHoODg+HZsUYDrtTU1F7PVVVVDapwIiIiIiKikehGayXerziFT6rPol3X2eOYtViK5RMW\nIVi2EA+seHSYKiQiGnseeOABrFu3Dp988gk8PT17HKuqqsLRo0exevVqnDlzxuAYRgOu2xoaGvDv\nf/8bhw8fRn5+PioqKkyvnIiIiIiIaIQQBAFZjXl4T3kSZ+tyeh13kzrhx5OW4seTlsLN2plrURER\nDbH+gisvLy88/vjjePzxx/sdo9+Aq7W1FcnJyTh8+DBycnKgVqvx8ccfIyYmxrSKiYiIiIiIRohO\nXRdO1ZzDe8qTKGop63U8wN4XifJ7Ee8RCRsr62GokIhofPnyyy8hEokgCAJEIpH++cWLFxs912DA\ntX79emRkZCA+Ph47d+5EbGws/P39ERcXNyRFExERERERWZogCKhqrsJH1Z8iqSQDtZ2NvfrEuM1H\novxehDkH9fgGi4iIzOuVV17R/73b3t4OhUKB0NBQfPHFF0bPNRhw5efnY+LEiQgMDERgYCCsrKyG\nrmIiIiIiIiIzEgQBNa01uFZ/Ddfqr6GkrgTXGq7hev11NHc2o1Wthkzy3bdDtmJrrPSMwTrvBEyR\nTRrGyomIxq9jx471aJeVleHJJ58c0LkGA66cnBzk5+fj8OHDWLJkCSZMmICmpiZUVVXBy8trcBUT\nERERERENAUEQUNtW+12IdTvQqi9Bc2ez0fMnWrviYe94POAVB2ep4d25iIjI8nx8fJCfnz+gvv2u\nwRUYGIiXXnoJL730ErKysnD48GEsXLgQPj4++Oabb4akWCIiIiIiImMEQUBdW50+wLodYl2rvwZ1\nh/qOxnKycYKfvRce87sXSz3CIREPeO8tIiIyo//3//6f/rFOp0NOTg5CQ0MHdO6A/yYPCwtDWFgY\nXnnlFZw9e/bOqyQiIiIiIhqgTm0nLlVfQoYyA7lVuSipL0FDe8MdjeFg7YDprtMxzXUaprlOw3S3\n7sfudu44/9lnCPXwMFP1RERkirCwMP1jiUSCxMRE3HXXXQM6945/VCEWixEbG3unp/Xp5MmT2Llz\nJ7RaLbZu3Yrnnnuuz36ZmZmIiorCBx98gAcffHBIrk1ERERERCOHIAgoa6/A1YvvIkOZgfOV59Gu\naR/QufbW9t0hlst3IdY012mYIJvAReKJiEaR+vp67Ny5s8dzb7zxxoDW4Rq2ubharRY7duzA6dOn\nIZfLER4ejlWrViEwMLBXv+eeew733nsvBEEYpmqJiIiIiGio3eqog6LhCjIaLkPRcAVlrbWQVTsZ\n7C+TyvThlX5Wlut0TLSfyCCLiGgM+Mc//tEr4Dp06NDIDrgUCgX8/f3h5+cHAFi3bh2Sk5N7BVx/\n+ctf8OMf/xiZmZnDUCUREREREQ2VFk0bzjdeRUbDZWQ0XMb11op++/s4+SBCHoEInwgEegTC08ET\nYpHYQtUSEZGlHD58GO+99x6uX7+OlStX6p9vamqCu7v7gMYwGnBVVVXhV7/6FZRKJU6ePIm8vDyc\nO3cOW7ZsMb1yAEqlEr6+vvq2j48PMjIyevVJTk7GF198gczMTP5UhoiIiIhoFNEKWlxpuoaM+u5A\n63JTCTSC1mB/Byt73D1tGSLkEVgoXwi5k9yC1RIR0XBZtGgRJk2ahJqaGuzatUt/B5+joyPmzZs3\noDGMBlyPPPIIHn30Ufz+978HAAQEBGDt2rWDDrgGElbt3LkTe/fuhUgkgiAI/d6iuHv3bv3juLg4\nxMXFDao+IiIiIiK6M4Ig4GbjTXxe+xXeu3UD2Q35aNa2GewvFUkw33kGIlzmIMJlDprb7RG+bLkF\nKyYiojvR3t6O2NhYdHR0oLOzE6tXr8aePXsAdN+Bt3//flhZWWHFihX44x//CADYs2cPDh48CCsr\nK+zbtw/x8fG9xp0yZQqmTJmC9PR0k2szGnCpVCo8/PDD2Lt3LwBAKpVCIhn8nY1yuRxlZWX6dllZ\nGXx8fHr0yc7Oxrp16/R1fPrpp5BKpVi1alWv8b4fcBERERER3ancc+egUatNOlfi5IR5UVFDXNHo\n0KXtwoWqC0i7mYazN8+irLEMrWo1ZAa+Z5hhP7k70HKdg/lOM2BrZaM/lt2hslTZRERkAltbW6Sk\npEAmk0Gj0SA6OhppaWno6urC0aNHcfHiRUilUtTU1AAA8vLycOTIEeTl5UGpVGLZsmUoLCyEWNzz\ndvO77roLX3/9NRwcHHpNiBKJRFAP4N9no0mVg4MDamtr9e309HQ4OzsP6IX3JywsDEVFRSgtLYW3\ntzeOHDmCw4cP9+hz7do1/eNHH30UK1eu7DPcIiIiIiIaLI1ajVAPD5POzVaNr2Cmrq0O35R9g7M3\nziJdmY6WzhaDfT1t3PQztMJdguBmPfjvJYiIaPjIZDIAQGdnJ7RaLVxdXfHSSy/h+eefh1QqBQBM\nmDABAJCcnIz169dDKpXCz88P/v7+UCgUiIyM7DHm119/DQBobm42uS6jAddrr72GlStX4tq1a1i0\naBFqamrw73//2+QL6i8skeDNN99EQkICtFottmzZgsDAQLz11lsAgG3btg36GkRERERENHiCIKCo\nrkg/S+vyrcsGlw+xk9phltMUrJoUjgiXOZhs58W1dImIxhCdToeQkBCUlJRg+/btmD17NgoLC/HV\nV18hKSkJtra2ePXVVxEWFoaKiooeYZaPjw+USmWvMVtbWyGRSGBtbQ0AKCgowPHjx+Hn54cHH3xw\nQHX1G3BptVp89dVX+Oqrr3D16lUIgoCZM2fqLzhYy5cvx/LlPe+xNxRsHTp0aEiuSURERERExnVo\nOpBVkYWzN88i7WYaqpqrDPb1dvRGzOQYxEyJQcikEFw6nWLybDgiIhrZxGIxcnJy0NjYiISEBKSm\npkKj0aC+vh7p6enIzMzE2rVre9yV9319/dAjISEBBw8eREBAAIqLixEZGYkNGzbg+PHjUCgU+mWz\n+tNvwGVlZYX33nsPTz31FObMmTPAlzo8UlNT9QvLp6amAgDbbLPNNttss8022ya0c8+dQ/rZswCA\nsG93LsrKzR1QOzImBvOiosxeb1ZuLpqcnREXFtZ9PCur+/hA232MX5Cbi9ClS00aLys3F002NiPi\n/RtMOyg8CGk303D4k8O4qroKG//u9bGaCpoAAI4zHQEAzYXNmO46HQ/f/zCiJ0fjRs4NiLpEiPSJ\n/O7rYcL74+jnZ7HXa473u/urM/zjpWZloaCxEaEJCX2/fhPGAwy/P0P9fo+Wzzffb77fwzGeud/v\n119/HTk5OfD7tt0fZ2dnrFixAllZWfDx8dHPtAoPD4dYLIZKpeq1/np5eTnk8t475DY0NCAgIAAA\n8I9//AOJiYn4y1/+gs7OToSEhAwo4BIJ/W1NCOCpp55CV1cXHn74Ydjb20MQBIhEIoSEhBgd3FJu\n77JIRERERIOXferUoNaiuv2fbnMyR42j4XUPNZ2gQ35Nvn6W1lXVVYN9HW0cschnEaInR2OR7yI4\n2xpeS8vUr6Ulv44j6c+QJf9MjqQxR0ON5hhzNNRojjFHQ43mGHM01NjfmD/MW1QqFSQSCVxcXNDW\n1oaEhAS88MILKC4uRkVFBV588UUUFhZi2bJluHnzJvLy8pCYmAiFQqFfZL64uLjXLK65c+fi4sWL\nAIBFixbhmWeewZo1a3od64/RNbguXLgAkUiE3/72tz2eT0lJMTo4ERERERGNHIIgQNmkhEKpgEKp\nQFZFFhraGwz293PxQ8zkGERPjsY8r3mQiAe/mzoREY1elZWV2LRpE3Q6HXQ6HTZu3IilS5di8eLF\n2Lx5M4KDg2FtbY1//vOfAICgoCCsXbsWQUFBkEgk2L9/f5+3KAYHB2PXrl3w9vZGSUkJ4uPjAQD1\n9fUDXsfR6L9Qt6e7ERERERHR6FPbWovMikxkKjOhqFCgsqnSYF+JWILQSaGInhyN6MnR8HX2tWCl\nREQ00gUHB+P8+fO9npdKpfjXv/7V5zlJSUlISkrqd9y3334bb7zxBm7cuIHPPvsM9vb2AID8/Hzs\n2rVrQLUZDbhefPFF/ZS076dmP5zRRUREREREw6+lswXZldnIVGYisyITxXXF/fZ3s3PDXb53IWZK\nDCLkEbC3trdQpURERN1kMhmef/75Xs8vWrQIixYtGtAYRgMue3t7fbDV1taGY8eOISgo6A5LJSIi\nIiIic+jUduJS9SUolApkVmTiSs0VaHVag/1lUhkWeC1AhE8Ewr3DMd1tOsQisQUrJiIiGnpGA64f\nTgV75pln9PdCEhERERGRZWl1WhTWFiKzIhMKpQI5VTlo17Qb7C8RSzDXcy7CvcOxUL4QsyfO5lpa\nREQ05tzxv2wtLS1QKpXmqIWIiIiIiABodBrUttaiprUGt1puQdWqwq2WW7jRcAPZldlQd6gNnisS\niTDTfSbCvcMRLg/HAq8FsJPaGeyfe+4cNGrD4/VH4uSEeVFRJp1LREQ0lIwGXMHBwfrHOp0Ot27d\n4vpbREREREQmEAQBjR2NqGmpQU1rDWpaegZYNa3dz9e11fXYlt0YX2dfLPReiIXyhQjzDoOzrfOA\nz9Wo1YPaVp6IiGioFBQU4NVXX0VpaSk0Gg2A7h/cfPHFF0bPNRpwHTt2TP+Pq0QigaenJ6RS6SBL\nJiIiovHO1FkjnDFCI5UgCKhrq0NVcxUqmytR2VTZawZWTWsNurRdg76Wh8xDP0NroXwhvBy8huAV\nEBERDa+HHnoI27dvx9atW2FlZQUAPTY87I/RgOvXv/51r60eN27caHD7RyIiIqKBMHXWCGeM0HDR\nClrUdNSjskOFynYVqjpq9Y/zmyrRrnwJHZqOIbmWSCSCm50bJsgmdP+y7/59ov1EBHsGY6rL1AH/\nh5+IiGi0kEql2L59u0nnGg24Ll++3KOt0WiQnZ1t0sWIiIiIiEaqLl0XbrZVoapd1R1cdahQ2V6L\nqg4VKtpVuNVZB62g6/PcVo0GMhunAV3HwdpBH1jdDq8m2k+Eh8wDE+0nYoJsAtxl7lwInoiIxp2V\nK1fir3/9Kx588EHY2Njon3dzczN6rsF/Nf/whz9gz549aGtrg6Ojo/55qVSKxx9/fJAlExERkblw\nwWii/ukEHcoay1BUV4TC2kIU1RahsK4QJcoCyCSDC5UcrB3g7eiNSQ6T4OXgBU8HT/3MKw+ZBybY\nT4BMKhuiV0JERDS2vPPOOxCJRHj11Vd7PH/9+nWj5xr8FzwpKQlJSUn45S9/ib179w6+SiIiIrII\nLhhN9J0WTRuKW8tQ1HwTRa1lKGy+iZzGa7C6aWvSeO7Wzphk4wEvG3dMsvHAJNvux7WtVrjnvrVw\nsHYY4ldAREQ0fpSWlpp8rtEfUe3duxf19fUoKipCe3u7/vnFixebfFEiIiIioqEkCAIq2mtQ1HIT\nhS03UdRShqKWmyhvv9Wrb4dOAxl6B1wiiHoEV/og63uPbays+7x+tqBiuEVERGSi//u//+t3bckH\nH3zQ6BhGA663334b+/btQ1lZGRYsWID09HRERUUNaItGIiIiIqKh1q5pR3FrKW5UXkSRPswqQ4u2\nbcBjuNm5IcAtADPcZyDAvfv32owCREzkboRERESW9sknn5g/4HrjjTeQmZmJqKgopKSk4OrVq3j+\n+efvrFIiIiIiIhOoO9QoUBWgoLYABaoCXK29ihsNN9Dc2DCg9bIkIiv4ySYhwH4yAux9McN+Clra\n7bH0/od69W0Ul5jjJRAREZER77zzzqDHMPq/AltbW9jZ2QEA2tvbMWvWLBQUFAz6wkREREREtwmC\ngJrWml5hVmVT5YDHcJE6fhtiTYb/t79PlclhLZb26Me15oiIiEaWd955Bxs2bIDEwA+vOjs78e67\n7+LRRx81OIbRgMvHxwf19fV44IEHcM8998DV1RV+fn4mF01ERERE45tO0KFcXa4Ps66qrqKgtgD1\nbfUDOl8kEmGSjSci3AL0gVaA/WRMsHbt9/YGIiIiGpmam5sRHh6OWbNmITw8HF5eXhAEAVVVVcjK\nysLVq1fx2GOP9TuG0YDr448/BgDs3r0bcXFxUKvVuPfee4fmFRARERHRmKbVaXG94Trya/L1YVZR\nXRFaOlsGdL7USorprtMx030mZnnMwkyPmQhwC0DeF1+ZvFsoERERjSw7duzAE088ga+//hppaWlI\nS0sDAEyZMgU7duzAokWLjP4Qq9+AS6PRYM6cObh69SoAIC4ubmgqJyIiIqIxqVXbhnNl53Cx+iIu\nVl/E5ZrLAw6zZFIZZrrPxEyPmfrfp7pMhdRKavxkIiIiGtVEIhGio6MRHR1t0vn9BlwSiQQzZ87E\njRs3MGXKFJMuQERERERjkyAIKGuvxkV1UfevpiJcaiyFXbmT0XPd7Nx6hVk+Tj4Qi8QWqJyIiIjG\nGqO3KNbV1WH27NlYuHAh7O3tAXSnakePHjV7cUREREQ0cnRoO5HXfA0X1cW42FSEXHURGrqaevQR\n+jhvgv0EzJkwRx9mzfKYBQ+ZB9fLIiIioiFjNOB6+eWXez3H/4wQERERjX3VHbW49G2YdVFdjKvN\npdAI2n7PEUOMwAmBmDtxLuZ5zcNcz7nwtPfk/x+JiIjIrIwGXHFxcSgtLUVxcTGWLVuG1tZWaDQa\nS9RGRERERBak7lDjzLUz+OTmB7h1rRxVHbVGz3GS2GOuUwDmOvpjntMMtHc64a7lqyxQLREREY0V\nZWVlKC0tRUxMDADgtddeQ3NzM0QiERITE+Hv7290DKMB14EDB/D222+jrq4OJSUlKC8vx/bt23Hm\nzJnBvwIiIiIiGpTcc+egUatNOlfi5ITAhaFIu5mGT4s+RVpZGrq0XWhVqyGT9P3fxKkyb8x1DMA8\npwDMdQrAZDuvHutmZatUJtVCRERE49czzzyDn/zkJ/r2gQMH8Pjjj6OlpQUvvPAC3n33XaNjGA24\n/vrXv0KhUCAyMhIAMGPGDNy6dWsQZRMRERHRUNGo1Qj18Lijc3SCDrnqQhzIfx95haVo6mjqs5+t\n2BpzHKdjntMMzHXyxxxHfzhLHUyq09QgTuLkhHlRUSZdk4iIiIZWe3s7YmNj0dHRgc7OTqxevRp7\n9uzB7t278T//8z/4/9u79/im6vt/4K+TpLe0TXpPLykUWnpJ77SCICr3CojXjQGb8t1kOPZVxpyK\nYw+H2+RbnPoT0LF5Ae86xiYDFcULBQWF0pa2QAqU0krvJb2l9zbJ+f0RCJQ2vdGkt9fz8cij+Zyc\n88k7CR/avvo5n+Pr6wsA+L//+z8sWLAAAJCamoodO3ZAKpVi69atmD9/fpd+z549i8WLF1vaLi4u\n+N3vfgcAfb6qYq8Bl5OTE5ycnCxtg8HANRSIiIiIRqCi5jJ8VvUdPrt0BGWtOjQbDJArOl/xUOOr\nwSS5P34cPBWTXIMhFaSD8twDCeIAzggjIiIaTpydnZGWlga5XA6DwYAZM2bg8OHDEAQBjz32GB57\n7A0ucKkAACAASURBVLFO+2u1WuzcuRNarRalpaWYO3cuzp07B4mk81WTW1tbO7WvPWtQ18efBXoN\nuG6//XZs3LgRzc3N+PLLL7Ft27ZOqRoRERERDV/V7XXYf+koPqs6grzGom73CXQPxIKwBVgwaQFC\nPEKQuX8/It36H0YRERHR6CeXywEA7e3tMBqN8PT0BACIYtdrKe/ZswfLli2Dg4MDQkJCEBYW1uks\nwSsUCgXOnj2LiIgIAIC3tzcA4MyZM1Bc98c4a3oNuDZt2oTt27cjNjYWr776KhYuXIiVK1f2qXMi\nIiIisr8WYysOVmfis6rvcLT2JEzo+gOnQuaKmxUx+OXCtYhTxXGGPhEREfWJyWTC5MmTUVBQgNWr\nVyM6Ohr//ve/8fLLL+Odd95BcnIyXnzxRXh4eKCsrKxTmKVWq1FaWtqlzz/96U9YvHgx/vCHP2Dy\n5MkAgMzMTGzcuBFbtmzpU129BlxSqRQrVqzA1KlTIQgCIiMj+QMQERER0TBjFI04XqfFvqojSKvO\nQIuxrcs+DoIMt3knYoHvdEz3isfJmnrE+8cPQbVE/XOjF1PgOm5ERINHIpEgOzsb9fX1SElJwcGD\nB7F69Wr88Y9/BAA8/fTT+N3vfoft27d3e3x3mdIdd9yBjz76CM899xy2bt0KAIiOjsbu3bsRExPT\np7p6Dbg+/fRT/OpXv8LEiRMBABcuXLDM5LpRn3/+OdauXQuj0YiVK1di3bp1nR5///338de//hWi\nKMLd3R1///vfERcX121fBw8exMyZMy33AbDNNttss832mGy7w+xgRob58eTkPrfP1tcjKSXFLvVm\n5OSgQansV30A4B4SYpf6RkJbFEXUtBTjmwv78eGR/ag3NMI9zBUA0HC+CQDgHuaKJGUkgitUSFRG\nYFGU+fLbvX7eA/j3A1j/fAb78x5of5Z2N+/n2ZwcJM2ZM6D+MnJy0ODkNKz+ffSrPcw/76PffouI\nAX7emTqd3T7vgf7/O9j9cXxf93r5efe7P4Cf91j9vDdv3ozs7GyEXG73RKlUYtGiRcjIyLA8PwCs\nXLnSsrRVUFAQiouLLY+VlJQgKCio2/5iYmLw7rvv9vq81ghidydJXiMiIgKffvopwsLCAAAFBQVY\nuHAhzp49O+AnBQCj0YiIiAh89dVXCAoKwk033YQPP/wQUVFRln2+//57aDQaKJVKfP7553jmmWdw\n9OjRri9CELo915OIiGgsyty/f0CLeQPmBb2v/MBkawOt0541DkfVzdVIL00338rSUViWD7ms698s\nJ8gDsdDvFtzhOx0Bzt2/z9beS1v8Gxrsz3s41dhTnyPBcHovR/rnzdc9+mq0RZ8joUZb9DkSarRF\nnyOhxp76vD5v0el0kMlk8PDwQEtLC1JSUrBhwwZER0fD398fAPDSSy/h+PHj+OCDD6DVarF8+XKk\np6dbFpk/f/58l1lc3377LS5cuIAVK1YAAO6//37U1NQAMM8Imz17dq+vodcZXAqFwhJuAcDEiRP7\nvMBXT9LT0xEWFmZJBZcuXYo9e/Z0CrimXTOVeOrUqSgpKbnh5yUiIiIaSZram3Ci4oQl1Dpfc97q\nvj6OHkjxvRkL/W5BuOt4LitBREREg6q8vBwrVqyAyWSCyWTCAw88gDlz5uDBBx9EdnY2BEHAhAkT\n8OqrrwIANBoNlixZAo1GA5lMhm3btnX788mGDRvw8ssvW9rnzp3DW2+9haamJmzcuHFwAq6kpCQs\nXLgQS5YsAQDs2rULycnJ+OijjwAA9913X9/eheuUlpYiODjY0lar1Th27JjV/bdv3z4op0USERER\nDWcGkwGnq07jWOkxpJem41TVKRhMBqv7u0icscjvZizwm46bPDSQClI7VktERERjSWxsLLKysrps\nf+edd6wes379eqxfv77HfvV6PaKjoy3tsLAwJCUlAQCeeuqpPtXWa8DV2toKPz8/HDp0CADg6+uL\n1tZWfPzxxwAGHnD15y+KaWlp2LFjB44cOWJ1n2eeecZyf+bMmZ3O/yQiIiIarkRRxIXaC5YZWpnl\nmWjuaLa6v0wiQ7wqHlPVUzElaAqasy5iiq/KjhUTERERDa66urpO7d27d1vuV1ZW9qmPXgOut956\nq39V9dH1C40VFxdDrVZ32S83Nxe//OUv8fnnn8PT09Nqf9cGXERERETDWWVjpSXQOl52HLpmXY/7\nR/hEYErgFEwJmoIE/wS4OLhYHssUul5qm4iIiGgkiYyMxCeffII777yz0/aPP/4YkZGRfeqj14Dr\nwoULePnll1FUVASDwTw9XhAE7N27dwAlX5WcnIz8/HwUFRUhMDAQO3fuxIcffthpn4sXL+K+++7D\ne++912kdMCIiIqKRwmgyoqiuCNpLWpy+dBoZZRkoqivq8ZgA9wBMDTLP0Lop8CZ4ulj/Ix8RERHR\nSPfSSy9h0aJF+M9//oPJkydDFEVkZWXhyJEj+OSTT/rUR68B1z333GO5xKNEIgHQv9MLrT6xTIZX\nXnkFKSkpMBqNeOihhxAVFWVZiOzhhx/Gn//8Z9TW1mL16tUAAAcHB6Snp9/wcxMRERHZgkk04WL9\nRWgvaZF3KQ95ujyc0Z1Bq6G1x+OUzkokByRbTjsMcg/iAvFEREQ0ZkyaNAm5ubl4//33odVqAQC3\n3XYb/vGPf8DZ2blPffQacDk7O2PNmjU3VqkVCxYswIIFCzpte/jhhy3333jjDbzxxhs2eW4iIiKi\nG2ESTSjRlyDvUp450LocZvW0ftYVjlJHJPonYkqQ+bTDcO9wSCVcHJ6IiIjGpvz8fFRWVuKhhx7q\ntP3w4cMICAhAaGhor330GnA9+uijeOaZZ5CSkgInJyfL9smTJw+gZCIiIqKRRxRFlDWUWYIs7SUt\nzujOoLG9sU/H+7n6IconChpfDWJVsYhXxcNJ5tT7gUTXyfn+exj0+gEdK1MoED9t2iBXREREdOPW\nrl2L1NTULtsVCgXWrl1rudBhT3oNuE6fPo13330XaWlpllMUAfOVDYmIiIhGI12zDicrT0J7SWsJ\ntfRtfQsVvOXe0PhoEOVrDrSifKLgLfe2ccU0Vhj0eiT5+Azo2ExdzxczICIiGiqVlZWIi4vrsj0u\nLg6FhYV96qPXgGvXrl0oLCyEo6Nj/yskIiIiGubaDG04W30Wp6pO4WTlSZysOomKxoo+Hevh7IFo\n32hE+UYhyicKUb5R8JX7cv0sIiIion6oq6uz+lhra89rmV7Ra8AVGxuL2tpaqFSqvldGRERENAxd\nOdXwVNUpnKw6iVNVp3BGdwYGk6HXY5XOSkR6R5pnZV2enaVyVTHMIiIiIrpBycnJeO2117Bq1apO\n219//XUkJSX1qY9eA67a2lpERkbipptusqzBJQgC9u7dO4CSiYiIiOynuaMZeZfycLLqJE5WnsSp\nS6dQ3Vzd63HOMmdofDWI8YuxnGYY6B7IMIuIiIjIBjZv3ox7770X77//viXQyszMRFtbG3bv3t2n\nPnoNuP70pz8BMIdaoiha7hMRERENJybRhIv1F81B1uUZWudrzsMkmno9drzHeMT4xiBWFYtYv1iE\neYXxqoZEREREduLv74/vv/8eBw4cwKlTpyAIAu68807Mnj27z330GnDNnDkTRUVFOH/+PObOnYvm\n5mYYDL1P4yciIiKypTZDG05VnUJOZQ5yKnJwsupknxaCd3N0Q4xfDGL9YhGrikW0bzSUzko7VExE\nRERE3UlPT4dOp8PChQs7hVr79u2DSqXq02mKvQZcr732Gl5//XXU1NSgoKAAJSUlWL16Nb7++usb\nq56IaJTgJduJ7KOmvR45+nx8Un4CL//3wz6tnSUIAkI9Qy1hVoxfDEI8QiARJD0eR0RERET2s27d\nOrz55ptdtms0Gvz85z9HWlpar330GnD97W9/Q3p6Om6++WYAQHh4OKqqqgZQLhHR6MRLttONGmhI\nOpoDUlEUcbGlAtn6s8jR5yNbfw4XW8xXNmw2GCBvV3R7nIezB+JUcZYZWhpfDVwdXe1ZOhERERH1\nU0NDA0JCQrpsDwkJga6PvzP1GnA5OTlZFpcHAIPBwDW4iIiIBtFAQ9LRFJC2mzpwprHIEmjl6PNR\n19HQ63ETPCcgXhWPBP8ExKvioVao+XMKERER0QhTV1dn9bGWlpY+9WE14HrllVfwyCOP4Pbbb8fG\njRvR3NyML7/8Etu2bcPixYv7Xy0RERHRZfo2PbL1p/B9QyVy9Pk43XgB7aaOHo9xEGSIdp8IT2kg\nFs9cinhVPNfOIiIiIhoF5syZgz/84Q949tlnLX+sNJlM2LBhQ58XmrcacG3fvh2PPPIINm3ahO3b\ntyM2NhavvvoqFi5ciJUrVw7OKyAiIqIxobKxEtkV2ThRcQLZFdk4X3MezXo95DLrk8mVMjfEKyYh\nXhGOBGU4Il1D4CR1RKZOh6Txt9mx+sHFU1KJiIiIOnvxxRexcuVKhIaGIiEhAQCQk5OD5ORkvPHG\nG33qo9dTFKVSKVatWoVVq1bdWLVEREQ0JoiiiB/qf8CJ8hOWUKusoazX49TOfkhQhFsCrfEuAaNy\nMXiekkpERETUmZubG/75z3+ioKAAp0+fhiAI0Gg0CA0N7XMfVgOu3NxcuLu7d/uYIAjQD/CKYURE\nRDS6GE1GnKs+Z5mddaLiBGpbans8RiqRYoLLOMz1i0O8IhzxiknwdvSwU8VERERENByFhob2K9S6\nltWAKy4uDidOnBhwUURERDQ6tRnacKrqlCXMyq3MRXNHc4/HOMucEeMXg0T/RCQGJCLGLwZ5B74d\n8BVIiYiIiIiu1espikRERDS26dv0yK3MtZxyqNVp0WHseUF4hZMCCf4JSPBPQKJ/IiJ9IuEgdbBT\nxUREREQ01lgNuH784x/bsw4iIiIaBhoNTUgvTccZ3RloL2lxRncGJfqSXo9TuamQoEpAYkAiEv0T\nMcFzwqhcP4uov3hRASIiIvuwGnCtX7/ennUQERGRnTUYmnCmsQh5jYXIazB/PddYBnmpotdjQzxC\nLKcbJvgnIMAtwHJJZyK6ihcVICKi0aS1tRW333472tra0N7ejrvvvhupqamWx1988UU88cQT0Ol0\n8PLyAgCkpqZix44dkEql2Lp1K+bPn9+l39zcXKxatQolJSVYuHAhnnvuOXh6egIApkyZgvT09F5r\n4ymKREQ0bHCmg+00GppxtvEHaBsv4ExjEbQNhShurezTsTKJDOHe4ZZTDhP8E+Dl4mXjiomIiIho\nuHF2dkZaWhrkcjkMBgNmzJiBw4cPY8aMGSguLsaXX36J8ePHW/bXarXYuXMntFotSktLMXfuXJw7\ndw4SSeeZ/qtXr8YzzzyDqVOnYvv27bjllluwd+9ehIWFoaOj56UxrmDARUREwwZnOgyOFmMrMuvy\nzDOzGguR11iEiy0VfTpWKkgR6ROJKJ8oRPlGIconCqFeoXCUOtq4aiIiIiIaCeRyOQCgvb0dRqPR\nMlPrsccew1//+lfcfffdln337NmDZcuWwcHBASEhIQgLC0N6ejpuvvnmTn02NDTgjjvuAAA8/vjj\nSEpKwh133IH33nuvz3X1K+B68MEH8c477/TnECIiIrIhg8mA/Op8nL50GqeqTuH0pdPQ/pADF1nv\n3+JlghRhrsGIdBsPjdtERLqFQN/igpsX3GmHyomIiIhoJDKZTJg8eTIKCgqwevVqaDQa7NmzB2q1\nGnFxcZ32LSsr6xRmqdVqlJaWdulTEATU19dDqVQCAGbNmoWPPvoI9913H2pra/tUl9WffhcvXgxB\nECCKomXbgQMHUFtbC0EQsHfv3j49AREREQ0OURRR1noJpxoKcLqxAAcvaVFd+me0Gdo679fNsTJB\nilC5GpFuIdC4T0CkWwjC5MFwum5mVmYbZ8MRERERkXUSiQTZ2dmor69HSkoK9u3bh9TUVHzxxReW\nfa7Nkq7X3bqtTz75JLRaLaZds+xIXFwcDhw4gL/85S99qstqwFVSUgKNRoOVK1dCIpFAFEVkZGTg\n8ccf71PH9nbw4EHMnDnTch8A22yzzbZd2mdzcpA0Z465nZFhfjw5uU/tjJwcNDg5DavXM5TtjJwc\nNCiVfX7/rrTdQ0KGRf0DbbsD3b6+T459i6LmMjhOdMDphgs4nJWNRmMz3MNcAQCXzurhLHeFe4S5\nh4azDQAAhwAJJrkGw+WiE8a5+OPH0+ciTB6M70/kAnpgZrj19/NsfT2SUlLs8vr5eQ/O/xcD7a/X\nz3sA/QHWP5/B/rwH2p+lbaf/z/l58/Meiv74eV/3evl597s/gJ/3WP28N2/ejOzsbIRcbvdEqVRi\n0aJFyMrKQmFhIeLj4wGY86SkpCQcO3YMQUFBKC4uthxTUlKCoKCgLn399Kc/tdxvbGwEALi5uWHc\nuHF4/fXXe60FAATRSqxmNBqxZcsW7Nu3D88//zwSExMxYcIEFBYW9qlje7p+phkRkT1l7t8/oHWj\nAPPaUVe+OdHA38uR/j5m7t+PGE8F8psu4lRDgXmGVsOFXheBbzYYIFcoEOgeiBi/GET7RiPGLwZN\nJy5iul/gwGqx43s5lj/vwXzdtvg/aDj1ORJqtEWfI6FGW/Q5Emq0RZ8joUZb9DkSarRFnyOhRlv0\nORJqtEWfI6HGnvq8Pm/R6XSQyWTw8PBAS0sLUlJSsGHDBsy5HCgCwIQJE5CZmQkvLy9otVosX74c\n6enplkXmz58/3+0srm3btmHTpk2dAq5169bhf//3f/v0GqzO4JJKpXjsscewZMkS/Pa3v4Wfnx8M\nBkOfOiUiIqKetXS0IKMsA0dLjuLQ+S+gM1TAIBp7Pc5dJke020REu4fCweSDexf9D7zl3p32yZT0\nbUF5IiIiIqL+KC8vx4oVK2AymWAymfDAAw90CreAzqcgajQaLFmyBBqNBjKZDNu2bes23Hr22Wfx\n3Xff4eDBg5g4cSIA4MKFC1izZg1qamrw9NNP91pbryvQqtVq7Nq1C5988ollsS8iIiLqH1EU8UP9\nD/iu+DscuXgEJypOoN3YDgBobtFD3s2i8DJBigi38YhxD7WEWsEuKkgECQDzX9quD7eIiIiIiGwl\nNjYWWVlZPe5z4cKFTu3169dj/fr1PR7zzjvvICcnBy4uLpZtEydOxK5duxAXF3djAVdNTU2n9vTp\n0zFt2jTL9iuXgSQiIqLutXS04HjZcXxX/B2+K/4OZQ1lPe4/zsUf0e4TLYFWuNt4OEoc7FQtERER\nEdHQkEgkncKtK1xcXCCVSvvUh9WAy8fHB2q1utuOBEHokshRz3K+/x4GvX5Ax8oUCsRfcyUBIiIa\nnkRRRFFdkSXQyqrIQoexw+r+oV6hmK6eDvcSE+5XT4bSwc2O1RIRERERDQ+BgYH46quvMHfu3E7b\nv/76awQEBPSpD6sB15o1a3DgwAHMmDEDS5cuxa233trteZLUNwa9/oYWeyMiouHh+j9YtBpboW3K\nR07DaeQ25EHXUWP1WBcHOWZFzcX04OmYHjwd/m7+AMwLgjLcIiIiIqKx6uWXX8bdd9+NGTNmICkp\nCaIoIjMzE4cPH8aePXv61IfVgGvz5s0wmUw4ePAg3nvvPTz66KOYP38+fv3rX2PChAmD9iJo4AY6\nK4wzwoiIBq6jvh5e8jZ8V5uL72pzcaL+LDrEqxdhuX4trTBXNaZ7xmO6ZxyM7V6YOn+RvUsmIiIi\nIhrWoqOjcfLkSXzwwQfQarUAgNtuuw2vvvoqnJ2d+9RHj4vMSyQSzJ49G5MnT8aHH36IP/7xj5g0\naRJWrVp149UD+Pzzz7F27VoYjUasXLkS69at67LPmjVr8Nlnn0Eul+Ott95CYmLioDz3aDDQWWHW\nZoTxNEoioq7aje3Ir85Hni4P2ktafHn2U7SIDVb3d5W6YKpHNKZ7xWOaZyxUTlcXgeeMXCIiIiKi\nrvLz81FZWYmHHnqo0/bDhw8jICAAoaGhvfZhNeBqbGzEnj17sHPnTly6dAn33XcfMjMzMW7cuBuv\nHIDRaMQjjzyCr776CkFBQbjppptw1113ISoqyrLPvn37cP78eeTn5+PYsWNYvXo1jh492m1/mfv3\n97sGhjKd8TRKIhrrjCYjLtReQJ4uD6erTkOr0yK/Oh8G09UZWs0dXa94OMk1GNM94zDdMx7xikmQ\nSXq9SDEREREREV22du1apKamdtmuUCiwdu1afPzxx732YfUncJVKhUmTJuEnP/kJwsPDAQAZGRk4\nfvw4BEHAfffddwOlA+np6QgLC0NISAgAYOnSpdizZ0+ngGvv3r1YsWIFAGDq1Kmoq6tDZWUlVCpV\nl/4GcyYTERGNfibRhOL6YmgvaaG9pEWeLg9ndGfQamjt9Vg3qQumesZgumccpnnGwc+JVxYmIiIi\nIhqoyspKxMXFddkeFxeHwsLCPvVhNeD68Y9/DEEQcO7cOZw7d67L4zcacJWWliI4ONjSVqvVOHbs\nWK/7lJSUdBtwERERWSOKIioaKyxh1pVAq7G9sU/HByuDofHRQOOrgXC+HkuCkzhLi4iIiIhokNTV\n1Vl9rLW19z9AAz0EXG+99Va/C+qPvl6RURTFAR1HRERjV3NHM3Irc5FTkWMOtHRa1LbU9ulYfzd/\nRPlEQeNrDrSifKOgcFJYHs8s389wi4iIiIhoECUnJ+O1117rsub766+/jqSkpD71YfUn9LVr12Lz\n5s0AgC1btuA3v/mN5bH/+Z//ueEALCgoCMXFxZZ2cXEx1Gp1j/uUlJQgKCio2/5WvfSS5X5SXByS\n4+N7rUGmUHS7/fvvc6DXG7p9rDcKhQzTpnV9bplCMeBTIq3VOdA+B7u/nvq0xXs50D4Hu7+R0udI\nqNEWfdqzxuE0dkbCZ9NTnwN9L9vkUnz7w7fIKs/CiYoTyNPlwWgyoqmpBUajaPU4N5k7xrlMxHiX\niRjnMgHjXCZA4eABiICiVYapav5/fsVw+rxHwvecnvocCZ/3cOpzJNRoiz5HQo226HMk1GiLPkdC\njbbocyTUaIs+R0KNtuhzJNRoiz5HQo099Wkvmzdvxr333ov333/fEmhlZmaira0Nu3fv7lMfgnj9\nFKnLEhMTceLEiS73u2sPhMFgQEREBL7++msEBgZiypQp+PDDD7ssMv/KK69g3759OHr0KNauXdvt\nIvOCIHSZ6XUj9u/PhI9P3xLC6+l0mUhJGdixo5Et3suB9jnY/Y2UPkdCjbbo05412sJIeN1D+V7W\ntdaZw6zyE8iqyMK56nPdfh/Q65shk8kBAC5SN0x012CCm8by1dtJZXVmMP8/78wWQc9gG23/zomI\niIiG2mDnLT0RRRFpaWk4deoUBEFAdHQ0Zs+e3efjh+wcC5lMhldeeQUpKSkwGo146KGHEBUVhVdf\nfRUA8PDDD2PhwoXYt28fwsLC4OrqijfffHOoyiUioiGka9YhqzzLcrtQe6HH/QVBwCSvSZA7+CLe\nfwEmumvg56yGRJDYqeLRxx4BFRERERGNXYIgYPbs2f0Kta5lNeAyGo2oqamBKIqW+wAs7cGwYMEC\nLFiwoNO2hx9+uFP7lVdeGZTnIiKikaOisQJZ5VnILMvEiYoTuFh/scf9JYIEkT6RmBwwGZMDJiNe\nFQ+ls/KGZt8QEREREdHIYTXg0uv1lvMeRVHs86JeRERE/VXWUIaMsgxklmUiqyIL5Q3lPe4vk8gQ\n7RuNyQGTkRiQiHhVPFwdXe1ULRERERERDTdWA65Dhw5h/Pjx9qyFiIjGCH1HHfaf34/jZcdxvOw4\nSvWlPe7vJHNCjG+MZYZWrCoWzjJnO1VLRERERETDndWA695770VWVpY9ayEiolGqydCAM/VZOF2X\njry6DBTpT0JRJre6v4uDC+L84pAUmITJAZOh8dXAUepox4qJiIiIiGgksRpw2WuVfCIiGn3ajK04\np8/G6brjyKvPQGFDHkSYrO7v4uCCRP9EJAcmY3LAZET6REImGbLroBBZKBQy6HSZAz6WiIiIiOzD\n6k9epaWlWLNmTbdBlyAI2Lp1q00LIyKikcNg6kBBw2lo645DW38c+fpcGEWD1f2lggxJAUlIDkzG\nlKAp0Phq4CB1sGPFRH3Dq0cSERERjQxWAy4XFxckJSVBFEUIgmDZfn2biIjGHqPJiOKWIhwtOY28\nugyc1Z9Am7HF6v4CJJjgHoUoZTJiPKbAq8OIxQum27FiGgs424qIiIho7LL605yXlxdWrFhhz1qI\niGgYEUURte2XkN+Yh6a8H1BcX4yL9RdRrC9Gib4Euto6yGTW19FSy0MR5ZGMaI8piFROhqvM3fLY\nQEOI/mLgMbZwthURERHR2GX1p3cnJyd71kFERDZkLegRRRENhnpcaq9EVVsFLrVX4lJbBarazfc7\nTO2QSgW4Vrv0+hy+zkHQKM2BVpRHMjwcvW3xUvqFgQcRERER0dhgNeD629/+1ukqioIgwMfHB8HB\nwXYpjIhGHs6WGb40iSEo1ptnYF2sv2iejaU3f21sb+x6gCPg4iiDi/VvE1DKPBHrOxPRHlOgUSbD\nzyXIhq+AiIiIiIjIOqu/uTz++ONdttXU1KC9vR0ffvghEhISbFoYEY08nC0zdIyiEbVtVTjbeBot\nZ8ynEJboS1DaUIoSfQka2hoG1K/CSYFxynEYpxyHYEWw+avS/PVI2ln4+CQN8ishIiIiIqLhqrW1\nFbfffjva2trQ3t6Ou+++G6mpqXj66aexd+9eCIIAb29vvPXWW5YJUqmpqdixYwekUim2bt2K+fPn\n26Q2qwFXWlpat9szMjKwZs0afPPNNzYpiIiIutdqbMGl1lJUtpSYv7aav1a1luBSaxmMogEGQzMU\n1dbXxeqO3EHeJcS6clM6K230aoiIiIiIaKRxdnZGWloa5HI5DAYDZsyYgcOHD+PJJ5/EX/7yFwDA\nyy+/jD/96U944403oNVqsXPnTmi1WpSWlmLu3Lk4d+4cJBLJoNfW73OCkpOT0dAwsJkAIwVP31RZ\nQwAAHvhJREFUsyIiezOYDKhvrUd9Wz3qWutwrPYw2poyUdVagqrLIVZ9e/WA+3eWOUOtUFtCrPEe\n4y1hlpeLF6+OS0REREREfSKXm/+g3t7eDqPRCC8vL7i7X72gVGNjI3x8fAAAe/bswbJly+Dg4ICQ\nkBCEhYUhPT0dN99886DX1e80prKy0iZJ23DC06yI6Ea0GlpR11rXKbCqb738ta3esv3ax65fB0uv\nb+7xCoXdUTh4QekYiKmTEhDkHgS1Qo0ghfmrt4s3QywiIiIiIrphJpMJkydPRkFBAVavXg2NRgMA\n+MMf/oB3330XLi4uSE9PBwCUlZV1CrPUajVKS0ttUpfVgOvRRx/tsq22thZHjhzBli1bbFIMEdFI\nYRJNyKnIwYHCA8ivye8UXLUZ2mzynFJBBl/nQPg6B8HPWQ2/a7+6BMFZKodOl4mUWVwXi4iIiIiI\nbEMikSA7Oxv19fVISUnBwYMHMXPmTGzcuBEbN27Epk2bsHbtWrz55pvdHm+rP7xbDbiSkpIgCAJE\nUYQgCJaFwl588UWoVCqbFHMjrryhV+4DYHuYtDMyzO3k5P61Q0Lcu+0vJycDSmXDkPd3pd3d68/J\nOYs5c5IG1F9OTgacnBq6vJ+A+7DoLyPjIOrrzyIlJcnq6x/M9nD6vE2iCW989AYyyzJR5FmES02X\n0HDWfMq2e4T5+QbaVkYqoXBSoKOgA64OrghV+WG8ZzIa8mvh4eiL22++C15OKpzI/BZoBZJjrtZX\nhVKMS55keX3dfd4jZXyzzTbbbLPNNttss80220PX3rx5M7KzsxESEoLeKJVKLFq0CBkZGZbjAWD5\n8uVYuHAhACAoKAjFxcWWx0pKShAUZJurrwuiKIr9PSg9PR1TpkyxRT0DciWIo+Fn//7MAV9lTafL\ntIQog9HnYPfXU5/ff58Dvd4woD4VClm3p8mOhNdtC0P9uk2iCecbTiL90lc4Uv4RTC69z85ylDpC\n6ayE0sl883D2gIezx9VtzuZtV+4rnZRwd3KHRJAMuM4rRsJnA9i3TiIiIiIiGpjr8xadTgeZTAYP\nDw+0tLQgJSUFGzZswPjx4xEWFgbAvMh8eno63n33XWi1Wixfvhzp6emWRebPnz9vk1lcVmdwmUwm\n7N69GwUFBYiJicHChQuRkZGB9evXo6qqCtnZ2YNeDNFowXXcRrZrQ6103deoba8CAPMVCl2urovl\n6eKJ2SGzMWPcDPi6+lrCLGeZM9e7IiIiIiKiUae8vBwrVqyAyWSCyWTCAw88gDlz5uBHP/oRzp49\nC6lUitDQUPz9738HAGg0GixZsgQajQYymQzbtm2z/ymKq1atQmFhIaZMmYJnn30W27dvx5kzZ7Bx\n40bcc889NimGiGiomEQTChpO4ZjuK6Rf+soSal3Pw9kDsyfMxtyJc5EUkASpRGrnSomIiIiIiIZG\nbGwssrKyumz/97//bfWY9evXY/369bYsC0APAdfRo0eRm5sLiUSC1tZW+Pv7o6CgAN7e3jYviojI\nHq6EWum6r5Gu+wo1bZXd7ufu4IFk79mIcAjC/977M4ZaREREREREw4zVgMvBwQESiXk9GGdnZ0yY\nMIHhFhGNeKIo4rz+JI7pvsJx3deobqvodj93Bw8kec/CVJ+5iPJIhlSQQqfLZLhFREREREQ0DFkN\nuM6cOYPY2FhLu6CgwNIWBAG5ubm2r46I6AYYTUaUNpTiQu0FXKi9gIKaAnx19hs0iC3d7u8mUyLJ\n53KopUyGTGL1v0giIiIiIiIaRqz+9paXl2fPOoiIBswkmlDdVoGSpgKUNl9ASXMBCmoz8OfSerQZ\nOl/1UN/RDJns6kLxrjIFkr1nYYrvXGiUNzHUIiIiIiIiGoGs/ibX0dGByspKzJgxo9P2w4cPIyAg\nwOaFEZFtKRQy6HSZAz52KIiiiJr2KkuQdSXMKm2+gDZj51lZBkMzFA7ybvu5Emrd5DMH0R5TGGoR\nERERERGNcFZ/q1u7di1SU1O7bFcoFFi7di0+/vhjmxZGRLY1bVr8UJfQLZNogr5ND12zDpeaLiFN\ndwD62s/NYVZTAVqMjf3qz0fug1DPUEz0nIiJnhNRkdeGycH3QyZxsNErICIiIiIiInuzGnBVVlYi\nLi6uy/a4uDgUFhbatCgiGn3aje2obq5GdUs1dM06VDebv+qadVe3tVSjurkaBpPBcpxe3/mUQmvc\nHTwQJA+FWh6KIPlEuHa04YGFd0LprOy03/6LmQy3iIiIiIiIRhmrAVddXZ3Vg1pbW21SDNFQGImn\n6g03+jY9CmsLUd5YbjW4qm+tH5TncpG6Idg1DEHyiVC7msOsIPlEKB28IAiCZT+dLrNLuEVERERE\nRESjk9XfzpOTk/Haa69h1apVnba//vrrSEpKsnlhRPYyXE/VG25EUURVUxUK6wpRVFeEwtrLX+sK\nUdNSM6jP5eboBh+5D7xdvNEiFRDuc6slzPJ09O0UZBERERERERFZDbg2b96Me++9F++//74l0MrM\nzERbWxt2795ttwKJyL4MJgOK64stQdaVEKuorggtHS29d2CFVCKFl4sXvF28LeGVj9wH3nLz12u3\nOcmcLMft358JHx+G6kRERERERGSd1YDL398f3333HdLS0nDq1CkIgoA777wTs2fPtmd9RGQjTe1N\nKKorwg/1P6CwttASYpXoSzqtgdUXTjInjFeOR7AiGL6uvl3CK28Xb3g4e0Aqkdro1RAREREREdFY\n1uMCQoIgYPbs2Qy1iEY4URRR0ViBExUnkF2RjeyKbFyovdDvfpTOSkzwmIDxyvGY4DkBEzwmIMQj\nBP5u/gyviIiIiIiIaMhwhWyiUcgkmlDafAFn67ORXbEPWz+sRGVjZZ+P93fzR4hHCCZ4TMAEz6uB\nlqezJ9e/IiIiIiIiomFnyAKumpoa/OQnP8EPP/yAkJAQ/Otf/4KHh0enfYqLi/Hggw+iqqoKgiBg\n1apVWLNmzRBVTDR8GUwduNCoxbn6bJzVZyNfn4Mmg978mKEZClHe5RiZRIZgZbA5xLo8EyvEIwTj\nPcZD7tB1fyIiIiIiIqLhasgCrk2bNmHevHl48skn8dxzz2HTpk3YtGlTp30cHBzw0ksvISEhAY2N\njUhKSsK8efMQFRU1RFUTDQ8thibkN+ReDrROoKDhNDpMbT0eI3eQI9YvFokBiUjwT0CMXwycZc52\nqpiIiIiIiIjIdoYs4Nq7dy8OHToEAFixYgVmzpzZJeDy9/eHv78/AMDNzQ1RUVEoKytjwEVjiiiK\nqGqqwon6dFTUp+GcPhs/NJ6DCFOPxykcvBCuSECgRIkV8+9DuHc418kiIiIiIiKiUWnIAq7Kykqo\nVCoAgEqlQmVlz+sDFRUV4cSJE5g6dao9yiOyq5aOFpQ1lKG0oRQl+hLzfX0pShtKUdZQhlZDK/T6\nZshk1k8d9HNWI1yRgAhlIiIUCfB3GQdBEKDTZSLKl6EwERERERERjV42DbjmzZuHioqKLts3btzY\nqS0IQo8LVzc2NuJHP/oRtmzZAjc3t0Gvk8jWjCYjLjVfsoRWlq+XA6zq5up+9SdAwDjXcIQrE8yh\nliIBnk6+NqqeiIiIiIiIaHizacD15ZdfWn1MpVKhoqIC/v7+KC8vh5+fX7f7dXR04P7778fPfvYz\n3HPPPVb7e+aZZyz3Z86ciZkzZw60bKJ+M4km1LTUoKKxApWNlZbZWNfOwjKYDAPuX+GkgK9rMOL9\n5iFckYBJijjIZWMj7FUoZNDpMgd0HBEREREREY0NQ/Yb4F133YW3334b69atw9tvv91teCWKIh56\n6CFoNBqsXbu2x/6uDbiIBltzRzMqGytR0VjR5VbZZN5+IwGWTCJDgHsAgtyDzDdFENQKNQLdAxHo\nHgiFkwL792fCxydpEF/VyDBtWvxQl0BERERERETD3JAFXE899RSWLFmC7du3IyQkBP/6178AAGVl\nZfjlL3+JTz/9FEeOHMF7772HuLg4JCYmAgBSU1Nxxx13DFXZNAoZTUbomnWdwipLmNVkDrHqW+tv\n+Hm8XLwQpAhCoFsg1Ao1ghRXwyxfuS8XgCciIiIiIiIaoCELuLy8vPDVV1912R4YGIhPP/0UADBj\nxgyYTD1fKY6oP9oMbThfcx55ujzkXcpDni4PF2ov3NDsqyuUzkr4u/nD39Uf/m7+nQKsQPdAyB2s\nLxDfFzxVj4iIiIiIiKh7/M2XRq0OUzuKm86jsDEPhY15OKs7gg3FtQMKsxykDlC5qswBVjc3lasK\nLg4uNngVV/FUPSIiIiIiIhpKra2tuP3229HW1ob29nbcfffdSE1NxRNPPIFPPvkEjo6OCA0NxZtv\nvgmlUgnAfCbejh07IJVKsXXrVsyfP98mtTHgolGhw9SOkqYCS5hV1JiH4qbzMIpXwyyDoRkKx+5n\nUXm5eHUKq64PsDxdPCERJPZ6OURERERERETDjrOzM9LS0iCXy2EwGDBjxgwcPnwY8+fPx3PPPQeJ\nRIKnnnoKqamp2LRpE7RaLXbu3AmtVovS0lLMnTsX586dg0Qy+L9fM+CiEcdgMqCo8QwuNGhR1HgG\nhY15KG7K7xRm9WScchyifKIQ5RuFKJ8oRPhEwM1xbFyRkIiIiIiIiOhGyOXmiSPt7e0wGo3w8vKC\nRqOxPD516lT85z//AQDs2bMHy5Ytg4ODA0JCQhAWFob09HTcfPPNg14XAy4aMqIootXYjCZDA5oN\nDWg2NqDZ0IhmQwOaDA2orDuJ098fQkNbAxraG9DQ1oD6tnqcKslDu6GjT8/h4+iHcS4TEewyHlE+\nk/DjWYvh7uRu41dGRERERERENDqZTCZMnjwZBQUFWL16dadwCwB27NiBZcuWATBfSPDaMEutVqO0\ntNQmdTHgokFhNBlR11qH6pZqVDdXo7qlGrpmHY6Wn4RQsw/NxkY0GfTmIOuaEEuE9YsIGAzNULR2\nPaXQWe4AZzh02a5WqBHpEwmNrwaRPpGI9ImEwkkxqK+TRr6BLtZ/5VgiIiIiIqKxTCKRIDs7G/X1\n9UhJScHBgwcxc+ZMAMDGjRvh6OiI5cuXWz1eEASb1DVqflu79g09ePAgALB9g+3bb78dzR3N+OSL\nT6Bv02NC4gTomnX4/tvvoW/TQxGhQHVLNc5lnoO+TQ+3cPNpfg1nGwAA7hHu0Oub0VZkBAC4hpln\nTjWdb+hT2ylE2qW/K21vuTduve1WRPlGoSW/BcHKYNw5/05L/c21zVAEKYbV+8n28Gi3tdXCyWn4\n1GOtDZj/vWdkmNvJyTP71M7JyYCTU4Pd6u1vfVfaISHudqmPbbbZZpttttlmm2222e57e/PmzcjO\nzkZISAh6o1QqsWjRImRkZGDmzJl46623sG/fPnz99deWfYKCglBcXGxpl5SUICgoqNe+B0IQRVG0\nSc92JAgCRsHLsIsrM63qWutQ21qL2pZay/261rpOs690zTq0Glpv6Pn0+mbIZN0v7A4ATlIXyKXu\ncJW5Q375duW+qaUGUxOj4e7oDncnd7g7usPN0Q2B7oFQOitvqC6i4W7//kz4+CT1+zidLhMpKf0/\nbiAGWiNg3zqJiIiIiGhgrs9bdDodZDIZPDw80NLSgpSUFGzYsAEdHR343e9+h0OHDsHHx8eyv1ar\nxfLly5Genm5ZZP78+fM2mcU1amZwjVWthtYuIVVdax1qW2otAVZ9W73lvr5Nb7NaPJw94C33hreL\n+eYj90HJ+TqoPOI6BVdX7rtI3SCTWP8nqNNlIiWOvwATERERERERDQfl5eVYsWIFTCYTTCYTHnjg\nAcyZMweTJk1Ce3s75s2bBwCYNm0atm3bBo1GgyVLlkCj0UAmk2Hbtm08RXEsaje2o7KxEuWN5aho\nrEB5QznKG8tR3lCOiqYKVDdX3/AMq944yZzgI/exhFbecu+rbfnVIMvLxQsO0q7rYu2vH/gMDyIi\nIiIiIiIaPmJjY5GVldVle35+vtVj1q9fj/Xr19uyLAAMuIZUc0dz59CqscJ8/3K7uqV60E+9FAQB\nSiclPJw94Onsaf7q4mm5f21o5S33hquDq83SVSIiIiIiIiKiwcCAy0aMJiOqW6pR1VSFqqaqTjOw\nrgRZ9a31N/w8DlKHq0HV5a/XhlaeLp5QOiktbYWTAlKJdBBeIRERERERERHR8MCAawAMJgN0zTpU\nNlaisqnSEmJVNlaiqtl8X9esg9FkvKHnkQgS+Ln6wd/NHwFuAQhwD+h031fuC7mDnDOsiIiIiIiI\niGhMY8B1HYPJ0H1w1VSFqmbz/cE6ddBB6nA1sHK7HF65X72vclP1uAg7EY1eCoUMOl3mgI4jIiIi\nIiIaa8bkb0Idxg6UNZShWF+M4vpilOhLzPf1xShvKIfBZBiU5/F08YTKVQU/Vz/4ufpZZl5d+erl\n4gWJIBmU5yKi0WXatPihLoGIiIiIiGjEGLUBV5uhDaUNpebwqr4YF+svWoKsisYKmETTgPsWBAHe\nLt5QuangJzeHVyq3q0GWylUFH7kPnGROg/iKiIiIiIiIiIioO6Mj4JoAvJPzDorrzbOwSvQlqGyq\nHPBphL6uvuZTBF1VnWZgXQmyfOQ+PHWQiIiIiIiIiGiYGB0pzTxg67Gtfd5dEASoXFUYpxyHYEUw\n1Ao1gpXBCFYEI0gRBGeZsw2LHVsGuo7QlWOJiIiIiIiIiHozahMEiSBBoHugObxSBCNYGWy5H+ge\nyNMH7YTrCBERERERERGRrY2OgOsisHTF0k5BVoBbABykDkNdGRERERERERER2djoCLg+Bx7/7PGh\nroKIiIiIiIiIiIaAZKgLICIiIiIiIiIiuhEMuIiIiIiIiIiIaERjwEVERERERERERCMaAy4iIiIi\nIiIiIhrRGHAREREREREREdGIxoCLiIiIiIiIiIhGNAZcREREREREREQ0osmGugAiIhqZFAoZdLrM\nAR9LREREREQ0WARRFMWhLuJGCYKAUfAyiIiIiIiIiIiGjZGUt/AURSIiIiIiIiIiGtEYcBERERER\nERER0Yg2JAFXTU0N5s2bh/DwcMyfPx91dXVW9zUajUhMTMTixYvtWCEREREREREREV2rtbUVU6dO\nRUJCAjQaDX7/+98DAHbt2oXo6GhIpVJkZWV1OiY1NRWTJk1CZGQkvvjiC5vVNiQB16ZNmzBv3jyc\nO3cOc+bMwaZNm6zuu2XLFmg0GgiCYLf6Dh48aLfnIhpNOHaIBoZjh6j/OG6IBoZjh2hgOHbMnJ2d\nkZaWhuzsbOTm5iItLQ2HDx9GbGwsdu/ejdtuu63T/lqtFjt37oRWq8Xnn3+OX//61zCZTDapbUgC\nrr1792LFihUAgBUrVuC///1vt/uVlJRg3759WLlypV0XNeM/XKKB4dghGhiOHaL+47ghGhiOHaKB\n4di5Si6XAwDa29thNBrh5eWFyMhIhIeHd9l3z549WLZsGRwcHBASEoKwsDCkp6fbpK4hCbgqKyuh\nUqkAACqVCpWVld3u99vf/hbPP/88JBIuFUZERERERERENNRMJhMSEhKgUqkwa9YsaDQaq/uWlZVB\nrVZb2mq1GqWlpTapS2aTXgHMmzcPFRUVXbZv3LixU1sQhG5PP/zkk0/g5+eHxMREJqVERERERERE\nRMOARCJBdnY26uvrkZKSgoMHD2LmzJl9Pt5mS1CJQyAiIkIsLy8XRVEUy8rKxIiIiC77/P73vxfV\narUYEhIi+vv7i3K5XHzggQe67S8+Pl4EwBtvvPHGG2+88cYbb7zxxhtvvPHG2yDd4uPje8x3/vzn\nP4vPP/+8pT1z5kwxMzPT0k5NTRVTU1Mt7ZSUFPHo0aP9ypD6ShBFOy5uddmTTz4Jb29vrFu3Dps2\nbUJdXV2PC80fOnQIL7zwAj7++GM7VklERERERERERFfodDrIZDJ4eHigpaUFKSkp2LBhA+bMmQMA\nmDVrFl544QUkJSUBMC8yv3z5cqSnp6O0tBRz587F+fPnbTKLa0gWt3rqqafw5ZdfIjw8HAcOHMBT\nTz0FwHxu5qJFi7o9xp5XUSQiIiIiIiIios7Ky8sxe/ZsJCQkYOrUqVi8eDHmzJmD3bt3Izg4GEeP\nHsWiRYuwYMECAIBGo8GSJUug0WiwYMECbNu2zWb5zpDM4CIiIiIiIiIiIhosY+byhL/4xS+gUqkQ\nGxtr2ZaTk4Np06YhLi4Od911FxoaGiyP5ebmYtq0aYiJiUFcXBza29sBAJmZmYiNjcWkSZPwm9/8\nxu6vg8ie+jNu3n//fSQmJlpuUqkUubm5ADhuaOzpz9hpbW3FsmXLEBcXB41G0+mUfY4dGmv6M3ba\n29vx85//HHFxcUhISMChQ4csx3Ds0FhSXFyMWbNmITo6GjExMdi6dSsAoKamBvPmzUN4eDjmz5+P\nuro6yzGpqamYNGkSIiMj8cUXX1i2c+zQWNLfsVNTU4NZs2bB3d0djz76aKe+OHaGCZus7DUMffPN\nN2JWVpYYExNj2ZacnCx+8803oiiK4o4dO8Snn35aFEVR7OjoEOPi4sTc3FxRFEWxpqZGNBqNoiiK\n4k033SQeO3ZMFEVRXLBggfjZZ5/Z82UQ2VV/xs21Tp48KYaGhlraHDc01vRn7Lz55pvi0qVLRVEU\nxebmZjEkJET84YcfRFHk2KGxpz9j55VXXhF/8YtfiKIoilVVVWJSUpLlGI4dGkvKy8vFEydOiKIo\nig0NDWJ4eLio1WrFJ554QnzuuedEURTFTZs2ievWrRNFURRPnz4txsfHi+3t7WJhYaEYGhoqmkwm\nURQ5dmhs6e/YaWpqEg8fPiz+4x//EB955JFOfXHsDA9jZgbXrbfeCk9Pz07b8vPzceuttwIA5s6d\ni//85z8AgC+++AJxcXGWvx56enpCIpGgvLwcDQ0NmDJlCgDgwQcfxH//+187vgoi++rPuLnWBx98\ngGXLlgEAxw2NSf0ZOwEBAWhqaoLRaERTUxMcHR2hUCg4dmhM6s/YycvLw6xZswAAvr6+8PDwwPHj\nxzl2aMzx9/dHQkICAMDNzQ1RUVEoLS3F3r17sWLFCgDAihUrLONgz549WLZsGRwcHBASEoKwsDAc\nO3aMY4fGnP6OHblcjltuuQVOTk6d+uHYGT7GTMDVnejoaOzZswcAsGvXLhQXFwMAzp07B0EQcMcd\ndyApKQnPP/88AKC0tBRqtdpyfFBQEEpLS+1fONEQsjZurvWvf/3LEnBx3BCZWRs7KSkpUCgUCAgI\nQEhICJ544gl4eHhw7BBdZm3sxMfHY+/evTAajSgsLERmZiZKSko4dmhMKyoqwokTJzB16lRUVlZC\npVIBAFQqFSorKwGYL+x17RhRq9UoLS3tsp1jh8aSvoydK65fIJ3fd4aPMR1w7dixA9u2bUNycjIa\nGxvh6OgIADAYDDh8+DA++OADHD58GLt378aBAwd4JUciWB83Vxw7dgxyuRwajWaIKiQanqyNnffe\new8tLS0oLy9HYWEhXnjhBRQWFg5xtUTDh7Wx84tf/AJqtRrJycn47W9/i+nTp0MqlfLnNRqzGhsb\ncf/992PLli1wd3fv9JggCBwbRFZw7IwesqEuYChFRERg//79AMyztj799FMAQHBwMG677TZ4eXkB\nABYuXIisrCz87Gc/Q0lJieX4kpISBAUF2b9woiFkbdxc8c9//hPLly+3tIOCgjhuiNB17Ozbtw8A\n8N133+Hee++FVCqFr68vbrnlFmRmZmLGjBkcO0Sw/n1HKpXi//2//2fZ75ZbbkF4eDiUSiXHDo05\nHR0duP/++/HAAw/gnnvuAWCeeVJRUQF/f3+Ul5fDz88PgPlns2tn4JeUlECtVvNnNhqT+jN2rOHY\nGT7G9AyuS5cuAQBMJhOeffZZrF69GoD5dJGTJ0+ipaUFBoMBhw4dQnR0NPz9/aFQKHDs2DGIooh3\n333XMgiIxgpr4+bKtl27dmHp0qWWbQEBARw3ROg6dn71q18BACIjI3HgwAEAQFNTE44ePYrIyEh+\nzyG6zNr3nZaWFjQ1NQEAvvzySzg4OCAyMpLfd2jMEUURDz30EDQaDdauXWvZftddd+Htt98GALz9\n9tuWcXDXXXfhn//8J9rb21FYWIj8/HxMmTKF33dozOnv2Ln2uGvx+84wMmTL29vZ0qVLxYCAANHB\nwUFUq9Xi9u3bxS1btojh4eFieHi4+Pvf/77T/u+9954YHR0txsTEWK6aIIqimJGRIcbExIihoaHi\no48+au+XQWRX/R03aWlp4rRp07r0w3FDY01/xk5ra6v405/+VIyJiRE1Go34wgsvWB7j2KGxpj9j\np7CwUIyIiBCjoqLEefPmiRcvXrQ8xrFDY8m3334rCoIgxsfHiwkJCWJCQoL42WefidXV1eKcOXPE\nSZMmifPmzRNra2stx2zcuFEMDQ0VIyIixM8//9yynWOHxpKBjJ3x48eLXl5eopubm6hWq8W8vDxR\nFDl2hgtBFK+LH4mIiIiIiIiIiEaQMX2KIhERERERERERjXwMuIiIiIiIiIiIaERjwEVERERERERE\nRCMaAy4iIiIiIiIiIhrRGHAREREREREREdGIxoCLiIiIiIiIiIhGNAZcREREREREREQ0ojHgIiIi\nIiIiIiKiEe3/A0REcJKJvCSoAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f17300250d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Correlation between TSI and Temperature: 91.0%\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(csv_data[[\"Year\", \"Average\",\"CH4\"]].dropna()[\"Year\"],\n",
      "        csv_data[[\"Year\", \"Average\",\"CH4\"]].dropna()[\"Average\"],\n",
      "        width=0.7, edgecolor=\"none\",\n",
      "        color=(csv_data[[\"Year\", \"Average\",\"CH4\"]].dropna()[\"Average\"]>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "\n",
      "ch4_ax = plt.twinx()\n",
      "ch4_ax.plot(csv_data[\"Year\"], csv_data[\"CH4\"], linewidth=3, c=\"b\", alpha=.8)\n",
      "plt.ylabel(u\"CH4 CCGG (Individual Flasks) ppb\")\n",
      "plt.legend(loc=\"best\")\n",
      "plt.grid()\n",
      "plt.xlim(np.min(csv_data[[\"Year\", \"Average\",\"CH4\"]].dropna()[\"Year\"]),\n",
      "         np.max(csv_data[[\"Year\", \"Average\",\"CH4\"]].dropna()[\"Year\"]))\n",
      "\n",
      "plt.show()\n",
      "print \"Correlation between TSI and Temperature: %s%%\" % (round(1000*pearson_def(\n",
      "                                        csv_data[[\"Average\",\"CH4\"]].dropna()[\"Average\"].values,\n",
      "                                        csv_data[[\"Average\",\"CH4\"]].dropna()[\"CH4\"].values))/10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y56/vkcrdw0/S58oMVp465fQnL7q7Wbm+ZxrX90wjr8DMd/sCWLe7DZv3BpZZ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\nqMsDA6qb4mJpwwZrwmvPnorXO4UfV2J3i7q2zJO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       "text": [
        "<matplotlib.figure.Figure at 0x7f1728bd16d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Correlation between TSI and Temperature: 80.6%\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Forecasting TSI, CO2 and CH4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr_rbf = SVR(kernel=\"rbf\")\n",
      "C = [30]\n",
      "gamma_1 = [0.015]\n",
      "gamma_2 = [0.005, 0.004, 0.003, 0.002, 0.001, 0.0009, 0.0008, 0.0007, 0.0006, 0.0005, 0.0004, 0.0003, 0.0002, 0.0001]\n",
      "epsilon=[0.01, 0.001]\n",
      "parameters_1 = {\"C\":C, \"gamma\":gamma_1, \"epsilon\":epsilon}\n",
      "parameters_2 = {\"C\":C, \"gamma\":gamma_2, \"epsilon\":epsilon}\n",
      "\n",
      "gs_1 = GridSearchCV(regr_rbf, parameters_1, scoring=\"r2\")\n",
      "gs_2 = GridSearchCV(regr_rbf, parameters_2, scoring=\"r2\")\n",
      "\n",
      "gs_1.fit(csv_data[[\"Year\",\"TSI\"]].dropna()[[\"Year\"]], csv_data[[\"Year\",\"TSI\"]].dropna()[\"TSI\"])\n",
      "gs_2.fit(csv_data[[\"Year\",\"TSI\"]].dropna()[[\"Year\"]], csv_data[[\"Year\",\"TSI\"]].dropna()[\"TSI\"])\n",
      "\n",
      "print \"Best Estimator:\\n%s\"  % gs_1.best_estimator_\n",
      "print \"Best Estimator:\\n%s\"  % gs_2.best_estimator_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Best Estimator:\n",
        "SVR(C=30, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.015,\n",
        "  kernel=rbf, max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)\n",
        "Best Estimator:\n",
        "SVR(C=30, cache_size=200, coef0=0.0, degree=3, epsilon=0.01, gamma=0.0001,\n",
        "  kernel=rbf, max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "annual_index_feature = np.arange(np.min(csv_data[[\"Year\",\"TSI\"]].dropna()[\"Year\"]),\n",
      "                                 np.max(csv_data[[\"Year\",\"TSI\"]].dropna()[\"Year\"])+10)\n",
      "annual_index_feature = [[item] for item in annual_index_feature]\n",
      "\n",
      "\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "\n",
      "tsi_ax = plt.twinx()\n",
      "tsi_ax.plot(csv_data[\"Year\"], csv_data[\"TSI\"], \"--\", linewidth=3, c=\"gray\", label=\"TSI\")\n",
      "tsi_ax.plot(annual_index_feature,\n",
      "            gs_1.best_estimator_.predict(annual_index_feature),\n",
      "            linewidth=3, c=\"green\", label=\"TSI Short-term Trend\", alpha=0.4)\n",
      "tsi_ax.plot(annual_index_feature,\n",
      "            gs_2.best_estimator_.predict(annual_index_feature),\n",
      "            linewidth=3, c=\"blue\", label=\"TSI Long-term Trend\", alpha=0.4)\n",
      "plt.ylabel(u\"TSI Reconstruction from IPCC AR5\")\n",
      "\n",
      "plt.legend(loc=\"upper left\")\n",
      "\n",
      "plt.xlim(np.min(annual_index_feature)-1, np.max(annual_index_feature))\n",
      "plt.title(\"Total Solar Irradiance (TSI) with a short term and long term predictions\")\n",
      "plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature), 10))\n",
      "plt.grid()\n",
      "plt.show()\n",
      "\n",
      "st_prediction = gs_1.best_estimator_.predict(annual_index_feature)\n",
      "lt_prediction = gs_2.best_estimator_.predict(annual_index_feature)\n",
      "print u\"Long-Term Wave Length \\u2248 (%s - %s) * 2 \\u2248 %s\" % (annual_index_feature[np.argmax(lt_prediction)][0],\n",
      "                        annual_index_feature[np.argmin(lt_prediction)][0],\n",
      "                        (annual_index_feature[np.argmax(lt_prediction)][0]-annual_index_feature[np.argmin(lt_prediction)][0])*2\n",
      "                        )\n",
      "st_min = csv_data[\"Year\"][np.argmin(csv_data[\"Year\"][5:13])]\n",
      "st_max = csv_data[\"Year\"][np.argmax(csv_data[\"Year\"][5:13])]\n",
      "print u\"Short-Term Wave Length \\u2248 (%s - %s) * 2 \\u2248 %s\" % (st_max,\n",
      "                        st_min,\n",
      "                        (st_max-st_min)*2\n",
      "                        )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u377d2GEQERERERERPdIe65FaTT2jSE8m3pdERERERET0KGjqf79yTi0iIiIi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cX+/aXrv/rqOWJzVRnldIw6g51gMoisyPjivrJV0VzGEdHT8ynC4QvEk14Gs68AokKsU3f5SrEr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\n03lSkQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f17288f2290>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Long-Term Wave Length \u2248 (1973 - 1882) * 2 \u2248 182\n",
        "Short-Term Wave Length \u2248 (1863 - 1856) * 2 \u2248 14\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr_rbf = SVR(kernel=\"rbf\")\n",
      "C = [1,10,20,30,50,100,1000]\n",
      "gamma_2 = [0.01, 0.005, 0.004, 0.003, 0.002, 0.001, 0.0009, 0.0008, 0.0007, 0.0006, 0.0005, 0.0004, 0.0003, 0.0002, 0.0001,\n",
      "           0.00009,0.00008,0.00007,0.00006,0.00005,0.00004,0.00003,0.00002,0.00001]\n",
      "epsilon=[0.01, 0.001]\n",
      "parameters_2 = {\"C\":C, \"gamma\":gamma_2, \"epsilon\":epsilon}\n",
      "\n",
      "gs_2 = GridSearchCV(regr_rbf, parameters_2, scoring=\"r2\")\n",
      "\n",
      "gs_2.fit(csv_data[[\"Year\",\"CO2\"]].dropna()[[\"Year\"]], csv_data[[\"Year\",\"CO2\"]].dropna()[\"CO2\"])\n",
      "\n",
      "print \"Best Estimator:\\n%s\"  % gs_2.best_estimator_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Best Estimator:\n",
        "SVR(C=1000, cache_size=200, coef0=0.0, degree=3, epsilon=0.001, gamma=5e-05,\n",
        "  kernel=rbf, max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "annual_index_feature = np.arange(np.min(csv_data[[\"Year\",\"CO2\"]].dropna()[\"Year\"]),\n",
      "                                 np.max(csv_data[[\"Year\",\"CO2\"]].dropna()[\"Year\"])+10)\n",
      "annual_index_feature = [[item] for item in annual_index_feature]\n",
      "\n",
      "\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "\n",
      "tsi_ax = plt.twinx()\n",
      "tsi_ax.plot(csv_data[\"Year\"], csv_data[\"CO2\"], \"--\", linewidth=3, c=\"gray\", label=\"CO2\")\n",
      "\n",
      "tsi_ax.plot(annual_index_feature,\n",
      "            gs_2.best_estimator_.predict(annual_index_feature),\n",
      "            linewidth=3, c=\"blue\", label=\"CO2 Trend\", alpha=0.4)\n",
      "plt.ylabel(u\"CO2 CCGG (In Situ) ppm\")\n",
      "\n",
      "plt.legend(loc=\"upper left\")\n",
      "\n",
      "plt.xlim(np.min(annual_index_feature)-1, np.max(annual_index_feature))\n",
      "plt.title(\"CO2 with trend\")\n",
      "plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature), 10))\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L8fPz49VXXyUtLa3Z5w8YMIABAwZQU1NDTU0NXq+31YOsWgy4Jk+e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\nahxwERERERERERGRqv0PufDpylIP+i0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1728d11490>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regr_rbf = SVR(kernel=\"rbf\")\n",
      "C = [1,10,20,30,40,50,60,70,80,90,100,1000]\n",
      "gamma_2 = [0.01,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09, 0.005, 0.004, 0.003, 0.002, 0.001]\n",
      "epsilon=[0.01, 0.001, 0.0001, 0.00001]\n",
      "parameters_2 = {\"C\":C, \"gamma\":gamma_2, \"epsilon\":epsilon}\n",
      "\n",
      "gs_2 = GridSearchCV(regr_rbf, parameters_2, scoring=\"r2\")\n",
      "\n",
      "gs_2.fit(csv_data[[\"Year\",\"CH4\"]].dropna()[[\"Year\"]], csv_data[[\"Year\",\"CH4\"]].dropna()[\"CH4\"])\n",
      "\n",
      "print \"Best Estimator:\\n%s\"  % gs_2.best_estimator_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Best Estimator:\n",
        "SVR(C=1000, cache_size=200, coef0=0.0, degree=3, epsilon=0.0001, gamma=0.004,\n",
        "  kernel=rbf, max_iter=-1, probability=False, random_state=None,\n",
        "  shrinking=True, tol=0.001, verbose=False)\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "annual_index_feature = np.arange(np.min(csv_data[[\"Year\",\"CH4\"]].dropna()[\"Year\"]),\n",
      "                                 np.max(csv_data[[\"Year\",\"CH4\"]].dropna()[\"Year\"])+10)\n",
      "annual_index_feature = [[item] for item in annual_index_feature]\n",
      "\n",
      "\n",
      "plt.figure(figsize=(20,5))\n",
      "plt.bar(annual_index, annual_temp,  width=0.7, edgecolor=\"none\", color=(annual_temp>0).map({True: 'r', False: 'b'}),\n",
      "        label=\"Annual Average Global Anomaly\", alpha=0.3)\n",
      "plt.ylabel(u\"CRUTEM4 Temperature Anomaly (\\u00B0C)\")\n",
      "\n",
      "tsi_ax = plt.twinx()\n",
      "tsi_ax.plot(csv_data[\"Year\"], csv_data[\"CH4\"], \"--\", linewidth=3, c=\"gray\", label=\"CH4\")\n",
      "\n",
      "tsi_ax.plot(annual_index_feature,\n",
      "            gs_2.best_estimator_.predict(annual_index_feature),\n",
      "            linewidth=3, c=\"blue\", label=\"CH4 Trend\", alpha=0.4)\n",
      "plt.ylabel(u\"CH4 CCGG (Individual Flasks) ppb\")\n",
      "\n",
      "plt.legend(loc=\"upper left\")\n",
      "\n",
      "plt.xlim(np.min(annual_index_feature)-1, np.max(annual_index_feature))\n",
      "plt.title(\"Methane (CH4) with trend\")\n",
      "plt.xticks(np.arange(np.min(annual_index_feature)-1, np.max(annual_index_feature), 10))\n",
      "plt.grid()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ddBIJCQmdeEcicjS5+eabWbJkCdnZ2WzYsAGA9evXc/vtt1NdXU3v3r159dVX\ncbvdADz++OPMnTsXm83GM888E82R1qxZw5QpU/D5fIwdO5b//d//7bA+Nxl8vfLKK1iWxY033tho\nu81mY+LEiR3WKREREREREWlebS0UFkaWNf/pQdm3Ofj8ZoNjvKEQ/9y7F28oFN1mGBa1Zhl9c+PJ\nTQuSneonNTFA3bxgpcl2rrj2siN5KyJyjLjpppv46U9/2iAruvXWW5kzZw7Dhw/n5Zdf5sknn2T2\n7Nls3LiRN954g40bN5Kfn8+oUaPYvHkzhmFwxx138NJLLzFkyBDGjh3LsmXLuPTSSzukz00GX7/7\n3e/46KOPGm2/6qqruOCCCxR8iYiIiIiIHCHhMJSUHAi6CguhouLA/qLSRHJSzUbnxdts2J21pCdU\nkphYjctVjctVQxeXk9P7GHTRaC4RaYPhw4ezffv2Bts2b97M8OHDARg1ahSXXnops2fPZtGiRUyY\nMAGHw0Hv3r3p27cvq1atolevXlRWVjJkyBAAbrzxRv72t78d+eArEAhEh6bVl5SURCAQ6JDOiIiI\niIiICFR5bewrj9TkWrXNTuHLUDdoKxgMUl1dRVVVNdXV1VRWethbWUXfJDfpCSbZqX6ykmujr1l7\ndrHJ46GP283JyRmc7O5Nalxc596giBw3Tj/9dBYtWsQPfvADFi5cyK5duwDYs2cPQ4cOjR7XvXt3\n8vPzcTgcdO/ePbo9NzeX/Pz8Dutfk8GXz+ejqqqKpKSkBtsrKysVfImIiIiIiLSTQNA4UJdrf22u\nqloblmVhMwzKPGF6HnhSkfXr17Nnzx4Mw8LlqiExsZpevWs489QyLuyV2qj9S3v0YJzNhln3LKOI\nSDuaO3cud955J48++ihXXnklzrpZMI4STQZft9xyC9dccw2///3v6d27NwB5eXlMnz6dW2655Uj1\nr9WWL1/OyJEjo+8BrWtd61rXutaPy/U6y/cXFB35ne8c1jrp6UfV/R3Ln+eG4mKuqGtP1+vw623Y\ntIkrcnN1PV3viF9vw6ZNuJcvb/bvl6auZ1mwePXXlFc76J09hMKKOP7+nzV4/H4yM79DVaCKbQVr\nqA2GuOCUCzjJ7SZ/3zriv7U455yRZGfD5s2rKS9fT79+OZimRV5eHh4PlFuDgNRG/V399ddH7P6O\n9OfZUdfTutZP1PWnn36aL774IpoDtcZpp53Ge++9B8CmTZtYsmQJEBnJVTf6C2D37t10796d3Nxc\ndu/e3WBWK9NGAAAgAElEQVR77v7/v5uyZs0aPv30U0zT5LzzzuPss89udf8My7Kspnb+4Q9/4PHH\nH6eyMjKdbVJSEjNnzuSOO+5o9QWOBMMwaOY2RESkk723cCH+kpJ2a8+ZkcEl11zTbu0daxb/4Q/R\nL//t0l5+Plfcfnu7tXesac/PszWfpa7Xftc70v8v6Hq6XmuvVf963lqT3aV2NheZlFcmUOtNIhBs\nOPJqU0UF39Yv2AXYbCHOzHFwWV83X3p3MnHGJOrKcX311VcsX76czMxM0tPTycjIYMu//sW1vXph\ntMOorqPt//XOuJ6IRMTKW7Zv384VV1wRndWxqKiIrKwswuEwU6ZM4aKLLmLKlCls3LiRiRMnsnr1\n6mhx+y1btmAYBueeey7PPPMMQ4YM4bLLLuPOO+9sssbX7NmzWbhwIVdffTWWZbFo0SJ++MMf8vDD\nD7fqHpoc8QVw++23c/vtt+PxeABITk5uVaMiIiL1+UtK2v2HGxERkUOVX13NX7ZtwwRshoG5f8lx\nuRgXY5RDsc/HR/n5mIaBzTDI93p5++23ycjI4LzzzoseFwpBcTFs3VrJB2tTWfIPk5JqA1/ID0Cv\nJDvfTW8cTCU57LhcXhITq6NLfLyPM9LTOOekk9ibX0X9GvRnnHEGZ5xxRoM29vz73+0SeomINGfC\nhAmsWLGC4uJievTowX//939TVVXFc889B8D48eOZMmUKAAMGDODaa69lwIAB2O12nn/++ejfU88/\n/zxTpkzB6/UyduzYZgvb//nPf+bLL78kPj4egJkzZzJw4MDDD76CwSDvvfceTqeTiy++uFWNiYiI\niIiIdLaCggJWrVpFUlISo0aNarQ/GA5TFaNucYLNFrO96mCQbw4akVWydh0ZGSeTlXVedJbF0tK6\n2Rf95BUkAuGD2olc0xUXoktqpPh8dkotOD2sLNpLRnw8GXFuMuKzyIiLw9lEf0REOsvrr78ec/ud\nd94Zc/sDDzzAAw880Gj7oEGDoiPGWpKbm4vX640GXz6fr0Fx/JY0GXxdc801XHHFFVRVVfHqq68y\nb968VjcqIiIiIiJyJFmWxTfffMNnn33Gjh07AHA6nQwfPpy4g2YwDDVRJqWp4u9hyyIQsFNdnVhv\ncZGWlk2syRHrPxZkmmGSEr10S/VzWrbFxNODJMaHDjojjquTTmr9zYqInAB++tOfApCSksLpp5/O\n6NGjAfjggw8YMmRIq9tpMvjavn071157LV6vl9dee+0wuysiIiLHKtVoE5GjXSAcZmNJCeveeKPB\ndr/fz6ZNm/jO/qLmdXomJTHjjDMIWRZhyyJMJAxz7A++giGDYo+TwvLITIu7SrvgKD+JZCzcwM5A\nDWeccQpOZ+PUKzUVunZ1Eiwr5pI+SZycYZAe78I0EvcfcXDoJSIisQwaNAjDMBg8eDDjxo2LPiY5\ncuTINj3a3WTw9cILL0SHqr344ouH2V0RERE5VqlGm4gc7RymSbzdTm0oEiqZpsmAAQM499xzYz4O\nYzdN3E4nAJYF5dUOSvaHXEUVTkqrnIQbPqVIN9eB9yX+AD179iIhAbKzDyxZWRBpNhljTy3n5mZ2\n0B2LiBz/6mqFAdTW1vLNN99gGAb9+vXDuf/v8NZoMvgaMmRIm4aOiYiIiIiIdCTLsgiEwzFrX2W7\nXPhNk0GDBnHOOec0OTFXTa0tOpKrsMJJsceJP2C2eG27zSIzOVKTy/Dt5voJ4HYf9i2JiEgLlixZ\nwu23306fPn0A2LZtGy+88AJjx45t1flNBl/Lly9n5MiRzZ788ccfc+GFF7a+tyIiIiIiIm0UDIfZ\nUFrKqqIisuPjufqkxvWwkhwO7r7jDhwOR3RbIABFRZHli69zqdySS5W35YLxhgGpiYFo8fnslFrS\nkgKY+/OxovxKhV4iIkfIjBkz+Pjjj+nbty8AW7duZezYsYcffL3zzjv87Gc/Y9SoUQwePJhu3boR\nDofZu3cvn3/+OR9++CEXXnjhYQVfy5Yt46677iIUCnHrrbdy//33N9i/fPlyfvCDH0RTvfHjx/PQ\nQw8d8vVEREREROTYURkI8HlREZ8XF1MTDAJQ5PVycW5u9FHFOuGwSWmpIxp0FRVBefmB/XuLk+mS\nGjv0csWFyE71k5VcG311OmIXwBcRkSMrOTk5GnoB9OnTp8lRvbE0GXz95je/obKykkWLFvHBBx9E\nZ0bp1asX559/Pg8++CBJSUmH3PFQKMRPfvITPvzwQ3JzcznnnHO48sor6d+/f4PjRowYwdtvv33I\n1xERERERkWNPIBzm+Y0b8YUaFoO3mSa7q2rIsidRVOGMLJ44lu9yU+hoorF67DaLrJQDIVd2Si1J\nCSo4LyJytBo0aBBjx47l2muvBWDhwoUMHjyYv/71rwBcffXVzZ7fZPAF4Ha7mTRpEpMmTWqn7h6w\nevVq+vbtS+/evQG4/vrrWbRoUaPgy2piqmEREZGjhWY9FBFpfw7T5PS0ND4vKsbrjcf0p9DDkUOG\nmc2/Po8nFG44o1fYajzDl2lCWlqk6Hwwv4Dxp3pJdwdow2RgIiLSyXw+H9nZ2axYsQKArKwsfD4f\nixcvBg4z+OpI+fn59OjRI7revXt3Vq1a1eAYwzD417/+xcCBA8nNzeU3v/kNAwYMONJdFRERaZZm\nPRQROXS+UIiaYJD0uDgsCzw19ugorrKiNHZsr6JXYgpdXS5MP1Q00Y5hQGpqZHbFzMxI2JWRAfb9\nP/FUbCwnIznxiN2XiIi0j3nz5h3W+Z0WfBmt+DXL2Wefza5du3C5XCxdupRx48axadOmI9A7ERER\nERHpSL5gkL9uLmDl7loSwxmck9yDohgzLA7Lil1FPtkVJCvFH51pMatqO1ddO+JIdF1ERI6grVu3\nctddd7Fy5UoMw+B73/seTz31VLQefEs6LfjKzc1l165d0fVdu3bRvXv3Bse4602VMmbMGKZNm0Zp\naSnp6emN2ps1a1b0/ciRI1uckVJERERERNqPZVkEwmG8oRDeYJBQOFxvH1RWQnExLF26ms2by9i5\nM5dA4MCPI0ndbCQ7zFhNkxgfitblykrxk5XiJ84RbnCM3RuOea6IiBzbJk6cyE9+8pNoTa833niD\nCRMmNHpqsCktBl+DBg3i5ptvZuLEiaSlpR1eb+sZPHgwmzdvZvv27eTk5PDGG2/w+uuvNzhm3759\nZGdnYxgGq1evxrKsmKEXNAy+ROTIaM+6RqppJCIicnSwLAt/OIw3GIyGWN5QiJ6JiY1mUgRYtGMH\nWyoq8IZChCyLcBh8vniy7F1ZuTISdpWUgN8fOX7tWovCQj/1fxRxOxwEwiHAQbwz3CjkcsWp+LyI\nyInK6/Vyww03RNcnTZrEk08+2erzWwy+/vKXv/Dyyy9zzjnnMHjwYG666SZGjx7dqkcVm72w3c6z\nzz7LJZdcQigU4pZbbqF///688MILAPz4xz/mzTff5Pe//z12ux2Xy8Vf/vKXw7qmiLSv9qxrpJpG\nIlJHobpI53pj2za+rWhcSeu6Pn3od1DwFQwZFJY72VaQSk2NC6/XhdebQDhskJmQQPKGxu07HAem\nXsxJcnJOrpPvdLWRnVJJZnIJbpdCLhEROWDMmDE8/vjjTJgwAYiM+BozZgylpaUATQ6QqtNi8HXK\nKafwq1/9iscee4x33nmHm2++GdM0ufnmm/mv//qvFi/QUufHjBnTYNuPf/zj6Pvp06czffr0Q25f\nREREjj0K1UU6XigUCZdsNlujffExtgGUe8Pkh+Ip8Tgo9jgpqXRSXu3gi5JEdlZVAWAaBk7TxBnj\nkcX4+EjR+e7de+FyZbHz82XccGpXDMPfjncmIiLHmzfeeAPDMHjxxRdjbt+2bVuz57eqxtf69et5\n+eWXWbp0KePHj2fixIl8+umnXHTRRXzxxReH3nsRERERETliampqWLNmDf/+978ZOXIkZ599dqNj\n4u12rGAcIV8SIb+bgDcRvy+RD3ekkZ2Q0Oj401JSODUlBadpYtv/VEhSQogt/j0MGhQJuzIzITE6\noWIGkMHib0Ic5kMkIiJyAti+ffthnd+qGl8pKSnceuut/PrXvyY+Ph6AoUOH8s9//vOwLi4iIiIi\nIh2v2Odjp8fDU089RTAYBOCzzz5j4MCz8HgMSkth0/Zs3i3IptiTS46/3qgvx/4lBsOArilhMt1+\nMpIDZCZHZlmMc4RZnJ/PoEEdf28iIiLNaTH4WrhwYZNTRL711lvt3iEREREREWk/u6ureenbb6mt\ndUZrcHm9CWzdmkYw6CU+3gXAtl0ZuFPjm2zHZlqkuwNkuCPhVsb+sMtus47UrYiIiLRZk8HXb3/7\n2+h7wzCwLKvB+owZMzq2ZyIiIiIi0ma1AZPSSgelVQ5KK52UVHZh8yY3Fb4wACkpKZx00knk5OTE\nrPEF4HSEyTgo5EpLCmA2Lt0lIiJyVGsy+KqsrIw5c6NlWYc9o6OIiIiIiByeYMigrMpBaaWDsioH\n+eUmnuo4AoHGzyX2dKWyJeThzMHnkJGR0eD7vNsNaWlQ1aOYUScHyUz2404Iqv6WiIgcNb7++mu2\nb9+OaZr06tWLfv36tfrcJoOvWbNmtUffRERERETkMFgWVFTbIyFXlXP/aC4nnho7lgUev59tlZXk\n19RwakoKpyQ3Dr56JyXhNWr57nczSU+PBF3p6ZHFsf/wwPYi+nR1HuG7ExERiS0vL4+nnnqKd999\nl9zcXHJycrAsi4KCAnbv3s3ll1/O3XffTe/evZttp8UaX16vl5deeomNGzfi9Xqjvx2aO3duu9yI\niIiIiLSfHTt2sHXrVi666KLO7oq0UTgMHg9UVEB5OWzYlENgR1fKqhyEwg2HX1lAodfLtspKin2+\n6PbtlZWclppEhjtIujtAWqKfdHeAdHeAj4p3cvnl+u9CRESODffffz9Tp07lt7/9LQ5Hw1/qBAIB\nPv74Y372s5+xYMGCZttpMfi64YYb6N+/P8uWLeORRx7hz3/+M/379z+83ouIiIhIu6qurubDDz/k\niy++AKB3796NJijyh0K8unUrZ2VkcEZaGnYVbOoUtQGT8io7u/emsHp1JOQqL4+EXuHwgePy96WQ\nmRp7BJbH7+ffxUXExflIS/OSkOAjIcFLn3QY37+GtPi4I3Q3IiIiHaO5QMvhcDB69GhGjx7dYjst\nBl9btmzhzTffZNGiRUyePJmJEydy/vnnt623IiIiItIhLMti3bp1fPDBB/jqjfz58MMPmTp1aoNj\n15WUsLOqip1VVXy0Zw/nZmUxKDOTBHuLXwmljSwLKr12yqsdlFfZqahxRN5XO/DWRgLHr8rjsX3R\nuvYS40ORkVtJkSLz6e4A6Xs2s7umEgMYkJrK0C5d6J6Y2HE3JSIi0gkWLFjApZdeSnJyMo8++ihr\n167l4Ycf5uyzz27V+S1+y3E6I79lSklJYcOGDXTt2pWioqLD67WIiIiItItVq1bx3nvvNdjWv39/\nLrnkkgYFzC3LYm1xcXS9KhDgoz17+GTvXi7t3p2zMzOPWJ+PJ4GgEQ20KqrtDd4f/HhiayQlQWpq\nZAns3svgbmV0T7PITmz8tX24lc32ygTOzc4mxanaXCIicnx69NFHufbaa/n000/56KOPuPfee7nj\njjtYtWpVq85vMfiaOnUqpaWlPPbYY1x55ZVUVVXx6KOPHnbHRUTa6r2FC/GXlLRLW86MDC655pp2\naUtEpDOdddZZ/Otf/6KyspLU1FTGjh3LKaec0ug4wzCYcuqprCkuZlVREVWBAACBcJi0OD0W1xzL\ngqoqKC5L5Kuge3+4Zaei2kG1z9bm9uw2i5TEIN0cHgYNioRcKSmRxeGAmpoaNm/ejDd+K4sLAwyx\nshiT2KNRO6empHBqSkp73KKIiMhRy2aL/Fv7zjvvMHXqVC6//HIefvjhVp/fquALYMSIEeTl5R1i\nN0VEDp+/pIQrcnPbpa3F+fnt0o6ISGeLi4tjzJgx7NmzhwsuuKBR8df6Eux2zu/alaHZ2XxVVsbK\nffuwGQa9k5JiHm9ZVoNRY8ezQNDAUxVHXl6k1lZlZeTV44mEXuEwrP+qJ4HU1Fa36YoLkZoUIDUx\nSGpigNTEACmuAEkJIQwj8m/RoEEHji8pKeFvf/sbu3fvbtDOFyUlXJiTQ7yt7SGbiIjIsS43N5fb\nbruNDz74gJ///Of4fD7C9YtitqDF4KusrIz58+ezfft2gsEgEPmN4TPPPHPovRYRERGRNimrrcUX\nCtHN5Wq0r3///m2afMhumpyZkcHA9HRqgsGY4VZlIMCfN2/mnKwsBmZk4DjGC+FbFtTU2vDU2PHU\n2Kn0Rl49XgeeGjs+v8n6cjfVyW1r12ZaJLv2B1v1Qq4UVwCnw2pTW263m4KCggbbTMOgf2oqgVBI\nwZeIiJyQFi5cyNKlS7nvvvtITU2loKCAJ598stXntxh8jR07lmHDhvHd7343+qXoRPnNn8jhas9H\n80CP54mInIiC4TD/2rePf+zdS6rTye39+2NrpxDKMAwSmxghtrqwkEKfjyW7dvFxQQHnZGYSaMNv\nVztDMGQcCLQahFt2KmsOreZWncRESEuuoV93+/6QKxJwuROCtParcYnPx2aPh60eDz886aRG+51O\nJ7169SIvL4/u3bsTKivj+pNOwt3MKD4REZHj3euvv84tt9wSXe/WrRtPP/10q2Z0hFYEX7W1tcyZ\nM+fQeyhyAmvPR/NAj+eJiJxo8ioreXfnToprawEorq3lX4WFDO/atUOva1kWX5WVRddrgkFW7N2L\nAZy8fj0DBw7s0Os3JRSKPHZYt2zZkcny0oxo0FVTe+gjomymRZLLT8+ekJwcWdzuA692Oyyu3MEF\nbfx3fUdVFV+Xl7O5ooLS/X+OANsqK2MeP2bMGFwuFy6Xi8V/+INCLxEROeG9+eabxMXFMWnSJACm\nT5+O1+tt9fktBl8TJ07kxRdf5IorriCuXuHT9PT0Q+iuiIiIiLTG+7t3s7KwsMG2rgkJnOR2d/i1\nDcPg9v79WVdSwmeFhVT4/QBYRH7L2hEsC6p9Nqp89ujrxl0h4t4/EHT5fA3P2bIzi8TUxFZfIyEu\njDshSHJCgGRXkGRXMLLuCuKKC/HOnnwuvfTidr2vNUVFbKgXItbZXFGBYW/8VTxTs2uKiIg08Ne/\n/pUrr7wSm83G0qVLSUtLY+7cua0+v8XgKz4+nvvuu49f/vKXmPuH1RuGwbZt2w691yIiIiLSrJx6\ntbycpsmFOTkMycrCPEIlJ+JsNoZmZzMkK4uNZWX8q7CQ6mCQ7OzsmMc3VwjfssDnN6ny2any2qiu\nbfjq8drw+Exshkn9hzh3lkPadti9ezc1NTUEg0FCoSDBYIhgMIh50KOXpglJ8UFMhxd3QoBst0V6\nYjgacjnsbau51RqWZZFfU4MJ5CQ2DuFOSUmJBl8O0+Qkt5tTkpM5NSWFFUVF7d4fERGR40VpaWn0\n/f/93//xgx/8gPPPP59HHnmE0tLSVg/IajH4+u1vf8vWrVv12ycRERGRI+j0tDTWlZSQYLMxunt3\nkp3OTumHaRickZ7O6WlpLDpotkGIhFq7dhXx5z//lQEDBvPNjgTK90Cl1yTTnko4GE+1z9agvtba\nkmLK/V6CYYtgOEzIigRS53ftSlqM+9y+PY+ysnIMw8LpDOB0+nE4/PTNiWf4yUSCrYQDsyW+umUL\nqz0eqAC7J1LHLNFuZ0yPHnSPEU6V19ZiMwzCVuuCMV8wyBaPh80eD1s8HmqCQU5LSeH6k09udGzf\n5GTOycri1ORkervd2I/xSQJERESOlLPPPrvBL9Usy2LJkiUsWbIEgLy8vFa102Lwdcopp5CQkHCI\n3RQRkaNVe06+oIkXRA5dbSiE3TAaFaw3DIMJJ5/caUFJMGTgrTWpqbVFFr+Nbbu68MknUFNzYPF6\n4Ysv9rBzZw4rV+4B6n77GmZYFzuZcY2/btaGQlQHgo22O+x+slMhMS5IUkKIWs8+Ro2CQGAve/Zs\nwuFoWEj+pNRU+vdoPMtldSBw4D4siwq/nwq/H6uJYOtvO3awo6oKgG+eeILExEQSExMZO3YsXbp0\naXBsfnU1L337LQe3tK2ykmA43OjPK8FuZ2yPHjGvKyIiIk3bvn17u7TTYvDlcrk488wzufDCC6M1\nvgzD4JlnnmmXDhyL9MOiiBwP2nPyBU28INJ2lmXxdXk5y3bvZkhWFufHKFjf3qGXZUFtIBJmef22\nA6HW/sXrPxB0+QONr7213CTpm4bbQqEQhQfVIqsTDB+Ih+IcYRLjQyTFB9ljKyfOX4bT6Y8uSXEh\nrji5N6ekpETPKc4vpU8fGDbsZEpL04iLiyMuLg6n00lcXBxf//3vMa+b5HCQEgpRFQhER5MBTc5g\nWT8o8/l8+Hw+Spr4rtclIQG7aTaY4TLRbueU5ORIiKkRXSIiIu1q4cKFXHrppbjdbh599FHWrVvH\nQw89xNlnn92q81sMvsaNG8e4ceOiw8uaq99wotAPiyIicqLZUVmJaRgk7X9kzGk79NnzBEpra1m6\naxdbPB4AVhQUcEZaGqn1JhJqrVAoUvTd64281r2vrY28rt3YndCuLtFA66CyWIfNZrMxZsyFFBZu\np7x8D9Wlu+idYsOdEGZwV4s+aQ4S40MN6mudW2sSttJx2mzEmSYO02z2+2VTX2y3rFgRc/vEvn2B\nyPdWfzhMdTBIdSBAShPBl8vhIDEUojrYcBRaYozHIu2mycluN55AgFNSUjglOZkcl+uE/34sIiLS\nUWbPns0111zDp59+ykcffcS9997L7bffzurVq1t1fovB15QpU6itrWXTpk0A9OvXD4emVRYRETnm\nWZZFTTBIpd/Pv//9b0pLSxk9enTMH+Df3rmT0tra6LrTNElyOPhR376kxwhrCr1enKZJosOBQyNg\nosLhMAVVVfy+sJBgvZFIcTYb5X4/qXFx1AZMfP79S8CGz2/i9dtirn9WmERBCx9vYYmbwlDb64OZ\nJrjiQg2W6uQihg8Hl+vAkpAApmkH+gJ9WfyHPxz0C8LGjzSmHULAdygMwyDOZiPOZov532mdm049\nFYC3d+9m1OTJVFdXU11djcvV+DFKgGv69DlikwyIiIic6Gz7f+H6zjvvMHXqVC6//HIefvjhVp/f\nYvC1fPlyJk+eTK9evQDYuXMnf/rTnxgxYsQhdllEREQ6k2VZ/HnLFgpqavCGQgBsfvddAL73ve/h\ndrsbnVP/UTAAfzhMaW0tcU2EWn/esoXK/efE2Wwk2e0kORxcc9JJMY+vqqoiISEh+sXmWGRZkULt\nJSUlVFdXY1nQrVtPamuJLl6vwZbdKVR4kwkG7QSDdk5yZZCTlMmnnzv4MNC2EVnBUNtDRacjfCDM\nckZeE+LCjUKuOEfjjpTlF9O/f5svecwwDAOXy4XL5SIrK6vJ4xR6iYiIHDm5ubncdtttfPDBB/z8\n5z/H5/MRbsMXphaDrxkzZvD+++9z2mmnAbBp0yauv/561q5de+i9FhERkXYX3l/Eu8jno8jno9jr\n5fu5uSQdNFLbMAyqg8Fo6FVfUVFRo+DLsix6JSVRFQxSHQxG6yYZRAp3H8yyrAZBWW0oRG0oRElt\nbZOjv5577jl8Ph/x8fFYgQB7PB7ibDYm9e0bs2bS+pISHKYZHc3j3P8+2eFot0fOLMuiNhymqjZA\nmc/CGzDp4nRTG7RFR2X5gyZ7Kv0s2bGPar9BIJDOax9/TDBoIzExnQsu6HlQqwYV+/qyt7oat8PB\nd9PTSTWdVNUcej9NE+LjIyOv4uMPvI+Li7wahbu5sqcvGmgdw9miiIiInIAWLFjAsmXLuO+++0hN\nTaWgoIAnn3yy1ee3GHwFg8Fo6AVw6qmnEgw2HrIuIiIinef/5eXxTXl5g8fnAM5IT28UfAFkxcez\nz+vFsb++Ut8BA8jKyiI1NbXRsYZhMGF/zSTYHwjtr4cUa+RLIBymS0JCJCgLBKj7fZzTNGPWBgsG\ng/h8PoDo616vFwOwxWg/bFn8bceOmJ/Dw2edxcFnWJbFn/70CqYZj82WgGHEY5rxQBwDB55D3u4M\nVlenUhswqQ2YFFaHeH/n/iAraCMUigcgyWHnwm6NC9BXB4PkF3obbff7A422QaRG1Pe6dCHF6SRW\nDOiwWyQ4Q8Q7w8Q7Iq8Hr8c7IqO0Mot2cfWtzY/C3/pJJV3Skps9RkRERORolZiYyPjx4yksLGTn\nzp1ApAxXa7UYfA0aNIhbb72VSZMmYVkWr776KoMHDz70HouIiEibWZbF9u3b6d27d5PHHBx6ART5\nfJyc3Dj0uCgnh1G5uSQ7HLyzZw9XXHVVq/tiGAbxdjvxMUZ7AThtNm7b/zycZVl498+uVxtjhBlE\nwq6kpKT9jwc2rHt1YHIdCAQNagMmnloLjyeJUMhGKGQnFLIRDNowLAefODKjAZY/aPJZYQJ7bSHe\nfjut/hUBH6Zp4vEYfJuXTXzZgc/IFwpRUtm4JlZtKPaQeqftQHxlGgbxCQnExTmJj0/A4YiMvIqP\nj7zGxUHZpjK+n2MQ76yOBlj1A622jMhy2Nu5Ur2IiIjIUebtt9/mnnvuYc+ePWRnZ7Njxw769+/P\nf/7zn1ad32Lw9fvf/57nnnuOZ555BoDhw4czbdq0w+u1iIiItIplWWzbto3ly5eze/duxo8fH/O4\nrPjIqKREu52s+Hgy4+PJio/npBj1uuDIFhd32e247HYCQYNqn0lldRx790ZqXvn9UFubxIUX3oPP\nF8bj8fHp4g84JTENX8Dg9RUuaoMmgaBJXSYWsMJ4SxMIhMOE9tfVClhhbIbJJmfDWfi8tQ683tgj\n1e3NBHd1bKZBvM3E5YQkp0VWSi0JzjBOe5h4Z5g4RxinPcTQ/g5SE0z+WVrAlbdNjoZcsZ7s9G7Z\ny+BcPW8oIiIi0hoPPfQQK1eu5OKLL2bdunV8/PHHvPLKK60+v8XgKz4+nnvuuYd77rnnsDoqIiIi\nbVgfmLEAACAASURBVJOXl8fy5cujQ7oBVqxYQY8Yaco5WVmck5UVs+ZWewmFoDZowx8woiOqmnqt\ne19/va4G6fryRKpi5nEm4KKsPJVaUjCAyhhHOQyTMzMyWt1vh8PBsGFDMYwAphkA/ICfuDiL006D\nii0lnJMDcY5IkBXvDDN6MKQmmCQ5LRz2EAeeuNzX7LU2Br3EeFpURERERA6Rw+EgMzOT/8/encdH\nWd77/3/dM5PJvpEFQhIIEJawQzAuJwh6FCgWFXoE0aPFtdpaa7XH1tPvw1ZPPcX2eI56lLo8bHvq\nz4JiXUCsSpXFjQAhEEAIgRAIIWQh+zLJbL8/hgwMScgEsuL7+XjMI3Pfc9/Xdd0MqLy9rs/lcrlw\nOp1cddVV/OQnP/H7/k7/6/iLL77giSeeoLCw0FvbyzAMCgoKzn/UIiIick4HDhxg5cqVPufMZjMp\nKSnY26lv5W/g5XZDi8NTmL01mDpeFsHevfjsPtj6stlgS9ZoysJicLr6dic7a4ALq8WN1eLyhlRW\ni8t73N7PqPJj3HjXLAIC4uio5n3tvjKmJbatgwZaRigiIiLS16Kjo6mrq2PmzJnceuutxMfHExYW\n5vf9nf5X8l133cWzzz7L9OnTB/QW4yIiIgNJamoqgwYNorKyEpPJxLRp05g5cyaRkZGsfekl7+yr\n1vDqzF0Gm+0mbHbfz858nS232orxZcdjaW6xdEvoZTG7CQxwERbSzJAhYLV6Xq3LAluPjbJjzE9q\naRNinc9mjcF1Dqxty3WJiIiIyADx3nvvERwczP/8z//wxhtvUFtby69+9Su/7+80+IqKiuI73/nO\nBQ1SpL/4ePVqWk6e7Lb2rDExzL3ppm5rT0S+ndxuNw6niaYWM7YWE2Unw8jPNzFkyDxsthOMGzcN\nszmMTz89NQPrq7GUhA/q9XGaTK3LAZ1YLe4OZ1m1NyPLanF5i7ZHFBez4PqO+8mPrSMxRrsQioiI\niAje2V1ms5lly5Z1+f5Og6+rrrqKf/u3f2PRokUEnlEId/r06V3uTKSvtZw8yYLExG5rb21xcbe1\n1R0U7In0Hy12A5vdTFOzZ/ZVa6hla/EEXE0tZorr7OwsqyXaEs6w0NNBz67qIJybAEYTGTmakhLf\ntp3Odiqmd0FrKNX6KguoZfx4350Hz3wNsuWzcNjgLu02KCIiIiJyIcLCwrw7fJ/NMAxqa2v9aqfT\n4GvLli0YhsH27dt9zm/YsMGvDkSk91zswZ5IX3M6oaEBGht9f+7aPxRzSbw31LK1mM65NLC6pYUD\nNTWUNjUBUGauIzEkHHMX1vKdOfsq6Kwgq/V8oOV0sfbWXQjbWzLoLC4mM7PjvgKtDoVeIiIiItKr\n6uvru6WdToOvjRs3tjl34sSJbulcRESkP3C7PSHWmYFW6/szA67m5vbvLymP5Jg9qNN+7G4XOytO\ncuJU4NXK5nRSbW9ieGQgwVYncaZ6xozxzMAKDm77M86ex8JhCd3x6CIiIiIifrvzzjtZt24d8fHx\n7N69G4CtW7fywAMPYLfbsVgsrFixgksuuYTCwkLS0tIYN24cAJdffjkrVqwAIDs7m2XLlmGz2Zg/\nfz7PPfdcm77eeecdFi1aBEBVVRXR0dHnNWa/9zyvrq7m7bffZuXKlezbt4/jx4+fV4ciIiI97cxl\nv3a7CVtLAM0tFmzNFprtFpqbTx23WLA1B9Bit+B2d9yeJSyUCekzujQGi9lNsNVJcKCLoAAnQVYX\nQQEOygKPEeSow2JxEBDgYHJsGP+cHEtCWMXpe4uLmT37XG1rt0ERERER6X133HEHP/7xj7n99tu9\n5x599FH+4z/+g7lz5/L3v/+dRx991LtKMDU1lZycnDbt3H///bz22mtkZGQwf/58PvroI+bNm+dz\nzX/8x394g6+rr7663Xb8cc7gq7Gxkffff5+VK1eyc+dOamtree+995g5c+Z5dSYiItKd3G7PTKy6\nOqit9fysq4PPPwtjYthIGmxmnyWHZiDk1MvnRCd2VVcDnuWFISEQGur701VynO8ktxBkdRFs9YRc\nFnP7SVpsgoU3DlUyPiqKWQnDiQ8OvpBfAhERERGRXjNz5kwKCwt9ziUkJFBTUwN4Jk0ldlJ+p6Sk\nhLq6OjIyMgC4/fbbee+999oEX92lw+Br6dKlZGVlMWfOHB566CFmzZpFamoqs8/1v6BFRES6ma3F\nRF2ThdpGC3VNntf2Y2YaVkF9PbjamfxUVRtCrcnvSc0ABAe6CAl0Ehro8PwMcnp/hlYWsei2WQQF\n0aY+FkDRlhqS48K8xxU2G+W1TaS1Mx17VEQEPxo/ntigzpdGioiIiIj0d8uXLyczM5Of/exnuFwu\nvv76a+9nhw8fZtq0aURGRvKb3/yGzMxMiouLSUpK8l6TmJhIcTv1pZuamtixYwdut9vnfWvBe383\nXezwbwX79u0jPj6etLQ00tLSMPfzqrYbN270hnKtdcl66nj3gQOEV1Yye9Ikz+en1rWe93EPj1fH\nZx1f6PfV1e9P/fl1zKBB/bO/i/24l349W7V3v9NlMH3UNOqaLHy6azeNzRZGD72E2kYL2/J34nDC\n2MRLAMgr3gaALXQ0tbWQl+dpf+xYT3+tx2B4rw+wuJk+ahqhgU4OFG8jyOpi5oTJhAY6yCnYQbDV\nxdzpEzCZTo/vyom+zxcxaBDBwZ0/3/vZ2ew6eRJiY7GaTBw9coRAs7lXfz3P5/hi7m93RQULWttT\nfxddf7sPHPBu6qL+1F9v9rf7wAHCO/n7h/ob2P3pWMff1uNnn32WnTt3kpKSgr/uuusunn/+eRYu\nXMjq1au58847Wb9+PUOHDqWoqIjo6Gh27NjBjTfeyN69e/1ud8iQITzyyCNt3rfyd9NFw+3uuKrJ\nvn37WLlyJW+99RZxcXHs27ePPXv2MGTIEL8H2hsMw+Acj9Ht1r70UrftnLe2uJgF993XLW1J57rz\nu4POvz/113199UV/F7ve+vV0u+Gt515jZlSyd8bWmbO3Gpu7/j9WdlVXM2XWLMCz1DA83POKiPD8\n/Prd11mYEktIoJMAy4X/+6Gz3y+rX3iBALeb3MpKzuxt5pAhXD10aLf3N5D/rPd2f/3xny3qr/v6\nG8i/N9XfwO6vv/1ZUH/d35+IeLSXtxQWFrJgwQJvcfuIiAhqa2sBcLvdREVFeZc+numqq67imWee\nISEhgauvvpp9+/YBsHLlSjZt2sRLL73UI89wznUgaWlpPPnkkzz55JNs376dlStXkpGRQVJSEl99\n9VWPDEhERAYmt9tTZ6uq6vSrutrzyt6WSk1U1Hm1G2BxEx7sICLEQXiw52WpPcbCmzwhl6Wdf5Md\niGogMvR0fy63myaHA4fbjcPl8v40GwZDQtoW+aq329lVWem51uXiWF0d69atIywsjFmnArczFdfV\nUdPS4nMuNSKCsZGR5/XMIiIiIiIDRWpqKps2bWLWrFl89tlnjBkzBoCKigqio6Mxm80UFBSQn5/P\nyJEjiYqKIiIigqysLDIyMnj99dd58MEHe2x8fhdAmTFjBjNmzOD3v/89n3/+eY8NSERE+jenE2qb\nAqiq97yqGwL48mggZX/0fNZVJhMEB9oxBTQSFGgn0NpMYGALgYHNRIU4mRgX3uae/Udr2LDhHex2\nOy0tLdjtdux2O9HR0SxevLjN9WVNTby8f3+b84ODg7kvLa3N+Xq7nX+cVWegbPt24uPj2w2+hoSF\nUVNZCcCo8HBmJSSQHBbW5joRERERkYFs6dKlbNq0iYqKCpKTk3nyySd55ZVX+NGPfkRzczPBwcG8\n8sorAGzevJnHH3+cgIAATCYTL7/8MlGn/mf4ihUrWLZsGU1NTcyfP7/HCttDF4KvViaTqd3/6BcR\nkQvz8erVtJw82S1tWWNimHvTTRfUhtMJ1Q2nw62q+gAq6wOobDDT4nRjP1VVPiIggLqGFp/Qq7Gx\nkby8PBwOBw6HnabmarLrKhgU6uJ7owf7zOAKDXJSZmvipVNTnbGfetVDXEMQE+PGtxmby+1mb2u9\nqDM4HI52n8ViMrV73tFeZfxzXd9B+6EBAcwaMoSREREMU+AlIiIiIheplStXtns+KyurzblFixax\naNGidq9PT0/3LpXsaV0OvkREpGe0nDzZrbUrzsVut1Pf0sKBmhrqm11U1FuoqLPQ1BRIknUwVfUB\n1DVZaF3OX++w88WJUhwul08Nq7AAC1clnK5hFRYGUVFgGC0cPPgVoaFNBAfbsFg8qVh0YCAzRrfd\nzTCgg6DJ3kEwZWpva8VTz9Uei2EQbDZjMZk8L8PAYjIRZbW2e32IxcLl8fHeaw/U1TF55kxC2lkW\n2Wr2edTyEhERERGR9mVnZ3t3cGzPBe/qKCIiFxe3G2pqoLwcCgoaWbc1hrebgmhpOR3+BFssXDM0\nuM29ZsPkE0IFBrYQFNTEoHAHsyYGElx3lCXLZtGaI9XVmdm6taJNOx0FWYEmE7GBgQSYTASYzZ6f\nhkFYQEC71weYTCxcuJCAgACfV2BgYLvXRwUG8uiUKR3+2pwtxGJhzhlbLNe4XGRkZPh9v4iIiIiI\nXJhHHnnknMGXv7s6dhp8nThxgl/+8pcUFxfz0Ucf8c033/D1119z1113+T9aERHpVa0hV0WFJ+iq\nqPC8Wuuvt7QEUVMT0ea+M5f+GQZEhDiIDrMTEmzjkLWQoKAmwkNaCAkwCDSbCQ8IYGxSDAeKbZw5\neSokJIQbbriBwMBAgoKCyPrgA/558GCs5vZ3cAwNCOBHEyb4/Xwmw2Dy5Ml+Xy8iIiIiIgPLxo0b\nu6WdToOvZcuWcccdd/DUU08BMHr0aBYvXqzgS0SkH6lrNFNeG0hFrZXyGiufHQnjhBlcLhemdpYR\nWiwBBJrNRFqtWM0GESF2BoW1EBPewlXDKogOsxMZYqc1p3K73UxPTSLQbMZ8jv/r0spsNjN16lTv\n8Z6AAOKC284kExERERER6czu3bvZt28fNpvNe+7222/3695Og6+KigqWLFnC8uXLAQgICMDS3t7x\nIiLSK+qbzJTXWKmos1Je4wm7bC2+4ZbdYaOiooI9e/aQlpbG4MGDAQgJgbg4iI01Ya6o4qZRg4gI\nceDJxgw8/1pobNOnYRiE6J/9IiIiIiLSy37961+zadMm9u7dy3XXXcff//53MjMzuy/4CgsL4+QZ\nu4xt2bKFyMjI8x+xiIj4rcFm9s7iqqi1Ul4bSFNz+4XgWzU5nVQ0NXJ82+eEhjZSU3OCW275F4YM\nsRAaevq649vqiApru9xRRERERESkv3j77bfZtWsX06dP509/+hOlpaXceuutft/fafD1zDPPsGDB\nAgoKCrjiiisoLy/n7bffvqBBi4hIW80tZo6WB58OuWqsNDa3XxPrbIEBLgaF2zjhOkFR0zFGjasj\nMNCzw6HVaiUw8AShoUmdtCIiIiIiItK/BAcHYzabsVgs1NTUEB8fT1FRkd/3nzP4cjqdbN68mc2b\nN7N//37cbjdjx47F2sH27yJd9fHq1bScMaPwQlljYph7003d1p5IT6quhpISz+vECfgyawyVUVGd\n3mcNcBEb0UJcRAtxkS3ERTQTHuLE7nKx4pv9hFtbvNdOmjSJa6+9lvDw8J58FBERERERkR5xySWX\nUFVVxT333MOMGTMIDQ3liiuu8Pv+cwZfZrOZv/71r/z0pz9l4sSJFzxYkbO1nDzJgsTEbmtvbXFx\nt7Ul0p3cbqiqOh10lZRAU1Pn9wVY3KdCrmbiIluIjWghIsRBe/XlA0wm5iYl8WZBAcEWC0v+9V8Z\nPnx49z+MiIiIiIhIL1mxYgUA9913H3PnzqWurq5LO7x3utQxMzOTBx54gCVLlhAaGorb7cYwDKZP\nn37+oxYRuci53VBZCcePn57RdcYGJO0ym10MiW72BF2nZnJFhrYfcnVkbGQki0eOJL++XqGXiIiI\niIgMeJs2bcI46y9Fmzdv5sorr/Tr/k6Dr5ycHAzD4PHHH/c5v2HDhi4MU0Rk4OnKUly3G2rrg6is\nCaGqNoSqmlDsjtNF6C1hoUxIn+FzT2AgJCScfg1x5nF90rlnQLrdbvbX1LC1rIxbUlMJMPkWujcM\ng7SoKA42NPj5lCIiIiIiIv3X73//e2/wZbPZ2Lp1K+np6Xz22Wd+3d9p8LVx48YLGqCIyEB1rqW4\nLheU11gpqQqipCqI0morLXYTQUACkBDme/2u6mqCg32DruhofGZzdTazq8Jm46OiIg7V1QHwVWkp\nsxISzv8BRURERERE+rkPPvjA57ioqIif/OQnft/fafD1xBNPYBiGd4ljq7NngImIXMycTiivDaSk\nMpCSqiBOVAXicJ47qQoJdJIwqJmEaBvhtsMsvW3WefXd4nSy6cQJtpSV4XK7ved3VFTwT4MHYzlr\n1peIiIiIiMjFKikpiX379vl9fafBV2hoqDfwampq4oMPPmD8+PHnP0IRkQHA6YST1SFkN0VSUhVI\nWXXnQVdYsJOEaJs37IoMdXg/O1Tcco47z62wvp6vSku9xwaQHhvL1UOHKvQSEREREZGL2o9//GPv\ne5fLxc6dO0lPT/f7/k6Dr5/97Gc+x//2b//GnDlzujBEEZGBobYWjh71vEpKYMfu4bRERXZ4fUSI\nwxtyJUTbCA9x9si4RkdEMDoigvzaWpJDQ/lOcjIJISE90peIiIiIiEh/cmbIZbFYWLp0KZmZmX7f\n32nwdbaGhgaKi4u7epuISL/jcnkCrqIiT9hVXX3u6yNDHd4ZXUMH2QgN6pmg62yGYTA3KYkJDQ1M\nHjSozY4mIiIiIiIiF6tly5Zd0P2dBl+TJk3yvne5XJSVlam+l4gMWE3NJo6WB5OzP4mTf4GWc6xA\nDAtpZvywem/YFRLYc0GX2+3mZFMTX5eWcvngwW0+jwkKIiYoqMf6FxERERER6U/OzKPOZhgGubm5\nfrXTafD1wQcf4D5VTNlisTB48GACAgL8HKaISN9yu6Gi1srR8mCOlgdTXmMFoLTazJCzQi+LBRIT\nYdgwz+udpn00WK3kOyG//FRjwJjISEZGRLTpa391NQV1dd5/ZrYaFxXV7ti++eYbDh06BMCJEyc4\nXlvLsbo6xkVFER0YeIFPLiIiIiIiMnCtXbsWgBUrVgBw22234Xa7eeONN7rUTqfB1//7f/+P119/\n3efcbbfd1uaciEh/0WI3OHbSE3QVVQTT1OxbAN7pdtPidHLs2DGczipmzIhnxozBDB0KZvPp6+pb\nWihoZ/1jWEBAu8FXUX0928rL25yPtFrbHWdxcTE7duxoM7bNJ05ww/Dh/jyqiIiIiIjIRSklJQWA\nTz75hJ07d3rPT548mWnTpvH000/71U6n24Ht2bPH59jhcJCdnd2FoXbso48+Yty4cYwePbrDAT/4\n4IOMHj2aKVOmkJOT0y39isjFp6o+gF2HI/hgWzx/2ZDMP3bGcqA41Cf0KqyvZ8OJYr6o/gZLdB4t\nLf8fVus7xMTkkZzsG3qBZ/fELrnA2lsGkDl4MN9JSrqgdkRERERERC4WbrebL774wnv85Zdftlll\ncy4dzvj6z//8T37729/S1NREeHi493xAQAD33nvveQ73NKfTyQMPPMA//vEPEhMTueSSS7j++utJ\nS0vzXvPhhx9y8OBB8vPzycrK4v7772fLli0X3LeIDHxOJxyvDOJoeTCF5cGU1Lqpt9upszcTEeDy\n2fUwONDFsLgmYoxizA15WCwun7bK25mlBRARGMiU6Gifc4ZhkBwa2u714yIjiWxdCn4qBDOApNBQ\ntldVtbk+LS2NQYMGAWA2mzn45Zf8c2KiX88vIiIiIiLybfDHP/6RO+64g5qaGgCioqL405/+5Pf9\nHQZf//7v/86///u/84tf/ILly5df+EjPsnXrVlJTU71T126++Wbef/99n+BrzZo1fP/73wfg0ksv\npbq6mtLSUga3U/hZRC5+TTYL+4rCOFoeTPHJIIrrbeyrrqbBXsWZUVZiaAiTEywMi2tiWFwTsREt\nGAYcqrWz56DnykCzmZTUVOLi4hg2bFi7/YVbre0Wmu9IclgYyWFh7X/YTvCVlJRE0hmzu4oU7IuI\niIiIiPhIT08nNzfXG3xFRkZ26f5Oa3wtX76cqqoq8vPzsdls3vNXXnllF4fqq7i4mOTkZO9xUlIS\nWVlZnV5z7NgxBV8i3wIOh4PS0lIOHCjlxIlQrNaxbNo2muozCsWbDIM6ux0As9lJREQtkZG1jB7S\nwsLJo9u0mRwayr3jxhEbFMRHJSUsuPnmXnseERERERER8d/rr7/ObbfdxjPPPINxRlkZt9uNYRg8\n/PDDfrXTafD16quv8vzzz1NUVMS0adPYsmULl19+OZ999tn5jx58Bn0uZ6/b7Oi+7353qff9mDGT\nGDOm420vzyUmxspNN8095zXWmBjWFhefV/vttdWZ1as/5uTJlk6v80dnz9edffnTX3f+Wra219nn\n6q9/9meNiaG6upovvviCgoJy8vLsVFRE0tgYQmRkJFdeORZLWCi7zig2HxRkY/DgUiIjaxgU0UiI\n1UKQxUKIxdLpuPz5s9fbf9Yv5v4G8u9N9Tew++tvfxbUX/f2N5B/b6q/gd1ff/uzoP66vz8R6XuN\njY0A1NXV+Z0htcdwd1IRbOLEiWzbto3LL7+cnTt3sn//fh577DHefffd8+4UYMuWLfz617/mo48+\nAuC3v/0tJpOJn//8595r7rvvPmbPns3Np2ZljBs3jk2bNrWZ8WUYBn/4w5oLGk8rf4Kv3vbSS2tJ\nTFzQLW0VF6/lvvs6bqs7+/KnP/n2cblc1NTUEH1W7ayGBti1q47nn19HQ4NvDS2TyWDevO9gtZpJ\nSIBhwzyv8HA3x48fJzY2lsDAwN58DBERERERkYueYRhdKiTfE8rKyoiPjz/v+zud8RUUFERwcDAA\nNpuNcePGkZeXd94dtpoxYwb5+fkUFhYydOhQ3nzzTVauXOlzzfXXX88LL7zAzTffzJYtW4iKiupw\nmaPCFZH+x+12c/LkSY4fP+59nThxAoBf/OIXNDebKCiAQ4fgxAlwu8NwOAYBzQCEhoYyaFAko0cH\nMWuWg9GjzbTWjvcwSFQxeBERERERkYtWZmYmKSkpLFmyhEWLFrWZRNGZToOvpKQkqqqquPHGG7n2\n2muJjo72FqS/EBaLhRdeeIG5c+fidDq56667SEtL4+WXXwbgBz/4AfPnz+fDDz8kNTWV0NDQLlXt\nF5H+4dVXX6Wl5fTyWbvdTHV1NH/9ay1NTVGc+T8PDMNg4sSJBAYGMH58BOPHBzJ8OFitfTBwERER\nERER6XMHDhwgKyuLVatW8dRTTzF+/HiWLFnCbbfd5tf9nS51PNPGjRupra1l3rx5WPvR30T7w9S7\nnqSljtIf1dbWcuzYMY4fP05JSQkLFiwg6ozC863+/Oc/c+hQEdXV0VRVRVNbG05QUAhTpkwhLi7O\ne51hQGIijBoFKSmglYsiIiIiIiJ9q7/lLRUVFfz0pz/ljTfewOVy+XXPOWd8ORwOJk6cyP79+wGY\nPXv2BQ9S+reYGCvFxWu7tT25uOzatYucnByOHDnic/748eM+wZfdDkeOQEVFOqWlaYSHRzF2bBSR\nkZEEBQUBnrArIQFGjoQRI+DUqmoRERERERERAGpqanj33Xd58803OXjwIAsXLmTbtm1+33/O4Mti\nsTB27FiOHDnC8OHDL3iw0v/1t8L+0v/k5eW1Cb3AE3yNGTOeo0c9NbuKisDhgIiIScyY4Xvt4MGe\nmV0jR0JISC8NXERERERERAacqVOncsMNN/D4449z2WWXdXmHx05rfFVWVjJhwgQyMjIIDfXstGYY\nBmvWdM8uitK57pyFpRlY4g+3243dbm93SfPkyZPZt28fhmEwfPhwhgxJxDCG0dSUyF/+4gm72hMX\ndzrsCgvr4QcQERERERGRi0JBQUGXw64zdVrja+PGjW1vMgxmzZp13p12t/625lRkoKqtrWX37t3s\n2rWL2NhYFi9e3OYap9PJtm3biYqayIkToRQWwhm1633ExHiCrlGjICKiZ8cuIiIiIiIi3asv85af\n/OQnPPfccyxY0LZueFcmZHU642v27NkUFhZy8OBBrrnmGhobG3F0NKVDRAYch8PB3r17yc3NpaCg\nwHu+srKSpqYmgs8ovFVbC3l5Zg4dupSGhvbbi4ryBF2jRnnei4iIiIiIiHRV666NjzzyyAW102nw\n9corr/Dqq69SWVnJoUOHOHbsGPfffz+ffvrpBXUsIv2D2+3mww8/pOWsaVsmk4mSkhKGDx/J4cOw\nfz8cP95+GxERp8OuQYN6YdAiIiIiIiJyUZtxqlj0hW602Gnw9eKLL7J161Yuu+wyAMaMGUNZWdkF\ndSoifcPtdrdZGx0QEMD48ePZuXMnACNGjGDKlCnEx6dRUGDl88+hubltW8HBMHo0pKZCbGxvjF5E\nRERERES+LSZNmtThZ4ZhkJub61c7nQZfgYGBBAYGeo8dDscFFRUTkd5VX1/Pnj172LVrF5dffjmT\nJ09uc82MGTOIiYlh3LjJlJVFsH8/ZGW1bcswIDkZxo2DYcPAZOqFBxAREREREZFvnbVrPZv8rVix\nAvAsfXS73bzxxhtdaqfT4GvWrFk89dRTNDY2sn79elasWNFuYTER6T/sdjt5eXnk5uZy8OBBbzHC\n3NzcdoMvkykRuz2RNWva35UxIgLGjoUxY+DU5q4iIiIiIiIiPSYlJQWATz75xLtCCWDy5MlMmzaN\np59+2q92Og2+li9fzmuvvcakSZN4+eWXmT9/Pnfffff5jVpEekVBQQF/+9vf2pw/evQoNpuNoKAg\nGhshL8/zqq1t24bZ7NmRcexYSEjwzPYSERERERER6U1ut5svvviCzMxMAL788ssu7TRpuP24urm5\nmf3792MYBuPGjcNqtZ7/iHtAX26vKdIfOZ1O/vu//5vGxkYAhg0bxpQpUxg3bjxlZUHk5cHRXzPl\nBgAAIABJREFUo9DeH5vYWE/YlZoKZ6xyFhERERERkW+Z/pC3ZGdnc8cdd1BTUwNAVFQUf/rTn5g+\nfbpf93cafK1bt4777ruPkSNHAp6ZJK0zv/qL/vBFiPSFsrIy4uLi2q2798UXX+B0Opk8eTImUzR5\neXDgAJzKwnxYrZ5C9WPHqlC9iIiIiIiIePSnvKU1+IqMjOzSfZ0GX2PHjmXdunWkpqYCcOjQIebP\nn09eXt55DrX79acvQqQ31NfX89lnn5GTk8ONN97IlClT2lzjcEBBgWcpY0lJ++0MHeoJu0aO9Cxt\nFBEREREREWnVH/IWm83G3/72NwoLC3GcKkptGAaPP/64X/d3WuMrIiLCG3oBjBw5koiIiPMcrohc\nCIfDQVZWFps3b6alpQWAf/zjH6SlpXmXIJeXw/79cOgQnLrER0iIJ+waO9ZTtF5ERERERESkv7rh\nhhuIiooiPT2doKCgLt/fafCVnp7O/PnzWbx4MQCrV69mxowZvPPOOwAsWrSoy52KSNdVVVXx+uuv\nU1VV5XM+ISGB+vpmSkqs7NsHlZVt7zWZYNgwGDcOkpNVqF5EREREREQGhuLiYj7++OPzvr/T4Mtm\nsxEfH8+mTZsAiIuLw2azsXbtWkDBl0hviYyMJCAgwHscGxvL5ZfPw2Ybxfvvg93e3j2esGvMGAgO\n7sXBioiIiIiIiHSDK664gtzcXCZPnnxe9/u1q2N/1x/WnIr0hsOHD/PWW2+RlnYNZvNUjh83t9mZ\n0WLx1OwaNw6GDOmbcYqIiIiIiMjA1x/ylrS0NA4ePMiIESMIDAz0jis3N9ev+zsNvgoKCvjf//3f\nNkXE1qxZc4FD7z794YsQ6S5Op5OKigoGDx7sc97hgPx8yMmxU18f0Oa+6GiYMAFSUz27NIqIiIiI\niIhciP6QtxQWFrZ7PiUlxa/7O13qeOONN3L33XezYMECTCYT4HlwEel++fn5fPzxxzQ1NfHjH/+Y\noKAg6uth715PwfrmZgDf0GvYMJg4EZKS+mTIIiIiIiIiIt2u8lQB6wvdYLHTGV8ZGRls3br1gjrp\naf0hgRS5EOXl5XzyySccPHjQe27MmFnExs6msJA2yxkDAjy7Mk6Y4KnjJSIiIiIiItLdzs5b7rzz\nTtatW0d8fDy7d+8GYOvWrTzwwAPY7XYsFgsrVqzgkksuAeC3v/0tf/zjHzGbzTz//PPMmTMHgOzs\nbJYtW4bNZmP+/Pk899xzbfpOSUnpcOKVYRgUFBT49wydBV+vv/46hw4dYu7cud61lADTp0/3q4Pe\noOBLBrKsrCw+/vhj3G43LpdBZWU0VVWJJCdPZcSIkT7XRkR4ZneNGaPljCIiIiIiItKzzs5bPv/8\nc8LCwrj99tu9wdfs2bN57LHHmDt3Ln//+9/53e9+x4YNG/jmm2+45ZZb2LZtG8XFxVxzzTXk5+dj\nGAYZGRm88MILZGRkMH/+fB588EHmzZvXI8/Q6VLHvXv38vrrr7NhwwbvUkeADRs29MiARL5tEhIS\naG42U14eR0VFHAkJI7n00nE+QXNioifwGjYMtNJYRERERERE+sLMmTPb1NxKSEigpqYGgOrqahIT\nEwF4//33Wbp0KQEBAaSkpJCamkpWVhbDhw+nrq6OjIwMAG6//Xbee++9NsFXQUEBI0f6TgY526FD\nhxg1atQ5r+k0+Fq9ejWHDx/GquklIt2uvBwOHhxGZeU87PZmLr98ApGn1i5aLDB6tCfwio7u44GK\niIiIiIiItGP58uVkZmbys5/9DJfLxddffw3A8ePHueyyy7zXJSUlUVxcTEBAAElnFKlOTEykuLi4\nTbuPPfYYDQ0NXH/99cyYMYOEhATcbjclJSVs376dNWvWEB4ezqpVq845vk6Dr0mTJlFVVdVmh7n+\nZuPGjcyePdv7HtCxjvvV8aRJkwgODmbLlq2UlEBo6GzKyiAvbyNBQU6mTr0awzA4enQjKSlw662z\nCQzsP+PXsY51rGMd61jHOtaxjnWsYx1f3MfPPvssO3fu9HvHRIC77rqL559/noULF7J69WruvPNO\n1q9f7/f9HXnzzTc5ePAgq1at4pe//CVHjhwBYPjw4WRmZvK///u/nc4IAz9qfM2aNYvc3FwuueQS\n79IrwzBYs2bNBT9Ed1GNL+nPbDYbmzdv5osvthMXdyWDBmXS0ND2uiFDYNIkSEnRckYRERERERHp\ne+3lLYWFhSxYsMBb4ysiIoLa2loA3G43UVFR1NTUsHz5cgB+8YtfADBv3jyeeOIJhg8fzlVXXcW+\nffsAWLlyJZs2beKll17qkWfodMbXE088Afg+bEdV9UXkNJfLxY4dO/jgg685ciScysqJuN3VXHll\nrXc7VrMZRo3yLGeMje3jAYuIiIiIiIh0UWpqKps2bWLWrFl89tlnjBkzBoDrr7+eW265hYcffpji\n4mLy8/PJyMjAMAwiIiLIysoiIyOD119/nQcffLDHxtdp8DV79mwKCws5ePAg11xzDY2NjTgcjh4b\nkMjFoLm5hf/6r9Xs3WtQV5fiPT9okKdYV0gIjB8PaWkQHNxHgxQRERERERHpgqVLl7Jp0yYqKipI\nTk7mySef5JVXXuFHP/oRzc3NBAcH88orrwAwfvx4Fi9ezPjx47FYLKxYscI7kWrFihUsW7aMpqYm\n5s+f32M7OoIfSx1feeUVXn31VSorKzl06BAHDhzg/vvv59NPP+2xQXWVljpKf+FywcGDsHMnbNiQ\nw7FjxwAIDg4mLS2NKVOGMmmSwahRYDL18WBFREREREREzuFiyFs6nfH14osvsnXrVm8l/jFjxlBW\nVtbjAxMZSBwO2L8fcnOhvt5zbvz48Zw8eZLhw5O55ppRTJ1qoZ/vESEiIiIiIiLSLzgcDpqamggP\nDwfg66+/xm63AzB16lRvCaHOdBp8BQYGeovat3asGl8iHs3NkJPTzIEDgdhsvp+Fhwfy0EOzmTLF\nQmho34xPREREREREZCD6+c9/Tnx8PD//+c8BuOWWW5g4cSI2m43p06fz9NNP+9VOh8HXCy+8wAMP\nPMCsWbN46qmnaGxsZP369axYsYIFCxZ0z1OIDFCNjbBzp5O1aw+yf38BV1xxBZGRkYCnZtfEiTBh\nAlitnWbLIiIiIiIiInKWTz/9lG3btnmPo6KiWLt2LW63m8zMTL/b6bDG17Rp08jJycHpdPLaa6/x\nySefADB37lzuvvvufjXr62JYcyoDQ10d7NoFX311kpycXOpPrWuMjo5izpx/Yto0E2PHgkV5l4iI\niIiIiAxwfZm3TJ48mdzcXO/xJ598wpw5cwCYMmUKu3bt8qudTv96bjabuffee7n33nvPc6giA19l\npadg/b59Lezd+w1FRUXez4KDbYweXcz11zcSERHWh6MUERERERERuTjY7XZqa2u9tbxaQ6+amhqa\nm5v9bqfD4Cs3N9dbQOxshmFQW1vblfGKDEilpZCTA0ePeo6dThcnTpQAEBrawLBhlSxcOJ1LL83A\npG0aRURERERERLrFPffcw80338wf/vAHhg8fDkBhYSH3338/d999t9/tdBh8TZ48mZycnAsfqcgA\nVFTkmeFVUuJ7PigoiCuvHEVR0VouvTSZefP+1e+dJERERERERETEPw8//DAhISHMnDnTW2YoLCyM\nxx57jPvvv9/vdjqt8TUQqMaXdAe3GwoKPDW8Kip8PzMMSEmBqVMhJsbFkSNHGDFiRJ+MU0RERERE\nRKQ39Je8pba2FsMwOlyZeC4dzvi66aabLmhQIgOF0wn5+Z7Aq6bGc668vJzi4mNMmzaVMWMMpkyB\nqKjWO0wKvURERERERER6UFFREYWFhcycOZOIiAieeeYZ6uvrMQyDW265hdTUVL/a6XDG10DSXxJI\nGVjsdti3D3bvhoYGz7nm5mb27t1LSUkRcXEVLFuWTmbm1L4dqIiIiIiIiEgf6Mu85eabb+bWW29l\nwYIFAIwdO5Z7772XhoYG8vLyeOONN/xqp9NdHUUuNs3NsGeP59W6EYTb7ebo0aPk5+8hKqqYSZPK\nCAhwkp1dzxVXTFbhehEREREREZFelJeX5w29AIKDg3nkkUcAyMzM9LsdBV/yrdHQALm5nlleDofv\nZ5WVx6is/JBx4yowm10ATJw4kblz5yr0EhEREREREellNpvN5/jTTz/1vq84uzD3OXQp+Lr99tv5\ny1/+0pVbRPpcQ4Nnh8b9+z31vM4UEQFTpkBqaiJ/+pOZkhIX0dHRzJ8/3+/1wiIiIiIiIiLSvSIi\nIsjLy2Ps2LEAxMTEALB//34iIiL8bqfD4GvBggVt1nJ+9tlnVFVVYRgGa9asOd+xi/SKxkZP4LVv\nX9vAKybGs0PjyJGeHRvBxHe/+1327dvHlVdeSUBAQF8MWURERERERESAJ554ggULFvDLX/6S6dOn\nA5Cdnc1TTz3Fc88953c7HRa3nzZtGuPHj+fuu+/GZDLhdrtZunQpq1atAmDWrFnd8BjdQ8Xt5UxN\nTacDr7OXNEZENDFsWAVXXJHcN4MTERERERERGSD6Om/Zs2cPTz/9NN988w0AEyZM4NFHH2XixIl+\nt9Fh8OV0Onnuuef48MMP+f3vf8+0adMYMWIEhw8f7p7Rd6O+/iKkf2hqgl274JtvTgdeTqeThoYG\nAgKqiI8vorDwcywWCz/60Y8ICQnp2wGLiIiIiIiI9GN9mbc0NTVRV1dHfHy8z/mysjLCw8MJDg72\nq50Og69Wx44d46c//Snx8fGsWbOGoqKi8x91D1Hw9e1ms8GuXW727jXazPA6ePBrHI4tREbW+pyf\nOnUqN9xwQy+OUkRERERERGRg6cu85Z577mHevHl873vf8zn/zjvvsH79ev7whz/41U6nwVerDz74\ngK+++or//M//7Ppoe5iCr2+P5uZmysvLKS8vp7i4gpwcJ/v2mRk+fBQjRoz0XhcbC+npUFi4iY0b\nN/q0ERMTw3XXXceIESN6efQiIiIiIiIiA0df5i3Tp09nx44d7X42fvx47/LHznRY3L6ystLn+Ior\nruDyyy/3nh80aJC/YxXpMqfTidlsbnM+KyuL9es3U1oaT1lZPE6nGbBTV1cPeIrWp6dDSorn+sbG\neKKiooiLiyM2NpaEhATS0tKwWLq0oamIiIiIiIiI9KLGxsYOP3O5XH630+Hf/mNjY0lKSmo3fDAM\ng4KCAr87EemIzWbj+PHj3llcFRUVlJeXM27cOBYsWOBzbUsLlJYmsXv3xFOB12lu90muvdYTeHl2\nafRIS0sjLS2tF55ERERERERERLpLfHw8WVlZXHrppT7nt27d2qbu17l0GHw9+OCDfPbZZ2RmZnLz\nzTczc+ZMjDMTBZFucPjwYd5666025ysqKrzvW1pgzx7IzYXKyjhCQ6MICwsnLCyMxMQQMjODmT49\nGk3iEhEREREREbk4/Nd//ReLFy9m2bJlpKen43a7yc7O5v/+7/9YtWqV3+2cs8aXy+Vi48aNrFq1\niqysLObMmcMPf/jDflcbSTW+Bq6KigpefPHFNucjIiJ44IGfegOv5mbfz6OiPEsaR470neElIiIi\nIiIiIt2jr/OW0tJSXnzxRfbu3QvAhAkTeOCBB7o048uv4vbV1dWsXLmSxx9/nKeeeop77733/Efd\nA/r6i5Dz53Q6+b//+z9iYmKIi4sjLi6OqKg4jh2LZPduA5vN9/qoKJg+HUaNUuAlIiIiIiIi0pP6\nMm8pKyujvLycCRMm+Jzfu3cv8fHxxMXF+dVOh8FXfX0977//Pm+++Sbl5eUsWrSIJUuWMGzYsAsf\nfTdT8DUwuN3ucy6XdThg717YtYs2gVdEhGeGV2qqAi8RERERERGR3tCXecuSJUv44Q9/yKxZs3zO\nb968mZdeeom//vWvfrXTYfAVGhrK6NGjWbJkCWPGjPFcfOqBDcNg0aJFF/gI3UfBV/+3detWCgsL\n+Zd/+RdMJpPPZw4HfPONJ/BqavK9LyLCM8Nr9GgFXiIiIiIiIiK9qS/zlvT0dLKzs9v9bMKECd7l\nj53psBz4TTfdhGEYHDhwgAMHDrT5vD8FX9J/ud1u1q9fz9dffw3ARx99xHe+8x0Mw8DpPB14nb1L\naXj46cDrrJxMRERERERERC5ydXV1HX5mt9v9bqfD4OvPf/5zlwYkcjaHw8F7773nk8KWlJTQ0uKg\noCCAHTugocH3nrAwmDYNxo5V4CUiIiIiIiLybZWamsq6deu47rrrfM5/+OGHjBo1yu92Olzq+NBD\nD/Hss88C8Nxzz/GTn/zE+9myZcv6VTCmpY79j81mY9WqVRw5csR7bsyYsUyb9j127Qqgpsb3+tbA\na8wYMJt7ebAiIiIiIiIi0kZf5i0HDhzgu9/9LldccQXp6em43W6ys7P56quv+OCDDxg7dqxf7XQ4\np2bTpk3e92eHXLt27Tq/Ucu3hsXiO5lw+PBMAgIWs3mzb+gVEgL/9E+wZAmkpSn0EhEREREREREY\nM2YMubm5XHnllRQWFnLkyBFmzZrF7t27/Q694BxLHUUuhMViYcmSJTz33CoM41Kam9NoaTldnT4w\nEKZOhQkTwKLfhSIiIiIiIiJylqCgIO68884LaqPDyMHpdFJZWYnb7fa+B7zHIudSWQnbtgUTGXk7\n5jOmcVksMGkSTJ7sCb9ERERERERERHpKhzW+UlJSMAzPDB232+193+rw4cM9Pzo/qcZX33O5XJhM\nJurqYPt2OHgQzvxKTCbPUsZp0zzLG0VERERERESkf7sY8pYOg68jR44wfPjwHum0srKSJUuWcOTI\nEVJSUnjrrbeIiopqc11KSgoRERGYzWYCAgLYunVru+1dDF/EQOV2u9m8eTP5+cWMGbOEAwfMuFyn\nPzcMSE2F9HSIiOi7cYqIiIiIiIhI1/S3vKWsrIz4+Pgu3dNh8DV9+nR27NjRLQM726OPPkpsbCyP\nPvooTz/9NFVVVSxfvrzNdSNGjCA7O5tBgwads73+9kV8WzidTt5770M+/vgEpaXxJCYOY8qUqd7Z\ngcOHwyWXQCdfn4iIiIiIiIj0Q32Zt7SW3GrldrtJT0/3ZlWdZUWtOqzx1ZMPtmbNGu+ukd///veZ\nPXt2u8FXT49Dzl9jYwvPPruebducOBxDAGhqsuFyOUlKspCRAYMH9/EgRURERERERGRAio2NbbMS\nsbi4mPT0dAzDoKCgwK92OpzxFR8fz80339xu8GQYBs8///x5DNsjOjqaqqoqwBNsDRo0yHt8ppEj\nRxIZGYnZbOYHP/gB99xzT/sPoRlfvcblgp07m3jhha8pL2/wnk9OTuKqqyZz2WVmkpP7cIAiIiIi\nIiIi0i36Mm955plnWL9+Pb/73e+YPHky4FkZ2NWa8x3O+AoODiY9Pb1NYfv2Ct2359prr+XEiRNt\nzj/11FM+x4ZhdNjel19+SUJCAuXl5Vx77bWMGzeOmTNndtq3dD+3Gw4d8hSur64OxDBCAU/wNWXK\nCJYtm8CoUQZ+/NYQERERERERETmnRx55hMWLF/Pwww+TlJTEE088cV7tdBh8DRo0iO9///vnPcD1\n69d3+NngwYM5ceIEQ4YMoaSkpMPCZAkJCQDExcWxcOFCtm7d2mHw9etf/9r7fvbs2cyePfu8xy6+\njh6Fbdvg5EnPsclkOrWu9nPmzYtn0aIJmEx9O0YRERERERERubgkJyezevVq3n//fa699loaGxu7\n3EaHSx0vu+wytmzZcsGDbM+jjz5KTEwMP//5z1m+fDnV1dVtanw1NjbidDoJDw+noaGBOXPm8Ktf\n/Yo5c+a0fQgtdewRJ07A1q2en2cKDISpUyEtzYXVqsRLRERERERE5GLUn/KWpqYmDh06xMSJE7t0\nX4fBV3Z2ts8SRMMwiI2NJbkbCjhVVlayePFijh49SkpKCm+99RZRUVEcP36ce+65h3Xr1lFQUMCi\nRYsAcDgc3HrrrTz22GPtP0Q/+iIuBidPemZ4HT3qOXY6nZjNZiwWmDQJpkwBq7VvxygiIiIiIiIi\nPauva3ydOQ44vQGiYRg8/PDDfrXTYfB11VVXtTlXWVlJS0sLK1euZOrUqV0edE9R8NU9ams9NbwO\nHfLU9HK73ezdu5fa2mruvPMyLrnEQnBwX49SRERERERERHpDX+Ytv/71r739v/zyy9x3330+n//q\nV7/yq50Og6+ObN++nYcffpjNmzd35bYepeDrwjQ1QU4OfPONZ9dG8Mzy2rkzh+bmPQwdWsKUKSO5\n6aabMKmYl4iIiIiIiMi3Qn/JW6ZNm0ZOTs553dthcfuOzJgxg7q6uvPqTPoXhwNyc2HXLrDbT59v\naWnh8OENREXtJCTEBnh+s7tcLgVfIiIiIiIiIjJgdDn4Ki0tVfgxwLlckJcH2dlw9oYIUVE2Dh36\nKxERRd5zGRkZzJ07V9+7iIiIiIiIiAwoHQZfP/7xj9ucq6qq4ssvv+S5557r0UFJzzl82FO4vrra\n93x0NGRkwLBhgbz9djjffOM5P2fOHC677DKfjQ5ERERERERERHrSpEmTvO8PHTrkc2wYBrm5uX61\n02GNrz//+c/etZyGYWAYBjExMcyYMYPBgwdf4PC7V39Zc9qfnTgBWVlQWup7PjQUZsyAMWOgNdty\nOBysXLmS6dOnM2HChN4frIiIiIiIiIj0ub7MWwoLC8/5eUpKil/tdLm4PcDWrVvJyMjo6m09RsFX\nx6qqYOtWOHLE93xDQxUWyx7uuutSQkKsbe5rDTxFRERERERE5Nvp7LzlzjvvZN26dcTHx7N7924A\nbr75ZvLy8gCorq4mKiqKnJwcCgsLSUtLY9y4cQBcfvnlrFixAoDs7GyWLVuGzWZj/vz57a4szM/P\np7S0lMzMTJ/zX3zxBQkJCYwaNcqvZ+iwaJPL5eJvf/sbv/vd7/jwww8Bz46Oc+bM4d577/Wrcek7\nDQ2weTO8/fbp0Mtut1NUVEhBwXtUVr5IZeVn5OXtbfd+hV4iIiIiIiIicqY77riDjz76yOfcqlWr\nyMnJIScnh+9973t873vf836Wmprq/aw19AK4//77ee2118jPzyc/P79NmwAPPfQQERERbc5HRETw\n0EMP+T3mDmt83XvvvRw+fJiMjAx+85vf8Nprr7F//36eeuopbrzxRr87kN7V0gI7d8KePZ5dG1sd\nOVJIaemXxMcfJTi4xXt+x44dTJs2rQ9GKiIiIiIiIiIDycyZMztcguh2u3nrrbfYsGHDOdsoKSmh\nrq7Ou5Lw9ttv57333mPevHk+15WWljJ58uQ290+ePJnDhw/7PeYOg68tW7aQm5uLyWTCZrMxZMgQ\nDh06RExMjN+NS+9xOuGbbyAnB2w238+Sk2HqVCd///tB7zmz2cyECRNIT0/v5ZGKiIiIiIiIyMXm\n888/Z/DgwT5LEA8fPsy0adOIjIzkN7/5DZmZmRQXF5OUlOS9JjExkeLi4jbtVZ+9K98ZbGcHH+fQ\nYfAVEBCAyeRZCRkUFMSIESMUevVDbjccPAjbt0NdHTQ2NhISEgJAbCxceikkJoLLNYKvvorEarUy\nffp0pkyZQnBwcB+PXkREREREREQuBitXruSWW27xHg8dOpSioiKio6PZsWMHN954I3v3tl9uqT0z\nZszglVdeaVNu69VXX+3SJJ4Og6/9+/d3uHVkV7aN7C0bN25k9uzZ3vfARX+cmjqbrVth06Z/cPJk\nBTCUuro60tICmDTJysKFszGM09ffddddhIWFsWnTJrKysvp8/DrWsY51rGMd61jHOtaxjnWsYx3r\nuP8cP/vss+zcudPvHRNbORwO3n33XXbs2OE9Z7VasVqtAEyfPp1Ro0aRn59PYmIix44d81537Ngx\nEhMT27T57LPPsnDhQt544w1v0JWdnU1zczPvvvuu32PrcFfH7to2sjd823Z1rKjw7NS4d281hYVH\nOH78OA6HA4vFQUJCCbfcMpXMzCv6epgiIiIiIiIiMoC1l7cUFhayYMEC766OAB999BFPP/20T32v\niooKoqOjMZvNFBQUcOWVV7Jnzx6ioqK49NJLef7558nIyOC6667jwQcfbFPjCzx1wzZs2MCePXsw\nDIMJEyZw9dVXd+kZOpzxZbfbz7ltpPS+ujrYts2ztBHg8OFCioqKMJlcJCSUMmRIKVarQXNzU98O\nVEREREREREQuOkuXLmXTpk2cPHmS5ORknnzySe644w7efPNNli5d6nPt5s2befzxx72ltF5++WWi\noqIAWLFiBcuWLaOpqYn58+e3G3qBJ3i7+uqruxx2+bTR0Yyv6667jt/+9rdtKujn5ubyy1/+krVr\n1553p93tYp/xZbPBjh2e4vUu1+nzNTVV7Nv3HgkJxxk6NIr09HTV7hIRERERERGRbnEx5C0dzvjq\nrm0j5fzZbA7eeSef7GwHY8dO8vksJQUuuSSK7OxkRo++mmHDhmEYRt8MVERERERERESkH+ow+Oqu\nbSOl69xu2LXLxvPPf0V5eQMASUkjCQ0NZcgQz06NgwcDGFxzzTV9OlYRERERERERkf7K1NEHrdtG\nnq2r20ZK1xQWwl//aue//3ubN/QCqKw8xNy5cP31raGXiIiIiIiIiIicS4c1vk6cOMHChQuxWq3t\nbhvZnwrcXwxrTktLISsLjh1zkJWVRWVlJQBWq50bb0xm0aIJhIaG9PEoRUREREREROTb4mLIWzoM\nvqB7to3sDQP5i6iuhq1bPTO9AKqqqvj666+BFoYMKWXZsnQuu2xGXw5RRERERERERL6FBnLe0uqc\nwddAMRC/iMZGyM6GvDzfnRpNJhg0qIQ9e15nzpwrueyyy/pukCIiIiIiIiLyrTUQ85azKfjqZXY7\n7NoFubngcPh+lpoKl1wC4eFQV1dHeHh43wxSRERERERERL71BlLe0hEFX73E5YJ9+zyzvM7eFDMx\n0bNTY2xs34xNRERERERERORsAyFv6YylrwdwsXO7oaAAtm2D2lrfzwYNcjNiRBnp6dqUD1K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       "text": [
        "<matplotlib.figure.Figure at 0x7f17280cda50>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### End of Lesson 3\n",
      "For questions please leave them on:\n",
      "\n",
      "- <a href=\"https://www.youtube.com/user/roshanRush\">YouTube</a>\n",
      "- <a href=\"https://twitter.com/twistedhardware\">Twitter</a>\n",
      "- <a href=\"https://plus.google.com/+Tcsaroshan/posts\">Google+</a>\n",
      "\n",
      "<a href=\"/notebooks/Lesson 2 - Intoroduction to Machine Learning.ipynb\">Previous Lesson - Introduction to Machine Learning</a>\n",
      "<br />\n",
      "<a href=\"/notebooks/Lesson 4 - Clustering.ipynb\">Next Lesson - Clustering</a>\n",
      "\n",
      "In the next lesson:\n",
      "- Why do we use Clustering?\n",
      "- Finding Certoids\n",
      "- K-Means Algorithm"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/Lesson 4 - Clustering.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:42aadde3139597ff8f8143e835f3473a4584a11d644f24156e679f8237ba3244"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Lesson Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this you will learn about the following:\n",
      "\n",
      "- <a href=\"#Why-do-we-use-clustering?\">Why do we use clustering?</a>\n",
      "- <a href=\"#Clustering-Examples\">Clustering Examples</a>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import HTML\n",
      "\n",
      "input_form = \"\"\"\n",
      "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
      "<p>User: root<br> Password: admin</p>\n",
      "\"\"\"\n",
      "\n",
      "javascript = \"\"\"\n",
      "<script type=\"text/Javascript\">\n",
      "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
      "</script>\n",
      "\"\"\"\n",
      "\n",
      "HTML(input_form + javascript)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "<a id=\"admin_link\" target=\"_blank\" href=\"#\">Ajenti Administration Interface</a>\n",
        "<p>User: root<br> Password: admin</p>\n",
        "\n",
        "<script type=\"text/Javascript\">\n",
        "document.getElementById('admin_link').href = \"https://\" + window.location.hostname + \":8000\"\n",
        "</script>\n"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<IPython.core.display.HTML at 0x7f6c9c2f87d0>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Why do we use clustering?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unlike classification and regression, clustering is unsupervised. That means the algorithm will classify your data not based on predeined categories. The algorithm will look for similar data points in the dataset and cluster them together. Different algorithms can solve specific problems but k-means is a general use algorithm and that what we will cover in this tutorial."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Clustering Examples"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Finding similarities between items with no categories\n",
      " - Categorizing text documents\n",
      " - Counting objects in an image\n",
      " - Grouping images based on content\n",
      "- Finding new patterns in categorized data\n",
      " - Looking at unkown features\n",
      " - Finding correlated stocks and commodities (possitive/negative)\n",
      "- Recommendation systems\n",
      " - Finding related news\n",
      " - Finding products/information to a user's taste"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from matplotlib.finance import candlestick, quotes_historical_yahoo, date2num\n",
      "from sklearn.cluster import KMeans\n",
      "from sklearn.preprocessing import normalize\n",
      "\n",
      "from datetime import datetime, timedelta"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Configure Pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.options.display.max_columns=50"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Retreiving Stock Price for Google"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def download_data(symbol, days_delta=30):\n",
      "    # Set Start and End Date\n",
      "    finish_date = datetime.today()\n",
      "    start_date = finish_date - timedelta(days=days_delta)\n",
      "\n",
      "    # Read from Yahoo! Finance\n",
      "    stocks_raw = quotes_historical_yahoo(symbol, start_date, finish_date)\n",
      "    stocks_df = pd.DataFrame(stocks_raw, columns=[\"n_date\", \"open\", \"close\", \"high\", \"low\", \"volume\"])\n",
      "    return stocks_df\n",
      "\n",
      "stocks_df = download_data(\"GOOG\")\n",
      "stocks_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_date</th>\n",
        "      <th>open</th>\n",
        "      <th>close</th>\n",
        "      <th>high</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td> 569.99</td>\n",
        "      <td> 567.88</td>\n",
        "      <td> 570.49</td>\n",
        "      <td> 566.00</td>\n",
        "      <td> 1211400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td> 564.52</td>\n",
        "      <td> 562.73</td>\n",
        "      <td> 565.90</td>\n",
        "      <td> 560.88</td>\n",
        "      <td> 1537800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td> 567.31</td>\n",
        "      <td> 574.78</td>\n",
        "      <td> 575.00</td>\n",
        "      <td> 565.75</td>\n",
        "      <td> 1435300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 735459</td>\n",
        "      <td> 576.18</td>\n",
        "      <td> 574.65</td>\n",
        "      <td> 577.90</td>\n",
        "      <td> 570.88</td>\n",
        "      <td>  982800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 735460</td>\n",
        "      <td> 577.86</td>\n",
        "      <td> 573.48</td>\n",
        "      <td> 579.38</td>\n",
        "      <td> 570.52</td>\n",
        "      <td> 1515000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 735463</td>\n",
        "      <td> 576.11</td>\n",
        "      <td> 582.16</td>\n",
        "      <td> 584.51</td>\n",
        "      <td> 576.00</td>\n",
        "      <td> 1280600</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 735464</td>\n",
        "      <td> 585.00</td>\n",
        "      <td> 586.86</td>\n",
        "      <td> 587.34</td>\n",
        "      <td> 584.00</td>\n",
        "      <td>  976000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 735465</td>\n",
        "      <td> 585.88</td>\n",
        "      <td> 584.49</td>\n",
        "      <td> 586.70</td>\n",
        "      <td> 582.57</td>\n",
        "      <td> 1033900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 735466</td>\n",
        "      <td> 583.82</td>\n",
        "      <td> 583.37</td>\n",
        "      <td> 584.50</td>\n",
        "      <td> 581.14</td>\n",
        "      <td>  912300</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 735467</td>\n",
        "      <td> 583.59</td>\n",
        "      <td> 582.56</td>\n",
        "      <td> 585.24</td>\n",
        "      <td> 580.64</td>\n",
        "      <td>  786900</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 735470</td>\n",
        "      <td> 584.72</td>\n",
        "      <td> 580.20</td>\n",
        "      <td> 585.00</td>\n",
        "      <td> 579.00</td>\n",
        "      <td> 1357700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 735471</td>\n",
        "      <td> 581.26</td>\n",
        "      <td> 577.86</td>\n",
        "      <td> 581.80</td>\n",
        "      <td> 576.58</td>\n",
        "      <td> 1635200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 735472</td>\n",
        "      <td> 577.27</td>\n",
        "      <td> 571.00</td>\n",
        "      <td> 578.49</td>\n",
        "      <td> 570.10</td>\n",
        "      <td> 1698700</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 735473</td>\n",
        "      <td> 569.56</td>\n",
        "      <td> 569.20</td>\n",
        "      <td> 573.25</td>\n",
        "      <td> 567.10</td>\n",
        "      <td> 1289400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 735474</td>\n",
        "      <td> 571.33</td>\n",
        "      <td> 571.60</td>\n",
        "      <td> 572.04</td>\n",
        "      <td> 567.07</td>\n",
        "      <td> 1080800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 735478</td>\n",
        "      <td> 571.85</td>\n",
        "      <td> 577.33</td>\n",
        "      <td> 577.83</td>\n",
        "      <td> 571.19</td>\n",
        "      <td> 1574100</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 735479</td>\n",
        "      <td> 580.00</td>\n",
        "      <td> 577.94</td>\n",
        "      <td> 582.99</td>\n",
        "      <td> 575.00</td>\n",
        "      <td> 1211800</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 735480</td>\n",
        "      <td> 580.00</td>\n",
        "      <td> 581.98</td>\n",
        "      <td> 586.00</td>\n",
        "      <td> 579.22</td>\n",
        "      <td> 1454200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 735481</td>\n",
        "      <td> 583.98</td>\n",
        "      <td> 586.08</td>\n",
        "      <td> 586.55</td>\n",
        "      <td> 581.95</td>\n",
        "      <td> 1627900</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>19 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "    n_date    open   close    high     low   volume\n",
        "0   735456  569.99  567.88  570.49  566.00  1211400\n",
        "1   735457  564.52  562.73  565.90  560.88  1537800\n",
        "2   735458  567.31  574.78  575.00  565.75  1435300\n",
        "3   735459  576.18  574.65  577.90  570.88   982800\n",
        "4   735460  577.86  573.48  579.38  570.52  1515000\n",
        "5   735463  576.11  582.16  584.51  576.00  1280600\n",
        "6   735464  585.00  586.86  587.34  584.00   976000\n",
        "7   735465  585.88  584.49  586.70  582.57  1033900\n",
        "8   735466  583.82  583.37  584.50  581.14   912300\n",
        "9   735467  583.59  582.56  585.24  580.64   786900\n",
        "10  735470  584.72  580.20  585.00  579.00  1357700\n",
        "11  735471  581.26  577.86  581.80  576.58  1635200\n",
        "12  735472  577.27  571.00  578.49  570.10  1698700\n",
        "13  735473  569.56  569.20  573.25  567.10  1289400\n",
        "14  735474  571.33  571.60  572.04  567.07  1080800\n",
        "15  735478  571.85  577.33  577.83  571.19  1574100\n",
        "16  735479  580.00  577.94  582.99  575.00  1211800\n",
        "17  735480  580.00  581.98  586.00  579.22  1454200\n",
        "18  735481  583.98  586.08  586.55  581.95  1627900\n",
        "\n",
        "[19 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Processing the Date"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def process_date(stocks_df):\n",
      "    stocks_df[\"n_date\"] = stocks_df[\"n_date\"].astype(np.int32)\n",
      "    stocks_df[\"date\"] = stocks_df[\"n_date\"].apply(datetime.fromordinal)\n",
      "    return stocks_df\n",
      "\n",
      "process_date(stocks_df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_date</th>\n",
        "      <th>open</th>\n",
        "      <th>close</th>\n",
        "      <th>high</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>date</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td> 569.99</td>\n",
        "      <td> 567.88</td>\n",
        "      <td> 570.49</td>\n",
        "      <td> 566.00</td>\n",
        "      <td> 1211400</td>\n",
        "      <td>2014-08-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td> 564.52</td>\n",
        "      <td> 562.73</td>\n",
        "      <td> 565.90</td>\n",
        "      <td> 560.88</td>\n",
        "      <td> 1537800</td>\n",
        "      <td>2014-08-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td> 567.31</td>\n",
        "      <td> 574.78</td>\n",
        "      <td> 575.00</td>\n",
        "      <td> 565.75</td>\n",
        "      <td> 1435300</td>\n",
        "      <td>2014-08-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 735459</td>\n",
        "      <td> 576.18</td>\n",
        "      <td> 574.65</td>\n",
        "      <td> 577.90</td>\n",
        "      <td> 570.88</td>\n",
        "      <td>  982800</td>\n",
        "      <td>2014-08-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 735460</td>\n",
        "      <td> 577.86</td>\n",
        "      <td> 573.48</td>\n",
        "      <td> 579.38</td>\n",
        "      <td> 570.52</td>\n",
        "      <td> 1515000</td>\n",
        "      <td>2014-08-15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 735463</td>\n",
        "      <td> 576.11</td>\n",
        "      <td> 582.16</td>\n",
        "      <td> 584.51</td>\n",
        "      <td> 576.00</td>\n",
        "      <td> 1280600</td>\n",
        "      <td>2014-08-18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 735464</td>\n",
        "      <td> 585.00</td>\n",
        "      <td> 586.86</td>\n",
        "      <td> 587.34</td>\n",
        "      <td> 584.00</td>\n",
        "      <td>  976000</td>\n",
        "      <td>2014-08-19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 735465</td>\n",
        "      <td> 585.88</td>\n",
        "      <td> 584.49</td>\n",
        "      <td> 586.70</td>\n",
        "      <td> 582.57</td>\n",
        "      <td> 1033900</td>\n",
        "      <td>2014-08-20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 735466</td>\n",
        "      <td> 583.82</td>\n",
        "      <td> 583.37</td>\n",
        "      <td> 584.50</td>\n",
        "      <td> 581.14</td>\n",
        "      <td>  912300</td>\n",
        "      <td>2014-08-21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 735467</td>\n",
        "      <td> 583.59</td>\n",
        "      <td> 582.56</td>\n",
        "      <td> 585.24</td>\n",
        "      <td> 580.64</td>\n",
        "      <td>  786900</td>\n",
        "      <td>2014-08-22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 735470</td>\n",
        "      <td> 584.72</td>\n",
        "      <td> 580.20</td>\n",
        "      <td> 585.00</td>\n",
        "      <td> 579.00</td>\n",
        "      <td> 1357700</td>\n",
        "      <td>2014-08-25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 735471</td>\n",
        "      <td> 581.26</td>\n",
        "      <td> 577.86</td>\n",
        "      <td> 581.80</td>\n",
        "      <td> 576.58</td>\n",
        "      <td> 1635200</td>\n",
        "      <td>2014-08-26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 735472</td>\n",
        "      <td> 577.27</td>\n",
        "      <td> 571.00</td>\n",
        "      <td> 578.49</td>\n",
        "      <td> 570.10</td>\n",
        "      <td> 1698700</td>\n",
        "      <td>2014-08-27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 735473</td>\n",
        "      <td> 569.56</td>\n",
        "      <td> 569.20</td>\n",
        "      <td> 573.25</td>\n",
        "      <td> 567.10</td>\n",
        "      <td> 1289400</td>\n",
        "      <td>2014-08-28</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 735474</td>\n",
        "      <td> 571.33</td>\n",
        "      <td> 571.60</td>\n",
        "      <td> 572.04</td>\n",
        "      <td> 567.07</td>\n",
        "      <td> 1080800</td>\n",
        "      <td>2014-08-29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 735478</td>\n",
        "      <td> 571.85</td>\n",
        "      <td> 577.33</td>\n",
        "      <td> 577.83</td>\n",
        "      <td> 571.19</td>\n",
        "      <td> 1574100</td>\n",
        "      <td>2014-09-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 735479</td>\n",
        "      <td> 580.00</td>\n",
        "      <td> 577.94</td>\n",
        "      <td> 582.99</td>\n",
        "      <td> 575.00</td>\n",
        "      <td> 1211800</td>\n",
        "      <td>2014-09-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 735480</td>\n",
        "      <td> 580.00</td>\n",
        "      <td> 581.98</td>\n",
        "      <td> 586.00</td>\n",
        "      <td> 579.22</td>\n",
        "      <td> 1454200</td>\n",
        "      <td>2014-09-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 735481</td>\n",
        "      <td> 583.98</td>\n",
        "      <td> 586.08</td>\n",
        "      <td> 586.55</td>\n",
        "      <td> 581.95</td>\n",
        "      <td> 1627900</td>\n",
        "      <td>2014-09-05</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>19 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "    n_date    open   close    high     low   volume       date\n",
        "0   735456  569.99  567.88  570.49  566.00  1211400 2014-08-11\n",
        "1   735457  564.52  562.73  565.90  560.88  1537800 2014-08-12\n",
        "2   735458  567.31  574.78  575.00  565.75  1435300 2014-08-13\n",
        "3   735459  576.18  574.65  577.90  570.88   982800 2014-08-14\n",
        "4   735460  577.86  573.48  579.38  570.52  1515000 2014-08-15\n",
        "5   735463  576.11  582.16  584.51  576.00  1280600 2014-08-18\n",
        "6   735464  585.00  586.86  587.34  584.00   976000 2014-08-19\n",
        "7   735465  585.88  584.49  586.70  582.57  1033900 2014-08-20\n",
        "8   735466  583.82  583.37  584.50  581.14   912300 2014-08-21\n",
        "9   735467  583.59  582.56  585.24  580.64   786900 2014-08-22\n",
        "10  735470  584.72  580.20  585.00  579.00  1357700 2014-08-25\n",
        "11  735471  581.26  577.86  581.80  576.58  1635200 2014-08-26\n",
        "12  735472  577.27  571.00  578.49  570.10  1698700 2014-08-27\n",
        "13  735473  569.56  569.20  573.25  567.10  1289400 2014-08-28\n",
        "14  735474  571.33  571.60  572.04  567.07  1080800 2014-08-29\n",
        "15  735478  571.85  577.33  577.83  571.19  1574100 2014-09-02\n",
        "16  735479  580.00  577.94  582.99  575.00  1211800 2014-09-03\n",
        "17  735480  580.00  581.98  586.00  579.22  1454200 2014-09-04\n",
        "18  735481  583.98  586.08  586.55  581.95  1627900 2014-09-05\n",
        "\n",
        "[19 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Calculate the Daily Average, Daily Change and Rage"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll be calculating the daily average using this equation to compile the daily price into one number:\n",
      "\n",
      "$Average = \\frac{(Close + High + Low)}{3}$\n",
      "\n",
      "Then we will be calculating the daily change amount and percentage using these equations:\n",
      "\n",
      "$Change Amount = Close - Open$\n",
      "\n",
      "$Change Percentage = \\frac{Change Amount}{Average}$\n",
      "\n",
      "$Range = \\frac{(High - Low)}{Average}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calculate_stats(stocks_df):\n",
      "    stocks_df[\"average\"] = (stocks_df[\"close\"] + stocks_df[\"high\"] + stocks_df[\"low\"]) / 3.0\n",
      "    stocks_df[\"change_amount\"] = stocks_df[\"close\"] - stocks_df[\"open\"]\n",
      "    stocks_df[\"change_per\"] = stocks_df[\"change_amount\"] / stocks_df[\"average\"]\n",
      "    stocks_df[\"range\"] = (stocks_df[\"high\"] - stocks_df[\"low\"]) / stocks_df[\"average\"]\n",
      "    stocks_df[\"change_1_amount\"] = pd.Series(0.0)\n",
      "    stocks_df[\"change_1_amount\"][1:] = stocks_df[\"average\"][1:].values - stocks_df[\"average\"][:-1].values\n",
      "    stocks_df[\"change_1_per\"] = stocks_df[\"change_1_amount\"] / stocks_df[\"average\"]\n",
      "    return stocks_df\n",
      "\n",
      "calculate_stats(stocks_df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_date</th>\n",
        "      <th>open</th>\n",
        "      <th>close</th>\n",
        "      <th>high</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>date</th>\n",
        "      <th>average</th>\n",
        "      <th>change_amount</th>\n",
        "      <th>change_per</th>\n",
        "      <th>range</th>\n",
        "      <th>change_1_amount</th>\n",
        "      <th>change_1_per</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td> 569.99</td>\n",
        "      <td> 567.88</td>\n",
        "      <td> 570.49</td>\n",
        "      <td> 566.00</td>\n",
        "      <td> 1211400</td>\n",
        "      <td>2014-08-11</td>\n",
        "      <td> 568.123333</td>\n",
        "      <td>-2.11</td>\n",
        "      <td>-0.003714</td>\n",
        "      <td> 0.007903</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td> 564.52</td>\n",
        "      <td> 562.73</td>\n",
        "      <td> 565.90</td>\n",
        "      <td> 560.88</td>\n",
        "      <td> 1537800</td>\n",
        "      <td>2014-08-12</td>\n",
        "      <td> 563.170000</td>\n",
        "      <td>-1.79</td>\n",
        "      <td>-0.003178</td>\n",
        "      <td> 0.008914</td>\n",
        "      <td>-4.953333</td>\n",
        "      <td>-0.008795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td> 567.31</td>\n",
        "      <td> 574.78</td>\n",
        "      <td> 575.00</td>\n",
        "      <td> 565.75</td>\n",
        "      <td> 1435300</td>\n",
        "      <td>2014-08-13</td>\n",
        "      <td> 571.843333</td>\n",
        "      <td> 7.47</td>\n",
        "      <td> 0.013063</td>\n",
        "      <td> 0.016176</td>\n",
        "      <td> 8.673333</td>\n",
        "      <td> 0.015167</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 735459</td>\n",
        "      <td> 576.18</td>\n",
        "      <td> 574.65</td>\n",
        "      <td> 577.90</td>\n",
        "      <td> 570.88</td>\n",
        "      <td>  982800</td>\n",
        "      <td>2014-08-14</td>\n",
        "      <td> 574.476667</td>\n",
        "      <td>-1.53</td>\n",
        "      <td>-0.002663</td>\n",
        "      <td> 0.012220</td>\n",
        "      <td> 2.633333</td>\n",
        "      <td> 0.004584</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 735460</td>\n",
        "      <td> 577.86</td>\n",
        "      <td> 573.48</td>\n",
        "      <td> 579.38</td>\n",
        "      <td> 570.52</td>\n",
        "      <td> 1515000</td>\n",
        "      <td>2014-08-15</td>\n",
        "      <td> 574.460000</td>\n",
        "      <td>-4.38</td>\n",
        "      <td>-0.007625</td>\n",
        "      <td> 0.015423</td>\n",
        "      <td>-0.016667</td>\n",
        "      <td>-0.000029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 735463</td>\n",
        "      <td> 576.11</td>\n",
        "      <td> 582.16</td>\n",
        "      <td> 584.51</td>\n",
        "      <td> 576.00</td>\n",
        "      <td> 1280600</td>\n",
        "      <td>2014-08-18</td>\n",
        "      <td> 580.890000</td>\n",
        "      <td> 6.05</td>\n",
        "      <td> 0.010415</td>\n",
        "      <td> 0.014650</td>\n",
        "      <td> 6.430000</td>\n",
        "      <td> 0.011069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 735464</td>\n",
        "      <td> 585.00</td>\n",
        "      <td> 586.86</td>\n",
        "      <td> 587.34</td>\n",
        "      <td> 584.00</td>\n",
        "      <td>  976000</td>\n",
        "      <td>2014-08-19</td>\n",
        "      <td> 586.066667</td>\n",
        "      <td> 1.86</td>\n",
        "      <td> 0.003174</td>\n",
        "      <td> 0.005699</td>\n",
        "      <td> 5.176667</td>\n",
        "      <td> 0.008833</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 735465</td>\n",
        "      <td> 585.88</td>\n",
        "      <td> 584.49</td>\n",
        "      <td> 586.70</td>\n",
        "      <td> 582.57</td>\n",
        "      <td> 1033900</td>\n",
        "      <td>2014-08-20</td>\n",
        "      <td> 584.586667</td>\n",
        "      <td>-1.39</td>\n",
        "      <td>-0.002378</td>\n",
        "      <td> 0.007065</td>\n",
        "      <td>-1.480000</td>\n",
        "      <td>-0.002532</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 735466</td>\n",
        "      <td> 583.82</td>\n",
        "      <td> 583.37</td>\n",
        "      <td> 584.50</td>\n",
        "      <td> 581.14</td>\n",
        "      <td>  912300</td>\n",
        "      <td>2014-08-21</td>\n",
        "      <td> 583.003333</td>\n",
        "      <td>-0.45</td>\n",
        "      <td>-0.000772</td>\n",
        "      <td> 0.005763</td>\n",
        "      <td>-1.583333</td>\n",
        "      <td>-0.002716</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 735467</td>\n",
        "      <td> 583.59</td>\n",
        "      <td> 582.56</td>\n",
        "      <td> 585.24</td>\n",
        "      <td> 580.64</td>\n",
        "      <td>  786900</td>\n",
        "      <td>2014-08-22</td>\n",
        "      <td> 582.813333</td>\n",
        "      <td>-1.03</td>\n",
        "      <td>-0.001767</td>\n",
        "      <td> 0.007893</td>\n",
        "      <td>-0.190000</td>\n",
        "      <td>-0.000326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 735470</td>\n",
        "      <td> 584.72</td>\n",
        "      <td> 580.20</td>\n",
        "      <td> 585.00</td>\n",
        "      <td> 579.00</td>\n",
        "      <td> 1357700</td>\n",
        "      <td>2014-08-25</td>\n",
        "      <td> 581.400000</td>\n",
        "      <td>-4.52</td>\n",
        "      <td>-0.007774</td>\n",
        "      <td> 0.010320</td>\n",
        "      <td>-1.413333</td>\n",
        "      <td>-0.002431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 735471</td>\n",
        "      <td> 581.26</td>\n",
        "      <td> 577.86</td>\n",
        "      <td> 581.80</td>\n",
        "      <td> 576.58</td>\n",
        "      <td> 1635200</td>\n",
        "      <td>2014-08-26</td>\n",
        "      <td> 578.746667</td>\n",
        "      <td>-3.40</td>\n",
        "      <td>-0.005875</td>\n",
        "      <td> 0.009019</td>\n",
        "      <td>-2.653333</td>\n",
        "      <td>-0.004585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 735472</td>\n",
        "      <td> 577.27</td>\n",
        "      <td> 571.00</td>\n",
        "      <td> 578.49</td>\n",
        "      <td> 570.10</td>\n",
        "      <td> 1698700</td>\n",
        "      <td>2014-08-27</td>\n",
        "      <td> 573.196667</td>\n",
        "      <td>-6.27</td>\n",
        "      <td>-0.010939</td>\n",
        "      <td> 0.014637</td>\n",
        "      <td>-5.550000</td>\n",
        "      <td>-0.009683</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 735473</td>\n",
        "      <td> 569.56</td>\n",
        "      <td> 569.20</td>\n",
        "      <td> 573.25</td>\n",
        "      <td> 567.10</td>\n",
        "      <td> 1289400</td>\n",
        "      <td>2014-08-28</td>\n",
        "      <td> 569.850000</td>\n",
        "      <td>-0.36</td>\n",
        "      <td>-0.000632</td>\n",
        "      <td> 0.010792</td>\n",
        "      <td>-3.346667</td>\n",
        "      <td>-0.005873</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 735474</td>\n",
        "      <td> 571.33</td>\n",
        "      <td> 571.60</td>\n",
        "      <td> 572.04</td>\n",
        "      <td> 567.07</td>\n",
        "      <td> 1080800</td>\n",
        "      <td>2014-08-29</td>\n",
        "      <td> 570.236667</td>\n",
        "      <td> 0.27</td>\n",
        "      <td> 0.000473</td>\n",
        "      <td> 0.008716</td>\n",
        "      <td> 0.386667</td>\n",
        "      <td> 0.000678</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 735478</td>\n",
        "      <td> 571.85</td>\n",
        "      <td> 577.33</td>\n",
        "      <td> 577.83</td>\n",
        "      <td> 571.19</td>\n",
        "      <td> 1574100</td>\n",
        "      <td>2014-09-02</td>\n",
        "      <td> 575.450000</td>\n",
        "      <td> 5.48</td>\n",
        "      <td> 0.009523</td>\n",
        "      <td> 0.011539</td>\n",
        "      <td> 5.213333</td>\n",
        "      <td> 0.009060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 735479</td>\n",
        "      <td> 580.00</td>\n",
        "      <td> 577.94</td>\n",
        "      <td> 582.99</td>\n",
        "      <td> 575.00</td>\n",
        "      <td> 1211800</td>\n",
        "      <td>2014-09-03</td>\n",
        "      <td> 578.643333</td>\n",
        "      <td>-2.06</td>\n",
        "      <td>-0.003560</td>\n",
        "      <td> 0.013808</td>\n",
        "      <td> 3.193333</td>\n",
        "      <td> 0.005519</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 735480</td>\n",
        "      <td> 580.00</td>\n",
        "      <td> 581.98</td>\n",
        "      <td> 586.00</td>\n",
        "      <td> 579.22</td>\n",
        "      <td> 1454200</td>\n",
        "      <td>2014-09-04</td>\n",
        "      <td> 582.400000</td>\n",
        "      <td> 1.98</td>\n",
        "      <td> 0.003400</td>\n",
        "      <td> 0.011641</td>\n",
        "      <td> 3.756667</td>\n",
        "      <td> 0.006450</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 735481</td>\n",
        "      <td> 583.98</td>\n",
        "      <td> 586.08</td>\n",
        "      <td> 586.55</td>\n",
        "      <td> 581.95</td>\n",
        "      <td> 1627900</td>\n",
        "      <td>2014-09-05</td>\n",
        "      <td> 584.860000</td>\n",
        "      <td> 2.10</td>\n",
        "      <td> 0.003591</td>\n",
        "      <td> 0.007865</td>\n",
        "      <td> 2.460000</td>\n",
        "      <td> 0.004206</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>19 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "    n_date    open   close    high     low   volume       date     average  \\\n",
        "0   735456  569.99  567.88  570.49  566.00  1211400 2014-08-11  568.123333   \n",
        "1   735457  564.52  562.73  565.90  560.88  1537800 2014-08-12  563.170000   \n",
        "2   735458  567.31  574.78  575.00  565.75  1435300 2014-08-13  571.843333   \n",
        "3   735459  576.18  574.65  577.90  570.88   982800 2014-08-14  574.476667   \n",
        "4   735460  577.86  573.48  579.38  570.52  1515000 2014-08-15  574.460000   \n",
        "5   735463  576.11  582.16  584.51  576.00  1280600 2014-08-18  580.890000   \n",
        "6   735464  585.00  586.86  587.34  584.00   976000 2014-08-19  586.066667   \n",
        "7   735465  585.88  584.49  586.70  582.57  1033900 2014-08-20  584.586667   \n",
        "8   735466  583.82  583.37  584.50  581.14   912300 2014-08-21  583.003333   \n",
        "9   735467  583.59  582.56  585.24  580.64   786900 2014-08-22  582.813333   \n",
        "10  735470  584.72  580.20  585.00  579.00  1357700 2014-08-25  581.400000   \n",
        "11  735471  581.26  577.86  581.80  576.58  1635200 2014-08-26  578.746667   \n",
        "12  735472  577.27  571.00  578.49  570.10  1698700 2014-08-27  573.196667   \n",
        "13  735473  569.56  569.20  573.25  567.10  1289400 2014-08-28  569.850000   \n",
        "14  735474  571.33  571.60  572.04  567.07  1080800 2014-08-29  570.236667   \n",
        "15  735478  571.85  577.33  577.83  571.19  1574100 2014-09-02  575.450000   \n",
        "16  735479  580.00  577.94  582.99  575.00  1211800 2014-09-03  578.643333   \n",
        "17  735480  580.00  581.98  586.00  579.22  1454200 2014-09-04  582.400000   \n",
        "18  735481  583.98  586.08  586.55  581.95  1627900 2014-09-05  584.860000   \n",
        "\n",
        "    change_amount  change_per     range  change_1_amount  change_1_per  \n",
        "0           -2.11   -0.003714  0.007903         0.000000      0.000000  \n",
        "1           -1.79   -0.003178  0.008914        -4.953333     -0.008795  \n",
        "2            7.47    0.013063  0.016176         8.673333      0.015167  \n",
        "3           -1.53   -0.002663  0.012220         2.633333      0.004584  \n",
        "4           -4.38   -0.007625  0.015423        -0.016667     -0.000029  \n",
        "5            6.05    0.010415  0.014650         6.430000      0.011069  \n",
        "6            1.86    0.003174  0.005699         5.176667      0.008833  \n",
        "7           -1.39   -0.002378  0.007065        -1.480000     -0.002532  \n",
        "8           -0.45   -0.000772  0.005763        -1.583333     -0.002716  \n",
        "9           -1.03   -0.001767  0.007893        -0.190000     -0.000326  \n",
        "10          -4.52   -0.007774  0.010320        -1.413333     -0.002431  \n",
        "11          -3.40   -0.005875  0.009019        -2.653333     -0.004585  \n",
        "12          -6.27   -0.010939  0.014637        -5.550000     -0.009683  \n",
        "13          -0.36   -0.000632  0.010792        -3.346667     -0.005873  \n",
        "14           0.27    0.000473  0.008716         0.386667      0.000678  \n",
        "15           5.48    0.009523  0.011539         5.213333      0.009060  \n",
        "16          -2.06   -0.003560  0.013808         3.193333      0.005519  \n",
        "17           1.98    0.003400  0.011641         3.756667      0.006450  \n",
        "18           2.10    0.003591  0.007865         2.460000      0.004206  \n",
        "\n",
        "[19 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Visualizing the Price"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting the Average Price"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10,6))\n",
      "plt.plot_date(stocks_df[\"n_date\"], stocks_df[\"average\"], fmt=\"-b\", linewidth=3, alpha=.4, label=\"Average\")\n",
      "plt.gcf().autofmt_xdate()\n",
      "\n",
      "plt.title(\"Google Average Stock Price - Daily\")\n",
      "plt.grid()\n",
      "plt.legend(loc=\"best\")\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6lyhdXFzwy1/+EgEBAQgLC8P27duxadMmtGvXDoBokvnYY4/Bz88P33zzDUaPHo0//vGP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\n82IAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84cd6510>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plotting More Data on a Different Y-Scale"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,6))\n",
      "\n",
      "axes_1 = fig.add_subplot(111)\n",
      "#axes_2 = fig.add_subplot(111)\n",
      "axes_2 = axes_1.twinx()\n",
      "\n",
      "line1 = axes_1.plot_date(stocks_df[\"n_date\"], stocks_df[\"average\"], \"o-b\", \n",
      "                    linewidth=3, alpha=.4, label=\"Average\")\n",
      "line2 = axes_2.plot_date(stocks_df[\"n_date\"], stocks_df[\"change_1_per\"], \".-r\",\n",
      "                    linewidth=2, alpha=.4, label=\"Change from 1 day\")\n",
      "\n",
      "axes_2.plot_date(stocks_df[\"n_date\"], np.zeros(len(stocks_df)), fmt=\"-k\", linewidth=1, alpha=.4)\n",
      "\n",
      "plt.gcf().autofmt_xdate()\n",
      "\n",
      "axes_1.set_title(\"Google Average Stock Price - Daily\")\n",
      "axes_1.grid()\n",
      "axes_1.legend(loc=\"upper right\")\n",
      "axes_2.legend(loc=\"lower right\")\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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clqpbaLpWDyh+QKAj20LTU9evH3D6dLzW+xIJMHs2f09FdTVw6hRw7BhQWWmc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/AAAg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\ngjwGg8FgMBgMI4QFeQwGg8FgMBhGyP8Boh8zPu/fPRUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84c43650>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Using Candle Stick Chart"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "```\n",
      "candlestick(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0)\n",
      "\n",
      "quotes is a sequence of (time, open, close, high, low, ...) sequences.\n",
      "\n",
      "ax          : an Axes instance to plot to\n",
      "width       : fraction of a day for the rectangle width\n",
      "colorup     : the color of the rectangle where close >= open\n",
      "colordown   : the color of the rectangle where close <  open\n",
      "alpha       : the rectangle alpha level\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,6))\n",
      "axes = fig.add_subplot(111)\n",
      "\n",
      "candlestick(axes, stocks_df[[\"n_date\", \"open\", \"close\", \"high\", \"low\"]].values,\n",
      "            width=0.6, colorup='g', colordown='r')\n",
      "\n",
      "axes.xaxis_date()\n",
      "plt.gcf().autofmt_xdate()\n",
      "\n",
      "plt.grid()\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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FO3fuxJo1a/Daa69h06ZNuP/++zFz5kwcPHgQL7/8MsaOHVvj6202WwOOloh8Qn4+IFL5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\nRKTM/wd0H6c1uZEHbwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84cc0f50>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,6))\n",
      "axes_1 = fig.add_subplot(111)\n",
      "axes_2 = axes_1.twinx()\n",
      "\n",
      "candlestick(axes_1, stocks_df[[\"n_date\", \"open\", \"close\", \"high\", \"low\"]].values,\n",
      "            width=0.6, colorup='g', colordown='r')\n",
      "\n",
      "axes_2.plot_date(stocks_df[\"n_date\"], stocks_df[\"change_1_per\"], \"o--b\", linewidth=3, alpha=0.4)\n",
      "\n",
      "axes_1.xaxis_date()\n",
      "plt.gcf().autofmt_xdate()\n",
      "\n",
      "axes_1.grid()\n",
      "\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rbIXTuRNOknyKkDL9cqS38vLgxhud2++/D+fOha49QojwYbfXU1RUy7lzOTQ3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       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84b94510>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Working with many stocks"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will download 50 stocks and process their stats. We will be working with these stocks:\n",
      "\n",
      "1. GOOG: Google\n",
      "2. FB: Facebook, Inc.\n",
      "3. AAPL: Apple Inc.\n",
      "4. MSFT: Microsoft Corporation\n",
      "5. HPQ: Hewlett-Packard Company\n",
      "6. INTC: Intel Corporation\n",
      "7. NVDA: NVIDIA Corporation\n",
      "8. TXN: Texas Instruments Incorporated\n",
      "9. IBM: International Business Machines Corp. (IBM)\n",
      "10. SAP: SAP SE (ADR)\n",
      "11. ADBE: Adobe Systems Incorporated\n",
      "12. ADSK: Autodesk, Inc.\n",
      "13. CRM: salesforce.com, inc.\n",
      "14. N: NetSuite Inc\n",
      "15. VMW: VMware, Inc.\n",
      "16. CTXS: Citrix Systems, Inc.\n",
      "17. RHT: Red Hat Inc\n",
      "18. RAX: Rackspace Hosting, Inc.\n",
      "19. AMZN: Amazon.com, Inc.\n",
      "20. NWSA: News Corp\n",
      "21. EBAY: eBay Inc\n",
      "22. CBS: CBS Corporation\n",
      "23. CMCSA: Comcast Corporation\n",
      "24. VIAB: Viacom, Inc.\n",
      "25. NFLX: Netflix, Inc.\n",
      "26. TWX: Time Warner Inc\n",
      "27. FOXA: Twenty-First Century Fox Inc\n",
      "28. NYT: The New York Times Company\n",
      "29. TRI: Thomson Reuters Corporation (USA)\n",
      "30. DIS: The Walt Disney Company\n",
      "31. SNE: Sony Corp (ADR)\n",
      "32. PCRFY: Panasonic Corporation (ADR)\n",
      "33. CAJ: Canon Inc (ADR)\n",
      "34. TOSYY: Toshiba Corp (USA)\n",
      "35. BBRY: BlackBerry Ltd\n",
      "36. CSC: Computer Sciences Corporation\n",
      "37. GE: General Electric Company\n",
      "38. HTHIY: Hitachi, Ltd. (ADR)\n",
      "39. SIEGY: Siemens AG (ADR)\n",
      "40. CVX: Chevron Corporation\n",
      "41. XOM: Exxon Mobil Corporation\n",
      "42. BP: BP plc (ADR)\n",
      "43. CAT: Caterpillar Inc.\n",
      "44. LXK: Lexmark International Inc\n",
      "45. BKS: Barnes & Noble, Inc.\n",
      "46. FJTSY: Fujitsu Ltd (ADR)\n",
      "47. EMC: EMC Corporation\n",
      "48. ORCL: Oracle Corporation\n",
      "49. CSCO: Cisco Systems, Inc.\n",
      "50. XRX: Xerox Corp"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stock_dict={\"GOOG\": \"Google\",\n",
      "            \"FB\": \"Facebook, Inc.\",\n",
      "            \"AAPL\": \"Apple Inc.\",\n",
      "            \"MSFT\": \"Microsoft Corporation\",\n",
      "            \"HPQ\": \"Hewlett-Packard Company\",\n",
      "            \"INTC\": \"Intel Corporation\",\n",
      "            \"NVDA\": \"NVIDIA Corporation\",\n",
      "            \"TXN\": \"Texas Instruments Incorporated\",\n",
      "            \"IBM\": \"International Business Machines Corp. (IBM)\",\n",
      "            \"SAP\": \"SAP SE (ADR)\",\n",
      "            \"ADBE\": \"Adobe Systems Incorporated\",\n",
      "            \"ADSK\": \"Autodesk, Inc.\",\n",
      "            \"CRM\": \"salesforce.com, inc.\",\n",
      "            \"N\": \"NetSuite Inc\",\n",
      "            \"VMW\": \"VMware, Inc.\",\n",
      "            \"CTXS\": \"Citrix Systems, Inc.\",\n",
      "            \"RHT\": \"Red Hat Inc\",\n",
      "            \"RAX\": \"Rackspace Hosting, Inc.\",\n",
      "            \"AMZN\": \"Amazon.com, Inc.\",\n",
      "            \"NWSA\": \"News Corp\",\n",
      "            \"EBAY\": \"eBay Inc\",\n",
      "            \"CBS\": \"CBS Corporation\",\n",
      "            \"CMCSA\": \"Comcast Corporation\",\n",
      "            \"VIAB\": \"Viacom, Inc.\",\n",
      "            \"NFLX\": \"Netflix, Inc.\",\n",
      "            \"TWX\": \"Time Warner Inc\",\n",
      "            \"FOXA\": \"Twenty-First Century Fox Inc\",\n",
      "            \"NYT\": \"The New York Times Company\",\n",
      "            \"TRI\": \"Thomson Reuters Corporation (USA)\",\n",
      "            \"DIS\": \"The Walt Disney Company\",\n",
      "            \"SNE\": \"Sony Corp (ADR)\",\n",
      "            \"PCRFY\": \"Panasonic Corporation (ADR)\",\n",
      "            \"CAJ\": \"Canon Inc (ADR)\",\n",
      "            \"TOSYY\": \"Toshiba Corp (USA)\",\n",
      "            \"BBRY\": \"BlackBerry Ltd\",\n",
      "            \"CSC\": \"Computer Sciences Corporation\",\n",
      "            \"GE\": \"General Electric Company\",\n",
      "            \"HTHIY\": \"Hitachi, Ltd. (ADR)\",\n",
      "            \"SIEGY\": \"Siemens AG (ADR)\",\n",
      "            \"CVX\": \"Chevron Corporation\",\n",
      "            \"XOM\": \"Exxon Mobil Corporation\",\n",
      "            \"BP\": \"BP plc (ADR)\",\n",
      "            \"CAT\": \"Caterpillar Inc.\",\n",
      "            \"LXK\": \"Lexmark International Inc\",\n",
      "            \"BKS\": \"Barnes & Noble, Inc.\",\n",
      "            \"FJTSY\": \"Fujitsu Ltd (ADR)\",\n",
      "            \"EMC\": \"EMC Corporation\",\n",
      "            \"ORCL\": \"Oracle Corporation\",\n",
      "            \"CSCO\": \"Cisco Systems, Inc.\",\n",
      "            \"XRX\": \"Xerox Corp\",\n",
      "           }\n",
      "symbols = stock_dict.keys()\n",
      "names = stock_dict.values()\n",
      "\n",
      "stocks_data = pd.DataFrame(symbols, columns=[\"symbol\"])\n",
      "stocks_data[\"name\"] = names\n",
      "stocks_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>symbol</th>\n",
        "      <th>name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  NFLX</td>\n",
        "      <td>                               Netflix, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>  AAPL</td>\n",
        "      <td>                                  Apple Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   NYT</td>\n",
        "      <td>                  The New York Times Company</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>    FB</td>\n",
        "      <td>                              Facebook, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>    BP</td>\n",
        "      <td>                                BP plc (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>  ADSK</td>\n",
        "      <td>                              Autodesk, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>  MSFT</td>\n",
        "      <td>                       Microsoft Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>   EMC</td>\n",
        "      <td>                             EMC Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>   BKS</td>\n",
        "      <td>                        Barnes &amp; Noble, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>   SAP</td>\n",
        "      <td>                                SAP SE (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>   RAX</td>\n",
        "      <td>                     Rackspace Hosting, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>     N</td>\n",
        "      <td>                                NetSuite Inc</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>    GE</td>\n",
        "      <td>                    General Electric Company</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>  AMZN</td>\n",
        "      <td>                            Amazon.com, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>   SNE</td>\n",
        "      <td>                             Sony Corp (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>  VIAB</td>\n",
        "      <td>                                Viacom, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>  FOXA</td>\n",
        "      <td>                Twenty-First Century Fox Inc</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> PCRFY</td>\n",
        "      <td>                 Panasonic Corporation (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>   RHT</td>\n",
        "      <td>                                 Red Hat Inc</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>  EBAY</td>\n",
        "      <td>                                    eBay Inc</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>   VMW</td>\n",
        "      <td>                                VMware, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>   LXK</td>\n",
        "      <td>                   Lexmark International Inc</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> CMCSA</td>\n",
        "      <td>                         Comcast Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>   TRI</td>\n",
        "      <td>           Thomson Reuters Corporation (USA)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>  ADBE</td>\n",
        "      <td>                  Adobe Systems Incorporated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>   IBM</td>\n",
        "      <td> International Business Machines Corp. (IBM)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>   CVX</td>\n",
        "      <td>                         Chevron Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> FJTSY</td>\n",
        "      <td>                           Fujitsu Ltd (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>   CRM</td>\n",
        "      <td>                        salesforce.com, inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>   CAT</td>\n",
        "      <td>                            Caterpillar Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>   CAJ</td>\n",
        "      <td>                             Canon Inc (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>  NVDA</td>\n",
        "      <td>                          NVIDIA Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>   TWX</td>\n",
        "      <td>                             Time Warner Inc</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td>   DIS</td>\n",
        "      <td>                     The Walt Disney Company</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>   TXN</td>\n",
        "      <td>              Texas Instruments Incorporated</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>  NWSA</td>\n",
        "      <td>                                   News Corp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>  ORCL</td>\n",
        "      <td>                          Oracle Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>  CTXS</td>\n",
        "      <td>                        Citrix Systems, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> HTHIY</td>\n",
        "      <td>                         Hitachi, Ltd. (ADR)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>  GOOG</td>\n",
        "      <td>                                      Google</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>  INTC</td>\n",
        "      <td>                           Intel Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>  BBRY</td>\n",
        "      <td>                              BlackBerry Ltd</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> TOSYY</td>\n",
        "      <td>                          Toshiba Corp (USA)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>   HPQ</td>\n",
        "      <td>                     Hewlett-Packard Company</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>   CSC</td>\n",
        "      <td>               Computer Sciences Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>   XRX</td>\n",
        "      <td>                                  Xerox Corp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>   XOM</td>\n",
        "      <td>                     Exxon Mobil Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>  CSCO</td>\n",
        "      <td>                         Cisco Systems, Inc.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>   CBS</td>\n",
        "      <td>                             CBS Corporation</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> SIEGY</td>\n",
        "      <td>                            Siemens AG (ADR)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>50 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "   symbol                                         name\n",
        "0    NFLX                                Netflix, Inc.\n",
        "1    AAPL                                   Apple Inc.\n",
        "2     NYT                   The New York Times Company\n",
        "3      FB                               Facebook, Inc.\n",
        "4      BP                                 BP plc (ADR)\n",
        "5    ADSK                               Autodesk, Inc.\n",
        "6    MSFT                        Microsoft Corporation\n",
        "7     EMC                              EMC Corporation\n",
        "8     BKS                         Barnes & Noble, Inc.\n",
        "9     SAP                                 SAP SE (ADR)\n",
        "10    RAX                      Rackspace Hosting, Inc.\n",
        "11      N                                 NetSuite Inc\n",
        "12     GE                     General Electric Company\n",
        "13   AMZN                             Amazon.com, Inc.\n",
        "14    SNE                              Sony Corp (ADR)\n",
        "15   VIAB                                 Viacom, Inc.\n",
        "16   FOXA                 Twenty-First Century Fox Inc\n",
        "17  PCRFY                  Panasonic Corporation (ADR)\n",
        "18    RHT                                  Red Hat Inc\n",
        "19   EBAY                                     eBay Inc\n",
        "20    VMW                                 VMware, Inc.\n",
        "21    LXK                    Lexmark International Inc\n",
        "22  CMCSA                          Comcast Corporation\n",
        "23    TRI            Thomson Reuters Corporation (USA)\n",
        "24   ADBE                   Adobe Systems Incorporated\n",
        "25    IBM  International Business Machines Corp. (IBM)\n",
        "26    CVX                          Chevron Corporation\n",
        "27  FJTSY                            Fujitsu Ltd (ADR)\n",
        "28    CRM                         salesforce.com, inc.\n",
        "29    CAT                             Caterpillar Inc.\n",
        "30    CAJ                              Canon Inc (ADR)\n",
        "31   NVDA                           NVIDIA Corporation\n",
        "32    TWX                              Time Warner Inc\n",
        "33    DIS                      The Walt Disney Company\n",
        "34    TXN               Texas Instruments Incorporated\n",
        "35   NWSA                                    News Corp\n",
        "36   ORCL                           Oracle Corporation\n",
        "37   CTXS                         Citrix Systems, Inc.\n",
        "38  HTHIY                          Hitachi, Ltd. (ADR)\n",
        "39   GOOG                                       Google\n",
        "40   INTC                            Intel Corporation\n",
        "41   BBRY                               BlackBerry Ltd\n",
        "42  TOSYY                           Toshiba Corp (USA)\n",
        "43    HPQ                      Hewlett-Packard Company\n",
        "44    CSC                Computer Sciences Corporation\n",
        "45    XRX                                   Xerox Corp\n",
        "46    XOM                      Exxon Mobil Corporation\n",
        "47   CSCO                          Cisco Systems, Inc.\n",
        "48    CBS                              CBS Corporation\n",
        "49  SIEGY                             Siemens AG (ADR)\n",
        "\n",
        "[50 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Download Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp_list = []\n",
      "for symbol in stocks_data[\"symbol\"]:\n",
      "    temp_data = download_data(symbol)\n",
      "    process_date(temp_data)\n",
      "    calculate_stats(temp_data)\n",
      "    temp_data[\"symbol\"] = symbol\n",
      "    temp_list.append(temp_data)\n",
      "\n",
      "stocks_df = pd.concat(temp_list)\n",
      "stocks_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>n_date</th>\n",
        "      <th>open</th>\n",
        "      <th>close</th>\n",
        "      <th>high</th>\n",
        "      <th>low</th>\n",
        "      <th>volume</th>\n",
        "      <th>date</th>\n",
        "      <th>average</th>\n",
        "      <th>change_amount</th>\n",
        "      <th>change_per</th>\n",
        "      <th>range</th>\n",
        "      <th>change_1_amount</th>\n",
        "      <th>change_1_per</th>\n",
        "      <th>symbol</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td> 449.22</td>\n",
        "      <td> 451.54</td>\n",
        "      <td> 457.65</td>\n",
        "      <td> 448.71</td>\n",
        "      <td>   1892400</td>\n",
        "      <td>2014-08-11</td>\n",
        "      <td> 452.633333</td>\n",
        "      <td> 2.32</td>\n",
        "      <td> 0.005126</td>\n",
        "      <td> 0.019751</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td> 451.35</td>\n",
        "      <td> 446.41</td>\n",
        "      <td> 453.00</td>\n",
        "      <td> 443.41</td>\n",
        "      <td>   1549300</td>\n",
        "      <td>2014-08-12</td>\n",
        "      <td> 447.606667</td>\n",
        "      <td>-4.94</td>\n",
        "      <td>-0.011036</td>\n",
        "      <td> 0.021425</td>\n",
        "      <td>-5.026667</td>\n",
        "      <td>-0.011230</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td> 448.60</td>\n",
        "      <td> 451.53</td>\n",
        "      <td> 454.22</td>\n",
        "      <td> 446.82</td>\n",
        "      <td>   1294000</td>\n",
        "      <td>2014-08-13</td>\n",
        "      <td> 450.856667</td>\n",
        "      <td> 2.93</td>\n",
        "      <td> 0.006499</td>\n",
        "      <td> 0.016413</td>\n",
        "      <td> 3.250000</td>\n",
        "      <td> 0.007208</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 735459</td>\n",
        "      <td> 452.21</td>\n",
        "      <td> 450.87</td>\n",
        "      <td> 455.00</td>\n",
        "      <td> 448.22</td>\n",
        "      <td>    951500</td>\n",
        "      <td>2014-08-14</td>\n",
        "      <td> 451.363333</td>\n",
        "      <td>-1.34</td>\n",
        "      <td>-0.002969</td>\n",
        "      <td> 0.015021</td>\n",
        "      <td> 0.506667</td>\n",
        "      <td> 0.001123</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 735460</td>\n",
        "      <td> 451.49</td>\n",
        "      <td> 459.09</td>\n",
        "      <td> 462.00</td>\n",
        "      <td> 448.60</td>\n",
        "      <td>   3148100</td>\n",
        "      <td>2014-08-15</td>\n",
        "      <td> 456.563333</td>\n",
        "      <td> 7.60</td>\n",
        "      <td> 0.016646</td>\n",
        "      <td> 0.029350</td>\n",
        "      <td> 5.200000</td>\n",
        "      <td> 0.011389</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 735463</td>\n",
        "      <td> 462.06</td>\n",
        "      <td> 466.00</td>\n",
        "      <td> 469.50</td>\n",
        "      <td> 461.25</td>\n",
        "      <td>   1928000</td>\n",
        "      <td>2014-08-18</td>\n",
        "      <td> 465.583333</td>\n",
        "      <td> 3.94</td>\n",
        "      <td> 0.008463</td>\n",
        "      <td> 0.017720</td>\n",
        "      <td> 9.020000</td>\n",
        "      <td> 0.019374</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 735464</td>\n",
        "      <td> 467.09</td>\n",
        "      <td> 468.15</td>\n",
        "      <td> 470.50</td>\n",
        "      <td> 462.43</td>\n",
        "      <td>   1556800</td>\n",
        "      <td>2014-08-19</td>\n",
        "      <td> 467.026667</td>\n",
        "      <td> 1.06</td>\n",
        "      <td> 0.002270</td>\n",
        "      <td> 0.017280</td>\n",
        "      <td> 1.443333</td>\n",
        "      <td> 0.003090</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 735465</td>\n",
        "      <td> 467.00</td>\n",
        "      <td> 472.19</td>\n",
        "      <td> 473.75</td>\n",
        "      <td> 464.54</td>\n",
        "      <td>   1737700</td>\n",
        "      <td>2014-08-20</td>\n",
        "      <td> 470.160000</td>\n",
        "      <td> 5.19</td>\n",
        "      <td> 0.011039</td>\n",
        "      <td> 0.019589</td>\n",
        "      <td> 3.133333</td>\n",
        "      <td> 0.006664</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 735466</td>\n",
        "      <td> 471.28</td>\n",
        "      <td> 472.05</td>\n",
        "      <td> 476.15</td>\n",
        "      <td> 467.67</td>\n",
        "      <td>   1678100</td>\n",
        "      <td>2014-08-21</td>\n",
        "      <td> 471.956667</td>\n",
        "      <td> 0.77</td>\n",
        "      <td> 0.001632</td>\n",
        "      <td> 0.017968</td>\n",
        "      <td> 1.796667</td>\n",
        "      <td> 0.003807</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 735467</td>\n",
        "      <td> 470.94</td>\n",
        "      <td> 479.19</td>\n",
        "      <td> 479.55</td>\n",
        "      <td> 470.05</td>\n",
        "      <td>   1962200</td>\n",
        "      <td>2014-08-22</td>\n",
        "      <td> 476.263333</td>\n",
        "      <td> 8.25</td>\n",
        "      <td> 0.017322</td>\n",
        "      <td> 0.019947</td>\n",
        "      <td> 4.306667</td>\n",
        "      <td> 0.009043</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 735470</td>\n",
        "      <td> 481.55</td>\n",
        "      <td> 480.93</td>\n",
        "      <td> 485.30</td>\n",
        "      <td> 477.53</td>\n",
        "      <td>   1926400</td>\n",
        "      <td>2014-08-25</td>\n",
        "      <td> 481.253333</td>\n",
        "      <td>-0.62</td>\n",
        "      <td>-0.001288</td>\n",
        "      <td> 0.016145</td>\n",
        "      <td> 4.990000</td>\n",
        "      <td> 0.010369</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 735471</td>\n",
        "      <td> 478.50</td>\n",
        "      <td> 479.36</td>\n",
        "      <td> 481.85</td>\n",
        "      <td> 474.55</td>\n",
        "      <td>   1448200</td>\n",
        "      <td>2014-08-26</td>\n",
        "      <td> 478.586667</td>\n",
        "      <td> 0.86</td>\n",
        "      <td> 0.001797</td>\n",
        "      <td> 0.015253</td>\n",
        "      <td>-2.666667</td>\n",
        "      <td>-0.005572</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 735472</td>\n",
        "      <td> 479.40</td>\n",
        "      <td> 474.70</td>\n",
        "      <td> 480.25</td>\n",
        "      <td> 473.63</td>\n",
        "      <td>   1484900</td>\n",
        "      <td>2014-08-27</td>\n",
        "      <td> 476.193333</td>\n",
        "      <td>-4.70</td>\n",
        "      <td>-0.009870</td>\n",
        "      <td> 0.013902</td>\n",
        "      <td>-2.393333</td>\n",
        "      <td>-0.005026</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 735473</td>\n",
        "      <td> 472.65</td>\n",
        "      <td> 475.21</td>\n",
        "      <td> 477.48</td>\n",
        "      <td> 470.81</td>\n",
        "      <td>   1083500</td>\n",
        "      <td>2014-08-28</td>\n",
        "      <td> 474.500000</td>\n",
        "      <td> 2.56</td>\n",
        "      <td> 0.005395</td>\n",
        "      <td> 0.014057</td>\n",
        "      <td>-1.693333</td>\n",
        "      <td>-0.003569</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 735474</td>\n",
        "      <td> 476.85</td>\n",
        "      <td> 477.64</td>\n",
        "      <td> 480.50</td>\n",
        "      <td> 474.14</td>\n",
        "      <td>   1405500</td>\n",
        "      <td>2014-08-29</td>\n",
        "      <td> 477.426667</td>\n",
        "      <td> 0.79</td>\n",
        "      <td> 0.001655</td>\n",
        "      <td> 0.013321</td>\n",
        "      <td> 2.926667</td>\n",
        "      <td> 0.006130</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 735478</td>\n",
        "      <td> 478.50</td>\n",
        "      <td> 476.60</td>\n",
        "      <td> 478.79</td>\n",
        "      <td> 474.59</td>\n",
        "      <td>   1258100</td>\n",
        "      <td>2014-09-02</td>\n",
        "      <td> 476.660000</td>\n",
        "      <td>-1.90</td>\n",
        "      <td>-0.003986</td>\n",
        "      <td> 0.008811</td>\n",
        "      <td>-0.766667</td>\n",
        "      <td>-0.001608</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 735479</td>\n",
        "      <td> 480.52</td>\n",
        "      <td> 477.39</td>\n",
        "      <td> 487.60</td>\n",
        "      <td> 476.53</td>\n",
        "      <td>   2317000</td>\n",
        "      <td>2014-09-03</td>\n",
        "      <td> 480.506667</td>\n",
        "      <td>-3.13</td>\n",
        "      <td>-0.006514</td>\n",
        "      <td> 0.023038</td>\n",
        "      <td> 3.846667</td>\n",
        "      <td> 0.008005</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 735480</td>\n",
        "      <td> 479.11</td>\n",
        "      <td> 472.67</td>\n",
        "      <td> 481.69</td>\n",
        "      <td> 472.13</td>\n",
        "      <td>   1708400</td>\n",
        "      <td>2014-09-04</td>\n",
        "      <td> 475.496667</td>\n",
        "      <td>-6.44</td>\n",
        "      <td>-0.013544</td>\n",
        "      <td> 0.020105</td>\n",
        "      <td>-5.010000</td>\n",
        "      <td>-0.010536</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 735481</td>\n",
        "      <td> 473.23</td>\n",
        "      <td> 475.68</td>\n",
        "      <td> 477.35</td>\n",
        "      <td> 470.07</td>\n",
        "      <td>   1707600</td>\n",
        "      <td>2014-09-05</td>\n",
        "      <td> 474.366667</td>\n",
        "      <td> 2.45</td>\n",
        "      <td> 0.005165</td>\n",
        "      <td> 0.015347</td>\n",
        "      <td>-1.130000</td>\n",
        "      <td>-0.002382</td>\n",
        "      <td> NFLX</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td>  95.27</td>\n",
        "      <td>  95.99</td>\n",
        "      <td>  96.08</td>\n",
        "      <td>  94.84</td>\n",
        "      <td>  36585000</td>\n",
        "      <td>2014-08-11</td>\n",
        "      <td>  95.636667</td>\n",
        "      <td> 0.72</td>\n",
        "      <td> 0.007528</td>\n",
        "      <td> 0.012966</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td>  96.04</td>\n",
        "      <td>  95.97</td>\n",
        "      <td>  96.88</td>\n",
        "      <td>  95.61</td>\n",
        "      <td>  33795000</td>\n",
        "      <td>2014-08-12</td>\n",
        "      <td>  96.153333</td>\n",
        "      <td>-0.07</td>\n",
        "      <td>-0.000728</td>\n",
        "      <td> 0.013208</td>\n",
        "      <td> 0.516667</td>\n",
        "      <td> 0.005373</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td>  96.15</td>\n",
        "      <td>  97.24</td>\n",
        "      <td>  97.24</td>\n",
        "      <td>  96.04</td>\n",
        "      <td>  31916000</td>\n",
        "      <td>2014-08-13</td>\n",
        "      <td>  96.840000</td>\n",
        "      <td> 1.09</td>\n",
        "      <td> 0.011256</td>\n",
        "      <td> 0.012392</td>\n",
        "      <td> 0.686667</td>\n",
        "      <td> 0.007091</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 735459</td>\n",
        "      <td>  97.33</td>\n",
        "      <td>  97.50</td>\n",
        "      <td>  97.57</td>\n",
        "      <td>  96.80</td>\n",
        "      <td>  28116000</td>\n",
        "      <td>2014-08-14</td>\n",
        "      <td>  97.290000</td>\n",
        "      <td> 0.17</td>\n",
        "      <td> 0.001747</td>\n",
        "      <td> 0.007914</td>\n",
        "      <td> 0.450000</td>\n",
        "      <td> 0.004625</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 735460</td>\n",
        "      <td>  97.90</td>\n",
        "      <td>  97.98</td>\n",
        "      <td>  98.19</td>\n",
        "      <td>  96.86</td>\n",
        "      <td>  48951000</td>\n",
        "      <td>2014-08-15</td>\n",
        "      <td>  97.676667</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.000819</td>\n",
        "      <td> 0.013616</td>\n",
        "      <td> 0.386667</td>\n",
        "      <td> 0.003959</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 735463</td>\n",
        "      <td>  98.49</td>\n",
        "      <td>  99.16</td>\n",
        "      <td>  99.37</td>\n",
        "      <td>  97.98</td>\n",
        "      <td>  47572000</td>\n",
        "      <td>2014-08-18</td>\n",
        "      <td>  98.836667</td>\n",
        "      <td> 0.67</td>\n",
        "      <td> 0.006779</td>\n",
        "      <td> 0.014064</td>\n",
        "      <td> 1.160000</td>\n",
        "      <td> 0.011737</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 735464</td>\n",
        "      <td>  99.41</td>\n",
        "      <td> 100.53</td>\n",
        "      <td> 100.68</td>\n",
        "      <td>  99.32</td>\n",
        "      <td>  69274700</td>\n",
        "      <td>2014-08-19</td>\n",
        "      <td> 100.176667</td>\n",
        "      <td> 1.12</td>\n",
        "      <td> 0.011180</td>\n",
        "      <td> 0.013576</td>\n",
        "      <td> 1.340000</td>\n",
        "      <td> 0.013376</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 735465</td>\n",
        "      <td> 100.44</td>\n",
        "      <td> 100.57</td>\n",
        "      <td> 101.09</td>\n",
        "      <td>  99.95</td>\n",
        "      <td>  52612800</td>\n",
        "      <td>2014-08-20</td>\n",
        "      <td> 100.536667</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 0.001293</td>\n",
        "      <td> 0.011339</td>\n",
        "      <td> 0.360000</td>\n",
        "      <td> 0.003581</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 735466</td>\n",
        "      <td> 100.57</td>\n",
        "      <td> 100.58</td>\n",
        "      <td> 100.94</td>\n",
        "      <td> 100.11</td>\n",
        "      <td>  33421900</td>\n",
        "      <td>2014-08-21</td>\n",
        "      <td> 100.543333</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.000099</td>\n",
        "      <td> 0.008255</td>\n",
        "      <td> 0.006667</td>\n",
        "      <td> 0.000066</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 735467</td>\n",
        "      <td> 100.29</td>\n",
        "      <td> 101.32</td>\n",
        "      <td> 101.47</td>\n",
        "      <td> 100.19</td>\n",
        "      <td>  44102400</td>\n",
        "      <td>2014-08-22</td>\n",
        "      <td> 100.993333</td>\n",
        "      <td> 1.03</td>\n",
        "      <td> 0.010199</td>\n",
        "      <td> 0.012674</td>\n",
        "      <td> 0.450000</td>\n",
        "      <td> 0.004456</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 735470</td>\n",
        "      <td> 101.79</td>\n",
        "      <td> 101.54</td>\n",
        "      <td> 102.17</td>\n",
        "      <td> 101.28</td>\n",
        "      <td>  40144700</td>\n",
        "      <td>2014-08-25</td>\n",
        "      <td> 101.663333</td>\n",
        "      <td>-0.25</td>\n",
        "      <td>-0.002459</td>\n",
        "      <td> 0.008754</td>\n",
        "      <td> 0.670000</td>\n",
        "      <td> 0.006590</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 735471</td>\n",
        "      <td> 101.42</td>\n",
        "      <td> 100.89</td>\n",
        "      <td> 101.50</td>\n",
        "      <td> 100.86</td>\n",
        "      <td>  33119800</td>\n",
        "      <td>2014-08-26</td>\n",
        "      <td> 101.083333</td>\n",
        "      <td>-0.53</td>\n",
        "      <td>-0.005243</td>\n",
        "      <td> 0.006331</td>\n",
        "      <td>-0.580000</td>\n",
        "      <td>-0.005738</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 735472</td>\n",
        "      <td> 101.02</td>\n",
        "      <td> 102.13</td>\n",
        "      <td> 102.57</td>\n",
        "      <td> 100.70</td>\n",
        "      <td>  46827400</td>\n",
        "      <td>2014-08-27</td>\n",
        "      <td> 101.800000</td>\n",
        "      <td> 1.11</td>\n",
        "      <td> 0.010904</td>\n",
        "      <td> 0.018369</td>\n",
        "      <td> 0.716667</td>\n",
        "      <td> 0.007040</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 735473</td>\n",
        "      <td> 101.59</td>\n",
        "      <td> 102.25</td>\n",
        "      <td> 102.78</td>\n",
        "      <td> 101.56</td>\n",
        "      <td>  68389800</td>\n",
        "      <td>2014-08-28</td>\n",
        "      <td> 102.196667</td>\n",
        "      <td> 0.66</td>\n",
        "      <td> 0.006458</td>\n",
        "      <td> 0.011938</td>\n",
        "      <td> 0.396667</td>\n",
        "      <td> 0.003881</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 735474</td>\n",
        "      <td> 102.86</td>\n",
        "      <td> 102.50</td>\n",
        "      <td> 102.90</td>\n",
        "      <td> 102.20</td>\n",
        "      <td>  44567000</td>\n",
        "      <td>2014-08-29</td>\n",
        "      <td> 102.533333</td>\n",
        "      <td>-0.36</td>\n",
        "      <td>-0.003511</td>\n",
        "      <td> 0.006827</td>\n",
        "      <td> 0.336667</td>\n",
        "      <td> 0.003283</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 735478</td>\n",
        "      <td> 103.06</td>\n",
        "      <td> 103.30</td>\n",
        "      <td> 103.74</td>\n",
        "      <td> 102.72</td>\n",
        "      <td>  53491400</td>\n",
        "      <td>2014-09-02</td>\n",
        "      <td> 103.253333</td>\n",
        "      <td> 0.24</td>\n",
        "      <td> 0.002324</td>\n",
        "      <td> 0.009879</td>\n",
        "      <td> 0.720000</td>\n",
        "      <td> 0.006973</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 735479</td>\n",
        "      <td> 103.10</td>\n",
        "      <td>  98.94</td>\n",
        "      <td> 103.20</td>\n",
        "      <td>  98.58</td>\n",
        "      <td> 125233100</td>\n",
        "      <td>2014-09-03</td>\n",
        "      <td> 100.240000</td>\n",
        "      <td>-4.16</td>\n",
        "      <td>-0.041500</td>\n",
        "      <td> 0.046089</td>\n",
        "      <td>-3.013333</td>\n",
        "      <td>-0.030061</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 735480</td>\n",
        "      <td>  98.85</td>\n",
        "      <td>  98.12</td>\n",
        "      <td> 100.09</td>\n",
        "      <td>  97.79</td>\n",
        "      <td>  85594800</td>\n",
        "      <td>2014-09-04</td>\n",
        "      <td>  98.666667</td>\n",
        "      <td>-0.73</td>\n",
        "      <td>-0.007399</td>\n",
        "      <td> 0.023311</td>\n",
        "      <td>-1.573333</td>\n",
        "      <td>-0.015946</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 735481</td>\n",
        "      <td>  98.80</td>\n",
        "      <td>  98.97</td>\n",
        "      <td>  99.39</td>\n",
        "      <td>  98.31</td>\n",
        "      <td>  58353200</td>\n",
        "      <td>2014-09-05</td>\n",
        "      <td>  98.890000</td>\n",
        "      <td> 0.17</td>\n",
        "      <td> 0.001719</td>\n",
        "      <td> 0.010921</td>\n",
        "      <td> 0.223333</td>\n",
        "      <td> 0.002258</td>\n",
        "      <td> AAPL</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td>  12.58</td>\n",
        "      <td>  12.48</td>\n",
        "      <td>  12.68</td>\n",
        "      <td>  12.46</td>\n",
        "      <td>    624200</td>\n",
        "      <td>2014-08-11</td>\n",
        "      <td>  12.540000</td>\n",
        "      <td>-0.10</td>\n",
        "      <td>-0.007974</td>\n",
        "      <td> 0.017544</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td>  12.39</td>\n",
        "      <td>  12.26</td>\n",
        "      <td>  12.50</td>\n",
        "      <td>  12.18</td>\n",
        "      <td>    704400</td>\n",
        "      <td>2014-08-12</td>\n",
        "      <td>  12.313333</td>\n",
        "      <td>-0.13</td>\n",
        "      <td>-0.010558</td>\n",
        "      <td> 0.025988</td>\n",
        "      <td>-0.226667</td>\n",
        "      <td>-0.018408</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>  12.26</td>\n",
        "      <td>  12.33</td>\n",
        "      <td>  12.23</td>\n",
        "      <td>    593000</td>\n",
        "      <td>2014-08-13</td>\n",
        "      <td>  12.273333</td>\n",
        "      <td>-0.01</td>\n",
        "      <td>-0.000815</td>\n",
        "      <td> 0.008148</td>\n",
        "      <td>-0.040000</td>\n",
        "      <td>-0.003259</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 735459</td>\n",
        "      <td>  12.25</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>  12.35</td>\n",
        "      <td>  12.16</td>\n",
        "      <td>    514800</td>\n",
        "      <td>2014-08-14</td>\n",
        "      <td>  12.260000</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.001631</td>\n",
        "      <td> 0.015498</td>\n",
        "      <td>-0.013333</td>\n",
        "      <td>-0.001088</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 735460</td>\n",
        "      <td>  12.41</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>  12.41</td>\n",
        "      <td>  12.06</td>\n",
        "      <td>    688600</td>\n",
        "      <td>2014-08-15</td>\n",
        "      <td>  12.246667</td>\n",
        "      <td>-0.14</td>\n",
        "      <td>-0.011432</td>\n",
        "      <td> 0.028579</td>\n",
        "      <td>-0.013333</td>\n",
        "      <td>-0.001089</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 735463</td>\n",
        "      <td>  12.42</td>\n",
        "      <td>  12.38</td>\n",
        "      <td>  12.47</td>\n",
        "      <td>  12.34</td>\n",
        "      <td>    548200</td>\n",
        "      <td>2014-08-18</td>\n",
        "      <td>  12.396667</td>\n",
        "      <td>-0.04</td>\n",
        "      <td>-0.003227</td>\n",
        "      <td> 0.010487</td>\n",
        "      <td> 0.150000</td>\n",
        "      <td> 0.012100</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 735464</td>\n",
        "      <td>  12.38</td>\n",
        "      <td>  12.29</td>\n",
        "      <td>  12.44</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>    412100</td>\n",
        "      <td>2014-08-19</td>\n",
        "      <td>  12.333333</td>\n",
        "      <td>-0.09</td>\n",
        "      <td>-0.007297</td>\n",
        "      <td> 0.013784</td>\n",
        "      <td>-0.063333</td>\n",
        "      <td>-0.005135</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 735465</td>\n",
        "      <td>  12.25</td>\n",
        "      <td>  12.29</td>\n",
        "      <td>  12.33</td>\n",
        "      <td>  12.20</td>\n",
        "      <td>    426200</td>\n",
        "      <td>2014-08-20</td>\n",
        "      <td>  12.273333</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.003259</td>\n",
        "      <td> 0.010592</td>\n",
        "      <td>-0.060000</td>\n",
        "      <td>-0.004889</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 735466</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>  12.41</td>\n",
        "      <td>  12.45</td>\n",
        "      <td>  12.24</td>\n",
        "      <td>    664100</td>\n",
        "      <td>2014-08-21</td>\n",
        "      <td>  12.366667</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.011321</td>\n",
        "      <td> 0.016981</td>\n",
        "      <td> 0.093333</td>\n",
        "      <td> 0.007547</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 735467</td>\n",
        "      <td>  12.41</td>\n",
        "      <td>  12.38</td>\n",
        "      <td>  12.46</td>\n",
        "      <td>  12.32</td>\n",
        "      <td>    556300</td>\n",
        "      <td>2014-08-22</td>\n",
        "      <td>  12.386667</td>\n",
        "      <td>-0.03</td>\n",
        "      <td>-0.002422</td>\n",
        "      <td> 0.011302</td>\n",
        "      <td> 0.020000</td>\n",
        "      <td> 0.001615</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 735470</td>\n",
        "      <td>  12.44</td>\n",
        "      <td>  12.32</td>\n",
        "      <td>  12.46</td>\n",
        "      <td>  12.24</td>\n",
        "      <td>    350900</td>\n",
        "      <td>2014-08-25</td>\n",
        "      <td>  12.340000</td>\n",
        "      <td>-0.12</td>\n",
        "      <td>-0.009724</td>\n",
        "      <td> 0.017828</td>\n",
        "      <td>-0.046667</td>\n",
        "      <td>-0.003782</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 735471</td>\n",
        "      <td>  12.34</td>\n",
        "      <td>  12.39</td>\n",
        "      <td>  12.42</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>    532000</td>\n",
        "      <td>2014-08-26</td>\n",
        "      <td>  12.360000</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.004045</td>\n",
        "      <td> 0.012136</td>\n",
        "      <td> 0.020000</td>\n",
        "      <td> 0.001618</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 735472</td>\n",
        "      <td>  12.40</td>\n",
        "      <td>  12.40</td>\n",
        "      <td>  12.44</td>\n",
        "      <td>  12.35</td>\n",
        "      <td>    586200</td>\n",
        "      <td>2014-08-27</td>\n",
        "      <td>  12.396667</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.007260</td>\n",
        "      <td> 0.036667</td>\n",
        "      <td> 0.002958</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 735473</td>\n",
        "      <td>  12.33</td>\n",
        "      <td>  12.35</td>\n",
        "      <td>  12.41</td>\n",
        "      <td>  12.25</td>\n",
        "      <td>    469100</td>\n",
        "      <td>2014-08-28</td>\n",
        "      <td>  12.336667</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.001621</td>\n",
        "      <td> 0.012969</td>\n",
        "      <td>-0.060000</td>\n",
        "      <td>-0.004864</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 735474</td>\n",
        "      <td>  12.36</td>\n",
        "      <td>  12.38</td>\n",
        "      <td>  12.41</td>\n",
        "      <td>  12.17</td>\n",
        "      <td>    342000</td>\n",
        "      <td>2014-08-29</td>\n",
        "      <td>  12.320000</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.001623</td>\n",
        "      <td> 0.019481</td>\n",
        "      <td>-0.016667</td>\n",
        "      <td>-0.001353</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 735478</td>\n",
        "      <td>  12.37</td>\n",
        "      <td>  12.33</td>\n",
        "      <td>  12.45</td>\n",
        "      <td>  12.19</td>\n",
        "      <td>    810500</td>\n",
        "      <td>2014-09-02</td>\n",
        "      <td>  12.323333</td>\n",
        "      <td>-0.04</td>\n",
        "      <td>-0.003246</td>\n",
        "      <td> 0.021098</td>\n",
        "      <td> 0.003333</td>\n",
        "      <td> 0.000270</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 735479</td>\n",
        "      <td>  12.38</td>\n",
        "      <td>  12.33</td>\n",
        "      <td>  12.43</td>\n",
        "      <td>  12.29</td>\n",
        "      <td>    968100</td>\n",
        "      <td>2014-09-03</td>\n",
        "      <td>  12.350000</td>\n",
        "      <td>-0.05</td>\n",
        "      <td>-0.004049</td>\n",
        "      <td> 0.011336</td>\n",
        "      <td> 0.026667</td>\n",
        "      <td> 0.002159</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 735480</td>\n",
        "      <td>  12.38</td>\n",
        "      <td>  12.35</td>\n",
        "      <td>  12.46</td>\n",
        "      <td>  12.26</td>\n",
        "      <td>    750900</td>\n",
        "      <td>2014-09-04</td>\n",
        "      <td>  12.356667</td>\n",
        "      <td>-0.03</td>\n",
        "      <td>-0.002428</td>\n",
        "      <td> 0.016186</td>\n",
        "      <td> 0.006667</td>\n",
        "      <td> 0.000540</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 735481</td>\n",
        "      <td>  12.31</td>\n",
        "      <td>  12.37</td>\n",
        "      <td>  12.45</td>\n",
        "      <td>  12.27</td>\n",
        "      <td>    559800</td>\n",
        "      <td>2014-09-05</td>\n",
        "      <td>  12.363333</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.004853</td>\n",
        "      <td> 0.014559</td>\n",
        "      <td> 0.006667</td>\n",
        "      <td> 0.000539</td>\n",
        "      <td>  NYT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 735456</td>\n",
        "      <td>  73.46</td>\n",
        "      <td>  73.44</td>\n",
        "      <td>  73.91</td>\n",
        "      <td>  73.06</td>\n",
        "      <td>  24591000</td>\n",
        "      <td>2014-08-11</td>\n",
        "      <td>  73.470000</td>\n",
        "      <td>-0.02</td>\n",
        "      <td>-0.000272</td>\n",
        "      <td> 0.011569</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>   FB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 735457</td>\n",
        "      <td>  73.09</td>\n",
        "      <td>  72.83</td>\n",
        "      <td>  73.33</td>\n",
        "      <td>  72.22</td>\n",
        "      <td>  27419000</td>\n",
        "      <td>2014-08-12</td>\n",
        "      <td>  72.793333</td>\n",
        "      <td>-0.26</td>\n",
        "      <td>-0.003572</td>\n",
        "      <td> 0.015249</td>\n",
        "      <td>-0.676667</td>\n",
        "      <td>-0.009296</td>\n",
        "      <td>   FB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 735458</td>\n",
        "      <td>  73.12</td>\n",
        "      <td>  73.77</td>\n",
        "      <td>  74.25</td>\n",
        "      <td>  73.05</td>\n",
        "      <td>  29198500</td>\n",
        "      <td>2014-08-13</td>\n",
        "      <td>  73.690000</td>\n",
        "      <td> 0.65</td>\n",
        "      <td> 0.008821</td>\n",
        "      <td> 0.016284</td>\n",
        "      <td> 0.896667</td>\n",
        "      <td> 0.012168</td>\n",
        "      <td>   FB</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>950 rows \u00d7 14 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "    n_date    open   close    high     low     volume       date     average  \\\n",
        "0   735456  449.22  451.54  457.65  448.71    1892400 2014-08-11  452.633333   \n",
        "1   735457  451.35  446.41  453.00  443.41    1549300 2014-08-12  447.606667   \n",
        "2   735458  448.60  451.53  454.22  446.82    1294000 2014-08-13  450.856667   \n",
        "3   735459  452.21  450.87  455.00  448.22     951500 2014-08-14  451.363333   \n",
        "4   735460  451.49  459.09  462.00  448.60    3148100 2014-08-15  456.563333   \n",
        "5   735463  462.06  466.00  469.50  461.25    1928000 2014-08-18  465.583333   \n",
        "6   735464  467.09  468.15  470.50  462.43    1556800 2014-08-19  467.026667   \n",
        "7   735465  467.00  472.19  473.75  464.54    1737700 2014-08-20  470.160000   \n",
        "8   735466  471.28  472.05  476.15  467.67    1678100 2014-08-21  471.956667   \n",
        "9   735467  470.94  479.19  479.55  470.05    1962200 2014-08-22  476.263333   \n",
        "10  735470  481.55  480.93  485.30  477.53    1926400 2014-08-25  481.253333   \n",
        "11  735471  478.50  479.36  481.85  474.55    1448200 2014-08-26  478.586667   \n",
        "12  735472  479.40  474.70  480.25  473.63    1484900 2014-08-27  476.193333   \n",
        "13  735473  472.65  475.21  477.48  470.81    1083500 2014-08-28  474.500000   \n",
        "14  735474  476.85  477.64  480.50  474.14    1405500 2014-08-29  477.426667   \n",
        "15  735478  478.50  476.60  478.79  474.59    1258100 2014-09-02  476.660000   \n",
        "16  735479  480.52  477.39  487.60  476.53    2317000 2014-09-03  480.506667   \n",
        "17  735480  479.11  472.67  481.69  472.13    1708400 2014-09-04  475.496667   \n",
        "18  735481  473.23  475.68  477.35  470.07    1707600 2014-09-05  474.366667   \n",
        "0   735456   95.27   95.99   96.08   94.84   36585000 2014-08-11   95.636667   \n",
        "1   735457   96.04   95.97   96.88   95.61   33795000 2014-08-12   96.153333   \n",
        "2   735458   96.15   97.24   97.24   96.04   31916000 2014-08-13   96.840000   \n",
        "3   735459   97.33   97.50   97.57   96.80   28116000 2014-08-14   97.290000   \n",
        "4   735460   97.90   97.98   98.19   96.86   48951000 2014-08-15   97.676667   \n",
        "5   735463   98.49   99.16   99.37   97.98   47572000 2014-08-18   98.836667   \n",
        "6   735464   99.41  100.53  100.68   99.32   69274700 2014-08-19  100.176667   \n",
        "7   735465  100.44  100.57  101.09   99.95   52612800 2014-08-20  100.536667   \n",
        "8   735466  100.57  100.58  100.94  100.11   33421900 2014-08-21  100.543333   \n",
        "9   735467  100.29  101.32  101.47  100.19   44102400 2014-08-22  100.993333   \n",
        "10  735470  101.79  101.54  102.17  101.28   40144700 2014-08-25  101.663333   \n",
        "11  735471  101.42  100.89  101.50  100.86   33119800 2014-08-26  101.083333   \n",
        "12  735472  101.02  102.13  102.57  100.70   46827400 2014-08-27  101.800000   \n",
        "13  735473  101.59  102.25  102.78  101.56   68389800 2014-08-28  102.196667   \n",
        "14  735474  102.86  102.50  102.90  102.20   44567000 2014-08-29  102.533333   \n",
        "15  735478  103.06  103.30  103.74  102.72   53491400 2014-09-02  103.253333   \n",
        "16  735479  103.10   98.94  103.20   98.58  125233100 2014-09-03  100.240000   \n",
        "17  735480   98.85   98.12  100.09   97.79   85594800 2014-09-04   98.666667   \n",
        "18  735481   98.80   98.97   99.39   98.31   58353200 2014-09-05   98.890000   \n",
        "0   735456   12.58   12.48   12.68   12.46     624200 2014-08-11   12.540000   \n",
        "1   735457   12.39   12.26   12.50   12.18     704400 2014-08-12   12.313333   \n",
        "2   735458   12.27   12.26   12.33   12.23     593000 2014-08-13   12.273333   \n",
        "3   735459   12.25   12.27   12.35   12.16     514800 2014-08-14   12.260000   \n",
        "4   735460   12.41   12.27   12.41   12.06     688600 2014-08-15   12.246667   \n",
        "5   735463   12.42   12.38   12.47   12.34     548200 2014-08-18   12.396667   \n",
        "6   735464   12.38   12.29   12.44   12.27     412100 2014-08-19   12.333333   \n",
        "7   735465   12.25   12.29   12.33   12.20     426200 2014-08-20   12.273333   \n",
        "8   735466   12.27   12.41   12.45   12.24     664100 2014-08-21   12.366667   \n",
        "9   735467   12.41   12.38   12.46   12.32     556300 2014-08-22   12.386667   \n",
        "10  735470   12.44   12.32   12.46   12.24     350900 2014-08-25   12.340000   \n",
        "11  735471   12.34   12.39   12.42   12.27     532000 2014-08-26   12.360000   \n",
        "12  735472   12.40   12.40   12.44   12.35     586200 2014-08-27   12.396667   \n",
        "13  735473   12.33   12.35   12.41   12.25     469100 2014-08-28   12.336667   \n",
        "14  735474   12.36   12.38   12.41   12.17     342000 2014-08-29   12.320000   \n",
        "15  735478   12.37   12.33   12.45   12.19     810500 2014-09-02   12.323333   \n",
        "16  735479   12.38   12.33   12.43   12.29     968100 2014-09-03   12.350000   \n",
        "17  735480   12.38   12.35   12.46   12.26     750900 2014-09-04   12.356667   \n",
        "18  735481   12.31   12.37   12.45   12.27     559800 2014-09-05   12.363333   \n",
        "0   735456   73.46   73.44   73.91   73.06   24591000 2014-08-11   73.470000   \n",
        "1   735457   73.09   72.83   73.33   72.22   27419000 2014-08-12   72.793333   \n",
        "2   735458   73.12   73.77   74.25   73.05   29198500 2014-08-13   73.690000   \n",
        "       ...     ...     ...     ...     ...        ...        ...         ...   \n",
        "\n",
        "    change_amount  change_per     range  change_1_amount  change_1_per symbol  \n",
        "0            2.32    0.005126  0.019751         0.000000      0.000000   NFLX  \n",
        "1           -4.94   -0.011036  0.021425        -5.026667     -0.011230   NFLX  \n",
        "2            2.93    0.006499  0.016413         3.250000      0.007208   NFLX  \n",
        "3           -1.34   -0.002969  0.015021         0.506667      0.001123   NFLX  \n",
        "4            7.60    0.016646  0.029350         5.200000      0.011389   NFLX  \n",
        "5            3.94    0.008463  0.017720         9.020000      0.019374   NFLX  \n",
        "6            1.06    0.002270  0.017280         1.443333      0.003090   NFLX  \n",
        "7            5.19    0.011039  0.019589         3.133333      0.006664   NFLX  \n",
        "8            0.77    0.001632  0.017968         1.796667      0.003807   NFLX  \n",
        "9            8.25    0.017322  0.019947         4.306667      0.009043   NFLX  \n",
        "10          -0.62   -0.001288  0.016145         4.990000      0.010369   NFLX  \n",
        "11           0.86    0.001797  0.015253        -2.666667     -0.005572   NFLX  \n",
        "12          -4.70   -0.009870  0.013902        -2.393333     -0.005026   NFLX  \n",
        "13           2.56    0.005395  0.014057        -1.693333     -0.003569   NFLX  \n",
        "14           0.79    0.001655  0.013321         2.926667      0.006130   NFLX  \n",
        "15          -1.90   -0.003986  0.008811        -0.766667     -0.001608   NFLX  \n",
        "16          -3.13   -0.006514  0.023038         3.846667      0.008005   NFLX  \n",
        "17          -6.44   -0.013544  0.020105        -5.010000     -0.010536   NFLX  \n",
        "18           2.45    0.005165  0.015347        -1.130000     -0.002382   NFLX  \n",
        "0            0.72    0.007528  0.012966         0.000000      0.000000   AAPL  \n",
        "1           -0.07   -0.000728  0.013208         0.516667      0.005373   AAPL  \n",
        "2            1.09    0.011256  0.012392         0.686667      0.007091   AAPL  \n",
        "3            0.17    0.001747  0.007914         0.450000      0.004625   AAPL  \n",
        "4            0.08    0.000819  0.013616         0.386667      0.003959   AAPL  \n",
        "5            0.67    0.006779  0.014064         1.160000      0.011737   AAPL  \n",
        "6            1.12    0.011180  0.013576         1.340000      0.013376   AAPL  \n",
        "7            0.13    0.001293  0.011339         0.360000      0.003581   AAPL  \n",
        "8            0.01    0.000099  0.008255         0.006667      0.000066   AAPL  \n",
        "9            1.03    0.010199  0.012674         0.450000      0.004456   AAPL  \n",
        "10          -0.25   -0.002459  0.008754         0.670000      0.006590   AAPL  \n",
        "11          -0.53   -0.005243  0.006331        -0.580000     -0.005738   AAPL  \n",
        "12           1.11    0.010904  0.018369         0.716667      0.007040   AAPL  \n",
        "13           0.66    0.006458  0.011938         0.396667      0.003881   AAPL  \n",
        "14          -0.36   -0.003511  0.006827         0.336667      0.003283   AAPL  \n",
        "15           0.24    0.002324  0.009879         0.720000      0.006973   AAPL  \n",
        "16          -4.16   -0.041500  0.046089        -3.013333     -0.030061   AAPL  \n",
        "17          -0.73   -0.007399  0.023311        -1.573333     -0.015946   AAPL  \n",
        "18           0.17    0.001719  0.010921         0.223333      0.002258   AAPL  \n",
        "0           -0.10   -0.007974  0.017544         0.000000      0.000000    NYT  \n",
        "1           -0.13   -0.010558  0.025988        -0.226667     -0.018408    NYT  \n",
        "2           -0.01   -0.000815  0.008148        -0.040000     -0.003259    NYT  \n",
        "3            0.02    0.001631  0.015498        -0.013333     -0.001088    NYT  \n",
        "4           -0.14   -0.011432  0.028579        -0.013333     -0.001089    NYT  \n",
        "5           -0.04   -0.003227  0.010487         0.150000      0.012100    NYT  \n",
        "6           -0.09   -0.007297  0.013784        -0.063333     -0.005135    NYT  \n",
        "7            0.04    0.003259  0.010592        -0.060000     -0.004889    NYT  \n",
        "8            0.14    0.011321  0.016981         0.093333      0.007547    NYT  \n",
        "9           -0.03   -0.002422  0.011302         0.020000      0.001615    NYT  \n",
        "10          -0.12   -0.009724  0.017828        -0.046667     -0.003782    NYT  \n",
        "11           0.05    0.004045  0.012136         0.020000      0.001618    NYT  \n",
        "12           0.00    0.000000  0.007260         0.036667      0.002958    NYT  \n",
        "13           0.02    0.001621  0.012969        -0.060000     -0.004864    NYT  \n",
        "14           0.02    0.001623  0.019481        -0.016667     -0.001353    NYT  \n",
        "15          -0.04   -0.003246  0.021098         0.003333      0.000270    NYT  \n",
        "16          -0.05   -0.004049  0.011336         0.026667      0.002159    NYT  \n",
        "17          -0.03   -0.002428  0.016186         0.006667      0.000540    NYT  \n",
        "18           0.06    0.004853  0.014559         0.006667      0.000539    NYT  \n",
        "0           -0.02   -0.000272  0.011569         0.000000      0.000000     FB  \n",
        "1           -0.26   -0.003572  0.015249        -0.676667     -0.009296     FB  \n",
        "2            0.65    0.008821  0.016284         0.896667      0.012168     FB  \n",
        "              ...         ...       ...              ...           ...    ...  \n",
        "\n",
        "[950 rows x 14 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Visualizing Change"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(10,6))\n",
      "axes = fig.add_subplot(111)\n",
      "for symbol in stocks_df[\"symbol\"].unique():\n",
      "    x = stocks_df[stocks_df[\"symbol\"] == symbol][\"n_date\"]\n",
      "    y = stocks_df[stocks_df[\"symbol\"] == symbol][\"change_1_per\"]\n",
      "    \n",
      "    axes.plot_date(x=x, y=y, fmt=\"-\", alpha=0.2, linewidth=2)\n",
      "    \n",
      "    axes.xaxis_date()\n",
      "    plt.gcf().autofmt_xdate()\n",
      "\n",
      "plt.grid()\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6m3S7rhNWVSzPI9UiLQiWO4x1CZPlMi7QretEt7DkbXUsqnVdWdveUsKcTqfJ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       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84655990>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Finding Similar Stocks"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Preparing Data for Clustering"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def pivot_data(stocks_df, values=\"change_1_per\"):\n",
      "    clustering_data = stocks_df.pivot(index=\"symbol\", columns=\"n_date\", values=values)\n",
      "    return clustering_data\n",
      "clustering_data = pivot_data(stocks_df, values=\"change_1_per\")\n",
      "clustering_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>n_date</th>\n",
        "      <th>735456</th>\n",
        "      <th>735457</th>\n",
        "      <th>735458</th>\n",
        "      <th>735459</th>\n",
        "      <th>735460</th>\n",
        "      <th>735463</th>\n",
        "      <th>735464</th>\n",
        "      <th>735465</th>\n",
        "      <th>735466</th>\n",
        "      <th>735467</th>\n",
        "      <th>735470</th>\n",
        "      <th>735471</th>\n",
        "      <th>735472</th>\n",
        "      <th>735473</th>\n",
        "      <th>735474</th>\n",
        "      <th>735478</th>\n",
        "      <th>735479</th>\n",
        "      <th>735480</th>\n",
        "      <th>735481</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>symbol</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAPL</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.005373</td>\n",
        "      <td> 0.007091</td>\n",
        "      <td> 0.004625</td>\n",
        "      <td> 0.003959</td>\n",
        "      <td> 0.011737</td>\n",
        "      <td> 0.013376</td>\n",
        "      <td> 0.003581</td>\n",
        "      <td> 0.000066</td>\n",
        "      <td> 0.004456</td>\n",
        "      <td> 0.006590</td>\n",
        "      <td>-0.005738</td>\n",
        "      <td> 0.007040</td>\n",
        "      <td> 0.003881</td>\n",
        "      <td> 0.003283</td>\n",
        "      <td> 0.006973</td>\n",
        "      <td>-0.030061</td>\n",
        "      <td>-0.015946</td>\n",
        "      <td> 0.002258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADBE</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.002185</td>\n",
        "      <td> 0.010283</td>\n",
        "      <td> 0.007582</td>\n",
        "      <td> 0.004037</td>\n",
        "      <td> 0.009363</td>\n",
        "      <td> 0.012268</td>\n",
        "      <td>-0.006643</td>\n",
        "      <td> 0.003217</td>\n",
        "      <td> 0.006578</td>\n",
        "      <td> 0.000648</td>\n",
        "      <td>-0.003437</td>\n",
        "      <td>-0.001256</td>\n",
        "      <td>-0.004954</td>\n",
        "      <td> 0.006732</td>\n",
        "      <td> 0.005219</td>\n",
        "      <td> 0.003177</td>\n",
        "      <td> 0.003212</td>\n",
        "      <td> 0.003430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADSK</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.004898</td>\n",
        "      <td> 0.017504</td>\n",
        "      <td> 0.013646</td>\n",
        "      <td>-0.033605</td>\n",
        "      <td>-0.015570</td>\n",
        "      <td> 0.004711</td>\n",
        "      <td>-0.004108</td>\n",
        "      <td> 0.003350</td>\n",
        "      <td>-0.002924</td>\n",
        "      <td>-0.001059</td>\n",
        "      <td> 0.010964</td>\n",
        "      <td>-0.002099</td>\n",
        "      <td>-0.002661</td>\n",
        "      <td>-0.000619</td>\n",
        "      <td>-0.007361</td>\n",
        "      <td> 0.011897</td>\n",
        "      <td> 0.007039</td>\n",
        "      <td>-0.006158</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AMZN</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.002078</td>\n",
        "      <td> 0.026988</td>\n",
        "      <td> 0.009929</td>\n",
        "      <td> 0.002949</td>\n",
        "      <td> 0.009095</td>\n",
        "      <td>-0.002142</td>\n",
        "      <td> 0.002989</td>\n",
        "      <td>-0.005231</td>\n",
        "      <td>-0.005269</td>\n",
        "      <td> 0.007005</td>\n",
        "      <td> 0.016890</td>\n",
        "      <td> 0.010134</td>\n",
        "      <td>-0.010366</td>\n",
        "      <td>-0.002032</td>\n",
        "      <td> 0.003425</td>\n",
        "      <td>-0.002177</td>\n",
        "      <td> 0.018375</td>\n",
        "      <td>-0.003090</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BBRY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.006726</td>\n",
        "      <td>-0.002840</td>\n",
        "      <td> 0.009494</td>\n",
        "      <td> 0.010783</td>\n",
        "      <td> 0.014736</td>\n",
        "      <td> 0.009504</td>\n",
        "      <td> 0.027402</td>\n",
        "      <td>-0.013043</td>\n",
        "      <td>-0.004030</td>\n",
        "      <td> 0.007333</td>\n",
        "      <td> 0.003322</td>\n",
        "      <td> 0.018265</td>\n",
        "      <td> 0.001628</td>\n",
        "      <td> 0.000651</td>\n",
        "      <td> 0.012849</td>\n",
        "      <td> 0.032027</td>\n",
        "      <td>-0.009733</td>\n",
        "      <td>-0.009189</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BKS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.004016</td>\n",
        "      <td> 0.001040</td>\n",
        "      <td> 0.009566</td>\n",
        "      <td>-0.002360</td>\n",
        "      <td> 0.014967</td>\n",
        "      <td> 0.008071</td>\n",
        "      <td>-0.018048</td>\n",
        "      <td>-0.007093</td>\n",
        "      <td> 0.002947</td>\n",
        "      <td>-0.008769</td>\n",
        "      <td> 0.013200</td>\n",
        "      <td> 0.027528</td>\n",
        "      <td> 0.008626</td>\n",
        "      <td> 0.001553</td>\n",
        "      <td>-0.010558</td>\n",
        "      <td> 0.016419</td>\n",
        "      <td> 0.002240</td>\n",
        "      <td>-0.004501</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BP</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001125</td>\n",
        "      <td>-0.005802</td>\n",
        "      <td> 0.005278</td>\n",
        "      <td>-0.000775</td>\n",
        "      <td> 0.009557</td>\n",
        "      <td> 0.007821</td>\n",
        "      <td>-0.000138</td>\n",
        "      <td> 0.001797</td>\n",
        "      <td>-0.001661</td>\n",
        "      <td> 0.006054</td>\n",
        "      <td>-0.004214</td>\n",
        "      <td> 0.002206</td>\n",
        "      <td>-0.007850</td>\n",
        "      <td>-0.002996</td>\n",
        "      <td>-0.011132</td>\n",
        "      <td> 0.010251</td>\n",
        "      <td>-0.041773</td>\n",
        "      <td>-0.001601</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CAJ</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.002323</td>\n",
        "      <td> 0.003221</td>\n",
        "      <td> 0.004908</td>\n",
        "      <td> 0.000501</td>\n",
        "      <td>-0.000501</td>\n",
        "      <td> 0.001900</td>\n",
        "      <td>-0.008164</td>\n",
        "      <td>-0.000403</td>\n",
        "      <td>-0.007006</td>\n",
        "      <td>-0.000102</td>\n",
        "      <td> 0.003441</td>\n",
        "      <td>-0.002638</td>\n",
        "      <td> 0.000608</td>\n",
        "      <td>-0.004584</td>\n",
        "      <td> 0.003351</td>\n",
        "      <td> 0.003541</td>\n",
        "      <td> 0.006134</td>\n",
        "      <td>-0.002217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CAT</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001084</td>\n",
        "      <td> 0.004349</td>\n",
        "      <td> 0.005242</td>\n",
        "      <td> 0.002017</td>\n",
        "      <td> 0.009583</td>\n",
        "      <td> 0.004691</td>\n",
        "      <td> 0.003653</td>\n",
        "      <td> 0.003178</td>\n",
        "      <td>-0.007492</td>\n",
        "      <td> 0.007283</td>\n",
        "      <td> 0.002002</td>\n",
        "      <td> 0.002672</td>\n",
        "      <td>-0.000861</td>\n",
        "      <td> 0.003523</td>\n",
        "      <td> 0.003237</td>\n",
        "      <td>-0.004016</td>\n",
        "      <td> 0.000919</td>\n",
        "      <td>-0.001718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CBS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.010617</td>\n",
        "      <td>-0.002817</td>\n",
        "      <td> 0.003984</td>\n",
        "      <td> 0.007462</td>\n",
        "      <td> 0.008284</td>\n",
        "      <td> 0.004180</td>\n",
        "      <td>-0.002702</td>\n",
        "      <td>-0.001215</td>\n",
        "      <td> 0.002478</td>\n",
        "      <td> 0.004387</td>\n",
        "      <td>-0.009521</td>\n",
        "      <td> 0.002485</td>\n",
        "      <td>-0.009082</td>\n",
        "      <td>-0.007353</td>\n",
        "      <td>-0.005758</td>\n",
        "      <td>-0.006992</td>\n",
        "      <td> 0.011908</td>\n",
        "      <td>-0.002816</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CMCSA</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000310</td>\n",
        "      <td> 0.005419</td>\n",
        "      <td> 0.002886</td>\n",
        "      <td> 0.004888</td>\n",
        "      <td> 0.003895</td>\n",
        "      <td>-0.003114</td>\n",
        "      <td>-0.000366</td>\n",
        "      <td>-0.000733</td>\n",
        "      <td>-0.005716</td>\n",
        "      <td> 0.005441</td>\n",
        "      <td> 0.000367</td>\n",
        "      <td> 0.001099</td>\n",
        "      <td>-0.002202</td>\n",
        "      <td> 0.003779</td>\n",
        "      <td> 0.000122</td>\n",
        "      <td> 0.002674</td>\n",
        "      <td> 0.003392</td>\n",
        "      <td> 0.007574</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CRM</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.029861</td>\n",
        "      <td>-0.004338</td>\n",
        "      <td> 0.006434</td>\n",
        "      <td> 0.003858</td>\n",
        "      <td> 0.013566</td>\n",
        "      <td> 0.010267</td>\n",
        "      <td> 0.005979</td>\n",
        "      <td> 0.006361</td>\n",
        "      <td> 0.066805</td>\n",
        "      <td>-0.006311</td>\n",
        "      <td> 0.002361</td>\n",
        "      <td> 0.009025</td>\n",
        "      <td>-0.025011</td>\n",
        "      <td> 0.010118</td>\n",
        "      <td> 0.012062</td>\n",
        "      <td>-0.004713</td>\n",
        "      <td>-0.002418</td>\n",
        "      <td> 0.003642</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CSC</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.003303</td>\n",
        "      <td> 0.002547</td>\n",
        "      <td> 0.006370</td>\n",
        "      <td> 0.002154</td>\n",
        "      <td> 0.010804</td>\n",
        "      <td> 0.004196</td>\n",
        "      <td> 0.002791</td>\n",
        "      <td> 0.006711</td>\n",
        "      <td>-0.004814</td>\n",
        "      <td>-0.001434</td>\n",
        "      <td> 0.000394</td>\n",
        "      <td> 0.004502</td>\n",
        "      <td>-0.004918</td>\n",
        "      <td>-0.000559</td>\n",
        "      <td> 0.006930</td>\n",
        "      <td> 0.002213</td>\n",
        "      <td>-0.002162</td>\n",
        "      <td> 0.000166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CSCO</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.002519</td>\n",
        "      <td>-0.000265</td>\n",
        "      <td>-0.021680</td>\n",
        "      <td>-0.005998</td>\n",
        "      <td> 0.004883</td>\n",
        "      <td> 0.002841</td>\n",
        "      <td> 0.000676</td>\n",
        "      <td> 0.007246</td>\n",
        "      <td>-0.003501</td>\n",
        "      <td>-0.000404</td>\n",
        "      <td> 0.002419</td>\n",
        "      <td>-0.000538</td>\n",
        "      <td> 0.000269</td>\n",
        "      <td> 0.004683</td>\n",
        "      <td>-0.001474</td>\n",
        "      <td> 0.006126</td>\n",
        "      <td>-0.002804</td>\n",
        "      <td> 0.000667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CTXS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.004566</td>\n",
        "      <td> 0.005074</td>\n",
        "      <td> 0.001158</td>\n",
        "      <td>-0.001741</td>\n",
        "      <td> 0.009672</td>\n",
        "      <td>-0.001151</td>\n",
        "      <td>-0.000288</td>\n",
        "      <td> 0.004345</td>\n",
        "      <td> 0.006168</td>\n",
        "      <td>-0.003571</td>\n",
        "      <td>-0.000286</td>\n",
        "      <td>-0.002100</td>\n",
        "      <td>-0.004555</td>\n",
        "      <td> 0.009922</td>\n",
        "      <td> 0.005148</td>\n",
        "      <td> 0.001368</td>\n",
        "      <td> 0.000801</td>\n",
        "      <td> 0.001553</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CVX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.008605</td>\n",
        "      <td> 0.005390</td>\n",
        "      <td>-0.003951</td>\n",
        "      <td> 0.001565</td>\n",
        "      <td>-0.000132</td>\n",
        "      <td> 0.008799</td>\n",
        "      <td>-0.000026</td>\n",
        "      <td> 0.003705</td>\n",
        "      <td>-0.005799</td>\n",
        "      <td> 0.005766</td>\n",
        "      <td> 0.004183</td>\n",
        "      <td> 0.001945</td>\n",
        "      <td> 0.000648</td>\n",
        "      <td> 0.004078</td>\n",
        "      <td>-0.009115</td>\n",
        "      <td> 0.001119</td>\n",
        "      <td>-0.007733</td>\n",
        "      <td>-0.001207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DIS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.003517</td>\n",
        "      <td> 0.005172</td>\n",
        "      <td> 0.008895</td>\n",
        "      <td> 0.007890</td>\n",
        "      <td> 0.009188</td>\n",
        "      <td> 0.000370</td>\n",
        "      <td>-0.001706</td>\n",
        "      <td> 0.004910</td>\n",
        "      <td> 0.002284</td>\n",
        "      <td> 0.001140</td>\n",
        "      <td>-0.004397</td>\n",
        "      <td> 0.000480</td>\n",
        "      <td>-0.000296</td>\n",
        "      <td>-0.004043</td>\n",
        "      <td> 0.008423</td>\n",
        "      <td> 0.003263</td>\n",
        "      <td>-0.005233</td>\n",
        "      <td> 0.002316</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EBAY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.009406</td>\n",
        "      <td>-0.012296</td>\n",
        "      <td> 0.001951</td>\n",
        "      <td>-0.001892</td>\n",
        "      <td> 0.003080</td>\n",
        "      <td> 0.009711</td>\n",
        "      <td>-0.001871</td>\n",
        "      <td> 0.036590</td>\n",
        "      <td> 0.001560</td>\n",
        "      <td> 0.001737</td>\n",
        "      <td> 0.006958</td>\n",
        "      <td> 0.001484</td>\n",
        "      <td>-0.012566</td>\n",
        "      <td>-0.000722</td>\n",
        "      <td>-0.006297</td>\n",
        "      <td>-0.005785</td>\n",
        "      <td>-0.002014</td>\n",
        "      <td>-0.014046</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EMC</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000455</td>\n",
        "      <td> 0.005656</td>\n",
        "      <td> 0.006630</td>\n",
        "      <td> 0.001683</td>\n",
        "      <td> 0.007350</td>\n",
        "      <td>-0.001897</td>\n",
        "      <td>-0.007079</td>\n",
        "      <td> 0.002018</td>\n",
        "      <td>-0.004166</td>\n",
        "      <td> 0.002919</td>\n",
        "      <td>-0.003832</td>\n",
        "      <td>-0.003393</td>\n",
        "      <td>-0.003176</td>\n",
        "      <td> 0.003054</td>\n",
        "      <td>-0.004202</td>\n",
        "      <td> 0.006880</td>\n",
        "      <td>-0.010486</td>\n",
        "      <td>-0.009782</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FB</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.009296</td>\n",
        "      <td> 0.012168</td>\n",
        "      <td> 0.005846</td>\n",
        "      <td>-0.004926</td>\n",
        "      <td> 0.008913</td>\n",
        "      <td> 0.009362</td>\n",
        "      <td>-0.003428</td>\n",
        "      <td>-0.001963</td>\n",
        "      <td>-0.005833</td>\n",
        "      <td> 0.009863</td>\n",
        "      <td> 0.007014</td>\n",
        "      <td>-0.009351</td>\n",
        "      <td>-0.011530</td>\n",
        "      <td> 0.007288</td>\n",
        "      <td> 0.019939</td>\n",
        "      <td> 0.003102</td>\n",
        "      <td>-0.002189</td>\n",
        "      <td> 0.007948</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FJTSY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.015135</td>\n",
        "      <td> 0.022376</td>\n",
        "      <td>-0.003667</td>\n",
        "      <td>-0.000367</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.007576</td>\n",
        "      <td>-0.008573</td>\n",
        "      <td> 0.002510</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.022526</td>\n",
        "      <td>-0.021060</td>\n",
        "      <td> 0.000679</td>\n",
        "      <td> 0.016783</td>\n",
        "      <td>-0.032388</td>\n",
        "      <td> 0.002945</td>\n",
        "      <td> 0.000883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FOXA</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.010004</td>\n",
        "      <td> 0.008595</td>\n",
        "      <td> 0.009264</td>\n",
        "      <td> 0.007339</td>\n",
        "      <td> 0.003148</td>\n",
        "      <td>-0.003998</td>\n",
        "      <td>-0.002143</td>\n",
        "      <td>-0.002522</td>\n",
        "      <td> 0.000560</td>\n",
        "      <td>-0.001777</td>\n",
        "      <td>-0.001123</td>\n",
        "      <td> 0.003359</td>\n",
        "      <td>-0.001776</td>\n",
        "      <td>-0.004979</td>\n",
        "      <td> 0.008014</td>\n",
        "      <td> 0.010968</td>\n",
        "      <td> 0.002666</td>\n",
        "      <td>-0.004432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GE</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005840</td>\n",
        "      <td> 0.005036</td>\n",
        "      <td> 0.001418</td>\n",
        "      <td>-0.005705</td>\n",
        "      <td> 0.011155</td>\n",
        "      <td> 0.003450</td>\n",
        "      <td> 0.006475</td>\n",
        "      <td> 0.005178</td>\n",
        "      <td>-0.006995</td>\n",
        "      <td>-0.001528</td>\n",
        "      <td>-0.002682</td>\n",
        "      <td> 0.000638</td>\n",
        "      <td>-0.003715</td>\n",
        "      <td>-0.001154</td>\n",
        "      <td>-0.004121</td>\n",
        "      <td> 0.001543</td>\n",
        "      <td> 0.002693</td>\n",
        "      <td> 0.000256</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOG</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.008795</td>\n",
        "      <td> 0.015167</td>\n",
        "      <td> 0.004584</td>\n",
        "      <td>-0.000029</td>\n",
        "      <td> 0.011069</td>\n",
        "      <td> 0.008833</td>\n",
        "      <td>-0.002532</td>\n",
        "      <td>-0.002716</td>\n",
        "      <td>-0.000326</td>\n",
        "      <td>-0.002431</td>\n",
        "      <td>-0.004585</td>\n",
        "      <td>-0.009683</td>\n",
        "      <td>-0.005873</td>\n",
        "      <td> 0.000678</td>\n",
        "      <td> 0.009060</td>\n",
        "      <td> 0.005519</td>\n",
        "      <td> 0.006450</td>\n",
        "      <td> 0.004206</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HPQ</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005128</td>\n",
        "      <td> 0.004443</td>\n",
        "      <td> 0.003673</td>\n",
        "      <td>-0.004162</td>\n",
        "      <td> 0.003487</td>\n",
        "      <td> 0.004130</td>\n",
        "      <td>-0.007852</td>\n",
        "      <td> 0.036108</td>\n",
        "      <td> 0.008050</td>\n",
        "      <td> 0.006559</td>\n",
        "      <td> 0.015742</td>\n",
        "      <td> 0.007287</td>\n",
        "      <td>-0.001935</td>\n",
        "      <td> 0.002807</td>\n",
        "      <td>-0.001934</td>\n",
        "      <td> 0.005072</td>\n",
        "      <td>-0.008555</td>\n",
        "      <td>-0.008718</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HTHIY</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001694</td>\n",
        "      <td> 0.018982</td>\n",
        "      <td>-0.004305</td>\n",
        "      <td>-0.004058</td>\n",
        "      <td> 0.007181</td>\n",
        "      <td> 0.003708</td>\n",
        "      <td>-0.015190</td>\n",
        "      <td>-0.000753</td>\n",
        "      <td>-0.007772</td>\n",
        "      <td> 0.012744</td>\n",
        "      <td>-0.001325</td>\n",
        "      <td> 0.000618</td>\n",
        "      <td>-0.003943</td>\n",
        "      <td> 0.007300</td>\n",
        "      <td> 0.016224</td>\n",
        "      <td> 0.001598</td>\n",
        "      <td>-0.011005</td>\n",
        "      <td>-0.003198</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IBM</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001297</td>\n",
        "      <td> 0.001703</td>\n",
        "      <td>-0.001457</td>\n",
        "      <td> 0.000124</td>\n",
        "      <td> 0.006844</td>\n",
        "      <td> 0.004827</td>\n",
        "      <td> 0.000105</td>\n",
        "      <td> 0.007076</td>\n",
        "      <td>-0.002131</td>\n",
        "      <td> 0.001917</td>\n",
        "      <td> 0.006684</td>\n",
        "      <td> 0.000692</td>\n",
        "      <td>-0.005183</td>\n",
        "      <td> 0.002135</td>\n",
        "      <td>-0.001269</td>\n",
        "      <td> 0.002254</td>\n",
        "      <td>-0.005807</td>\n",
        "      <td>-0.000681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INTC</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001112</td>\n",
        "      <td> 0.024832</td>\n",
        "      <td> 0.004219</td>\n",
        "      <td> 0.001763</td>\n",
        "      <td> 0.007486</td>\n",
        "      <td> 0.002231</td>\n",
        "      <td> 0.000969</td>\n",
        "      <td> 0.015269</td>\n",
        "      <td> 0.002475</td>\n",
        "      <td>-0.004206</td>\n",
        "      <td>-0.000670</td>\n",
        "      <td>-0.001725</td>\n",
        "      <td>-0.002787</td>\n",
        "      <td> 0.004401</td>\n",
        "      <td>-0.005967</td>\n",
        "      <td>-0.000096</td>\n",
        "      <td> 0.005932</td>\n",
        "      <td> 0.004097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LXK</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.007975</td>\n",
        "      <td> 0.008664</td>\n",
        "      <td> 0.010324</td>\n",
        "      <td>-0.007590</td>\n",
        "      <td> 0.000110</td>\n",
        "      <td> 0.003788</td>\n",
        "      <td>-0.005231</td>\n",
        "      <td>-0.002781</td>\n",
        "      <td>-0.008994</td>\n",
        "      <td>-0.001651</td>\n",
        "      <td> 0.001430</td>\n",
        "      <td>-0.000267</td>\n",
        "      <td> 0.001469</td>\n",
        "      <td> 0.009064</td>\n",
        "      <td>-0.008811</td>\n",
        "      <td>-0.008414</td>\n",
        "      <td>-0.004055</td>\n",
        "      <td>-0.000609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.003428</td>\n",
        "      <td> 0.012751</td>\n",
        "      <td> 0.006959</td>\n",
        "      <td> 0.010214</td>\n",
        "      <td> 0.006049</td>\n",
        "      <td> 0.010608</td>\n",
        "      <td>-0.001848</td>\n",
        "      <td> 0.000370</td>\n",
        "      <td> 0.002874</td>\n",
        "      <td>-0.000295</td>\n",
        "      <td>-0.002216</td>\n",
        "      <td>-0.005348</td>\n",
        "      <td>-0.001190</td>\n",
        "      <td> 0.009283</td>\n",
        "      <td>-0.002437</td>\n",
        "      <td>-0.005944</td>\n",
        "      <td> 0.004806</td>\n",
        "      <td> 0.012413</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.003774</td>\n",
        "      <td>-0.002940</td>\n",
        "      <td>-0.001937</td>\n",
        "      <td>-0.004743</td>\n",
        "      <td> 0.010588</td>\n",
        "      <td> 0.011145</td>\n",
        "      <td>-0.001350</td>\n",
        "      <td>-0.000040</td>\n",
        "      <td> 0.012627</td>\n",
        "      <td>-0.002477</td>\n",
        "      <td> 0.012155</td>\n",
        "      <td> 0.013787</td>\n",
        "      <td>-0.014610</td>\n",
        "      <td> 0.013834</td>\n",
        "      <td> 0.015171</td>\n",
        "      <td> 0.001545</td>\n",
        "      <td>-0.006065</td>\n",
        "      <td>-0.003195</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NFLX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.011230</td>\n",
        "      <td> 0.007208</td>\n",
        "      <td> 0.001123</td>\n",
        "      <td> 0.011389</td>\n",
        "      <td> 0.019374</td>\n",
        "      <td> 0.003090</td>\n",
        "      <td> 0.006664</td>\n",
        "      <td> 0.003807</td>\n",
        "      <td> 0.009043</td>\n",
        "      <td> 0.010369</td>\n",
        "      <td>-0.005572</td>\n",
        "      <td>-0.005026</td>\n",
        "      <td>-0.003569</td>\n",
        "      <td> 0.006130</td>\n",
        "      <td>-0.001608</td>\n",
        "      <td> 0.008005</td>\n",
        "      <td>-0.010536</td>\n",
        "      <td>-0.002382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NVDA</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.004594</td>\n",
        "      <td> 0.007043</td>\n",
        "      <td>-0.006938</td>\n",
        "      <td> 0.004801</td>\n",
        "      <td> 0.011358</td>\n",
        "      <td> 0.012215</td>\n",
        "      <td>-0.002593</td>\n",
        "      <td>-0.007490</td>\n",
        "      <td>-0.002795</td>\n",
        "      <td> 0.003828</td>\n",
        "      <td> 0.009650</td>\n",
        "      <td>-0.002072</td>\n",
        "      <td> 0.000690</td>\n",
        "      <td> 0.004124</td>\n",
        "      <td> 0.002229</td>\n",
        "      <td> 0.009847</td>\n",
        "      <td> 0.015379</td>\n",
        "      <td> 0.001502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWSA</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000192</td>\n",
        "      <td>-0.021825</td>\n",
        "      <td> 0.017388</td>\n",
        "      <td> 0.000386</td>\n",
        "      <td>-0.000386</td>\n",
        "      <td>-0.005830</td>\n",
        "      <td> 0.010955</td>\n",
        "      <td> 0.010272</td>\n",
        "      <td>-0.001715</td>\n",
        "      <td> 0.005872</td>\n",
        "      <td> 0.002457</td>\n",
        "      <td>-0.000378</td>\n",
        "      <td> 0.000567</td>\n",
        "      <td>-0.000756</td>\n",
        "      <td>-0.005705</td>\n",
        "      <td> 0.003222</td>\n",
        "      <td> 0.003965</td>\n",
        "      <td> 0.001320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NYT</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.018408</td>\n",
        "      <td>-0.003259</td>\n",
        "      <td>-0.001088</td>\n",
        "      <td>-0.001089</td>\n",
        "      <td> 0.012100</td>\n",
        "      <td>-0.005135</td>\n",
        "      <td>-0.004889</td>\n",
        "      <td> 0.007547</td>\n",
        "      <td> 0.001615</td>\n",
        "      <td>-0.003782</td>\n",
        "      <td> 0.001618</td>\n",
        "      <td> 0.002958</td>\n",
        "      <td>-0.004864</td>\n",
        "      <td>-0.001353</td>\n",
        "      <td> 0.000270</td>\n",
        "      <td> 0.002159</td>\n",
        "      <td> 0.000540</td>\n",
        "      <td> 0.000539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ORCL</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001671</td>\n",
        "      <td> 0.007382</td>\n",
        "      <td> 0.000332</td>\n",
        "      <td> 0.002069</td>\n",
        "      <td> 0.007881</td>\n",
        "      <td> 0.014721</td>\n",
        "      <td> 0.002340</td>\n",
        "      <td> 0.005058</td>\n",
        "      <td> 0.001203</td>\n",
        "      <td> 0.005741</td>\n",
        "      <td> 0.001592</td>\n",
        "      <td>-0.006087</td>\n",
        "      <td>-0.005719</td>\n",
        "      <td> 0.002811</td>\n",
        "      <td> 0.002404</td>\n",
        "      <td> 0.005340</td>\n",
        "      <td>-0.003680</td>\n",
        "      <td>-0.005793</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PCRFY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001397</td>\n",
        "      <td> 0.016763</td>\n",
        "      <td> 0.006823</td>\n",
        "      <td>-0.001093</td>\n",
        "      <td> 0.005975</td>\n",
        "      <td> 0.000814</td>\n",
        "      <td>-0.015431</td>\n",
        "      <td> 0.001101</td>\n",
        "      <td>-0.015372</td>\n",
        "      <td> 0.005006</td>\n",
        "      <td> 0.003602</td>\n",
        "      <td> 0.005237</td>\n",
        "      <td> 0.001651</td>\n",
        "      <td> 0.010887</td>\n",
        "      <td> 0.007295</td>\n",
        "      <td> 0.014643</td>\n",
        "      <td>-0.002134</td>\n",
        "      <td>-0.010515</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RAX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.036414</td>\n",
        "      <td>-0.004699</td>\n",
        "      <td> 0.033312</td>\n",
        "      <td> 0.009003</td>\n",
        "      <td> 0.017585</td>\n",
        "      <td> 0.035446</td>\n",
        "      <td>-0.000203</td>\n",
        "      <td> 0.012440</td>\n",
        "      <td> 0.021210</td>\n",
        "      <td>-0.007319</td>\n",
        "      <td> 0.020159</td>\n",
        "      <td>-0.008504</td>\n",
        "      <td>-0.005109</td>\n",
        "      <td> 0.012419</td>\n",
        "      <td> 0.020992</td>\n",
        "      <td> 0.031463</td>\n",
        "      <td> 0.019219</td>\n",
        "      <td> 0.009385</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RHT</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000507</td>\n",
        "      <td> 0.007825</td>\n",
        "      <td> 0.002064</td>\n",
        "      <td> 0.001281</td>\n",
        "      <td> 0.014115</td>\n",
        "      <td> 0.021549</td>\n",
        "      <td>-0.002640</td>\n",
        "      <td>-0.002376</td>\n",
        "      <td> 0.004356</td>\n",
        "      <td>-0.005406</td>\n",
        "      <td> 0.003287</td>\n",
        "      <td>-0.007984</td>\n",
        "      <td>-0.015275</td>\n",
        "      <td> 0.007824</td>\n",
        "      <td> 0.004141</td>\n",
        "      <td> 0.006711</td>\n",
        "      <td>-0.004622</td>\n",
        "      <td>-0.003985</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SAP</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.006488</td>\n",
        "      <td>-0.001993</td>\n",
        "      <td> 0.002852</td>\n",
        "      <td>-0.007576</td>\n",
        "      <td> 0.008291</td>\n",
        "      <td> 0.009113</td>\n",
        "      <td>-0.008675</td>\n",
        "      <td> 0.006645</td>\n",
        "      <td>-0.005908</td>\n",
        "      <td> 0.014241</td>\n",
        "      <td> 0.005454</td>\n",
        "      <td> 0.003329</td>\n",
        "      <td>-0.015968</td>\n",
        "      <td>-0.000986</td>\n",
        "      <td> 0.008287</td>\n",
        "      <td>-0.008875</td>\n",
        "      <td>-0.002407</td>\n",
        "      <td> 0.001502</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SIEGY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.012527</td>\n",
        "      <td> 0.014653</td>\n",
        "      <td> 0.014548</td>\n",
        "      <td>-0.004600</td>\n",
        "      <td> 0.004551</td>\n",
        "      <td> 0.005068</td>\n",
        "      <td>-0.001160</td>\n",
        "      <td> 0.014180</td>\n",
        "      <td>-0.008505</td>\n",
        "      <td> 0.016414</td>\n",
        "      <td> 0.007543</td>\n",
        "      <td>-0.000786</td>\n",
        "      <td>-0.015761</td>\n",
        "      <td>-0.002001</td>\n",
        "      <td> 0.008176</td>\n",
        "      <td> 0.011301</td>\n",
        "      <td> 0.000680</td>\n",
        "      <td>-0.000131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNE</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.029412</td>\n",
        "      <td> 0.005369</td>\n",
        "      <td> 0.019070</td>\n",
        "      <td> 0.007033</td>\n",
        "      <td> 0.016146</td>\n",
        "      <td> 0.006522</td>\n",
        "      <td>-0.002651</td>\n",
        "      <td>-0.000707</td>\n",
        "      <td> 0.007548</td>\n",
        "      <td>-0.005826</td>\n",
        "      <td>-0.001414</td>\n",
        "      <td> 0.002293</td>\n",
        "      <td> 0.010991</td>\n",
        "      <td> 0.013425</td>\n",
        "      <td> 0.020566</td>\n",
        "      <td>-0.006789</td>\n",
        "      <td>-0.022029</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TOSYY</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.012976</td>\n",
        "      <td>-0.004764</td>\n",
        "      <td>-0.001632</td>\n",
        "      <td> 0.010191</td>\n",
        "      <td> 0.012761</td>\n",
        "      <td>-0.008040</td>\n",
        "      <td> 0.007245</td>\n",
        "      <td>-0.008420</td>\n",
        "      <td> 0.001731</td>\n",
        "      <td>-0.002230</td>\n",
        "      <td>-0.015474</td>\n",
        "      <td> 0.001005</td>\n",
        "      <td>-0.005942</td>\n",
        "      <td> 0.013593</td>\n",
        "      <td>-0.010841</td>\n",
        "      <td>-0.004177</td>\n",
        "      <td> 0.005038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TRI</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.004010</td>\n",
        "      <td>-0.004197</td>\n",
        "      <td> 0.009253</td>\n",
        "      <td> 0.001130</td>\n",
        "      <td> 0.005721</td>\n",
        "      <td>-0.004335</td>\n",
        "      <td>-0.000177</td>\n",
        "      <td> 0.001681</td>\n",
        "      <td>-0.000531</td>\n",
        "      <td>-0.001064</td>\n",
        "      <td> 0.001328</td>\n",
        "      <td> 0.006857</td>\n",
        "      <td> 0.000352</td>\n",
        "      <td>-0.002025</td>\n",
        "      <td> 0.000968</td>\n",
        "      <td> 0.003943</td>\n",
        "      <td> 0.003232</td>\n",
        "      <td>-0.003242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TWX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.008865</td>\n",
        "      <td> 0.006782</td>\n",
        "      <td> 0.026300</td>\n",
        "      <td> 0.007408</td>\n",
        "      <td> 0.005309</td>\n",
        "      <td> 0.001090</td>\n",
        "      <td> 0.000651</td>\n",
        "      <td>-0.002313</td>\n",
        "      <td> 0.002966</td>\n",
        "      <td> 0.002401</td>\n",
        "      <td>-0.002507</td>\n",
        "      <td> 0.002647</td>\n",
        "      <td>-0.000390</td>\n",
        "      <td> 0.000866</td>\n",
        "      <td> 0.003151</td>\n",
        "      <td> 0.005495</td>\n",
        "      <td>-0.004095</td>\n",
        "      <td>-0.006769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TXN</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001002</td>\n",
        "      <td> 0.010276</td>\n",
        "      <td> 0.003460</td>\n",
        "      <td> 0.004779</td>\n",
        "      <td> 0.002664</td>\n",
        "      <td> 0.002796</td>\n",
        "      <td> 0.003344</td>\n",
        "      <td> 0.004577</td>\n",
        "      <td> 0.000901</td>\n",
        "      <td>-0.008384</td>\n",
        "      <td>-0.004774</td>\n",
        "      <td> 0.002800</td>\n",
        "      <td> 0.005708</td>\n",
        "      <td> 0.005124</td>\n",
        "      <td>-0.002917</td>\n",
        "      <td> 0.004425</td>\n",
        "      <td> 0.002345</td>\n",
        "      <td> 0.002820</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>VIAB</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005008</td>\n",
        "      <td>-0.000787</td>\n",
        "      <td> 0.002314</td>\n",
        "      <td> 0.007547</td>\n",
        "      <td> 0.009547</td>\n",
        "      <td>-0.006172</td>\n",
        "      <td>-0.002993</td>\n",
        "      <td>-0.004944</td>\n",
        "      <td>-0.001899</td>\n",
        "      <td>-0.000950</td>\n",
        "      <td>-0.002112</td>\n",
        "      <td> 0.001860</td>\n",
        "      <td> 0.001980</td>\n",
        "      <td> 0.000948</td>\n",
        "      <td> 0.007079</td>\n",
        "      <td>-0.002872</td>\n",
        "      <td>-0.000739</td>\n",
        "      <td>-0.000041</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>VMW</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.007897</td>\n",
        "      <td> 0.004582</td>\n",
        "      <td> 0.009869</td>\n",
        "      <td> 0.005238</td>\n",
        "      <td> 0.015535</td>\n",
        "      <td> 0.000745</td>\n",
        "      <td> 0.003810</td>\n",
        "      <td>-0.003434</td>\n",
        "      <td>-0.003283</td>\n",
        "      <td>-0.014676</td>\n",
        "      <td>-0.028492</td>\n",
        "      <td>-0.005011</td>\n",
        "      <td>-0.001810</td>\n",
        "      <td> 0.008902</td>\n",
        "      <td> 0.002936</td>\n",
        "      <td>-0.002571</td>\n",
        "      <td>-0.017769</td>\n",
        "      <td>-0.010790</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>XOM</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005729</td>\n",
        "      <td> 0.006333</td>\n",
        "      <td> 0.000303</td>\n",
        "      <td>-0.001247</td>\n",
        "      <td> 0.005199</td>\n",
        "      <td> 0.002409</td>\n",
        "      <td>-0.000770</td>\n",
        "      <td>-0.000100</td>\n",
        "      <td>-0.010319</td>\n",
        "      <td> 0.001992</td>\n",
        "      <td> 0.009432</td>\n",
        "      <td>-0.001306</td>\n",
        "      <td>-0.000872</td>\n",
        "      <td>-0.001578</td>\n",
        "      <td>-0.006624</td>\n",
        "      <td> 0.005612</td>\n",
        "      <td>-0.006018</td>\n",
        "      <td> 0.003000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>XRX</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.005475</td>\n",
        "      <td> 0.010589</td>\n",
        "      <td>-0.002964</td>\n",
        "      <td>-0.007214</td>\n",
        "      <td> 0.005935</td>\n",
        "      <td>-0.002976</td>\n",
        "      <td> 0.006162</td>\n",
        "      <td> 0.003684</td>\n",
        "      <td> 0.001471</td>\n",
        "      <td> 0.002202</td>\n",
        "      <td> 0.003657</td>\n",
        "      <td> 0.002189</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.007005</td>\n",
        "      <td> 0.003850</td>\n",
        "      <td> 0.009061</td>\n",
        "      <td>-0.013778</td>\n",
        "      <td>-0.013971</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>50 rows \u00d7 19 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "n_date  735456    735457    735458    735459    735460    735463    735464  \\\n",
        "symbol                                                                       \n",
        "AAPL         0  0.005373  0.007091  0.004625  0.003959  0.011737  0.013376   \n",
        "ADBE         0 -0.002185  0.010283  0.007582  0.004037  0.009363  0.012268   \n",
        "ADSK         0  0.004898  0.017504  0.013646 -0.033605 -0.015570  0.004711   \n",
        "AMZN         0  0.002078  0.026988  0.009929  0.002949  0.009095 -0.002142   \n",
        "BBRY         0 -0.006726 -0.002840  0.009494  0.010783  0.014736  0.009504   \n",
        "BKS          0 -0.004016  0.001040  0.009566 -0.002360  0.014967  0.008071   \n",
        "BP           0 -0.001125 -0.005802  0.005278 -0.000775  0.009557  0.007821   \n",
        "CAJ          0 -0.002323  0.003221  0.004908  0.000501 -0.000501  0.001900   \n",
        "CAT          0  0.001084  0.004349  0.005242  0.002017  0.009583  0.004691   \n",
        "CBS          0 -0.010617 -0.002817  0.003984  0.007462  0.008284  0.004180   \n",
        "CMCSA        0  0.000310  0.005419  0.002886  0.004888  0.003895 -0.003114   \n",
        "CRM          0 -0.029861 -0.004338  0.006434  0.003858  0.013566  0.010267   \n",
        "CSC          0 -0.003303  0.002547  0.006370  0.002154  0.010804  0.004196   \n",
        "CSCO         0 -0.002519 -0.000265 -0.021680 -0.005998  0.004883  0.002841   \n",
        "CTXS         0 -0.004566  0.005074  0.001158 -0.001741  0.009672 -0.001151   \n",
        "CVX          0 -0.008605  0.005390 -0.003951  0.001565 -0.000132  0.008799   \n",
        "DIS          0 -0.003517  0.005172  0.008895  0.007890  0.009188  0.000370   \n",
        "EBAY         0 -0.009406 -0.012296  0.001951 -0.001892  0.003080  0.009711   \n",
        "EMC          0  0.000455  0.005656  0.006630  0.001683  0.007350 -0.001897   \n",
        "FB           0 -0.009296  0.012168  0.005846 -0.004926  0.008913  0.009362   \n",
        "FJTSY        0 -0.015135  0.022376 -0.003667 -0.000367  0.000000 -0.007576   \n",
        "FOXA         0  0.010004  0.008595  0.009264  0.007339  0.003148 -0.003998   \n",
        "GE           0 -0.005840  0.005036  0.001418 -0.005705  0.011155  0.003450   \n",
        "GOOG         0 -0.008795  0.015167  0.004584 -0.000029  0.011069  0.008833   \n",
        "HPQ          0 -0.005128  0.004443  0.003673 -0.004162  0.003487  0.004130   \n",
        "HTHIY        0  0.001694  0.018982 -0.004305 -0.004058  0.007181  0.003708   \n",
        "IBM          0  0.001297  0.001703 -0.001457  0.000124  0.006844  0.004827   \n",
        "INTC         0 -0.001112  0.024832  0.004219  0.001763  0.007486  0.002231   \n",
        "LXK          0  0.007975  0.008664  0.010324 -0.007590  0.000110  0.003788   \n",
        "MSFT         0  0.003428  0.012751  0.006959  0.010214  0.006049  0.010608   \n",
        "N            0 -0.003774 -0.002940 -0.001937 -0.004743  0.010588  0.011145   \n",
        "NFLX         0 -0.011230  0.007208  0.001123  0.011389  0.019374  0.003090   \n",
        "NVDA         0 -0.004594  0.007043 -0.006938  0.004801  0.011358  0.012215   \n",
        "NWSA         0  0.000192 -0.021825  0.017388  0.000386 -0.000386 -0.005830   \n",
        "NYT          0 -0.018408 -0.003259 -0.001088 -0.001089  0.012100 -0.005135   \n",
        "ORCL         0 -0.001671  0.007382  0.000332  0.002069  0.007881  0.014721   \n",
        "PCRFY        0 -0.001397  0.016763  0.006823 -0.001093  0.005975  0.000814   \n",
        "RAX          0 -0.036414 -0.004699  0.033312  0.009003  0.017585  0.035446   \n",
        "RHT          0  0.000507  0.007825  0.002064  0.001281  0.014115  0.021549   \n",
        "SAP          0 -0.006488 -0.001993  0.002852 -0.007576  0.008291  0.009113   \n",
        "SIEGY        0 -0.012527  0.014653  0.014548 -0.004600  0.004551  0.005068   \n",
        "SNE          0  0.000000  0.029412  0.005369  0.019070  0.007033  0.016146   \n",
        "TOSYY        0  0.000000  0.012976 -0.004764 -0.001632  0.010191  0.012761   \n",
        "TRI          0  0.004010 -0.004197  0.009253  0.001130  0.005721 -0.004335   \n",
        "TWX          0 -0.008865  0.006782  0.026300  0.007408  0.005309  0.001090   \n",
        "TXN          0  0.001002  0.010276  0.003460  0.004779  0.002664  0.002796   \n",
        "VIAB         0 -0.005008 -0.000787  0.002314  0.007547  0.009547 -0.006172   \n",
        "VMW          0 -0.007897  0.004582  0.009869  0.005238  0.015535  0.000745   \n",
        "XOM          0 -0.005729  0.006333  0.000303 -0.001247  0.005199  0.002409   \n",
        "XRX          0  0.005475  0.010589 -0.002964 -0.007214  0.005935 -0.002976   \n",
        "\n",
        "n_date    735465    735466    735467    735470    735471    735472    735473  \\\n",
        "symbol                                                                         \n",
        "AAPL    0.003581  0.000066  0.004456  0.006590 -0.005738  0.007040  0.003881   \n",
        "ADBE   -0.006643  0.003217  0.006578  0.000648 -0.003437 -0.001256 -0.004954   \n",
        "ADSK   -0.004108  0.003350 -0.002924 -0.001059  0.010964 -0.002099 -0.002661   \n",
        "AMZN    0.002989 -0.005231 -0.005269  0.007005  0.016890  0.010134 -0.010366   \n",
        "BBRY    0.027402 -0.013043 -0.004030  0.007333  0.003322  0.018265  0.001628   \n",
        "BKS    -0.018048 -0.007093  0.002947 -0.008769  0.013200  0.027528  0.008626   \n",
        "BP     -0.000138  0.001797 -0.001661  0.006054 -0.004214  0.002206 -0.007850   \n",
        "CAJ    -0.008164 -0.000403 -0.007006 -0.000102  0.003441 -0.002638  0.000608   \n",
        "CAT     0.003653  0.003178 -0.007492  0.007283  0.002002  0.002672 -0.000861   \n",
        "CBS    -0.002702 -0.001215  0.002478  0.004387 -0.009521  0.002485 -0.009082   \n",
        "CMCSA  -0.000366 -0.000733 -0.005716  0.005441  0.000367  0.001099 -0.002202   \n",
        "CRM     0.005979  0.006361  0.066805 -0.006311  0.002361  0.009025 -0.025011   \n",
        "CSC     0.002791  0.006711 -0.004814 -0.001434  0.000394  0.004502 -0.004918   \n",
        "CSCO    0.000676  0.007246 -0.003501 -0.000404  0.002419 -0.000538  0.000269   \n",
        "CTXS   -0.000288  0.004345  0.006168 -0.003571 -0.000286 -0.002100 -0.004555   \n",
        "CVX    -0.000026  0.003705 -0.005799  0.005766  0.004183  0.001945  0.000648   \n",
        "DIS    -0.001706  0.004910  0.002284  0.001140 -0.004397  0.000480 -0.000296   \n",
        "EBAY   -0.001871  0.036590  0.001560  0.001737  0.006958  0.001484 -0.012566   \n",
        "EMC    -0.007079  0.002018 -0.004166  0.002919 -0.003832 -0.003393 -0.003176   \n",
        "FB     -0.003428 -0.001963 -0.005833  0.009863  0.007014 -0.009351 -0.011530   \n",
        "FJTSY  -0.008573  0.002510  0.000000  0.000000  0.000000 -0.022526 -0.021060   \n",
        "FOXA   -0.002143 -0.002522  0.000560 -0.001777 -0.001123  0.003359 -0.001776   \n",
        "GE      0.006475  0.005178 -0.006995 -0.001528 -0.002682  0.000638 -0.003715   \n",
        "GOOG   -0.002532 -0.002716 -0.000326 -0.002431 -0.004585 -0.009683 -0.005873   \n",
        "HPQ    -0.007852  0.036108  0.008050  0.006559  0.015742  0.007287 -0.001935   \n",
        "HTHIY  -0.015190 -0.000753 -0.007772  0.012744 -0.001325  0.000618 -0.003943   \n",
        "IBM     0.000105  0.007076 -0.002131  0.001917  0.006684  0.000692 -0.005183   \n",
        "INTC    0.000969  0.015269  0.002475 -0.004206 -0.000670 -0.001725 -0.002787   \n",
        "LXK    -0.005231 -0.002781 -0.008994 -0.001651  0.001430 -0.000267  0.001469   \n",
        "MSFT   -0.001848  0.000370  0.002874 -0.000295 -0.002216 -0.005348 -0.001190   \n",
        "N      -0.001350 -0.000040  0.012627 -0.002477  0.012155  0.013787 -0.014610   \n",
        "NFLX    0.006664  0.003807  0.009043  0.010369 -0.005572 -0.005026 -0.003569   \n",
        "NVDA   -0.002593 -0.007490 -0.002795  0.003828  0.009650 -0.002072  0.000690   \n",
        "NWSA    0.010955  0.010272 -0.001715  0.005872  0.002457 -0.000378  0.000567   \n",
        "NYT    -0.004889  0.007547  0.001615 -0.003782  0.001618  0.002958 -0.004864   \n",
        "ORCL    0.002340  0.005058  0.001203  0.005741  0.001592 -0.006087 -0.005719   \n",
        "PCRFY  -0.015431  0.001101 -0.015372  0.005006  0.003602  0.005237  0.001651   \n",
        "RAX    -0.000203  0.012440  0.021210 -0.007319  0.020159 -0.008504 -0.005109   \n",
        "RHT    -0.002640 -0.002376  0.004356 -0.005406  0.003287 -0.007984 -0.015275   \n",
        "SAP    -0.008675  0.006645 -0.005908  0.014241  0.005454  0.003329 -0.015968   \n",
        "SIEGY  -0.001160  0.014180 -0.008505  0.016414  0.007543 -0.000786 -0.015761   \n",
        "SNE     0.006522 -0.002651 -0.000707  0.007548 -0.005826 -0.001414  0.002293   \n",
        "TOSYY  -0.008040  0.007245 -0.008420  0.001731 -0.002230 -0.015474  0.001005   \n",
        "TRI    -0.000177  0.001681 -0.000531 -0.001064  0.001328  0.006857  0.000352   \n",
        "TWX     0.000651 -0.002313  0.002966  0.002401 -0.002507  0.002647 -0.000390   \n",
        "TXN     0.003344  0.004577  0.000901 -0.008384 -0.004774  0.002800  0.005708   \n",
        "VIAB   -0.002993 -0.004944 -0.001899 -0.000950 -0.002112  0.001860  0.001980   \n",
        "VMW     0.003810 -0.003434 -0.003283 -0.014676 -0.028492 -0.005011 -0.001810   \n",
        "XOM    -0.000770 -0.000100 -0.010319  0.001992  0.009432 -0.001306 -0.000872   \n",
        "XRX     0.006162  0.003684  0.001471  0.002202  0.003657  0.002189  0.000000   \n",
        "\n",
        "n_date    735474    735478    735479    735480    735481  \n",
        "symbol                                                    \n",
        "AAPL    0.003283  0.006973 -0.030061 -0.015946  0.002258  \n",
        "ADBE    0.006732  0.005219  0.003177  0.003212  0.003430  \n",
        "ADSK   -0.000619 -0.007361  0.011897  0.007039 -0.006158  \n",
        "AMZN   -0.002032  0.003425 -0.002177  0.018375 -0.003090  \n",
        "BBRY    0.000651  0.012849  0.032027 -0.009733 -0.009189  \n",
        "BKS     0.001553 -0.010558  0.016419  0.002240 -0.004501  \n",
        "BP     -0.002996 -0.011132  0.010251 -0.041773 -0.001601  \n",
        "CAJ    -0.004584  0.003351  0.003541  0.006134 -0.002217  \n",
        "CAT     0.003523  0.003237 -0.004016  0.000919 -0.001718  \n",
        "CBS    -0.007353 -0.005758 -0.006992  0.011908 -0.002816  \n",
        "CMCSA   0.003779  0.000122  0.002674  0.003392  0.007574  \n",
        "CRM     0.010118  0.012062 -0.004713 -0.002418  0.003642  \n",
        "CSC    -0.000559  0.006930  0.002213 -0.002162  0.000166  \n",
        "CSCO    0.004683 -0.001474  0.006126 -0.002804  0.000667  \n",
        "CTXS    0.009922  0.005148  0.001368  0.000801  0.001553  \n",
        "CVX     0.004078 -0.009115  0.001119 -0.007733 -0.001207  \n",
        "DIS    -0.004043  0.008423  0.003263 -0.005233  0.002316  \n",
        "EBAY   -0.000722 -0.006297 -0.005785 -0.002014 -0.014046  \n",
        "EMC     0.003054 -0.004202  0.006880 -0.010486 -0.009782  \n",
        "FB      0.007288  0.019939  0.003102 -0.002189  0.007948  \n",
        "FJTSY   0.000679  0.016783 -0.032388  0.002945  0.000883  \n",
        "FOXA   -0.004979  0.008014  0.010968  0.002666 -0.004432  \n",
        "GE     -0.001154 -0.004121  0.001543  0.002693  0.000256  \n",
        "GOOG    0.000678  0.009060  0.005519  0.006450  0.004206  \n",
        "HPQ     0.002807 -0.001934  0.005072 -0.008555 -0.008718  \n",
        "HTHIY   0.007300  0.016224  0.001598 -0.011005 -0.003198  \n",
        "IBM     0.002135 -0.001269  0.002254 -0.005807 -0.000681  \n",
        "INTC    0.004401 -0.005967 -0.000096  0.005932  0.004097  \n",
        "LXK     0.009064 -0.008811 -0.008414 -0.004055 -0.000609  \n",
        "MSFT    0.009283 -0.002437 -0.005944  0.004806  0.012413  \n",
        "N       0.013834  0.015171  0.001545 -0.006065 -0.003195  \n",
        "NFLX    0.006130 -0.001608  0.008005 -0.010536 -0.002382  \n",
        "NVDA    0.004124  0.002229  0.009847  0.015379  0.001502  \n",
        "NWSA   -0.000756 -0.005705  0.003222  0.003965  0.001320  \n",
        "NYT    -0.001353  0.000270  0.002159  0.000540  0.000539  \n",
        "ORCL    0.002811  0.002404  0.005340 -0.003680 -0.005793  \n",
        "PCRFY   0.010887  0.007295  0.014643 -0.002134 -0.010515  \n",
        "RAX     0.012419  0.020992  0.031463  0.019219  0.009385  \n",
        "RHT     0.007824  0.004141  0.006711 -0.004622 -0.003985  \n",
        "SAP    -0.000986  0.008287 -0.008875 -0.002407  0.001502  \n",
        "SIEGY  -0.002001  0.008176  0.011301  0.000680 -0.000131  \n",
        "SNE     0.010991  0.013425  0.020566 -0.006789 -0.022029  \n",
        "TOSYY  -0.005942  0.013593 -0.010841 -0.004177  0.005038  \n",
        "TRI    -0.002025  0.000968  0.003943  0.003232 -0.003242  \n",
        "TWX     0.000866  0.003151  0.005495 -0.004095 -0.006769  \n",
        "TXN     0.005124 -0.002917  0.004425  0.002345  0.002820  \n",
        "VIAB    0.000948  0.007079 -0.002872 -0.000739 -0.000041  \n",
        "VMW     0.008902  0.002936 -0.002571 -0.017769 -0.010790  \n",
        "XOM    -0.001578 -0.006624  0.005612 -0.006018  0.003000  \n",
        "XRX     0.007005  0.003850  0.009061 -0.013778 -0.013971  \n",
        "\n",
        "[50 rows x 19 columns]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Normalize the data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Assuming $X_{i,j}$ is a matrix representing our dataset where $i$ is th number of rows and $j$ is the number of columns. This is the function to return $X_{scaled}$ which is the normalized version of the dataset:\n",
      "\n",
      "$X_{std} = \\frac{X - min(X_j)}{max(X_j) - min(X_j)}$\n",
      "\n",
      "$X_{scaled} = X_{std} * (max - min) + min$\n",
      "\n",
      "\n",
      "**The most common Max and Min are:**\n",
      "\n",
      "$max  = 1$\n",
      "\n",
      "$min  = -1$\n",
      "\n",
      "$X_{scaled} = X_{std} * (1 - (-1)) + (-1) = X_{std} * 2 - 1$\n",
      "\n",
      "**And:**\n",
      "\n",
      "$max  = 1$\n",
      "\n",
      "$min  = 0$\n",
      "\n",
      "$X_{scaled} = X_{std} * (1 - 0) + 0 = X_{std}$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for item in clustering_data.values:\n",
      "    plt.plot(item)\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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owmvuqMhyZBCZiJDvlV/nqeRyOQ4ePIh27drh6NGjlfZFrotE/I/xqtkkEvGC\n4OqqWnsVaei7s0kBZFUXDUeVQGHF4yQSCc2ZM4cOHTpEOjo1V4zctm1b+Y+Li0uj7W0IQUFBFJGQ\nQNMnT6bfjxyhzz//nJydnWns2LGkra1NX3zxBe3bt49GjhxJQ4YMoX379tF7Y8eSdZcuNP3QIZKX\nzTZMTiaKiOCDxh071n9hTU1+7sH69S/K4H71FdGuXS9lHeFSpZKSS0v5RTo2bSL69VfepjpQAnQ0\nOZkG+PqSQ7t25OfgQMN0dam1mhpt7NSJfqxpBSg1tfLyFNJUKcX+L5Zsz9iSmkbdt6RATUAGEw0o\n27nmCWiO2dnUuXVrGquvT2oq3J+F/pUrlbZSU6OphoYUXFRE3bW06GbFuR5ubkQDBhDZ2xM9ecL/\nX27f5icP1kNr89bEFXMkz/0X69SUlvK1cHbtqrVJfr6Y4uK+ot69b5GGhm6jLqOn9ybp679N8fHf\nNtbSlkEkIurfn9ClC0We7UveudNJoahe4iTu6zgynmlM7QfVnnzg4eFBgwYNolu3btH+/fvp8OHD\nxFWYYVzjfIPaGD6c6NIl/nnw9m7w1yrDxcWl0ruywTRFedzd3Su5iXbs2FEtiLxixQpcunSp/HNF\nN5FMJsP48eNxoI40qyaa2GhWrl6NtosX46a3N0xMTFBSUoJZs2bhtxO/IWFPAmQyGaysrODu7o6Y\nmBgYGBggJSUFDx8+hHbnztga/dyvvWIFoKurcu+6nOXLgTVrXnweMQL4448am0okEixatAjBNQRk\nG4JPQQHsPT3RQShEybRpdaZ/lhFUWIghvr4Y4eeHUImk2v4ihQImIhGCa9gHFxdw/fvj6fSniN1S\ni0umBtIvpePp1KfVtis5DqZiMaY+fYpfUlNVOlf0xmjEfR9XadudrCyM8PPD1WfPMMrPjx/F/PQT\n71ZwdKx8goUL+X0q4DPIB3nifzFV+LffgH79+LhTcXG13SUliRCLzZCV5dTkS0mlmRCJOqCgwK/J\n5/rXUSh4V62JCXD7NmJivoSPzyCEhn6IsLAllZrme+VDZCIqdxtWJTs7Gx9//DHMzMzw+++/g+M4\ncByHPn364N69ey8uWaSAq44r5Hm1JIvUhKMjP0KowV3eGBr67mzSm1Yul6Nr166Ii4uDVCqtN4Ds\n7u5eHkDmOA4ffvghPvvss7oNbAExKC4uRls9PYy5dw9Lly7Fd999h+LiYrRv3x4BCwLwWPAYWU5Z\nOHjwIObbcEL7AAAgAElEQVTOnQsA2LRpExYvXgyO4zBw8GC0274dvunpgJYWsGVLw43IzuZvXm9v\n/rOzM9CzJ/9iqoBCocD06dMxbNgwdOzYERERDQ9SypRKbIuLg7FIhD/S03H5yBEkdO0KZU3B8OeU\nKBT4JjYWRiIRfk5JqdMlszM+HgtCasiuUCig0DVGgPV1KEvrdg9VsjdbBtd2rlCUVPZ3e+bnw9bD\nAx1EIsTXYXtFAsYFIOtuVqVtUqUS+kIh4oqLYe/khIJJk4DBg/lgelXc3Pi88arupBoIXRiK1F9U\nE6lGMWgQcPs2MGkSLwwVUCiK4e09EAkJ9cyHeY6yVIlnV5/VGShOTT0DH59BDY47vFRSUoAxY4BR\no4DkZCQm7oWnpy2k0kzI5YVwd++KzEw+gMspOPg4+CDtXFq103Ach3PnzsHExASrVq1CbhVX8OnT\npzFlypRK2wLGByDjrwyVTc2Xy/HbxYvgOnQATp1q+HetwksVAwBwcnJC9+7d0a1bN+zYsQMA8PPP\nP+PnCqlaq1atQrdu3dCnTx/4+voCAIRCIQQCAfr27Yt+/fqhX79+cHZ2rm5gC4jB2XPnoDVkCBwj\nI6Gvr4/MzEzcvn0by3otg7uVO1K/84bPIG/k5+fDwMAAsbGxyM/Ph4mJCXx9fXH37l1Y2Nri9IIF\n4DQ0gPyafY8cp6w7K+O33/iJMAoFH/QaOBC4caNSkw0bNmDUqFGQSqU4c+YMLCwsENOAiWGhEgkc\nfHwwISCAz55JTQVnaoqlv/6KA7Vk4zzJzUUPDw/MCgpCSn0ZN+BvckOhEFFVUmSlmVKkaU1H8art\nKttbhu9wX2Tfz6607euYGHwUGgprDw+VzsFxHIQGQpSmVv8Oi0JDceXWLeR27IiHH31UewCf44C+\nfYG//673evE74+vNhGo0Hh5A1678vXLrFh/TKDeRQ0jIfISEzFc5Cyjikwg8VnuM9N9rn4jGcUr4\n+Y1AcvLxJpv/r+DkBJiaAtu3AwoFUlPPws2tE0pKXtzXeXkiiMWmkErTkXw8mU9rrvI3Cg4OxsiR\nIzFw4EB4l3XOqlBUVAQjIyNER7/4/ybuS0T4x+Eqm7s5OhqCx49xx90dsLHhY1JNSOd+6WLwb9MS\nYtBj8GDY7t2LLVu24JNPPgEArPxgJe7r3kfOB/sg09ODp/pvyJrwLTbOnYvP1q4FwIvgm2++CaVS\niWn29iho3RrRgwbVep3Y2G8RHDy39geU4/gezeHD/Ofr1/mspOftT548CRsbG2RlvejZHj9+HFZW\nVkhIqDsrR8lx2JeYWN6z5zgOePCAz33evRsxxcUwEokqZeTkymT4ODwc5mIx/spQvccDAN/GxmJZ\neOUHI2R+CJJnnQPeeKNB5wKA+B/iEbkustK2Pl5eWBMZiU9UHB2VJJRAbCquvoPjELJ9O7INDJD1\n55/QFwpRUNvcEIDPUZ85s97rZd7MROAU1eY+NJgFC4C9e5FYUoK8khI+Ay0gAACQkLAb3t4DoVBU\ndx3VROrZVHj08ECua26dwXoAKCwMgkhkhNLS6r3pFkMqBTZs4P8GLi4AgMzMmxCLTSGRhFVoxgt8\nTMyXCPCeAqGREIVPX9zvEokEmzZtgpGREY4dOwZFPS/mjRs3Yv369S+OD5HArbObSgIcU1wMA6EQ\nF9LS0M3dHdLMTOCtt4B33gFqS8+uByYGTSQkJAQahoa4HBMDY2NjREREQKFQYHub7fB2OA/XEZZo\n96MOft13AD6Wd5DYtx/0BQLkLlgA+Z076NWzJ65fu4a0AQNwUEsLmz//HK61lETw9LSHSNQBaWnn\nazcoNJTP809J4V0R9vbA/ft48OABOnTogMjIyGqH7N+/H926dUNKLdlLscXFeNPPDyP8/BBdXMxP\ngvvqK14IHjwob3c+LQ12np6QyOW4lpGBjmIxPomIQF5dL8YKlJaWlj8IWTIZ9IVCJD5332TezoR7\nV3co8kr471eTC6YOCvwK4GHzYgQQV1wMY5EIEwMDcU1Focq8mYnASVVeztnZwNSpUL7xBvpcvYrk\n0lLMDgrCseQ68s0LCgB9faCuNgCKIorg3tW92vbS0jSkpZ1vfO5+Whqgp4cbERHQFwphKhbDe8MG\nKFesQFbWXYjFZigpUW3CW4FfAURGIkhC+BhPwu4E+L3pV2eKZXT0ZoSEqJA2/TKIieHdZe+8A2Rm\nAgByc10gEhkjP/9Fr37H9h2YPnI6AD5dVnjNFgEHd5Tvv3nzJjp16oQPPvgAaWl1C11WljOKi2MQ\nFxcHAwOD8qxIjuPgZuEGSVgN8bIqzAkOxvdxcQCAiYGBOJyUxMcZly/nswsb+HwATAyazMxPPoHR\n4sU4fvw4pk2bBgBw3emKqxqXIencFd33d8UPT36AxX4LrJ61Gpl3MrFgxgzseecdYMgQ/N2uHZZr\naUFpbo6+amr44fhxdHV3R2GVF2hxcczzAJwvRCKjSkPXanz9NfDuu/zvFy4gxMEBxsbGcHne66mJ\nnTt3wtbWttKcDo7jcDolBUYiEX5KSICC44CEBGDYMD59NT0dHMeV9yA5jsOMp09h5e4OW09PCFWo\n81OGSCSCmZkZZs6cWf5wbIyOxprISMjz+Hov5TOJly4F9u9X+dxltonNxCiK4l1Ph5OS8GFoKNq5\nuiJbxWB97LexiPm6gkvNwwPo3JmfECeVYlFoKA4lJeFRTg561levaOXKapPoqqKUK/GkzRMoiiv3\nMCMiVsLVtR2CgmZCJlP9b1wGt307fN9/H5ZubjialISbmZmYdu8eCtq3het9A+TlqZbSKsuWwb2L\nO55deZGayik4+I3yQ8Lu2keaCoUEbm6dkZ39oNY2L4XLl/mOxcGD5aPnggI/iETGyMn5p7yZ6LoI\npzVO4ybdRH5kPnKf5EI05AKEQiOEhT3B1KlTYWtri0cVa4XVgkJRBKHQAB4ePSCT5WLGjBk4fvyF\n2yx8WTiSDtYtxK65uejk5obi5yOPwMJCmIhEyJfL+e+xfz+fhq2i+7MMJgZNoKSkBJp6evjJzQ3d\nu3fHkydPIM2UwrnNbextMwhfXVmB2ZdmAidOIDk7Dr139cb0JdPh4eUBCwsLyGQyIDISaZqaeKyn\nh4sCAYYZG2PR/ftYUSWwnpR0GGFhHwEA4uN/gL//WHBcLUHI4mLeH+zsjGcpKeiioYFzKmT6bN26\nFb169UJmZiZSS0sxJTAQ/b29EVTm+vnrLz5DZs8eQKmEQlGC4OC5cHOzQKk0A8eSk2EoFELX1RXX\n6sldL4PjOBw6dAjGxsa4ceMGlixZgj59+iA+Ph5ppaXQFwohWhtS2Zfq7MwLUgMJWxKGpMP8g/Z2\nQAC+j4uDQ5U5LFW5cuUKzp07BwB4OvUpMq5lvHjgOnSoFJMpyyriOA52np5wqUsMAwMBc/PaS408\nx9PeE4UBL1wRUmk6hEJ9lJQkIiJiFdzduzUoQ6e0tBTZHTrg3UuXsDw8HBZubjASiXAuOQbPRrXF\nmQ0TMC8kpHxEVhuckkPgpEBErY+qtq9sRm2BX+3uiszM2/DwsIFC0QLF+IqK+B60tTU/iat8cwTE\nYjNkZFzne9lxcUjf9ie8BesRPvAjnNefjMfdH8PDzgNJF5OwadNk6Opq4Icfvi93IdVHSsopPH06\n9fkkvol4+PBv2Nvbl3ccMq5lIHBi7a5BJcdhoLc3LoYmI3ZrLPxG+EGaLsXC0FB8U3HSo6MjL3QV\nMjPrg4lBE9j2yy9oM2gQ/rpxAw4ODuA4DsGjH+FLwae49MsPMN5jjOTj2xC8TQ2BvxjDz2ccTlww\nwLEbhjh6whBOTm/g2cqeyHrLFDe3qMH/jA06m7XG6U8NsNVpLB6c74eI+5MQGfoJ3Nw6ITDwHeTl\nuUGplMPHZzCSko7UbpyzM5RdumDUG2/g60mT+IyReuA4Dps3b4ZV794wcnbGlthYSJVKoKQEWLWK\nr5fkzrstSiVZ8HwwFK43rHDzn/b4+VFvDPN2R7BEAnFeHkxEonqDxRKJBPPnz0ffvn1x/34MZs4E\nLl7kcODAAZiamkIoFGLZo6dY8EWVlDupVCU3S1XKHrQ8uRztXF2xOToa/6sjeC4SiWBsbIyOHTvi\n8uXLEJuLUeyXAkyfzrsWqsw4LssqSiktxZGkJLxbX+rusGHVAvxVCZodhPRLL0ZrMTFfIiLi0/LS\nBc+eXYZIZISUlJ/rdRtlSqX4dudOBDo4YISvLyYHBiJHJoNvfh6snlzD1kMbIOvTB9/GxMBAKMT2\nuDgU1eL3jtsWB7+RflDKau6QpP+eDk97z2qjmkrfLWgm4uLqHh01O0FBvOt01izg8WPg2jXgwAHI\n132MrDHakA7oCnTsCE5TE/J2pshWs0OQRU9gwwZkt2sHsd5qHDY4DFtbW0yZMhl37gxBQkLNNdaq\nwnEcHohsserWHLgniuHvPwaRkevRs2fP8moKstznmW+1/N0u+CXiuwViCPWFCFsShsi1kfAd6ou4\n3CIYPL/3ygkI4MvabNv2okJBHTAxaAJGgwZhyYkTGDlyJC5duoRnO93hqnYBXTp0hMMpB/zidRKJ\ny/UR+HgIsr4eh8zF3RFx8Ri++mYkZpw0wPr5plAa6CDD5xAip2vh9JY3cODAEowYYYt//L/Hhw/X\nIvJ/tkhY1AYuD9QQ7TwLbmJ+JmdRUQSEQkMUFdUc/OQ4Dm6WlrhmZwdlcTHfC32emVUbWTIZ3gsO\nhv5776HnwIHIz8/nZ/z27QvMmQNlRjaynLIQ9OkDPD7fGcJD47H1wTQY/fMQV52t8MhJC6G/HYIs\nX4ZtcXEYFxBQawppZGQkevXqhfffX4Q1a4pgZARs3cpnx966Bdy7dw/GRsZY1flL6D+qwZWzcCFf\nIqAByPPkcG3nikuJaZgUGIihvr54WEu9oMTERJiZmeHvv2fC3X05jA2N8UfrdeCsrIB162rNFloY\nGorDSUnIk8uhJxQitS5BvHCBL99RB7HfxCL2W1505PJ8CIUGiDvrBrG5GLmu/MijqCgcXl69EBKy\nAHJ5zSU1wiQSdHV3R/iAAVj+44/YGhtb/r+Jjt4MN7/xWBoShHgLC4Q+fIi44mLMDQ5GJzc3XHlW\nOWU0624WxOZilKaV8i+ZrCzeJVFhRizHcQh+LxiRa6vHqMooKUl8fg/X3qbJODry8a0FC/iMGzU1\nQF2dn1sxYAAwfToUny5DwuoOyDzyPiASQeobDf/R3nCydcIw22EoeT5K+v2zr/EuCWCmboDTy06D\n4ziUlCRAJDJGQUHdpUM4jsMvHl/DaKcGpl6ciq6HuiK7MB7u7t2wa9dCTJ8+vbxtTZlvRVFFCFoa\nitvtHsN1ZXB5eXNOySH43WCEzA/BhqgoLK+SdIG0ND7h4v33a5xLUhEmBo3EKSAAAn19/OPqik6d\nOqHIJRAitRs4NOEzDN8wHGN+GwPlmdNwv94Gubnu/EPzv/+B624LL1sRdp/ZDUdrgviDd4DgYCgs\nLGBkaIiAgAB07twZYrEYayIjMT8kBBmRZxBw2xbo3x+hF3ohOnoDACAp6Qh8fAZDqazuavj2228x\npX9/cEZG/JoI+/cDs2fX+n3uZmWho1iMdZGRKJLL8cknn2C4jQ0KDA0hWbsPYUtCITQUwmvmRbje\nN0XM0x9wSDgdBs6PcH6wEEFLhHji3AFPLlrj8fHeePq9I4a6++CnGrKUbt68CWNjY8yd+zMMDDiM\nGsVnxX3wAZ8IVTaP5v6i++jcrjN6LlyILVUD346OfOZUA/Eb5Ye5D/yxPyEBOq6uKKmh51tUVIQB\nAwbgl1/eh7t7VwiF+gic/z4ySIDIetahKHMVAcCK8HBsfx7kq5GSEv7LRlV3tZSR/kc6gt/lRxgJ\nCXsQ5P8eRB1ESNybCJGJCPE748EpOSgURQgLWwxPTztIJJXnaDzIzoaRUIivr11DcocOuFMhwJme\nfgnu7l0glfLB04CtW3Fp0iTsSUiAkuPgkpuLvp6emHH/PiLv3YN0989I1F4I6ZiZfOqyri7/4+DA\nl1BJeuHvluXI4GbpVu3FVpHExH0ICBj37xSyS0zkR5BffsmP5J67Tiu+FOXyQvj4DEZ09EYAQK5r\nLsTmYnit9IKxgTEuX76MY8eOYcSIk9AQPIGNQAMFRh3gY3AFuS68GKel/QYvr161urzicuMw+Y/J\n6La/HW76bQIALL21FMtuLYNEEoIHDwyhr98esc9HmnHfxSHqc/6eKAwoRMi8EIiMRPh1jR8Wi6pX\nx1UUK+AzyAeh22JgJBJVn8xZXAy89x4wZAi/DkMtMDFoJL2WLsXw5cvx3nvvYe833yBIay9iJt9A\nn1F90P6H9oh6FobMWWbYsf1jaBvk4o73857DgQPIMJiBqE678MxYDzqbNeG/9l1g9Wrs378fEydO\nxIkTJzB58mQUKRTo7uEBZ/95SEo6BIjFkI7sBZHIGIWFQeA4JQICxiE+/odKtl24cAFWVlZ8MPjw\nYWD0aH5BnA4dqlUTLZDLsTw8HJ3d3PDoeS+Zy81H8bi5WNiqHRw07CAaIELCTwlIC70NkcgYz55d\nxZ9eP0DvzkP8usgbhU8LkXIqBX4rLuPxTV2IxpzA4/dX4dLHX0Df6TFOnU/HmTPA5s0K2Np+CQ0N\nSwgEHlBXB+zs+Fj0hAn8PC1DQ2DtWsBIX4mTBoFIj0rH8DFjoPnGG0h4nu0BgH+R6unVeXPXRMzu\neOjdc8GvqakY9zyVsiIcx2HevHlYtWoqRCIjFKa6InqvDUK/NsQf476Hqakpwqv2vipQ0VUUWFgI\n87J6RRzHzwivWl5840b+pxYK/Arg1dsLCkUJxGIzhHzriOC5wUjYk4DihGL4DvVF4ORASDP5kUpq\n6hmIREZIS7sAADiZkgJjoRATAgJwfepUZFUIWsvlBRCJTFBQ4MNnniUkAH/9BaWWFq689x5cx46F\nrHdvcG3bosjICN72vZCgOwEZE9bz38XTkx8VlL3IV62qNmEy52EOxOZiyLJqDtIrlXJ4efVVuSx2\ng9iyBZgzh3dvrlrF3zOVrl2KgIC3ERa2BDKZDA++eICv2n+FxZMWQ1tbG5qamujTpw8++GAtdNpI\noK9eDPOOHyL5q6+gsLCGRwdnlCSWgOM4BAXNLu+klSFXyrFXvBeGuw2x7Z/P8eiJARQK/v9fUFqA\nroe64lb4LWRl3cG8eW3x2WcrAPCzmt2s3BA4JRBiMzESfkpAfIYEBkIhEmqJ5ZSmlsLN0g2/HAvB\n9KfVZ9uD4/ihd+fOfFXkKkikEiYGjSE6Px8CfX3c+vtvGOjrI9JkHrxMnZAYkwiNJRrY6boT+P13\n7P96HFrpZMJq+C20sbuPiRcmwSPJA9yZsyghY2Qv2gkDWwP4dtLEb3s/RGlpKWxsbHDr1i2Ym5vD\nx8cHbrk5uPFYHwl5obxrQkcHyVH74Oc34vkwNREikVF5ENHV1RXGxsYvSk0oFHwP7vx54IcfKhVK\ne5Kbiy7u7lgSFoY8qQy5wlwkvHsDxeoWyDCYjtitQXhv2nuYMGECEhNPQyTqgNxcV4h9XGBw4yGO\nbfeGj5cvvv/+KB494idBHj54Go5XbbC9qwduGf+Fryc5w+7nBzitdxvddUegvdYctG2Vg5E2xRhg\nXoI2GgpY6eRjjH403tYJRTfLbPTuocQWnXAYtVciMJCfud79gw9g1LVr5RnT8+cDJ0406H/n7JeK\nHmddsCYyErtqGLXs3LkTQ4f2h4eHHVJjjwIODpCuXQQXZ10kXPTEmTNnYGVlheQ64hVlriIAGOHn\nx6eufvUV0KYNsGhRZf9tVBQ/OqjlIVdIFHjS5glSkk7CTzweIiMRfIb6wLW9K5KOJEEpUyJ6YzTc\nLN2QJ+JLVxQWBsLDwwbnvN6Djfgx7D09sVwsBqenB1RIo42L3YrMFb0AW1veNnNzfrTVrRuUkyfj\n8tGjGHf2LP6Oi+OrxC4MwYV3PGAkFOJAYmL1onzBwXwWSxWXXtTnUQiaHVRr7z8vzx1isVmjMqNq\nRSbj/656evx8mwpwHIe4uBjs2zcUH31kjTeHj0RbjbawaG2BudPm4s0338Sbb76JgrwCFEUW4YMB\neZimkYK9szJhZhbKF5lbvx4lXYfAd6A7FCUKSKWZEIs7IjeXz9jzTvFG/5/7Y+xvYxGVHYWoqM8Q\nHc2PChAWBqSkQJQgguleUzyTPINQuAm6uuqIvxYNvxF+eKz2GPE74stnzS8ICakcIAZ4Aa8wqa3A\nrwBCIyFG/SKCqLYVDy9e5APLd+6Ub3oQ8wBdDnZhYtAYJh8+DMvBg7F+1SqsN+oEkfZ9FPgWYOH+\nhTD4nwGkshKsnz0Eunrp+Gb/dnh7T0TXbqGYvn4zLPeZ4bdZ3ZBm2wc+Gqdxfeo0SFq1wrATgzD/\n+nxc/esq7O3tsW/fPsycORP5+Z64LbLBO0+f8g/T2LHgbt+Ej88gpKXxWS5paefh5dUL4eHBMDEx\nqVTzBAB/w5ia8gFPQ0MooqOxMToaZmIxLokSEPVZFMQdRYg3+wIKbQOU7jtbfqhMJsPOnfa4eVML\nubkBCH6UCNPL/+CHX+7D3T0GWq3M0Eagh6WG53CmWwj+6h6Ih9tm4fFPb+KBriuOCHzxjroI6nQb\nRLloRQo4aGZiloUvZg06hrlTluLL1ZtwYddx3N56Ht3UcrFjZDxOdniK3r04mJnxK2gGFRai3ebN\nMO7QAffLyvdevw6MG9eg/93nUVFYusYVtkL3atVRb9++jY4dzeDjMxuhQQvAjXmLrxXFcRB+8RGC\n3Hkh3blzJ3r16oWcWuINtzMzMfK5q+hSejqOffEF0KMHn/vds2f10gHjx/Pxg1oQWwnh5toN3kvP\nIPSjUHj29ERRVBHEpmJk/827YDIdMyHqIELCngQUSGWYHeCG/a5j8etjGxyLcQW3axcvRM+RSjMQ\n9bk2lD278z3FiiMWoZC3l+PwOCcHFm5uWPaXP0T9PKGQKBAqkWB8QABsPT3hnFW5NAdGjgT+/LPS\nJkWJAl69vWos21BGePgKRER8Wuv+BnPlCu++2rsXGRkZuHv3LrZt24bJkyfD2NgYxsbaGDXKCF+v\n+BIHTA/A7V03pF9Nx9kPz8JU2xQPej7AE60n+NXAH/okhXhuGJ6YukG/fQHGjv0aUCjATZuGHKsZ\nCFscCo7jkJV1Fw9dLbHWaSVMfjLBbwG/geM4yOUFEAoNUFKSAHlWPLIm6ENm2R6YMwdfnpmPaX9M\nRdqlNAzTGYiNHVYg7fc0BL8XjJTT/Lwfj/x8dBSLq6Wb45dfAKJKf++MGxl4YCbEBCfv2l1vbm6A\nmRmKd/+IpTeXwHK/Je5G3mVi0FCyZDJoDBqEPYcPw0BDA/c7Hkbst7FIL0xHq69bYfu57Rj3zWTo\nGiTg+x2n8PixMaKjM+HlJYWRkQTOP1ujqL0GhmwxxeCPHRBoagUfdXUkpiVi7tW5GH5mOIaPH44D\nBw7AxMQEzs4rEBG1EX29vHAmNRX47jtgwwYUFPhAJDKBTJYNjuMgEr2Drl0NcKKGnjKn4KD4aCWk\nMxYjb9Y6OE6chWGX3eDUUwyPHh6I3+AL+ejJvN+3wvR4pVKO8PDl8PLqh3nz3sYEhynocuZvrL52\nEid2p8JA0AWr1D7GScEUmAna4mHXb3DK/hI+tXPFuVP9seiD72HUJhpEv0Dd6DE+H/M50szaIV/T\nAsVkAhm1h1KtFTg1dXDtdKHQbY8jZj/BlIrxzFeCtWv5gHLHjrwHY0ZQENZeuwZTU1McPHgQnETC\n+6kruo/Ar7Dl6zscfn4jEBg4BSEh8xERsRLR0ZuxWvgprpz8EtMefoNnmY7IzXVFYWEggoIeoHNn\nQ7i6boanZ08o5k4F5s4FFArI8+VwMb4DkdAIRUUR4DgO69evx/Dhw1FUw6pypRVcRfKzZ5FsYoLI\nsOczWcPD+Z5ZxTIFN24Aw4fXes95r/sJ4pv94d7dHW5d3coFIPcJP+O3bHGfkvgSuA3yxpERTzDi\noRfMRCI8jtwNkcgYGbOMK10z6dJcyA21+JnQV69WuWE4voz687z5OI9sjNztgj5CT4Q//74cx+F2\nZiasPTwwJTAQEWV/h0uX+No+VSgM5FcBK46tOYgpk+VALDZFfn7ta080iO7d4W5hDv1O+mij0wZD\nRg7B5k2bcfHIRTw4vxzCW73gPVSMx+qP4aLpAncrdzwc+xDGbY3x15d/Id87Hxl3s9BLIx8HN/Lf\nLfN2Jpbo+qJN63v8i1YiAdevP5I6rETysWQ4hjui4x4dzDjXDZlF/D2pVEoRGbkOIlEv/PPPEDxw\n1sKF073hdNcaBUe+Q7z5YnT/xApb3l6LK/87C2trLcTGfoe0c2kInhMMjuMw1NcXZ6sWUszI4N2+\nZ87w91OFzLW4XfG40P0J/oqtXXz//uc0wkw14DrBDvmFvKA39N0peH7QK4tAIKhWArsxREQQZWUR\nde7ML0Oqrs5vX+fqSqdmzKBvTU3J+5k6fdXpVxrgOZDmXJtDt6/fJstBfSh/9x80c8ZV0jWwoPOX\nZ1J2tg6N3XKLDJL60/pbn1H3+QUUOD2Inni/TcOP3yKxpYz0DAfQ2j896VuXrfSb729UdJqjd8d8\nTJGRh2j27Fv0j9cQuhtcQN+MyqAtwoVE3t4UGbmawCmoY+ufaPLc8WTeJZC+n3CBBOF9SZos5X+S\npCRLk1Fr3VLqV/Ahrfvsc9pz7Af6atVk6j9yPM3XsiTtj5YTvfsu0Y4dRK1aERGRQiGh0ND3CFCS\nve1VCtqcRKMefET6RlJalPUrXQ9dTAN029OyUo720WZ6IBtMRAIarxVAgzkxWegEUPsTD2j3bkMa\nnjuY7OYa0v/GvUvOeX+SuuQqGeotp9I/JlP83wX0eKgb3eriSNk6CfTPOTVametDvbq2ohPhXejk\nSQFt3Eikp0f0i7CAlqWF0EMTE5o7Ywa98cYbdDwvj1pNmkS0dCkREQFKCggYTXp6b5G+/tukUOST\nUpJ9m0oAACAASURBVJlPCkUepRVn0qW0aBouU1JaUSaN7NKKFIp8kslyKDU1mvT01EhNIKMuZzVI\nN1ydovZ3JXVtfWpXOIMKNo8hoyv3qKgolOzt/yCO42jRokWUl5dHf/31F2lWKdu9KCyM5nh40NQv\nv6TDFy5QtJUVHbax4Xdeu0a0cSORry+RgQFfatzKisjZmah3byIiknEcpUillFhaSkVuw0nt+If0\npM8YcrNWUH6fVmSjpUWXe/akZ2fSKOmnJBrgMYAC1Utopt9TWnyaaMAjJfW+0pOs3zSigtt7KESx\nhYx7r6KuXXeRLM6fBEOGklqvgaSpqU8UGMiXRB4z5sUXOHaMyNWVZEcvkK+DL3U72I3+ekNO38TF\n0Z5u3egjU1MSCAQk5Tg6nJxMuxMTaaW5OX1vbk7UqRNfsrtHj0p/k6R9SZR5I5P6P+lPAvXq5cLT\n03+n5OT9NGCAF6mpaVTbryr4+286MHEC/U9LjYbNeItMjYzpcZtH1Kq0FY3Q7EDD7DJo4KELRDmt\nyPqgNRlMNCA1bTWaOHEiDR06lLZv304FXgW0e9wzcu7YhXxCNUjteaX0gCURNOCsHt11ktOkSRZE\nqamU/KYDLR2sT9F9S+nnmYeobeanpKMznrKy0kmpFFJamjl5er5DRVkjqcvfIZQ5xZp6tA6inhNP\nkNbV40RDCmha3kpyv9Ca3ilUo7Vb1WjGxJ8pdkhHSg6ypp/Sksl74MDKJdY/+ohIX59o/36i334j\n+vFHIi8vIj09AkD/fPCUglLyadXD4dRKQ738sMyiTFp7by15p3jT2TGHaeT/jvOlzP/8kwQGBg16\nd/6/EINLl4jWrePXmE9I4MvPm5sTWXQCuSWvJ/vCOErJcaUdrffSyMvvUZjVI1p2cxFJCpVkfiWI\n2hYVUrKsM1m+EUBh00E91SQU9NU4svnsd7pw5AB9MH0MTZ2dQyOV6TRlhpgWvz+P7hrfoLaybmSc\nvI8SCuIpf9AmMhN9SUWhn9P777vTqFGupKu3m6SlaqQUDqXOER+SPKY9SX94n/atsqNCmTr9tHgm\nSf+PvfcMjvJc9n1/k0cjjXLOCQkFJCEQCBAimJyjySaZHIzBJJuMTc5gkkkm2ySTo8lJSICEApJA\nOecsTZ7zQXuttX3WOveefW/VqXv33k/VfJm3n6l5ut/uf6en+osduKXewcTZBpmbDKGzkPfG95zP\neU7dn+UsPX2S7AALnCxdSDWU0vPOJ36dH03wpCX09O6JRCRBoykhMXEApqat8Hb8maRx6SwcVItR\n8oGKab9QIiill4WSr5rCuNi0ihLzKqpHrCLuj0cYeimwKVpG8bNL9OspZemiVNrcGYjJ88/8FBTE\nn+3acfP+Ze55JHPCtoqHRiORxnBafw4jv5MHnz9c4ccTKkbUv+ZKn89EnfHjyTsJgwc3202/PxIY\n7WTHGHNzJkyYQHlqKpecnLB/+BCA3NzNVFTcJizsTwQC0V/kujU3lyyVijq1Dod1ZWw61gmBQkD/\n/v0JDvZi5MgHeL5qid3ZIjS3TqKT69Bqy0l+MxlFynhC5i0jJsaXsLCHmJoGodVqGTRoEA4ODhw/\nfvwvczdeXrtG4MSJWN69S15ICKFxceRGRmImFqM3GilasYK80lLyfvyRPI2GwK1bEZSWsmrxYvLU\naiq1WhylUrqK3vNl4TaqfjjGu3Ah7Rd5Y2b6BysKdERKS/jOQU/jlh4UpyoZvVaHXCpisK0tK5Mt\nyZzxCfdl7rjenIzu6y9JDb6OVl2O96I0DKYSZA29uKLsRUS4Ev+D0+H+fQgNbT5ATQ1GT0+Sgy+i\niPLCe6M3AEn19Yz5+JEghYKDfn5Y/hsIFqvVRMfHs9vXl77bt0NTE+zc+Rf+Gw1GEnokYNXTCo/l\nHv+kd0ajkYSEL7C1HYyr6zf/j3S3qqKCsW5OfJDoGLd2L7uXj8bVwoIpo7UEjtjM9fhfeP5ZQYV5\nJQNDBjIkeAg9vXuyf/d+rl69yuPHj1Elq3jeI4mJ+nbcuCskIuIfv6+r19HS+hiWXl15k+rLobhD\n7HyynPnFjYSYtUYVmUd9o4SYmO5UVfXE2toXN7djTPrqZ0x7diLeZwLpZ+0QBApp+qoWc//lGI2X\nyHGK48L7M4y+bsuDNw9Z8qsR0ca9/PBVMKu/DCLa0vIff+LJExg/HlJSQKls/m7u3GZjdfUqCIXo\n1XpOd3iJWQdzhv8citFo5HzSeb69+y0TQiawtttaFBIF6PXNjsnNmwjS0/8bDP62jMZmgP3lF7hx\n4+9OGmo15OXBtlcFHJsZRrSoK4XqbIa7XOSkxJzcYZ6Q8QXCK2cR64QMGXaQz91s+eihpE+ultn+\nwzh5VsXlCyr0e1JQ33JDcs+G/vW3mMs+lorOky7JIipwMzFRT9HqWiHP/QJJ29VMFCtoH6QjLm4o\nF+o/USerYIAmin7Rd2io9iXlo5ZLV1O4c6cIdw9LUlOnUK9V8VbdhbsZd3mY9RAHp24UuM9klo2U\nzd9tQNiuPWzdCh07UnF4N+ernnE68TQZlRlMDe5FH+Vj3F2+xl63mKS+sZztoUdYYqD/bQ0VxjKU\nGjv0aMnEGXfJG6yljqQMvs75Oj1Pr/dCIJ6HXXcBq8YZaWEuRmLSiOBTK2rLFJyrmYKsqADb/AzC\nS5T45SsRGsxRSS3QC03JnXGTww4ZeP8wlSptLxaa5GDd1xpdd0e6f2+DsHUNyp9S+RTZDiGwevly\nTm3dytUnT/AOU/LhQ0/atIlDLv9nY9P5/XuWu7kxLT2dg7vkRI51Y/PzzSQkxLNjhwWyj0X4ra2B\np0/Bxubv+5LmPKB6yGS8An9Ar6+ntvY1wcEXAWhoaOCLL74gOjqaLVu2NG+Ij8fYqxfDvv+en2fN\nwkEqpcO7d9Tr9TTo9RRpNNiIxbhlZeEml+MeFkZAVRWTevUiPjERFzs7HKVSRAIB7990p3ZjJKKn\n/XCY4IDdmhKSk0fgEPSYTknF7LN5j7TiCtqFE3nt6obdqiSmuHhiYdEZfYEZKYPeIv30ipY5cxDb\ny8neEUp+ixTUO8ez6VUB8aJ4TNQmXJu2ktbX18GLF83hMFAbNor6enscU3f/ZYhQk17PdxkZ3Kqs\n5ExAAB3Mzam4UcG7iwUsmK0iwc4OSUREs9IoFH+RgSpPxds2bwm5HYKyjfKfZNTYmMa7d52IiEhA\nJnP5D+nvsxfPGN6nJz0MamZcuM7gtVV0nTIZZ/EcLDN60FF9DMn+BZguaIHzYjU3P93gatpVXue9\nRvtJy7px6xhpO5LifsUcCwtD6Kbg8OF/MwyHD8O1a9CrF7veF7Ls7DK+3tKPyBYfsBaKKYxrRbe4\nBDIyt2LcMA1nz58wGt9gMGhwdByH9VkdnzbUcam+LUdkMpwDLvD69RZev75HWdk4Yt78wnOvvXR1\n6cyeMXu4v2UwVdY3+HB8LQvDS2D69GaPVKOBsDD48UcYNuwfh9domiO7nj1h9WoAYrOryOyUgP9y\nW1baLCerKotjg4/RzqXdPzPv0CEEM2f+NxhAMy+nT4fk5GaZW1tDSUnz8LCaGiivMjDh+B4s7uxF\nJ5lLkCgK52Et+L3kJzRvJkGpHxKRgKguF8lf1MinD21hgz/iJhlGI6wQ/ESe0YVT8gmwKgWjcxPb\nLl7Bs94JQWI77rXI5krqNa7bPeJ+eDWNg3R0stVxMxsur4b2EdDoas9Is1ZM178jKLkbXSjHo9tL\nWoUYSc5oycEXAZTbx/Bzz3xuxvjhVtgex+BI1gX5cVKrpa+NDTQ2Nk9I+u03aNcOCgshPx9tagEp\nec8pizrDp5tRCG/0w7vYG71IQa67kFbFH0iWv+FqZSKjzcRMqrmOl2gve3ZdQrdjKbIsH45wmXuc\nYaH4GyJF7RFoxWhN6xEu3orKqCfl1EDqMVAb1B778iyavGz54G5JG00qDtlHsUo0we3TQuL2/8zZ\nrEYyNt9ilyKV6BUuVNysoD61iUVNwXzYnMHOXs7M9nME4HxEBPNSU1m80oqvvvoRR8ev/km+5RoN\nPjExPAkLY0hSEk9fu3Du6jl+yf+FW7emUpt5iPB5IHz8olnp/t2KDYnF84iUz/qBuLsvJydnPa1a\n3USpbA1ARUUFnTt3ZsqUKXw3ZAh06QK7dzM2IACN0ci7+nrkQiH1ej2Xg4JoZWqKTCRq5n3bts1h\nfs+ezcrduzfMmAFAbW0s8c8GY332FuXnqokobMmHrHb4+R3AxqY/V8rKmPDxIyZCITaNAg7O1mIz\nJRPjgIvU1LxAJnPFd7uI4k9TqKlsi3ePHErOpHHWMYVDVZcZObw9O/fc48DqQ2zduZVLw+YQlXQO\nnj+n7JmBolk3aCVdiyAz4x950n+3/igrY3pKGl8+EjPuggBjtZ7926REdnZk7syZzeeZMuWf9pWc\nKyFnXQ5t3rZBpPjn383J2UBR0RF8ffdgazvg/1Z3jUYjqzasYuvGTRzTGujXviN+5oNYMH4VrkYP\nlNafqSm2xmf/dj50GMLOG0qUSpg5EwYNqqPjFyEMWjiIAnEBd9Pv4t4wkJyTv3DvVQEdbKwQTJtG\nVVoZaV8IUYd+ROtRwbZf1lL1yY+pXepw7TmJqGgJypP7UH+/g7yh5/E5HkZcXBgqVT4eiW/4ZV4D\n5019yVOVMOxLBy789hm1ujUikZDi4je8fz+IM5cXc8t7M30yemBj5YJdRBFdahPpHB+J8Mzv0KNH\nc760qKh5Yt7/PJmvqAgiIponHw4YgNFoZODBbXy92I+sDZnMmTMHqUj6L3loMGgRiaT/DQaVlc3v\nrZUVnD7d7BhOm9bMawuL5k99YyGpmWNo6dCfjE/2BLTvw9tkIQKn97Q0aUVGmo59PQ+y4tu2ROnu\nE6zNZVXf65wrK+NG3FuOTxiPKDaOuVu8yc42kmBaDl++ooO3I98XucC6Qrb7fsmU8RoUxkLs7kL+\nIBh2H+qKATsBX0V/xUD/gUie5zA09ghGUSrWNv54O2ayPkgLRhN0TR7U5nTEwu0aS+5cIaU7WK71\nJrSwlEBRGkHaBPrUXEJm9KRJ4EuD2JcGgwe6ju8wLtyN5d7hSOO8ydJ4cb+nLSfGq/F9OgPHshLi\nrsBemR/TVQ8ZKD7GE6sthJUNwMUwDzPDZXzxQCh8zR6bJCxHWfLe8RVNei2mYjjZXkmA90qUJhP4\nJi2NiwYDnmVluN67ylvpVZqaGrF5IuGI4Rs8imVc/OUpvx6PxuvJULZ7VdE6sSuaQg25x4oZ/kBA\nxuwiniQ5E/KNA/Inl3i2aykj8yqZM2c5K1as+KcRqyeLi7laXk5XS0sS6uv5KrGUwaMH8zBuH41V\nswlfKMPk9+fg5/eXfWnJaTxt85Qp1VNQGzKIj++GpeUX6PVVtGp1/e90eXl5DIuM5KFWS+2mTezt\n0oX9hYVIBQKut2pFe3Nzhicn87S6GrXBgKdcjpeJCT0TEpi6cCHP7t7FNzcXr1WrEMXHg0BA/LPB\n1B7zxDx7MrWxtVg82oypVQDm5kt4n57OGhMT4hsaUIpEpEREoMjR8T7qPYG/BWIRraSh8AWmQX1J\n3jmI6g2dyc0wZ6fZWtRuGvYf2oBSeQIwEBBwhjObb/Pdmu8402U0XWryeZ+/hFY3QjCf2wPWrIF+\n/f7CF02ZhuzV2SQ/LGXLdjEyJxk/3TBFVKln0MhKMioqUK5f/78cy5gyLgWxlRi/fX7/8nll5T0+\nfZqLQhGIr+8uTEw8/yVdRUUFA0cNJO5THA/NHGlTZmSSf2s6ffMAj/S2HIhaQ/xdI/39ZzPKLR83\nx5G4u6/kxQsfDhyAW7cm4uMj5dSefWinvcf+OwcGnbDEpt0dMl2/waSskm8K+hMwMA5dYgin9Lnk\nVzXReElAWm0C8fb98DSmwYABMGgQhht3qTsbS8OWC3x22c7JBcO5mtueEPsCCq32MWJ1Bw67uFI+\n1JN96w3MmuUGNEdEr1715syfkfxRF0vjmSrmPHjMwPuzcejjT0vPHQh27YL168HLCxYsgAkTwPx/\nGrX58iUMGULm3XNMS9xAWVM11uVLWL/XlbYvwjHxMfknHqo0Vbz4MIweEY//Q7bz/9UM5P8vrowM\n6NCh2UE7fhwWLoRZs+DUqeYoNykJni24QOOkC4gNRopygtFKR5IouIXN7OHMGG9PfraEEd9fYfmM\nSGayi2HGC7Qw/Y3g24l8/WcOI6b+zNauI/HMKSNscTFGI0SWCRBMbkcrpQWbLfaQv2okU/pWcvtA\nKe3uT0QkN8UyE3pU2iJwFWI8beTsq7PMvTWXIR8XYTj/EWm9GyrTbKJbreWVbALJ0s7YhPxAYOdC\nZOa1rBw1hpSuUhLvOPH9YS8CunqhE3xBivEgnxiB0FiDpeA+9hO/Q7x4K2Gv+yDO705xbScuRblz\nYoaC7VbbaG1Rwr1rUDnEl0m6B4wXHyarbSd6l9zkle4FBwyd+VVxCnN7e4QOjghap/Ls+FMGq8OZ\nHzgNW7mS7+Lr+JS5jPmZ50hVKOhlY0NLbwcyg57y7YhvaTqs4vq5M2ywOkiRMYgZc/UMGP2WGDNz\n4rIL+TT0KXJvOf4/eRH72B2BXsKIOHgZEMf7u+V0SCnk1fUH3LhxgzFjxtDY2PgXOV8rL2egjQ0P\nqqoIV6sZ++1YVnrNp6n8W/y3gcmxO/8EBPfu3SGk9QXm6KoYNHwQaWkNhIbep6rqHjU1r6ipef13\nWjczM3729yd65kxaennRaDAQEx6OAfCUyxEKBFwJDqYiKorCjh05GxjI105OGLp04c9Jk/CaOJFR\nNjZkVVTQ69AhesdcpKL6KRmC4RTnNxC76jUf6428S+yAj7cPfXt2I/7+AyY2muEilXKhrAxFCwWB\nZwNJGZ2COluLdF8MdRZdqFwyg5vZx5grm0SkaQd+dlqBoGElDY3plDcV8zo2hEGz7fll2y+MfXKO\ngynheDnfxbyNabP7fPDg389p0BjI25FHbGAsAqmAAS/b87xfezrIzOgZ9IwnSRUMMLdhtb9/cwdG\nXNy/1LsWP7eg4nrF/3I2tbV1LyIiElEq2/L2bVtycjZgMKj/QvP8+XN8g3xJ0CbwcsxUvIotKJBU\n0Gvhn4QZhhBbupH4fS74l9hzou4UI/THOFnSQMy7dri4f82oUbtxd3/DgB7b6dtHwDxaszXJC6nO\ngoemH8jZK+VY2VICRj7j+bXuvL7XgckX9vBg8EGsJbVEi0+wvnwFTT9fgJAQ2LsX4flT5EvkfDe3\nkAFDt1Mmtmffsi7M3vKchqgGDrq5stzECeGsEn7crOBvtleh8Ccq6gVfD0hmcisRarkZlvfeYP1m\nG9WlLyioPQkxMc1g8MsvzXUDT89mg/XvZp3rI9uza2kX2l3oTV+37rybFkPYl+HEzVCQODARXU0z\nrdFoJLa2luUfn/LbywjeqG3/L+3kv1r/qSKDFy+aMyZr1kBQEEycCN26Nde9/ga4+tv3WLkihm0F\nIxFV2qJz34X9pKt09Y/gw8MW5F5YQuPU7bi28mRa1QaC/T4y7+k9CjqKMLnsxrTTtwjRrmSn0xM+\n+NjD+BwkMiNW37rS3vdPZi6dj3U5nJEt4HdlZ1QndrHLQYdfxzpSYir4wSIFywcCLGrtKJYKqehX\njdlJM2aKTDkpvUHk8HXctbuGnZkra1rWckS+khxJBAHCApY2fY1Aq0ZWIEd8sT31T2ZgHyrAdV0r\npFHe3L+poihtFrbOd1Fs6Ulocirmxgy0Yg0JAR4Y3AoQmVax7iJ4egdwIvcZwbp0Pkj9aOdzmgU5\nzjyfNwcJ8FJcQUalFfuPH+J6x4NoPsdR3ijklkFDSZ/BTOjfGlXTXha55pKvcSJHMpQ9xo4sdrJh\nY2jvv8vk3bvuvNjfhhbH29PJYgqDvlxK3G9DuFsTh8eSFrhs6gjAhcJyvvozG8msIC6f96PtPEs+\nNg3m4DetuXx/D/VZBhzXjyHEw4I2Chm7Csv4M7AbfTLz8Vq8jEFd+tKv1Q1s4lNx9L2Axr8DhiYD\nBpWBsrJS9j6fxLm77khUR5AIwTN0ATm5D+jQoQNLloxFq52CXO5Nm7bvuVNUxI5bt/jo4sJgMzN+\nmzCB62fP0rFjR8anJGOtLUGQe4+nyZ8YExXJnHazMZWa/uNFNBqbQ1NXV4weHqgTEngzUoP2uTma\nixN5ucjAbc+XpO57TcP717B4PkKtGOGGtcjHCOnWNIGnY7/khsSPTl2dyFqdRcGePNo2jufJoNks\nvb4GW5mBOSdcGNQ/nY+LMim8XkzCyrOE+p3GiAGdwAwz6yFkT2vHophlbPRozfS+fs21JQ8PjO/e\nUZFgSsaiDEz8TPDZ7oNpS1MaNA38/PowK2/vwCjRYlA6YJX1A1X9nbi87xGR4hysrxxF/C+ag6oe\nVfFx/EfaJrRFavuv0xcATU1ZfP78DY2NabRo8TOWlt35ceOPbNy6EadxTmwNOU632cN40V6AaGEt\nVu5LeTB2ImuqXPF0TiKrqQZjdmdEynoEXR5iEw2DQv5ggPEyVcKeuGyahkfHYGJaeDJ9qo4D+mmE\nuzRQEh6EMPgRZTu/x9K+CINEj0mlBaa9GrnZIoHCZB0X//iRZEMrHCZ041mXFWw96czLZwKG6lOY\nwgXC5SeJn2TBlMiJ5LlH4GEw4ZNBhXeeloz1bTi/zYrhw/8RxWo0lZx91YV7t9P5fY8bu4Y/5wvx\nY2TKaXj8YYpo806oqoLycoxZ2WjuPETm5Qw//shHT1OmPvoWsVDM0Vd2tKgRw/nzlGu1tHzzhtun\nrdFnqLl/wJLTFaU461NZpl+GnfN8WnsvQygU/tdME/2tY+jIkea00NmzcOgQDBzY/Ly2Fg59n82B\nA1DkY4UhfwmK6D9Qe8iQxvxKTfBWhA8uYrLpHabFMTw5uonsQ9V8L9qPxNie7Pm+jMt/xceaPny2\ncqKysoEIl69Ith3D8PEHCHN6yfx5L7AyqWJyUROhknpyApvYPK8OtaaWb632sCvxEz+X98D11xsM\nFmrQCMDFUUm+qY6Ilm0YHjuNlfmj2T/hG477xfJM/Z4ZfubE1K7l6E87kCk9yFP2QmRqRDr9IVqH\neOwdR+PkNA1T00BSUyfRWJWDYc0Gyh6KOYo7V4Pk2Cx9xjexp+mec5WcP4xEyKxxUjdRhCPZSKl0\nK8ev7Gv+HHWX1To/GhKG0W3yZSbfi0TY2Ijp6wDmiqdTJ6pi0iQBXfo4oa23pNXzLJIiRFRb1bIq\nGfq2jOKy9VriIyLxNGkuNFZU3CQrayWGnQfJuX2H/qzG3ToJP7NTbPkQSavTXliMDcVgNNIqJo7K\nbTLcNCmEej+lb9x7Js4YgmJHNyo+BKE3GJA6FmFop0UXpkXolo6hIAnx+3us6JRBX0UFj0+1p9iv\nAw4Ge6ytanhXdwWFbRrnznpTW/aO7YrPYF/GkpxOfL+uPWrNAPbvP0nHToHMmvmEmyYzeVwQzaK0\nNL5c/j01VWJ2HzrPjk2zsW2zlkL7lhi7KxF92wYThRHb8Gc09prM0uhvmdl2JiaSfwvbq6sxtm1L\nzbTFCPev4O0+NR9XP0CYIaJq6SB2bjTgpwkmYcZ7WtUMxlxtTmX1e7ITk+jyfTduCDV4ayezaZUE\nC40Spc1rjpds4jdjHVvFYgIe+GL0WM2DqkqklScIeWSFdM883L63oqzz1zQ2ZVFjMEGvl1CavpK1\n89ew0MqVpQuGovlYQOUrA7nyqfjs8MGmjw2VTZXse7OPfW/2IS6IJrRuOdcOhbFq8xa2sQOZVRu8\nBBN5MW82IaaZtO5uRc+e0KtXc5fe37J4GYszaPrcRNDlIAQCAYVqNU+qqynRaFCKxShFIsxEIpQi\nEbq652Qlr2HP9mrSCyrpNqcr1rEnmXWhNwlD0mk1tJy0okP8emQSadlqjCveUB0hZJitLVNlYsZ3\n88DSI5vPWXoM5W4ovNPo0jGBQd0OkOpgzdOds/F8pqWTy2sCXcyRFztQkhFEQZ9PTDkRSVRkEAe9\n36B/0EjuhG1sefOSsLD3FN01pUKtpq5BzEBJOW1nlfCm50Vy33vyMiCUanMJ5o31zLpzj9AYAddz\nnhO7bDmZNT7Y/upH7txfEFaUQXk5RQUFFJflU/H1J8au13C0Fjo32KAU19LgJqDOsQeFGjdSKqw5\nm/+ZRP07gsQ16LvWEddazfx3Fiw37YJpWGs4cwZGj6Z85UqmpKXxuqKa75caMPNTEPBTDuTNpkWL\n/djbj/gP2c6/29r/v4PBv+8Y2rIF1q2DwEA4cABsbZtTQ7Nnw71bOvoYblIZYU9s9GUMZ3ZjbCPg\nzNRPrH7/HTkP9qH46TPBGSks2byGRz8GoHTQ8DHtCA9X+rBM8p77ZV2oFdnTza8j3qpyjgriSCuv\npGd0OKufFdCh9xtq/3DCKNMz7cRqujyvRXB0CAtG/kzZ0IkEaV7yVdNLAi5ac/L3u7y0EmHjpWHR\npu48iK/mVlUaoVe30JjXl29aXuebqUYEpcsY87Y/0c/G0CA1pXD0S7z736Jz5E3MxRqKi49RVHQE\nrbYRUXYbdN8uRlUj5tc2r7k26hTG0G10eJVG6QFn6grXo5Luo1jTmfZcZZHFDFwnVmH9WzR1oiRy\nqn2Y3HgVG8kfFAv6YDV0OiOsWzDk2hDyrQ1Ml/2IOu4JX1gEMCEsCo8uVZS4v8TGvQihyEi5GuKl\n0WThzVBxBiEuUbg6jiM5eTg+Tgf5FC2jxvdXlDFxfCG8x9yAMQyNnca9kxWctdEhEatQYUXh1IE4\n2qdxJ34644IT8Wkh4sAmDU9fvWTq3AOYBk5CYRJNZgzojHo8govo5XOSJpEdif4ibP1fUpx+i0CT\nWgZ7woIFcurK3zJRZ6RsxANCggIo3+3IHZWSH4514I3Dz5w8/SddS47z5RAViqOt+Kn+IWmfFWj1\nWgR2qViZXaM+5yjrttzjx8BaEiMisDLIGDcOCsvqsZ00m7dVf9JbsQzr7GkkJ8jRv0vgQnUPgLho\nKgAAIABJREFUkuYEoJeZoz+0hCv9v+P3+0lMtF3J5+qOPNN7YGZTiDrgHjUfumOpOYBe84AfhrXB\n581AzrUU8siwFtHFctobbNmBgbcX55JgG0uwIQ6Z1J4WbrPxd56AOgs+jv1IY3oDeo9kEKsxTDiP\nITieh8kDOLkwhv6iIKZLRhAmXoGgOIcSXSU7Xu3g6PujRHt0ofhFT6rrVXQd9plqdSVRJlFYLLFl\nx5pnxKefZkaBAyscJ/PEZzn37zd3r0ql/B0YWrZv4o9V8aQNlfPGQUOZVku0hQXucjn1ej11ej11\nOh11ej0ltbVklZRgMJMhNDHFYJRgotXSTv2KGcLD/HjkJJ+edMYlOovceflIJAbGOroiEgi4WVHB\nAkELdgy1o9/oxZzXX6TL23WISwfxtMwEiaicNhH3cXasYlCsCRbl1hxXOvM+8CHpH/oiqHPDIugl\nngGV/FThjvoPE2YK4miw+IraGnPCB36gdnAt+W4GgvKyaZf+AZ/MEk516070g2Qm1yQhS/tMem4N\n46nGS2CC9McNvD01iQP+t5nRPhutjQ3zqqqYGhJCuIc7Y3d2Izu1Buek33lZ05FamSUBAW9wcvrI\n27cbCAhoQdcx/dmYvA1BpYHIdFtKKurJzM3FWaHAWyAgoL4eT6kUSx8fClq2ZET3gVRvtkE/7Bwh\nq2ZjYdEBaG6cCQ7+LwQGf+sYSkxsTgedOgW7dsHo0fDhA8yaZSTutY5+8odsESzB9/Ay2toUk7hg\nKT5Cb6KjonEc0JaNv3VGMaWSc6H+LJs+mfJx4zlgM5cLZ7eR9GgsiwtjOWkYRY24HuvRavoMNsVe\nVs/rxAic1w/kiGQnlXXVVHqtxZg/B1QypJ0+oVj5gdHn1dx0nIaLwoSPduvR2AUw9KyOzDc3+Zi1\nl2+cWxO2PJnaei3a34ys7y6kvGoIptGTWXEQgpOF5Dulcz78KSHlfZHKPSn7+neSGkJJlbtTZ2WK\nQAc+hgwqq3zwTReS4a2h1tUAAh3jDCdJv2dBwpbuVBl7Y0RItPQwv9j9wKXNQm7G+PPOLANBZktU\nl+5iZjKdHu5/kJLZn7SmoyhFm2jjv5vQitEER6lwmHodE6kOowHUagFivRJZoznYlqCvM6PcqCbf\nsgUWgno8jRkIhFCoVvCkwpzAgsUE7ggk6cuNXLy8kvdtZUwwbaJDrJbXezIZpVjPTMkFNtmHMC/K\nCk1JJaFWeZz6EPr3hqC8vDx8+/QhyNycxLR0dv58mqq3v5IT25fHNeFkp/qBdQOG4EbM7UrRJCdj\ntIjE0Rzkdg2UOlVS3STC4p4XnqkKGl01uOw5hPUTC/qejcVm++/sOGQgs86FnlNHM3XgMCJd2yMS\niti1axcHDhwg9NgxQq3cqX1uRd4zUx7cFVBeDk6hydBjGQ1m8Yz3+IFF3adge+k4b32+w3quE5M1\nUipNarCov0ZqvSe9ul1AEHmCJ5YarLWVVDZCfVMJ+psaqJVj0d0bYVwW2rwGTjQaMPUVUN+7FSa9\nP/Gi0ZnkLCXmVsH4e/vjY+WDj7UP3lbeyDPlvJtyDtq8wbpuJjU58WjGbKOiQsTS9XW4KJ1Zbith\n45A8XpnlIxfL0eg0mOo8MZb7Mqa3D57mAsSGCmIrtdx8f5sWNi0wuISTlxGDqDaF1cN2M73tDIo1\nes4mV3Mlq5oPwmoaxVocsswY+rqWNkMDGT/YBrnsr8V/vV7Phg0b2LprK6KhYkI0f1AcE8n0iSPp\n9fQeZ1qP5NjuLYSFaGkrz+XIgipqzCX0KCwjNjOTpg4dWOjlxbmKClQfhJSsKGKjTRMbei/BPn4g\nj2NuMEL0AC+5mjTzBhKLWmLmUE5TRwOWnTQMiNJhU6jm9i8y3mbaINTJkKVY0MFYQd3kTLLjvPB0\nyeHriXPoVCVBmZyN4a4cQ7kex+oKUo0teUEUsfY6ztecRf71ajyvnkFfkkrD+HPkPehGXpwJq+5U\n8PSlEc9Mh+b7Y7aFFBe5cGybgJw/n7L5QQECwXd4eTWyc+dFci3zWXRvEVt6bKPg197s2idk1pYc\ncnrKuJKYiHdZGf7JyUjOnCHD0ZGkggLEei2dcGSObi95jidwaS8kUzmZdTf7U14l+a8BBpWVzfUB\ngQCqq8HBAY4ebb63sWCujow0HVNlp5jrcJSkeYFcdq2jQNvAy6QYpNtUKCwV7LtwgTFP5di0NXKh\nU0umxz1ClV/DT3WvUCvecPmHq8yqyGSX2UJUTSlIplkzcaiIWENrbmp6k9/UxMB77hz+dR3DRrXl\nRfw7KKmD+t+gsR0sSkTsPBehXIi97AtCMrtjYoD7bWtRCVR0+uMGr08dJ9BEjoePI4IwT+Kc+9I/\nyYXeT+yID9Nwb4CYt22EGIVaNKoyxFpwq7QhyvUakqt98Im1xKHEQI04l3pLc14sEpLlqSRHrUdv\nFGJolGJIV0BGBuKMcIJ4ygDhHA530KOrtabHw+5czy9CVXYKr4DdbN+4lxq3pbiJ7Zl79jql+7dg\nr8rFXzuDGKORakklajMjUmc3wiTWpBXmI7SyoteoXkzueIqyPVvRJXoz52cBXrfk9On8E91sbyKT\nCIjVtqBx73LqBC5c7ZlO6aIJeC9ezqpDbtgvP8BDZxWXZBPJvjkWw2UfeqkfMNtmL6vaD2XJnAaC\nQwaTrZMz+cELyn74Hp2bB/NHSxBZOpHn0p3nqTIaZGY0VjqjzbOEFCV8NIcSOSZODRhalSO0bcTQ\nvh61dyMStRDRQwc8n0toWfaK0XUXcZ+Xg75TCSd+q+HKH1KGDBnOihUr8PLyQq2GMWO+53ZuAqpV\nS5DIhbTUWrDHPpBP8WJ++AFOngTrVm9Y/Xg1H8s+cqi6M69SY9j3WwYOktGITBfQa9AeOne6xOpC\nCfXS7lQ13OLKge8QCXpRN1LEkLKDqF8/gtIyZJ7tWaetQWFdytahjdQI62nQCjAXmGGrd0RaLkUo\nEmK0NVIv11NSLEOkU+FiLSDQq5rqejUF6bbUmFUw3lWPg2kta1eIUSl1jHQazIjk8Tj5OJIrsCau\nqpqxK+/T5HgMiak5Uqkden09isfbeZWdxru+7zj48U8UAlfUTu3QWwZjYmJPDxt7ulpa0tXSkhZi\nU2JeC0j7KQ/ZqzIWCMLoFC0kJKS5bmc0lnDqzBgKVB8wGeiB+tI1RvZxocPYO4h3fceu7L3kZQRw\nTjqNcvsFTFtXit7GknbbLtL5iTPBqlZ8CFBzeJkNDoUCzF6lU/hFOKH3LFlme4QF4t+Z9ugwTplW\nmA7/HUXgeeae+4Nhk56SmiolLv5LMjKdkITU0STSYXhviW+/OiItiplz5DFq3NGbvsat4RyuwgIy\nTDx4IYnkeUMXctwsMWSqmSHKwld/gC7KWhTffImxa1/UTTXo9/+OISMTgfEx+l6poCyjk7kNg3ys\n6d5aiputjhkRncho2cBP0wQ8feaMTjcAqVSIb+fbFGtK6OndHYGyIw/UvuwvtKQiy5SwIhvOzPPE\n11rWbPy2b0f/+2lSDnlxPzsVtWE5zg8asDviwXcm1aTXdUBsHIaa9//5wSAjo7kzztGx2fj/+GPz\nXZi1SxooL9bxrWQHXgOece8LAddqY3E0c0StV1NYX4b9Z1sc4+xQBgXyvO90LHWw4As3NudmYnPv\nLMu26VBvzqYp2x+fs8PZwUKMlako53Zm6KACZLIu5N4/ROpHIaVVSdT5fsmzw5lc8tjO0sjnFDa8\ngphSKD0IpiEwbSabuunYXrKCKmdTRBgwqyzHSiQi184OrVCI0GikdWwdgy6LCfmo4E5ANpf9Gilt\ndRWpUYtrgy0/hz2i/tBEdra4RblTOes7RWGtLaBxzCae9JXhppCSPsWMz00XyGxypTTGiab1ERgt\n9yL3TUEVOQ5p209oLK0xKFwRFWdiv/ceFcmxiCSvCAp5zoxJP7K7dhtJgQqMYiNCowF5xhlUxzoj\nzxrC9x5n8X8DOSaLOWfaSKLGEkNDAzYKE6r0eloFifj2GzE79zwmQWoNM7PhiR30S8dZXsR8yQH8\nBJ/QXv4SXVg95x92pCHZAYtFlwkSGmm64Mv58gmUVNkwfs06QpQv8RN+pNhoR41AirlIzS4WkGAM\nRaauw6a6lNLsEvQVPlDSHltjJdamn1FVf6Yp9w7iYlMa8q6wyficS4GveOrpirY4DHI8MNYpwCkN\nKw8zqt3kSMNLkdaq0L5y48j4Pkhv2CPp944Xh1w4El9IC5f+pKs2YDpPT1lgBvp7d0hZ8h37S8t4\nqNFwLTSUwrdyRgwzsHx4OpPd/+TEaTXX7b7n2QtHVht9mej8hpR99bz5IGG90JTpD+bzrvVjWiUG\ns9RxAC4P5pJ76TRjFy7hfWIsCqWAYX107D4rpnPfS9Sn9aCyrILAlklYOcRSphVTVNaShlI/aood\n0KgVYFWDQCUClRlSs2JsnT5jtMqjVJGFzLYQg/knBvlkkX24CWVOOZLN2/BWudEp5Q3O5ZaIc0PQ\nZSlRWZiQYa5E9UMSVR6XiI8dQ2pnR8q0WizyP+OQ94IPpsloKuJwMLVlYeRCZkXMwkxqBvzb7eRe\nCcjaWZLU2pP0dEhKesjV62PQh+oROA1D/GAvnq4yKpzOE1hfwNvkrxCJtfxeNZliv2hudAxHLlaR\n4G/Ol0vjeN2mkOwoEZq7f+JaGkDRmO7kRXkwd4+ezjEKdFItMpWQa0YXWny/lLaNb5h8+B6SgUm0\n/vI21mIVYfIETBvquPrHl1Q/8WFs2Rt6ND3HylBCjtAXraE1aYzjAxasJxxFxzJUXcuxvn6W3pnF\nVA+x56b/e/Q7XmCcPh13t0Z6Sk9SlduSCncVhTuqaMxpQ4X8OLqDRxGbWaGWeyLQVkNDJob6TKjO\nwkSbgSP5iMSm6NQCjAY1UoEfMicLurqY0UGUgh3FaKuUfPqzPbdzhmPVMxWJezEiQz0T625irBex\n3HEiSRbdCU09R+7GjdiUWxLd/SBtX3gxsX7if24wePEChgxpvqvh6AgdO+g5f6AGXX0TQ5y2UDn+\nOTct0zCTmaPWqzERmzDYfzDhHj14tlvGxYIvaYr2h+DNSJ5IaDHcBFFjLa32JVFzxYRCnxZs3N+X\n6hm/8pv2IBklL4ga2w/PidlQEMyeFbtZuvQIvr4XkErzcLtnRfWDDF5UbyMkKozI/V3YPOopO2q/\no/bFGtA3YWamQTsnCL38Cn5OLuS5uNM3VkrbWwJ0IjCvBbsKuDHMQH+X1bSITSPpupY5kkdo+oxB\n45FOxzwB344TcHBzGxQySx53fMF4azNG2gRQ1u1bfiqzpG/qaQ7UDqeu1A7ZT1doa7GLtxGgClVg\nK5IyNauSm7ZN5H40RfdMQHDb/uTpd9FkU4L2u2waRGYY1SLE8VaYmwpQ+9VgpRRioysj8bIew7He\nDGqRSV99Cb9MSEaJhmqtiqK8UmpSC1F/TkVcnIqLt4xcj2gID0faqiNqEwuEMg1IjMiMBuQiNXJD\nAzLU5H/yI8LlMVZZdcRtmknbNnf4at5q0qQtMRGKEF2QcynjK2x7JxEsT6G1/UPevO5B1JtMTGcn\nMXPpWMoqz/FFTxuWbNpFg96RuRGr0Ksy0GkeMkxUzb2ZEzH1E+L29jNVVY08UwqQWFjQVzeJ3Ldy\nGhMmkooveoERHGswtlTj5JVBSbI/4fIYIjLiSWifxYvRg/B495op+Y/wfhXPoCwtYiNIdQKa5DIk\nOj1avQkZBk9qEKARJSO1EuNhDMLY5i3uD+FSuJTn9hIiGsZglq7mQsc7rCreQnW2JVdtzrH3YzJt\nuwfj0cKDV+9akfNcgqvQmyzLUAx1IhQ2DVhKzagqAjkqnEJe0qnHAVqFfuJniwWojbaUG81YskSG\n3sYH1Yg4zry4TU1FAI66jjSVO1BWoMDQKEcqzgVtDuY+pZTLDQjr8qAsA11dKm07O1E0bRwV5gok\n6daEiWOY62fC0H5Tmf/2LVt69GDzrVscUxUTVHqWR5+vYzAa6OzemVFBoxjoPxDrGmvetnlL4NVA\ndt3exd4De2nqa0CctZ3lfaayaJGRqZuvcX1XG6ICH2JT0kTHKj0+jc5UmZqT7BPPloVemJ+6gEnH\nSAISwnkWF4y4Vkhd/ScMTbU4RUnp76Bm4E0h2RITYqUiikI+EWr9gcJrE3DT1uGt/ICbPBFnknFs\nSEFsX8nHyVK0YSqKr3Th3NXF/NnQHb+W0DXpAt3DWiHQ6En/pGP7D3coe/0JC+9Yqm2zEYo7Ydib\ngO2YXvwe9xnTbjlUdaxCKlBTXOPCIatxlC18SlbSRgZ4JXB1nh59fCyGvCKEpkqkg/vQpFJjcHNC\nqLTBwZBDY2022toGVFllGNQfQFmNstIOP0kLvP3M8TVUoBOJUDkZUQvNMEgNNOKE8pWBSjc/blsE\noEoxx96qCVt1E7XVuRQcWYU2N+f/LBjcuXOHBQsWoNfr+frrr1m6dOk/0cyfP5/bt2+jUCg4ceIE\nrVu3/t/e++/B4Ny55hqBQADhQWrS3tZiIijGof0ukrqfRySXoNKpCHUIZVjAMNp79UIjdyepsZEX\nL4oJn/aEHWM+UzVwKMaP2xErTRmaMJJep91YI/ImqPYtC6/OxeRRZ86eLedp5UO+bxtG1nqQpNri\nZhWKm9thbG0HYG//JcXFJ3CZcx+TiT/wNCyMsj5l1IfX0yBpYFuXbRwLe0j/XrYIRYcR6G5i7jqL\nUYHO9H8JiIUI1IAYKuwFLNptpFEpZNqliwROsUNUF0v11RFsONqVNY7hLO/XwFJfBY0F7ry75E6P\n4n78NGoNSluwvzqfurq2pI+0B9tUet/bR77bS5LtjZh8Hk+Auh3RLY9yMD0e4SMZrfVt+cb7W874\nhpJfmI7vuhv8Lh/JQBtbRtnbU63Wc/eWgD/vg0d0PWn2vyKqT0RQ6IjixBbCq9S0Cyxmu24TYSaO\n9JKn0b4in2ILASbOnuQ73ON+ijUPn9ZgbGpEJrJCJR2DgBYIG1bgY17H2uH9sR38ApXIyLNnQzl0\ncAsDehxg8ltTKhQb+GrfWSylpnx38ya9X35kWMl58lVJiEQ3uTPrCLrwfPJLlJhIFezfv4mEhNPo\nhBV46osRVhYjsriMtaYNa37Kon3+VRqPHWJGpITbL4XYmduRXppOPxmcUMGASTI0z7Lwz1LRYsR2\njr2S4xAcTeqndjRl24JSC26NeJYnM7XoGL00j3htVcp2Nwc02hyCvOxJsZ0DXYOwOiXB6+Nx6ko+\n06uLOfYafxos6yn63ApBfgPGRhsKRP40ihUUiLToVXZUC5Xk6KzQY46jshAb+ybaeMnxfnoKgSaN\nHwX57Di6mJXuegKb3HknUKNJN+GrbWZk1Sl5EQS6xWnYxiQy1bgBfaQbezTRSFP3o21S4VImxqVI\nQ1q+EGoM1FWAVm/CwA4+VKhdiUvwxsnZm9racLRNntQb7NFpZDgJSwhT59GKRJyFFYA55oEiAoZ3\nJe+PU4S4uPIxqgsXS8sYZK6ktOglMXkx2MpsqW2oxbzJHHmBnNrcWvS2UBBciVn1XJYtDUZnLWHo\nvDz0tXpWjJ1D56OOJBgGU9zSwPlhIpIfbUPi4IJFp9F4N1jwMs0LwRk3Zs0/jNGmhDtNQXQ5qqNv\npj1VvlLODjbyoZ2O4I31jBKfxymnltCad1gbNGRZhPNWFEy8KoTUujYUCn3QGUS4e6UyaO5GXAJu\n8qjRlze235OzajXRVpE8aJXIuFu9CM0I5cjo57yPCkJS6Uzt5oUIrEbhZOtAb7+11L5rIC8fBG5S\nfDCiCZZxzXs+VocLqMhbw++uQ5k2axzGh+vwaa+gxE5KYVwhhkcGDNOltHRpSS9vD+KJ4kONFbUi\newxGPYiqQN+IUKdFUK/GUNWIWb0Ge/MmrEyU+HtIsJAW8jnei2ePR9DH8zmdLYUUCSs4uGENYyz7\ncrjk/P85MNDr9fj7+/PgwQNcXFyIiIjg3LlzBAQE/J3m1q1b7Nu3j1u3bhETE8M333zD69ev/7f2\n/g0MDAYjy5bB7t0gF2swqGuRWyVS22MLtHyIUCQiwr0bLd36Y9bYmoYMMepPjbiU1OGvqsBOW4pB\nUcbEjlZUWIdh3B+O89f7cS86Rrq+Ec1vFxnX8jmDovahCKzn1DJfnhQlcklkTsIpc1wl+QgFzWwS\nCuX/xmAjSlEgYX3TIDsToY0jj+c/5u3Nt6ycsJJrfa9hNknO1eiL7D68hS84RxzLcMaNSKuvyZjo\nxtsWb6nzCWPOPjNC0wUc321CrKAKc5kUa2kDNZpGSpuc0Uh1iDU6rPXZHFZ8yzeZ+8ktCkOmMyAW\n/EyT4DxCzTAENuVIa+7jVakl/ZUZirwXyO1UCDr0o/5yOdZaW5Y5rCKgJpRtPetIeeuIYvsrrJ07\ncT4wEEeZ7C+8f/quiEFf5aCtdsRzgo68knwalLMx/f13bEq8mD40g01XounT7zCpBaF0E3zHsIqb\nBDYZSdgv4/WVZZz5M5pusnMk28aQmJeLqqGe1q31uNkL6KUbzu/VPbj/chIjhy/HnJ04pcwhOsOV\noxPecz06klivcPz6D+HCnj3MWLaMNS29iBj1gnovMSYm1pTWOrDuvh/uOfcouVbPO72RQJsr1FVH\nMaftceL0a4i105JraUBvbkBkI0JkFOFTak+HknxiTCRkaTQ0uNggPZzMEsFnroz9CkW7Wub6C/mm\naRtDH6SRXhj4P7h7z+gmr2/d96duy5LlXnDDBmNjYzqY3muoCTX0FkILIUCoCaEmQAgEAqFDSCCU\nQIBQbLrpphhsg42NwQX3KjfJVl3nA/+Tff9n77P33Wecsce9e36Txpx657uWtJ5Xa675PCTY2yHS\nnTG9cUdiFQhbDsjcweoMUvN7igeLDGQ1eCnNODfIR6GsIVenAZknnVP1tNdfwqD14XHTOt4FxzM0\nyMjeH8vA/BErGtuZW7YfdWU19RI/3BxfUufig654DFbbbhTNz8E6O71PJZCbF0vG8+f0HLOH+x/o\naPF7Na/i66jwy4W225AozNBqLsGXbhBV/4iAANBqZXh4tKVv3xVcvTqIFStk3PLsxLEWL/krzsDs\nLn1JiPyMwVdrcM705WEAmHPkZFrdeG1TUoAvKuw0JZ9gSSktyEbbSolziBNJEif8lTIaVJcRXxyP\nXqJHXqJCIXTU6vQorQ2Izu6Ou9zCr4ZArlv9mThwN8O6H0O5dg5l9obkN7ZjcNajT4qjormGN/36\nYrYqkPwZisFm4stVQ/DxzCVnQ29CEsci902nVnmJ8Bc1eKnfcLJ9TxZ9Noeel5KRFkDCx/5k159H\n6RhEs5vfUHJLx+FDEnr1AmET1NfX87j0MSv2HCc66ld6eNVxp9iBK6V9GLW3E3s9quj4fCrjyObr\nz8t4dXgxUkMtzop6ggKt+Pg4Y6lswoInr0hrDUnNDVxNhcoCR8xFNjD9hQuXueX8B1PnzSf77D7m\nTZ5M9y6dGTJ0CJ69PfHo7cHNcYd59XIIZ84IunVbhtl8n40bF1Lmnom19wpUDgJ/BydeF7+g3CxF\nUemN8bUaacJyJFVd6Rw5j+bdC0lNzSX1RRXrekfRMGkGfVM+/68Dg4cPH7JmzRpiY2MB2LhxIwDL\nli3722fWrFn07NmTMWPGABAeHk5cXBxZWVn/YSy8B4P27Ww8Tn+H1KSFoDvYu25CG/iacNGCFvUR\nBNq1yNUW6p2tVHjZ0OukVDkqqZC4Ump3pwIdVVJXrDeL4PBwdJ9MYG2wP4diAsjI9UQ24Evmplrp\n4TSIc4U3OXnbzB2ZnQsHomjm+oRGDcYQFPQ1ZnMJBQU/odffxt9/AY7XknDcF0vSVoGyog251xry\nhftVFvy+EJPMRKecSDBpuSE82SaNYLPvC3bp5/HGmolZ2gbluI8xt85C8ewhc54vp9NbwdL2PhRX\nOmMq1CItVeLlkUt5RQD9hgk8XdNp4LEctza3ibveBk9TJDGhRvwaGsgseE7PejU9qpNYvssdk/Q2\n5pADSGq34Z4FE9260sFnOopaF870TuPKhbE4bn/Ggd7d6efm9q/m9mLqVSZuPUyT0iW8vtOS2loJ\nAWFWamY8oM46l8gT60lL+ACVKhad6Rsm6vZx1C0MRd5bFk1fibU8hrDxAk2ODEOoDc0bcEmWU/5E\nyb2XKm5JnYm1HEGBkcEOc2li1fJx6xc4IajLaYlDtZWTQ5pSHXeVqSVVXJJKcYpuRKduGeT00vJp\n5RLyE7cw39mRwVHFJDyQsD4RTB4emHUapLocHCoFkjoZUpM71pRqZmk/5fW01xhrSli86wU7Ixvw\nILicKqeq9zdtdkKWPoQR5W48GezCoafbaBNTR10QJLUNwj62kHSHISTrvuFMmglDqhrfhxWUPVyL\ng8MbKsrKiOoYwQcei+g2cRH6xFDOFLoS9Lgl3bIiudjzAisT/0Amr6ftRCvL68JZsbWOAEV34pRx\n3Gq1lD+qBlKf/JqVbKA9T3gracqKoPZcL/kdo7EG19mz0XTrzFhTIUelFVSWpmJ++xibaxGSCg2B\nyq6QOJvShJ64dEmncJYB2bYmNNYX0qndSby80olomoxOV0ZRgh+dzxqw5bVhs1MDYvKPMytyCT6D\nigjudIyHRe04pZnA9k+cSe9+k1/uX8fm6UuzHm3Iu9Ec52w1L2lGBR4ESF6hEs8plxSRJ4KQNzUj\n7/cTSrUev/gl2JMH09FSyvnaCLw8kpj19TTCHBW0+ayc7c7buTtNi3faaz6LiedonzEE19twf6dC\nl6HEVVGHyq4EmwKJqMeZNIKdfkcaaqLkVQT6sY85a2vG7phNeLYtI3uQGrtfIbZD+5FNmYxVZCGp\nL6dT2EjaywqQVyfxLPcOj/IfEe4ejmN5M14/d0XjmU03j6cMbZ1L5bNG5F0exjcDR9C+poCKHStp\nFF7P/M2gdTFS57KI9U9ieV32hpBUOWd+dcWmK2RlUDln0iUMCGhM7Pgd1G6IwkcRxR0+AG0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QcqT5Kf6QqZ51BJ09A45jKu9xzcvGzQwAelyhul0hOFwgvZ7USeGGR8cHcBpZZSbra5SY2XiXul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\n2Z7VAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c847397d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "norm_data = normalize(clustering_data.values, axis=1)\n",
      "norm_data = pd.DataFrame(norm_data)\n",
      "for item in norm_data.values:\n",
      "    plt.plot(item)\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wkdtjbePBmSQF333vy3ffXf1h7IrdRdCiIJRyJednnadXg16VxtPDrwcnUq7e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CUomB1jZaIZ3yLeUaZvxNhF66gL9EG7bzgm+xRRTYlHrimT9p6KdI8X4pafTU5oJVo1+Wy8/oOr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\nnG2pQYg4yvCyVsxnHom6Pzja+yMd909AsSsdzjVh/RMTVqz4jq9vS8zM4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iunN6GhASI/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\nO96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AS31LSAVdX8VOfiHtAkFOQQCc9PPDOSQEfS0tSkvjnkvo2w6wJe9YHkYFRqQsScFl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\nPoVCoQwhqNOnUCiUIQR1+hQKhTKE+P9AyyujOGPE4gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84bd34d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Perform Clustering"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cluster_data(data, n_clusters=8, normalize_data=False):\n",
      "    if normalize_data:\n",
      "        data = normalize(data.values, norm='l2', axis=1, copy=True)\n",
      "    cluster_model = KMeans(n_clusters=n_clusters)\n",
      "    prediction = cluster_model.fit_predict(data)\n",
      "    return prediction, cluster_model, data\n",
      "\n",
      "prediction, model, data = cluster_data(clustering_data, n_clusters=8, normalize_data=True)\n",
      "print \"Cluster Count: %s\" % len(np.unique(prediction))\n",
      "clustering_data[\"Cluster\"] = prediction\n",
      "clustering_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster Count: 8\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>n_date</th>\n",
        "      <th>735456</th>\n",
        "      <th>735457</th>\n",
        "      <th>735458</th>\n",
        "      <th>735459</th>\n",
        "      <th>735460</th>\n",
        "      <th>735463</th>\n",
        "      <th>735464</th>\n",
        "      <th>735465</th>\n",
        "      <th>735466</th>\n",
        "      <th>735467</th>\n",
        "      <th>735470</th>\n",
        "      <th>735471</th>\n",
        "      <th>735472</th>\n",
        "      <th>735473</th>\n",
        "      <th>735474</th>\n",
        "      <th>735478</th>\n",
        "      <th>735479</th>\n",
        "      <th>735480</th>\n",
        "      <th>735481</th>\n",
        "      <th>Cluster</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>symbol</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>AAPL</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.005373</td>\n",
        "      <td> 0.007091</td>\n",
        "      <td> 0.004625</td>\n",
        "      <td> 0.003959</td>\n",
        "      <td> 0.011737</td>\n",
        "      <td> 0.013376</td>\n",
        "      <td> 0.003581</td>\n",
        "      <td> 0.000066</td>\n",
        "      <td> 0.004456</td>\n",
        "      <td> 0.006590</td>\n",
        "      <td>-0.005738</td>\n",
        "      <td> 0.007040</td>\n",
        "      <td> 0.003881</td>\n",
        "      <td> 0.003283</td>\n",
        "      <td> 0.006973</td>\n",
        "      <td>-0.030061</td>\n",
        "      <td>-0.015946</td>\n",
        "      <td> 0.002258</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADBE</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.002185</td>\n",
        "      <td> 0.010283</td>\n",
        "      <td> 0.007582</td>\n",
        "      <td> 0.004037</td>\n",
        "      <td> 0.009363</td>\n",
        "      <td> 0.012268</td>\n",
        "      <td>-0.006643</td>\n",
        "      <td> 0.003217</td>\n",
        "      <td> 0.006578</td>\n",
        "      <td> 0.000648</td>\n",
        "      <td>-0.003437</td>\n",
        "      <td>-0.001256</td>\n",
        "      <td>-0.004954</td>\n",
        "      <td> 0.006732</td>\n",
        "      <td> 0.005219</td>\n",
        "      <td> 0.003177</td>\n",
        "      <td> 0.003212</td>\n",
        "      <td> 0.003430</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADSK</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.004898</td>\n",
        "      <td> 0.017504</td>\n",
        "      <td> 0.013646</td>\n",
        "      <td>-0.033605</td>\n",
        "      <td>-0.015570</td>\n",
        "      <td> 0.004711</td>\n",
        "      <td>-0.004108</td>\n",
        "      <td> 0.003350</td>\n",
        "      <td>-0.002924</td>\n",
        "      <td>-0.001059</td>\n",
        "      <td> 0.010964</td>\n",
        "      <td>-0.002099</td>\n",
        "      <td>-0.002661</td>\n",
        "      <td>-0.000619</td>\n",
        "      <td>-0.007361</td>\n",
        "      <td> 0.011897</td>\n",
        "      <td> 0.007039</td>\n",
        "      <td>-0.006158</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AMZN</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.002078</td>\n",
        "      <td> 0.026988</td>\n",
        "      <td> 0.009929</td>\n",
        "      <td> 0.002949</td>\n",
        "      <td> 0.009095</td>\n",
        "      <td>-0.002142</td>\n",
        "      <td> 0.002989</td>\n",
        "      <td>-0.005231</td>\n",
        "      <td>-0.005269</td>\n",
        "      <td> 0.007005</td>\n",
        "      <td> 0.016890</td>\n",
        "      <td> 0.010134</td>\n",
        "      <td>-0.010366</td>\n",
        "      <td>-0.002032</td>\n",
        "      <td> 0.003425</td>\n",
        "      <td>-0.002177</td>\n",
        "      <td> 0.018375</td>\n",
        "      <td>-0.003090</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BBRY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.006726</td>\n",
        "      <td>-0.002840</td>\n",
        "      <td> 0.009494</td>\n",
        "      <td> 0.010783</td>\n",
        "      <td> 0.014736</td>\n",
        "      <td> 0.009504</td>\n",
        "      <td> 0.027402</td>\n",
        "      <td>-0.013043</td>\n",
        "      <td>-0.004030</td>\n",
        "      <td> 0.007333</td>\n",
        "      <td> 0.003322</td>\n",
        "      <td> 0.018265</td>\n",
        "      <td> 0.001628</td>\n",
        "      <td> 0.000651</td>\n",
        "      <td> 0.012849</td>\n",
        "      <td> 0.032027</td>\n",
        "      <td>-0.009733</td>\n",
        "      <td>-0.009189</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BKS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.004016</td>\n",
        "      <td> 0.001040</td>\n",
        "      <td> 0.009566</td>\n",
        "      <td>-0.002360</td>\n",
        "      <td> 0.014967</td>\n",
        "      <td> 0.008071</td>\n",
        "      <td>-0.018048</td>\n",
        "      <td>-0.007093</td>\n",
        "      <td> 0.002947</td>\n",
        "      <td>-0.008769</td>\n",
        "      <td> 0.013200</td>\n",
        "      <td> 0.027528</td>\n",
        "      <td> 0.008626</td>\n",
        "      <td> 0.001553</td>\n",
        "      <td>-0.010558</td>\n",
        "      <td> 0.016419</td>\n",
        "      <td> 0.002240</td>\n",
        "      <td>-0.004501</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BP</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001125</td>\n",
        "      <td>-0.005802</td>\n",
        "      <td> 0.005278</td>\n",
        "      <td>-0.000775</td>\n",
        "      <td> 0.009557</td>\n",
        "      <td> 0.007821</td>\n",
        "      <td>-0.000138</td>\n",
        "      <td> 0.001797</td>\n",
        "      <td>-0.001661</td>\n",
        "      <td> 0.006054</td>\n",
        "      <td>-0.004214</td>\n",
        "      <td> 0.002206</td>\n",
        "      <td>-0.007850</td>\n",
        "      <td>-0.002996</td>\n",
        "      <td>-0.011132</td>\n",
        "      <td> 0.010251</td>\n",
        "      <td>-0.041773</td>\n",
        "      <td>-0.001601</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CAJ</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.002323</td>\n",
        "      <td> 0.003221</td>\n",
        "      <td> 0.004908</td>\n",
        "      <td> 0.000501</td>\n",
        "      <td>-0.000501</td>\n",
        "      <td> 0.001900</td>\n",
        "      <td>-0.008164</td>\n",
        "      <td>-0.000403</td>\n",
        "      <td>-0.007006</td>\n",
        "      <td>-0.000102</td>\n",
        "      <td> 0.003441</td>\n",
        "      <td>-0.002638</td>\n",
        "      <td> 0.000608</td>\n",
        "      <td>-0.004584</td>\n",
        "      <td> 0.003351</td>\n",
        "      <td> 0.003541</td>\n",
        "      <td> 0.006134</td>\n",
        "      <td>-0.002217</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CAT</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001084</td>\n",
        "      <td> 0.004349</td>\n",
        "      <td> 0.005242</td>\n",
        "      <td> 0.002017</td>\n",
        "      <td> 0.009583</td>\n",
        "      <td> 0.004691</td>\n",
        "      <td> 0.003653</td>\n",
        "      <td> 0.003178</td>\n",
        "      <td>-0.007492</td>\n",
        "      <td> 0.007283</td>\n",
        "      <td> 0.002002</td>\n",
        "      <td> 0.002672</td>\n",
        "      <td>-0.000861</td>\n",
        "      <td> 0.003523</td>\n",
        "      <td> 0.003237</td>\n",
        "      <td>-0.004016</td>\n",
        "      <td> 0.000919</td>\n",
        "      <td>-0.001718</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CBS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.010617</td>\n",
        "      <td>-0.002817</td>\n",
        "      <td> 0.003984</td>\n",
        "      <td> 0.007462</td>\n",
        "      <td> 0.008284</td>\n",
        "      <td> 0.004180</td>\n",
        "      <td>-0.002702</td>\n",
        "      <td>-0.001215</td>\n",
        "      <td> 0.002478</td>\n",
        "      <td> 0.004387</td>\n",
        "      <td>-0.009521</td>\n",
        "      <td> 0.002485</td>\n",
        "      <td>-0.009082</td>\n",
        "      <td>-0.007353</td>\n",
        "      <td>-0.005758</td>\n",
        "      <td>-0.006992</td>\n",
        "      <td> 0.011908</td>\n",
        "      <td>-0.002816</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CMCSA</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000310</td>\n",
        "      <td> 0.005419</td>\n",
        "      <td> 0.002886</td>\n",
        "      <td> 0.004888</td>\n",
        "      <td> 0.003895</td>\n",
        "      <td>-0.003114</td>\n",
        "      <td>-0.000366</td>\n",
        "      <td>-0.000733</td>\n",
        "      <td>-0.005716</td>\n",
        "      <td> 0.005441</td>\n",
        "      <td> 0.000367</td>\n",
        "      <td> 0.001099</td>\n",
        "      <td>-0.002202</td>\n",
        "      <td> 0.003779</td>\n",
        "      <td> 0.000122</td>\n",
        "      <td> 0.002674</td>\n",
        "      <td> 0.003392</td>\n",
        "      <td> 0.007574</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CRM</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.029861</td>\n",
        "      <td>-0.004338</td>\n",
        "      <td> 0.006434</td>\n",
        "      <td> 0.003858</td>\n",
        "      <td> 0.013566</td>\n",
        "      <td> 0.010267</td>\n",
        "      <td> 0.005979</td>\n",
        "      <td> 0.006361</td>\n",
        "      <td> 0.066805</td>\n",
        "      <td>-0.006311</td>\n",
        "      <td> 0.002361</td>\n",
        "      <td> 0.009025</td>\n",
        "      <td>-0.025011</td>\n",
        "      <td> 0.010118</td>\n",
        "      <td> 0.012062</td>\n",
        "      <td>-0.004713</td>\n",
        "      <td>-0.002418</td>\n",
        "      <td> 0.003642</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CSC</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.003303</td>\n",
        "      <td> 0.002547</td>\n",
        "      <td> 0.006370</td>\n",
        "      <td> 0.002154</td>\n",
        "      <td> 0.010804</td>\n",
        "      <td> 0.004196</td>\n",
        "      <td> 0.002791</td>\n",
        "      <td> 0.006711</td>\n",
        "      <td>-0.004814</td>\n",
        "      <td>-0.001434</td>\n",
        "      <td> 0.000394</td>\n",
        "      <td> 0.004502</td>\n",
        "      <td>-0.004918</td>\n",
        "      <td>-0.000559</td>\n",
        "      <td> 0.006930</td>\n",
        "      <td> 0.002213</td>\n",
        "      <td>-0.002162</td>\n",
        "      <td> 0.000166</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CSCO</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.002519</td>\n",
        "      <td>-0.000265</td>\n",
        "      <td>-0.021680</td>\n",
        "      <td>-0.005998</td>\n",
        "      <td> 0.004883</td>\n",
        "      <td> 0.002841</td>\n",
        "      <td> 0.000676</td>\n",
        "      <td> 0.007246</td>\n",
        "      <td>-0.003501</td>\n",
        "      <td>-0.000404</td>\n",
        "      <td> 0.002419</td>\n",
        "      <td>-0.000538</td>\n",
        "      <td> 0.000269</td>\n",
        "      <td> 0.004683</td>\n",
        "      <td>-0.001474</td>\n",
        "      <td> 0.006126</td>\n",
        "      <td>-0.002804</td>\n",
        "      <td> 0.000667</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CTXS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.004566</td>\n",
        "      <td> 0.005074</td>\n",
        "      <td> 0.001158</td>\n",
        "      <td>-0.001741</td>\n",
        "      <td> 0.009672</td>\n",
        "      <td>-0.001151</td>\n",
        "      <td>-0.000288</td>\n",
        "      <td> 0.004345</td>\n",
        "      <td> 0.006168</td>\n",
        "      <td>-0.003571</td>\n",
        "      <td>-0.000286</td>\n",
        "      <td>-0.002100</td>\n",
        "      <td>-0.004555</td>\n",
        "      <td> 0.009922</td>\n",
        "      <td> 0.005148</td>\n",
        "      <td> 0.001368</td>\n",
        "      <td> 0.000801</td>\n",
        "      <td> 0.001553</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CVX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.008605</td>\n",
        "      <td> 0.005390</td>\n",
        "      <td>-0.003951</td>\n",
        "      <td> 0.001565</td>\n",
        "      <td>-0.000132</td>\n",
        "      <td> 0.008799</td>\n",
        "      <td>-0.000026</td>\n",
        "      <td> 0.003705</td>\n",
        "      <td>-0.005799</td>\n",
        "      <td> 0.005766</td>\n",
        "      <td> 0.004183</td>\n",
        "      <td> 0.001945</td>\n",
        "      <td> 0.000648</td>\n",
        "      <td> 0.004078</td>\n",
        "      <td>-0.009115</td>\n",
        "      <td> 0.001119</td>\n",
        "      <td>-0.007733</td>\n",
        "      <td>-0.001207</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DIS</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.003517</td>\n",
        "      <td> 0.005172</td>\n",
        "      <td> 0.008895</td>\n",
        "      <td> 0.007890</td>\n",
        "      <td> 0.009188</td>\n",
        "      <td> 0.000370</td>\n",
        "      <td>-0.001706</td>\n",
        "      <td> 0.004910</td>\n",
        "      <td> 0.002284</td>\n",
        "      <td> 0.001140</td>\n",
        "      <td>-0.004397</td>\n",
        "      <td> 0.000480</td>\n",
        "      <td>-0.000296</td>\n",
        "      <td>-0.004043</td>\n",
        "      <td> 0.008423</td>\n",
        "      <td> 0.003263</td>\n",
        "      <td>-0.005233</td>\n",
        "      <td> 0.002316</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EBAY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.009406</td>\n",
        "      <td>-0.012296</td>\n",
        "      <td> 0.001951</td>\n",
        "      <td>-0.001892</td>\n",
        "      <td> 0.003080</td>\n",
        "      <td> 0.009711</td>\n",
        "      <td>-0.001871</td>\n",
        "      <td> 0.036590</td>\n",
        "      <td> 0.001560</td>\n",
        "      <td> 0.001737</td>\n",
        "      <td> 0.006958</td>\n",
        "      <td> 0.001484</td>\n",
        "      <td>-0.012566</td>\n",
        "      <td>-0.000722</td>\n",
        "      <td>-0.006297</td>\n",
        "      <td>-0.005785</td>\n",
        "      <td>-0.002014</td>\n",
        "      <td>-0.014046</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EMC</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000455</td>\n",
        "      <td> 0.005656</td>\n",
        "      <td> 0.006630</td>\n",
        "      <td> 0.001683</td>\n",
        "      <td> 0.007350</td>\n",
        "      <td>-0.001897</td>\n",
        "      <td>-0.007079</td>\n",
        "      <td> 0.002018</td>\n",
        "      <td>-0.004166</td>\n",
        "      <td> 0.002919</td>\n",
        "      <td>-0.003832</td>\n",
        "      <td>-0.003393</td>\n",
        "      <td>-0.003176</td>\n",
        "      <td> 0.003054</td>\n",
        "      <td>-0.004202</td>\n",
        "      <td> 0.006880</td>\n",
        "      <td>-0.010486</td>\n",
        "      <td>-0.009782</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FB</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.009296</td>\n",
        "      <td> 0.012168</td>\n",
        "      <td> 0.005846</td>\n",
        "      <td>-0.004926</td>\n",
        "      <td> 0.008913</td>\n",
        "      <td> 0.009362</td>\n",
        "      <td>-0.003428</td>\n",
        "      <td>-0.001963</td>\n",
        "      <td>-0.005833</td>\n",
        "      <td> 0.009863</td>\n",
        "      <td> 0.007014</td>\n",
        "      <td>-0.009351</td>\n",
        "      <td>-0.011530</td>\n",
        "      <td> 0.007288</td>\n",
        "      <td> 0.019939</td>\n",
        "      <td> 0.003102</td>\n",
        "      <td>-0.002189</td>\n",
        "      <td> 0.007948</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FJTSY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.015135</td>\n",
        "      <td> 0.022376</td>\n",
        "      <td>-0.003667</td>\n",
        "      <td>-0.000367</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.007576</td>\n",
        "      <td>-0.008573</td>\n",
        "      <td> 0.002510</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td>-0.022526</td>\n",
        "      <td>-0.021060</td>\n",
        "      <td> 0.000679</td>\n",
        "      <td> 0.016783</td>\n",
        "      <td>-0.032388</td>\n",
        "      <td> 0.002945</td>\n",
        "      <td> 0.000883</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FOXA</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.010004</td>\n",
        "      <td> 0.008595</td>\n",
        "      <td> 0.009264</td>\n",
        "      <td> 0.007339</td>\n",
        "      <td> 0.003148</td>\n",
        "      <td>-0.003998</td>\n",
        "      <td>-0.002143</td>\n",
        "      <td>-0.002522</td>\n",
        "      <td> 0.000560</td>\n",
        "      <td>-0.001777</td>\n",
        "      <td>-0.001123</td>\n",
        "      <td> 0.003359</td>\n",
        "      <td>-0.001776</td>\n",
        "      <td>-0.004979</td>\n",
        "      <td> 0.008014</td>\n",
        "      <td> 0.010968</td>\n",
        "      <td> 0.002666</td>\n",
        "      <td>-0.004432</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GE</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005840</td>\n",
        "      <td> 0.005036</td>\n",
        "      <td> 0.001418</td>\n",
        "      <td>-0.005705</td>\n",
        "      <td> 0.011155</td>\n",
        "      <td> 0.003450</td>\n",
        "      <td> 0.006475</td>\n",
        "      <td> 0.005178</td>\n",
        "      <td>-0.006995</td>\n",
        "      <td>-0.001528</td>\n",
        "      <td>-0.002682</td>\n",
        "      <td> 0.000638</td>\n",
        "      <td>-0.003715</td>\n",
        "      <td>-0.001154</td>\n",
        "      <td>-0.004121</td>\n",
        "      <td> 0.001543</td>\n",
        "      <td> 0.002693</td>\n",
        "      <td> 0.000256</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GOOG</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.008795</td>\n",
        "      <td> 0.015167</td>\n",
        "      <td> 0.004584</td>\n",
        "      <td>-0.000029</td>\n",
        "      <td> 0.011069</td>\n",
        "      <td> 0.008833</td>\n",
        "      <td>-0.002532</td>\n",
        "      <td>-0.002716</td>\n",
        "      <td>-0.000326</td>\n",
        "      <td>-0.002431</td>\n",
        "      <td>-0.004585</td>\n",
        "      <td>-0.009683</td>\n",
        "      <td>-0.005873</td>\n",
        "      <td> 0.000678</td>\n",
        "      <td> 0.009060</td>\n",
        "      <td> 0.005519</td>\n",
        "      <td> 0.006450</td>\n",
        "      <td> 0.004206</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HPQ</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005128</td>\n",
        "      <td> 0.004443</td>\n",
        "      <td> 0.003673</td>\n",
        "      <td>-0.004162</td>\n",
        "      <td> 0.003487</td>\n",
        "      <td> 0.004130</td>\n",
        "      <td>-0.007852</td>\n",
        "      <td> 0.036108</td>\n",
        "      <td> 0.008050</td>\n",
        "      <td> 0.006559</td>\n",
        "      <td> 0.015742</td>\n",
        "      <td> 0.007287</td>\n",
        "      <td>-0.001935</td>\n",
        "      <td> 0.002807</td>\n",
        "      <td>-0.001934</td>\n",
        "      <td> 0.005072</td>\n",
        "      <td>-0.008555</td>\n",
        "      <td>-0.008718</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HTHIY</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001694</td>\n",
        "      <td> 0.018982</td>\n",
        "      <td>-0.004305</td>\n",
        "      <td>-0.004058</td>\n",
        "      <td> 0.007181</td>\n",
        "      <td> 0.003708</td>\n",
        "      <td>-0.015190</td>\n",
        "      <td>-0.000753</td>\n",
        "      <td>-0.007772</td>\n",
        "      <td> 0.012744</td>\n",
        "      <td>-0.001325</td>\n",
        "      <td> 0.000618</td>\n",
        "      <td>-0.003943</td>\n",
        "      <td> 0.007300</td>\n",
        "      <td> 0.016224</td>\n",
        "      <td> 0.001598</td>\n",
        "      <td>-0.011005</td>\n",
        "      <td>-0.003198</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IBM</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001297</td>\n",
        "      <td> 0.001703</td>\n",
        "      <td>-0.001457</td>\n",
        "      <td> 0.000124</td>\n",
        "      <td> 0.006844</td>\n",
        "      <td> 0.004827</td>\n",
        "      <td> 0.000105</td>\n",
        "      <td> 0.007076</td>\n",
        "      <td>-0.002131</td>\n",
        "      <td> 0.001917</td>\n",
        "      <td> 0.006684</td>\n",
        "      <td> 0.000692</td>\n",
        "      <td>-0.005183</td>\n",
        "      <td> 0.002135</td>\n",
        "      <td>-0.001269</td>\n",
        "      <td> 0.002254</td>\n",
        "      <td>-0.005807</td>\n",
        "      <td>-0.000681</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>INTC</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001112</td>\n",
        "      <td> 0.024832</td>\n",
        "      <td> 0.004219</td>\n",
        "      <td> 0.001763</td>\n",
        "      <td> 0.007486</td>\n",
        "      <td> 0.002231</td>\n",
        "      <td> 0.000969</td>\n",
        "      <td> 0.015269</td>\n",
        "      <td> 0.002475</td>\n",
        "      <td>-0.004206</td>\n",
        "      <td>-0.000670</td>\n",
        "      <td>-0.001725</td>\n",
        "      <td>-0.002787</td>\n",
        "      <td> 0.004401</td>\n",
        "      <td>-0.005967</td>\n",
        "      <td>-0.000096</td>\n",
        "      <td> 0.005932</td>\n",
        "      <td> 0.004097</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LXK</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.007975</td>\n",
        "      <td> 0.008664</td>\n",
        "      <td> 0.010324</td>\n",
        "      <td>-0.007590</td>\n",
        "      <td> 0.000110</td>\n",
        "      <td> 0.003788</td>\n",
        "      <td>-0.005231</td>\n",
        "      <td>-0.002781</td>\n",
        "      <td>-0.008994</td>\n",
        "      <td>-0.001651</td>\n",
        "      <td> 0.001430</td>\n",
        "      <td>-0.000267</td>\n",
        "      <td> 0.001469</td>\n",
        "      <td> 0.009064</td>\n",
        "      <td>-0.008811</td>\n",
        "      <td>-0.008414</td>\n",
        "      <td>-0.004055</td>\n",
        "      <td>-0.000609</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MSFT</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.003428</td>\n",
        "      <td> 0.012751</td>\n",
        "      <td> 0.006959</td>\n",
        "      <td> 0.010214</td>\n",
        "      <td> 0.006049</td>\n",
        "      <td> 0.010608</td>\n",
        "      <td>-0.001848</td>\n",
        "      <td> 0.000370</td>\n",
        "      <td> 0.002874</td>\n",
        "      <td>-0.000295</td>\n",
        "      <td>-0.002216</td>\n",
        "      <td>-0.005348</td>\n",
        "      <td>-0.001190</td>\n",
        "      <td> 0.009283</td>\n",
        "      <td>-0.002437</td>\n",
        "      <td>-0.005944</td>\n",
        "      <td> 0.004806</td>\n",
        "      <td> 0.012413</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.003774</td>\n",
        "      <td>-0.002940</td>\n",
        "      <td>-0.001937</td>\n",
        "      <td>-0.004743</td>\n",
        "      <td> 0.010588</td>\n",
        "      <td> 0.011145</td>\n",
        "      <td>-0.001350</td>\n",
        "      <td>-0.000040</td>\n",
        "      <td> 0.012627</td>\n",
        "      <td>-0.002477</td>\n",
        "      <td> 0.012155</td>\n",
        "      <td> 0.013787</td>\n",
        "      <td>-0.014610</td>\n",
        "      <td> 0.013834</td>\n",
        "      <td> 0.015171</td>\n",
        "      <td> 0.001545</td>\n",
        "      <td>-0.006065</td>\n",
        "      <td>-0.003195</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NFLX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.011230</td>\n",
        "      <td> 0.007208</td>\n",
        "      <td> 0.001123</td>\n",
        "      <td> 0.011389</td>\n",
        "      <td> 0.019374</td>\n",
        "      <td> 0.003090</td>\n",
        "      <td> 0.006664</td>\n",
        "      <td> 0.003807</td>\n",
        "      <td> 0.009043</td>\n",
        "      <td> 0.010369</td>\n",
        "      <td>-0.005572</td>\n",
        "      <td>-0.005026</td>\n",
        "      <td>-0.003569</td>\n",
        "      <td> 0.006130</td>\n",
        "      <td>-0.001608</td>\n",
        "      <td> 0.008005</td>\n",
        "      <td>-0.010536</td>\n",
        "      <td>-0.002382</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NVDA</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.004594</td>\n",
        "      <td> 0.007043</td>\n",
        "      <td>-0.006938</td>\n",
        "      <td> 0.004801</td>\n",
        "      <td> 0.011358</td>\n",
        "      <td> 0.012215</td>\n",
        "      <td>-0.002593</td>\n",
        "      <td>-0.007490</td>\n",
        "      <td>-0.002795</td>\n",
        "      <td> 0.003828</td>\n",
        "      <td> 0.009650</td>\n",
        "      <td>-0.002072</td>\n",
        "      <td> 0.000690</td>\n",
        "      <td> 0.004124</td>\n",
        "      <td> 0.002229</td>\n",
        "      <td> 0.009847</td>\n",
        "      <td> 0.015379</td>\n",
        "      <td> 0.001502</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NWSA</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000192</td>\n",
        "      <td>-0.021825</td>\n",
        "      <td> 0.017388</td>\n",
        "      <td> 0.000386</td>\n",
        "      <td>-0.000386</td>\n",
        "      <td>-0.005830</td>\n",
        "      <td> 0.010955</td>\n",
        "      <td> 0.010272</td>\n",
        "      <td>-0.001715</td>\n",
        "      <td> 0.005872</td>\n",
        "      <td> 0.002457</td>\n",
        "      <td>-0.000378</td>\n",
        "      <td> 0.000567</td>\n",
        "      <td>-0.000756</td>\n",
        "      <td>-0.005705</td>\n",
        "      <td> 0.003222</td>\n",
        "      <td> 0.003965</td>\n",
        "      <td> 0.001320</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NYT</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.018408</td>\n",
        "      <td>-0.003259</td>\n",
        "      <td>-0.001088</td>\n",
        "      <td>-0.001089</td>\n",
        "      <td> 0.012100</td>\n",
        "      <td>-0.005135</td>\n",
        "      <td>-0.004889</td>\n",
        "      <td> 0.007547</td>\n",
        "      <td> 0.001615</td>\n",
        "      <td>-0.003782</td>\n",
        "      <td> 0.001618</td>\n",
        "      <td> 0.002958</td>\n",
        "      <td>-0.004864</td>\n",
        "      <td>-0.001353</td>\n",
        "      <td> 0.000270</td>\n",
        "      <td> 0.002159</td>\n",
        "      <td> 0.000540</td>\n",
        "      <td> 0.000539</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ORCL</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001671</td>\n",
        "      <td> 0.007382</td>\n",
        "      <td> 0.000332</td>\n",
        "      <td> 0.002069</td>\n",
        "      <td> 0.007881</td>\n",
        "      <td> 0.014721</td>\n",
        "      <td> 0.002340</td>\n",
        "      <td> 0.005058</td>\n",
        "      <td> 0.001203</td>\n",
        "      <td> 0.005741</td>\n",
        "      <td> 0.001592</td>\n",
        "      <td>-0.006087</td>\n",
        "      <td>-0.005719</td>\n",
        "      <td> 0.002811</td>\n",
        "      <td> 0.002404</td>\n",
        "      <td> 0.005340</td>\n",
        "      <td>-0.003680</td>\n",
        "      <td>-0.005793</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PCRFY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.001397</td>\n",
        "      <td> 0.016763</td>\n",
        "      <td> 0.006823</td>\n",
        "      <td>-0.001093</td>\n",
        "      <td> 0.005975</td>\n",
        "      <td> 0.000814</td>\n",
        "      <td>-0.015431</td>\n",
        "      <td> 0.001101</td>\n",
        "      <td>-0.015372</td>\n",
        "      <td> 0.005006</td>\n",
        "      <td> 0.003602</td>\n",
        "      <td> 0.005237</td>\n",
        "      <td> 0.001651</td>\n",
        "      <td> 0.010887</td>\n",
        "      <td> 0.007295</td>\n",
        "      <td> 0.014643</td>\n",
        "      <td>-0.002134</td>\n",
        "      <td>-0.010515</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RAX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.036414</td>\n",
        "      <td>-0.004699</td>\n",
        "      <td> 0.033312</td>\n",
        "      <td> 0.009003</td>\n",
        "      <td> 0.017585</td>\n",
        "      <td> 0.035446</td>\n",
        "      <td>-0.000203</td>\n",
        "      <td> 0.012440</td>\n",
        "      <td> 0.021210</td>\n",
        "      <td>-0.007319</td>\n",
        "      <td> 0.020159</td>\n",
        "      <td>-0.008504</td>\n",
        "      <td>-0.005109</td>\n",
        "      <td> 0.012419</td>\n",
        "      <td> 0.020992</td>\n",
        "      <td> 0.031463</td>\n",
        "      <td> 0.019219</td>\n",
        "      <td> 0.009385</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>RHT</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000507</td>\n",
        "      <td> 0.007825</td>\n",
        "      <td> 0.002064</td>\n",
        "      <td> 0.001281</td>\n",
        "      <td> 0.014115</td>\n",
        "      <td> 0.021549</td>\n",
        "      <td>-0.002640</td>\n",
        "      <td>-0.002376</td>\n",
        "      <td> 0.004356</td>\n",
        "      <td>-0.005406</td>\n",
        "      <td> 0.003287</td>\n",
        "      <td>-0.007984</td>\n",
        "      <td>-0.015275</td>\n",
        "      <td> 0.007824</td>\n",
        "      <td> 0.004141</td>\n",
        "      <td> 0.006711</td>\n",
        "      <td>-0.004622</td>\n",
        "      <td>-0.003985</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SAP</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.006488</td>\n",
        "      <td>-0.001993</td>\n",
        "      <td> 0.002852</td>\n",
        "      <td>-0.007576</td>\n",
        "      <td> 0.008291</td>\n",
        "      <td> 0.009113</td>\n",
        "      <td>-0.008675</td>\n",
        "      <td> 0.006645</td>\n",
        "      <td>-0.005908</td>\n",
        "      <td> 0.014241</td>\n",
        "      <td> 0.005454</td>\n",
        "      <td> 0.003329</td>\n",
        "      <td>-0.015968</td>\n",
        "      <td>-0.000986</td>\n",
        "      <td> 0.008287</td>\n",
        "      <td>-0.008875</td>\n",
        "      <td>-0.002407</td>\n",
        "      <td> 0.001502</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SIEGY</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.012527</td>\n",
        "      <td> 0.014653</td>\n",
        "      <td> 0.014548</td>\n",
        "      <td>-0.004600</td>\n",
        "      <td> 0.004551</td>\n",
        "      <td> 0.005068</td>\n",
        "      <td>-0.001160</td>\n",
        "      <td> 0.014180</td>\n",
        "      <td>-0.008505</td>\n",
        "      <td> 0.016414</td>\n",
        "      <td> 0.007543</td>\n",
        "      <td>-0.000786</td>\n",
        "      <td>-0.015761</td>\n",
        "      <td>-0.002001</td>\n",
        "      <td> 0.008176</td>\n",
        "      <td> 0.011301</td>\n",
        "      <td> 0.000680</td>\n",
        "      <td>-0.000131</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNE</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.029412</td>\n",
        "      <td> 0.005369</td>\n",
        "      <td> 0.019070</td>\n",
        "      <td> 0.007033</td>\n",
        "      <td> 0.016146</td>\n",
        "      <td> 0.006522</td>\n",
        "      <td>-0.002651</td>\n",
        "      <td>-0.000707</td>\n",
        "      <td> 0.007548</td>\n",
        "      <td>-0.005826</td>\n",
        "      <td>-0.001414</td>\n",
        "      <td> 0.002293</td>\n",
        "      <td> 0.010991</td>\n",
        "      <td> 0.013425</td>\n",
        "      <td> 0.020566</td>\n",
        "      <td>-0.006789</td>\n",
        "      <td>-0.022029</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TOSYY</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.012976</td>\n",
        "      <td>-0.004764</td>\n",
        "      <td>-0.001632</td>\n",
        "      <td> 0.010191</td>\n",
        "      <td> 0.012761</td>\n",
        "      <td>-0.008040</td>\n",
        "      <td> 0.007245</td>\n",
        "      <td>-0.008420</td>\n",
        "      <td> 0.001731</td>\n",
        "      <td>-0.002230</td>\n",
        "      <td>-0.015474</td>\n",
        "      <td> 0.001005</td>\n",
        "      <td>-0.005942</td>\n",
        "      <td> 0.013593</td>\n",
        "      <td>-0.010841</td>\n",
        "      <td>-0.004177</td>\n",
        "      <td> 0.005038</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TRI</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.004010</td>\n",
        "      <td>-0.004197</td>\n",
        "      <td> 0.009253</td>\n",
        "      <td> 0.001130</td>\n",
        "      <td> 0.005721</td>\n",
        "      <td>-0.004335</td>\n",
        "      <td>-0.000177</td>\n",
        "      <td> 0.001681</td>\n",
        "      <td>-0.000531</td>\n",
        "      <td>-0.001064</td>\n",
        "      <td> 0.001328</td>\n",
        "      <td> 0.006857</td>\n",
        "      <td> 0.000352</td>\n",
        "      <td>-0.002025</td>\n",
        "      <td> 0.000968</td>\n",
        "      <td> 0.003943</td>\n",
        "      <td> 0.003232</td>\n",
        "      <td>-0.003242</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TWX</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.008865</td>\n",
        "      <td> 0.006782</td>\n",
        "      <td> 0.026300</td>\n",
        "      <td> 0.007408</td>\n",
        "      <td> 0.005309</td>\n",
        "      <td> 0.001090</td>\n",
        "      <td> 0.000651</td>\n",
        "      <td>-0.002313</td>\n",
        "      <td> 0.002966</td>\n",
        "      <td> 0.002401</td>\n",
        "      <td>-0.002507</td>\n",
        "      <td> 0.002647</td>\n",
        "      <td>-0.000390</td>\n",
        "      <td> 0.000866</td>\n",
        "      <td> 0.003151</td>\n",
        "      <td> 0.005495</td>\n",
        "      <td>-0.004095</td>\n",
        "      <td>-0.006769</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TXN</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001002</td>\n",
        "      <td> 0.010276</td>\n",
        "      <td> 0.003460</td>\n",
        "      <td> 0.004779</td>\n",
        "      <td> 0.002664</td>\n",
        "      <td> 0.002796</td>\n",
        "      <td> 0.003344</td>\n",
        "      <td> 0.004577</td>\n",
        "      <td> 0.000901</td>\n",
        "      <td>-0.008384</td>\n",
        "      <td>-0.004774</td>\n",
        "      <td> 0.002800</td>\n",
        "      <td> 0.005708</td>\n",
        "      <td> 0.005124</td>\n",
        "      <td>-0.002917</td>\n",
        "      <td> 0.004425</td>\n",
        "      <td> 0.002345</td>\n",
        "      <td> 0.002820</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>VIAB</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005008</td>\n",
        "      <td>-0.000787</td>\n",
        "      <td> 0.002314</td>\n",
        "      <td> 0.007547</td>\n",
        "      <td> 0.009547</td>\n",
        "      <td>-0.006172</td>\n",
        "      <td>-0.002993</td>\n",
        "      <td>-0.004944</td>\n",
        "      <td>-0.001899</td>\n",
        "      <td>-0.000950</td>\n",
        "      <td>-0.002112</td>\n",
        "      <td> 0.001860</td>\n",
        "      <td> 0.001980</td>\n",
        "      <td> 0.000948</td>\n",
        "      <td> 0.007079</td>\n",
        "      <td>-0.002872</td>\n",
        "      <td>-0.000739</td>\n",
        "      <td>-0.000041</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>VMW</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.007897</td>\n",
        "      <td> 0.004582</td>\n",
        "      <td> 0.009869</td>\n",
        "      <td> 0.005238</td>\n",
        "      <td> 0.015535</td>\n",
        "      <td> 0.000745</td>\n",
        "      <td> 0.003810</td>\n",
        "      <td>-0.003434</td>\n",
        "      <td>-0.003283</td>\n",
        "      <td>-0.014676</td>\n",
        "      <td>-0.028492</td>\n",
        "      <td>-0.005011</td>\n",
        "      <td>-0.001810</td>\n",
        "      <td> 0.008902</td>\n",
        "      <td> 0.002936</td>\n",
        "      <td>-0.002571</td>\n",
        "      <td>-0.017769</td>\n",
        "      <td>-0.010790</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>XOM</th>\n",
        "      <td> 0</td>\n",
        "      <td>-0.005729</td>\n",
        "      <td> 0.006333</td>\n",
        "      <td> 0.000303</td>\n",
        "      <td>-0.001247</td>\n",
        "      <td> 0.005199</td>\n",
        "      <td> 0.002409</td>\n",
        "      <td>-0.000770</td>\n",
        "      <td>-0.000100</td>\n",
        "      <td>-0.010319</td>\n",
        "      <td> 0.001992</td>\n",
        "      <td> 0.009432</td>\n",
        "      <td>-0.001306</td>\n",
        "      <td>-0.000872</td>\n",
        "      <td>-0.001578</td>\n",
        "      <td>-0.006624</td>\n",
        "      <td> 0.005612</td>\n",
        "      <td>-0.006018</td>\n",
        "      <td> 0.003000</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>XRX</th>\n",
        "      <td> 0</td>\n",
        "      <td> 0.005475</td>\n",
        "      <td> 0.010589</td>\n",
        "      <td>-0.002964</td>\n",
        "      <td>-0.007214</td>\n",
        "      <td> 0.005935</td>\n",
        "      <td>-0.002976</td>\n",
        "      <td> 0.006162</td>\n",
        "      <td> 0.003684</td>\n",
        "      <td> 0.001471</td>\n",
        "      <td> 0.002202</td>\n",
        "      <td> 0.003657</td>\n",
        "      <td> 0.002189</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.007005</td>\n",
        "      <td> 0.003850</td>\n",
        "      <td> 0.009061</td>\n",
        "      <td>-0.013778</td>\n",
        "      <td>-0.013971</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>50 rows \u00d7 20 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "n_date  735456    735457    735458    735459    735460    735463    735464  \\\n",
        "symbol                                                                       \n",
        "AAPL         0  0.005373  0.007091  0.004625  0.003959  0.011737  0.013376   \n",
        "ADBE         0 -0.002185  0.010283  0.007582  0.004037  0.009363  0.012268   \n",
        "ADSK         0  0.004898  0.017504  0.013646 -0.033605 -0.015570  0.004711   \n",
        "AMZN         0  0.002078  0.026988  0.009929  0.002949  0.009095 -0.002142   \n",
        "BBRY         0 -0.006726 -0.002840  0.009494  0.010783  0.014736  0.009504   \n",
        "BKS          0 -0.004016  0.001040  0.009566 -0.002360  0.014967  0.008071   \n",
        "BP           0 -0.001125 -0.005802  0.005278 -0.000775  0.009557  0.007821   \n",
        "CAJ          0 -0.002323  0.003221  0.004908  0.000501 -0.000501  0.001900   \n",
        "CAT          0  0.001084  0.004349  0.005242  0.002017  0.009583  0.004691   \n",
        "CBS          0 -0.010617 -0.002817  0.003984  0.007462  0.008284  0.004180   \n",
        "CMCSA        0  0.000310  0.005419  0.002886  0.004888  0.003895 -0.003114   \n",
        "CRM          0 -0.029861 -0.004338  0.006434  0.003858  0.013566  0.010267   \n",
        "CSC          0 -0.003303  0.002547  0.006370  0.002154  0.010804  0.004196   \n",
        "CSCO         0 -0.002519 -0.000265 -0.021680 -0.005998  0.004883  0.002841   \n",
        "CTXS         0 -0.004566  0.005074  0.001158 -0.001741  0.009672 -0.001151   \n",
        "CVX          0 -0.008605  0.005390 -0.003951  0.001565 -0.000132  0.008799   \n",
        "DIS          0 -0.003517  0.005172  0.008895  0.007890  0.009188  0.000370   \n",
        "EBAY         0 -0.009406 -0.012296  0.001951 -0.001892  0.003080  0.009711   \n",
        "EMC          0  0.000455  0.005656  0.006630  0.001683  0.007350 -0.001897   \n",
        "FB           0 -0.009296  0.012168  0.005846 -0.004926  0.008913  0.009362   \n",
        "FJTSY        0 -0.015135  0.022376 -0.003667 -0.000367  0.000000 -0.007576   \n",
        "FOXA         0  0.010004  0.008595  0.009264  0.007339  0.003148 -0.003998   \n",
        "GE           0 -0.005840  0.005036  0.001418 -0.005705  0.011155  0.003450   \n",
        "GOOG         0 -0.008795  0.015167  0.004584 -0.000029  0.011069  0.008833   \n",
        "HPQ          0 -0.005128  0.004443  0.003673 -0.004162  0.003487  0.004130   \n",
        "HTHIY        0  0.001694  0.018982 -0.004305 -0.004058  0.007181  0.003708   \n",
        "IBM          0  0.001297  0.001703 -0.001457  0.000124  0.006844  0.004827   \n",
        "INTC         0 -0.001112  0.024832  0.004219  0.001763  0.007486  0.002231   \n",
        "LXK          0  0.007975  0.008664  0.010324 -0.007590  0.000110  0.003788   \n",
        "MSFT         0  0.003428  0.012751  0.006959  0.010214  0.006049  0.010608   \n",
        "N            0 -0.003774 -0.002940 -0.001937 -0.004743  0.010588  0.011145   \n",
        "NFLX         0 -0.011230  0.007208  0.001123  0.011389  0.019374  0.003090   \n",
        "NVDA         0 -0.004594  0.007043 -0.006938  0.004801  0.011358  0.012215   \n",
        "NWSA         0  0.000192 -0.021825  0.017388  0.000386 -0.000386 -0.005830   \n",
        "NYT          0 -0.018408 -0.003259 -0.001088 -0.001089  0.012100 -0.005135   \n",
        "ORCL         0 -0.001671  0.007382  0.000332  0.002069  0.007881  0.014721   \n",
        "PCRFY        0 -0.001397  0.016763  0.006823 -0.001093  0.005975  0.000814   \n",
        "RAX          0 -0.036414 -0.004699  0.033312  0.009003  0.017585  0.035446   \n",
        "RHT          0  0.000507  0.007825  0.002064  0.001281  0.014115  0.021549   \n",
        "SAP          0 -0.006488 -0.001993  0.002852 -0.007576  0.008291  0.009113   \n",
        "SIEGY        0 -0.012527  0.014653  0.014548 -0.004600  0.004551  0.005068   \n",
        "SNE          0  0.000000  0.029412  0.005369  0.019070  0.007033  0.016146   \n",
        "TOSYY        0  0.000000  0.012976 -0.004764 -0.001632  0.010191  0.012761   \n",
        "TRI          0  0.004010 -0.004197  0.009253  0.001130  0.005721 -0.004335   \n",
        "TWX          0 -0.008865  0.006782  0.026300  0.007408  0.005309  0.001090   \n",
        "TXN          0  0.001002  0.010276  0.003460  0.004779  0.002664  0.002796   \n",
        "VIAB         0 -0.005008 -0.000787  0.002314  0.007547  0.009547 -0.006172   \n",
        "VMW          0 -0.007897  0.004582  0.009869  0.005238  0.015535  0.000745   \n",
        "XOM          0 -0.005729  0.006333  0.000303 -0.001247  0.005199  0.002409   \n",
        "XRX          0  0.005475  0.010589 -0.002964 -0.007214  0.005935 -0.002976   \n",
        "\n",
        "n_date    735465    735466    735467    735470    735471    735472    735473  \\\n",
        "symbol                                                                         \n",
        "AAPL    0.003581  0.000066  0.004456  0.006590 -0.005738  0.007040  0.003881   \n",
        "ADBE   -0.006643  0.003217  0.006578  0.000648 -0.003437 -0.001256 -0.004954   \n",
        "ADSK   -0.004108  0.003350 -0.002924 -0.001059  0.010964 -0.002099 -0.002661   \n",
        "AMZN    0.002989 -0.005231 -0.005269  0.007005  0.016890  0.010134 -0.010366   \n",
        "BBRY    0.027402 -0.013043 -0.004030  0.007333  0.003322  0.018265  0.001628   \n",
        "BKS    -0.018048 -0.007093  0.002947 -0.008769  0.013200  0.027528  0.008626   \n",
        "BP     -0.000138  0.001797 -0.001661  0.006054 -0.004214  0.002206 -0.007850   \n",
        "CAJ    -0.008164 -0.000403 -0.007006 -0.000102  0.003441 -0.002638  0.000608   \n",
        "CAT     0.003653  0.003178 -0.007492  0.007283  0.002002  0.002672 -0.000861   \n",
        "CBS    -0.002702 -0.001215  0.002478  0.004387 -0.009521  0.002485 -0.009082   \n",
        "CMCSA  -0.000366 -0.000733 -0.005716  0.005441  0.000367  0.001099 -0.002202   \n",
        "CRM     0.005979  0.006361  0.066805 -0.006311  0.002361  0.009025 -0.025011   \n",
        "CSC     0.002791  0.006711 -0.004814 -0.001434  0.000394  0.004502 -0.004918   \n",
        "CSCO    0.000676  0.007246 -0.003501 -0.000404  0.002419 -0.000538  0.000269   \n",
        "CTXS   -0.000288  0.004345  0.006168 -0.003571 -0.000286 -0.002100 -0.004555   \n",
        "CVX    -0.000026  0.003705 -0.005799  0.005766  0.004183  0.001945  0.000648   \n",
        "DIS    -0.001706  0.004910  0.002284  0.001140 -0.004397  0.000480 -0.000296   \n",
        "EBAY   -0.001871  0.036590  0.001560  0.001737  0.006958  0.001484 -0.012566   \n",
        "EMC    -0.007079  0.002018 -0.004166  0.002919 -0.003832 -0.003393 -0.003176   \n",
        "FB     -0.003428 -0.001963 -0.005833  0.009863  0.007014 -0.009351 -0.011530   \n",
        "FJTSY  -0.008573  0.002510  0.000000  0.000000  0.000000 -0.022526 -0.021060   \n",
        "FOXA   -0.002143 -0.002522  0.000560 -0.001777 -0.001123  0.003359 -0.001776   \n",
        "GE      0.006475  0.005178 -0.006995 -0.001528 -0.002682  0.000638 -0.003715   \n",
        "GOOG   -0.002532 -0.002716 -0.000326 -0.002431 -0.004585 -0.009683 -0.005873   \n",
        "HPQ    -0.007852  0.036108  0.008050  0.006559  0.015742  0.007287 -0.001935   \n",
        "HTHIY  -0.015190 -0.000753 -0.007772  0.012744 -0.001325  0.000618 -0.003943   \n",
        "IBM     0.000105  0.007076 -0.002131  0.001917  0.006684  0.000692 -0.005183   \n",
        "INTC    0.000969  0.015269  0.002475 -0.004206 -0.000670 -0.001725 -0.002787   \n",
        "LXK    -0.005231 -0.002781 -0.008994 -0.001651  0.001430 -0.000267  0.001469   \n",
        "MSFT   -0.001848  0.000370  0.002874 -0.000295 -0.002216 -0.005348 -0.001190   \n",
        "N      -0.001350 -0.000040  0.012627 -0.002477  0.012155  0.013787 -0.014610   \n",
        "NFLX    0.006664  0.003807  0.009043  0.010369 -0.005572 -0.005026 -0.003569   \n",
        "NVDA   -0.002593 -0.007490 -0.002795  0.003828  0.009650 -0.002072  0.000690   \n",
        "NWSA    0.010955  0.010272 -0.001715  0.005872  0.002457 -0.000378  0.000567   \n",
        "NYT    -0.004889  0.007547  0.001615 -0.003782  0.001618  0.002958 -0.004864   \n",
        "ORCL    0.002340  0.005058  0.001203  0.005741  0.001592 -0.006087 -0.005719   \n",
        "PCRFY  -0.015431  0.001101 -0.015372  0.005006  0.003602  0.005237  0.001651   \n",
        "RAX    -0.000203  0.012440  0.021210 -0.007319  0.020159 -0.008504 -0.005109   \n",
        "RHT    -0.002640 -0.002376  0.004356 -0.005406  0.003287 -0.007984 -0.015275   \n",
        "SAP    -0.008675  0.006645 -0.005908  0.014241  0.005454  0.003329 -0.015968   \n",
        "SIEGY  -0.001160  0.014180 -0.008505  0.016414  0.007543 -0.000786 -0.015761   \n",
        "SNE     0.006522 -0.002651 -0.000707  0.007548 -0.005826 -0.001414  0.002293   \n",
        "TOSYY  -0.008040  0.007245 -0.008420  0.001731 -0.002230 -0.015474  0.001005   \n",
        "TRI    -0.000177  0.001681 -0.000531 -0.001064  0.001328  0.006857  0.000352   \n",
        "TWX     0.000651 -0.002313  0.002966  0.002401 -0.002507  0.002647 -0.000390   \n",
        "TXN     0.003344  0.004577  0.000901 -0.008384 -0.004774  0.002800  0.005708   \n",
        "VIAB   -0.002993 -0.004944 -0.001899 -0.000950 -0.002112  0.001860  0.001980   \n",
        "VMW     0.003810 -0.003434 -0.003283 -0.014676 -0.028492 -0.005011 -0.001810   \n",
        "XOM    -0.000770 -0.000100 -0.010319  0.001992  0.009432 -0.001306 -0.000872   \n",
        "XRX     0.006162  0.003684  0.001471  0.002202  0.003657  0.002189  0.000000   \n",
        "\n",
        "n_date    735474    735478    735479    735480    735481  Cluster  \n",
        "symbol                                                             \n",
        "AAPL    0.003283  0.006973 -0.030061 -0.015946  0.002258        3  \n",
        "ADBE    0.006732  0.005219  0.003177  0.003212  0.003430        3  \n",
        "ADSK   -0.000619 -0.007361  0.011897  0.007039 -0.006158        4  \n",
        "AMZN   -0.002032  0.003425 -0.002177  0.018375 -0.003090        1  \n",
        "BBRY    0.000651  0.012849  0.032027 -0.009733 -0.009189        6  \n",
        "BKS     0.001553 -0.010558  0.016419  0.002240 -0.004501        2  \n",
        "BP     -0.002996 -0.011132  0.010251 -0.041773 -0.001601        0  \n",
        "CAJ    -0.004584  0.003351  0.003541  0.006134 -0.002217        1  \n",
        "CAT     0.003523  0.003237 -0.004016  0.000919 -0.001718        6  \n",
        "CBS    -0.007353 -0.005758 -0.006992  0.011908 -0.002816        5  \n",
        "CMCSA   0.003779  0.000122  0.002674  0.003392  0.007574        3  \n",
        "CRM     0.010118  0.012062 -0.004713 -0.002418  0.003642        5  \n",
        "CSC    -0.000559  0.006930  0.002213 -0.002162  0.000166        6  \n",
        "CSCO    0.004683 -0.001474  0.006126 -0.002804  0.000667        0  \n",
        "CTXS    0.009922  0.005148  0.001368  0.000801  0.001553        5  \n",
        "CVX     0.004078 -0.009115  0.001119 -0.007733 -0.001207        0  \n",
        "DIS    -0.004043  0.008423  0.003263 -0.005233  0.002316        6  \n",
        "EBAY   -0.000722 -0.006297 -0.005785 -0.002014 -0.014046        0  \n",
        "EMC     0.003054 -0.004202  0.006880 -0.010486 -0.009782        0  \n",
        "FB      0.007288  0.019939  0.003102 -0.002189  0.007948        4  \n",
        "FJTSY   0.000679  0.016783 -0.032388  0.002945  0.000883        4  \n",
        "FOXA   -0.004979  0.008014  0.010968  0.002666 -0.004432        2  \n",
        "GE     -0.001154 -0.004121  0.001543  0.002693  0.000256        6  \n",
        "GOOG    0.000678  0.009060  0.005519  0.006450  0.004206        4  \n",
        "HPQ     0.002807 -0.001934  0.005072 -0.008555 -0.008718        0  \n",
        "HTHIY   0.007300  0.016224  0.001598 -0.011005 -0.003198        4  \n",
        "IBM     0.002135 -0.001269  0.002254 -0.005807 -0.000681        0  \n",
        "INTC    0.004401 -0.005967 -0.000096  0.005932  0.004097        3  \n",
        "LXK     0.009064 -0.008811 -0.008414 -0.004055 -0.000609        3  \n",
        "MSFT    0.009283 -0.002437 -0.005944  0.004806  0.012413        3  \n",
        "N       0.013834  0.015171  0.001545 -0.006065 -0.003195        5  \n",
        "NFLX    0.006130 -0.001608  0.008005 -0.010536 -0.002382        6  \n",
        "NVDA    0.004124  0.002229  0.009847  0.015379  0.001502        1  \n",
        "NWSA   -0.000756 -0.005705  0.003222  0.003965  0.001320        2  \n",
        "NYT    -0.001353  0.000270  0.002159  0.000540  0.000539        5  \n",
        "ORCL    0.002811  0.002404  0.005340 -0.003680 -0.005793        0  \n",
        "PCRFY   0.010887  0.007295  0.014643 -0.002134 -0.010515        1  \n",
        "RAX     0.012419  0.020992  0.031463  0.019219  0.009385        5  \n",
        "RHT     0.007824  0.004141  0.006711 -0.004622 -0.003985        4  \n",
        "SAP    -0.000986  0.008287 -0.008875 -0.002407  0.001502        4  \n",
        "SIEGY  -0.002001  0.008176  0.011301  0.000680 -0.000131        4  \n",
        "SNE     0.010991  0.013425  0.020566 -0.006789 -0.022029        1  \n",
        "TOSYY  -0.005942  0.013593 -0.010841 -0.004177  0.005038        4  \n",
        "TRI    -0.002025  0.000968  0.003943  0.003232 -0.003242        2  \n",
        "TWX     0.000866  0.003151  0.005495 -0.004095 -0.006769        6  \n",
        "TXN     0.005124 -0.002917  0.004425  0.002345  0.002820        3  \n",
        "VIAB    0.000948  0.007079 -0.002872 -0.000739 -0.000041        7  \n",
        "VMW     0.008902  0.002936 -0.002571 -0.017769 -0.010790        7  \n",
        "XOM    -0.001578 -0.006624  0.005612 -0.006018  0.003000        0  \n",
        "XRX     0.007005  0.003850  0.009061 -0.013778 -0.013971        0  \n",
        "\n",
        "[50 rows x 20 columns]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Visualize Clusters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def visualize_clusters(data_df, values=\"change_1_per\", n_clusters=8, normalize_data=False):\n",
      "    data = pivot_data(data_df, values)\n",
      "    prediction, model, c_data = cluster_data(data, n_clusters=n_clusters, normalize_data=normalize_data)\n",
      "    c_data = pd.DataFrame(c_data, index=data.index,columns=data.columns)\n",
      "    data[\"Cluster\"] = prediction\n",
      "    c_data[\"Cluster\"] = prediction\n",
      "    plt.figure\n",
      "    for cluster in np.unique(prediction):\n",
      "        plt.plot(model.cluster_centers_[cluster], \"o-\", alpha=0.5, linewidth=2)\n",
      "    plt.show()\n",
      "    for cluster in np.unique(prediction):\n",
      "        temp_cluster_data = c_data[c_data[\"Cluster\"]==cluster]\n",
      "        print \"Cluster: %s\" % cluster\n",
      "        print \"Members: %s\" % [\"%s: %s\"% (symbol, stock_dict[symbol]) for symbol in list(temp_cluster_data.index)]\n",
      "        plt.figure()\n",
      "        plt.title(\"Cluster#: %s\" % cluster)\n",
      "        plt.plot(model.cluster_centers_[cluster], \"o--\", alpha=0.5, linewidth=2)\n",
      "        for symbol in temp_cluster_data.index:\n",
      "            plt.plot(np.ravel(temp_cluster_data.loc[[symbol]].drop(\"Cluster\", 1).values),\n",
      "                     alpha=0.2, linewidth=2)\n",
      "            \n",
      "        plt.grid()\n",
      "        plt.show();\n",
      "    return prediction, model, c_data\n",
      "\n",
      "prediction, model, c_data = visualize_clusters(stocks_df, values=\"average\", n_clusters=3, normalize_data=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wVFVBfn4eBsPQDmZv7zzS05ejUMj1nr//QKd0/1yd/sc+PvDGG3k0NQ2cp/+C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\nkPsf/t+jUPGrHjEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84690e50>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 0\n",
        "Members: ['ADSK: Autodesk, Inc.', 'BP: BP plc (ADR)', 'CAJ: Canon Inc (ADR)', 'CBS: CBS Corporation', 'CSCO: Cisco Systems, Inc.', 'EMC: EMC Corporation', 'FJTSY: Fujitsu Ltd (ADR)', 'GE: General Electric Company', 'GOOG: Google', 'LXK: Lexmark International Inc', 'NYT: The New York Times Company', 'TOSYY: Toshiba Corp (USA)', 'VIAB: Viacom, Inc.', 'VMW: VMware, Inc.', 'XOM: Exxon Mobil Corporation']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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X24D3PBGvGBsThSCHD8Px4/T+8z8Ly2ULsRX98VtRprWwMY7wdaQw0E9iez2q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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84979e10>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 1\n",
        "Members: ['BBRY: BlackBerry Ltd', 'CRM: salesforce.com, inc.', 'HPQ: Hewlett-Packard Company', 'RAX: Rackspace Hosting, Inc.', 'SNE: Sony Corp (ADR)']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tVgOBsxc9E1UvBYLzZXDQvz1bScPR1lZ1tf/nHxX+4WFf/NNpv6qWpvm3hw6N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\nxsPrhfvvhx077OaN//7v9uh6/fpsPGVldnVKQwMsWTJk9Kt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3bh35+flDbnv33Xd56KGH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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84442950>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 2\n",
        "Members: ['AAPL: Apple Inc.', 'ADBE: Adobe Systems Incorporated', 'AMZN: Amazon.com, Inc.', 'BKS: Barnes & Noble, Inc.', 'CAT: Caterpillar Inc.', 'CMCSA: Comcast Corporation', 'CSC: Computer Sciences Corporation', 'CTXS: Citrix Systems, Inc.', 'CVX: Chevron Corporation', 'DIS: The Walt Disney Company', 'EBAY: eBay Inc', 'FB: Facebook, Inc.', 'FOXA: Twenty-First Century Fox Inc', 'HTHIY: Hitachi, Ltd. (ADR)', 'IBM: International Business Machines Corp. (IBM)', 'INTC: Intel Corporation', 'MSFT: Microsoft Corporation', 'N: NetSuite Inc', 'NFLX: Netflix, Inc.', 'NVDA: NVIDIA Corporation', 'NWSA: News Corp', 'ORCL: Oracle Corporation', 'PCRFY: Panasonic Corporation (ADR)', 'RHT: Red Hat Inc', 'SAP: SAP SE (ADR)', 'SIEGY: Siemens AG (ADR)', 'TRI: Thomson Reuters Corporation (USA)', 'TWX: Time Warner Inc', 'TXN: Texas Instruments Incorporated', 'XRX: Xerox Corp']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zqXAMTXNj+31rK2F7I3aTjRA+spzGNC8BBJZ1kChymBgtEz01Sd7Uab/2GkRL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xEEDUXJI8sFJOmTm3lN3psQ2uE9p1a37bD2N+/hWz6ZJ+bpexmCWpdsuE+pcg05LyH2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\nNQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c8477cd10>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Measuring Performance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def measure_error(prediction, model, c_data):\n",
      "    error_score = []\n",
      "    for counter in range(len(c_data)):\n",
      "        true_val = c_data.drop(\"Cluster\",1).values[counter]\n",
      "        center_val = model.cluster_centers_[c_data[\"Cluster\"][counter]]\n",
      "\n",
      "        error_score.append(np.average(np.abs(true_val - center_val)) / np.average(center_val))\n",
      "    \n",
      "    cluster_counts = c_data[\"Cluster\"].value_counts()\n",
      "    \n",
      "    return np.average(error_score), len(cluster_counts[cluster_counts==1])\n",
      "\n",
      "measure_error(prediction, model, c_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(0.008035569568482899, 0)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Tuning Performance using Widgets"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def visualize_clusters_widget(values=\"change_1_per\", n_clusters=8, normalize_data=False):\n",
      "    prediction, model, c_data = visualize_clusters(data_df=stocks_df, \n",
      "                                                   values=values, \n",
      "                                                   n_clusters=n_clusters, \n",
      "                                                   normalize_data=normalize_data\n",
      "                                                   )\n",
      "    print measure_error(prediction, model, c_data)\n",
      "interact(visualize_clusters_widget, \n",
      "         values=[\"change_1_per\",\"close\",\"average\",\"change_per\"],\n",
      "         n_clusters=(2,50),\n",
      "         normalize=False\n",
      "         );"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MZ2HicxH5CrVNF5+r6OLFOxtsslhgKKVuVqmkE+EnBgMDA3z00UdDL/WJSSen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bl6tZ7w8BB0ugT0+qTevw5vVQlyl669e/ficrlITU3l8ccfR6vVjjh2oiiVOiyWjUQE/Rd1yhIu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\nt07cqnIzPwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c82bc4e50>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 0\n",
        "Members: ['CRM: salesforce.com, inc.']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ugSwCUllZy+bNxzEalw7dt3nzDtauZUIWhMbTexJlhnhPojMN7U90nq2shQgX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\n9p3Ex0QPdTuN1TJ9ETqTs4w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364ZH8rlSs4gtBvH6JP7xD9j0Bwt2x9iv2BxccZwSk4JW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\nHAaSJEkSgL8ADzourl11UvoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84c9e790>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 1\n",
        "Members: ['AAPL: Apple Inc.', 'ADBE: Adobe Systems Incorporated', 'CTXS: Citrix Systems, Inc.', 'DIS: The Walt Disney Company', 'FB: Facebook, Inc.', 'FJTSY: Fujitsu Ltd (ADR)', 'GOOG: Google', 'HTHIY: Hitachi, Ltd. (ADR)', 'INTC: Intel Corporation', 'MSFT: Microsoft Corporation', 'N: NetSuite Inc', 'NFLX: Netflix, Inc.', 'NVDA: NVIDIA Corporation', 'ORCL: Oracle Corporation', 'RHT: Red Hat Inc', 'TOSYY: Toshiba Corp (USA)', 'TXN: Texas Instruments Incorporated', 'VIAB: Viacom, Inc.', 'VMW: VMware, Inc.']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ko2dLlLqrULfqqJJKJnyuCZ2itFDVzlCsn8XULCqWgVUfBVxEVUWWI5SyGVJi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\n/wPeVJ7Lk8PbaQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84505e10>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 2\n",
        "Members: ['RAX: Rackspace Hosting, Inc.']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E6phPVBRY1Qaclk9YudLzJMdrFYvVyfneRhw4XIZ7Xj43zQu3qq84eLCB3NT7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\nEEIIgP8HohoWxr+aKTYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c82c01cd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 3\n",
        "Members: ['AMZN: Amazon.com, Inc.', 'FOXA: Twenty-First Century Fox Inc', 'PCRFY: Panasonic Corporation (ADR)', 'SNE: Sony Corp (ADR)', 'TWX: Time Warner Inc']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gA5EJZTDdb9no8yHZCRKWjSVNrVHIrZKjtqv9F+IqckwHJwmyHEKNykVbWs/l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RrF70TsHxQxEyBmLs6kDGdSTtRcamguBtnZ2bDZbCgvL0daWhqys7MHtHE6nXjllVdQUFCAsrIy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\nDqRqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84cb2ed0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 4\n",
        "Members: ['ADSK: Autodesk, Inc.', 'BP: BP plc (ADR)', 'CAJ: Canon Inc (ADR)', 'CAT: Caterpillar Inc.', 'CBS: CBS Corporation', 'CMCSA: Comcast Corporation', 'CSC: Computer Sciences Corporation', 'CSCO: Cisco Systems, Inc.', 'CVX: Chevron Corporation', 'EMC: EMC Corporation', 'GE: General Electric Company', 'IBM: International Business Machines Corp. (IBM)', 'LXK: Lexmark International Inc', 'NWSA: News Corp', 'NYT: The New York Times Company', 'SAP: SAP SE (ADR)', 'SIEGY: Siemens AG (ADR)', 'TRI: Thomson Reuters Corporation (USA)', 'XOM: Exxon Mobil Corporation', 'XRX: Xerox Corp']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OsrzxJhdFBVnuwLZLWFaBQOA6+YnX8cq+Q7aB53mYptl61Jt1Ti+fxjRMxJhI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ktFFzMmEYvmkWNlDNWAzj1aYQhhSO7ePS6fPw9Klp4ToCoWy+kOcmSbZkNEE9bYvZ9jKegQqdvM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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c847a0290>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 5\n",
        "Members: ['BKS: Barnes & Noble, Inc.']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bt26loKAAgJSUFFauXDk2+P7p2Wz6fOlSB2fPpiNL62lv/RhJUtHSUsfWrdDe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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84847790>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 6\n",
        "Members: ['BBRY: BlackBerry Ltd']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5+GI/f/yjxKULCaiU/rdC9wVDsud3Ggt9tIQxCIBo+3dHIxhNA4MO9h5vpaWn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\ngwFjjDHwYMAYYwzAfwHe92wgsHVYnQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84c309d0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Cluster: 7\n",
        "Members: ['EBAY: eBay Inc', 'HPQ: Hewlett-Packard Company']\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NiygtnS8eTEoSKaZtbSKYvHjxDAqXzAZkzEAya+l19/LKO9VUvuzihvxUbtgy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\n0qQGJ4Le9csAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84655a50>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(5.4891499235401122, 4)\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Searching for the Best Number of Cluster"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max_clusters = 30\n",
      "feature = \"average\"\n",
      "clustering_data = pivot_data(stocks_df, values=feature)\n",
      "clustering_data[\"Cluster\"] = pd.Series()\n",
      "for normalize_data in [True, False]:\n",
      "    fig = plt.figure(figsize=(10,6))\n",
      "    plt.title(\"K-Means - Feature: %s Normalized: %s\" % (feature, normalize_data))\n",
      "    axes_1 = fig.add_subplot(111)\n",
      "    axes_2 = axes_1.twinx()\n",
      "    score_error_list = []\n",
      "    failed_clusters_list = []\n",
      "    \n",
      "    for n_clusters in range(2,max_clusters):\n",
      "        prediction, model, data = cluster_data(clustering_data.drop(\"Cluster\",1), n_clusters=n_clusters,\n",
      "                                               normalize_data=normalize_data)\n",
      "        data = pd.DataFrame(data, index=clustering_data.index,columns=clustering_data.drop(\"Cluster\",1).columns)\n",
      "        data[\"Cluster\"] = prediction\n",
      "        score_error, failed_clusters =  measure_error(prediction, model, data)\n",
      "        score_error_list.append(score_error)\n",
      "        failed_clusters_list.append(failed_clusters)\n",
      "    axes_1.plot(range(2,max_clusters), score_error_list, \"ro-\", label = \"Average Error\")\n",
      "    axes_2.plot(range(2,max_clusters), failed_clusters_list, \"bo-\", label = \"Failed Cluster\")\n",
      "    \n",
      "    axes_1.grid()\n",
      "    axes_1.legend(loc = \"lower center\")\n",
      "    axes_2.legend(loc = \"upper center\")\n",
      "    axes_1.set_ylabel(\"Average Error\")\n",
      "    axes_2.set_ylabel(\"Failed Cluster\")\n",
      "    axes_1.set_xlabel(\"Clusters\")\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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g7q6ne/euzJsXgp+fYcg7rej1MH48/PAD/PRTCD/8AHFx08jKMsQ1cWJXqz5M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qAAAJ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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c82b6e810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ztNk9QwwcOBDt27fHpUuX4Ovri6+//hojR47ElStX0KRJEwwcOBCbNm0yawxc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\nRERE5OQY+IiIiIic3P8DzlrR6PL7yi4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c84468a10>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### End of Lesson 4\n",
      "For questions please leave them on:\n",
      "\n",
      "- <a href=\"https://www.youtube.com/user/roshanRush\">YouTube</a>\n",
      "- <a href=\"https://twitter.com/twistedhardware\">Twitter</a>\n",
      "- <a href=\"https://plus.google.com/+Tcsaroshan/posts\">Google+</a>\n",
      "\n",
      "<a href=\"/notebooks/Lesson 3 - Regression.ipynb\">Previous Lesson - Regression</a>\n",
      "<br />\n",
      "<a href=\"/notebooks/Lesson 5 - Preprocessing.ipynb\">Next Lesson - Preprocessing</a>\n",
      "\n",
      "In the next lesson:\n",
      "- Normalization\n",
      "- Features Matrix\n",
      "- Processing Text"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/Lesson 5 - Preprocesing.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Preprocessing is the transformations to your data the happens before training your model. This includes converting your data to its basic numerical components.\n",
      "\n",
      "**Video Tutorial**:\n",
      "\n",
      "http://youtu.be/NWp6DFtnqYk"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data Set"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "My Youtube stats as of Sep 29, 2014.\n",
      "\n",
      "###Columns:\n",
      "\n",
      "- Video\n",
      "- Upload Time\tVideo length (minutes)\n",
      "- Views\tEstimated minutes watched\n",
      "- Average view duration (minutes)\n",
      "- Average percentage viewed\n",
      "- Subscriber views\n",
      "- Subscriber minutes watched\n",
      "- Likes\n",
      "- Dislikes\n",
      "- Shares\n",
      "- Comments\n",
      "- Favorites\n",
      "- Subscribers\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Core Libraries\n",
      "import os\n",
      "from fnmatch import fnmatch\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "from IPython.html import widgets\n",
      "from IPython.html.widgets import interact\n",
      "from IPython.display import display\n",
      "import urllib2\n",
      "from datetime import datetime\n",
      "\n",
      "# Feature Extraction\n",
      "from sklearn.feature_extraction import DictVectorizer\n",
      "\n",
      "# Preprocessing\n",
      "from sklearn import preprocessing\n",
      "\n",
      "# External\n",
      "from sklearn.externals import joblib\n",
      "\n",
      "# Hide Warnings\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "\n",
      "# Configure Pandas\n",
      "pd.options.display.max_columns = 100\n",
      "pd.options.display.width = 120"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Loading Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "success_alert = \"\"\"\n",
      "<div class=\"alert alert-success\" role=\"alert\">Loading data from %s was successful.</div>\n",
      "\"\"\"\n",
      "\n",
      "error_alert = \"\"\"\n",
      "<div class=\"alert alert-danger\" role=\"alert\">Error loading data from %s. %s</div>\n",
      "\"\"\"\n",
      "\n",
      "\n",
      "def load_data(widget):\n",
      "    global csv_data\n",
      "    path = wgt_file_location.value\n",
      "    try:\n",
      "        if wgt_header.value:\n",
      "            csv_data = pd.read_csv(path, sep=wgt_separator.value)\n",
      "        else:\n",
      "            csv_data = pd.read_csv(path, sep=wgt_separator.value, names=wgt_manual_header.value.split(\",\"))\n",
      "        wgt_alert.value = success_alert % path\n",
      "    except Exception as ex:\n",
      "        print ex\n",
      "        print path\n",
      "        wgt_alert.value = error_alert % (path, ex)\n",
      "    wgt_alert.visible = True\n",
      "    \n",
      "\n",
      "def preview_file(widget):\n",
      "    path = wgt_file_location.value\n",
      "    if path.startswith(\"http://\") or path.startswith(\"https://\") or path.startswith(\"ftp://\"):\n",
      "        raw_file = urllib2.urlopen(path)\n",
      "        wgt_file_preview.value = \"<pre>%s</pre>\" % raw_file.read(1000)\n",
      "        raw_file.close()\n",
      "    else:\n",
      "        raw_file = open(path)\n",
      "        wgt_file_preview.value = \"<pre>%s</pre>\" % raw_file.read(1000)\n",
      "        raw_file.close()\n",
      "    \n",
      "\n",
      "def manual_columns(name,old,new):\n",
      "    wgt_manual_header.visible = old\n",
      "    \n",
      "def update_path(name,old,new):\n",
      "    wgt_file_location.value = new\n",
      "    \n",
      "    \n",
      "def load_files(widget):\n",
      "    files_list = {}\n",
      "    root = os.curdir\n",
      "    patterns = [\"*.txt\", \"*.csv\"]\n",
      "\n",
      "    for path, subdirs, files in os.walk(root):\n",
      "        for name in files:\n",
      "            for pattern in patterns:\n",
      "                if fnmatch(name, pattern):\n",
      "                    files_list[os.path.join(path, name)] = os.path.join(path, name)\n",
      "    widget.values = files_list\n",
      "\n",
      "container = widgets.ContainerWidget()\n",
      "\n",
      "wgt_alert = widgets.HTMLWidget()\n",
      "wgt_file_location = widgets.TextWidget(description=\"Path/URL:\")\n",
      "wgt_file_path = widgets.DropdownWidget(description=\"Files List\")\n",
      "wgt_separator = widgets.TextWidget(description=\"Separator\", value=\",\")\n",
      "wgt_header = widgets.CheckboxWidget(description=\"First columns is a header?\", value=True)\n",
      "wgt_manual_header = widgets.TextWidget(description=\"Columns seperated by commas\", visible=False)\n",
      "wgt_load_data = widgets.ButtonWidget(description=\"Load Data\")\n",
      "wgt_preview_file = widgets.ButtonWidget(description=\"Preview File\")\n",
      "wgt_file_preview = widgets.HTMLWidget()\n",
      "\n",
      "wgt_alert.visible = False\n",
      "\n",
      "wgt_load_data.on_click(load_data)\n",
      "wgt_preview_file.on_click(preview_file)\n",
      "wgt_file_path.on_displayed(load_files)\n",
      "wgt_file_path.on_trait_change(update_path, \"value\")\n",
      "wgt_header.on_trait_change(manual_columns, \"value\")\n",
      "\n",
      "container.children = (wgt_alert, wgt_file_path, wgt_file_location, wgt_separator, wgt_header, wgt_manual_header,\n",
      "                      wgt_load_data, wgt_preview_file, wgt_file_preview)\n",
      "\n",
      "display(container)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Type Conversion"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "success_alert = \"\"\"\n",
      "<div class=\"alert alert-success\" role=\"alert\">Type conversion was successful.</div>\n",
      "\"\"\"\n",
      "\n",
      "error_alert = \"\"\"\n",
      "<div class=\"alert alert-danger\" role=\"alert\">Error in type conversion. %s.</div>\n",
      "\"\"\"\n",
      "\n",
      "def get_stats(column):\n",
      "    series = pd.Series(csv_data[column].values.ravel())\n",
      "    stats = \"Max:<span class='badge'>%s</span>, Min:<span class='badge'>%s</span>,\"\n",
      "    stats += \"Avg:<span class='badge'>%s</span>, Median:<span class='badge'>%s</span>\"\n",
      "    if str(series.dtype) in [\"int32\", \"int64\", \"float32\", \"float64\"]:\n",
      "        return stats % (series.max(), series.min(), series.mean(), series.median())\n",
      "    else:\n",
      "        return \"Not numerical\"\n",
      "\n",
      "\n",
      "def get_column_type(column):\n",
      "    column_type = str(pd.Series(csv_data[column].values.ravel()).dtype)\n",
      "    if column_type == \"int64\":\n",
      "        return \"Int\"\n",
      "    elif column_type == \"int32\":\n",
      "        return \"Int\"\n",
      "    elif column_type == \"float32\":\n",
      "        return \"Float\"\n",
      "    elif column_type == \"float64\":\n",
      "        return \"Float\"\n",
      "    elif column_type == \"object\":\n",
      "        return \"Object\"\n",
      "\n",
      "def process_column(column):\n",
      "    column_name = column.children[1].value\n",
      "    data_type = column.children[2].value\n",
      "    if data_type == \"Float\":\n",
      "        csv_data[column_name] = csv_data[column_name].astype(np.float64)\n",
      "    elif data_type == \"Int\":\n",
      "        csv_data[column_name] = csv_data[column_name].astype(np.int64)\n",
      "    elif data_type == \"Ordinal Date\":\n",
      "        csv_data[column_name + \"_date\"] = csv_data[column_name].apply(datetime.fromordinal)\n",
      "    elif data_type == \"Text Date\":\n",
      "        csv_data[column_name + \"_date\"] = csv_data[column_name].astype(str).apply(datetime.strptime,\n",
      "                                                                                       args=(wgt_date_format.value,))\n",
      "\n",
      "\n",
      "def process_columns(widget):\n",
      "    try:\n",
      "        for column in main_container.children:\n",
      "            if isinstance(column, widgets.ContainerWidget):\n",
      "                process_column(column)\n",
      "        \n",
      "        wgt_alert.value = success_alert\n",
      "        wgt_alert.visible = True\n",
      "    except Exception as ex:\n",
      "        wgt_alert.value = error_alert % ex\n",
      "        wgt_alert.visible = True\n",
      "\n",
      "\n",
      "main_container = widgets.ContainerWidget()\n",
      "display(main_container)\n",
      "\n",
      "columns = []\n",
      "\n",
      "wgt_alert = widgets.HTMLWidget(visible=False)\n",
      "wgt_date_format = widgets.TextWidget(description=\"Text Date Format:\" ,value=\"%Y%m%d\")\n",
      "wgt_process = widgets.ButtonWidget(description=\"Process Columns\")\n",
      "wgt_process.on_click(process_columns)\n",
      "\n",
      "main_container.children = (wgt_alert, wgt_date_format,)\n",
      "\n",
      "for column in csv_data.columns:\n",
      "    temp_container = widgets.ContainerWidget()\n",
      "    \n",
      "    main_container.children += (temp_container,)\n",
      "    \n",
      "    temp_container.remove_class('vbox')\n",
      "    temp_container.add_class('hbox')\n",
      "    temp_container.add_class('start')\n",
      "    \n",
      "    w1 = widgets.CheckboxWidget(value=True)\n",
      "    w2 = widgets.TextWidget(value=column, disabled=True)\n",
      "    w3 = widgets.DropdownWidget(values=[\"Float\", \"Int\", \"Ordinal Date\", \"Text Date\", \"Boolean\", \"Object\"],\n",
      "                                value = get_column_type(column))\n",
      "    w4 = widgets.HTMLWidget()\n",
      "    \n",
      "    w2.set_css(\"width\",\"200px\")\n",
      "    \n",
      "    stats = get_stats(column)\n",
      "    w4.value = \"<pre>%s</pre>\" % stats\n",
      "    \n",
      "    children = [w1, w2, w3, w4]\n",
      "\n",
      "    temp_container.children = children\n",
      "\n",
      "main_container.children += (wgt_process,)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Text Date Format:\n",
      "\n",
      "ref: https://docs.python.org/2/library/datetime.html\n",
      "\n",
      "<table>\n",
      "<colgroup>\n",
      "<col width=\"15%\">\n",
      "<col width=\"43%\">\n",
      "<col width=\"32%\">\n",
      "<col width=\"9%\">\n",
      "</colgroup>\n",
      "<thead valign=\"bottom\">\n",
      "<tr class=\"row-odd\"><th class=\"head\">Directive</th>\n",
      "<th class=\"head\">Meaning</th>\n",
      "<th class=\"head\">Example</th>\n",
      "<th class=\"head\">Notes</th>\n",
      "</tr>\n",
      "</thead>\n",
      "<tbody valign=\"top\">\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%a</span></tt></td>\n",
      "<td>Weekday as locale\u2019s\n",
      "abbreviated name.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">Sun, Mon, ..., Sat\n",
      "(en_US);</div>\n",
      "<div class=\"line\">So, Mo, ..., Sa\n",
      "(de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%A</span></tt></td>\n",
      "<td>Weekday as locale\u2019s full name.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">Sunday, Monday, ...,\n",
      "Saturday (en_US);</div>\n",
      "<div class=\"line\">Sonntag, Montag, ...,\n",
      "Samstag (de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%w</span></tt></td>\n",
      "<td>Weekday as a decimal number,\n",
      "where 0 is Sunday and 6 is\n",
      "Saturday.</td>\n",
      "<td>0, 1, ..., 6</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%d</span></tt></td>\n",
      "<td>Day of the month as a\n",
      "zero-padded decimal number.</td>\n",
      "<td>01, 02, ..., 31</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%b</span></tt></td>\n",
      "<td>Month as locale\u2019s abbreviated\n",
      "name.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">Jan, Feb, ..., Dec\n",
      "(en_US);</div>\n",
      "<div class=\"line\">Jan, Feb, ..., Dez\n",
      "(de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%B</span></tt></td>\n",
      "<td>Month as locale\u2019s full name.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">January, February,\n",
      "..., December (en_US);</div>\n",
      "<div class=\"line\">Januar, Februar, ...,\n",
      "Dezember (de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%m</span></tt></td>\n",
      "<td>Month as a zero-padded\n",
      "decimal number.</td>\n",
      "<td>01, 02, ..., 12</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%y</span></tt></td>\n",
      "<td>Year without century as a\n",
      "zero-padded decimal number.</td>\n",
      "<td>00, 01, ..., 99</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%Y</span></tt></td>\n",
      "<td>Year with century as a decimal\n",
      "number.</td>\n",
      "<td>1970, 1988, 2001, 2013</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%H</span></tt></td>\n",
      "<td>Hour (24-hour clock) as a\n",
      "zero-padded decimal number.</td>\n",
      "<td>00, 01, ..., 23</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%I</span></tt></td>\n",
      "<td>Hour (12-hour clock) as a\n",
      "zero-padded decimal number.</td>\n",
      "<td>01, 02, ..., 12</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%p</span></tt></td>\n",
      "<td>Locale\u2019s equivalent of either\n",
      "AM or PM.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">AM, PM (en_US);</div>\n",
      "<div class=\"line\">am, pm (de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1),\n",
      "(2)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%M</span></tt></td>\n",
      "<td>Minute as a zero-padded\n",
      "decimal number.</td>\n",
      "<td>00, 01, ..., 59</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%S</span></tt></td>\n",
      "<td>Second as a zero-padded\n",
      "decimal number.</td>\n",
      "<td>00, 01, ..., 59</td>\n",
      "<td>(3)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%f</span></tt></td>\n",
      "<td>Microsecond as a decimal\n",
      "number, zero-padded on the\n",
      "left.</td>\n",
      "<td>000000, 000001, ...,\n",
      "999999</td>\n",
      "<td>(4)</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%z</span></tt></td>\n",
      "<td>UTC offset in the form +HHMM\n",
      "or -HHMM (empty string if the\n",
      "the object is naive).</td>\n",
      "<td>(empty), +0000, -0400,\n",
      "+1030</td>\n",
      "<td>(5)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%Z</span></tt></td>\n",
      "<td>Time zone name (empty string\n",
      "if the object is naive).</td>\n",
      "<td>(empty), UTC, EST, CST</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%j</span></tt></td>\n",
      "<td>Day of the year as a\n",
      "zero-padded decimal number.</td>\n",
      "<td>001, 002, ..., 366</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%U</span></tt></td>\n",
      "<td>Week number of the year\n",
      "(Sunday as the first day of\n",
      "the week) as a zero padded\n",
      "decimal number. All days in a\n",
      "new year preceding the first\n",
      "Sunday are considered to be in\n",
      "week 0.</td>\n",
      "<td>00, 01, ..., 53</td>\n",
      "<td>(6)</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%W</span></tt></td>\n",
      "<td>Week number of the year\n",
      "(Monday as the first day of\n",
      "the week) as a decimal number.\n",
      "All days in a new year\n",
      "preceding the first Monday\n",
      "are considered to be in\n",
      "week 0.</td>\n",
      "<td>00, 01, ..., 53</td>\n",
      "<td>(6)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%c</span></tt></td>\n",
      "<td>Locale\u2019s appropriate date and\n",
      "time representation.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">Tue Aug 16 21:30:00\n",
      "1988 (en_US);</div>\n",
      "<div class=\"line\">Di 16 Aug 21:30:00\n",
      "1988 (de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%x</span></tt></td>\n",
      "<td>Locale\u2019s appropriate date\n",
      "representation.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">08/16/88 (None);</div>\n",
      "<div class=\"line\">08/16/1988 (en_US);</div>\n",
      "<div class=\"line\">16.08.1988 (de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-even\"><td><tt class=\"docutils literal\"><span class=\"pre\">%X</span></tt></td>\n",
      "<td>Locale\u2019s appropriate time\n",
      "representation.</td>\n",
      "<td><div class=\"first last line-block\">\n",
      "<div class=\"line\">21:30:00 (en_US);</div>\n",
      "<div class=\"line\">21:30:00 (de_DE)</div>\n",
      "</div>\n",
      "</td>\n",
      "<td>(1)</td>\n",
      "</tr>\n",
      "<tr class=\"row-odd\"><td><tt class=\"docutils literal\"><span class=\"pre\">%%</span></tt></td>\n",
      "<td>A literal <tt class=\"docutils literal\"><span class=\"pre\">'%'</span></tt> character.</td>\n",
      "<td>%</td>\n",
      "<td>&nbsp;</td>\n",
      "</tr>\n",
      "</tbody>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Feature Extraction"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Text"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "success_alert = \"\"\"\n",
      "<div class=\"alert alert-success\" role=\"alert\">Text features extraction was successful.</div>\n",
      "\"\"\"\n",
      "\n",
      "error_alert = \"\"\"\n",
      "<div class=\"alert alert-danger\" role=\"alert\">Error in features extraction. %s.</div>\n",
      "\"\"\"\n",
      "\n",
      "def get_map_dict(column):\n",
      "    keys = csv_data[column].unique()\n",
      "    values = xrange(len(keys))\n",
      "    return {key:value for key,value in zip(keys,values)}\n",
      "\n",
      "\n",
      "def process_text_column(column):\n",
      "    column_name = column.children[0].value\n",
      "    text_process = column.children[1].value\n",
      "    map_dict = eval(column.children[2].value)\n",
      "    if text_process == \"Map\":\n",
      "        csv_data[column_name + \"_mapped\"] = csv_data[column_name].map(map_dict)\n",
      "    elif text_process == \"Binary Vectorize\":\n",
      "        temp_dict = [{column_name: item} for item in csv_data[column_name]]\n",
      "        vec = DictVectorizer(separator=\"_is_\")\n",
      "        vec_list = vec.fit_transform(temp_dict).toarray()\n",
      "        columns = vec.get_feature_names()\n",
      "        for counter in range(len(columns)):\n",
      "            column = columns[counter]\n",
      "            values = vec_list[:,counter]\n",
      "            csv_data[column] = values\n",
      "\n",
      "\n",
      "def process_text_columns(widget):\n",
      "    try:\n",
      "        for column in main_container.children:\n",
      "            if isinstance(column, widgets.ContainerWidget):\n",
      "                process_text_column(column)\n",
      "        wgt_alert.value = success_alert\n",
      "        wgt_alert.visible = True\n",
      "    except Exception as ex:\n",
      "        wgt_alert.value = error_alert % ex\n",
      "        wgt_alert.visible = True\n",
      "\n",
      "    \n",
      "main_container = widgets.ContainerWidget()\n",
      "display(main_container)\n",
      "\n",
      "wgt_alert = widgets.HTMLWidget(visible=False)\n",
      "wgt_process = widgets.ButtonWidget(description=\"Process Columns\")\n",
      "wgt_process.on_click(process_text_columns)\n",
      "\n",
      "main_container.children = (wgt_alert,)\n",
      "\n",
      "columns = []\n",
      "\n",
      "for column in csv_data.columns:\n",
      "    if str(pd.Series(csv_data[column].values).dtype) == \"object\":\n",
      "        temp_container = widgets.ContainerWidget()\n",
      "\n",
      "        main_container.children += (temp_container,)\n",
      "\n",
      "        temp_container.remove_class('vbox')\n",
      "        temp_container.add_class('hbox')\n",
      "        temp_container.add_class('start')\n",
      "\n",
      "        w1 = widgets.TextWidget(value=column, disabled=True)\n",
      "        w2 = widgets.DropdownWidget(values=[\"Map\", \"Binary Vectorize\", \"Don't Process\"])\n",
      "        w3 = widgets.TextWidget(description=\"Dict: {'m': 0, 'f': 1}\")\n",
      "\n",
      "        w1.set_css(\"width\",\"200px\")\n",
      "\n",
      "        w3.value = str(get_map_dict(column))\n",
      "\n",
      "        children = [w1, w2, w3]\n",
      "\n",
      "        temp_container.children = children;\n",
      "\n",
      "main_container.children += (wgt_process,)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Data Preview"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_preview():\n",
      "    print \"Data Sample:\"\n",
      "    print csv_data.head(5)\n",
      "    print \".\\n\" * 3\n",
      "    print csv_data.tail(5)\n",
      "    \n",
      "print_preview()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data Sample:\n",
        "                                               Video                  Upload Time  Video length (minutes)  Views  \\\n",
        "0  1. Notebooks and Cells - IPython Notebook Tuto...       July 26, 2014 02:47 AM                    7.55    327   \n",
        "1        4. NumPy Basics - IPython Notebook Tutorial     August 07, 2014 06:04 AM                   30.72    249   \n",
        "2     Load Balancing - IPython Parallel Computing #1  September 10, 2014 01:58 AM                    8.57    244   \n",
        "3     6. IPython Widgets - IPython Notebook Tutorial     August 10, 2014 10:59 AM                   10.07    234   \n",
        "4    2. Markdown & LaTeX - IPython Notebook Tutorial       July 31, 2014 02:39 AM                   10.77    230   \n",
        "\n",
        "   Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\n",
        "0                        858                             2.62                      34.76                16   \n",
        "1                       1829                             7.34                      23.91                26   \n",
        "2                        712                             2.92                      34.06                 6   \n",
        "3                        823                             3.52                      34.93                15   \n",
        "4                        821                             3.57                      33.15                12   \n",
        "\n",
        "   Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\n",
        "0                          63      3         1       2         2          0            8 2014-07-26 02:47:00   \n",
        "1                         159      4         0       2         3          0            4 2014-08-07 06:04:00   \n",
        "2                          10      6         0       1         3          1            4 2014-09-10 01:58:00   \n",
        "3                          53      6         0       1         8          0            1 2014-08-10 10:59:00   \n",
        "4                          66      3         1       3         2          1            3 2014-07-31 02:39:00   \n",
        "\n",
        "   Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\n",
        "0             0                   0                                                  1             \n",
        "1             1                   1                                                  0             \n",
        "2             2                   2                                                  0             \n",
        "3             3                   3                                                  0             \n",
        "4             4                   4                                                  0             \n",
        "\n",
        "   Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\n",
        "0                                                  0                                                         0      \n",
        "1                                                  0                                                         0      \n",
        "2                                                  0                                                         0      \n",
        "3                                                  0                                                         0      \n",
        "4                                                  1                                                         0      \n",
        "\n",
        "   Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\n",
        "0                                                  0      \n",
        "1                                                  1      \n",
        "2                                                  0      \n",
        "3                                                  0      \n",
        "4                                                  0      \n",
        "\n",
        "   Video_is_5. Plotting Charts with Matplotlib - IPython Notebook Tutorial  \\\n",
        "0                                                  0                         \n",
        "1                                                  0                         \n",
        "2                                                  0                         \n",
        "3                                                  0                         \n",
        "4                                                  0                         \n",
        "\n",
        "   Video_is_6. IPython Widgets - IPython Notebook Tutorial  Video_is_7. Pandas - IPython Notebook Tutorial  \\\n",
        "0                                                  0                                                     0   \n",
        "1                                                  0                                                     0   \n",
        "2                                                  0                                                     0   \n",
        "3                                                  1                                                     0   \n",
        "4                                                  0                                                     0   \n",
        "\n",
        "   Video_is_8. SymPy - IPython Notebook Tutorial  Video_is_Can computers DISCRIMINATE  against race and gender?  \\\n",
        "0                                              0                                                  0               \n",
        "1                                              0                                                  0               \n",
        "2                                              0                                                  0               \n",
        "3                                              0                                                  0               \n",
        "4                                              0                                                  0               \n",
        "\n",
        "   Video_is_Container Widgets - IPython Widgets #1  Video_is_Folder Management - IPython Notebook Tips  \\\n",
        "0                                                0                                                  0    \n",
        "1                                                0                                                  0    \n",
        "2                                                0                                                  0    \n",
        "3                                                0                                                  0    \n",
        "4                                                0                                                  0    \n",
        "\n",
        "   Video_is_Handling Events - IPython Widgets #2  Video_is_How does SVM work?  \\\n",
        "0                                              0                            0   \n",
        "1                                              0                            0   \n",
        "2                                              0                            0   \n",
        "3                                              0                            0   \n",
        "4                                              0                            0   \n",
        "\n",
        "   Video_is_How does kNN (k-Nearest Neighbors) work?  \\\n",
        "0                                                  0   \n",
        "1                                                  0   \n",
        "2                                                  0   \n",
        "3                                                  0   \n",
        "4                                                  0   \n",
        "\n",
        "   Video_is_Indian Elections 2014 - IPython Notebook Tutorial (Exercise)  \\\n",
        "0                                                  0                       \n",
        "1                                                  0                       \n",
        "2                                                  0                       \n",
        "3                                                  0                       \n",
        "4                                                  0                       \n",
        "\n",
        "   Video_is_Load Balancing - IPython Parallel Computing #1  \\\n",
        "0                                                  0         \n",
        "1                                                  0         \n",
        "2                                                  1         \n",
        "3                                                  0         \n",
        "4                                                  0         \n",
        "\n",
        "   Video_is_Machine Learning 1 - Setup Development Environment  Video_is_Machine Learning 2 - Introduction to ML  \\\n",
        "0                                                  0                                                           0   \n",
        "1                                                  0                                                           0   \n",
        "2                                                  0                                                           0   \n",
        "3                                                  0                                                           0   \n",
        "4                                                  0                                                           0   \n",
        "\n",
        "   Video_is_Machine Learning 3 - Clustering  Video_is_Machine Learning 3 - Regression  \\\n",
        "0                                         0                                         0   \n",
        "1                                         0                                         0   \n",
        "2                                         0                                         0   \n",
        "3                                         0                                         0   \n",
        "4                                         0                                         0   \n",
        "\n",
        "   Video_is_Styling Widgets - IPython Widgets #3  Video_is_Web DICOM Viewer  \\\n",
        "0                                              0                          0   \n",
        "1                                              0                          0   \n",
        "2                                              0                          0   \n",
        "3                                              0                          0   \n",
        "4                                              0                          0   \n",
        "\n",
        "   Video_is_Widgets Alignment - IPython Widgets #4  \n",
        "0                                                0  \n",
        "1                                                0  \n",
        "2                                                0  \n",
        "3                                                0  \n",
        "4                                                0  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        ".\n",
        ".\n",
        ".\n",
        "\n",
        "                                                Video                  Upload Time  Video length (minutes)  Views  \\\n",
        "18               Handling Events - IPython Widgets #2  September 11, 2014 09:54 PM                    5.63     31   \n",
        "19                                   Web DICOM Viewer        May 30, 2014 11:56 PM                    1.72     28   \n",
        "20  Indian Elections 2014 - IPython Notebook Tutor...  September 06, 2014 03:08 PM                   30.58     23   \n",
        "21               Styling Widgets - IPython Widgets #3  September 13, 2014 04:26 AM                    8.25     23   \n",
        "22             Widgets Alignment - IPython Widgets #4  September 27, 2014 10:39 PM                    3.18      4   \n",
        "\n",
        "    Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\n",
        "18                         89                             2.87                      50.88                 4   \n",
        "19                         32                             1.13                      71.10                10   \n",
        "20                         90                             3.93                      12.86                 6   \n",
        "21                         87                             3.79                      45.99                 9   \n",
        "22                          5                             1.14                      35.68                 2   \n",
        "\n",
        "    Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\n",
        "18                          12      2         0       2         2          1            1 2014-09-11 21:54:00   \n",
        "19                          13      3         0       2         2          0            0 2014-05-30 23:56:00   \n",
        "20                          21      3         0       1         1          0            2 2014-09-06 15:08:00   \n",
        "21                          35      2         0       0         1          0            0 2014-09-13 04:26:00   \n",
        "22                           1      2         0       0         1          0            0 2014-09-27 22:39:00   \n",
        "\n",
        "    Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\n",
        "18            18                  18                                                  0             \n",
        "19            19                  19                                                  0             \n",
        "20            20                  20                                                  0             \n",
        "21            21                  21                                                  0             \n",
        "22            22                  22                                                  0             \n",
        "\n",
        "    Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\n",
        "18                                                  0                                                         0      \n",
        "19                                                  0                                                         0      \n",
        "20                                                  0                                                         0      \n",
        "21                                                  0                                                         0      \n",
        "22                                                  0                                                         0      \n",
        "\n",
        "    Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\n",
        "18                                                  0      \n",
        "19                                                  0      \n",
        "20                                                  0      \n",
        "21                                                  0      \n",
        "22                                                  0      \n",
        "\n",
        "    Video_is_5. Plotting Charts with Matplotlib - IPython Notebook Tutorial  \\\n",
        "18                                                  0                         \n",
        "19                                                  0                         \n",
        "20                                                  0                         \n",
        "21                                                  0                         \n",
        "22                                                  0                         \n",
        "\n",
        "    Video_is_6. IPython Widgets - IPython Notebook Tutorial  Video_is_7. Pandas - IPython Notebook Tutorial  \\\n",
        "18                                                  0                                                     0   \n",
        "19                                                  0                                                     0   \n",
        "20                                                  0                                                     0   \n",
        "21                                                  0                                                     0   \n",
        "22                                                  0                                                     0   \n",
        "\n",
        "    Video_is_8. SymPy - IPython Notebook Tutorial  Video_is_Can computers DISCRIMINATE  against race and gender?  \\\n",
        "18                                              0                                                  0               \n",
        "19                                              0                                                  0               \n",
        "20                                              0                                                  0               \n",
        "21                                              0                                                  0               \n",
        "22                                              0                                                  0               \n",
        "\n",
        "    Video_is_Container Widgets - IPython Widgets #1  Video_is_Folder Management - IPython Notebook Tips  \\\n",
        "18                                                0                                                  0    \n",
        "19                                                0                                                  0    \n",
        "20                                                0                                                  0    \n",
        "21                                                0                                                  0    \n",
        "22                                                0                                                  0    \n",
        "\n",
        "    Video_is_Handling Events - IPython Widgets #2  Video_is_How does SVM work?  \\\n",
        "18                                              1                            0   \n",
        "19                                              0                            0   \n",
        "20                                              0                            0   \n",
        "21                                              0                            0   \n",
        "22                                              0                            0   \n",
        "\n",
        "    Video_is_How does kNN (k-Nearest Neighbors) work?  \\\n",
        "18                                                  0   \n",
        "19                                                  0   \n",
        "20                                                  0   \n",
        "21                                                  0   \n",
        "22                                                  0   \n",
        "\n",
        "    Video_is_Indian Elections 2014 - IPython Notebook Tutorial (Exercise)  \\\n",
        "18                                                  0                       \n",
        "19                                                  0                       \n",
        "20                                                  1                       \n",
        "21                                                  0                       \n",
        "22                                                  0                       \n",
        "\n",
        "    Video_is_Load Balancing - IPython Parallel Computing #1  \\\n",
        "18                                                  0         \n",
        "19                                                  0         \n",
        "20                                                  0         \n",
        "21                                                  0         \n",
        "22                                                  0         \n",
        "\n",
        "    Video_is_Machine Learning 1 - Setup Development Environment  Video_is_Machine Learning 2 - Introduction to ML  \\\n",
        "18                                                  0                                                           0   \n",
        "19                                                  0                                                           0   \n",
        "20                                                  0                                                           0   \n",
        "21                                                  0                                                           0   \n",
        "22                                                  0                                                           0   \n",
        "\n",
        "    Video_is_Machine Learning 3 - Clustering  Video_is_Machine Learning 3 - Regression  \\\n",
        "18                                         0                                         0   \n",
        "19                                         0                                         0   \n",
        "20                                         0                                         0   \n",
        "21                                         0                                         0   \n",
        "22                                         0                                         0   \n",
        "\n",
        "    Video_is_Styling Widgets - IPython Widgets #3  Video_is_Web DICOM Viewer  \\\n",
        "18                                              0                          0   \n",
        "19                                              0                          1   \n",
        "20                                              0                          0   \n",
        "21                                              1                          0   \n",
        "22                                              0                          0   \n",
        "\n",
        "    Video_is_Widgets Alignment - IPython Widgets #4  \n",
        "18                                                0  \n",
        "19                                                0  \n",
        "20                                                0  \n",
        "21                                                0  \n",
        "22                                                1  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Date"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "success_alert = \"\"\"\n",
      "<div class=\"alert alert-success\" role=\"alert\">Date features extraction was successful.</div>\n",
      "\"\"\"\n",
      "\n",
      "error_alert = \"\"\"\n",
      "<div class=\"alert alert-danger\" role=\"alert\">Error in date extraction. %s.</div>\n",
      "\"\"\"\n",
      "\n",
      "def process_date_column(column):\n",
      "    column_name = column.children[0].value\n",
      "    if column.children[1].value: # Year\n",
      "        csv_data[column_name + \"_year\"] = csv_data[column_name].apply(lambda x: x.year)\n",
      "    if column.children[2].value: # Month\n",
      "        csv_data[column_name + \"_month\"] = csv_data[column_name].apply(lambda x: x.month)\n",
      "    if column.children[3].value: # Day\n",
      "        csv_data[column_name + \"_day\"] = csv_data[column_name].apply(lambda x: x.day)\n",
      "    if column.children[4].value: # Day of week\n",
      "        csv_data[column_name + \"_dayofweek\"] = csv_data[column_name].apply(lambda x: x.dayofweek)\n",
      "    if column.children[5].value: # Hour\n",
      "        csv_data[column_name + \"_hour\"] = csv_data[column_name].apply(lambda x: x.hour)\n",
      "    if column.children[6].value: # Minute\n",
      "        csv_data[column_name + \"_minute\"] = csv_data[column_name].apply(lambda x: x.minute)\n",
      "    if column.children[7].value: # Second\n",
      "        csv_data[column_name + \"_second\"] = csv_data[column_name].apply(lambda x: x.second)\n",
      "    #if column.children[8].value: # Micro Second\n",
      "    #    csv_data[column_name + \"_microsecond\"] = csv_data[column_name].apply(lambda x: datetime.microsecond)\n",
      "\n",
      "def process_date_columns(widget):\n",
      "    try:\n",
      "        for column in main_container.children:\n",
      "            if isinstance(column, widgets.ContainerWidget):\n",
      "                process_date_column(column)\n",
      "        wgt_alert.value = success_alert\n",
      "        wgt_alert.visible = True\n",
      "    except Exception as ex:\n",
      "        wgt_alert.value = error_alert % ex\n",
      "        wgt_alert.visible = True\n",
      "\n",
      "\n",
      "main_container = widgets.ContainerWidget()\n",
      "display(main_container)\n",
      "\n",
      "wgt_alert = widgets.HTMLWidget(visible=False)\n",
      "wgt_process = widgets.ButtonWidget(description=\"Process Columns\")\n",
      "wgt_process.on_click(process_date_columns)\n",
      "\n",
      "main_container.children = (wgt_alert,)\n",
      "\n",
      "columns = []\n",
      "\n",
      "for column in csv_data.columns:\n",
      "    if str(pd.Series(csv_data[column].values).dtype) == \"datetime64[ns]\":\n",
      "        temp_container = widgets.ContainerWidget()\n",
      "\n",
      "        main_container.children += (temp_container,)\n",
      "\n",
      "        temp_container.remove_class('vbox')\n",
      "        temp_container.add_class('hbox')\n",
      "        temp_container.add_class('start')\n",
      "\n",
      "        w1 = widgets.TextWidget(value=column, disabled=True)\n",
      "        w2 = widgets.CheckboxWidget(description=\"Year\", value=True)\n",
      "        w3 = widgets.CheckboxWidget(description=\"Month\", value=True)\n",
      "        w4 = widgets.CheckboxWidget(description=\"Day\", value=True)\n",
      "        w5 = widgets.CheckboxWidget(description=\"DayOfWeek\", value=True)\n",
      "        w6 = widgets.CheckboxWidget(description=\"Hour\", value=True)\n",
      "        w7 = widgets.CheckboxWidget(description=\"Minute\", value=True)\n",
      "        w8 = widgets.CheckboxWidget(description=\"Second\", value=True)\n",
      "        #w9 = widgets.CheckboxWidget(description=\"MS\", value=True)\n",
      "\n",
      "        w1.set_css(\"width\",\"200px\")\n",
      "\n",
      "        children = [w1, w2, w3, w4, w5, w6, w7, w8]\n",
      "\n",
      "        temp_container.children = children;\n",
      "\n",
      "main_container.children += (wgt_process,)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print_preview()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Data Sample:\n",
        "                                               Video                  Upload Time  Video length (minutes)  Views  \\\n",
        "0  1. Notebooks and Cells - IPython Notebook Tuto...       July 26, 2014 02:47 AM                    7.55    327   \n",
        "1        4. NumPy Basics - IPython Notebook Tutorial     August 07, 2014 06:04 AM                   30.72    249   \n",
        "2     Load Balancing - IPython Parallel Computing #1  September 10, 2014 01:58 AM                    8.57    244   \n",
        "3     6. IPython Widgets - IPython Notebook Tutorial     August 10, 2014 10:59 AM                   10.07    234   \n",
        "4    2. Markdown & LaTeX - IPython Notebook Tutorial       July 31, 2014 02:39 AM                   10.77    230   \n",
        "\n",
        "   Estimated minutes watched  Average view duration (minutes)  Average percentage viewed  Subscriber views  \\\n",
        "0                        858                             2.62                      34.76                16   \n",
        "1                       1829                             7.34                      23.91                26   \n",
        "2                        712                             2.92                      34.06                 6   \n",
        "3                        823                             3.52                      34.93                15   \n",
        "4                        821                             3.57                      33.15                12   \n",
        "\n",
        "   Subscriber minutes watched  Likes  Dislikes  Shares  Comments  Favorites  Subscribers    Upload Time_date  \\\n",
        "0                          63      3         1       2         2          0            8 2014-07-26 02:47:00   \n",
        "1                         159      4         0       2         3          0            4 2014-08-07 06:04:00   \n",
        "2                          10      6         0       1         3          1            4 2014-09-10 01:58:00   \n",
        "3                          53      6         0       1         8          0            1 2014-08-10 10:59:00   \n",
        "4                          66      3         1       3         2          1            3 2014-07-31 02:39:00   \n",
        "\n",
        "   Video_mapped  Upload Time_mapped  Video_is_1. Notebooks and Cells - IPython Notebook Tutorial  \\\n",
        "0             0                   0                                                  1             \n",
        "1             1                   1                                                  0             \n",
        "2             2                   2                                                  0             \n",
        "3             3                   3                                                  0             \n",
        "4             4                   4                                                  0             \n",
        "\n",
        "   Video_is_2. Markdown & LaTeX - IPython Notebook Tutorial  Video_is_3. Basic Python - IPython Notebook Tutorial  \\\n",
        "0                                                  0                                                         0      \n",
        "1                                                  0                                                         0      \n",
        "2                                                  0                                                         0      \n",
        "3                                                  0                                                         0      \n",
        "4                                                  1                                                         0      \n",
        "\n",
        "   Video_is_4. NumPy Basics - IPython Notebook Tutorial  \\\n",
        "0                                                  0      \n",
        "1                                                  1      \n",
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        "   Upload Time_date_dayofweek  Upload Time_date_hour  Upload Time_date_minute  \n",
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        "20  Indian Elections 2014 - IPython Notebook Tutor...  September 06, 2014 03:08 PM                   30.58     23   \n",
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        "\n",
        "    Upload Time_date_dayofweek  Upload Time_date_hour  Upload Time_date_minute  \n",
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       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Normalization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "success_alert = \"\"\"\n",
      "<div class=\"alert alert-success\" role=\"alert\">Numbers processing was successful.</div>\n",
      "\"\"\"\n",
      "\n",
      "error_alert = \"\"\"\n",
      "<div class=\"alert alert-danger\" role=\"alert\">Error in numbers processing. %s.</div>\n",
      "\"\"\"\n",
      "\n",
      "def process_number_column(column):\n",
      "    column_name = column.children[0].value\n",
      "    number_process = column.children[1].value\n",
      "    scale_min = column.children[2].value\n",
      "    scale_max = column.children[3].value\n",
      "    if number_process == \"Scale\":\n",
      "        pre_process = preprocessing.MinMaxScaler(feature_range=(scale_min, scale_max))\n",
      "        csv_data[column_name + \"_scaled\"] = pre_process.fit_transform(csv_data[[column_name]].astype(np.float64).values)\n",
      "    elif number_process == \"Standard Scaler\":\n",
      "        pre_process = preprocessing.StandardScaler()\n",
      "        csv_data[column_name + \"_standardscaler\"] = pre_process.fit_transform(csv_data[column_name].copy().astype(np.float64))\n",
      "\n",
      "def process_number_columns(widget):\n",
      "    if True: #try:\n",
      "        for column in main_container.children:\n",
      "            if isinstance(column, widgets.ContainerWidget):\n",
      "                process_number_column(column)\n",
      "        wgt_alert.value = success_alert\n",
      "        wgt_alert.visible = True\n",
      "    else: #except Exception as ex:\n",
      "        raise ex\n",
      "        wgt_alert.value = error_alert % ex\n",
      "        wgt_alert.visible = True\n",
      "\n",
      "    \n",
      "main_container = widgets.ContainerWidget()\n",
      "display(main_container)\n",
      "\n",
      "wgt_alert = widgets.HTMLWidget(visible=False)\n",
      "wgt_process = widgets.ButtonWidget(description=\"Process Columns\")\n",
      "wgt_process.on_click(process_number_columns)\n",
      "\n",
      "main_container.children = (wgt_alert,)\n",
      "\n",
      "columns = []\n",
      "\n",
      "for column in csv_data.columns:\n",
      "    data_type = str(pd.Series(csv_data[column].values).dtype)\n",
      "    if data_type in [\"float32\", \"float64\", \"int32\", \"int64\"]:\n",
      "        temp_container = widgets.ContainerWidget()\n",
      "\n",
      "        main_container.children += (temp_container,)\n",
      "\n",
      "        temp_container.remove_class('vbox')\n",
      "        temp_container.add_class('hbox')\n",
      "        temp_container.add_class('start')\n",
      "\n",
      "        w1 = widgets.TextWidget(value=column, disabled=True)\n",
      "        w2 = widgets.DropdownWidget(values=[\"Scale\", \"Standard Scaler\", \"Don't Process\"], value=\"Don't Process\")\n",
      "        w3 = widgets.FloatTextWidget(description=\"Scale Min:\", value=0)\n",
      "        w4 = widgets.FloatTextWidget(description=\"Scale Max:\", value=1)\n",
      "\n",
      "        w1.set_css(\"width\",\"200px\")\n",
      "        w3.set_css(\"width\",\"50px\")\n",
      "        w4.set_css(\"width\",\"50px\")\n",
      "\n",
      "        children = [w1, w2, w3, w4]\n",
      "\n",
      "        temp_container.children = children;\n",
      "\n",
      "main_container.children += (wgt_process,)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Visualize Preprocessing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def display_feature(feature_name):\n",
      "    plt.figure()\n",
      "    plt.scatter(list(csv_data.index), csv_data[feature_name])\n",
      "    plt.grid()\n",
      "    plt.ylabel(feature_name)\n",
      "    plt.show()\n",
      "    print \"Mean: %4f\" % np.mean(csv_data[feature_name])\n",
      "    print \"Std : %4f\" % np.std(csv_data[feature_name])\n",
      "\n",
      "wgt_column = widgets.SelectWidget(values=list(csv_data._get_numeric_data().columns))\n",
      "\n",
      "interact(display_feature, feature_name=wgt_column);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l3MD51V3q+ZU1U2NwAjAFXBMRn4+IY4HCtx0zWAP8paQXA7cDp7fpuLWybt26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\n4cbAzMxwY2BmZrgxMDMz3BiYmRnw/wFyE5UEyKXgaAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fec0ca24610>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mean: 0.000000\n",
        "Std : 1.000000\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Further Preprocessing:\n",
      "\n",
      "- Missing Values:\n",
      " - Drop Columns (Features/Attributes) \n",
      " - Drop Rows (Samples/Data Points)\n",
      " - Impute Values (Mean/Median/Mode)\n",
      "- Denoising Time Series\n",
      "- Counting Vectorizer For Long Text"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Save Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "csv_data.save(\"processed_data.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "joblib.dump(csv_data, \"processed_data.pkl\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "['processed_data.pkl',\n",
        " 'processed_data.pkl_01.npy',\n",
        " 'processed_data.pkl_02.npy',\n",
        " 'processed_data.pkl_03.npy',\n",
        " 'processed_data.pkl_04.npy',\n",
        " 'processed_data.pkl_05.npy']"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/Lesson 6 - Features Extraction.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:2f47664872cfaa34ab4161cd438a77f10d2905ddc7b40effff295fc6a1989117"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h3>This Tutorial is also available on <a href=\"https://www.youtube.com/user/roshanRush\">YouTube</a></h3>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import YouTubeVideo\n",
      "# a talk about IPython at Sage Days at U. Washington, Seattle.\n",
      "# Video credit: William Stein.\n",
      "YouTubeVideo('http://youtu.be/HuDIbSCnsqo')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "\n",
        "        <iframe\n",
        "            width=\"400\"\n",
        "            height=300\"\n",
        "            src=\"https://www.youtube.com/embed/http://youtu.be/HuDIbSCnsqo\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
        "        ></iframe>\n",
        "        "
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "<IPython.lib.display.YouTubeVideo at 0x7f41bca4e990>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extracting Features from Text"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One of the most common ways to analyze is to split it in to words and count how many times each word was used in a text. This may not sound very useful, but with large amounts of text you can build a fairly accurate classifier and this is what we will do in this tutorial."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Import Required Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import lxml\n",
      "import requests\n",
      "from sklearn.feature_extraction.text import CountVectorizer\n",
      "from sklearn.cross_validation import train_test_split\n",
      "from sklearn.naive_bayes import MultinomialNB\n",
      "import warnings\n",
      "\n",
      "warnings.filterwarnings(\"ignore\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Fetch the RSS news feed from Reuters"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url_list = [\"http://feeds.reuters.com/reuters/businessNews\",\n",
      "            \"http://feeds.reuters.com/reuters/technologyNews\",\n",
      "            \"http://feeds.reuters.com/reuters/sportsNews\",\n",
      "            ]\n",
      "documents = []\n",
      "\n",
      "for url in url_list:\n",
      "    response = requests.get(url)\n",
      "    xml_page = response.text\n",
      "    parser = lxml.etree.XMLParser(recover=True, encoding='utf-8')\n",
      "    documents.append(lxml.etree.fromstring(xml_page.encode(\"utf-8\"), parser=parser))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extracting Articles from the XML Feed"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Examining the XML Feed Format"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_tag(node):\n",
      "    print \"<%s %s>%s\" % (node.tag, \" \".join([\"%s=%s\" % (k,v)for k,v in node.attrib.iteritems()]), node.text)\n",
      "    for item in node[:25]:\n",
      "        print \"  <%s %s>%s</%s>\" % (item.tag, \" \".join([\"%s=%s\" % (k,v)for k,v in item.attrib.iteritems()]), item.text, item.tag)\n",
      "    print \"</%s>\" % node.tag"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp_node = documents[0]\n",
      "print_tag(temp_node)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<rss version=2.0>None\n",
        "  <channel >None</channel>\n",
        "</rss>\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp_node = temp_node[0]\n",
      "print_tag(temp_node)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<channel >None\n",
        "  <title >Reuters: Business News</title>\n",
        "  <link >http://www.reuters.com</link>\n",
        "  <description >Reuters.com is your source for breaking news, business, financial and investing news, including personal finance and stocks. Reuters is the leading global provider of news, financial information and technology solutions to the world's media, financial institutions, businesses and individuals.</description>\n",
        "  <language >en-us</language>\n",
        "  <copyright >All rights reserved. Users may download and print extracts of content from this website for their own personal and non-commercial use only. Republication or redistribution of Reuters content, including by framing or similar means, is expressly prohibited without the prior written consent of Reuters. Reuters and the Reuters sphere logo are registered trademarks or trademarks of the Reuters group of companies around the world. \u00a9 Reuters 2015</copyright>\n",
        "  <pubDate >Sat, 07 Feb 2015 14:34:41 GMT</pubDate>\n",
        "  <lastBuildDate >Sat, 07 Feb 2015 14:34:41 GMT</lastBuildDate>\n",
        "  <ttl >5</ttl>\n",
        "  <image >None</image>\n",
        "  <{http://www.w3.org/2005/Atom}link rel=self type=application/rss+xml href=http://feeds.reuters.com/reuters/businessNews>None</{http://www.w3.org/2005/Atom}link>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}info uri=reuters/businessnews>None</{http://rssnamespace.org/feedburner/ext/1.0}info>\n",
        "  <{http://www.w3.org/2005/Atom}link rel=hub href=http://pubsubhubbub.appspot.com/>None</{http://www.w3.org/2005/Atom}link>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://add.my.yahoo.com/rss?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://us.i1.yimg.com/us.yimg.com/i/us/my/addtomyyahoo4.gif>Subscribe with My Yahoo!</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://www.newsgator.com/ngs/subscriber/subext.aspx?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://www.newsgator.com/images/ngsub1.gif>Subscribe with NewsGator</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://feeds.my.aol.com/add.jsp?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://o.aolcdn.com/favorites.my.aol.com/webmaster/ffclient/webroot/locale/en-US/images/myAOLButtonSmall.gif>Subscribe with My AOL</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://www.bloglines.com/sub/http://feeds.reuters.com/reuters/businessNews src=http://www.bloglines.com/images/sub_modern11.gif>Subscribe with Bloglines</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://www.netvibes.com/subscribe.php?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://www.netvibes.com/img/add2netvibes.gif>Subscribe with Netvibes</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://fusion.google.com/add?feedurl=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://buttons.googlesyndication.com/fusion/add.gif>Subscribe with Google</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}feedFlare href=http://www.pageflakes.com/subscribe.aspx?url=http%3A%2F%2Ffeeds.reuters.com%2Freuters%2FbusinessNews src=http://www.pageflakes.com/ImageFile.ashx?instanceId=Static_4&fileName=ATP_blu_91x17.gif>Subscribe with Pageflakes</{http://rssnamespace.org/feedburner/ext/1.0}feedFlare>\n",
        "  <item >None</item>\n",
        "  <item >None</item>\n",
        "  <item >None</item>\n",
        "  <item >None</item>\n",
        "  <item >None</item>\n",
        "  <item >None</item>\n",
        "</channel>\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "temp_node = temp_node.xpath(\"item\")[0]\n",
      "print_tag(temp_node)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<item >None\n",
        "  <title >Exclusive: Top Fox investors seek to convert voting shares, Murdoch may benefit</title>\n",
        "  <link >http://feeds.reuters.com/~r/reuters/businessNews/~3/Ti_4toCmS6w/story01.htm</link>\n",
        "  <description >NEW YORK (Reuters) - Several top investors in Twenty-First Century Fox Inc are pressing for the right to swap their voting shares for ordinary shares, which are trading at an unusual premium, even though the move could hand even more control of the company to Rupert Murdoch, according to people familiar with the matter.<img width='1' height='1' src='http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/mf.gif' border='0'/><br clear='all'/><div class=\"feedflare\">\n",
        "<a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:yIl2AUoC8zA\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?d=yIl2AUoC8zA\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\" border=\"0\"></img></a>\n",
        "</div><img src=\"//feeds.feedburner.com/~r/reuters/businessNews/~4/Ti_4toCmS6w\" height=\"1\" width=\"1\" alt=\"\"/></description>\n",
        "  <category domain=>businessNews</category>\n",
        "  <pubDate >Sat, 07 Feb 2015 14:30:10 GMT</pubDate>\n",
        "  <guid isPermaLink=false>http://www.reuters.com/article/2015/02/07/us-fox-votingshares-exclusive-idUSKBN0LA2HP20150207?feedType=RSS&feedName=businessNews</guid>\n",
        "  <{http://rssnamespace.org/feedburner/ext/1.0}origLink >http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/l/0L0Sreuters0N0Carticle0C20A150C0A20C0A70Cus0Efox0Evotingshares0Eexclusive0EidUSKBN0ALA2HP20A150A20A70DfeedType0FRSS0GfeedName0FbusinessNews/story01.htm</{http://rssnamespace.org/feedburner/ext/1.0}origLink>\n",
        "</item>\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Processing All Feeds to a DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "title_list = []\n",
      "description_list = []\n",
      "category_list = []\n",
      "\n",
      "for xml_doc in documents:\n",
      "    articles = xml_doc.xpath(\"//item\")\n",
      "    for article in articles:\n",
      "        title_list.append(article[0].text)\n",
      "        description_list.append(article[2].text)\n",
      "        category_list.append(article[3].text)\n",
      "        \n",
      "\n",
      "news_data = pd.DataFrame(title_list, columns=[\"title\"])\n",
      "news_data[\"description\"] = description_list\n",
      "news_data[\"category\"] = category_list\n",
      "print len(news_data)\n",
      "news_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "60\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>title</th>\n",
        "      <th>description</th>\n",
        "      <th>category</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Exclusive: Top Fox investors seek to convert v...</td>\n",
        "      <td> NEW YORK (Reuters) - Several top investors in ...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> Different delivery, one message to Greece's ne...</td>\n",
        "      <td> ROME/PARIS (Reuters) - In Paris and Rome, it w...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> Isolated Greece wants no more bailout money wi...</td>\n",
        "      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> Valuations may hurt small caps, despite job gr...</td>\n",
        "      <td> NEW YORK (Reuters) - The good news from Friday...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> Shippers suspend weekend cargo loading at U.S....</td>\n",
        "      <td> LOS ANGELES (Reuters) - The loading and unload...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> Building unions return to Kentucky refinery de...</td>\n",
        "      <td> NEW YORK (Reuters) - Workers represented by th...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> Wall St. ends down on interest rate, Greece ji...</td>\n",
        "      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Strong U.S. job, wage gains open door to mid-y...</td>\n",
        "      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> Building unions return to Kentucky refinery de...</td>\n",
        "      <td> NEW YORK (Reuters) - Workers represented by th...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> JPMorgan under scrutiny over hiring of Chinese...</td>\n",
        "      <td> (Reuters) - JPMorgan Chase &amp; Co is under feder...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> Exclusive: Top Fox investors seek to convert v...</td>\n",
        "      <td> NEW YORK (Reuters) - Several top investors in ...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> Isolated Greece wants no more bailout money wi...</td>\n",
        "      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> Wall St. ends down on interest rate, Greece ji...</td>\n",
        "      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>  RadioShack would accept liquidation bids: lawyer</td>\n",
        "      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> Strong U.S. job, wage gains open door to mid-y...</td>\n",
        "      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> Wall Street firms waver over June 2015 rate hi...</td>\n",
        "      <td> NEW YORK (Reuters) - Economists at Wall Street...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> Oil climbs, Brent posts best two weeks since 1998</td>\n",
        "      <td> NEW YORK (Reuters) - Oil rallied again on Frid...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> Brazil's Petrobras taps state banker as CEO; s...</td>\n",
        "      <td> BRASILIA (Reuters) - Brazil's President Dilma ...</td>\n",
        "      <td>   businessNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> Anthem warns U.S. customers of email scam afte...</td>\n",
        "      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> U.S. court orders Symantec to pay $17 mln for ...</td>\n",
        "      <td> (Reuters) - Symantec Corp, maker of the popula...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>  RadioShack would accept liquidation bids: lawyer</td>\n",
        "      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> Anthem warns U.S. customers of email scam afte...</td>\n",
        "      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> Google can disrupt car industry but is no auto...</td>\n",
        "      <td> FRANKFURT (Reuters) - Technology companies suc...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> Chipmaker Intel promotes senior executives, du...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> Google panel backs firm on EU limit to 'right ...</td>\n",
        "      <td> BRUSSELS (Reuters) - A panel of experts appoin...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> Family money puts its faith in European techno...</td>\n",
        "      <td> LONDON (Reuters) - Some of Europe's wealthiest...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> U.S. states probe massive data breach at healt...</td>\n",
        "      <td> NEW YORK (Reuters) - Several U.S. states are i...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> Health insurer Anthem hit by massive cybersecu...</td>\n",
        "      <td> (Reuters) - Health insurer Anthem Inc , which ...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>        China to have most robots in world by 2017</td>\n",
        "      <td> FRANKFURT (Reuters) - China will have more rob...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> U.S. businesses ask White House to help on Chi...</td>\n",
        "      <td> WASHINGTON (Reuters) - U.S. business lobbies c...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> Sharp says not considering sale of any oversea...</td>\n",
        "      <td> TOKYO (Reuters) - Japanese electronics maker S...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> RadioShack files for bankruptcy; Sprint to tak...</td>\n",
        "      <td> (Reuters) - Electronics retailer RadioShack Co...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> SEC probes Blackberry options trading ahead of...</td>\n",
        "      <td> NEW YORK (Reuters) - The U.S. Securities and E...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td> Twitter tops Wall Street revenue target, user ...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Twitter Inc said on ...</td>\n",
        "      <td> technologyNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> U.S. Golf Association to launch Senior Women's...</td>\n",
        "      <td> NEW YORK (Reuters) - The United States Golf As...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> In-form Wiesberger goes two shots clear in Kua...</td>\n",
        "      <td> KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> Jansrud signals downhill ambition in final tra...</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - Unheralded ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> NFL, players union spar in court over Peterson...</td>\n",
        "      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> English takes charge as Mickelson exits Torrey...</td>\n",
        "      <td> LA JOLLA, California (Reuters) - Local favorit...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>        No pressure for double medallist Fenninger</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - A Super-G v...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> Referees association backs rookie in flare-up ...</td>\n",
        "      <td> (Reuters) - The National Basketball Referees A...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> Rangers goalie Lundqvist out for three weeks w...</td>\n",
        "      <td> (Reuters) - New York Rangers goalie Henrik Lun...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>         Miracle now needed to win gold, says Vonn</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - Denied gold...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> NFL and players union spar in court over Peter...</td>\n",
        "      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>           No sharing world downhill gold for Maze</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - Tina Maze h...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>   Doping should be criminal offense: Russia chief</td>\n",
        "      <td> MOSCOW (Reuters) - Russia's Athletics Federati...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> Kansas police drop drug charges against Dallas...</td>\n",
        "      <td> DALLAS, Texas (Reuters) - Authorities have dro...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>      Thompson leads by one stroke at Torrey Pines</td>\n",
        "      <td> LA JOLLA, California (Reuters - American Nicho...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> Borzakovskiy takes over as Russia's caretaker ...</td>\n",
        "      <td> MOSCOW (Reuters) - Former Olympic 800 meters c...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>   Clubs need long-term mental health plan: FIFPro</td>\n",
        "      <td> (Reuters) - Soccer clubs need to take a longer...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> European Games will convince skeptics, attract...</td>\n",
        "      <td> (Reuters) - The inaugural European Games this ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td> Departing VFLA President to continue fight aga...</td>\n",
        "      <td> MOSCOW (Reuters) - Russian Athletics Federatio...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> Canizares joins champion Westwood at the top i...</td>\n",
        "      <td> KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td> Force India opposed Marussia's bid to use old car</td>\n",
        "      <td> LONDON (Reuters) - Marussia's hopes of rising ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
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        "                                                title  \\\n",
        "0   Exclusive: Top Fox investors seek to convert v...   \n",
        "1   Different delivery, one message to Greece's ne...   \n",
        "2   Isolated Greece wants no more bailout money wi...   \n",
        "3   Valuations may hurt small caps, despite job gr...   \n",
        "4   Shippers suspend weekend cargo loading at U.S....   \n",
        "5   Building unions return to Kentucky refinery de...   \n",
        "6         Motorola exploring possible sale: Bloomberg   \n",
        "7   Wall St. ends down on interest rate, Greece ji...   \n",
        "8   Strong U.S. job, wage gains open door to mid-y...   \n",
        "9   Building unions return to Kentucky refinery de...   \n",
        "10        Motorola exploring possible sale: Bloomberg   \n",
        "11  JPMorgan under scrutiny over hiring of Chinese...   \n",
        "12  Exclusive: Top Fox investors seek to convert v...   \n",
        "13  Isolated Greece wants no more bailout money wi...   \n",
        "14  Wall St. ends down on interest rate, Greece ji...   \n",
        "15   RadioShack would accept liquidation bids: lawyer   \n",
        "16  Strong U.S. job, wage gains open door to mid-y...   \n",
        "17  Wall Street firms waver over June 2015 rate hi...   \n",
        "18  Oil climbs, Brent posts best two weeks since 1998   \n",
        "19  Brazil's Petrobras taps state banker as CEO; s...   \n",
        "20  Feds can't indefinitely gag Yahoo about subpoe...   \n",
        "21        Motorola exploring possible sale: Bloomberg   \n",
        "22  Anthem warns U.S. customers of email scam afte...   \n",
        "23  U.S. court orders Symantec to pay $17 mln for ...   \n",
        "24        Motorola exploring possible sale: Bloomberg   \n",
        "25   RadioShack would accept liquidation bids: lawyer   \n",
        "26  Anthem warns U.S. customers of email scam afte...   \n",
        "27  Google can disrupt car industry but is no auto...   \n",
        "28  Chipmaker Intel promotes senior executives, du...   \n",
        "29  Feds can't indefinitely gag Yahoo about subpoe...   \n",
        "30  Google panel backs firm on EU limit to 'right ...   \n",
        "31  Family money puts its faith in European techno...   \n",
        "32  U.S. states probe massive data breach at healt...   \n",
        "33  Health insurer Anthem hit by massive cybersecu...   \n",
        "34         China to have most robots in world by 2017   \n",
        "35  U.S. businesses ask White House to help on Chi...   \n",
        "36  Sharp says not considering sale of any oversea...   \n",
        "37  RadioShack files for bankruptcy; Sprint to tak...   \n",
        "38  SEC probes Blackberry options trading ahead of...   \n",
        "39  Twitter tops Wall Street revenue target, user ...   \n",
        "40  U.S. Golf Association to launch Senior Women's...   \n",
        "41  In-form Wiesberger goes two shots clear in Kua...   \n",
        "42  Jansrud signals downhill ambition in final tra...   \n",
        "43  NFL, players union spar in court over Peterson...   \n",
        "44  English takes charge as Mickelson exits Torrey...   \n",
        "45         No pressure for double medallist Fenninger   \n",
        "46  Referees association backs rookie in flare-up ...   \n",
        "47  Rangers goalie Lundqvist out for three weeks w...   \n",
        "48          Miracle now needed to win gold, says Vonn   \n",
        "49  NFL and players union spar in court over Peter...   \n",
        "50            No sharing world downhill gold for Maze   \n",
        "51    Doping should be criminal offense: Russia chief   \n",
        "52  Kansas police drop drug charges against Dallas...   \n",
        "53       Thompson leads by one stroke at Torrey Pines   \n",
        "54  Borzakovskiy takes over as Russia's caretaker ...   \n",
        "55    Clubs need long-term mental health plan: FIFPro   \n",
        "56  European Games will convince skeptics, attract...   \n",
        "57  Departing VFLA President to continue fight aga...   \n",
        "58  Canizares joins champion Westwood at the top i...   \n",
        "59  Force India opposed Marussia's bid to use old car   \n",
        "\n",
        "                                          description        category  \n",
        "0   NEW YORK (Reuters) - Several top investors in ...    businessNews  \n",
        "1   ROME/PARIS (Reuters) - In Paris and Rome, it w...    businessNews  \n",
        "2   ATHENS/BRUSSELS (Reuters) - Greece's new lefti...    businessNews  \n",
        "3   NEW YORK (Reuters) - The good news from Friday...    businessNews  \n",
        "4   LOS ANGELES (Reuters) - The loading and unload...    businessNews  \n",
        "5   NEW YORK (Reuters) - Workers represented by th...    businessNews  \n",
        "6   (Reuters) - Walkie-talkie and radio systems ma...    businessNews  \n",
        "7   NEW YORK (Reuters) - Wall Street stocks fell o...    businessNews  \n",
        "8   WASHINGTON (Reuters) - U.S. job growth rose so...    businessNews  \n",
        "9   NEW YORK (Reuters) - Workers represented by th...    businessNews  \n",
        "10  (Reuters) - Walkie-talkie and radio systems ma...    businessNews  \n",
        "11  (Reuters) - JPMorgan Chase & Co is under feder...    businessNews  \n",
        "12  NEW YORK (Reuters) - Several top investors in ...    businessNews  \n",
        "13  ATHENS/BRUSSELS (Reuters) - Greece's new lefti...    businessNews  \n",
        "14  NEW YORK (Reuters) - Wall Street stocks fell o...    businessNews  \n",
        "15  (Reuters) - A lawyer for RadioShack on Friday ...    businessNews  \n",
        "16  WASHINGTON (Reuters) - U.S. job growth rose so...    businessNews  \n",
        "17  NEW YORK (Reuters) - Economists at Wall Street...    businessNews  \n",
        "18  NEW YORK (Reuters) - Oil rallied again on Frid...    businessNews  \n",
        "19  BRASILIA (Reuters) - Brazil's President Dilma ...    businessNews  \n",
        "20  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews  \n",
        "21  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews  \n",
        "22  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews  \n",
        "23  (Reuters) - Symantec Corp, maker of the popula...  technologyNews  \n",
        "24  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews  \n",
        "25  (Reuters) - A lawyer for RadioShack on Friday ...  technologyNews  \n",
        "26  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews  \n",
        "27  FRANKFURT (Reuters) - Technology companies suc...  technologyNews  \n",
        "28  SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...  technologyNews  \n",
        "29  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews  \n",
        "30  BRUSSELS (Reuters) - A panel of experts appoin...  technologyNews  \n",
        "31  LONDON (Reuters) - Some of Europe's wealthiest...  technologyNews  \n",
        "32  NEW YORK (Reuters) - Several U.S. states are i...  technologyNews  \n",
        "33  (Reuters) - Health insurer Anthem Inc , which ...  technologyNews  \n",
        "34  FRANKFURT (Reuters) - China will have more rob...  technologyNews  \n",
        "35  WASHINGTON (Reuters) - U.S. business lobbies c...  technologyNews  \n",
        "36  TOKYO (Reuters) - Japanese electronics maker S...  technologyNews  \n",
        "37  (Reuters) - Electronics retailer RadioShack Co...  technologyNews  \n",
        "38  NEW YORK (Reuters) - The U.S. Securities and E...  technologyNews  \n",
        "39  SAN FRANCISCO (Reuters) - Twitter Inc said on ...  technologyNews  \n",
        "40  NEW YORK (Reuters) - The United States Golf As...      sportsNews  \n",
        "41  KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...      sportsNews  \n",
        "42  BEAVER CREEK, Colorado (Reuters) - Unheralded ...      sportsNews  \n",
        "43  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews  \n",
        "44  LA JOLLA, California (Reuters) - Local favorit...      sportsNews  \n",
        "45  BEAVER CREEK, Colorado (Reuters) - A Super-G v...      sportsNews  \n",
        "46  (Reuters) - The National Basketball Referees A...      sportsNews  \n",
        "47  (Reuters) - New York Rangers goalie Henrik Lun...      sportsNews  \n",
        "48  BEAVER CREEK, Colorado (Reuters) - Denied gold...      sportsNews  \n",
        "49  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews  \n",
        "50  BEAVER CREEK, Colorado (Reuters) - Tina Maze h...      sportsNews  \n",
        "51  MOSCOW (Reuters) - Russia's Athletics Federati...      sportsNews  \n",
        "52  DALLAS, Texas (Reuters) - Authorities have dro...      sportsNews  \n",
        "53  LA JOLLA, California (Reuters - American Nicho...      sportsNews  \n",
        "54  MOSCOW (Reuters) - Former Olympic 800 meters c...      sportsNews  \n",
        "55  (Reuters) - Soccer clubs need to take a longer...      sportsNews  \n",
        "56  (Reuters) - The inaugural European Games this ...      sportsNews  \n",
        "57  MOSCOW (Reuters) - Russian Athletics Federatio...      sportsNews  \n",
        "58  KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...      sportsNews  \n",
        "59  LONDON (Reuters) - Marussia's hopes of rising ...      sportsNews  "
       ]
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     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Clean up data"
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    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "news_data[\"description\"].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0    NEW YORK (Reuters) - Several top investors in ...\n",
        "1    ROME/PARIS (Reuters) - In Paris and Rome, it w...\n",
        "2    ATHENS/BRUSSELS (Reuters) - Greece's new lefti...\n",
        "3    NEW YORK (Reuters) - The good news from Friday...\n",
        "4    LOS ANGELES (Reuters) - The loading and unload...\n",
        "Name: description, dtype: object"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print news_data[\"description\"][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "NEW YORK (Reuters) - Several top investors in Twenty-First Century Fox Inc are pressing for the right to swap their voting shares for ordinary shares, which are trading at an unusual premium, even though the move could hand even more control of the company to Rupert Murdoch, according to people familiar with the matter.<img width='1' height='1' src='http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/mf.gif' border='0'/><br clear='all'/><div class=\"feedflare\">\n",
        "<a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:yIl2AUoC8zA\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?d=yIl2AUoC8zA\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\" border=\"0\"></img></a>\n",
        "</div><img src=\"//feeds.feedburner.com/~r/reuters/businessNews/~4/Ti_4toCmS6w\" height=\"1\" width=\"1\" alt=\"\"/>\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Visualizing article in HTML"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%HTML\n",
      "NEW YORK (Reuters) - Several top investors in Twenty-First Century Fox Inc are pressing for the right to swap their voting shares for ordinary shares, which are trading at an unusual premium, even though the move could hand even more control of the company to Rupert Murdoch, according to people familiar with the matter.<img width='1' height='1' src='http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/mf.gif' border='0'/><br clear='all'/><div class=\"feedflare\">\n",
      "<a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:yIl2AUoC8zA\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?d=yIl2AUoC8zA\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\" border=\"0\"></img></a>\n",
      "</div><img src=\"//feeds.feedburner.com/~r/reuters/businessNews/~4/Ti_4toCmS6w\" height=\"1\" width=\"1\" alt=\"\"/>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "NEW YORK (Reuters) - Several top investors in Twenty-First Century Fox Inc are pressing for the right to swap their voting shares for ordinary shares, which are trading at an unusual premium, even though the move could hand even more control of the company to Rupert Murdoch, according to people familiar with the matter.<img width='1' height='1' src='http://reuters.us.feedsportal.com/c/35217/f/654199/s/4325e2cf/sc/2/mf.gif' border='0'/><br clear='all'/><div class=\"feedflare\">\n",
        "<a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:yIl2AUoC8zA\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?d=yIl2AUoC8zA\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:F7zBnMyn0Lo\" border=\"0\"></img></a> <a href=\"http://feeds.reuters.com/~ff/reuters/businessNews?a=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\"><img src=\"http://feeds.feedburner.com/~ff/reuters/businessNews?i=Ti_4toCmS6w:UwKWzqPQZuQ:V_sGLiPBpWU\" border=\"0\"></img></a>\n",
        "</div><img src=\"//feeds.feedburner.com/~r/reuters/businessNews/~4/Ti_4toCmS6w\" height=\"1\" width=\"1\" alt=\"\"/>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7f41bca4e710>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Extracting text from the description"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "news_data[\"short_description\"] = [item[item.find(\" - \")+3:item.find(\"<\")] for item in news_data[\"description\"]]\n",
      "news_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>title</th>\n",
        "      <th>description</th>\n",
        "      <th>category</th>\n",
        "      <th>short_description</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> Exclusive: Top Fox investors seek to convert v...</td>\n",
        "      <td> NEW YORK (Reuters) - Several top investors in ...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Several top investors in Twenty-First Century ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> Different delivery, one message to Greece's ne...</td>\n",
        "      <td> ROME/PARIS (Reuters) - In Paris and Rome, it w...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> In Paris and Rome, it was sugar coated; in Ber...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> Isolated Greece wants no more bailout money wi...</td>\n",
        "      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Greece's new leftist-led government, isolated ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> Valuations may hurt small caps, despite job gr...</td>\n",
        "      <td> NEW YORK (Reuters) - The good news from Friday...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> The good news from Friday's jobs report may al...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> Shippers suspend weekend cargo loading at U.S....</td>\n",
        "      <td> LOS ANGELES (Reuters) - The loading and unload...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> The loading and unloading of freighters will b...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> Building unions return to Kentucky refinery de...</td>\n",
        "      <td> NEW YORK (Reuters) - Workers represented by th...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Workers represented by the Building Trades Uni...</td>\n",
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        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Walkie-talkie and radio systems maker Motorola...</td>\n",
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        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> Wall St. ends down on interest rate, Greece ji...</td>\n",
        "      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Wall Street stocks fell on Friday as a better-...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> Strong U.S. job, wage gains open door to mid-y...</td>\n",
        "      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> U.S. job growth rose solidly in January and wa...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> Building unions return to Kentucky refinery de...</td>\n",
        "      <td> NEW YORK (Reuters) - Workers represented by th...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Workers represented by the Building Trades Uni...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Walkie-talkie and radio systems maker Motorola...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> JPMorgan under scrutiny over hiring of Chinese...</td>\n",
        "      <td> (Reuters) - JPMorgan Chase &amp; Co is under feder...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> JPMorgan Chase &amp; Co is under federal scrutiny ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> Exclusive: Top Fox investors seek to convert v...</td>\n",
        "      <td> NEW YORK (Reuters) - Several top investors in ...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Several top investors in Twenty-First Century ...</td>\n",
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        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> Isolated Greece wants no more bailout money wi...</td>\n",
        "      <td> ATHENS/BRUSSELS (Reuters) - Greece's new lefti...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Greece's new leftist-led government, isolated ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> Wall St. ends down on interest rate, Greece ji...</td>\n",
        "      <td> NEW YORK (Reuters) - Wall Street stocks fell o...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Wall Street stocks fell on Friday as a better-...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>  RadioShack would accept liquidation bids: lawyer</td>\n",
        "      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> A lawyer for RadioShack on Friday said the ban...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> Strong U.S. job, wage gains open door to mid-y...</td>\n",
        "      <td> WASHINGTON (Reuters) - U.S. job growth rose so...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> U.S. job growth rose solidly in January and wa...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> Wall Street firms waver over June 2015 rate hi...</td>\n",
        "      <td> NEW YORK (Reuters) - Economists at Wall Street...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Economists at Wall Street's biggest banks are ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> Oil climbs, Brent posts best two weeks since 1998</td>\n",
        "      <td> NEW YORK (Reuters) - Oil rallied again on Frid...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Oil rallied again on Friday, with benchmark Br...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> Brazil's Petrobras taps state banker as CEO; s...</td>\n",
        "      <td> BRASILIA (Reuters) - Brazil's President Dilma ...</td>\n",
        "      <td>   businessNews</td>\n",
        "      <td> Brazil's President Dilma Rousseff tapped a con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Law enforcement cannot indefinitely forbid Yah...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Walkie-talkie and radio systems maker Motorola...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> Anthem warns U.S. customers of email scam afte...</td>\n",
        "      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Health insurer Anthem Inc on Friday warned U.S...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> U.S. court orders Symantec to pay $17 mln for ...</td>\n",
        "      <td> (Reuters) - Symantec Corp, maker of the popula...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Symantec Corp, maker of the popular Norton ant...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>       Motorola exploring possible sale: Bloomberg</td>\n",
        "      <td> (Reuters) - Walkie-talkie and radio systems ma...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Walkie-talkie and radio systems maker Motorola...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>  RadioShack would accept liquidation bids: lawyer</td>\n",
        "      <td> (Reuters) - A lawyer for RadioShack on Friday ...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> A lawyer for RadioShack on Friday said the ban...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> Anthem warns U.S. customers of email scam afte...</td>\n",
        "      <td> (Reuters) - Health insurer Anthem Inc on Frida...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Health insurer Anthem Inc on Friday warned U.S...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> Google can disrupt car industry but is no auto...</td>\n",
        "      <td> FRANKFURT (Reuters) - Technology companies suc...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Technology companies such as Google are unlike...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> Chipmaker Intel promotes senior executives, du...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Chipmaker Intel Corp said on Friday it promote...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> Feds can't indefinitely gag Yahoo about subpoe...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Law enforcement cann...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Law enforcement cannot indefinitely forbid Yah...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> Google panel backs firm on EU limit to 'right ...</td>\n",
        "      <td> BRUSSELS (Reuters) - A panel of experts appoin...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> A panel of experts appointed by Google to advi...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> Family money puts its faith in European techno...</td>\n",
        "      <td> LONDON (Reuters) - Some of Europe's wealthiest...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Some of Europe's wealthiest families are inves...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> U.S. states probe massive data breach at healt...</td>\n",
        "      <td> NEW YORK (Reuters) - Several U.S. states are i...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Several U.S. states are investigating a massiv...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> Health insurer Anthem hit by massive cybersecu...</td>\n",
        "      <td> (Reuters) - Health insurer Anthem Inc , which ...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Health insurer Anthem Inc , which has nearly 4...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>        China to have most robots in world by 2017</td>\n",
        "      <td> FRANKFURT (Reuters) - China will have more rob...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> China will have more robots operating in its p...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> U.S. businesses ask White House to help on Chi...</td>\n",
        "      <td> WASHINGTON (Reuters) - U.S. business lobbies c...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> U.S. business lobbies called on the White Hous...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> Sharp says not considering sale of any oversea...</td>\n",
        "      <td> TOKYO (Reuters) - Japanese electronics maker S...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Japanese electronics maker Sharp Corp said a s...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> RadioShack files for bankruptcy; Sprint to tak...</td>\n",
        "      <td> (Reuters) - Electronics retailer RadioShack Co...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Electronics retailer RadioShack Corp filed for...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> SEC probes Blackberry options trading ahead of...</td>\n",
        "      <td> NEW YORK (Reuters) - The U.S. Securities and E...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> The U.S. Securities and Exchange Commission is...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td> Twitter tops Wall Street revenue target, user ...</td>\n",
        "      <td> SAN FRANCISCO (Reuters) - Twitter Inc said on ...</td>\n",
        "      <td> technologyNews</td>\n",
        "      <td> Twitter Inc said on Thursday the social media ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> U.S. Golf Association to launch Senior Women's...</td>\n",
        "      <td> NEW YORK (Reuters) - The United States Golf As...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> The United States Golf Association will launch...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> In-form Wiesberger goes two shots clear in Kua...</td>\n",
        "      <td> KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Austrian Bernd Wiesberger will take a two-shot...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> Jansrud signals downhill ambition in final tra...</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - Unheralded ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Unheralded Frenchman Brice Roger posted the fa...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> NFL, players union spar in court over Peterson...</td>\n",
        "      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> The National Football League and its players u...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> English takes charge as Mickelson exits Torrey...</td>\n",
        "      <td> LA JOLLA, California (Reuters) - Local favorit...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Local favorite Phil Mickelson has joined Tiger...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>        No pressure for double medallist Fenninger</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - A Super-G v...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> A Super-G victory at last year's Winter Olympi...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> Referees association backs rookie in flare-up ...</td>\n",
        "      <td> (Reuters) - The National Basketball Referees A...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> The National Basketball Referees Association c...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> Rangers goalie Lundqvist out for three weeks w...</td>\n",
        "      <td> (Reuters) - New York Rangers goalie Henrik Lun...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> New York Rangers goalie Henrik Lundqvist will ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>         Miracle now needed to win gold, says Vonn</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - Denied gold...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Denied gold medals in her best events, America...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> NFL and players union spar in court over Peter...</td>\n",
        "      <td> MINNEAPOLIS (Reuters) - The National Football ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> The National Football League and its players u...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>           No sharing world downhill gold for Maze</td>\n",
        "      <td> BEAVER CREEK, Colorado (Reuters) - Tina Maze h...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Tina Maze had to share Olympic downhill gold b...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>   Doping should be criminal offense: Russia chief</td>\n",
        "      <td> MOSCOW (Reuters) - Russia's Athletics Federati...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Russia's Athletics Federation (VFLA) president...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> Kansas police drop drug charges against Dallas...</td>\n",
        "      <td> DALLAS, Texas (Reuters) - Authorities have dro...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Authorities have dropped drug charges against ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>      Thompson leads by one stroke at Torrey Pines</td>\n",
        "      <td> LA JOLLA, California (Reuters - American Nicho...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> American Nicholas Thompson has a one-shot lead...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> Borzakovskiy takes over as Russia's caretaker ...</td>\n",
        "      <td> MOSCOW (Reuters) - Former Olympic 800 meters c...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Former Olympic 800 meters champion Yury Borzak...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>   Clubs need long-term mental health plan: FIFPro</td>\n",
        "      <td> (Reuters) - Soccer clubs need to take a longer...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Soccer clubs need to take a longer-term approa...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> European Games will convince skeptics, attract...</td>\n",
        "      <td> (Reuters) - The inaugural European Games this ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> The inaugural European Games this year will co...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td> Departing VFLA President to continue fight aga...</td>\n",
        "      <td> MOSCOW (Reuters) - Russian Athletics Federatio...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Russian Athletics Federation (VFLA) President ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> Canizares joins champion Westwood at the top i...</td>\n",
        "      <td> KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Spaniard Alejandro Canizares picked up three s...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td> Force India opposed Marussia's bid to use old car</td>\n",
        "      <td> LONDON (Reuters) - Marussia's hopes of rising ...</td>\n",
        "      <td>     sportsNews</td>\n",
        "      <td> Marussia's hopes of rising from the dead to ra...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "                                                title  \\\n",
        "0   Exclusive: Top Fox investors seek to convert v...   \n",
        "1   Different delivery, one message to Greece's ne...   \n",
        "2   Isolated Greece wants no more bailout money wi...   \n",
        "3   Valuations may hurt small caps, despite job gr...   \n",
        "4   Shippers suspend weekend cargo loading at U.S....   \n",
        "5   Building unions return to Kentucky refinery de...   \n",
        "6         Motorola exploring possible sale: Bloomberg   \n",
        "7   Wall St. ends down on interest rate, Greece ji...   \n",
        "8   Strong U.S. job, wage gains open door to mid-y...   \n",
        "9   Building unions return to Kentucky refinery de...   \n",
        "10        Motorola exploring possible sale: Bloomberg   \n",
        "11  JPMorgan under scrutiny over hiring of Chinese...   \n",
        "12  Exclusive: Top Fox investors seek to convert v...   \n",
        "13  Isolated Greece wants no more bailout money wi...   \n",
        "14  Wall St. ends down on interest rate, Greece ji...   \n",
        "15   RadioShack would accept liquidation bids: lawyer   \n",
        "16  Strong U.S. job, wage gains open door to mid-y...   \n",
        "17  Wall Street firms waver over June 2015 rate hi...   \n",
        "18  Oil climbs, Brent posts best two weeks since 1998   \n",
        "19  Brazil's Petrobras taps state banker as CEO; s...   \n",
        "20  Feds can't indefinitely gag Yahoo about subpoe...   \n",
        "21        Motorola exploring possible sale: Bloomberg   \n",
        "22  Anthem warns U.S. customers of email scam afte...   \n",
        "23  U.S. court orders Symantec to pay $17 mln for ...   \n",
        "24        Motorola exploring possible sale: Bloomberg   \n",
        "25   RadioShack would accept liquidation bids: lawyer   \n",
        "26  Anthem warns U.S. customers of email scam afte...   \n",
        "27  Google can disrupt car industry but is no auto...   \n",
        "28  Chipmaker Intel promotes senior executives, du...   \n",
        "29  Feds can't indefinitely gag Yahoo about subpoe...   \n",
        "30  Google panel backs firm on EU limit to 'right ...   \n",
        "31  Family money puts its faith in European techno...   \n",
        "32  U.S. states probe massive data breach at healt...   \n",
        "33  Health insurer Anthem hit by massive cybersecu...   \n",
        "34         China to have most robots in world by 2017   \n",
        "35  U.S. businesses ask White House to help on Chi...   \n",
        "36  Sharp says not considering sale of any oversea...   \n",
        "37  RadioShack files for bankruptcy; Sprint to tak...   \n",
        "38  SEC probes Blackberry options trading ahead of...   \n",
        "39  Twitter tops Wall Street revenue target, user ...   \n",
        "40  U.S. Golf Association to launch Senior Women's...   \n",
        "41  In-form Wiesberger goes two shots clear in Kua...   \n",
        "42  Jansrud signals downhill ambition in final tra...   \n",
        "43  NFL, players union spar in court over Peterson...   \n",
        "44  English takes charge as Mickelson exits Torrey...   \n",
        "45         No pressure for double medallist Fenninger   \n",
        "46  Referees association backs rookie in flare-up ...   \n",
        "47  Rangers goalie Lundqvist out for three weeks w...   \n",
        "48          Miracle now needed to win gold, says Vonn   \n",
        "49  NFL and players union spar in court over Peter...   \n",
        "50            No sharing world downhill gold for Maze   \n",
        "51    Doping should be criminal offense: Russia chief   \n",
        "52  Kansas police drop drug charges against Dallas...   \n",
        "53       Thompson leads by one stroke at Torrey Pines   \n",
        "54  Borzakovskiy takes over as Russia's caretaker ...   \n",
        "55    Clubs need long-term mental health plan: FIFPro   \n",
        "56  European Games will convince skeptics, attract...   \n",
        "57  Departing VFLA President to continue fight aga...   \n",
        "58  Canizares joins champion Westwood at the top i...   \n",
        "59  Force India opposed Marussia's bid to use old car   \n",
        "\n",
        "                                          description        category  \\\n",
        "0   NEW YORK (Reuters) - Several top investors in ...    businessNews   \n",
        "1   ROME/PARIS (Reuters) - In Paris and Rome, it w...    businessNews   \n",
        "2   ATHENS/BRUSSELS (Reuters) - Greece's new lefti...    businessNews   \n",
        "3   NEW YORK (Reuters) - The good news from Friday...    businessNews   \n",
        "4   LOS ANGELES (Reuters) - The loading and unload...    businessNews   \n",
        "5   NEW YORK (Reuters) - Workers represented by th...    businessNews   \n",
        "6   (Reuters) - Walkie-talkie and radio systems ma...    businessNews   \n",
        "7   NEW YORK (Reuters) - Wall Street stocks fell o...    businessNews   \n",
        "8   WASHINGTON (Reuters) - U.S. job growth rose so...    businessNews   \n",
        "9   NEW YORK (Reuters) - Workers represented by th...    businessNews   \n",
        "10  (Reuters) - Walkie-talkie and radio systems ma...    businessNews   \n",
        "11  (Reuters) - JPMorgan Chase & Co is under feder...    businessNews   \n",
        "12  NEW YORK (Reuters) - Several top investors in ...    businessNews   \n",
        "13  ATHENS/BRUSSELS (Reuters) - Greece's new lefti...    businessNews   \n",
        "14  NEW YORK (Reuters) - Wall Street stocks fell o...    businessNews   \n",
        "15  (Reuters) - A lawyer for RadioShack on Friday ...    businessNews   \n",
        "16  WASHINGTON (Reuters) - U.S. job growth rose so...    businessNews   \n",
        "17  NEW YORK (Reuters) - Economists at Wall Street...    businessNews   \n",
        "18  NEW YORK (Reuters) - Oil rallied again on Frid...    businessNews   \n",
        "19  BRASILIA (Reuters) - Brazil's President Dilma ...    businessNews   \n",
        "20  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews   \n",
        "21  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews   \n",
        "22  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews   \n",
        "23  (Reuters) - Symantec Corp, maker of the popula...  technologyNews   \n",
        "24  (Reuters) - Walkie-talkie and radio systems ma...  technologyNews   \n",
        "25  (Reuters) - A lawyer for RadioShack on Friday ...  technologyNews   \n",
        "26  (Reuters) - Health insurer Anthem Inc on Frida...  technologyNews   \n",
        "27  FRANKFURT (Reuters) - Technology companies suc...  technologyNews   \n",
        "28  SAN FRANCISCO (Reuters) - Chipmaker Intel Corp...  technologyNews   \n",
        "29  SAN FRANCISCO (Reuters) - Law enforcement cann...  technologyNews   \n",
        "30  BRUSSELS (Reuters) - A panel of experts appoin...  technologyNews   \n",
        "31  LONDON (Reuters) - Some of Europe's wealthiest...  technologyNews   \n",
        "32  NEW YORK (Reuters) - Several U.S. states are i...  technologyNews   \n",
        "33  (Reuters) - Health insurer Anthem Inc , which ...  technologyNews   \n",
        "34  FRANKFURT (Reuters) - China will have more rob...  technologyNews   \n",
        "35  WASHINGTON (Reuters) - U.S. business lobbies c...  technologyNews   \n",
        "36  TOKYO (Reuters) - Japanese electronics maker S...  technologyNews   \n",
        "37  (Reuters) - Electronics retailer RadioShack Co...  technologyNews   \n",
        "38  NEW YORK (Reuters) - The U.S. Securities and E...  technologyNews   \n",
        "39  SAN FRANCISCO (Reuters) - Twitter Inc said on ...  technologyNews   \n",
        "40  NEW YORK (Reuters) - The United States Golf As...      sportsNews   \n",
        "41  KUALA LUMPUR (Reuters) - Austrian Bernd Wiesbe...      sportsNews   \n",
        "42  BEAVER CREEK, Colorado (Reuters) - Unheralded ...      sportsNews   \n",
        "43  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews   \n",
        "44  LA JOLLA, California (Reuters) - Local favorit...      sportsNews   \n",
        "45  BEAVER CREEK, Colorado (Reuters) - A Super-G v...      sportsNews   \n",
        "46  (Reuters) - The National Basketball Referees A...      sportsNews   \n",
        "47  (Reuters) - New York Rangers goalie Henrik Lun...      sportsNews   \n",
        "48  BEAVER CREEK, Colorado (Reuters) - Denied gold...      sportsNews   \n",
        "49  MINNEAPOLIS (Reuters) - The National Football ...      sportsNews   \n",
        "50  BEAVER CREEK, Colorado (Reuters) - Tina Maze h...      sportsNews   \n",
        "51  MOSCOW (Reuters) - Russia's Athletics Federati...      sportsNews   \n",
        "52  DALLAS, Texas (Reuters) - Authorities have dro...      sportsNews   \n",
        "53  LA JOLLA, California (Reuters - American Nicho...      sportsNews   \n",
        "54  MOSCOW (Reuters) - Former Olympic 800 meters c...      sportsNews   \n",
        "55  (Reuters) - Soccer clubs need to take a longer...      sportsNews   \n",
        "56  (Reuters) - The inaugural European Games this ...      sportsNews   \n",
        "57  MOSCOW (Reuters) - Russian Athletics Federatio...      sportsNews   \n",
        "58  KUALA LUMPUR (Reuters) - Spaniard Alejandro Ca...      sportsNews   \n",
        "59  LONDON (Reuters) - Marussia's hopes of rising ...      sportsNews   \n",
        "\n",
        "                                    short_description  \n",
        "0   Several top investors in Twenty-First Century ...  \n",
        "1   In Paris and Rome, it was sugar coated; in Ber...  \n",
        "2   Greece's new leftist-led government, isolated ...  \n",
        "3   The good news from Friday's jobs report may al...  \n",
        "4   The loading and unloading of freighters will b...  \n",
        "5   Workers represented by the Building Trades Uni...  \n",
        "6   Walkie-talkie and radio systems maker Motorola...  \n",
        "7   Wall Street stocks fell on Friday as a better-...  \n",
        "8   U.S. job growth rose solidly in January and wa...  \n",
        "9   Workers represented by the Building Trades Uni...  \n",
        "10  Walkie-talkie and radio systems maker Motorola...  \n",
        "11  JPMorgan Chase & Co is under federal scrutiny ...  \n",
        "12  Several top investors in Twenty-First Century ...  \n",
        "13  Greece's new leftist-led government, isolated ...  \n",
        "14  Wall Street stocks fell on Friday as a better-...  \n",
        "15  A lawyer for RadioShack on Friday said the ban...  \n",
        "16  U.S. job growth rose solidly in January and wa...  \n",
        "17  Economists at Wall Street's biggest banks are ...  \n",
        "18  Oil rallied again on Friday, with benchmark Br...  \n",
        "19  Brazil's President Dilma Rousseff tapped a con...  \n",
        "20  Law enforcement cannot indefinitely forbid Yah...  \n",
        "21  Walkie-talkie and radio systems maker Motorola...  \n",
        "22  Health insurer Anthem Inc on Friday warned U.S...  \n",
        "23  Symantec Corp, maker of the popular Norton ant...  \n",
        "24  Walkie-talkie and radio systems maker Motorola...  \n",
        "25  A lawyer for RadioShack on Friday said the ban...  \n",
        "26  Health insurer Anthem Inc on Friday warned U.S...  \n",
        "27  Technology companies such as Google are unlike...  \n",
        "28  Chipmaker Intel Corp said on Friday it promote...  \n",
        "29  Law enforcement cannot indefinitely forbid Yah...  \n",
        "30  A panel of experts appointed by Google to advi...  \n",
        "31  Some of Europe's wealthiest families are inves...  \n",
        "32  Several U.S. states are investigating a massiv...  \n",
        "33  Health insurer Anthem Inc , which has nearly 4...  \n",
        "34  China will have more robots operating in its p...  \n",
        "35  U.S. business lobbies called on the White Hous...  \n",
        "36  Japanese electronics maker Sharp Corp said a s...  \n",
        "37  Electronics retailer RadioShack Corp filed for...  \n",
        "38  The U.S. Securities and Exchange Commission is...  \n",
        "39  Twitter Inc said on Thursday the social media ...  \n",
        "40  The United States Golf Association will launch...  \n",
        "41  Austrian Bernd Wiesberger will take a two-shot...  \n",
        "42  Unheralded Frenchman Brice Roger posted the fa...  \n",
        "43  The National Football League and its players u...  \n",
        "44  Local favorite Phil Mickelson has joined Tiger...  \n",
        "45  A Super-G victory at last year's Winter Olympi...  \n",
        "46  The National Basketball Referees Association c...  \n",
        "47  New York Rangers goalie Henrik Lundqvist will ...  \n",
        "48  Denied gold medals in her best events, America...  \n",
        "49  The National Football League and its players u...  \n",
        "50  Tina Maze had to share Olympic downhill gold b...  \n",
        "51  Russia's Athletics Federation (VFLA) president...  \n",
        "52  Authorities have dropped drug charges against ...  \n",
        "53  American Nicholas Thompson has a one-shot lead...  \n",
        "54  Former Olympic 800 meters champion Yury Borzak...  \n",
        "55  Soccer clubs need to take a longer-term approa...  \n",
        "56  The inaugural European Games this year will co...  \n",
        "57  Russian Athletics Federation (VFLA) President ...  \n",
        "58  Spaniard Alejandro Canizares picked up three s...  \n",
        "59  Marussia's hopes of rising from the dead to ra...  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extract Features with CountVictrizer"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corpus = news_data[\"short_description\"]\n",
      "vectorizer = CountVectorizer(min_df=1)\n",
      "X = vectorizer.fit_transform(corpus).toarray()\n",
      "print X.shape\n",
      "X"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(60, 809)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([[0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 1, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 1],\n",
        "       ..., \n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0],\n",
        "       [0, 0, 0, ..., 0, 0, 0]])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vectorizer.get_feature_names()[:25]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[u'11',\n",
        " u'14',\n",
        " u'17',\n",
        " u'18',\n",
        " u'2012',\n",
        " u'2017',\n",
        " u'2018',\n",
        " u'29',\n",
        " u'40',\n",
        " u'400',\n",
        " u'65',\n",
        " u'800',\n",
        " u'about',\n",
        " u'abroad',\n",
        " u'abuse',\n",
        " u'accept',\n",
        " u'according',\n",
        " u'account',\n",
        " u'added',\n",
        " u'admitted',\n",
        " u'adrian',\n",
        " u'advise',\n",
        " u'affiliate',\n",
        " u'after',\n",
        " u'again']"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "categories = news_data[\"category\"].unique()\n",
      "category_dict = {value:index for index, value in enumerate(categories)}\n",
      "results = news_data[\"category\"].map(category_dict)\n",
      "category_dict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "{'businessNews': 0, 'sportsNews': 2, 'technologyNews': 1}"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"corpus size: %s\" % len(vectorizer.get_feature_names())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "corpus size: 809\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Multinomial Naive Bayes Cassifier"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_train,x_test, y_train,y_test = train_test_split(X, results, test_size=0.2, random_state=1, )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf = MultinomialNB()\n",
      "clf.fit(x_train, y_train)\n",
      "clf.score(x_test, y_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "0.83333333333333337"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y_test"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([1, 2, 0, 2, 2, 2, 1, 1, 2, 1, 1, 2])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf.predict(x_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "array([1, 2, 0, 2, 2, 2, 1, 0, 2, 1, 0, 2])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "category_dict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "{'businessNews': 0, 'sportsNews': 2, 'technologyNews': 1}"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Manual Testing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text = [\"Who won the Superbawl?\"]\n",
      "vec_text = vectorizer.transform(text).toarray()\n",
      "category_dict.keys()[category_dict.values().index(clf.predict(vec_text)[0])]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "'sportsNews'"
       ]
      }
     ],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/data/temp_data_features.csv
================================================
Year,Jan,Feb,Mar,Apr,May,Jun,Jul,Aug,Sep,Oct,Nov,Dec,Average,TSI,CO2,CH4
1851,0.823,0.357,-0.564,0.51,0.062,0.062,0.367,0.279,0.172,0.672,-0.132,-0.24,0.1973333333,1360.8289,,
1852,0.132,0.185,-0.016,-0.889,0.427,0.485,0.539,0.064,0.147,-0.252,0.126,1.834,0.2318333333,1360.8634,,
1853,0.711,-0.656,-1.348,-0.478,-0.213,-0.196,0.418,0.325,-0.333,0.082,-0.527,-1.23,-0.2870833333,1360.7908,,
1854,-0.444,0.256,0.2,-0.355,0.428,0.107,0.616,0.399,0.171,0.702,-0.501,0.346,0.1604166667,1360.642,,
1855,0.059,-1.359,-0.521,0.444,0.243,0.197,0.277,0.127,-0.303,0.346,-0.431,-1.622,-0.2119166667,1360.5151,,
1856,-0.303,-0.751,-1.241,-0.022,-0.592,0.232,-0.413,-0.293,-0.601,-0.599,-1.486,-0.141,-0.5175,1360.4386,,
1857,-1.157,0.283,-0.708,-1.46,-1.028,-0.316,-0.007,0.147,-0.325,-0.412,-0.726,0.647,-0.4218333333,1360.4407,,
1858,0.054,-1.353,-0.734,-0.397,-0.365,0.146,-0.149,-0.344,-0.361,-0.059,-1.819,-0.382,-0.48025,1360.5687,,
1859,-0.167,0.153,-0.024,-0.149,0.021,-0.159,0.049,0.044,-0.551,-0.152,-0.31,-1.072,-0.1930833333,1360.6651,,
1860,0.156,-1.111,-1.565,-0.538,-0.291,0.056,-0.252,-0.011,-0.268,-0.335,-1.291,-1.61,-0.5883333333,1360.9295,,
1861,-2.001,0.241,0.101,-0.782,-1.015,0.173,-0.099,0.079,-0.3,-0.264,-0.705,-0.448,-0.4183333333,1360.9756,,
1862,-1.255,-1.314,-0.414,-0.308,-0.154,-0.36,-0.433,-0.725,-0.359,-0.535,-1.006,-1.171,-0.6695,1360.8084,,
1863,1.155,0.468,-0.366,-0.302,-0.214,-0.374,-0.457,-0.249,-0.485,-0.604,-0.381,-0.377,-0.1821666667,1360.7553,,
1864,-1.422,-0.618,-0.505,-0.762,-0.627,0.165,-0.034,-0.465,-0.279,-1.356,-1.319,-1.285,-0.7089166667,1360.588,,
1865,-0.131,-1.352,-1.429,-0.157,-0.161,-0.172,0.161,-0.331,0.282,-0.597,0.057,-0.564,-0.3661666667,1360.5792,,
1866,0.599,-0.528,-0.918,-0.214,-0.729,0.077,0.011,-0.492,-0.009,-0.857,-0.654,-0.38,-0.3411666667,1360.5595,,
1867,-0.729,0.612,-1.931,-0.499,-1.195,-0.259,-0.321,-0.171,-0.079,-0.079,-0.641,-0.982,-0.5228333333,1360.445,,
1868,-1.426,-0.888,-0.197,-0.682,0.195,0.136,0.496,0.142,-0.355,-0.116,-1.024,-0.201,-0.3266666667,1360.43,,
1869,-0.154,0.891,-1.167,-0.363,-0.449,-0.313,-0.14,0.28,-0.04,-1.005,-0.622,-0.424,-0.2921666667,1360.6744,,
1870,0.136,-0.968,-1.156,-0.458,0.013,0.244,0.166,-0.747,-0.317,-0.485,-0.133,-1.854,-0.46325,1360.5065,,
1871,-1.03,-1.2,-0.007,-0.34,-0.571,-0.382,0.059,0.163,-0.604,-0.616,-1.33,-1.234,-0.591,1360.8971,,
1872,-0.105,-0.507,-0.617,-0.109,-0.004,-0.061,-0.109,-0.426,-0.163,-0.142,-0.5,-0.809,-0.296,1360.8028,,
1873,-0.004,-0.64,-0.535,-1.022,-0.578,0.277,-0.13,0.05,-0.378,-0.4,-0.945,0.032,-0.3560833333,1360.8236,,
1874,0.291,-0.481,-1.132,-0.681,-0.871,-0.407,-0.247,-0.374,-0.192,-0.244,-0.833,-0.368,-0.4615833333,1360.7149,,
1875,-1.263,-1.638,-1.709,-1.093,-0.251,-0.177,-0.316,-0.296,-0.665,-0.836,-1.327,-0.94,-0.8759166667,1360.6017,,
1876,-0.265,-0.224,-0.451,-0.383,-0.769,-0.055,0.128,-0.163,-0.199,-0.45,-1.34,-1.515,-0.4738333333,1360.5014,,
1877,-0.308,-0.026,-0.636,-0.765,-0.924,-0.038,0.211,0.25,-0.367,-0.57,0.094,0.119,-0.2466666667,1360.4721,,
1878,-0.343,0.442,0.675,0.428,-0.446,-0.076,-0.061,0,0.301,0.156,-0.13,-1.011,-0.0054166667,1360.4132,,
1879,-0.485,-0.498,-0.47,-0.648,-0.535,-0.623,-0.377,-0.381,-0.348,-0.118,-0.912,-1.238,-0.55275,1360.3903,,
1880,0.108,-0.269,-0.498,-0.346,-0.089,-0.103,-0.19,0.172,-0.165,-1.021,-1.088,-0.624,-0.34275,1360.4251,,
1881,-1.303,-0.626,-0.737,-0.46,-0.02,-0.609,-0.283,-0.138,-0.112,-0.663,-0.709,0.14,-0.46,1360.6637,,
1882,0.682,0.102,0.137,-0.714,-0.74,-0.679,-0.532,-0.074,-0.082,-0.379,-0.768,-0.918,-0.3304166667,1360.5734,,
1883,-1.103,-0.947,-1.128,-0.722,-0.559,0.076,-0.242,-0.43,-0.542,-0.559,-0.584,-0.355,-0.59125,1360.4887,,
1884,-0.615,-0.416,-0.83,-1.099,-1.05,-0.698,-0.736,-0.239,-0.422,-0.526,-1.031,-0.641,-0.6919166667,1360.8455,,
1885,-1.362,-0.808,-1.074,-0.923,-0.91,-0.715,-0.344,-0.621,-0.342,-0.501,-0.439,0.037,-0.6668333333,1360.7314,,
1886,-0.783,-1.071,-0.98,-0.301,-0.411,-0.66,-0.319,-0.393,-0.197,-0.572,-0.484,-0.277,-0.5373333333,1360.6114,,
1887,-0.71,-0.796,-0.576,-0.676,-0.439,-0.421,-0.292,-0.458,-0.297,-0.894,-0.687,-0.613,-0.5715833333,1360.475,,
1888,-1.206,-1.035,-1.484,-0.33,-0.771,-0.47,-0.405,-0.443,-0.209,-0.387,-0.485,-0.296,-0.62675,1360.4428,,
1889,-0.437,-0.428,-0.275,0.029,0.161,-0.139,-0.351,-0.386,-0.564,-0.332,-0.699,0.054,-0.2805833333,1360.4102,,
1890,-0.138,-0.364,-0.506,-0.255,-0.628,-0.285,-0.426,-0.43,-0.236,-0.528,-0.83,-0.721,-0.4455833333,1360.4097,,
1891,-0.979,-0.867,-0.699,-0.745,-0.471,-0.503,-0.511,-0.529,-0.217,-0.508,-0.934,0.191,-0.5643333333,1360.5689,,
1892,-0.726,-0.049,-0.885,-0.882,-0.82,-0.421,-0.482,-0.342,-0.269,-0.468,-0.787,-1.37,-0.6250833333,1360.7463,,
1893,-2.125,-1.556,-0.601,-0.683,-0.772,-0.485,-0.169,-0.372,-0.486,-0.241,-0.518,-0.386,-0.6995,1360.972,,
1894,-0.557,-0.452,-0.205,-0.429,-0.549,-0.603,-0.424,-0.387,-0.758,-0.425,-0.434,-0.417,-0.47,1361.2425,,
1895,-1.115,-1.555,-0.773,-0.47,-0.49,-0.367,-0.656,-0.344,-0.165,-0.318,-0.448,-0.249,-0.5791666667,1361.0936,,
1896,-0.346,-0.353,-0.856,-0.829,-0.376,-0.25,-0.228,-0.332,-0.286,-0.015,-0.965,-0.122,-0.4131666667,1360.8795,,
1897,-0.607,-0.37,-0.812,-0.059,-0.034,-0.15,-0.103,-0.26,-0.048,-0.288,-0.499,-0.582,-0.3176666667,1360.692,,
1898,0.35,-0.41,-1.349,-0.687,-0.642,-0.202,-0.328,-0.127,-0.13,-0.575,-0.419,-0.117,-0.3863333333,1360.6241,,
1899,-0.14,-0.735,-0.816,-0.239,-0.475,-0.58,-0.265,-0.214,0.022,-0.196,0.339,-0.619,-0.3265,1360.5902,,
1900,-0.426,-0.406,-0.468,-0.413,-0.276,-0.008,-0.279,-0.147,-0.225,0.326,-0.176,0.16,-0.1948333333,1360.5106,,
1901,-0.175,-0.534,-0.166,-0.043,-0.137,0.098,-0.009,-0.078,-0.125,-0.176,-0.32,-0.457,-0.1768333333,1360.3975,,
1902,0.052,-0.075,-0.307,-0.475,-0.459,-0.362,-0.441,-0.503,-0.465,-0.55,-0.62,-0.864,-0.4224166667,1360.3649,,
1903,-0.117,0.144,-0.114,-0.539,-0.573,-0.596,-0.534,-0.493,-0.398,-0.574,-0.61,-0.763,-0.4305833333,1360.4474,,
1904,-0.823,-0.72,-0.924,-0.544,-0.539,-0.504,-0.542,-0.468,-0.586,-0.396,-0.284,-0.498,-0.569,1360.7916,,
1905,-0.571,-1.272,-0.54,-0.706,-0.39,-0.206,-0.313,-0.333,-0.234,-0.532,-0.04,-0.002,-0.42825,1360.596,,
1906,0.125,-0.466,-0.594,0.159,0.018,-0.093,-0.164,-0.192,-0.323,-0.228,-0.635,-0.143,-0.2113333333,1360.9176,,
1907,-0.715,-0.815,-0.553,-0.942,-0.994,-0.726,-0.494,-0.616,-0.422,-0.129,-0.805,-0.568,-0.64825,1360.7087,,
1908,-0.211,-0.391,-0.892,-0.584,-0.448,-0.375,-0.411,-0.576,-0.3,-0.596,-0.751,-0.413,-0.4956666667,1360.8591,,
1909,-0.656,-0.679,-0.889,-0.852,-0.809,-0.404,-0.421,-0.04,-0.126,-0.114,-0.076,-0.687,-0.4794166667,1360.7022,,
1910,-0.089,-0.367,-0.273,-0.224,-0.336,-0.299,-0.196,-0.231,-0.207,-0.414,-0.811,-0.691,-0.3448333333,1360.6694,,
1911,-0.611,-0.949,-0.839,-0.738,-0.423,-0.442,-0.268,-0.249,-0.296,-0.376,-0.174,-0.059,-0.452,1360.4789,,
1912,-0.466,-0.069,-0.585,-0.438,-0.324,-0.119,-0.625,-0.717,-0.706,-0.833,-0.595,-0.288,-0.4804166667,1360.3846,,
1913,-0.263,-0.735,-0.665,-0.248,-0.871,-0.701,-0.383,-0.205,-0.337,-0.365,0.201,0.293,-0.3565833333,1360.4054,,
1914,0.487,0.172,-0.142,-0.312,-0.115,-0.099,-0.193,-0.143,-0.199,0.022,-0.346,-0.336,-0.1003333333,1360.4695,,
1915,-0.079,0.331,-0.657,0.079,-0.323,-0.215,0.011,-0.185,-0.044,-0.23,-0.004,-0.088,-0.117,1360.8119,,
1916,0.036,-0.101,-0.746,-0.355,-0.378,-0.529,-0.221,-0.248,-0.32,-0.369,-0.479,-0.974,-0.3903333333,1361.2152,,
1917,-0.653,-1.387,-1.257,-0.788,-1.237,-0.448,-0.092,-0.333,-0.166,-0.62,-0.352,-1.325,-0.7215,1361.3453,,
1918,-0.817,-0.591,-0.542,-0.636,-0.775,-0.428,-0.466,-0.377,-0.289,0.152,-0.3,-0.328,-0.44975,1361.6055,,
1919,-0.078,-0.299,-0.597,-0.09,-0.474,-0.13,-0.217,-0.176,-0.061,-0.273,-0.852,-0.548,-0.31625,1361.2481,,
1920,-0.116,-0.429,-0.107,-0.332,-0.198,-0.271,-0.134,-0.207,-0.09,-0.375,-0.555,-0.522,-0.278,1361.0023,,
1921,0.367,-0.122,-0.01,-0.021,0.069,0.086,-0.01,-0.379,-0.205,-0.213,-0.63,-0.142,-0.1008333333,1360.8043,,
1922,-0.645,-0.516,-0.372,-0.096,-0.165,-0.162,-0.277,-0.273,-0.205,-0.287,-0.152,-0.188,-0.2781666667,1360.6155,,
1923,0.067,-0.602,-0.367,-0.735,-0.388,-0.296,-0.458,-0.481,-0.193,-0.044,0.159,0.212,-0.2605,1360.5334,,
1924,-0.493,-0.516,-0.493,-0.655,-0.331,-0.198,-0.203,-0.232,-0.075,-0.171,-0.143,-0.594,-0.342,1360.5019,,
1925,-0.361,-0.076,-0.297,-0.132,-0.414,-0.328,-0.417,-0.109,-0.254,-0.547,-0.067,0.202,-0.2333333333,1360.6004,,
1926,0.429,0.515,0.043,-0.37,-0.526,-0.202,-0.249,-0.058,-0.023,-0.076,0.097,-0.456,-0.073,1360.8627,,
1927,-0.27,-0.178,-0.402,-0.446,-0.514,-0.253,-0.062,-0.156,0.04,0.247,-0.051,-0.873,-0.2431666667,1361.1797,,
1928,0.109,-0.108,-0.511,-0.29,-0.348,-0.585,-0.097,-0.126,-0.026,-0.059,0.122,-0.001,-0.16,1360.9539,,
1929,-0.71,-1.39,-0.505,-0.72,-0.456,-0.442,-0.436,-0.168,-0.28,0.032,0.029,-0.744,-0.4825,1360.9219,,
1930,-0.355,-0.139,-0.055,-0.154,-0.322,-0.043,0.005,0.047,-0.16,-0.219,0.119,-0.099,-0.1145833333,1361.0652,,
1931,0.026,-0.43,-0.322,-0.409,-0.351,0.016,0.114,0.077,-0.047,0.176,-0.118,0.137,-0.09425,1360.7765,,
1932,0.93,-0.43,-0.681,0.05,-0.225,-0.197,-0.078,-0.037,0.095,0.085,-0.275,-0.147,-0.0758333333,1360.5965,,
1933,-0.482,-0.651,-0.557,-0.3,-0.26,-0.248,-0.052,-0.07,-0.073,-0.026,-0.381,-0.919,-0.3349166667,1360.5046,,
1934,-0.025,0.211,-0.418,-0.368,0.229,-0.028,0.015,-0.003,-0.136,0.04,0.229,0.142,-0.0093333333,1360.5146,,
1935,-0.459,0.754,-0.06,-0.588,-0.498,-0.209,-0.061,-0.04,-0.152,0.145,-0.524,-0.314,-0.1671666667,1360.6565,,
1936,-0.331,-0.925,-0.387,-0.327,-0.133,-0.027,0.3,0.122,-0.013,0.045,0.079,0.257,-0.1116666667,1361.2101,,
1937,-0.254,0.116,-0.716,-0.328,-0.034,0.096,-0.001,0.226,0.263,0.352,0.133,-0.308,-0.0379166667,1361.3661,,
1938,0.157,0.168,0.378,0.357,0.052,-0.037,0.082,0.114,0.282,0.476,0.351,-0.376,0.167,1361.3043,,
1939,0.109,0.059,-0.496,-0.191,0.054,0.115,-0.031,0.066,-0.155,-0.294,-0.02,0.723,-0.0050833333,1361.4078,,
1940,-0.804,-0.108,-0.158,0.01,-0.169,0.163,0.123,0.022,0.164,-0.025,-0.036,0.3,-0.0431666667,1361.2262,,
1941,-0.219,0.101,-0.379,-0.026,-0.043,0.126,0.186,0.029,-0.205,0.212,-0.213,-0.171,-0.0501666667,1361.131,,
1942,0.1,-0.428,-0.295,-0.114,-0.113,0.095,-0.106,-0.012,0.111,0.102,0.079,0.028,-0.0460833333,1360.9361,,
1943,-0.39,0.28,-0.262,0.172,0.055,-0.266,-0.01,-0.168,0.039,0.414,-0.007,0.291,0.0123333333,1360.675,,
1944,0.736,0.307,0.042,-0.149,-0.049,-0.024,0.057,0.149,0.42,0.344,-0.054,-0.373,0.1171666667,1360.637,,
1945,-0.268,-0.446,-0.2,0.21,-0.289,-0.126,-0.201,0.431,0.151,0.133,-0.131,-0.705,-0.1200833333,1360.8539,,
1946,0.115,0.134,-0.317,0.189,-0.095,-0.259,0.007,-0.028,0.063,-0.16,0.012,-0.587,-0.0771666667,1360.8947,,
1947,-0.301,-0.319,0.012,0.163,-0.032,0.063,-0.053,0.088,0.063,0.435,0.103,-0.243,-0.00175,1361.3986,,
1948,0.441,-0.083,-0.415,-0.005,0.176,0.14,-0.1,-0.048,0.02,0.153,0.045,-0.238,0.0071666667,1361.7898,,
1949,0.382,-0.356,-0.399,-0.109,-0.117,-0.25,-0.179,-0.048,-0.082,0.139,-0.009,-0.36,-0.1156666667,1361.6193,,
1950,-0.638,-0.389,-0.248,-0.311,-0.164,-0.093,-0.239,-0.299,-0.157,-0.203,-0.701,-0.332,-0.3145,1361.5186,,
1951,-0.555,-0.834,-0.442,-0.081,-0.023,-0.101,-0.048,0.125,0.206,0.156,-0.02,0.417,-0.1,1360.984,,
1952,0.294,0.072,-0.406,-0.03,-0.045,-0.013,0.117,0.089,0.127,-0.139,-0.549,-0.208,-0.0575833333,1361.0445,,
1953,0.16,0.186,0.221,0.22,0.053,0.179,-0.009,0.146,0.112,0.199,-0.136,0.257,0.1323333333,1360.8951,,
1954,-0.506,-0.229,-0.365,-0.206,-0.301,-0.105,-0.187,-0.061,-0.004,0.079,0.248,-0.198,-0.1529166667,1360.7328,,
1955,0.411,-0.131,-0.797,-0.361,-0.204,-0.104,-0.211,0.036,-0.032,0.006,-0.383,-0.497,-0.1889166667,1360.827,,
1956,-0.341,-0.7,-0.578,-0.536,-0.434,-0.263,-0.292,-0.39,-0.419,-0.321,-0.478,-0.377,-0.4274166667,1361.181,,
1957,-0.27,-0.23,-0.46,-0.165,-0.115,0.062,-0.181,0.036,0.061,-0.06,0.151,0.36,-0.0675833333,1361.66,,
1958,0.505,0.244,-0.065,0.027,0.042,-0.081,0.019,-0.04,-0.125,0.011,0.08,-0.009,0.0506666667,1362.1425,,
1959,0.127,0.064,0.198,0.069,-0.127,0.127,0.047,0.025,0.054,-0.162,-0.172,-0.02,0.0191666667,1361.8834,315.97,
1960,0.034,0.348,-0.717,-0.316,-0.343,0.052,-0.102,-0.019,0.04,0.017,-0.223,0.317,-0.076,1361.822,316.91,
1961,0.09,0.278,0.096,0.103,0.09,0.087,-0.034,-0.013,0.051,-0.053,0.006,-0.186,0.0429166667,1361.5247,317.64,
1962,0.186,0.273,-0.012,0.047,-0.089,-0.147,-0.067,-0.07,-0.016,0.124,0.083,0.048,0.03,1361.0859,318.45,
1963,-0.074,0.415,-0.316,-0.234,-0.181,-0.139,0.069,0.049,0.141,0.4,0.276,-0.122,0.0236666667,1360.9942,318.99,
1964,-0.038,-0.287,-0.522,-0.379,-0.191,-0.247,-0.202,-0.282,-0.294,-0.41,-0.297,-0.449,-0.2998333333,1360.8564,319.62,
1965,-0.067,-0.453,-0.284,-0.472,-0.233,-0.11,-0.289,-0.224,-0.205,-0.063,-0.252,-0.16,-0.2343333333,1360.7718,320.04,
1966,-0.231,-0.169,-0.132,-0.297,-0.194,0.047,-0.007,-0.014,-0.026,-0.181,-0.161,-0.395,-0.1466666667,1360.7994,321.38,
1967,-0.286,-0.447,-0.103,-0.08,0.115,-0.245,-0.118,-0.089,-0.132,0.348,0.015,-0.124,-0.0955,1361.1175,322.16,
1968,-0.369,-0.257,0.36,-0.216,-0.325,-0.307,-0.221,-0.204,-0.209,-0.135,-0.26,-0.449,-0.216,1361.3468,323.04,
1969,-0.651,-0.745,-0.321,-0.016,-0.023,-0.099,0.038,-0.026,-0.086,-0.057,0.189,0.259,-0.1281666667,1361.2814,324.62,
1970,-0.055,0.298,-0.263,0.007,-0.111,0.089,-0.008,-0.075,0.009,-0.185,-0.117,-0.329,-0.0616666667,1361.3926,325.68,
1971,-0.072,-0.436,-0.484,-0.379,-0.298,-0.314,-0.168,-0.111,0.001,-0.067,-0.003,-0.089,-0.2016666667,1361.317,326.32,
1972,-0.805,-0.654,-0.294,-0.177,-0.193,-0.039,-0.08,-0.084,-0.234,-0.104,-0.285,0.073,-0.2396666667,1361.2703,327.45,
1973,0.167,0.53,0.386,0.274,0.163,0.264,0.086,0.033,-0.035,0.001,-0.132,-0.064,0.1394166667,1361.0355,329.68,
1974,-0.474,-0.634,-0.203,-0.157,-0.236,-0.189,-0.072,-0.118,-0.253,-0.357,-0.326,-0.351,-0.2808333333,1360.8742,330.18,
1975,0.122,-0.029,0.031,0.037,0.019,-0.001,-0.016,-0.143,0.051,-0.18,-0.288,-0.283,-0.0566666667,1360.7604,331.08,
1976,-0.071,-0.362,-0.747,-0.182,-0.319,-0.29,-0.312,-0.287,-0.228,-0.705,-0.453,-0.297,-0.3544166667,1360.8495,332.05,
1977,-0.433,0.11,0.269,0.271,0.111,0.218,0.071,0,0.023,-0.052,0.397,0.03,0.0845833333,1360.9971,333.78,
1978,0.048,-0.037,0.141,0.009,-0.075,-0.176,-0.082,-0.274,-0.077,-0.071,0.148,-0.168,-0.0511666667,1361.5371,335.41,
1979,-0.15,-0.317,0.014,-0.266,-0.092,0.098,0.025,0.052,0.084,0.197,0.132,0.646,0.03525,1361.8607,336.78,
1980,0.114,0.27,-0.103,0.177,0.258,0.142,0.059,0.078,0.099,0.124,0.271,0.133,0.1351666667,1361.798,338.68,
1981,0.782,0.627,0.524,0.429,0.116,0.151,0.096,0.26,0.111,0.029,0.157,0.526,0.3173333333,1361.8106,340.1,
1982,-0.204,0.106,-0.256,0.065,0.021,-0.125,0.035,0.016,0.097,-0.129,-0.113,0.459,-0.0023333333,1361.3654,341.44,
1983,0.755,0.5,0.373,0.143,0.148,0.113,0.253,0.342,0.398,0.215,0.579,0.148,0.3305833333,1361.3431,343.03,1718.4488888889
1984,0.17,-0.093,0.024,-0.136,0.227,0.03,-0.004,0.038,-0.138,0.083,-0.214,-0.528,-0.0450833333,1360.821,344.58,1744.4258333333
1985,0.011,-0.308,-0.094,-0.001,0.067,-0.061,-0.135,0.118,-0.028,0.037,-0.046,0.041,-0.03325,1360.7718,346.04,1754.435
1986,0.452,0.207,0.233,0.298,0.201,0.153,-0.044,0.022,-0.066,-0.003,-0.036,0.028,0.1204166667,1360.7799,347.39,1776.4766666667
1987,0.179,0.676,-0.122,0.185,0.167,0.207,0.368,0.188,0.305,0.237,0.106,0.531,0.25225,1360.7853,349.16,1780.5516666667
1988,0.641,0.261,0.414,0.331,0.369,0.429,0.345,0.325,0.389,0.378,0.109,0.509,0.375,1361.2885,351.56,1796.0975
1989,0.15,0.459,0.415,0.244,0.178,0.155,0.216,0.219,0.218,0.308,0.062,0.32,0.2453333333,1361.7128,353.07,1810.4516666667
1990,0.39,0.527,1.186,0.619,0.413,0.436,0.286,0.356,0.195,0.491,0.589,0.419,0.49225,1361.7209,354.35,1819.6
1991,0.522,0.533,0.255,0.599,0.33,0.583,0.455,0.368,0.388,0.363,0.259,0.106,0.39675,1361.814,355.57,1824.685
1992,0.73,0.561,0.432,0.071,0.155,0.047,-0.258,-0.086,-0.241,-0.222,-0.247,0.179,0.0934166667,1361.7312,356.38,1828.8966666667
1993,0.485,0.459,0.361,0.208,0.25,0.198,0.17,0.114,-0.125,0.105,-0.366,0.241,0.175,1361.1668,357.07,1828.9458333333
1994,0.349,-0.184,0.333,0.428,0.397,0.444,0.274,0.279,0.342,0.514,0.515,0.419,0.3425,1360.9068,358.82,1843.7925
1995,0.89,1.252,0.511,0.528,0.364,0.536,0.427,0.616,0.486,0.691,0.587,0.211,0.5915833333,1360.8431,360.8,1845.2466666667
1996,0.195,0.458,0.136,-0.013,0.295,0.267,0.289,0.217,0.078,0.166,0.234,0.408,0.2275,1360.7208,362.59,1846.25
1997,0.423,0.651,0.55,0.41,0.345,0.525,0.397,0.497,0.574,0.664,0.546,0.635,0.5180833333,1360.8221,363.71,1847.0366666667
1998,0.725,1.355,0.785,0.965,0.831,0.889,0.984,0.919,0.701,0.7,0.285,0.874,0.8344166667,1361.1838,366.65,1859.6883333333
1999,0.773,1.18,0.211,0.488,0.393,0.466,0.501,0.43,0.571,0.546,0.372,0.808,0.5615833333,1361.4072,368.33,1863.75
2000,0.321,0.985,0.771,0.953,0.505,0.427,0.394,0.547,0.395,0.263,0.05,0.194,0.48375,1361.613,369.52,1861.4525
2001,0.718,0.523,0.798,0.667,0.702,0.565,0.646,0.808,0.55,0.615,1.032,0.546,0.6808333333,1361.553,371.13,1860.1308333333
2002,1.212,1.368,1.174,0.697,0.598,0.659,0.788,0.622,0.7,0.501,0.744,0.272,0.7779166667,1361.5834,373.22,1862.225
2003,1.002,0.576,0.572,0.612,0.853,0.71,0.628,0.796,0.757,0.933,0.676,1.12,0.7695833333,1361.0274,375.77,1877.5841666667
2004,0.81,1.073,0.916,0.705,0.345,0.527,0.388,0.465,0.549,0.772,1.076,0.459,0.67375,1360.9153,377.49,1866.2883333333
2005,0.96,0.566,0.872,1.022,0.762,0.869,0.828,0.709,0.91,1.093,1.205,0.787,0.8819166667,1360.746,379.8,1867.5908333333
2006,0.608,0.93,0.775,0.574,0.506,0.867,0.792,0.732,0.77,0.974,0.884,1.313,0.8104166667,1360.6647,381.9,1865.4491666667
2007,1.697,0.904,1.01,1.126,0.786,0.582,0.622,0.784,0.827,0.989,0.778,0.798,0.9085833333,1360.5636,383.76,1872.7608333333
2008,0.385,0.499,1.204,0.557,0.479,0.605,0.73,0.569,0.563,1.011,1.065,0.67,0.69475,1360.5324,385.59,1883.7858333333
2009,0.779,0.792,0.633,0.829,0.581,0.659,0.587,0.876,0.976,0.724,0.845,0.492,0.7310833333,1360.5516,387.37,1885.5558333333
2010,0.778,0.826,1.111,1.062,0.936,0.996,0.974,0.863,0.732,0.888,1.195,0.448,0.90075,1360.7969,389.85,1896.4875
2011,0.467,0.382,0.585,0.83,0.523,0.778,0.775,0.766,0.852,0.941,0.609,0.827,0.6945833333,1361.068,391.63,1896.0341666667
2012,0.557,0.323,0.645,1.03,0.963,0.947,0.792,0.891,0.9,0.847,0.849,0.261,0.7504166667,1361.2332,393.82,1899.78
2013,0.935,0.949,0.669,0.581,0.773,0.827,0.671,0.706,0.776,0.821,0.984,0.795,0.7905833333,1361.2924,396.48,1906.75
2014,0.95,0.408,0.929,1.048,,,,,,,,,0.83375


================================================
FILE: notebooks/data-mining/1. Web Scraping.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Web scrpping a one of the most common method of collecting data. This tutorial covers the basics of web scraping.\n",
      "\n",
      "**Video Tutorial**:\n",
      "\n",
      "http://youtu.be/wT66i7jeyL8\n",
      "\n",
      "More About LXML:\n",
      "\n",
      "http://lxml.de/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "\n",
      "from lxml import html\n",
      "import requests\n",
      "\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "pd.options.display.max_columns=50"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Data and Data Source"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be working with the Nobel Prize data. We will get that from Wikipedia\n",
      "![](http://upload.wikimedia.org/wikipedia/en/e/ed/Nobel_Prize.png)\n",
      "\n",
      "Webpage URL:\n",
      "\n",
      "http://en.wikipedia.org/wiki/List_of_Nobel_laureates"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Before you Web Scrape - Read the ToS"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Please make sure that you read the ToS (Terms of Service) of the website before you web scrape any data. Most websites don't allows web scraping of their content. Some allow moderate use.\n",
      "\n",
      "For Wikipedia:\n",
      "\n",
      "> ## 4. Refraining from Certain Activities\n",
      "> ### Engaging in Disruptive and Illegal Misuse of Facilities\n",
      ">  Engaging in automated uses of the site that are abusive or disruptive of the services and have not been approved by the Wikimedia community;\n",
      "> ##### [Wikimedia Foundation Terms of Use](wikimediafoundation.org/wiki/Terms_of_Use#4._Refraining_from_Certain_Activities)\n",
      "\n",
      "**From my basic technical understanding**:\n",
      "\n",
      "This means \"automated uses\" like web scraping are not allowed if they are \"abusive or disruptive of the services\" but I would argue web scraping a single page is not \"abusive or disruptive of the services\". Web scraping will actually consume less servers time and bandwidth than reading the page in a browser. This is due to the fact that we will only request the page and none of the static files linked to it like CSS, images, javascript ... etc.\n",
      "\n",
      "**DISCLAIMERL**: I'm not a lawyer\n",
      "\n",
      "##Basic Rules:\n",
      "\n",
      "- Check if there is an API use it. It will make your life easier.\n",
      "- Don't use scrape too much in a short time. It slows down the servers and might gets you banned from the website.\n",
      "- Never scrape anything that is not public.\n",
      "- Check /robots.txt for allowed paths"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Fetch page & build HTML tree"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_element(element):\n",
      "    print \"<%s %s>%s ...\" % (element.tag, element.attrib, element.text_content()[:200].replace(\"\\n\", \" \"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "page = requests.get('http://en.wikipedia.org/wiki/List_of_Nobel_laureates')\n",
      "tree = html.fromstring(page.text)\n",
      "print_element(tree)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<html {'lang': 'en', 'class': 'client-nojs', 'dir': 'ltr'}>   List of Nobel laureates - Wikipedia, the free encyclopedia              a:lang(ar),a:lang(kk-arab),a:lang(mzn),a:lang(ps),a:lang(ur){text-decoration:none} /* cache key: enwiki:resourceloader:filter ...\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Locate The table"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "First we find all tables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tables = tree.xpath('//table')\n",
      "for table in tables:\n",
      "    print_element(table)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<table {'class': 'wikitable sortable'}>  Year Physics Chemistry Physiology or Medicine Literature Peace Economics   1901 R\u00f6ntgen, WilhelmWilhelm R\u00f6ntgen Hoff, Jacobus Henricus van 'tJacobus Henricus van 't Hoff von Behring, Emil AdolfEmil  ...\n",
        "<table {'style': 'border:1px solid #aaa;background-color:#f9f9f9', 'class': 'mbox-small plainlinks'}>   Wikimedia Commons has media related to Nobel laureates.   ...\n",
        "<table {'style': 'border-spacing:0;', 'cellspacing': '0', 'class': 'navbox'}>        v t e   Nobel Prizes       Prizes    Chemistry Economics1 Literature Peace Physics Physiology or Medicine         Laureates     by subject    Chemistry Economics Literature Peace Physics Physi ...\n",
        "<table {'style': 'border-spacing:0;background:transparent;color:inherit;', 'cellspacing': '0', 'class': 'nowraplinks hlist collapsible collapsed navbox-inner'}>     v t e   Nobel Prizes       Prizes    Chemistry Economics1 Literature Peace Physics Physiology or Medicine         Laureates     by subject    Chemistry Economics Literature Peace Physics Physiolo ...\n",
        "<table {'style': 'border-spacing:0;', 'cellspacing': '0', 'class': 'nowraplinks navbox-subgroup'}>  by subject    Chemistry Economics Literature Peace Physics Physiology or Medicine         by criterion    Country University affiliation Female Black Christians Chinese Indian Muslim Japanese Jewish ...\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**When locating the table watchout for client side javascript alteration to the HTML code**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table = tree.xpath('//table[@class=\"wikitable sortable\"]')[0]\n",
      "print_element(table)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<table {'class': 'wikitable sortable'}>  Year Physics Chemistry Physiology or Medicine Literature Peace Economics   1901 R\u00f6ntgen, WilhelmWilhelm R\u00f6ntgen Hoff, Jacobus Henricus van 'tJacobus Henricus van 't Hoff von Behring, Emil AdolfEmil  ...\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extract the Subjects & Years"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subjects = [subject[0].text_content().replace(\"\\n\",\" \") for subject in table.xpath('tr')[0][1:]]\n",
      "subjects"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "['Physics',\n",
        " 'Chemistry',\n",
        " 'Physiology or Medicine',\n",
        " 'Literature',\n",
        " 'Peace',\n",
        " 'Economics']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = [item[0].text for item in table.xpath('tr')[1:-1]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extract Winners Data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Testing for a sigle years"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for index, item in enumerate(table.xpath('tr')[1][1:]):\n",
      "    subject = subjects[index]\n",
      "    print \"%s:\" % subject\n",
      "    for winner in item.xpath('span[@class=\"vcard\"]/span/a'):\n",
      "        winner_name = winner.attrib[\"title\"]\n",
      "        winner_url = winner.attrib[\"href\"]\n",
      "        print \" - %s\" % winner_name\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Physics:\n",
        " - Wilhelm R\u00f6ntgen\n",
        "Chemistry:\n",
        " - Jacobus Henricus van 't Hoff\n",
        "Physiology or Medicine:\n",
        " - Emil Adolf von Behring\n",
        "Literature:\n",
        " - Sully Prudhomme\n",
        "Peace:\n",
        " - Henry Dunant\n",
        " - Fr\u00e9d\u00e9ric Passy\n",
        "Economics:\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Extract The complete table"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "year_list = []\n",
      "subject_list = []\n",
      "name_list = []\n",
      "url_list = []\n",
      "for y_index, year in enumerate(years):\n",
      "    #print year\n",
      "    for index, item in enumerate(table.xpath('tr')[y_index + 1][1:]):\n",
      "        subject = subjects[index]\n",
      "        #print \"%s:\" % subject\n",
      "        for winner in item.xpath('span[@class=\"vcard\"]/span/a'):\n",
      "            winner_name = winner.attrib[\"title\"]\n",
      "            winner_url = winner.attrib[\"href\"]\n",
      "            #print \" - %s\" % winner_name\n",
      "            year_list.append(year)\n",
      "            subject_list.append(subject)\n",
      "            name_list.append(winner_name)\n",
      "            url_list.append(winner_url)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Post Processing in Pandas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_set = pd.DataFrame(name_list, columns=[\"winner_name\"])\n",
      "data_set[\"subject\"] = subject_list\n",
      "data_set[\"year\"] = year_list\n",
      "data_set[\"year\"] = data_set[\"year\"].astype(np.int32)\n",
      "data_set[\"url\"] = url_list\n",
      "data_set.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>winner_name</th>\n",
        "      <th>subject</th>\n",
        "      <th>year</th>\n",
        "      <th>url</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>              Wilhelm R\u00f6ntgen</td>\n",
        "      <td>                Physics</td>\n",
        "      <td> 1901</td>\n",
        "      <td>           /wiki/Wilhelm_R%C3%B6ntgen</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Jacobus Henricus van 't Hoff</td>\n",
        "      <td>              Chemistry</td>\n",
        "      <td> 1901</td>\n",
        "      <td> /wiki/Jacobus_Henricus_van_%27t_Hoff</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>       Emil Adolf von Behring</td>\n",
        "      <td> Physiology or Medicine</td>\n",
        "      <td> 1901</td>\n",
        "      <td>         /wiki/Emil_Adolf_von_Behring</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>              Sully Prudhomme</td>\n",
        "      <td>             Literature</td>\n",
        "      <td> 1901</td>\n",
        "      <td>                /wiki/Sully_Prudhomme</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>                 Henry Dunant</td>\n",
        "      <td>                  Peace</td>\n",
        "      <td> 1901</td>\n",
        "      <td>                   /wiki/Henry_Dunant</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                    winner_name                 subject  year                                   url\n",
        "0               Wilhelm R\u00f6ntgen                 Physics  1901            /wiki/Wilhelm_R%C3%B6ntgen\n",
        "1  Jacobus Henricus van 't Hoff               Chemistry  1901  /wiki/Jacobus_Henricus_van_%27t_Hoff\n",
        "2        Emil Adolf von Behring  Physiology or Medicine  1901          /wiki/Emil_Adolf_von_Behring\n",
        "3               Sully Prudhomme              Literature  1901                 /wiki/Sully_Prudhomme\n",
        "4                  Henry Dunant                   Peace  1901                    /wiki/Henry_Dunant"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Looking at the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years_df = data_set[\"year\"].value_counts().sort_index()\n",
      "years_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "1901    6\n",
        "1902    7\n",
        "1903    7\n",
        "1904    5\n",
        "1905    5\n",
        "1906    6\n",
        "1907    6\n",
        "1908    7\n",
        "1909    7\n",
        "1910    4\n",
        "1911    6\n",
        "1912    6\n",
        "1913    5\n",
        "1914    3\n",
        "1915    4\n",
        "...\n",
        "1999     6\n",
        "2000    13\n",
        "2001    14\n",
        "2002    13\n",
        "2003    11\n",
        "2004    12\n",
        "2005    12\n",
        "2006     8\n",
        "2007    11\n",
        "2008    12\n",
        "2009    13\n",
        "2010    11\n",
        "2011    13\n",
        "2012    10\n",
        "2013    13\n",
        "Length: 110, dtype: int64"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Number of Prizes per Year"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(15,5))\n",
      "plt.plot(years_df.index, years_df.values, linewidth=2, alpha=.6)\n",
      "plt.grid()\n",
      "plt.xlabel(\"Year\")\n",
      "plt.ylabel(\"Number of Prizes\")\n",
      "plt.show();\n",
      "print \"Total Prizes: %s\" % len(data_set)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0uQs4ZWVMWafSOX0Ltdbd44+7ASys8PFJDZK12HiSXWgrJAq0l/LJmujbvFmK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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc3daf60dd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total Prizes: 853\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years_df.value_counts()\n",
      "plt.bar(years_df.value_counts().index, years_df.value_counts())\n",
      "plt.box(on=\"off\")\n",
      "plt.grid()\n",
      "plt.xlabel(\"Number of Nober Prizes/Year\")\n",
      "plt.xlabel(\"\")\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc3da7bb910>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "By Subject"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(13,5))\n",
      "\n",
      "for subject in subjects:\n",
      "    df = data_set[data_set[\"subject\"]==subject][\"year\"].value_counts().sort_index().cumsum()\n",
      "    plt.plot(df.index, df, label=subject, linewidth=2, alpha=.6)\n",
      "\n",
      "\n",
      "plt.grid()\n",
      "plt.legend(loc=\"best\")\n",
      "plt.xlabel(\"Year\")\n",
      "plt.ylabel(\"Cumulative Sum of Given Nobel Prizes\")\n",
      "plt.xticks(np.arange(1900, 2020, 10))\n",
      "\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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sJhmIjTXJQXrWuJ7u7tCiBbRrZwY2KsFLtgIrbILwudb6GaVUNLkMN6q1zr3j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GkNV68GXYbeeCMYUNzuqhOKxeDBB4OBxI88EnQjejssy+Xa5Rozl2ssXGvQPN/AWrDxfA2uy0pf\ni6ulRWaKU3hehd5amYFqGTPWojmYYGUshZcxUEqRT+YppAoUUgWKZpF8Mk/RLLa3FVIFesweMonb\n7OMkhBAR5zjLLC39Oc3m69j2NFp7644xjGx7WtJUaoxUqkQyOSJTk4pdTboYCXGDcjkoCs6cgZmZ\ntfv6+9d2Hbr33qA70a3QWuNbfjBmYMVlpWwzs9BkfqnF8kyLxngLd84B1w+mNnIdcByspE15aBa7\n+CYJ9wqDlSUGqsuk/RazD43hPHGSfYffQalQYn9+Pz1mD7lkjpiSwcNCCHEneF6LcvnrlMt/0bFw\nmSKZHOwoBoKCIB6X8Vui+0iB0MWi1h8O7kxmxwmmH712DV58ES5eXO3DH9z8nzoFJ04EhcGmi46F\nfNen/koda9zCqwaFgFNxWVmyqSzb1Jsudd+j5no4WgMaLBtsK2iecB2MVJ1UukI6vsDFuad5rDCH\n6dSp9qSpDBToPXqCgyfex/DJ91BM79tzv3ii9r6OWl6IXmbJu3do7bOy8l0WF/83rlsBIJ9/jPPn\nc3zoQz9NLBaNbpp7+RpvJmqZZQyCiAytgwXIblyUbG4uWCJgVSIRFARPPRV0H3qrgcVaa1pXW1TO\nVFg6W6FatYPBw55PzXNp+H674PAT4OQUTsoFr0qhMU8uvkA6NUfOHydjvUEj1mCpaLLQn+VKT4vq\nk0foP3KcU4fezQdHHiOdSN+5fyQhhBBrBAONV2g232Bx8SvY9jQA6fS9DAz8NOn0fVy69K3IFAdC\nSAuC6FqeF0w7uloMrBYEtdr6Y2MxGB4OugwdOxasRPxWU49qrZmZazL+9BJLZ1ZoTFvUPR/L92kM\nx1g+mggKgXwMJ6co9CQZSfuMzk5QOv8yg69fwlseZ742x2TG49p9/SwO5ij356jnU/Rm+hgrjnGw\n5yBPjj5Jf6b/zvxDCSGEaAsGGs+sGWQcDDS+/ssjkRhgYOBj5HKP7bkWXCFWSRcj0fXq9fWtAtPT\n6wcUQzDTUOcYglIpWJvgZoOLLd9n0rKYsCwmKk3KL1Zxn6uT+aGDCt9uTk6xeDxB5TGT3lKKUqPB\n2NISpZkZRq9dwxwfx2vUKTeXmK3PMuNXuXy4n8vHBqmXBjkx/AgHeg5QKpQoFUoycFgIIe4wz6vT\nao2vmYLUtqduOtA4l3uEYvE9MsBY7HlSIHSxqPWH83344he/xYED71tTDGy0KBnAwMD6YqC3d/Pp\nR7XWlF03KAQ6HnOWRe6yR/85m32vOhjheLREMkb8WJz8EYf9hSXGZqYYuHqV2OwsttOibtep2TXq\nTp26XWcp4TI3lOfysUGmD/XzwMjDPFV6iuNDx0kYG1coUbvGEL3MUcsL0csseXeW1j6OM49lTawp\nCFx3o18etzfQeLdlvtOilheil1nGIIhdybLWdw+anAwWI9u/f+2xyWSwIvHqgmSrKxTfbECx6/tM\n2zYTlsV4q8VUucX8bBNv0SW57JFasEkuuvSXXUpLHvmmR851yToOhXyV0d4ZBtPjxK7VaLzZoG4H\nRcAFu0bNbbBQiFPuy1Luz1LuH6Tcn8POmpQKJU6XTvHk6JMUUrc4/ZEQQohb4nktbHtyTTFg25Md\nMw1dF4ulbigESqRSozKWQIi3IC0I4o7TOmgBuLGL0Pz89ZmEOu3bt3Z14lIpaCm42UDiiuMwvtBk\narrO7FyL8myL+oJFfMklNdciuehgNIIpRROuS9a2ybpuUBC4LhnPI5myyAzMEe+5ihVfpG7XqNt1\nlmMWC70Zlvuz7YJguTdL0sy0uwuNFcfaU5AmjeSd+8cUQoiICAYOL63pHtRqjeM48xsen0j0rlmH\nIJUqkUgMyBgCITYhXYzEXeO6wWJjnS0DExPBGIIbGUbQUtDZPahUglxu86/veT5T43UmL9dYuNqg\nMt6kPmejl1xiLgRTilrQbIJtk3Ycso5L1nXI0iIXq5KOVSDVwM3bWAWXZtGm2tNi3qwxG2/RyiRo\nZpK0MkmamSRO0qA/O9AuBlYLgr50n/ziEUKIbeD7DrY9taYYCAYON9Ydq1ScVGr/DQuTjWIY2R04\ncyG6lxQIXWw39YfTOpghqFJZ/1heDroHTU+vnU50VS63fqzA8DDEN+jktpq56XmMt1pMvlZl6bkK\ntctNnCkLnPWvMRyHlFslac2Qdhcw9Tx5dxY/WWG532VmyGZhOEG5L0e1aGKlExsOVEgYCUbzo2uK\ngdHC6B0dULybrvHdErXMUcsL0csseW+d61bWzB4UdBGaQev1vzwMI98uBExzdYXiYZQy3maCrZNr\nvPdFLbOMQRC3TGu4fBnOnw9u+lcLgJUVqFY3vvnvpNT16UQ7C4Ji8eYDhxcdJxgrYFn83/l5vvX8\n66jnG/S95GAurf2fxvYZGAMesdQCSfcyfVefJT97EcuvgtKUR3K8fGyQyQO9VHv62//jdCJNMVXk\nQKpAYYNHf6afodyQrE4shBDbQGt/w+lEVxcf66RUjGRyZM1YAdMcwzAK0lIrxA6QFgQBwNISPPNM\n8Jid3fy4bDZYefjGR7EIIyNBl6HkBl3wXd+n4nlUXJeK57Hsukx1zCLUsjzScx7ZSY++8w75qx4x\nBWmliKWa+KNzxI1L5Jdewpx6g1hjbbNzK5Pk6rFh6o89TPHwQ5QKJQazg2sKgM1mERJCCPH2eF5j\nXfcgy5pC6/VNvrGYeUP3oBKp1H5iMRm/JcTdJF2MxDpaw+IivP46nDkDP/jB9X3FIjz+eHCz31kA\n5PMbdwlyfZ/Xm03KrhsUAI7LSsulZrnUwo8tyyPmgXI1MQcMR2PO+WRmPdIzHrkFD9N1Ma0GiXqF\nRGOJZPw1jNhLYE6j1Npr7mcyqFKJ5IF7yRw/Se/j72akOEpc5q0WQog7RmuN4yx0FAFBy4DjLG54\nfCLRv2460URCxm8JsRtIgdDFtqM/XKvmM3HBYfqaz/S4ZnZKMz+tsZuamO+jtCapNIfv1TxwRFMa\n1uD5aEejXR18DJ/7jt/+fKlpM1FvMVO38Wwf5elgoLAH4ONrHT48tPaI+R4x30X5LjHPJtFqYtZr\nZGpVss0mRmwZZS7zff88j442UIYLSuEP9BMbu4f0oaMUDz/E4NHHKA4fQN1sWqMuErU+jxC9zFHL\nC9HLvBfzau3Tal3r6B40jmVN4vstzp6d4tSp6/NRK5UglRpdN6WoYaR3MMH22ovX+Gailheil1nG\nIESE72uWxl0mXrSYu2BRvmTRuGrhLTjgXy+q0sA9QCIJhYxH3z6P/n0+xpwPMx5l38dzHTzXxvMc\nPNfBd118z6HpeywSYzEWp6VioH201piOhelYGK6D4TnEfYuEb5PwLFJui7h2UMoD5RE0JXiQrIFZ\nhv4yiXQDI5vCHh7AcEbo+ZEP0nv0BINHHiGVlbUEhBDibrGsSVZWzlCtPovrrqzbH48XSadT9Pa+\nv10IJJNDKBm/JcSeIS0IXcq1NRMv2Uy91GLhNYvqmxatcQtdX798PDFFoi/OvlyD3uQKxdgKOV0m\n1ViCVh1XW9h+k5bfwPIbWH4d229RS5ssZ9OUczkW81mW8lkqGRM/pvENTcprcXj+CkfnrtBfXwpu\n+pUPyseIGcRjBvFYnISRJBFPETfTJMwscTNDMpMnMTRC8sC9pA8ewbjnQLDYgTQ7CyHEXee6FarV\n51hZOYNljbe3JxIDpNP3rllbIB7P7+CZCiG2i3Qx6nKVOY/xFyxmzrcoX7KoX7FwZmzw1v87qLRB\neiRGb3+Vod4lhgrTDMSukZifwLVarLgNZnCZw2Eu5jOfUCyn01TSaepmmpqZoZ4yqaez+IkUyjBQ\nRpyYEUcZBsmYwVHP4hHf4f5EDDOdI2lmMdMFUpk8qXQeM5MnbmaC0cqJRPDRuPtT0gkhhFjP8+ob\nDCieaE8zahgZ8vknKBROY5qHZLyAEHuUFAhdwvc10686TJ6zmH/VYuX1FucufJuj+vENjzd6ExRG\nXAb6VxgqzjGcnSDfvAaL80wb8EZS8WYqzhUzxZSZYiFjUslnsMw4ViqBZcZxkgZm3CSbzJJL5sgm\nsmSTWcx4mt54nFIqRSmVYsw0KaVSDCQSxO7wL4uo9QGMWl6IXuao5YXoZd7NebXWtFpvsrJyhkbj\nAo6ztO4YpWJksw9TKJwmmz1O7C0mfNjNee+UqGWOWl6IXmYZg7CLTV2wOfeFCgvPN7AnLXDWzvmv\n6x70xEgNx+kdqDPYV2a4MM1g4hqp8gROq8VUOs2bsQTfsF3eTBtcu3+USiaFZSawUnGsdPCRuEEu\nkaE/lWHIzDOa6WEs28dgKkMhHqdgGO2PxXictPzVXwghupZtL1CtnmVl5QyOM9/eHoslSSZHb1h8\nbBTDMHfwbIUQu4m0IOyA6oLHi5+vcvXPKtiXm2v2xYpxcsM+fX0VhnsXGM5Oss+7Smxhjkosxngm\nw0T4uJY2uZJOUk64LBsuLTMetgrEyRgxxkyTezN5Hiz083DPCMeKoxTiCWkuFkKIPUZrjeuutLsL\nNRoXaDQutffH4z0UCqfJ558gldovA4qFENLFaKe5LkxNasa/U2fiaxWqL9XA0YAmpZoMHq5z37F5\n9ufHyaxcxa/VmEmnmUinrxcE2SwrhQK2maCZirFsOMxQw4lp0rpFnhYPF4d59/D9nOq/l7HMPmJ7\nZBpQIYQQa2ntU6+/SqPxWrso8LzammNisSS53KMUCk+RyRyTokAIsYYUCHdRtQoTE9cfcxdaeOcr\n7Jupkm1UyNhlUk6dnuI8peHL9N5XZrbHZCKTCYqBdJrpfB4rl6WVMrh68QLmyYdYMRxabo20VyOr\nm+R0nazf5MHiEO8eO83j+x8nm8zudPxtEbU+gFHLC9HLHLW8EL3MdytvqzVBpfIM1epZXLeyZp9h\nZNszDZnmQbLZ43es21DUri9EL3PU8kL0MssYhDvA92Fu7nohMD4efFxehrjt0j9XoW+uwmClRr41\nR68zi5FZpnW0Tv2EzfmhBF/NjrBYOIabTtFMxagnoBL3WFEWOAvkdANLX2bAajCiG6S0TX+mj1Kh\nxIHiCU7uP8lwbnin/ymEEELcQa5boVJ5lkrlmTVTkCaTQ+Tzj2OaB0mlxojHe6QLqRBiW0gLwi1o\ntda2CkxMwOQk2HbHefk++xZrDC1WGGlVSKslDG8Jx6gxf9jmzQc8FkditPblaJgxanHNStyl4TVI\neVWyfoOcbpD1G2R1g7RhMJIboVQoMVYco1QoUSqUyCQydzW7EEKIu8/3Her1l9szD12fgjRLPv84\nhcJTmOZBKQiEELdNuhjdIq1haWlti8DEBMzPb3z8vh6PA8Y0vTOTpMareI5NC5cGPuVRi5mDNeZG\nalSLSZbycWppl4yuk9XNdkGQ1i2KqUK7AFgtBoZzw8Skv6gQQkRG53Sk1erz+H4wiYVSBtnsO255\nClIhhLgVUiBswHFgamptq8D4ODSb64+Nx2FoyKO3p4ap6iRWluDqDO54A7+h0fj42qeyr8XUgQrT\no8vEklX8vI/VHyOZdMj6DUzlM5wbbhcDqwVBIVXY9Dyj1h8Oopc5ankhepmjlheil3mreX3fxbZn\nblisbHzNQGPTvIdC4Sny+Sd23erFUbu+EL3MUcsL0cssYxA6vPEGfOlLwUffD7oGxR2PpO2Ssj0G\n4i59RZt0roURt1C+jWvZOK/Z+LUGbqOGY9n4+GjtY6VarIwtsHJoBmewBlmPniKkzQxjhbE1xcD+\n/H4SRmKn/wmEEELsAM9rUq2+QLV6lmbzh2jtrTsmmI70SQqFp0il9u/AWQohRERaELSvmXzF5huf\nsxh/0SJdtzAth2LCo5D0yGTByzjUEw51XCzfx3NsPLuFb1toxwbbxk9WiSWXSCTnaQ7XWbzfZuE+\ng4HC4LpioDfdK31DhRAi4rT2aTReY2XlDLXaObR2wj2KZHIgnHVorD37UDy+T353CCHuish1MdJa\n07raonq2yvJrTV4/YzEzodE+xAwolYJHC49pq8lVXWXFaNCK1WmpBr6uknHLZFlTYcIAAAwESURB\nVL0yRXeBfa15eu0ZKmNZ6o89jPH4E4wO3EepUGK0MIoZl9UnhRBCgO9bWNZku8tQrfYSrrvS3p/J\nHKNQeIpc7hEMI72DZyqEiLquKhCUUh8BPgMYwH/UWv/GDfs3LRCcskPlbIXKmQrVqzazs8HYAs8F\ny4zTc6xK5ugEs6lpXkk2mEq4uLoBQG+9wkOTb3Jkdpx9jSp+T5HY2BjmgcPkDz9I34OPMzB27K4P\nHI5afziIXuao5YXoZY5aXtj7mYOVi5exrHEsa4JvfvP/cPJkFtueA9b+jkomhygUTlMonCKR6NuZ\nE95me/36biRqmaOWF6KXuWvGICilDOB3gR8BJoHnlFJf1lq/ttlrfMun+v0qlWcq1C40WFyA6RmP\n2eYis8MzzJ6cprF/hWavj2Osvbk3bYt3zI7zoFNltL+X7DtP0XP47zJ47DFyvbtjfYFz585F6s0K\n0csctbwQvcxRywt7L7PnNanVzrULgmDl4np7/wsvvMzDDx9HKYNkcqTdZSidvg/TPLTnug3ttet7\nK6KWOWp5IZqZN7OrCgTgSeANrfUVAKXUF4CPApsWCAsX5zn7z19moVyj5rWYHqlw5WSVueEm8UQw\nA1EsBhDDxGfAgP35LMcGBjl17/2MHD5OPLl7uwgtLy/v9CncdVHLHLW8EL3MUcsLey+z71vMzPzn\nNduClYuD8QNauxw48M9IJocjMQ3pXru+tyJqmaOWF6KZeTO77afYKDDe8fkEcOpmL/je5A/5bvYy\niweaTO+v4ad9EgaMGB5DpkGp2MN9o2McP3KcseEDxAzjjgYQQgix98TjRYrFd5JIDLSLgs6Vi03z\nAqZZ2uGzFEKI7bHbCoQtD4j48Psf5XNnv85AKs1fOTjCo8fu4+GjJyjke+7E+d11V65c2elTuOui\nljlqeSF6maOWF/ZeZqUUw8Of3HT/Xsv7VqKWF6KXOWp5IZqZN7OrBikrpU4Dn9ZafyT8/NcAv3Og\nslJq95ywEEIIIYQQXaorZjFSSsWBHwAfBKaAZ4G/frNBykIIIYQQQojts6u6GGmtXaXULwBfI5jm\n9PelOBBCCCGEEOLu2VUtCEIIIYQQQoiddXdX/dqAUuoPlFKzSqnzHdtOKKXOKKVeVkp9WSmV79j3\na0qp15VSF5VSH+7YflIpdT7c99t3O8et2kpepVSvUuqbSqmqUup3bvg6XZEXtpz5Q0qp58Ptzyul\n3t/xmq7IvMW8Tyqlvh8+XlZK/bWO13RFXtj693G4/x6lVE0p9Usd27oi8xav8UGlVLPjOn+24zVd\nkRdu62f18XDfK+H+ZLi9KzJv8Rr/XMf1/b5SylNKHQ/3dUVe2HJmUyn1+XD7q0qpf9zxmq7IvMW8\nSaXUfwq3n1NKvbfjNd2Sd0wF9xQXwu/LT4Xbe5VSf6GUuqSU+rpSqqfjNd1+z7WlzGqP3HdtC631\njj6AdwOPAuc7tj0HvDt8/reBfxU+fxA4BySAg8AbXG8FeRZ4Mnz+VeAjO51tG/JmgL8E/D3gd274\nOl2R9zYyPwIMh88fAia6LfMW86aBWPh8GFgAjG7Ku9XMHfv/BPhj4Jf2+DU+2HncDV+nK/LeRuY4\n8BLwcPj5vo73eVdkvp33dLj9HQTr+ez1a/y3gM+Hz9PAZeCebsq8xbz/gKDbM8AA8Hy3XWOC3zGP\nhM9zBGM+HwB+E/iVcPuvAr8ePt8L91xbzbwn7ru247HjLQha6+8A5Rs2Hwm3A3wD+Knw+UcJfiA5\nOlhM7Q3glFJqBMhrrZ8Nj/sj4Cfu7Jnfnq3k1Vo3tNZPA1bnwd2UF7ac+ZzWeibc/iqQVkoluinz\nFvM2tdZ+uD0NrGitvW7KC1v+PkYp9RPAmwTXeHVb12Teat6NdFNe2HLmDwMva63Ph68ta639bsr8\nNq7x3wA+D3v+Gk8DWaWUAWQBG6h0U+Yt5n0A+Gb4unlgWSn1RJflndFanwuf1wgWoR0Ffhz4w/Cw\nP+T6+e+Fe64tZd4r913bYccLhE1cUEp9NHz+cWAsfL6fYPG0VRMEF/rG7ZPh9m6xWd5VNw4UGaW7\n88JbZ4bgB/MLWmuH7s+8aV4VdDO6AFwAfjHc3O15YZPMSqkc8CvAp284vtsz3+w9fSjsevItpdS7\nwm3dnhc2z3wU0EqpP1dKvaCU+kfh9m7PfCs/t36GsECg+/PCJpm11l8DKgSFwhXg32mtl+n+zJtd\n45eAH1dKGUqpQ8BJoESX5lVKHSRoPTkLDGmtZ8Nds8BQ+HxP3XPdYuZVe/G+a0t2a4Hwd4C/r5R6\nnqBJyN7h87nTopYX3iKzUuoh4NcJmvn2gk3zaq2f1Vo/BDwG/LZSqrhD57jdNsv8aeC3tNYNYMP5\nl7vUZnmngDGt9aMEBeDn1A3jMbrYZpnjwLsI/pr+LuBjSqkPcBuLYe4yb/Vz6xTQ0Fq/utGLu9SG\nmZVSP0/Q6jkCHAJ+Obxx7nabXeM/ILhBfB74LeB7gEcXvqfDP9L8D+Afaq2rnft00H+m6zK9lShm\nfrt21TSnq7TWPwB+FEApdRT4sXDXJGv/YlMi+IadDJ93bp+882e6PW6SdzNdnRdunlkpVQL+J/AJ\nrfXlcHNXZ76Va6y1vqiU+iFwmOB93bV5YcPMfznc9STwU0qp3wR6AF8p1SS45l2bebNrrLW2CW8y\ntNYvhtf4CF3+noabvq/Hgf+ntV4K932VoAD+r3Rx5lv4Pv5Z4HMdn+/Fa7z6ffxO4H9prT1gXin1\nNMFf1b9LF2e+yfexx/UWXsK8l4AVuiivUipBcKP8X7TWXww3zyqlhrXWM2FXmrlw+56459pi5s10\nVebtsCtbEJRSA+HHGPBPgf8Q7voy8LMqmE3gEMEv2WfDPusVpdQppZQCPgF8cYMvvSvdJG/7kM5P\ntNbTdHFe2DxzOJPAnwK/qrU+s3p8t2e+Sd6DKlggEKXUAYL39Ovd/p6GDTP/HoDW+j1a60Na60PA\nZ4B/o7X+bLdnvsk17g/7aaOUupfgGr/Z7e9puOnPrq8BDyul0uH7+73Ahb16jTu2fRz4wuq2PXqN\nfy/cdRH4QLgvC5wGLu7Vaxy+l7Ph8w8Bjtb6Yjdd4/D8fh94VWv9mY5dXwY+GT7/JNfPv+vvuW4j\nc/ulnZ9003XeNm93lPPbfRD01Zwi+AvbOEHz3qcIRpr/APi3Nxz/TwgGylwEfrRj+0ngfLjv3+90\nrm3MewVYBKrh8fd3U96tZib4gVwDvt/x6O+mzFvM+/PAK2HOZ+mYFaFb8t7O+7rjdf8C+MVuy7zF\na/yTHdf4BeDHui3v7Vxj4OfC3OcJZwjppsy3kfd9wPc2+DpdkXermYEUQYvQeYLxU52zkXVF5i3m\nPUhw3/Eq8HWCboPdlvddgE8wM9Hq79ePAL0EA7Ivhdl6Ol7T7fdct5P5Cl1+37UdD1koTQghhBBC\nCNG2K7sYCSGEEEIIIXaGFAhCCCGEEEKINikQhBBCCCGEEG1SIAghhBBCCCHapEAQQgghhBBCtEmB\nIIQQQgghhGiTAkEIIcQtUYHvKKU+0rHt40qpP9vJ8xJCCLG9ZB0EIYQQt0wp9RDw34FHgQTwIsEC\nSpdv42vFtdbuNp+iEEKIt0kKBCGEEFuilPoNoAFkCVY+PwC8g6Bg+LTW+stKqYPAH4XHAPyC1vqM\nUup9wL8GlghWKD12d89eCCHEW5ECQQghxJYopTIELQc28BXggtb6vymleoCzBK0LGvC11pZS6gjw\nOa31E2GB8BXgIa311Z1JIIQQ4mbiO30CQgghuovWuqGU+mOC1oOfAf6qUuqXw90pYAyYAX5XKXUC\n8IAjHV/iWSkOhBBi95ICQQghxO3ww4cCflJr/XrnTqXUp4FprfUnlFIG0OrYXb9rZymEEGLLZBYj\nIYQQb8fXgE+tfqKUejR8WiBoRQD4m4Bxl89LCCHEbZICQQghxO3SBAOOE0qpl5VSrwD/Mtz3WeCT\nSqlzwDGC7kidrxNCCLFLySBlIYQQQgghRJu0IAghhBBCCCHapEAQQgghhBBCtEmBIIQQQgghhGiT\nAkEIIYQQQgjRJgWCEEIIIYQQok0KBCGEEEIIIUSbFAhCCCGEEEKINikQhBBCCCGEEG3/HwKudZY5\nzlIzAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc3daf01710>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "The effects of WW I and WW II"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(13,5))\n",
      "\n",
      "for subject in subjects:\n",
      "    df = data_set[(data_set[\"subject\"]==subject) &\n",
      "                  (data_set[\"year\"].astype(np.int32)<1950)][\"year\"].value_counts().sort_index().cumsum()\n",
      "    plt.plot(df.index, df, label=subject, linewidth=2, alpha=.6)\n",
      "\n",
      "plt.grid()\n",
      "plt.legend(loc=\"best\")\n",
      "plt.xlabel(\"Year\")\n",
      "plt.ylabel(\"Cumulative Sum of Given Nobel Prizes\")\n",
      "plt.xticks(np.arange(1900, 1950, 5))\n",
      "\n",
      "gca = plt.gca()\n",
      "\n",
      "gca.add_patch(plt.Rectangle((1914,0), 4, 60, alpha=.3, color=\"orange\"))\n",
      "gca.add_patch(plt.Rectangle((1939,0), (45-39), 60, alpha=.3, color=\"orange\"))\n",
      "\n",
      "plt.annotate(s=\"WW I\", xy=(1915,55))\n",
      "plt.annotate(s=\"WW II\", xy=(1941,55))\n",
      "plt.show();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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puOUWu1QC0tJgwwZ47TUjouijj2DnTigogHbt4I9/hOnTr/1jXK5eNffe++29drvWe6Pe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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fc3d2e79390>"
       ]
      }
     ],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/data-mining/2. YouTube Data.ipynb
================================================
  {
 "metadata": {
  "name": "",
  "signature": "sha256:3dd2f282369fba3f33bc12f052ab125052158b319a86dc7b60158f9eac201e91"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will cover some functions from YouTube Data API v3 from Google Developer Console.\n",
      "\n",
      "**Important Links**:\n",
      "\n",
      "- [Google Developer Console](https://console.developers.google.com)\n",
      "- [YouTube Data API v3 Documentation](https://developers.google.com/youtube/v3/)\n",
      "\n",
      "**We will use the following function**:\n",
      "\n",
      "- youtube.search.list [Doc](https://developers.google.com/youtube/v3/docs/search/list)\n",
      "- youtube.videos.list [Doc](https://developers.google.com/youtube/v3/docs/videos/list)\n",
      "\n",
      "**Video Tutorial**:\n",
      "\n",
      "https://www.youtube.com/watch?v=bCrCkfSyuNE"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Google APIs"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is a Python Google Library. But we will be using HTTP requests to access the API."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "api_key = \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 73
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Import Libraries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "from datetime import datetime \n",
      "import requests\n",
      "from lxml import html, etree\n",
      "import json\n",
      "from textblob import TextBlob\n",
      "\n",
      "import pandas as pd\n",
      "\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore')\n",
      "\n",
      "pd.options.display.max_columns = 100\n",
      "pd.options.display.max_rows = 35\n",
      "pd.options.display.width = 120"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Searching YouTube Using `youtube.search.list`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Documentation:\n",
      "\n",
      "https://developers.google.com/youtube/v3/docs/search\n",
      "\n",
      "### HTTPS Request:\n",
      "\n",
      "```\n",
      "GET https://www.googleapis.com/youtube/v3/search\n",
      "```\n",
      "\n",
      "### Parameters:\n",
      "\n",
      "<table class=\"matchpre\">\n",
      "  <thead>\n",
      "    <tr>\n",
      "      <th>Parameter name</th>\n",
      "      <th>Value</th>\n",
      "      <th>Description</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "        <tr id=\"required-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Required parameters</b></td>\n",
      "    </tr>\n",
      "    <tr id=\"part\">\n",
      "      <td><code itemprop=\"property\">part</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>part</strong></code> parameter specifies a comma-separated list of one or more <code>search</code> resource properties that the API response will include. Set the parameter value to <code>snippet</code>. The <code>snippet</code> part has a quota cost of 1 unit.\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"filter-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Filters <span style=\"color:#555\">(specify 0 or 1 of the following parameters)</span></b></td>\n",
      "    </tr>\n",
      "    <tr id=\"forContentOwner\">\n",
      "      <td><code itemprop=\"property\">forContentOwner</code></td>\n",
      "      <td><code class=\"apitype\">boolean</code></td>\n",
      "      <td>This parameter can only be used in a properly <a href=\"/youtube/v3/guides/authentication\">authorized request</a>. <strong>Note:</strong> This parameter is intended exclusively for YouTube content partners.<br><br>The <code><strong>forContentOwner</strong></code> parameter restricts the search to only retrieve resources owned by the content owner specified by the <code><strong>onBehalfOfContentOwner</strong></code> parameter. The user must be authenticated using a CMS account linked to the specified content owner and <code><strong>onBehalfOfContentOwner</strong></code> must be provided.</td>\n",
      "    </tr>\n",
      "    <tr id=\"forMine\">\n",
      "      <td><code itemprop=\"property\">forMine</code></td>\n",
      "      <td><code class=\"apitype\">boolean</code></td>\n",
      "      <td>This parameter can only be used in a properly <a href=\"/youtube/v3/guides/authentication\">authorized request</a>. The <code><strong>forMine</strong></code> parameter restricts the search to only retrieve videos owned by the authenticated user. If you set this parameter to <code>true</code>, then the <code>type</code> parameter's value must also be set to <code>video</code>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"relatedToVideoId\">\n",
      "      <td><code itemprop=\"property\">relatedToVideoId</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>relatedToVideoId</strong></code> parameter retrieves a list of videos that are related to the video that the parameter value identifies. The parameter value must be set to a YouTube video ID and, if you are using this parameter, the <code>type</code> parameter must be set to <code>video</code>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"optional-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Optional parameters</b></td>\n",
      "    </tr>\n",
      "    <tr id=\"channelId\">\n",
      "      <td><code itemprop=\"property\">channelId</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>channelId</strong></code> parameter indicates that the API response should only contain resources created by the channel</td>\n",
      "    </tr>\n",
      "    <tr id=\"channelType\">\n",
      "      <td><code itemprop=\"property\">channelType</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>channelType</strong></code> parameter lets you restrict a search to a particular type of channel.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Return all channels.</li>\n",
      "<li><code><strong>show</strong></code> \u2013 Only retrieve shows.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"eventType\">\n",
      "      <td><code itemprop=\"property\">eventType</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>eventType</strong></code> parameter restricts a search to broadcast events. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>completed</strong></code> \u2013 Only include completed broadcasts.</li>\n",
      "<li><code><strong>live</strong></code> \u2013 Only include active broadcasts.</li>\n",
      "<li><code><strong>upcoming</strong></code> \u2013 Only include upcoming broadcasts.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"location\">\n",
      "      <td><code itemprop=\"property\">location</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>location</strong></code> parameter, in conjunction with the <code>locationRadius</code> parameter, defines a circular geographic area and also restricts a search to videos that specify, in their metadata, a geographic location that falls within that area. The parameter value is a string that specifies latitude/longitude coordinates e.g. (<code>37.42307,-122.08427</code>).<br><br><ul><li>The <code>location</code> parameter value identifies the point at the center of the area.</li><li>The <code>locationRadius</code> parameter specifies the maximum distance that the location associated with a video can be from that point for the video to still be included in the search results.</li></ul>The API returns an error if your request specifies a value for the <code>location</code> parameter but does not also specify a value for the <code>locationRadius</code> parameter.</td>\n",
      "    </tr>\n",
      "    <tr id=\"locationRadius\">\n",
      "      <td><code itemprop=\"property\">locationRadius</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>locationRadius</strong></code> parameter, in conjunction with the <code>location</code> parameter, defines a circular geographic area.<br><br>The parameter value must be a floating point number followed by a measurement unit. Valid measurement units are <code>m</code>, <code>km</code>, <code>ft</code>, and <code>mi</code>. For example, valid parameter values include <code>1500m</code>, <code>5km</code>, <code>10000ft</code>, and <code>0.75mi</code>. The API does not support <code>locationRadius</code> parameter values larger than 1000 kilometers.<br><br><strong>Note:</strong> See the definition of the <code><a href=\"#location\">location</a></code> parameter for more information.</td>\n",
      "    </tr>\n",
      "    <tr id=\"maxResults\">\n",
      "      <td><code itemprop=\"property\">maxResults</code></td>\n",
      "      <td><code class=\"apitype\">unsigned integer</code></td>\n",
      "      <td>The <code><strong>maxResults</strong></code> parameter specifies the maximum number of items that should be returned in the result set. Acceptable values are <code>0</code> to <code>50</code>, inclusive. The default value is <code>5</code>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"onBehalfOfContentOwner\">\n",
      "      <td><code itemprop=\"property\">onBehalfOfContentOwner</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>This parameter can only be used in a properly <a href=\"/youtube/v3/guides/authentication\">authorized request</a>. <strong>Note:</strong> This parameter is intended exclusively for YouTube content partners.<br><br>The <code><strong>onBehalfOfContentOwner</strong></code> parameter indicates that the request's authorization credentials identify a YouTube CMS user who is acting on behalf of the content owner specified in the parameter value. This parameter is intended for YouTube content partners that own and manage many different YouTube channels. It allows content owners to authenticate once and get access to all their video and channel data, without having to provide authentication credentials for each individual channel. The CMS account that the user authenticates with must be linked to the specified YouTube content owner.</td>\n",
      "    </tr>\n",
      "    <tr id=\"order\">\n",
      "      <td><code itemprop=\"property\">order</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>order</strong></code> parameter specifies the method that will be used to order resources in the API response. The default value is <code>relevance</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>date</strong></code> \u2013 Resources are sorted in reverse chronological order based on the date they were created.</li>\n",
      "<li><code><strong>rating</strong></code> \u2013 Resources are sorted from highest to lowest rating.</li>\n",
      "<li><code><strong>relevance</strong></code> \u2013 Resources are sorted based on their relevance to the search query. This is the default value for this parameter.</li>\n",
      "<li><code><strong>title</strong></code> \u2013 Resources are sorted alphabetically by title.</li>\n",
      "<li><code><strong>videoCount</strong></code> \u2013 Channels are sorted in descending order of their number of uploaded videos.</li>\n",
      "<li><code><strong>viewCount</strong></code> \u2013 Resources are sorted from highest to lowest number of views.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"pageToken\">\n",
      "      <td><code itemprop=\"property\">pageToken</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>pageToken</strong></code> parameter identifies a specific page in the result set that should be returned. In an API response, the <code>nextPageToken</code> and <code>prevPageToken</code> properties identify other pages that could be retrieved.</td>\n",
      "    </tr>\n",
      "    <tr id=\"publishedAfter\">\n",
      "      <td><code itemprop=\"property\">publishedAfter</code></td>\n",
      "      <td><code class=\"apitype\">datetime</code></td>\n",
      "      <td>The <code><strong>publishedAfter</strong></code> parameter indicates that the API response should only contain resources created after the specified time. The value is an RFC 3339 formatted date-time value (1970-01-01T00:00:00Z).</td>\n",
      "    </tr>\n",
      "    <tr id=\"publishedBefore\">\n",
      "      <td><code itemprop=\"property\">publishedBefore</code></td>\n",
      "      <td><code class=\"apitype\">datetime</code></td>\n",
      "      <td>The <code><strong>publishedBefore</strong></code> parameter indicates that the API response should only contain resources created before the specified time. The value is an RFC 3339 formatted date-time value (1970-01-01T00:00:00Z).</td>\n",
      "    </tr>\n",
      "    <tr id=\"q\">\n",
      "      <td><code itemprop=\"property\">q</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>q</strong></code> parameter specifies the query term to search for.<br><br>Your request can also use the Boolean NOT (<code>-</code>) and OR (<code>|</code>) operators to exclude videos or to find videos that are associated with one of several search terms. For example, to search for videos matching either \"boating\" or \"sailing\", set the <code>q</code> parameter value to <code>boating|sailing</code>. Similarly, to search for videos matching either \"boating\" or \"sailing\" but not \"fishing\", set the <code>q</code> parameter value to <code>boating|sailing -fishing</code>. Note that the pipe character must be URL-escaped when it is sent in your API request. The URL-escaped value for the pipe character is <code>%7C</code>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"regionCode\">\n",
      "      <td><code itemprop=\"property\">regionCode</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>regionCode</strong></code> parameter instructs the API to return search results for the specified country. The parameter value is an <a href=\"http://www.iso.org/iso/country_codes/iso_3166_code_lists/country_names_and_code_elements.htm\">ISO 3166-1 alpha-2</a> country code.</td>\n",
      "    </tr>\n",
      "    <tr id=\"safeSearch\">\n",
      "      <td><code itemprop=\"property\">safeSearch</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>safeSearch</strong></code> parameter indicates whether the search results should include restricted content as well as standard content.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>moderate</strong></code> \u2013 YouTube will filter some content from search results and, at the least, will filter content that is restricted in your locale. Based on their content, search results could be removed from search results or demoted in search results. This is the default parameter value.</li>\n",
      "<li><code><strong>none</strong></code> \u2013 YouTube will not filter the search result set.</li>\n",
      "<li><code><strong>strict</strong></code> \u2013 YouTube will try to exclude all restricted content from the search result set. Based on their content, search results could be removed from search results or demoted in search results.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"topicId\">\n",
      "      <td><code itemprop=\"property\">topicId</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>topicId</strong></code> parameter indicates that the API response should only contain resources associated with the specified topic. The value identifies a <a href=\"//wiki.freebase.com/wiki/Freebase_API\">Freebase topic ID</a>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"type\">\n",
      "      <td><code itemprop=\"property\">type</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>type</strong></code> parameter restricts a search query to only retrieve a particular type of resource. The value is a comma-separated list of resource types. The default value is <code>video,channel,playlist</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>channel</strong></code></li><li><code><strong>playlist</strong></code></li><li><code><strong>video</strong></code></li></ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoCaption\">\n",
      "      <td><code itemprop=\"property\">videoCaption</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoCaption</strong></code> parameter indicates whether the API should filter video search results based on whether they have captions. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Do not filter results based on caption availability.</li>\n",
      "<li><code><strong>closedCaption</strong></code> \u2013 Only include videos that have captions.</li>\n",
      "<li><code><strong>none</strong></code> \u2013 Only include videos that do not have captions.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoCategoryId\">\n",
      "      <td><code itemprop=\"property\">videoCategoryId</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoCategoryId</strong></code> parameter filters video search results based on their <a href=\"/youtube/v3/docs/videoCategories\">category</a>. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoDefinition\">\n",
      "      <td><code itemprop=\"property\">videoDefinition</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoDefinition</strong></code> parameter lets you restrict a search to only include either high definition (HD) or standard definition (SD) videos. HD videos are available for playback in at least 720p, though higher resolutions, like 1080p, might also be available. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Return all videos, regardless of their resolution.</li>\n",
      "<li><code><strong>high</strong></code> \u2013 Only retrieve HD videos.</li>\n",
      "<li><code><strong>standard</strong></code> \u2013 Only retrieve videos in standard definition.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoDimension\">\n",
      "      <td><code itemprop=\"property\">videoDimension</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoDimension</strong></code> parameter lets you restrict a search to only retrieve 2D or 3D videos. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>2d</strong></code> \u2013 Restrict search results to exclude 3D videos.</li>\n",
      "<li><code><strong>3d</strong></code> \u2013 Restrict search results to only include 3D videos.</li>\n",
      "<li><code><strong>any</strong></code> \u2013 Include both 3D and non-3D videos in returned results. This is the default value.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoDuration\">\n",
      "      <td><code itemprop=\"property\">videoDuration</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoDuration</strong></code> parameter filters video search results based on their duration. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Do not filter video search results based on their duration. This is the default value.</li>\n",
      "<li><code><strong>long</strong></code> \u2013 Only include videos longer than 20 minutes.</li>\n",
      "<li><code><strong>medium</strong></code> \u2013 Only include videos that are between four and 20 minutes long (inclusive).</li>\n",
      "<li><code><strong>short</strong></code> \u2013 Only include videos that are less than four minutes long.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoEmbeddable\">\n",
      "      <td><code itemprop=\"property\">videoEmbeddable</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoEmbeddable</strong></code> parameter lets you to restrict a search to only videos that can be embedded into a webpage. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Return all videos, embeddable or not.</li>\n",
      "<li><code><strong>true</strong></code> \u2013 Only retrieve embeddable videos.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoLicense\">\n",
      "      <td><code itemprop=\"property\">videoLicense</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoLicense</strong></code> parameter filters search results to only include videos with a particular license. YouTube lets video uploaders choose to attach either the Creative Commons license or the standard YouTube license to each of their videos. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Return all videos, regardless of which license they have, that match the query parameters.</li>\n",
      "<li><code><strong>creativeCommon</strong></code> \u2013 Only return videos that have a Creative Commons license. Users can reuse videos with this license in other videos that they create. <a href=\"http://www.google.com/support/youtube/bin/answer.py?answer=1284989\">Learn more</a>.</li>\n",
      "<li><code><strong>youtube</strong></code> \u2013 Only return videos that have the standard YouTube license.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoSyndicated\">\n",
      "      <td><code itemprop=\"property\">videoSyndicated</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoSyndicated</strong></code> parameter lets you to restrict a search to only videos that can be played outside youtube.com. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Return all videos, syndicated or not.</li>\n",
      "<li><code><strong>true</strong></code> \u2013 Only retrieve syndicated videos.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoType\">\n",
      "      <td><code itemprop=\"property\">videoType</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoType</strong></code> parameter lets you restrict a search to a particular type of videos. If you specify a value for this parameter, you must also set the <code><a href=\"#type\">type</a></code> parameter's value to <code>video</code>.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>any</strong></code> \u2013 Return all videos.</li>\n",
      "<li><code><strong>episode</strong></code> \u2013 Only retrieve episodes of shows.</li>\n",
      "<li><code><strong>movie</strong></code> \u2013 Only retrieve movies.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "\n",
      "  </tbody>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### The important parameters:\n",
      "\n",
      "- **`part`**:\n",
      " - `id`: Returns only resource ID data\n",
      " - `snippet`: Returns some basic meta data about the resource\n",
      "\n",
      "- **`channelId`**:\n",
      " - Filter results to a single channelId.\n",
      "\n",
      "- **`maxResults`**:\n",
      " - Between 0 and 50 results per page. The default is 5.\n",
      "\n",
      "- **`order`**:\n",
      " - date: Resources are sorted in reverse chronological order based on the date they were uploaded.\n",
      " - rating: Resources are sorted from highest to lowest rating.\n",
      " - **relevance**: Resources are sorted based on their relevance to the search query. This is the default value for this parameter.\n",
      " - title: Resources are sorted alphabetically by title.\n",
      " - videoCount: Channels are sorted in descending order of their number of uploaded videos.\n",
      " - viewCount: Resources are sorted from highest to lowest number of views.\n",
      "\n",
      "- **`pageToken`**:\n",
      " - A string token to select results page\n",
      "\n",
      "- **`publishedAfter`**:\n",
      " - Use RFC 3339 format for Date Time 2000-12-31T23:59:59\n",
      "\n",
      "- **`publishedBefore`**:\n",
      " - Use RFC 3339 format for Date Time 2000-12-31T23:59:59\n",
      "\n",
      "- **`q`**:\n",
      " - Query term(s)\n",
      " - You can use multiple search terms\n",
      " - For OR operator use `|`\n",
      " - For NOT operator use `-`\n",
      "\n",
      "- **`key`**:\n",
      " - You API Key code"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Preparing The HTTP Request"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters = {\"part\": \"snippet\",\n",
      "              \"maxResults\": 5,\n",
      "              \"order\": \"date\",\n",
      "              \"pageToken\": \"\",\n",
      "              \"publishedAfter\": \"2008-08-04T00:00:00Z\",\n",
      "              \"publishedBefore\": \"2008-11-04T00:00:00Z\",\n",
      "              \"q\": \"\",\n",
      "              \"key\": api_key,\n",
      "              \"type\": \"video\",\n",
      "              }\n",
      "url = \"https://www.googleapis.com/youtube/v3/search\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Fetch Results for a Single Page"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters[\"q\"] = \"Mark Udall\"\n",
      "page = requests.request(method=\"get\", url=url, params=parameters)\n",
      "j_results = json.loads(page.text)\n",
      "print page.text"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{\n",
        " \"kind\": \"youtube#searchListResponse\",\n",
        " \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/_2hFMhP6zvFl7CAy5D9Ir40dMWE\\\"\",\n",
        " \"nextPageToken\": \"CAUQAA\",\n",
        " \"pageInfo\": {\n",
        "  \"totalResults\": 2325,\n",
        "  \"resultsPerPage\": 5\n",
        " },\n",
        " \"items\": [\n",
        "  {\n",
        "   \"kind\": \"youtube#searchResult\",\n",
        "   \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/tmAMwya2pvXlrX05odd04vzKBSQ\\\"\",\n",
        "   \"id\": {\n",
        "    \"kind\": \"youtube#video\",\n",
        "    \"videoId\": \"5Q98TvXjIZg\"\n",
        "   },\n",
        "   \"snippet\": {\n",
        "    \"publishedAt\": \"2008-11-03T15:31:30.000Z\",\n",
        "    \"channelId\": \"UC52X5wxOL_s5yw0dQk7NtgA\",\n",
        "    \"title\": \"Cousins Vying to Ride Democratic Wave to Senate\",\n",
        "    \"description\": \"Cousins Tom and Mark Udall are vying to become U.S. Senators in New Mexico and Colorado. The two are hoping to ride an emerging Democratic wave in the ...\",\n",
        "    \"thumbnails\": {\n",
        "     \"default\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/5Q98TvXjIZg/default.jpg\"\n",
        "     },\n",
        "     \"medium\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/5Q98TvXjIZg/mqdefault.jpg\"\n",
        "     },\n",
        "     \"high\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/5Q98TvXjIZg/hqdefault.jpg\"\n",
        "     }\n",
        "    },\n",
        "    \"channelTitle\": \"AssociatedPress\",\n",
        "    \"liveBroadcastContent\": \"none\"\n",
        "   }\n",
        "  },\n",
        "  {\n",
        "   \"kind\": \"youtube#searchResult\",\n",
        "   \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/Pqtrk7f6rZM5jwPLKtXoJ98nNtg\\\"\",\n",
        "   \"id\": {\n",
        "    \"kind\": \"youtube#video\",\n",
        "    \"videoId\": \"nnghUTeSKW0\"\n",
        "   },\n",
        "   \"snippet\": {\n",
        "    \"publishedAt\": \"2008-11-03T00:06:40.000Z\",\n",
        "    \"channelId\": \"UC9ZGcEDoHfuY8lB5_SknuLA\",\n",
        "    \"title\": \"mark udall\",\n",
        "    \"description\": \"gov project.\",\n",
        "    \"thumbnails\": {\n",
        "     \"default\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/nnghUTeSKW0/default.jpg\"\n",
        "     },\n",
        "     \"medium\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/nnghUTeSKW0/mqdefault.jpg\"\n",
        "     },\n",
        "     \"high\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/nnghUTeSKW0/hqdefault.jpg\"\n",
        "     }\n",
        "    },\n",
        "    \"channelTitle\": \"g072091\",\n",
        "    \"liveBroadcastContent\": \"none\"\n",
        "   }\n",
        "  },\n",
        "  {\n",
        "   \"kind\": \"youtube#searchResult\",\n",
        "   \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/ay0GP1CevugYOb4FvBtJzXG_A0c\\\"\",\n",
        "   \"id\": {\n",
        "    \"kind\": \"youtube#video\",\n",
        "    \"videoId\": \"Pq-KnAMpDHs\"\n",
        "   },\n",
        "   \"snippet\": {\n",
        "    \"publishedAt\": \"2008-11-01T00:55:26.000Z\",\n",
        "    \"channelId\": \"UC5QhjJAjxtRvJ9ujFxNiJbA\",\n",
        "    \"title\": \"Eden Lane One on One with Congressman Mark Udall\",\n",
        "    \"description\": \"Senate candidate, Congressman Mark Udall spoke with me at a campaign event. Congressional candidate Jared Polis, and State Senate Candidate Joe ...\",\n",
        "    \"thumbnails\": {\n",
        "     \"default\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/Pq-KnAMpDHs/default.jpg\"\n",
        "     },\n",
        "     \"medium\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/Pq-KnAMpDHs/mqdefault.jpg\"\n",
        "     },\n",
        "     \"high\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/Pq-KnAMpDHs/hqdefault.jpg\"\n",
        "     }\n",
        "    },\n",
        "    \"channelTitle\": \"missedenlane\",\n",
        "    \"liveBroadcastContent\": \"none\"\n",
        "   }\n",
        "  },\n",
        "  {\n",
        "   \"kind\": \"youtube#searchResult\",\n",
        "   \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/y4ELzTNng5HKENL9FoHgkDV304k\\\"\",\n",
        "   \"id\": {\n",
        "    \"kind\": \"youtube#video\",\n",
        "    \"videoId\": \"aITDlrkKOoY\"\n",
        "   },\n",
        "   \"snippet\": {\n",
        "    \"publishedAt\": \"2008-11-01T00:42:17.000Z\",\n",
        "    \"channelId\": \"UCT3P1V7_N5HzV1vEZUSdNNQ\",\n",
        "    \"title\": \"CO: AFGE, APWU, NALC, and NPMHU leaflet with Mark Udall\",\n",
        "    \"description\": \"APWU, NALC, and NPMHU are out at the worksite when it matters most!\",\n",
        "    \"thumbnails\": {\n",
        "     \"default\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/aITDlrkKOoY/default.jpg\"\n",
        "     },\n",
        "     \"medium\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/aITDlrkKOoY/mqdefault.jpg\"\n",
        "     },\n",
        "     \"high\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/aITDlrkKOoY/hqdefault.jpg\"\n",
        "     }\n",
        "    },\n",
        "    \"channelTitle\": \"shubi10\",\n",
        "    \"liveBroadcastContent\": \"none\"\n",
        "   }\n",
        "  },\n",
        "  {\n",
        "   \"kind\": \"youtube#searchResult\",\n",
        "   \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/JgS_14GwklWRyGGiyMW8NbsyiQA\\\"\",\n",
        "   \"id\": {\n",
        "    \"kind\": \"youtube#video\",\n",
        "    \"videoId\": \"JAHI1pSiEPM\"\n",
        "   },\n",
        "   \"snippet\": {\n",
        "    \"publishedAt\": \"2008-10-30T04:43:12.000Z\",\n",
        "    \"channelId\": \"UCxdp8upAlGFfB4jjTH3wAHw\",\n",
        "    \"title\": \"[SEN-CO] Udall: Reason\",\n",
        "    \"description\": \"http://politicalrealm.blogspot.com A new campaign ad from Democrat Mark Udall.\",\n",
        "    \"thumbnails\": {\n",
        "     \"default\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/JAHI1pSiEPM/default.jpg\"\n",
        "     },\n",
        "     \"medium\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/JAHI1pSiEPM/mqdefault.jpg\"\n",
        "     },\n",
        "     \"high\": {\n",
        "      \"url\": \"https://i.ytimg.com/vi/JAHI1pSiEPM/hqdefault.jpg\"\n",
        "     }\n",
        "    },\n",
        "    \"channelTitle\": \"PoliticalRealm\",\n",
        "    \"liveBroadcastContent\": \"none\"\n",
        "   }\n",
        "  }\n",
        " ]\n",
        "}\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "YouTube Video Meta Data Using youtube.video.list"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Documentation:\n",
      "\n",
      "https://developers.google.com/youtube/v3/docs/videos/list\n",
      "\n",
      "### HTTPS Request:\n",
      "\n",
      "```\n",
      "GET https://www.googleapis.com/youtube/v3/videos\n",
      "```\n",
      "\n",
      "### Parameters:\n",
      "\n",
      "<table class=\"matchpre\">\n",
      "  <thead>\n",
      "    <tr>\n",
      "      <th>Parameter name</th>\n",
      "      <th>Value</th>\n",
      "      <th>Description</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "        <tr id=\"required-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Required parameters</b></td>\n",
      "    </tr>\n",
      "    <tr id=\"part\">\n",
      "      <td><code itemprop=\"property\">part</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>part</strong></code> parameter specifies a comma-separated list of one or more <code>video</code> resource properties that the API response will include.<br><br>If the parameter identifies a property that contains child properties, the child properties will be included in the response. For example, in a <code>video</code> resource, the <code>snippet</code> property contains the <code>channelId</code>, <code>title</code>, <code>description</code>, <code>tags</code>, and <code>categoryId</code> properties. As such, if you set <code><strong>part=snippet</strong></code>, the API response will contain all of those properties.<br><br>The list below contains the <code>part</code> names that you can include in the parameter value and the <a href=\"/youtube/v3/getting-started#quota\">quota cost</a> for each part:<br><ul>\n",
      "<li><code>contentDetails</code>: 2</li>\n",
      "<li><code>fileDetails</code>: 1</li>\n",
      "<li><code>id</code>: 0</li>\n",
      "<li><code>liveStreamingDetails</code>: 2</li>\n",
      "<li><code>player</code>: 0</li>\n",
      "<li><code>processingDetails</code>: 1</li>\n",
      "<li><code>recordingDetails</code>: 2</li>\n",
      "<li><code>snippet</code>: 2</li>\n",
      "<li><code>statistics</code>: 2</li>\n",
      "<li><code>status</code>: 2</li>\n",
      "<li><code>suggestions</code>: 1</li>\n",
      "<li><code>topicDetails</code>: 2</li>\n",
      "\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"filter-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Filters <span style=\"color:#555\">(specify exactly one of the following parameters)</span></b></td>\n",
      "    </tr>\n",
      "    <tr id=\"chart\">\n",
      "      <td><code itemprop=\"property\">chart</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>chart</strong></code> parameter identifies the chart that you want to retrieve.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>mostPopular</strong></code> \u2013 Return the most popular videos for the specified <a href=\"#regionCode\">content region</a> and <a href=\"#videoCategoryId\">video category</a>.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"id\">\n",
      "      <td><code itemprop=\"property\">id</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>id</strong></code> parameter specifies a comma-separated list of the YouTube video ID(s) for the resource(s) that are being retrieved. In a <code>video</code> resource, the <code>id</code> property specifies the video's ID.</td>\n",
      "    </tr>\n",
      "    <tr id=\"myRating\">\n",
      "      <td><code itemprop=\"property\">myRating</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>This parameter can only be used in a properly <a href=\"/youtube/v3/guides/authentication\">authorized request</a>. Set this parameter's value to <code>like</code> or <code>dislike</code> to instruct the API to only return videos liked or disliked by the authenticated user.<br><br>Acceptable values are:\n",
      "<ul>\n",
      "<li><code><strong>dislike</strong></code> \u2013 Returns only videos disliked by the authenticated user.</li>\n",
      "<li><code><strong>like</strong></code> \u2013 Returns only video liked by the authenticated user.</li>\n",
      "</ul>\n",
      "</td>\n",
      "    </tr>\n",
      "    <tr id=\"optional-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Optional parameters</b></td>\n",
      "    </tr>\n",
      "    <tr id=\"maxResults\">\n",
      "      <td><code itemprop=\"property\">maxResults</code></td>\n",
      "      <td><code class=\"apitype\">unsigned integer</code></td>\n",
      "      <td>The <code><strong>maxResults</strong></code> parameter specifies the maximum number of items that should be returned in the result set.<br><br><strong>Note:</strong> This parameter is supported for use in conjunction with the <code><a href=\"#myRating\">myRating</a></code> parameter, but it is not supported for use in conjunction with the <code><a href=\"#id\">id</a></code> parameter. Acceptable values are <code>1</code> to <code>50</code>, inclusive. The default value is <code>5</code>.</td>\n",
      "    </tr>\n",
      "    <tr id=\"onBehalfOfContentOwner\">\n",
      "      <td><code itemprop=\"property\">onBehalfOfContentOwner</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>This parameter can only be used in a properly <a href=\"/youtube/v3/guides/authentication\">authorized request</a>. <strong>Note:</strong> This parameter is intended exclusively for YouTube content partners.<br><br>The <code><strong>onBehalfOfContentOwner</strong></code> parameter indicates that the request's authorization credentials identify a YouTube CMS user who is acting on behalf of the content owner specified in the parameter value. This parameter is intended for YouTube content partners that own and manage many different YouTube channels. It allows content owners to authenticate once and get access to all their video and channel data, without having to provide authentication credentials for each individual channel. The CMS account that the user authenticates with must be linked to the specified YouTube content owner.</td>\n",
      "    </tr>\n",
      "    <tr id=\"pageToken\">\n",
      "      <td><code itemprop=\"property\">pageToken</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>pageToken</strong></code> parameter identifies a specific page in the result set that should be returned. In an API response, the <code>nextPageToken</code> and <code>prevPageToken</code> properties identify other pages that could be retrieved.<br><br><strong>Note:</strong> This parameter is supported for use in conjunction with the <code><a href=\"#myRating\">myRating</a></code> parameter, but it is not supported for use in conjunction with the <code><a href=\"#id\">id</a></code> parameter.</td>\n",
      "    </tr>\n",
      "    <tr id=\"regionCode\">\n",
      "      <td><code itemprop=\"property\">regionCode</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>regionCode</strong></code> parameter instructs the API to select a video chart available in the specified region. This parameter can only be used in conjunction with the <code><a href=\"#chart\">chart</a></code> parameter. The parameter value is an ISO 3166-1 alpha-2 country code.</td>\n",
      "    </tr>\n",
      "    <tr id=\"videoCategoryId\">\n",
      "      <td><code itemprop=\"property\">videoCategoryId</code></td>\n",
      "      <td><code class=\"apitype\">string</code></td>\n",
      "      <td>The <code><strong>videoCategoryId</strong></code> parameter identifies the <a href=\"/youtube/v3/docs/videoCategories\">video category</a> for which the chart should be retrieved. This parameter can only be used in conjunction with the <code><a href=\"#chart\">chart</a></code> parameter. By default, charts are not restricted to a particular category. The default value is <code>0</code>.</td>\n",
      "    </tr>\n",
      "\n",
      "  </tbody>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Preparing The HTTP Request"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters = {\"part\": \"statistics\",\n",
      "              \"id\": \"5Q98TvXjIZg\",\n",
      "              \"key\": api_key,\n",
      "              }\n",
      "url = \"https://www.googleapis.com/youtube/v3/videos\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "page = requests.request(method=\"get\", url=url, params=parameters)\n",
      "j_results = json.loads(page.text)\n",
      "print page.text"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{\n",
        " \"kind\": \"youtube#videoListResponse\",\n",
        " \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/RI2HLqoe4gS1QbNV867B5089lmY\\\"\",\n",
        " \"pageInfo\": {\n",
        "  \"totalResults\": 1,\n",
        "  \"resultsPerPage\": 1\n",
        " },\n",
        " \"items\": [\n",
        "  {\n",
        "   \"kind\": \"youtube#video\",\n",
        "   \"etag\": \"\\\"PSjn-HSKiX6orvNhGZvglLI2lvk/HdPfiQBFpxUe-eEq-EYCkg3p4b8\\\"\",\n",
        "   \"id\": \"5Q98TvXjIZg\",\n",
        "   \"statistics\": {\n",
        "    \"viewCount\": \"58\",\n",
        "    \"likeCount\": \"0\",\n",
        "    \"dislikeCount\": \"0\",\n",
        "    \"favoriteCount\": \"0\",\n",
        "    \"commentCount\": \"0\"\n",
        "   }\n",
        "  }\n",
        " ]\n",
        "}\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Process Data Range"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I'll check the coorelation between the results of 2008 Senate elections results and YouTube Stats."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Colorado Senate - Gardner vs. Udall\n",
      "Cory Gardner (R)\n",
      "Mark Udall (D)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def _search_list(q=\"\", publishedAfter=None, publishedBefore=None, pageToken=\"\"):\n",
      "    parameters = {\"part\": \"id\",\n",
      "                  \"maxResults\": 50,\n",
      "                  \"order\": \"viewCount\",\n",
      "                  \"pageToken\": pageToken,\n",
      "                  \"q\": q,\n",
      "                  \"type\": \"video\",\n",
      "                  \"key\": api_key,\n",
      "                  }\n",
      "    url = \"https://www.googleapis.com/youtube/v3/search\"\n",
      "    \n",
      "    if publishedAfter: parameters[\"publishedAfter\"] = publishedAfter\n",
      "    if publishedBefore: parameters[\"publishedBefore\"] = publishedBefore\n",
      "    \n",
      "    page = requests.request(method=\"get\", url=url, params=parameters)\n",
      "    return json.loads(page.text)\n",
      "\n",
      "def search_list(q=\"\", publishedAfter=None, publishedBefore=None, max_requests=10):\n",
      "    more_results = True\n",
      "    pageToken=\"\"\n",
      "    results = []\n",
      "    \n",
      "    for counter in range(max_requests):\n",
      "        j_results = _search_list(q=q, publishedAfter=publishedAfter, publishedBefore=publishedBefore, pageToken=pageToken)\n",
      "        items = j_results.get(\"items\", None)\n",
      "        if items:\n",
      "            results += [item[\"id\"][\"videoId\"] for item in j_results[\"items\"]]\n",
      "            if j_results.has_key(\"nextPageToken\"):\n",
      "                pageToken = j_results[\"nextPageToken\"]\n",
      "            else:\n",
      "                return results\n",
      "        else:\n",
      "            return results\n",
      "    return results\n",
      "\n",
      "def _video_list(video_id_list):\n",
      "    parameters = {\"part\": \"statistics\",\n",
      "                  \"id\": \",\".join(video_id_list),\n",
      "                  \"key\": api_key,\n",
      "                  \"maxResults\": 50\n",
      "                  }\n",
      "    url = \"https://www.googleapis.com/youtube/v3/videos\"\n",
      "    page = requests.request(method=\"get\", url=url, params=parameters)\n",
      "    j_results = json.loads(page.text)\n",
      "    df = pd.DataFrame([item[\"statistics\"] for item in j_results[\"items\"]], dtype=np.int64)\n",
      "    df[\"video_id\"] = [item[\"id\"] for item in j_results[\"items\"]]\n",
      "    \n",
      "    parameters[\"part\"] = \"snippet\"\n",
      "    page = requests.request(method=\"get\", url=url, params=parameters)\n",
      "    j_results = json.loads(page.text)\n",
      "    df[\"publishedAt\"] = [item[\"snippet\"][\"publishedAt\"] for item in j_results[\"items\"]]\n",
      "    df[\"publishedAt\"] = df[\"publishedAt\"].apply(lambda x: datetime.strptime(x, \"%Y-%m-%dT%H:%M:%S.000Z\"))\n",
      "    df[\"date\"] = df[\"publishedAt\"].apply(lambda x: x.date())\n",
      "    df[\"week\"] = df[\"date\"].apply(lambda x: x.isocalendar()[1])\n",
      "    df[\"channelId\"] = [item[\"snippet\"][\"channelId\"] for item in j_results[\"items\"]]\n",
      "    df[\"title\"] = [item[\"snippet\"][\"title\"] for item in j_results[\"items\"]]\n",
      "    df[\"description\"] = [item[\"snippet\"][\"description\"] for item in j_results[\"items\"]]\n",
      "    df[\"channelTitle\"] = [item[\"snippet\"][\"channelTitle\"] for item in j_results[\"items\"]]\n",
      "    df[\"categoryId\"] = [item[\"snippet\"][\"categoryId\"] for item in j_results[\"items\"]]\n",
      "    return df\n",
      "\n",
      "def video_list(video_id_list):\n",
      "    values = []\n",
      "    for index, item in enumerate(video_id_list[::50]):\n",
      "        t_index = index * 50\n",
      "        values.append(_video_list(video_id_list[t_index:t_index+50]))\n",
      "    return pd.concat(values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get Data for Two Candidates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_data(candidates, publishedAfter, publishedBefore):\n",
      "    results_list = []\n",
      "    for q in candidates:\n",
      "        results = search_list(q=q,\n",
      "                              publishedAfter=publishedAfter,\n",
      "                              publishedBefore=publishedBefore,\n",
      "                              max_requests=50)\n",
      "\n",
      "        stat_data_set = video_list(results)\n",
      "        stat_data_set[\"candidate_name\"] = q\n",
      "        results_list.append(stat_data_set)\n",
      "    data_set = pd.concat(results_list)\n",
      "    return data_set\n",
      "\n",
      "def get_2008_data(candidates):\n",
      "    return get_data(candidates, publishedAfter=\"2008-08-04T00:00:00Z\", publishedBefore=\"2008-11-04T00:00:00Z\")\n",
      "\n",
      "def get_2010_data(candidates):\n",
      "    return get_data(candidates, publishedAfter=\"2010-08-04T00:00:00Z\", publishedBefore=\"2010-11-04T00:00:00Z\")\n",
      "\n",
      "def get_2012_data(candidates):\n",
      "    return get_data(candidates, publishedAfter=\"2012-08-04T00:00:00Z\", publishedBefore=\"2012-11-04T00:00:00Z\")\n",
      "\n",
      "def get_2014_data(candidates):\n",
      "    return get_data(candidates, publishedAfter=\"2014-08-04T00:00:00Z\", publishedBefore=\"2014-11-04T00:00:00Z\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Analyzing Colorado Senate Race for 2014"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "candidates = [\"Cory Gardner\", \"Mark Udall\"] # Cory Gardner (R), Mark Udall (D)*\n",
      "colorado_2014_ds = get_2014_data(candidates)\n",
      "pd.pivot_table(colorado_2014_ds, values=[\"commentCount\", \"favoriteCount\", \"dislikeCount\", \"likeCount\", \"viewCount\"],\n",
      "               aggfunc='sum', rows=\"candidate_name\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>commentCount</th>\n",
        "      <th>dislikeCount</th>\n",
        "      <th>favoriteCount</th>\n",
        "      <th>likeCount</th>\n",
        "      <th>viewCount</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>candidate_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Cory Gardner</th>\n",
        "      <td> 304</td>\n",
        "      <td> 167</td>\n",
        "      <td> 0</td>\n",
        "      <td> 437</td>\n",
        "      <td> 234669</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mark Udall</th>\n",
        "      <td> 195</td>\n",
        "      <td> 470</td>\n",
        "      <td> 0</td>\n",
        "      <td> 450</td>\n",
        "      <td> 144744</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount\n",
        "candidate_name                                                                 \n",
        "Cory Gardner             304           167              0        437     234669\n",
        "Mark Udall               195           470              0        450     144744"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = colorado_2014_ds[colorado_2014_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = cand[\"week\"].value_counts()\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos Published\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uWu7mzWHHDqhXr9JTIrUS3DXAEOApVd3v+VT/u0rOKZeqbhCRV4G1QCFuSvC/\nA42AOSJyG25a8GtCvYYxxsSrKrc3eO8aRowIKDGEqtI7BwAR6QZciGsHWK6qGyIWUcVx2J2DMSah\ndeoEW7fCunUhrMmTmwtt2sDBg/DJJ3D22QGdFsqdQyC9lcYAM4FWuB5EM0VkdDAXMcYYA9u3u8TQ\nuHHA7+slzZ3rEkOvXiEWELhAqpV+DVygqocBRORx4CNgaiQDM8aY6sZbpdSvX4hDE7xjGyIwIrq0\nQAfBFZazbYwxJkBVam/44gtYtgxOOAGuvTascfkTyJ3Dy8AqT5dTwY1cnh7RqIwxphqqUnLw3jUM\nHw6NGoUtpvIE2iB9PtDP83S5qq6LaFTlx2EN0saYhLR3L7Ro4daKPnAgyDWjjx+HU06BH35wC0/3\n7RvUtSPSIO3RADikqlOB7SISmSF5xhhTTa1Y4b726hVkYgC3ZsMPP0CXLtCnT9hj8yeQ3koZwO8p\nniW1Lq73kjHGmABVqUopQqu9VSSQO4crgMuBwwCqugM3YM0YY0yAQk4O27fDggVQp44b+BYlgSSH\no6pa1ENJRE6IYDzGGFPt5ObC2rXuQ3/QtUKvvAKFhXD55dCqVSTC8yuQ5PCGiPwNaCoid+AW4vlH\nZMMyxpjqY9Uqt/rbuedCkyZBnFhYCNM9nUOjMLbBV6VdWVX1KRFJA34COuOm8F4S8ciMMaaaCLlK\naelS+PpraN/eTc8dRYGMc0BVFwOLIxyLMcZUSyGv3+BtiL711ois9laRcsc5iMghyl9wR1W1ccSi\nKoeNczDGJJr8fGjWDA4dgu++C2LN6B9/hLZt3RiHrVvd3UOIwjplt6o29BT6GG45T2/31RtwK7gZ\nY4ypxPr1LjGcdloQiQHgtdfg2DFIT69SYghVINVKw1T1HJ/nL4rIJ8DECMVkjDHVRkjtDapuVSCI\nekO0VyC9lQ6LyI0iUtvzuAE4FOnAjDGmOggpOaxZA5s2QcuWMGxYROKqTCDJ4Xrcqmy7PI9rPPtC\nJiJNRWSuiHwmIptF5AIRaS4iS0TkCxFZLCJNq3INY4yJNdUQG6O9k+zddBPUrRv2uAIR0MR7Yb+o\nyAwgW1Wni0gScAIwHtijqk+KyINAM1tD2hiTyLZscdMhtW4NO3cGOPPFoUNutbdDh+DTT6Fr1yrH\nEdYGaRF5UFWfEJHn/bysqhrSanAi0gS4UFVHegrKBw6IyDBgoOewGUAWxfM5GWNMwvGtUgp4SqQ3\n3nCJoW+LyjJLAAAgAElEQVTfsCSGUFXUIL3Z8/U/PvsUt6ZDVT6+dwJ2i8jLwLme8u8BTlLVXZ5j\nduGWJDXGmIQVUnuD7yR7MVRRcvi5iOxT1VcicM3zgLtVdY2IPEupOwRVVRHxm4AyMjKKtlNTU0lN\nTQ1zeMYYEx5BJ4fPPoMPP4SGDeHqq0O+blZWFllZWSGfDxUPgrsHGI4b0zAbmBWORX5EpDWwUlU7\neZ73B8YCpwIXqer3ItIGWKqqZ5Y619ocjDEJYft2aNcOGjd2C/0ENMD5gQdgyhS4/Xb4+9/DFktY\nF/tR1WdVtQ+uHWAvMF1EPheRSSLSOdQgVfV74FufMgYDnwLvAiM9+0YCb4d6DWOMiTXvXUO/fgEm\nhmPHYMYMtx3jKiUIbOK9rcDjwOMi0h23pvQfgKpM9DEKeE1E6gI5wC2e8uaIyG3AVlyXWWOMSUhB\nVynNmwd79sDPfgY9e0YsrkBVmhw8XU0vBa4FLgaWApOqclFV3QD4++6jO+2gMcZESNDJwTu2IYqr\nvVWkojaHNFxCGAqsBmYB81Q1ZqOjrc3BGJMI9u6FFi3cWtEHDgSwZvQ330DHjm61t5073clhFNZx\nDrgeRLOAB1R1b5UiM8ZUKjNzGVOnLubo0SSSk/MZPTqNoUMHxDosE4IVK9zXXr0CSAwAL7/shlNf\neWXYE0OoKpqVdVA0AzGmJsvMXMaYMYvIyZlctC8nZzyAJYgEFFSVUkFB8Wpvt90WsZiCFcjcSsaY\nCJs6dXGJxACQkzOZ55+3RRcTkTc59O8fwMHvv19crTQofj6TW3IwJg4cOeL/Jj4vL7qrf5mqy82F\ntWtdm3LfvgGc4B0RfdttUCt+3pIDWibUGBM5P/4Imzbl+30tKakgytEkhmWZmSyeOpWko0fJT04m\nbfRoBgwdGuuwAFi1yq3+1q0bNGlSycG7d8Pbb7ukcPPN0QgvYJYcjImhzz6Dyy6DffvSqF17PAUF\nvlVL4zh4cAiFhXH1gTLmlmVmsmjMGCbn5BTtG+/ZjocEEdQU3f/8p1sG9NJL4ZRTIhpXsOxPzpgY\nWbgQeveGnBw477wB/OMf6aSnT2TgwAwuvHAiJ5wwhDVrBjCpSqOKqpmvvmLxPfeUSAwAk3NyWPK8\nvwmkoy/gxmjVkmMb4ozdORgTZaowdSrcdx8UFsKvfuVmTWjQYAA331zcM2nJEhgyBB57DM491x1X\nI331lZvGes4cWL++3Det2nl5UQ3Ln/x8WLnSbVeaHD76CDZvhhNPhF/8IuKxBcvuHIyJomPH4M47\n4Z57XGL4wx9g9mxo0KDssZdcAk895bZHjoRPPolurDH11Vfwpz9B9+5w+ukwbhysXw+NG5Pfpo3f\nUwq++85l3hhav94txXDaaW6Bnwp5G6JvvtkNfos3qpowDxeuMYlpzx7VgQNVQbVePdVZsyo/p7BQ\ndcQId07Hjqq7d0c8zNj58kvVP/5RtVs39w17H40bq954o+o776geOaLZ8+fruJSUEseMBc0G1Ztu\nUs3Li0n48+dn6xlnjFeYpG3bjtf587PLP/jgQdUTTnDxb9kS8dg8753Bvd8Ge0IsH5YcTKLavFnV\n+37Wpo3qqlWBn5ubq9qzpzt30CDV48cjF2fUBZgQSsueP18npKfrpIEDdUJ6umaPHavaoIE7t18/\n1V27ovptzJ+frSkp40p8Cykp48pPEC+95A668MKoxGfJwZg4tGCBe68D1fPOU/322+DL+PZb1ZNO\ncmWMGRP+GKMqxIRQqY8/Vj3lFFdWhw6qGzaEPXR/tm1T7dx5fIlvxftIT5/g/6QLLnAHzJgRlRgt\nORgTRwoLVZ99VrVWLfef9qtfqR4+HHp5K1ao1qnjypo+PXxxRsUXX6hOnhz+hFDazp3Fb7wNG6rO\nm1f1Mv3Ytk11ypTiS8Ekv8lh4MBJZU/euLH4e6/KH0QQLDkYEyeOHlW9/fbiN4k//EG1oKDq5Xpr\nI+rWVf3oo6qXF1HRSgil5eaqXn+9u5aI6hNPuExdRWUTgns0aKB60klB3Dncc4978Te/qXJMgbLk\nYEwcCKXhORh33aVFbRc7doS37CqLVUIorbDQxeG9fogN1RUlhKuvVn3jDdVDh8prcxhbts0hL0+1\neXN3wNq1YfpmK5dQyQG38ts64F3P8+bAEuALYDHQ1M854f+pGRNGVWl4DtSxY8XJ54ILovNeW6F4\nSQj+/PvfQTdUB5oQSps/P1vT0yfowIGTND19gv/G6H/9yxXWrVtY7mYClWjJ4T7gNdwCQgBPAr/3\nbD8IPO7nnLD/0IwJl3A0PAfqhx9U27d317r55si+z2TPn6/j09J00sCBOj4tTbPnz4/vhFBaAA3V\noSaEoA0e7Ar+y1/CUFjgEiY5AKcA7wEX+dw5bAFO8my3Brb4OS/8PzVjqijcDc+BWrdOtX59d83n\nnovMNfyNKRhXt64bUxDPCaE0Pw3VUUsIXv/9r7tAcrLq3r1hLLhyiZQc3gC6AwN9ksM+n9fF97nP\n/rD/0Iypikg1PAfKW0tRu7bq+++Hv/zxaWkl3zk9jwm1a8d/QijtyBHddvkoncK9egEro5MQfE2c\n6C52ww0RukD5QkkOUZ9bSUR+AfygqutEJNXfMaqqIqL+XsvIyCjaTk1NJTXVbxHGRNyPP8JVV0F2\nNtSr51Z6vPba6MYwfDhs2OBmmrj6areOQKdOYSp8zx6SNmzw+1Ltvn3djKIJ4JtvYO5cmDOnHqtW\nTS3a34DDDO3wKdf8qTs/H1aHE06IYBC+q71FYZK9rKwssrKyqlZIsNmkqg/gj8C3wNfAd8Bh4J+4\naqXWnmPaYNVKJo5Fo+E5UPn5qpde6mI55xzVn36qYoGFhar//Kdqy5Y63l//TNAJ6elhiT1SKq0y\nemClHqrfUoNpqK6SzEx3rdNOi2pDtBeJUq1UdPGS1UpPAg96th/CGqSrjfnzszUtbbwOHDhJ09Iq\nmXMmAUSz4TlQ+/ernnGGi+mqq6rw/pOTo3rJJUXvptlnn63jvC3fnsfYlBTXKB1D/v6mgm5DiOaI\n6iuvdNf54x8jd40KJGpy8PZWao5rpLaurNVI0HPOxLFYNTwHasuW4qT16KNBnnzsmBss5m3hbt5c\n9eWXVQsLy85jFAeJofTfVHLyOIXs4NsQvvuuZEP1O++EP+Dvv1dNSnINQzEamJJwySHoYC05JJy0\nNP8jR9PSyplzJk7FuuE5UPPnu0HBEMT73OrVqueeW/zN3XBD1CeuC0Z5f1O1ak0IrVH5yJGIjKgu\n8uSTruxhw8JXZpAsOZi406fPpHL+kSfp5ZerPv+86mefxaQaNmCRHvEcbn/8o4u1USPXNlKun35y\nUzl4b4U6dnR1ZnHs+HHVjh39/0317z8p9ILDNKLab7mdO7syIzTPUyAsOZi4snmzar16/j/lwYQS\nz085xQ3mmjnT3enHi3hqeA5UYaHq8OEu5tNPL6dL/fz5xaPoatVSfeCBCPbhDI+NG1XPP18VgpwB\nNRghjKiu0PLlxX88MZxr3ZKDiRtLl6o2baoK2Z764OJ/4pSUsTp9erZOn6563XWqrVqV/Uc/+2zV\ne+91nTyq3PsmRPHY8ByoQ4eKBy8PGeJ6NKmqy7zXXFP8gz7/fNX//CemsVbm+HHVxx4rnpG2Vats\nbdMmgHmMQhXOhuqRI105Y8eGJ7YQWXIwceGf/yz+R/7lL1Xnzq14zpmCAvf/N2WKeyPzfnDzPurU\nUR0wQPWRR1Q//DDyH8DiveE5UFu3qrb09Nb8/e8K3ZSuLmO7H/Izz8T9ykHFdwvuceedbhG1gOYx\nqopwNFTv31/cwP/ll+GNL0iWHExMFRaqPvxw8T/yPff4fGINQl6ealaW6oQJ7v/T+ybtfTRu7Nr2\nItFekSgNz4HKylJNSipUUH2N69w39fOfq379daxDq1Dpu4X27VWXLIlyEFVtqH7xRXduamrkYgyQ\nJQcTM0ePujYDbxX21KnhK3vfPtW33lL97W+L2/Z8Hyef7O7eq9pekWgNz5XKy1N9+GH9S+3R7nsi\nV9dOXhjfrf9a/t1CTFSlobpHD3fOzJmRjTEAlhxMTOzb59Y29tZWRKKruK9t21SnTSu/veJnPwu+\nvSIRG54rtHy5apcuqqCFoL/unF3U8P/997EOzr+4uFsoz5tvBtdQvW6dO7ZpU7f4UIxZcjBRt3Wr\nateu7i/ppJNU16yJ7vW97RVPP+2/vSIpya3hXrq9wneE7Xnnjdf69bMTsuG5jH373Apj3h9A586q\nS5dqXp5qnz7q6fLp7vTiSVzdLZQnmIbqu+92x919d9TCq4glBxNVa9aotm7t/oq6dnWJIta87RXj\nx5ffXtGrV7a2bDmu1B3HOO3XLzshG55V1VV/zJ3rbnu8rfgTJ5aYMXXnTlcFB1FdobJCcX234E8g\nDdW5ucUN/+vWRT9GPyw5mKiZN6/4U/qgQe4Dazzy314RwX7ysfDNN66F3vuN9O2rummT30NXr3bL\nCYDqX/8a5ThLSYi7BX+OHHGjyMtrqH7tNS3qJhwnLDmYqHj++eJP5DffHH9VFBXZtk21c+dJfpPD\nwIGTYh1ecPLzXct/w4ZadFv0wguVdq969VUtqnJbtixKsfpIuLsFfypqqL7oIrfvxRdjG6MPSw4m\novLzXfdU7//DI4/EfccXv8qbmyeh7hw2bFDt1as4+CuvVN2+PeDT77vPnXbiie7GI1oS9m6hPD4N\n1dlduuj4Xr10Euj4WrU0e/bsWEdXxJKDiZjDh92ANm919quvxjqi0PmfKTaMI2wjKTfXjbZNStKi\nfrxvvRV0McePF8/Mfd55kR/kVy3uFsrz8cea3aKFjiv1aWNcHExt7mXJwUTE998Xf0ht2tRNjZHo\nIj7CNhLee6+4v62I6wlz4EDIxf34o+qpp7rirr8+cneB1e5uwY/x3gEypR7xsiiSJQcTdps3u8k6\nwX2tcJZPExl79hTP0eMdyLFyZViK3rhR9YQTXLFPPhmWIotU67uFUiaVkxwmDRwY69BUNbTkUCuI\nFUVNDZOVBX37wtat0KsXfPQRdOkS66hqEFWYORPOPBNmzIDkZJg8Gf7zH+jdOyyX+NnPipeCfugh\nWLgwLMWycaMLccIEOH4c7rwTNm2CwYPDU368yU9O9ru/oF69KEcSRsFmk6o+gHbAUuBTYBMw2rO/\nObAEWwkuLpSePC9h+/8nkOz583V8WppOGjhQx/fvr9nduxd/Cr3oItUvvojYtTMy3GWaNKnaZY4d\nqzl3C76y58/Xcd4qP88jHpZT9SIRqpWA1kA3z3ZD4HOgC24N6d979j+IrSEdE+GaPM8Ex9+byzjQ\n7IYNi5brjKSCguIOB126hNaU8cknrnG7OrctVCTellP1lRDJoUwA8DYwGNgCnKTFCWSLn2PD/TMz\nPiI5eZ7xUVjohisvWaL63HOqd9yh45s0KZEYiho0L7ooamEdPKh61lnu0pddFvhstDX1biGRhJIc\nkiJWXxUAEekIdAdW4RLDLs9Lu4CTYhRWjbR/P1x1Ffzf/0GDBjBrFgwbFuuoEpwqfP89fPopbN7s\nvnq39+0rcWh5/4i1CwsjH6dHo0bwzjvQsye8+y5kZMAjj1R8zsaNcPPN8PHH7vmdd8JTT7myTGKL\nWXIQkYbAv4ExqvqTiBS9pqoqIurvvIyMjKLt1NRUUlNTIxtoDbBtG1x6qXvPOukkmD8fevSIdVQJ\nxDcJ+CYCP0mgSNOmcNZZRY/8V191Dc2lRLtBMyUFZs+GIUPg0Ufh3HPdh4bSjh+HJ5+Ehx922+3b\nw7Rp1bfBOdFkZWWRlZVVpTLE3XFEl4jUAeYDC1T1Wc++LUCqqn4vIm2Apap6ZqnzNBbxliczcxlT\npy7m6NEkkpPzGT06jaFDB8Q6rKCsXQuXXebe27p2hf/9X+jQIdZRxakwJAG6dnVfW7cGnw9EyzIz\nWTRmDJNzcor2jUtJYchzzzFg6NBIf2dlPPMM3H8/nHACfPghnHNO8Wt2t5B4RARVlcqPLBb1Owdx\ntwjTgM3exOAxDxgJPOH5+na0YwtGZuYyRo9exH//O7loX07OeICESRDvvgvXXgu5uTBoEPz73+69\nrCZYlpnJ4qlTSTp6lPzkZNJGjy5+E45gEiiP99oTn3+e2nl5FNSrx5BRo2KSGADuvRfWr3fdXAcP\nXsZZZy2moCCJ777L5+uv0ygoGGB3C9VdsI0UVX0A/YFCYD2wzvMYguvK+h5x3JU1P98tAjN5smqz\nZv7n5+nSZYJ+8kn8Ly05dWriTp5XVX57BrVsqdnp6W4hF+90y/4eTZu6Y+64wzUmL1niGpcTcZKp\nSuTmqp5+erZC2enNhwzJrlE9kRIdidAgraofQLmD7+LqM4gqfPUVvPceLFkCS5e6hlvH/4/us89q\nc8450KSJG0DWvz/06+cGkdWvH7XQy1VQAA88AM967tkeecQNVArgw221sfjZZ0tU3wBM3rOHiYsW\nUXTPV8U7geqgfn1o02YxX345udQrk1GdSKNGiXGHbEIT095K8eiHH1yPHW9C+Oabkq+npLjb6PcW\n/UjO1rLnt2h2mIaNXSPvggXuAVCnDpx/fnGy6NcPWrWK+LdTQm4u3HADvP22i2faNBgxIroxxNTO\nnfDXv5JUTkNd7ZQU+Otfa1wSqIiI/7eIvLzaUY7ERFuNTw65ubB8eXEy2LCh5OstWsDFF7uEMHgw\ndOoEHDzI8KUrgeHkMLvo2BSuoUfHXfzrY/j2W1ixAj74wH3dsMFNP/HRR/D00+74M85wSaJ/f/c4\n7bTIvR/t2uW6pq5e7T4Uv/UW1JiOXqtXw3PPwZw5kJ9PfjmHFZx2mlWgl5Kc7P+nVa9eQZQjMdEW\nk95KoQpHb6WCAtdj8L333GPFCjh2rPj1evXgwgs9yaD3IbolbaLWZ6X6qW/fTgbQk4Y8z5nkcQL1\nOMwotrCmSzsy1qxx3Tx8HDjgEsMHH7jHqlVw5EjJ2E48sWSy6N7dfcKvqs8+c11Vt26Fjh1dj6Rq\nP0fS8eOuhX3qVFi50u2rVQuuuIJlvXqx6O9/j5ueQfEsM3MZY8YsIienuGopJWUczz03JGE6XpjQ\neitV++Tg227w3nuuyqi43cB9Uj+/WwGDf/Y9g0/cQL/j2dT7fENREvArOZkJderw2KFDZV6aCDza\noAEMHQpXX+3elUslCnDvXevWFd9ZfPCBq9LyVb8+XHBBcbLo3du1ZQQjKwuuuMJ9z97BTSdV5+GF\ne/bA3/8OL7wAO3a4fU2bwu23w113FfXTXZaZyRKfnkGXxLBnULzLzFzG888vIS+vNvXqFTBq1CWW\nGBKMJQeP3bvh/fcraDdodZDBbTYxWP6PQbtn03znJv8FJSe7GTG9jZHehslTT2XZokVl+6W3aMGQ\nli0Z8PnnxWUEkCigOIn5JgvfYtz37/qbe9st+veHdu1KHuM79uLHH/P57DPX7fCXv4TXXnPhVEsb\nN7qqo9deg7w8t69LFxg92jWslPNzN6YmqLHJwbfd4L33XP9sXy3qHODipGUMPjKPwbxHJ7aWPKCC\nJEBS+c0y5X76/OYbmDsX3njD1SV5BZgovHbvdonCmyz+8x93x+GrffviZFFYuIxnny1ZBQDjGTYs\nnTffHEDt6taGWFDghnM/95zrSuZ16aUwZgxccok1KhtDDUkOnVqkc/v/DObiyx7gvfl5vPe/R1mx\noSHH8ovf+epxhAtZzmDeYzDv0Y311EJDTgJVEqZEAS4JrllTsqH74EHfIyYAj5U5Lz19IgsXPlrl\nbyVuHDgA06fDX/4C//2v29ewoRu2O2oUdO4c0/CMiTc1IjmAAg8Bl4KnV7pQyPn8pygZ9Ku7lnpd\nOkU3CQSiokRx6aVwzTUBJwpwH5w//bQ4Ucydm8GxYxlljhs4MIOsrLL7E84XX8Dzz8Mrr4C3vadT\nJ5cQbr01+AYZY2qIGpQcIIn7ua3WmQxu9zkXnX+QFud1iJ8kEIjyEkX9+u6OIshEAZCePoHFi6vZ\nnYOqazh67jnXzcpr0CBXdTR0KNWvvsyY8KpRyaFDw8vYuu+t+E8CgQhToqhW3Q4PH3YT+0yd6vri\ngutnfOONrpH57LNjG58xCaRGJYdTW/ycnD0LYhxRBFQxUSR8t8Nt21xbwj/+Udzn+OSTXTfU22+H\nli1jG58xCajGJIemSTfw0PjuPJjxQKxDiqwIVD3FJVXX3ey559zcHt4Fbvr0cVVHV14ZntGAxtRQ\nNSI5nNpiCHfcfXH1TwylBZgolmVllT8VdbzJy4N//ctVHa1b5/bVqeO+lzFj3Kg9Y0yV1YjkkEjx\nRkw5iWJZ3bosqlOHyYcPF+0b37Ej6U89xYArrohZw22ZtRNuvJEBX37pJrnbvdsd1KoV/OY38D//\nA23axCROY6orSw41kU+imPDRR35GOXim9AD3qbx+fdewW9HXMB6z7P33WXTvvSVGko8H0vF0RO7W\nzd0lXHutO88YE3YJnxxEZAjwLFAb+IeqPlHqdUsOFcjo3ZuMVavK7hchI0Y/N//D8mDiiSfy6Ny5\nbni3jWI2JqISYpnQ8ohIbeAvuAV/dgBrRGSeqn4W28gql5WVRWoczH+d7zMILAtI9WwXpKW5hSWO\nHXNTweblBfc1lHM8X5N8pp71jal2ly5u+tsYi5ffna94jAniMy6LKXLiJjkAvYCvVHUrgIj8C7gc\nsOQQoLTRoxmfk8PknJyiN+JxKSkMGTXKfTpPTnaPKMpPT4fFi4FSCStOqpDi5XfnKx5jgviMy2KK\nnHhKDicD3/o83w5cEKNYEpLvIvXLt2xh4plnxnSReiiZsLyKEpYxJm7FU3KwxoQwGDB0KAOGDiUj\nI4OMjIxYhxOXCcsYU7m4aZAWkd5AhqoO8TwfCxT6Nkq7QXDGGGOClbC9lcStZP45cDGwE1gNXJcI\nDdLGGFPdxE21kqrmi8jdwCJcV9ZplhiMMSY24ubOwRhjTPyoFesAyiMi9URklYisF5HNIvInz/6n\nROQzEdkgIm+KSNRWeKkgpkc98awXkfdFpF1lZUU6Jp/X7xeRQhFpHuuYRCRDRLaLyDrPY0i0Yqoo\nLs9rozx/V5tE5ImKyolGTCIy2+fn9LWIrIuDmHqJyGpPTGtEJGqTX1UQ07kislJEPhGReSLSKFox\n+cRW2/MzedfzvLmILBGRL0RksYg0jXZM5cR1tYh8KiIFInJepQWoatw+gAaer0nAR0B/4BKglmf/\n48DjcRBTI5/XR+FGd8c0Js/zdsBC4GugeaxjAiYB98Xh39RFwBKgjue1VrGOqdTrTwMTYh0TsBRI\n9+z/ObA0DmJaA1zo2X8L8EgM/qbuA14D5nmePwn83rP9YLTfoyqI60ygs+f3eF5l58ftnQOAquZ6\nNuvi2iH2quoSVfXM6cwq4JQ4iOknn0MaAntiHZPn+TPA76MZSwUx7fM8j+lcGeXE9RvgT6p63HPM\n7hjH5P39ISICXAPMinFM+4DvAe+delPcTAaxjul0VV3u2f8ecFU0YxKRU3BrFv+D4r/tYcAMz/YM\n4JfRjKm8uFR1i6p+EWgZcZ0cRKSWiKwHduE+pWwudcitwP+WPTP6MYnIZBH5BhiJu6OJaUwicjmw\nXVU/iWYsFcT0qeelUZ4quGmxuN0uJ67OwAAR+UhEskSkR4xj8v07vxDYpao5/s+OWkyf4hZvn+L5\nO38KGBsHMX3q+VsHuBp3txxNfwZ+BxT67DtJVXd5tncBJ0U5JvAfV1DiOjmoaqGqdsPdHQwQkVTv\nayIyHjimqq/HQ0yqOl5V2wOv4H4xsYzpUtw/7iSfw6L6ib2cn9OLQCegG/AdMCWaMVUQVxLQTFV7\n4/6h5sRBTF7XAVH9G68gpmnAaM/f+b3A9DiI6VbgtyKyFnfXfixa8YjIL4AfVHUd5fx/qavPiWqv\nn0DiCkRcJwcvVT0AZAI9AETkZtwt0w3xEpOP14GYrFLjE9N5uDfhDSLyNe6f6T8icmIMY+qhqj+o\nB+52t1e04/EXF26qljc9+9cAhSLSIsYxecf+XAHMjnYs5cTUS1Xf8rw0lxj9/kr9TX2uqumq2gP4\nFxDNO6y+wDDP/9gsYJCI/BPYJSKtAUSkDfBDFGMqL65Xgy0kbpODiLT0VjuISH1cQ7S3h8vvgMtV\nNS9OYjrN57DLgWj2LPEX00pVPUlVO6lqJ9yb33mqGpU/0gp+Tq19DrsC2BiNeCqLC3gbGOTZ3xmo\nq6o/xjgmcDMUf6aqO6MRSyUxrQe+EpGBnsMGAQHXX0copnUi0sqzrxZuhvgXoxWTqo5T1Xae/7Fr\ngf9T1RHAPFz1Mp6vb0crpgriuqnUYZXeUcTNIDg/2gAzPL/0WsA/VfV9EfkS1yC1xLXVsVJVfxvj\nmOaKyBlAAe6Ty/9EKZ5yYyp1TLQHs5T3c3pVRLp54vkauDNO4loGTBeRjbhqidL/SFGPyfPacKLc\nEF1BTO+JyB3A/xORZOAIcEeMY3pfRMaIiPf//9+q+koUYyrN+3/2ODBHRG4DtuI6FMSSAojIFcBU\noCWQKSLrVPXn5Z1kg+CMMcaUEbfVSsYYY2LHkoMxxpgyLDkYY4wpw5KDMcaYMiw5GGOMKcOSgzHG\nmDIsORhTioj8WUTG+DxfJCIv+TyfIiL3BlnmKyIS1UnhjKkKSw7GlPUBbgoC78jbFkBXn9f7ACuC\nLNMGFJmEYsnBmLJW4hIAwFnAJuAnEWnqGR3cBcAzg+taEVnoM5dOiogs8Oxf5hk57+UdqfqoiLzs\nSTzGxKV4nj7DmJhQ1Z0iki9uRb8+uGRxsmf7IPAZbubdy1V1j4gMByYDtwF/B+5U1a9E5ALgBeBi\nT9EiIk8BJ6jqLdH9rowJjiUHY/z7EFe11Be3aNLJnu0DuEVu0iie36s2sFNETvAc84ZnP7h5wMBN\ndDYRWKWq0Z5TypigWXIwxr8VQD/gbNzssd8CD+CSQxZwsqr29T1BRBoD+1S1u5/yFLek5fki0kxV\n9+ANuqgAAAC4SURBVPk5xpi4YXWexvj3IfAL4EfPEhT7cEtj9sHNlNpKRHoDiEgdEemqqgeBr0Xk\nV579IiLn+JS5EDdjZ6aINIzmN2NMsCw5GOPfJlwvpY989n0C7PesMf0r4Alxy1auo7gB+wbgNs/+\nTbj1hL1UVecCLwHzPI3bxsQlm7LbGGNMGXbnYIwxpgxLDsYYY8qw5GCMMaYMSw7GGGPKsORgjDGm\nDEsOxhhjyrDkYIwxpgxLDsYYY8r4/4K9BcySN94FAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0805e8e50>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = colorado_2014_ds[colorado_2014_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"viewCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos viewCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xixVmJ9661VvGd/JkaNgwcvKWlDZtoGpVY6ZLT3dbmpghEIWyAfjaKXx1v7NF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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0804fa310>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = colorado_2014_ds[colorado_2014_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"likeCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos likeCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LTLFrlz6pJyXBued6POXrrzW7aI8emqo7WJQyLTVrpk6NQ4dgy5bgDRRGsrJU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\nZmbWxMXBzMyauDiYmVkTFwczM2vyfzXse0CGFIOvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078bd3c50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = colorado_2014_ds[colorado_2014_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"dislikeCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos dislikeCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2OA0cCJ984uod0tOj/ezGGBN/grly2CgifweGA7ki0iDI/WoN6yltjDGlBXOS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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078b29890>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "How Predective Was It in 2012?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Virginia Senate - Allen vs. Kaine"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "candidates = [\"George Allen\", \"Tim Kaine\"] # George Allen (R), Tim Kaine (D)Winner\n",
      "va_2012_ds = get_2012_data(candidates)\n",
      "pd.pivot_table(va_2012_ds, values=[\"commentCount\", \"favoriteCount\", \"dislikeCount\", \"likeCount\", \"viewCount\"],\n",
      "               aggfunc='sum', rows=\"candidate_name\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>commentCount</th>\n",
        "      <th>dislikeCount</th>\n",
        "      <th>favoriteCount</th>\n",
        "      <th>likeCount</th>\n",
        "      <th>viewCount</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>candidate_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>George Allen</th>\n",
        "      <td> 297</td>\n",
        "      <td> 352</td>\n",
        "      <td> 0</td>\n",
        "      <td> 475</td>\n",
        "      <td> 203297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tim Kaine</th>\n",
        "      <td> 174</td>\n",
        "      <td>  97</td>\n",
        "      <td> 0</td>\n",
        "      <td> 553</td>\n",
        "      <td> 248367</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount\n",
        "candidate_name                                                                 \n",
        "George Allen             297           352              0        475     203297\n",
        "Tim Kaine                174            97              0        553     248367"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = va_2012_ds[va_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = cand[\"week\"].value_counts()\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos Published\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uaqkpxHvmAK1TOQBXAyep6m9UdSYwBvhZbMUyDKNZ4p01jBnjVj97aNeu0u/h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\nJQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078b29050>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = va_2012_ds[va_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"viewCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos viewCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PeZqIPOuUvysifcPpryU5CTW02cUKTRwJJ/2MixWahCEQoclS1fsxYoOqHoxA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\nyxIPXEsqimM0paUmWCSzRRn9+RouvTS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       "text": [
        "<matplotlib.figure.Figure at 0x7ff078959c10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = va_2012_ds[va_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"likeCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos likeCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XBVK4iDwhIptE5KsqaR1F5G0R+VZE8kSkfZV9E0TkOxFZISKZDTojwzCMMBGp\nuEpeGmW3kojc4vmd4WMJdJzDk8DIGmm3Am+r6gDcgLpbPfUcBpwLHObJ87CIBBoY0DAMI+w0526l\nugzSyzy/n1ZJU9ycDgFFAlHV90WkT43k0UC6Z302kI9TEGOA51S1BFglIt8Dx+JmnTMMw4g6kVYO\n7dtDUhLs3AlFRdCyZWTqaQh1KYdTRWS7qv4zzHV2U1VvI2oTbl5qcO6xVRXBWqBXmOs2DMMImEi7\nsiYkuLLXr3d19e4dmXoaQl3K4VvgHhHpCfwL91Uf1kl+VFVFpK5WiM99U6ZMqVjPyMggIyMjnGIZ\nhmEAkW85eMtev97ZHcKpHPLz88nPz29wftF6YsV6uoXOw9kD0oBncYri24AqcPlfU9UjPNsrgAxV\n3SgiPYD3VPVnInIrgKpO8xz3JpCjqh/XKE/rk9kwDCNUVF03z/79LoxGampk6hk5Et56C3JzYdSo\nyNQBICKoasDjvAPxVlqlqtNUdTBOSfwGWB6CjK8Cl3jWLwFeqZJ+nogke2aa6w8sCqEewzCMBrNr\nl1MMbdpETjFA/BqlA5kJLklERovIs8CbwArgzEAKF5HngIXAoSKyRkQuA6YBI0TkW+Bkzzaqugx4\nHmcIfwO42poIhmHEiki7sXqJ17EOdQXey8S1FEbhvuCfA/6gqgGPjlbV8/3s+pWf4+8E7gy0fMMw\njEgRDXsDxO9Yh7oM0rfiFMJNqrotSvIYhmHEBZH2VPISr8H36orKenI0BTEMw4gnmnvLwUYgG4Zh\n+CDayiHeWg6mHAzDMHwQLeUQrwZpUw6GYRg+iJa3krf8LVugrCyydQWDKQfDMAwfRKvl0KIFdOoE\n5eWwdWtk6woGUw6GYRg+iJa3UtU64qlrKZBpQo1myoJ588ibPp2k4mJKU1LIHD+e4ZEc328YcUS0\nWg7gjNLLl8eXUdqUg+GTBfPm8da11zJ15cqKtImedVMQRlOnrMzZAAA6d458ffHYcrBuJcMnedOn\nV1MMAFNXruTtGTNiJJFhRI9t25wNoFMnN99CpInHsQ6mHAyfJBUX+0xPLCqKsiSGEX2i2aUE8TnW\nwbqVDJ+UJif7TC+Lp6mqDCMM+LKtlaW5rtNIu7F6icduJVMOhk8yBw9m4ttvM7VKWna/fowcNy5m\nMhlGuPFnW2vx2x7AUVFvOZhyMOIbVYa/+SYAkw88kMQff6QsKYmR991nxmijSeHPtjby1Y+IpnKI\nx+B7phwCWbP0AAAgAElEQVSM2uTmwpdfMrxnT4Z/+y384hfwzTfQqlWsJTOMsOLPtrZvb2sg+jaH\neGo5mEHaqI4q3HGHW7/pJkhJgd/+1m2/+GLs5DKMCFCakuIzvbDc+a/GouUQL1OcmXIwqjN/Pixa\n5Jy7//AHl+ZVDi+/HF/BXwwjRDJ/9zsm1kjLTkmh1QFHAdFTDq1auaW42E1PGg+YcjCqM9Vjgr7u\nuspupMGDoU8f2LgRFi6MmWiGEW6G79xJFjC5SxemHHcck1NSGFlcTPn6UiB63koQf11LphyMShYu\nhPx8aNsW/vSnynQR61oymiZz5zIcuP3++5ny4YfcnpvLcOCnH/cB0Ws5QPyNdTDlYFTibTVccw20\nb199n1c5vPRS/HSKGkYorF0LH3wALVvC6NEu7Ve/gmuvZTOuydC19d6oiRNvYx1MORiOzz+H11+H\ntDTXpVSTIUOgZ09YswY++ST68jUTFsybx6SsLKZkZDApK4sF8+bFWqSmy/PPu9/TToM2bSqS9992\nFzvoQBIltP/rhKiJE28tB3NlNRx33ul+x4713dGakABnngl//7vrWjr22OjK1wywYIdRZu5c93vu\nudWSNxemAtCFzST8fTqMPg1GjIi4ONZyMOKP5cvdCz852bmv+uOss9zviy9a11IEsGCHUeR//3Mt\n4Nat4de/rrarIq5S90S3cumlLhJfhDGDtBF/TJvmXvaXXea6jvxxwgnu82blSvjyy+jJ10zwG+xw\n584oS9IM+Ne/3O+YMa4rtQoV04Me3gWGDYP166s7aESIeOtWMuXQ3PnhB3jmGUhMhFtuqfvYxEQ4\n4wy3bl5LYcdvsMNFi+C++6CkJOCy5s1bQFbWJDIyppCVNYl58xaES8ymgZ8uJajScuiWAHPmOJfu\nuXPhueciKpJ1Kxnxxd13u4FtF1wAffvWf7y5tEaMzMGDaw/ISktjRHk53HijG29SUFBvOfPmLeDa\na98iL+8OCgqmkJd3B9de+5YpCC/Ll7uWb/v2kJlZa3e16UEPOQTuv98lXH2183CKENatZMQP69fD\nE0+4cQwTAvTKOOkk6NABli1zfzIjbAxfuNANyBo4kCnp6UzOymLk888zPDcXDj4Yvv4aMjLgwgvd\nvatBURF89x3k5OSxcuXUavtWrpzKjBlvR+dE4h1vl9KZZ7rwMDWoNZfDFVc4j6YdO5z9obw8ImLF\nW/A9Uw7NmXvvhf373Z9k4MDA8rRoUekTbq2H8LFkCXzwAbtTu7Ko52jyyWCRHsNu2sCoUejSr9l+\n61/5IvmXvPrsbmb0vY+bMhZzzlnlDBkC3btDaioMGACffurbCbGoKDHKJxWHqNbZpQQ+lIMI/OMf\nLqTM/PnOYy8CdOjg/l67djlFH3NUNSYLsAr4EvgcWORJ6wi8DXwL5AHtfeRTIwxs3qyalqYKqp99\nFlzeV191+Y48MjKyNUcuv1xzaa392l2p7g3mlrS0bD3ooAJt00arpftakpJU+/RRbd9+os/9WVmT\nYn2WsWfJEncxOndWLSnxecioUe6QV1+tsePll92Oli1Vv/46IuL16uWqWL06/GV73p0Bv6Nj2XJQ\nIENVB6uq12n+VuBtVR0AzPdsG5HgwQdh71449VTXlx0MI0Y4F8AlS5xLoBEaW7fCM88wnZ+xcudj\n1Xbt3TuV1avfZvduZxc97DB3y646dTV3driHZ7iADzieH8eMo+h/6/nhB3j66Uz69atuvUhMzOby\nyyPvqx/3eFsNZ5/td3Jov1OEnnGG8+grKoLf/c61usNMXBmlg9Ek4VyAH4BONdJWAN08692BFT7y\nhVWbNkt27FBt1859onzwQcPKOP98l//uu8MrW3Pk7rtVQdM7/MbnF/9RR+Xo1q2q5eU18u3bp3rb\nbe5LFlRbt1a9917V/fs1N7dAs7Im6fDhOdq27SSFAr3uupicXfxQXu6aVqCan+/3sIMOcoesXOlj\n586dlWVMnBh2EUeOdEW/9lrYi250LYd3RGSxiFzpSeumql6duQnoFhvRmjgPPww7d0J6Ohx/fMPK\nMK+l8FBW5u4HUNrD9+PepUsZHTu6ru9qtGwJf/mLcw4YPRoKCyu8mtp8tZBjdBEnST5nH7aFxIQT\nmD4dPv00wucTzyxaBKtWubE8J5zg9zC/LQdwQSnnzHE346674KOPwipiXI11CEaThHMBenh+uwBL\ngBOB7TWO2eYjX5j1aTOjsND1t4Lq22+HVk5qqitnzZrwydfc+M9/3DU8+GAdmfWeQna1VkO/fhM0\nN7cgsLJyc1UPPlgLQLNrND+GtHvc0wrx29Xe9Ln+enc96mhCFRa6Q1JTfbTUqnLLLd4bpLp7d9hE\nvPlmV+ydd4atyAoIsuUQs9hKqrrB87tZRF4GjgU2iUh3Vd0oIj0An/pzypQpFesZGRlkZGREXuCm\nwuOPw5YtLjbSKac0vJxWrVzn90svuWX8+PDJWAfz5i1g+vQ8iouTSEkpZfz4TEaNGh6VuiOCx/Nl\n4+9u5r1pGUACJ5wwmcTERFq2LGPcuJGBn9+oUXDKKeQNGlQrDMc7O6+jV8vT+eyzbjz0EFx7bXhP\nI1AWzJtH3vTpJBUXU5qSQub48dGJG1VeXunC6sdLCaq3Gmq11Kpy223wxhtuvMSNN8Kjj4ZFzHC2\nHPLz88nPz294AcFoknAtQBrQxrPeCvgQyATuBm7xpN8KTPORN9wKtflQVKTas6f7NPnPf0Iv75ln\nXFnDh4deVgDk5hZov341v6yzA/+yjjeWL1ePS5Jm37BXQXXMmNCLzUlPr224AD1/0K0Vpokffwy9\nnmApyM3V7IMPriZTdr9+WpCbG4XKC1ydBx1UZ5Pgv/91h/3ylwGU+eWXqsnJYTUSzJnjijv//LAU\nVw2CbDnESjn0xXUlLQGWAhM86R2BdzBX1sjw6KPulh9xhGpZWejl7dzp/hwiqhs2hF5ePWRm+nbR\nHDRokm7bFvHqw88116iC7rp0nLZv785l4cLQi52YmelTOUwaNEh/+1u3ecYZodcTtFwnn+xbrqys\nyFd+9dWuvltuqfMwr5f2qFEBlvu3v7kM3bqp/vRTyGK+9ZYr7uSTQy6qFsEqh5gYpFX1B1U90rMM\nUtW7POnbVPVXqjpAVTNVdUcs5GuSlJbCX//q1rOzXQjuUGnb1rm1qsIrr4ReXj0UF/vuBV26NJGu\nXV0khJkzYcOGiIsSOrt2wT//CcDjnSewY4ezkQ4bFnrRmePHM7Ffv2pp2cCIpUt58JinaNPG3a4o\n3LJK1q4l6b//9bkrMdIjvkpL4YUX3HodXUpQJeheoNODXn+9G7W+aZObc11Di1YcTwZpGyHdXJg7\n141J6N/f+XiHiyh6Le3fX+ozvVOnMlTh7bfhj3+EXr2cE9a998bxMIw5c6CwkP0nnsJ9z/UA6o97\nGCjDR40i68EHmZyVVRmG47LLGA70mnAxd/7qXQDGjYPdu8NTZ5189RUMHUrpXt+zqpW1bBnZ+t97\nzwVMGjAAjjyyzkPr9FTyRUKCU/Jt2zptO3t2SKLaOIfQuqRCbFw1Q8rKVA87zLVXZ80Kb9lbtqgm\nJrply5bwll2F8nLVX/yiwK83z9atqv/8p+ro0aopKdV7Lo48UvX//k916dJ6PFCiRVmZ6qGHqoL+\n80+LFNztCUdPX538/e+qoKUk6C97ravPcSc8vPOOatu2qqAFAwdqtneMgGeZ0KlT5G0Ol1/u6vvL\nX+o91OvQ9Le/BVnHnDlaADoxMVFzjj1WJ2ZmNui8SkpcL61I+L3KaAw2h1AWUw4N4MUX3a3u3Vu1\nuDj85Y8Y4cp/4onwl+3h9dfVY7st0JNPnqTp6TmalTXJpzF6927V559XPe88rRV2YsAA1VtvVf34\nY9XXXivQzMyJmp6eo5mZE6Nn2M7LUwUt69VbDzusXMEptqgwa5aqiH7OLzRRSjUhoVwXL45QXXPm\nqLZo4S78WWep7tunBbm5OikrS3MGDdJJoAWpqWHpq/dLcbFWGHQCCHlx4YXu0Dlzgqum4LXXNLtV\nq2oPW0ON7V5P840bg85aJ6YcjOqUlzvndlCdMSMydcycqcFZ8YKjpKSy4XPvvcHlLSpSnTfPfTx2\n6lT1v1ugiYkx8nw6/XRV0NcumqugesABkdHZfnn2WdXERL2Bv3nGPpSH9yu1vFx16tTKC3v99b6b\nRd7hwFddFcbKa/Daa1rhhBEA3u+cN98Mrhq/TgANMLZ7n/Uvvgg6a52YcjCq88YbWuFNsXdvZOrY\nuNG1g5OTnQdTmHnsMXcKffu6l31DKSlRfe891XHjVFNSYhSc7n//q7hWJwwpbpDCCwsvv6y7W3TQ\n3qxWUH3g/jD1aZWUqP7hD+5iiqg+8ID/Y7/+2nVHJiSofvVVeOqvyUUXOVmmTg3o8F/8wh0ebCxK\nf+7DOenpQYt80kkueyhjVH0RrHIwg3RTZ6onrv8NN7iYzpGgWzc48UQXiCw3N6xF794Nkye79WnT\nfIbfD5ikJOdYMn06DBkSo7DWDz8Mqiw8aSIffJxM+/Zw5ZX1Zws7Z5xB61ef5aEW1wMw6eZi1vzg\n2+AfMIWFLjjdY4+50B4vvFD3aLvDDoOrrnID1G64IWRPn1rs21fpklWPl5KXoA3SHkr9PJhlicE/\nT/FilDbl0JRZsAA++MAFiv/jHyNbV4S8lu65x/1Jhg4Nr5PVvt2+/V39pYeFvXth1iwA7t43DnCT\ni7VpE7kq62TkSE7PG8eZif+hsDSV8Sd81vBIo5s2Oc07bx507OjmPfA+E3UxZQq0a+dczV5/vWF1\n++P1153COuYYqOHa6wvVylngOncOriq/7sP79gWt9OJmRrhgmhnxsGDdSoHj7QedMiXyda1Z4+pK\nTXUBasLA2rWV4Zs+/DAsRVZwzmFDtB/nVOsFSOF6PXtwRngrqso//qEKuuwIV29KSviNjg1h7X8W\naxt2Kqi+fPTtLtprMKxY4fr8vH1/K1YEl/+++1zeQw9V3b8/uLx1cfbZGozr0bZt7vB27RpWXYWx\nPT1dJ514ojO2g+rDDwdVjtdcU894vaDBbA6GqqouWuRub+vWqlu3RqfOIUNcnS+8EJbiLrtMKxxd\nwsKyZap33KF69NGaA5pLa83iGD2ekzWFGxUKdMTBwf2RA8b54qqC/n74dwqqY8dGpqqGMP3mH51x\nnB91V8bpgSv4Dz5Q7djR3ahjjmmYtisuVu3f35Xx4IPB5/fFrl2VXxYBxgpZscId3r9/eETQf/1L\nK74CliwJONvjj7tsl14aJjk8mHJo5hTk5urEzEzN6dRJJ4IW/Pa30avcMy+BnndeyEV9/rmzZ7Zo\nofrddw0spLxcdfFi1exs1Z/9TKs2EyYmJFTbnsepCqpJCUX6zTchi1+b999XBV3b6efaokW5ioRw\nXhGgtFT1mMP3uLEP3Kd6wgn1Oxf8+9+Vg0pOOy20FqM3Om2HDuH5mPHG/Tr++ICzLFgQdJb68Rrn\nDz004OvjDeHx61+HUQ415dCsKcjN1ex+/aq99LL79IlOYDNVNzuKt7USbNdEFcrLVU85xRUV9CCt\n0lIXZO2661QPPLDatdCOHd3n2KuvasGLL9a6VoNkjoJ7L4Z9QNo556iC3jxsQXhbQ2Hks89UExLK\nNYFSXcxRLvqcvxf1/fc77e11RQ3VF7a83AUUAtXx40MrS9WNhgTV6dMDzvLvf7ssv/lN6NVXsGeP\n6uGHu4IvuyygLN7gf0cfHUY51JRDsyacvtYNZvBgV2+tCXgDxzvgrX372oOuK1pG6emVo1CLi53L\n7pVXqnbpUv38e/RwQdfeeadWf3ZFH/Hxx+uk1q31NTpo9+Qtwb5T6mftWtWkJN2e0FHbtC5TcL1+\n8cgNN7jLdlTyl1pCourPf666aVPlAWVlTvF6r+9dd4Vv2PkXXzi31qQkF7G2oWzb5pqcCQlBBYR8\n+GGNTHff0qWVXVxPP13v4T/84A494IDwimHKoRmTM3y4T+XQEF/rBnPHHa7eSy5pUPaqA95q2hF9\ntoxat9aCtLTq53zwwW7WlIULA28CrF6t2r27vswYBdW0tHL93/8adAq1mTxZFXTaoKcUnB97vLJ7\ntxtID6oPdHH3suCAA3RierrmnHiiTuzWTQvAvXyfeir8Alx5pas8lAGVTzzhyggytOmUKS7bpEgM\ndfEO1mndWvXbb+s8dM8ed2hycnjDvZhyaMZM9Br1Ytly8M5R0KFDgzxP6hrw5rdlBG4EbE6O+/ps\n6D9q8WLVtDQ9l+cq3i0h/zmLilS7dtV9pGj3jkUKwY++jTbePu/Wrcr0hV5Da80qly2iBQEOKgua\njRsrY57k5TWsDO9z8uijQWXzRvUOa6vRS3l5RdeiHnVUvaM5W7d2h27fHj4RTDk0V2bP9jk95IRo\nTaZSFe+n/1tvBZVt1y43kBuco0dNco47zqdyyAloZpYA+c9/9Ce6aGd+asj7pTZPP60K+nivHAXn\nsBQXwf/q4cwz3eUd0Pn96H9wTJvm6hk0KHhbxk8/uVHXSUmqmzcHldXr+Tp3bnBVBsyOHZUuv/UY\n07wN5GC9guvClENz5I033J8BtOAPf6j0tc7Kir5iUK3oRtErr2xQtiFDfLxAV63SiTUCm0XsRfXg\ngzrXMwaiTVpJaLOmDR2qpSTogG7bFZwTTWNg7drKD/iXGVNbIUeyq3LfPlVv9NZHHgku7yOPuHyn\nnhp0td4IGO++G3TWwPn444r/al12Oe930IIF4avalENzY/FiVe9L889/jrU0jiVLnDxdujjvoQCo\nOuDtgw9q7PzsM9Xu3V3LyBvlM8Ito/Jx4/U3vKigOvLE3Q372v/kE1XQl9IuVHAzVIY7DHMkmT7d\nXeYD+FF30TqyCrkmL7zg6urc2X1xB4r3Dd+AMLcDB7qsS5cGnTU47rnHVdSxoxs86oMzznCHhGnI\nkKqacmhefP+9ateu7jZedFEUJgQIkPLyynbxe+8FlMU74K3WsIw336zsgD3pJC2YOzc6LaPSUl0/\n4mLtwFb3rpnegICCl1yi5aBDeqyKXF92BCktVT20/3aFAj2QCzWddM3kGD2re/8GX/fc3ADDpJeX\nq554orvvN90UWOHr1lUGgAxGoXjwRu2NZARxVXX/01NPdZWdeKLPL4axY93uhx4KX7WmHJoLP/2k\nesgh7haOGBHlmM8B8Oc/O9muuabeQ/0OeJs1y/Ufgwu0H0pI1oawe7fOPmiygmr7xJ26bmUQYzd+\n+kk1JUULGK7gXjxhiioSVR58sEBhQrVepZ7dr21QaPPc3ALt1y+IMOmLFwc3EvKBB1yhDZggu6TE\nZRUJuLEbGps2OTdrcI4UNfB2sQYwP1HAmHJoDhQWugFK4MYV7NoVa4lq4w3f0bNnnS0anwPeysvd\nH8b7BpkwIWatovK16/TXLd9RUB3d8xMtLw1QjjvvVAX9dddF/v7/jYLMTN+hzdPSJumAARrUkpbm\nu6yMjDp8Ry+5xB0UyMi0oUPdsc89F/R5btigFT2hUWP+fKeNEhJqtbA9k/aFdcyFKYemzv79blw9\nOKNdEIN8okp5eeUI5YUL/R5Wa8Db/v2VfUwJCcEbJCPAmrxl2pYd7r0zJoAXT0mJau/e+iWDFJwt\nJUjHmbghPT3H5wsd/KXXtfgvq0cPF4EjJ8fNz7N+vUeAdetUveNY6uqi9I4cS0trUBPtyy9d9sMP\nb8BFCoVJkyo/oqr0Z3lNLuEcrR2scvAd1N6IT1Rd/PvXX4dOneCtt6B791hL5RsROPNMeOABF8Z7\n2LBah5SWwk03ufVJk6BTi11w2tmQl+fmnvjXv+D006MseG0OGDGQe6/7misfaMc1//kVJ9/3NF1v\nuMh/hldfhTVruKfNdNgNl18efAjoeCElxfccDyecUMY//hFcWZdfXsqHH9ZOT0wsY8MGNxVI1elA\nevSAo4/uydHHvs7R+X/j6GvupOcXJ0JiIvPmLWD69DyKi5NISSllfM9djAL3vLRqFZxgNHweh5DJ\nyYH33oMPP4RLL3UXQCQ+5nQIRpPEw0Jzbjl4OyJTU1U/+ijW0tSPJ9ic9unj07n/0Ufd7r59VYv+\nt65yGq4uXZzLXxxRXq56ys/WKqieI/+qe4DWSSfpanprUkKpJia6j9rGim87wYQw2hwm6KuvFui3\n37reoJtuUs3IUG3b1nfro0e7PXrMMQXaoUONcpJ/p7m0Vn3ppQad57PPunLOPbdB2UNj9Wo3aBRc\n+HJVnTmzQGGitmwZvvnNsW6lJop3nuaEhJDiFkWVsjLV7t2d3J9+Wm1XtQFvf/uxMmZD//7OCysO\n+eEH1VYt3CjnF1Mv9D215VdfqYJe12KGgur550ddzLCTm1ugWVmTND09R7OyJoX0ogq0rLIyra4w\nBm6s6NoDP1O8JgxtcMBHry173LgGn1povPKKE6BFC829/3Ht2zf885ubcmiKvPKKUwrggr03Jv74\nR60wKlehYsDbwJ1a3rad2xg2LO475/8+wwXO68YG3dLr51U6xz1cdZVupYO2SnJK5PPPYyNnk6O8\nXMuGDNNvOUQHdvqjT+XQOunP+uyzDZsvKDvblXH77eEXPWDGjVMFzUw90bfyC3F+c1MOTY2FC1Vb\ntnS3KhozuoWbd95xsg8YUNG1VG3AW1K6Vlje9u6NrawBUFamOvyEUgXVi5jjJrjxGkC3b1dNS9Pb\nPV+2mZmxlbXJ8dFH7uUpx/oxbE9SUO3VywWLDWZaiCuucGXMnBk58etl3z7VI4/UdNJ9nl96ek5I\nxZtyaEosX145y9aVVzaOoDw1KSmpHF3k6Ya57NJyBdXf4nHJGD8+Ss7l4eG771RTU905vMYo1TFj\nnPz33697aaldWmxTcJ6KRpi54ALNpbX2a/X7ai/OvnK+/umP71aMcvaa5q66KrDo397pHxposggf\n33yjmQlDrOXQkKXZKId161y8BXA+fo0p7kJNLr9cvS2fzxeXqlCmLSjW7+ineu+9jVLp3XuvO6We\nsk5zaacTDzxQc1q21JFcpeAmammEpxX/rF6t2rKlm+J1yJ80/cCLNYtjNDfLzZ5UVuZCjWVlVX+x\nnnqq8yHwd0+GDXPH1QrdEgOmnfE7bV9jfvP2SRfotJx7Qiq30SsHYCSwAvgOuMXH/pAuUKNg585K\nz50hQ4Ly2w44PEEUyyrIydGJoH9pmap9WhQoqF6X8KDv0KuNhNLSyjFXv8BN+ltKgh7M904P3ro4\n1iI2XSZP1gLQiW3bak7Llm463DvuqHXY11+7Bre3VxbcOIbHH6/dg+mN9lLPVAtRYWJmZsX85umk\nO+VHa530y182KCyIl0atHIBE4HugD9ACWAIMrHFMgy9OpHgvwPhBAVFcXDlkeMCAoAy01V0F36vT\ny2H/ftdFvm6d6yb54gvXpfvOO84Z6s9/LtCuXat7TPTqla2PPlqg27YF/lVckJurZ3c7RDM5Rvtw\ngsJEbUGuvvqXME0kHwYaev+WLVNNlGIF1Tx+pf/ibHfN+U6zM4OPChoOmSJJvMhU8MILmu0Jq/Ke\n5+HMriMA4+bNbg4qb7QKcPH8Jk92Y0hzcws0MXGiQo6efHLobqOhXqccb/DAGkuOd71/fzdP+9/+\n5gYGBqgwglUO8TYI7ljge1VdBSAic4ExwPKqB2VlTWL8+ExGjRoedAUL5s0jb/p0koqLKU1JIXP8\neIaPGtUgYb1lffDNN5xw6KGMGDee40eOoqQE9u+HkhL/S839ny/8hMX/eR353xpKdndjYOof6XvJ\n/1Hyr84Bl/Hmm3ls2DDVI10+kMHKlVM577zJdOs2nD17YO9et5T6HttUhTxgarWUdeumMnbsZMaO\nHU5iohvY1aVL3b/33DSHbzYdy2qeAaYAU2jLWJ55bQWn3za+Qdc93OTn55ORkRF0voEDIb3PP3n3\nh58xmp+TiACTyORbWhTvjYlMkSReZMp7/HGmlpUB3qccpq5cyeQZM3z+lzt3hokT4eab4fnn4f77\n4bPP4Pbb4c47F5Ca+hZlZe5Zf/ddWL16IkCD3i8Q+nUqTUnxmV7Wti0UFcF337ll7tzKnf37w9FH\nwzHHuN/Bg6FdO6DyPRUs8aYcegFrqmyvBYbUPCgv7w6WLp3I2LFw1FHD631pepeVy7/ju7fWMnTH\nSEpoQQktGPdBId0GLqdjx26UlAolpcJ+z29dS+GeEvYWDiW5/Hj2cDcLV09hal5yCKf+S8/i+M8+\nYGKwZfi+nYWFiRQWVk9LTHQDSdPS3FJ1PS0NPv44iS1bapeVkpJIcjLs3u1Gb9Y/grM/cEe1lK08\nyuJVWQGfVTzTse2zJHM0RdxbkfYW5/HLXRpDqZo2ScXFPtMTi4rqzJecDBddBBdeCB984JTEyy/n\nUVhY/SNo5cqpzJgxucHKIVQyx49n4sqVTF25siItu18/Rj74IIwYAV9/DYsXw6efuuXLL/0qjAU9\nevDW118zdevWGp969RNvyiHgf9T69VPJyZkMBHMD+wP9+W/VpL3Ap0EUUUEK0Jp9ACR7FhDKSKaE\nZPZ71I/vper+1ZQw0McxX/bqyrDRp9KiBX6X5OTK9QceKGXp0tqSHndcGbNnV3/5J9ejx7KySsnL\nq52ekVHGm29CcTFs2VK5bN7s+/f9ghJKy2uXU0LwIQ7ikdUJh7K/imIA+B9z6SxjYyRR08fvl3XL\nlgHlF4ETT3TLkCFJLFpU+5iiosRQRAwJb+tn8owZJBYVUdayJSPHjatsFQ0e7JYrr3Tb+/c7hfHp\np5VKw6Mw8r77Lmil4EVcV1R8ICJDgSmqOtKzPQEoV9W/VjkmfgQ2DMNoRKiqBHpsvCmHJOAb4BRg\nPbAIOF9Vl9eZ0TAMwwgrcdWtpKqlInIN8BbOc2mWKQbDMIzoE1ctB8MwDCM+SIi1AP4Qkd4i8p6I\nfC0iS0VkvCf9WBFZJCKfi8gnIvLL+soKo0wtReRjEVkiIstE5C5PekcReVtEvhWRPBFpHy2Z6pHr\nHqA7UwoAAAXCSURBVBFZLiJfiMhLItIu1jJV2X+jiJSLSMd4kElExnmu1VIR+Wtd5URDplg+51Vk\nS/TU/5pnO6bPeR1yxew59ydTlfSoP+d1yRTUcx7MoIhoLkB34EjPemucLWIgzrU5y5N+KvBelOVK\n8/wmAf8FTgDuBv7sSb8FmBaD6+VLrhFAgid9WrTl8iWTZ7s38CbwA9Ax1jIBJwFvAy08+7rEgUzv\nxfI599R7A/AM8KpnO+bPuR+5Yvqc+5LJkxaz59zPdQrqOY/bloOqblTVJZ71QtxAuF7ABsD7ZdAe\nWBdlubyjm5JxdpHtwGhgtid9NnBGNGXyI9c2VX1bVb2OpB8DB8RaJs/2fcCfoylLHTJtB64C7lLV\nEs8xm+NApo3E8DkXkQOAXwP/ALweLjF/zn3JFevn3M+1ghg+535k+iNBPOdxqxyqIiJ9gMG4r6pb\ngXtF5EfgHmBClGVJEJElwCbc19zXQDdV9Q4H2wR0i6ZMfuRaVuOQ3wOvx1omERkDrFXVL6MpSx0y\nfQ0MAIaLyH9FJF9EjokDmWL6nAP3AzcDVUepxPw5x7dcVYn6c44PmWL9nPuSCTfQK+DnPO6Vg4i0\nBv4NXOtpQcwCxqvqgcD1wBPRlEdVy1X1SNzXyXAROanGfiWIwXwRlCvDu09EJgL7VfXZGMv0a9xL\nLqfKYQH7XUdIpgxcd04HVR2K+0M9Hwcyxew5F5HTgJ9U9XP83J9YPOf1yRWL59yXTCKSBmQTo+e8\njusU1HMeV66sNRGRFsCLwNOq+oon+VhV/ZVn/d+4ZlPUUdWdIjIPOBrYJCLdVXWjiPQAfoqFTDXk\nOgbIF5FLcc3LU+JApqOAvsAXIgLuZfipiByrqlG9ZjWu01rgJU/6Jx4DYidV3RpDmWL5nB8HjPYo\n85ZAWxF5itg/577kmqOqF8fwOa8lEzAHFzw0Vs+5v/sX3HMebSNJEMYUwV3k+2ukfwake9ZPAT6J\nokydgfae9VRggUeGu/GEF8d1B0Tb8OtPrpHA10DnGNw/nzLVOCaqhro6rtNY4DZP+gDgxxjL9KtY\nPuc15EsHXvOsx/Q5r0OumD3n/mSqkR4Tg7SP6xTUcx7PLYfjgYuAL0Xkc09aNvAH4CERSQH2ebaj\nRQ9gtogk4LrknlLV+R75nheRy4FVwDlRlKkuub7DGTnf9nzBfKSqV8dSphrHRLv7zd91WgA8ISJf\nAfuBi2Ms0zsiEsvnvCbe+zSN2D7nVREq5ZpB7J7zmvh6pmM9mMxb/xME8ZzbIDjDMAyjFnFvkDYM\nwzCijykHwzAMoxamHAzDMIxamHIwDMMwamHKwTAMw6iFKQfDMAyjFqYcDMMHInK/iFxbZfstEXm8\nyva9InJ9kGX+U0R+G045DSNSmHIwDN98gAtDgGeAWifgsCr7hwEfBlmmDSoyGg2mHAzDNx/hFADA\n4cBSYLeItPeMWh4I4IluuVhE3hSR7p60fiLyhid9gYgcWqVc9Rxzu4g86VE8hhF3xHP4DMOIGaq6\nXkRKRaQ3Tkl8hJtPZBiwCze/yP3AGFXdIiLnAlOBy4HHgLGq+r2IDAEepjIgnIjIPUArVb0sumdl\nGIFjysEw/LMQ17V0HG7ill6e9Z24yXcyqYznkwisF5FWnmNe8KSDi/sDLh7QZOBjVR0bpXMwjAZh\nysEw/PMhLgDkEcBXwBrgJpxyyAd6qepxVTOISFtgu6oO9lGeAp8AR4tIB1XdHkHZDSMkrL/TMPyz\nEDgN2KqO7bgpO4cBzwFdRGQouLlHROQwVd0F/CAiZ3nSRUR+XqXMN3HRTed5JrIyjLjElINh+Gcp\nzkvpv1XSvgR2qJt/9yzgr54pPj+n0oB9IXC5J30pbu5lL6qq/wYeB171GLcNI+6wkN2GYRhGLazl\nYBiGYdTClINhGIZRC1MOhmEYRi1MORiGYRi1MOVgGIZh1MKUg2EYhlELUw6GYRhGLUw5GIZhGLX4\nf/oeH1XVkxNPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff07891d5d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = va_2012_ds[va_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"dislikeCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos dislikeCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JvHmpFikuIolW2igis4CTgKki0hzzVRhG6onG3+DRt68zRX3+ORx7bP31I8Uv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\nHA9/+pNrK9qFhuth717VoiKXKQOcmfz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HbuGdAqry+WQCm0SkZaDOfwLl4PL+gMsHdAPw\ntqpemaTPYBgxY8rBMELzOi754wDgI+BL4Pc45bAY6KqqRwe/QURaAdtUdWCI9hR4FxgkIm1VdZuP\nshtG3JjN0zBC8wbwc2CrOrbhluscCjwOdBSRo8CtOyIiB6nqd8AaETkzUC4ickhQmy/iMpvOCyxi\nZRhpiykHwwjNx7gopbeCyj4Etqtbf/dM4C+B5T2XU+XAvgC4PFD+MW7dZQ9V1aeA+4BnA85tw0hL\nLGW3YRiGUQsbORiGYRi1MOVgGIZh1MKUg2EYhlELUw6GYRhGLUw5GIZhGLUw5WAYhmHUwpSDYRiG\nUQtTDoZhGEYt/j+5tXDtl8d7oAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078bdb110>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Nevada Senate - Heller vs. Berkley"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "candidates = [\"Dean Heller\", \"Shelley Berkley\"] # Dean Heller (R)*Winnner, Shelley Berkley (D)\n",
      "nv_2012_ds = get_2012_data(candidates)\n",
      "print pd.pivot_table(nv_2012_ds, values=[\"commentCount\", \"favoriteCount\", \"dislikeCount\", \"likeCount\", \"viewCount\"],\n",
      "               aggfunc='sum', rows=\"candidate_name\")\n",
      "\n",
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = nv_2012_ds[nv_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = cand[\"week\"].value_counts()\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos Published\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()\n",
      "\n",
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = nv_2012_ds[nv_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"viewCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos viewCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()\n",
      "\n",
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = nv_2012_ds[nv_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"likeCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos likeCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()\n",
      "\n",
      "for candidate, color in zip(candidates, [\"r\", \"b\"]):\n",
      "    cand = nv_2012_ds[nv_2012_ds[\"candidate_name\"]==candidate]\n",
      "    by_date = pd.pivot_table(cand, rows=[\"week\"], values=[\"dislikeCount\"], aggfunc=\"sum\")\n",
      "    by_date = by_date.sort_index()\n",
      "    dates = by_date.index\n",
      "    plt.plot(dates, by_date.values, \"-o\", label=candidate, c=color, linewidth=2)\n",
      "plt.legend(loc=\"best\")\n",
      "plt.ylabel(\"Videos dislikeCount\")\n",
      "plt.xlabel(\"Week\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount\n",
        "candidate_name                                                                  \n",
        "Dean Heller               248           644              0        926     870677\n",
        "Shelley Berkley           222           206              0        472     679636\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3ST/LUy7PyxAm0TSVD4XxC9gjVGJYMI4l5/DEidmkpITol1BRoZ8qlNLRQdFg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\nz3o7YHWA46JyjsSKI0dE/vEPkX79anw76ekiN90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       "text": [
        "<matplotlib.figure.Figure at 0x7ff078bdb450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qW4C9ItLPcxgYCbztV8ZX12U4hwOAbCBDRFqLSBtgCDA/GLuN2ODAAdi7163A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\nf/qp5nU884yb9d+4cegcAuo7w4bBkUe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       "text": [
        "<matplotlib.figure.Figure at 0x7ff078c13850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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su0KzogoKCnD11VdjxYoVlUrREa1c6axZs5z2M2bMGNx777349ddfkZGRgRdeeEG3DILv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\np5TEROe/BzExzE8+qUK0tm6tnixQB54qh0CbFe0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       "text": [
        "<matplotlib.figure.Figure at 0x7ff07880f210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hIleIyP4gxiwBKoBmSqkkoBmwDZ27abbjmNno9RSGKMNfs1JMKgfXWUPNxDuy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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078778450>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Current Senate $113^{th}$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data Sources:\n",
      "\n",
      "- http://www.fec.gov/data/CandidateSummary.do?format=html\n",
      "- http://www.senate.gov/general/contact_information/senators_cfm.cfm"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get Current Senate Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"http://www.senate.gov/general/contact_information/senators_cfm.xml\"\n",
      "response = requests.get(url)\n",
      "tree = etree.fromstring(str(response.text))\n",
      "print tree"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<Element contact_information at 0x7ff0804d4170>\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Store Data In Pandas Data Frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "member_full = [member.xpath(\"member_full\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators = pd.DataFrame(member_full, columns=[\"member_full\"])\n",
      "\n",
      "senators[\"member_full\"] = member_full\n",
      "senators[\"last_name\"] = [member.xpath(\"last_name\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"first_name\"] = [member.xpath(\"first_name\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"party\"] = [member.xpath(\"party\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"state\"] = [member.xpath(\"state\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"address\"] = [member.xpath(\"address\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"phone\"] = [member.xpath(\"phone\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"website\"] = [member.xpath(\"website\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"bioguide_id\"] = [member.xpath(\"bioguide_id\")[0].text for member in tree.xpath(\"//member\")]\n",
      "senators[\"class\"] = [member.xpath(\"class\")[0].text for member in tree.xpath(\"//member\")]\n",
      "\n",
      "senators"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 100 entries, 0 to 99\n",
        "Data columns (total 10 columns):\n",
        "member_full    100  non-null values\n",
        "last_name      100  non-null values\n",
        "first_name     100  non-null values\n",
        "party          100  non-null values\n",
        "state          100  non-null values\n",
        "address        100  non-null values\n",
        "phone          100  non-null values\n",
        "website        100  non-null values\n",
        "bioguide_id    100  non-null values\n",
        "class          100  non-null values\n",
        "dtypes: object(10)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 100 entries, 0 to 99\n",
        "Data columns (total 10 columns):\n",
        "member_full    100  non-null values\n",
        "last_name      100  non-null values\n",
        "first_name     100  non-null values\n",
        "party          100  non-null values\n",
        "state          100  non-null values\n",
        "address        100  non-null values\n",
        "phone          100  non-null values\n",
        "website        100  non-null values\n",
        "bioguide_id    100  non-null values\n",
        "class          100  non-null values\n",
        "dtypes: object(10)"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Control By Party"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "by_party = senators[\"party\"].value_counts()\n",
      "by_party.sort(ascending=False)\n",
      "print by_party\n",
      "\n",
      "color_dict = {\"D\": \"b\",\n",
      "              \"R\": \"r\",\n",
      "              \"I\": \"g\"}\n",
      "\n",
      "\n",
      "labels = [\"%s: %s\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\n",
      "colors = list(pd.Series(by_party.index).map(color_dict))\n",
      "\n",
      "plt.figure()\n",
      "plt.axis(\"equal\")\n",
      "plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\n",
      "plt.show()\n",
      "\n",
      "\n",
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "axes.barh(range(len(by_party.index)), by_party.values, color=colors)\n",
      "plt.box(on=\"off\")\n",
      "axes.axvline(x=50, color=\"black\", alpha=0.7, linewidth=2)\n",
      "axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\n",
      "axes.yaxis.set_ticklabels(by_party.index, minor=False)\n",
      "plt.xlabel(\"$113^{th}$ Senate Seats Controlled by Party\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D    53\n",
        "R    45\n",
        "I     2\n",
        "dtype: int64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff0786b7e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff07874a990>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Who is up for Re-election?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Class II senators are up for re-election."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class_2_senators = senators[senators[\"class\"]==\"Class II\"]\n",
      "by_party =class_2_senators[\"party\"].value_counts()\n",
      "by_party.sort(ascending=False)\n",
      "print by_party\n",
      "\n",
      "labels = [\"%s: %s\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\n",
      "colors = list(pd.Series(by_party.index).map(color_dict))\n",
      "\n",
      "plt.figure()\n",
      "plt.axis(\"equal\")\n",
      "plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\n",
      "plt.show()\n",
      "\n",
      "color_dict = {\"D\": \"b\",\n",
      "              \"R\": \"r\",\n",
      "              \"I\": \"g\"}\n",
      "\n",
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "axes.barh(range(len(by_party.index)), by_party.values, color=colors)\n",
      "plt.box(on=\"off\")\n",
      "axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\n",
      "axes.yaxis.set_ticklabels(by_party.index, minor=False)\n",
      "plt.xlabel(\"$113^{th}$ Senate Seats of $Class II$ Controlled by Party\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D    20\n",
        "R    13\n",
        "dtype: int64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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z1fAHgp1rI/rBpilw05XQdG5wypQUuNeAvSGVghl7wlEvOqe/1gKnU8aKQL1Z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\nKyISIpWuiEiIVLoiIiFS6YqIhOh/Aa5lRiyJeaDFAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0786b7b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff078adc510>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Looking at the other classes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class_3_senators = senators[senators[\"class\"]==\"Class III\"]\n",
      "by_party =class_3_senators[\"party\"].value_counts()\n",
      "by_party.sort(ascending=False)\n",
      "print by_party\n",
      "\n",
      "labels = [\"%s: %s\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\n",
      "colors = list(pd.Series(by_party.index).map(color_dict))\n",
      "\n",
      "plt.figure()\n",
      "plt.axis(\"equal\")\n",
      "plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\n",
      "plt.show()\n",
      "\n",
      "color_dict = {\"D\": \"b\",\n",
      "              \"R\": \"r\",\n",
      "              \"I\": \"g\"}\n",
      "\n",
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "axes.barh(range(len(by_party.index)), by_party.values, color=colors)\n",
      "plt.box(on=\"off\")\n",
      "axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\n",
      "axes.yaxis.set_ticklabels(by_party.index, minor=False)\n",
      "plt.xlabel(\"$113^{th}$ Senate Seats of $Class III$ Controlled by Party\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "R    24\n",
        "D    10\n",
        "dtype: int64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ogp45cNM4uPGbsGp/6L0O6NzlZ9o5B+ZH+y3c0+fwWMg0wcbxcGr/v4mSsgg4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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078ae45d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff0787413d0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class_1_senators = senators[senators[\"class\"]==\"Class I\"]\n",
      "by_party =class_1_senators[\"party\"].value_counts()\n",
      "by_party.sort(ascending=False)\n",
      "print by_party\n",
      "\n",
      "labels = [\"%s: %s\" % (by_party.index[index], value) for index, value in enumerate(by_party)]\n",
      "colors = list(pd.Series(by_party.index).map(color_dict))\n",
      "\n",
      "plt.figure()\n",
      "plt.axis(\"equal\")\n",
      "plt.pie(by_party.values, labels=labels, colors=colors, shadow=True, explode=np.zeros(len(by_party)) + 0.04)\n",
      "plt.show()\n",
      "\n",
      "color_dict = {\"D\": \"b\",\n",
      "              \"R\": \"r\",\n",
      "              \"I\": \"g\"}\n",
      "\n",
      "fig = plt.figure()\n",
      "axes = fig.add_subplot(111)\n",
      "axes.barh(range(len(by_party.index)), by_party.values, color=colors)\n",
      "plt.box(on=\"off\")\n",
      "axes.yaxis.set_ticks([item + 0.4 for item in range(len(by_party.index))])\n",
      "axes.yaxis.set_ticklabels(by_party.index, minor=False)\n",
      "plt.xlabel(\"$113^{th}$ Senate Seats of $Class I$ Controlled by Party\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "D    23\n",
        "R     8\n",
        "I     2\n",
        "dtype: int64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gM3OPL62myeUbuJ41Pfxz2bVMdhG6Y0DO7etxJnZmVheLL6VtkEfLLq0KVuPu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0787789d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAERCAYAAABfI52mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDzGUd1HHiQTdAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff078729c50>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Forecasting Results of Senate Elections 2014"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Start with listing all seat in $Class II$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class_2_senators = senators[senators[\"class\"]==\"Class II\"].sort(\"state\")\n",
      "class_2_senators"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 33 entries, 4 to 29\n",
        "Data columns (total 10 columns):\n",
        "member_full    33  non-null values\n",
        "last_name      33  non-null values\n",
        "first_name     33  non-null values\n",
        "party          33  non-null values\n",
        "state          33  non-null values\n",
        "address        33  non-null values\n",
        "phone          33  non-null values\n",
        "website        33  non-null values\n",
        "bioguide_id    33  non-null values\n",
        "class          33  non-null values\n",
        "dtypes: object(10)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 33 entries, 4 to 29\n",
        "Data columns (total 10 columns):\n",
        "member_full    33  non-null values\n",
        "last_name      33  non-null values\n",
        "first_name     33  non-null values\n",
        "party          33  non-null values\n",
        "state          33  non-null values\n",
        "address        33  non-null values\n",
        "phone          33  non-null values\n",
        "website        33  non-null values\n",
        "bioguide_id    33  non-null values\n",
        "class          33  non-null values\n",
        "dtypes: object(10)"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get Competitors"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Fetch Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"http://www.fec.gov/data/CandidateSummary.do?format=xml\"\n",
      "response = requests.get(url)\n",
      "page = html.fromstring(str(response.text))\n",
      "print response.text[:1000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<data.fec.gov xmlns:fecdc=\"http://www.w3.org/2001/XMLSchema-instance\" fecdc:schemaLocation=\"/data /finance/disclosure/schema/CandidateSummary.xsd\"><title>Candidate Summary</title><description>This file contains information for each candidate who has registered with the FEC or appears on an official state ballot for an election to the U.S. House of Representatives, U.S. Senate or U.S. President. The table is available for the current election cycle and for election cycles through 2008.</description><timestamp>2014-10-09T05:06:27-05:00</timestamp><copyright>Copyright 2014, Federal Election Commission.</copyright><can_sum><lin_ima>http://www.fec.gov/fecviewer/CandidateCommitteeDetail.do?candidateCommitteeId=H4UT04052&amp;tabIndex=1</lin_ima><can_id>H4UT04052</can_id><can_nam>AALDERS, TIM</can_nam><can_off>H</can_off><can_off_sta>UT</can_off_sta><can_off_dis>04</can_off_dis><can_par_aff>IAP</can_par_aff><can_inc_cha_ope_sea>OPEN</can_inc_cha_ope_sea><can_str1>5306 WEST 10320 NORTH</can_str\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Process the data into an XML Tree"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for item in page[:10]:\n",
      "    print item.tag"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "title\n",
        "description\n",
        "timestamp\n",
        "copyright\n",
        "can_sum\n",
        "can_sum\n",
        "can_sum\n",
        "can_sum\n",
        "can_sum\n",
        "can_sum\n"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice `<can_sum>` encapsulates the candidates data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for item in page.xpath(\"//can_sum\")[0]:\n",
      "    print \"<%s>%s</%s>\" % (item.tag, str(item.text), item.tag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<lin_ima>http://www.fec.gov/fecviewer/CandidateCommitteeDetail.do?candidateCommitteeId=H4UT04052&tabIndex=1</lin_ima>\n",
        "<can_id>H4UT04052</can_id>\n",
        "<can_nam>AALDERS, TIM</can_nam>\n",
        "<can_off>H</can_off>\n",
        "<can_off_sta>UT</can_off_sta>\n",
        "<can_off_dis>04</can_off_dis>\n",
        "<can_par_aff>IAP</can_par_aff>\n",
        "<can_inc_cha_ope_sea>OPEN</can_inc_cha_ope_sea>\n",
        "<can_str1>5306 WEST 10320 NORTH</can_str1>\n",
        "<can_str2>None</can_str2>\n",
        "<can_cit>HIGHLAND</can_cit>\n",
        "<can_sta>UT</can_sta>\n",
        "<can_zip>84003</can_zip>\n",
        "<ind_ite_con>None</ind_ite_con>\n",
        "<ind_uni_con>None</ind_uni_con>\n",
        "<ind_con>None</ind_con>\n",
        "<par_com_con>None</par_com_con>\n",
        "<oth_com_con>None</oth_com_con>\n",
        "<can_con>None</can_con>\n",
        "<tot_con>None</tot_con>\n",
        "<tra_fro_oth_aut_com>None</tra_fro_oth_aut_com>\n",
        "<can_loa>None</can_loa>\n",
        "<oth_loa>None</oth_loa>\n",
        "<tot_loa>None</tot_loa>\n",
        "<off_to_ope_exp>None</off_to_ope_exp>\n",
        "<off_to_fun>None</off_to_fun>\n",
        "<off_to_leg_acc>None</off_to_leg_acc>\n",
        "<oth_rec>None</oth_rec>\n",
        "<tot_rec>None</tot_rec>\n",
        "<ope_exp>None</ope_exp>\n",
        "<exe_leg_acc_dis>None</exe_leg_acc_dis>\n",
        "<fun_dis>None</fun_dis>\n",
        "<tra_to_oth_aut_com>None</tra_to_oth_aut_com>\n",
        "<can_loa_rep>None</can_loa_rep>\n",
        "<oth_loa_rep>None</oth_loa_rep>\n",
        "<tot_loa_rep>None</tot_loa_rep>\n",
        "<ind_ref>None</ind_ref>\n",
        "<par_com_ref>None</par_com_ref>\n",
        "<oth_com_ref>None</oth_com_ref>\n",
        "<tot_con_ref>None</tot_con_ref>\n",
        "<oth_dis>None</oth_dis>\n",
        "<tot_dis>None</tot_dis>\n",
        "<cas_on_han_beg_of_per>None</cas_on_han_beg_of_per>\n",
        "<cas_on_han_clo_of_per>None</cas_on_han_clo_of_per>\n",
        "<net_con>None</net_con>\n",
        "<net_ope_exp>None</net_ope_exp>\n",
        "<deb_owe_by_com>None</deb_owe_by_com>\n",
        "<deb_owe_to_com>None</deb_owe_to_com>\n",
        "<cov_sta_dat>None</cov_sta_dat>\n",
        "<cov_end_dat>None</cov_end_dat>\n"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cand_list = [cand for cand in page.xpath(\"//can_sum\") if cand.xpath(\"can_off\")[0].text==\"S\"]\n",
      "lin_ima = [cand.xpath(\"lin_ima\")[0].text for cand in cand_list]\n",
      "len(lin_ima)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "412"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Store data into Pandas Data Frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "senate_cadidate = pd.DataFrame(lin_ima, columns=[\"lin_ima\"])\n",
      "senate_cadidate[\"can_id\"] = [cand.xpath(\"can_id\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_nam\"] = [cand.xpath(\"can_nam\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_off\"] = [cand.xpath(\"can_off\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_off_sta\"] = [cand.xpath(\"can_off_sta\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_par_aff\"] = [cand.xpath(\"can_par_aff\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_inc_cha_ope_sea\"] = [cand.xpath(\"can_inc_cha_ope_sea\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"ind_ite_con\"] = [cand.xpath(\"ind_ite_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"ind_uni_con\"] = [cand.xpath(\"ind_uni_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"ind_con\"] = [cand.xpath(\"ind_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"par_com_con\"] = [cand.xpath(\"par_com_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"oth_com_con\"] = [cand.xpath(\"oth_com_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_con\"] = [cand.xpath(\"can_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tot_con\"] = [cand.xpath(\"tot_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tra_fro_oth_aut_com\"] = [cand.xpath(\"tra_fro_oth_aut_com\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_loa\"] = [cand.xpath(\"can_loa\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"oth_loa\"] = [cand.xpath(\"oth_loa\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tot_loa\"] = [cand.xpath(\"tot_loa\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"off_to_ope_exp\"] = [cand.xpath(\"off_to_ope_exp\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"off_to_fun\"] = [cand.xpath(\"off_to_fun\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"off_to_leg_acc\"] = [cand.xpath(\"off_to_leg_acc\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"oth_rec\"] = [cand.xpath(\"oth_rec\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tot_rec\"] = [cand.xpath(\"tot_rec\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"ope_exp\"] = [cand.xpath(\"ope_exp\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"fun_dis\"] = [cand.xpath(\"fun_dis\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"exe_leg_acc_dis\"] = [cand.xpath(\"exe_leg_acc_dis\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tra_to_oth_aut_com\"] = [cand.xpath(\"tra_to_oth_aut_com\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"can_loa_rep\"] = [cand.xpath(\"can_loa_rep\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"oth_loa_rep\"] = [cand.xpath(\"oth_loa_rep\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tot_loa_rep\"] = [cand.xpath(\"tot_loa_rep\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"ind_ref\"] = [cand.xpath(\"ind_ref\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"par_com_ref\"] = [cand.xpath(\"par_com_ref\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"oth_com_ref\"] = [cand.xpath(\"oth_com_ref\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tot_con_ref\"] = [cand.xpath(\"tot_con_ref\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"oth_dis\"] = [cand.xpath(\"oth_dis\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"tot_dis\"] = [cand.xpath(\"tot_dis\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"cas_on_han_beg_of_per\"] = [cand.xpath(\"cas_on_han_beg_of_per\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"cas_on_han_clo_of_per\"] = [cand.xpath(\"cas_on_han_clo_of_per\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"net_con\"] = [cand.xpath(\"net_con\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"net_ope_exp\"] = [cand.xpath(\"net_ope_exp\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"deb_owe_by_com\"] = [cand.xpath(\"deb_owe_by_com\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"deb_owe_to_com\"] = [cand.xpath(\"deb_owe_to_com\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"cov_sta_dat\"] = [cand.xpath(\"cov_sta_dat\")[0].text for cand in cand_list]\n",
      "senate_cadidate[\"cov_end_dat\"] = [cand.xpath(\"cov_end_dat\")[0].text for cand in cand_list]\n",
      "senate_cadidate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 412 entries, 0 to 411\n",
        "Data columns (total 44 columns):\n",
        "lin_ima                  412  non-null values\n",
        "can_id                   412  non-null values\n",
        "can_nam                  412  non-null values\n",
        "can_off                  412  non-null values\n",
        "can_off_sta              412  non-null values\n",
        "can_par_aff              412  non-null values\n",
        "can_inc_cha_ope_sea      411  non-null values\n",
        "ind_ite_con              197  non-null values\n",
        "ind_uni_con              186  non-null values\n",
        "ind_con                  204  non-null values\n",
        "par_com_con              37  non-null values\n",
        "oth_com_con              116  non-null values\n",
        "can_con                  101  non-null values\n",
        "tot_con                  220  non-null values\n",
        "tra_fro_oth_aut_com      61  non-null values\n",
        "can_loa                  103  non-null values\n",
        "oth_loa                  11  non-null values\n",
        "tot_loa                  102  non-null values\n",
        "off_to_ope_exp           104  non-null values\n",
        "off_to_fun               0  non-null values\n",
        "off_to_leg_acc           0  non-null values\n",
        "oth_rec                  76  non-null values\n",
        "tot_rec                  223  non-null values\n",
        "ope_exp                  221  non-null values\n",
        "fun_dis                  0  non-null values\n",
        "exe_leg_acc_dis          0  non-null values\n",
        "tra_to_oth_aut_com       22  non-null values\n",
        "can_loa_rep              34  non-null values\n",
        "oth_loa_rep              4  non-null values\n",
        "tot_loa_rep              38  non-null values\n",
        "ind_ref                  117  non-null values\n",
        "par_com_ref              4  non-null values\n",
        "oth_com_ref              47  non-null values\n",
        "tot_con_ref              121  non-null values\n",
        "oth_dis                  86  non-null values\n",
        "tot_dis                  221  non-null values\n",
        "cas_on_han_beg_of_per    69  non-null values\n",
        "cas_on_han_clo_of_per    192  non-null values\n",
        "net_con                  215  non-null values\n",
        "net_ope_exp              217  non-null values\n",
        "deb_owe_by_com           108  non-null values\n",
        "deb_owe_to_com           4  non-null values\n",
        "cov_sta_dat              231  non-null values\n",
        "cov_end_dat              231  non-null values\n",
        "dtypes: object(44)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 412 entries, 0 to 411\n",
        "Data columns (total 44 columns):\n",
        "lin_ima                  412  non-null values\n",
        "can_id                   412  non-null values\n",
        "can_nam                  412  non-null values\n",
        "can_off                  412  non-null values\n",
        "can_off_sta              412  non-null values\n",
        "can_par_aff              412  non-null values\n",
        "can_inc_cha_ope_sea      411  non-null values\n",
        "ind_ite_con              197  non-null values\n",
        "ind_uni_con              186  non-null values\n",
        "ind_con                  204  non-null values\n",
        "par_com_con              37  non-null values\n",
        "oth_com_con              116  non-null values\n",
        "can_con                  101  non-null values\n",
        "tot_con                  220  non-null values\n",
        "tra_fro_oth_aut_com      61  non-null values\n",
        "can_loa                  103  non-null values\n",
        "oth_loa                  11  non-null values\n",
        "tot_loa                  102  non-null values\n",
        "off_to_ope_exp           104  non-null values\n",
        "off_to_fun               0  non-null values\n",
        "off_to_leg_acc           0  non-null values\n",
        "oth_rec                  76  non-null values\n",
        "tot_rec                  223  non-null values\n",
        "ope_exp                  221  non-null values\n",
        "fun_dis                  0  non-null values\n",
        "exe_leg_acc_dis          0  non-null values\n",
        "tra_to_oth_aut_com       22  non-null values\n",
        "can_loa_rep              34  non-null values\n",
        "oth_loa_rep              4  non-null values\n",
        "tot_loa_rep              38  non-null values\n",
        "ind_ref                  117  non-null values\n",
        "par_com_ref              4  non-null values\n",
        "oth_com_ref              47  non-null values\n",
        "tot_con_ref              121  non-null values\n",
        "oth_dis                  86  non-null values\n",
        "tot_dis                  221  non-null values\n",
        "cas_on_han_beg_of_per    69  non-null values\n",
        "cas_on_han_clo_of_per    192  non-null values\n",
        "net_con                  215  non-null values\n",
        "net_ope_exp              217  non-null values\n",
        "deb_owe_by_com           108  non-null values\n",
        "deb_owe_to_com           4  non-null values\n",
        "cov_sta_dat              231  non-null values\n",
        "cov_end_dat              231  non-null values\n",
        "dtypes: object(44)"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Retrieive YouTube Data for All Candidates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_state_data(candidates):\n",
      "    data_set = get_2014_data(candidates)\n",
      "    t_ds = pd.pivot_table(data_set, values=[\"commentCount\", \"favoriteCount\", \"dislikeCount\", \"likeCount\", \"viewCount\"],\n",
      "                   aggfunc='sum', rows=\"candidate_name\")\n",
      "    t_ds[\"like_dislike_r\"] = t_ds[\"likeCount\"] / (t_ds[\"dislikeCount\"] + t_ds[\"likeCount\"])\n",
      "    t_ds[\"views_share\"] = t_ds[\"viewCount\"] / t_ds[\"viewCount\"].sum()\n",
      "    t_ds[\"msgs_share\"] = t_ds[\"commentCount\"] / t_ds[\"commentCount\"].sum()\n",
      "    t_ds[\"likes_share\"] = t_ds[\"likeCount\"] / t_ds[\"likeCount\"].sum()\n",
      "    t_ds[\"dislikes_share\"] = t_ds[\"dislikeCount\"] / t_ds[\"dislikeCount\"].sum()\n",
      "    print t_ds\n",
      "    return t_ds\n",
      "\n",
      "def fix_name(val_name):\n",
      "    val_names = val_name.split(\", \")\n",
      "    return \"%s %s\" % (val_names[1].split(\" \")[0].capitalize(), val_names[0].capitalize())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "values_list = []\n",
      "for index, state in zip(class_2_senators.index, class_2_senators[\"state\"]):\n",
      "    print \"%s: %s\" % (state,\n",
      "                           class_2_senators[\"member_full\"][index])\n",
      "    candidates = senate_cadidate[senate_cadidate[\"can_off_sta\"]==state]\n",
      "    candidates = candidates[~senate_cadidate[\"tot_rec\"].isnull()]\n",
      "    candidates[\"tot_rec_num\"] = candidates[\"tot_rec\"].apply(lambda x: x[1:].replace(\",\",\"\")).astype(np.float64)\n",
      "    top_candidates = candidates.sort(\"tot_rec_num\", ascending=False)[:2][[\"can_nam\",\n",
      "                                                                      \"can_par_aff\",\n",
      "                                                                      \"can_inc_cha_ope_sea\",\n",
      "                                                                      \"tot_rec_num\",\n",
      "                                                                      \"can_off_sta\"]]\n",
      "    top_candidates[\"full_name\"] = [fix_name(name) for name in top_candidates.values[:,0]]\n",
      "    top_candidates = top_candidates.sort(\"full_name\")\n",
      "    print top_candidates[\"full_name\"]\n",
      "    try:\n",
      "        ds = get_state_data([fix_name(name) for name in top_candidates.values[:,0]])\n",
      "        ds[\"state\"] = state\n",
      "        ds[\"party\"] = top_candidates[\"can_par_aff\"].values\n",
      "        ds[\"donations\"] = top_candidates[\"tot_rec_num\"].values\n",
      "        values_list.append(ds)\n",
      "    except:\n",
      "        print \"NA\"\n",
      "        \n",
      "        \n",
      "sentate_2014 = pd.concat(values_list)\n",
      "sentate_2014"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "AK: Begich (D-AK)\n",
        "359    Dan Sullivan\n",
        "27      Mark Begich\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Dan Sullivan             228            90              0        496     189278        0.846416     0.644151   \n",
        "Mark Begich               65            96              0        157     104563        0.620553     0.355849   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Dan Sullivan      0.778157     0.759571        0.483871  \n",
        "Mark Begich       0.221843     0.240429        0.516129  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "AL: Sessions (R-AL)\n",
        "335    Jeff Sessions\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Jeff Sessions            137            10              0         29       4800         0.74359            1   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Jeff Sessions            1            1               1  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "AR: Pryor (D-AR)\n",
        "290       Mark Pryor\n",
        "89     Thomas Cotton\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Mark Pryor               139            37              0        175     228610        0.825472     0.263103   \n",
        "Thomas Cotton            152            73              0        270     640288        0.787172     0.736897   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Mark Pryor        0.477663     0.393258        0.336364  \n",
        "Thomas Cotton     0.522337     0.606742        0.663636  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CO: Udall (D-CO)\n",
        "137    Cory Gardner\n",
        "375      Mark Udall\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Cory Gardner             327           157              0        467     282673        0.748397     0.659577   \n",
        "Mark Udall               263           455              0        411     145894        0.474596     0.340423   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Cory Gardner      0.554237     0.531891        0.256536  \n",
        "Mark Udall        0.445763     0.468109        0.743464  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "DE: Coons (D-DE)\n",
        "85     Christopher Coons\n",
        "379           Kevin Wade\n",
        "Name: full_name, dtype: object\n",
        "                   commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                    \n",
        "Christopher Coons           200            20              0        110      20040        0.846154      0.54844   \n",
        "Kevin Wade                  110            30              0        190      16500        0.863636      0.45156   \n",
        "\n",
        "                   msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                              \n",
        "Christopher Coons    0.645161     0.366667             0.4  \n",
        "Kevin Wade           0.354839     0.633333             0.6  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "GA: Chambliss (R-GA)\n",
        "197    John Kingston\n",
        "261        Mary Nunn\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "John Kingston            113             4              0         60       2597        0.937500     0.727247   \n",
        "Mary Nunn                 70             8              0         18        974        0.692308     0.272753   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "John Kingston     0.617486     0.769231        0.333333  \n",
        "Mary Nunn         0.382514     0.230769        0.666667  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "IA: Harkin (D-IA)\n",
        "43     Bruce Braley\n",
        "180     Mark Jacobs\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bruce Braley             260            70              0        278      87802        0.798851     0.056535   \n",
        "Mark Jacobs             7341          1053              0      66086    1465259        0.984316     0.943465   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Bruce Braley      0.034206     0.004189        0.062333  \n",
        "Mark Jacobs       0.965794     0.995811        0.937667  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ID: Risch (R-ID)\n",
        "253    Briane Mitchell\n",
        "306        James Risch\n",
        "Name: full_name, dtype: object\n",
        "NA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "IL: Durbin (D-IL)\n",
        "264    James Oberweis\n",
        "115    Richard Durbin\n",
        "Name: full_name, dtype: object\n",
        "NA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "KS: Roberts (R-KS)\n",
        "403    Milton Wolf\n",
        "307    Pat Roberts\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Milton Wolf               20            20              0         20       5210        0.500000     0.039943   \n",
        "Pat Roberts              488            98              0       1000     125227        0.910747     0.960057   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Milton Wolf        0.03937     0.019608        0.169492  \n",
        "Pat Roberts        0.96063     0.980392        0.830508  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "KY: McConnell (R-KY)\n",
        "152      Alison Grimes\n",
        "239    Mitch Mcconnell\n",
        "Name: full_name, dtype: object\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Alison Grimes            1362           376              0       2797     706090        0.881500     0.432752   \n",
        "Mitch Mcconnell          2247           291              0       4839     925538        0.943275     0.567248   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                            \n",
        "Alison Grimes       0.37739     0.366291        0.563718  \n",
        "Mitch Mcconnell     0.62261     0.633709        0.436282  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "LA: Landrieu (D-LA)\n",
        "206      Mary Landrieu\n",
        "69     William Cassidy\n",
        "Name: full_name, dtype: object\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Mary Landrieu             744            79              0       2113     407306        0.963960     0.984916   \n",
        "William Cassidy            84             1              0         94       6238        0.989474     0.015084   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                            \n",
        "Mary Landrieu      0.898551     0.957408          0.9875  \n",
        "William Cassidy    0.101449     0.042592          0.0125  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MA: Markey (D-MA)\n",
        "231    Edward Markey\n",
        "146    Gabriel Gomez\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Edward Markey              0            10              0         70       7994        0.875000     0.004272   \n",
        "Gabriel Gomez            920           515              0       1235    1863296        0.705714     0.995728   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Edward Markey            0      0.05364        0.019048  \n",
        "Gabriel Gomez            1      0.94636        0.980952  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ME: Collins (R-ME)\n",
        "29    Shenna Bellows\n",
        "80     Susan Collins\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Shenna Bellows            15             0              0          6      57891         1.00000     0.163068   \n",
        "Susan Collins             90            18              0        351     297121         0.95122     0.836932   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Shenna Bellows    0.142857     0.016807               0  \n",
        "Susan Collins     0.857143     0.983193               1  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MI: Levin (D-MI)\n",
        "280    Gary Peters\n",
        "205     Terri Land\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Gary Peters               89            45              0        424      37748        0.904051     0.012098   \n",
        "Terri Land               130           558              0       1784    3082566        0.761742     0.987902   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Gary Peters       0.406393     0.192029        0.074627  \n",
        "Terri Land        0.593607     0.807971        0.925373  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MN: Franken (D-MN)\n",
        "133          Al Franken\n",
        "242    Michael Mcfadden\n",
        "Name: full_name, dtype: object\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Al Franken                 186            79              0        235     262735        0.748408     0.894219   \n",
        "Michael Mcfadden            57            40              0        106      31080        0.726027     0.105781   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                             \n",
        "Al Franken          0.765432      0.68915        0.663866  \n",
        "Michael Mcfadden    0.234568      0.31085        0.336134  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MS: Cochran (R-MS)\n",
        "241    Christopher Mcdaniel\n",
        "79             Thad Cochran\n",
        "Name: full_name, dtype: object\n",
        "                      commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                       \n",
        "Christopher Mcdaniel             0             0              0          6       1062        1.000000     0.061698   \n",
        "Thad Cochran                   109            10              0         55      16151        0.846154     0.938302   \n",
        "\n",
        "                      msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                                 \n",
        "Christopher Mcdaniel           0     0.098361               0  \n",
        "Thad Cochran                   1     0.901639               1  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MT: Walsh (D-MT)\n",
        "384       John Walsh\n",
        "104    Steven Daines\n",
        "Name: full_name, dtype: object\n",
        "NA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NC: Hagan (D-NC)\n",
        "155      Kay Hagan\n",
        "370    Thom Tillis\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Kay Hagan                278           128              0        271      51704        0.679198     0.056229   \n",
        "Thom Tillis              231           206              0        561     867825        0.731421     0.943771   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Kay Hagan         0.546169     0.325721        0.383234  \n",
        "Thom Tillis       0.453831     0.674279        0.616766  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NE: Johanns (R-NE)\n",
        "325    Benjamin Sasse\n",
        "111      Sid Dinsdale\n",
        "Name: full_name, dtype: object\n",
        "NA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NH: Shaheen (D-NH)\n",
        "336    Jeanne Shaheen\n",
        "50        Scott Brown\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Jeanne Shaheen            30            34              0         84      37123        0.711864     0.085959   \n",
        "Scott Brown              721            96              0       2465     394746        0.962515     0.914041   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Jeanne Shaheen    0.039947     0.032954        0.261538  \n",
        "Scott Brown       0.960053     0.967046        0.738462  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NJ: Booker (D-NJ)\n",
        "36       Cory Booker\n",
        "272    Frank Pallone\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Cory Booker              299            43              0        688      93738        0.941176     0.997956   \n",
        "Frank Pallone              1             0              0          0        192             inf     0.002044   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Cory Booker       0.996667            1               1  \n",
        "Frank Pallone     0.003333            0               0  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NM: Udall (D-NM)\n",
        "391    Allen Weh\n",
        "376    Tom Udall\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Allen Weh                800            70              0        250    1950780        0.781250     0.987187   \n",
        "Tom Udall                700            40              0        670      25320        0.943662     0.012813   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Allen Weh         0.533333     0.271739        0.636364  \n",
        "Tom Udall         0.466667     0.728261        0.363636  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "OK: Inhofe (R-OK)\n",
        "178      James Inhofe\n",
        "207    James Lankford\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "James Inhofe            1927           412              0       2116     172284        0.837025     0.985471   \n",
        "James Lankford            30            10              0         10       2540        0.500000     0.014529   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "James Inhofe       0.98467     0.995296        0.976303  \n",
        "James Lankford     0.01533     0.004704        0.023697  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "OR: Merkley (D-OR)\n",
        "249    Jeffrey Merkley\n",
        "392       Monica Wehby\n",
        "Name: full_name, dtype: object\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Jeffrey Merkley           156            36              0        392      43632        0.915888     0.310716   \n",
        "Monica Wehby              135           305              0        660      96792        0.683938     0.689284   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                            \n",
        "Jeffrey Merkley    0.536082     0.372624        0.105572  \n",
        "Monica Wehby       0.463918     0.627376        0.894428  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "RI: Reed (D-RI)\n",
        "301        Jack Reed\n",
        "410    Mark Zaccaria\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Jack Reed                 70             9              0        180      41526        0.952381     0.945277   \n",
        "Mark Zaccaria             10             0              0          0       2404             inf     0.054723   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Jack Reed            0.875            1               1  \n",
        "Mark Zaccaria        0.125            0               0  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "SC: Graham (R-SC)\n",
        "148    Lindsey Graham\n",
        "333     Timothy Scott\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Lindsey Graham          1274           184              0       1555     125933        0.894192     0.877735   \n",
        "Timothy Scott            198             0              0        332      17542        1.000000     0.122265   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "Lindsey Graham    0.865489     0.824059               1  \n",
        "Timothy Scott     0.134511     0.175941               0  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "SD: Johnson (D-SD)\n",
        "40     Annette Bosworth\n",
        "315       Marion Rounds\n",
        "Name: full_name, dtype: object\n",
        "NA"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "TN: Alexander (R-TN)\n",
        "131       George Flinn\n",
        "8      Lamar Alexander\n",
        "Name: full_name, dtype: object\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "George Flinn              279            27              0         51       5247        0.653846     0.437505   \n",
        "Lamar Alexander            37             4              0         49       6746        0.924528     0.562495   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                            \n",
        "George Flinn       0.882911         0.51        0.870968  \n",
        "Lamar Alexander    0.117089         0.49        0.129032  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "TX: Cornyn (R-TX)\n",
        "7     David Alameel\n",
        "88      John Cornyn\n",
        "Name: full_name, dtype: object\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "David Alameel              3             2              0          2        105        0.500000     0.004871   \n",
        "John Cornyn              166            20              0        260      21450        0.928571     0.995129   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                           \n",
        "David Alameel     0.017751     0.007634        0.090909  \n",
        "John Cornyn       0.982249     0.992366        0.909091  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "VA: Warner (D-VA)\n",
        "140    Edward Gillespie\n",
        "386         Mark Warner\n",
        "Name: full_name, dtype: object\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Edward Gillespie             2             0              0          8        434        1.000000     0.021218   \n",
        "Mark Warner                 53             9              0         44      20020        0.830189     0.978782   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                             \n",
        "Edward Gillespie    0.036364     0.153846               0  \n",
        "Mark Warner         0.963636     0.846154               1  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "WV: Rockefeller (D-WV)\n",
        "366    Natalie Tennant\n",
        "61      Shelley Capito\n",
        "Name: full_name, dtype: object\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Natalie Tennant           350           271              0        830     149271        0.753860     0.945921   \n",
        "Shelley Capito             48            24              0        152       8534        0.863636     0.054079   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                            \n",
        "Natalie Tennant    0.879397     0.845214        0.918644  \n",
        "Shelley Capito     0.120603     0.154786        0.081356  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "WY: Enzi (R-WY)\n",
        "72     Elizabeth Cheney\n",
        "121        Michael Enzi\n",
        "Name: full_name, dtype: object\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Elizabeth Cheney            48             0              0         30       4482               1     0.749875   \n",
        "Michael Enzi                25             0              0         70       1495               1     0.250125   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  \n",
        "candidate_name                                             \n",
        "Elizabeth Cheney    0.657534          0.3             inf  \n",
        "Michael Enzi        0.342466          0.7             inf  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Index: 55 entries, Dan Sullivan to Michael Enzi\n",
        "Data columns (total 13 columns):\n",
        "commentCount      55  non-null values\n",
        "dislikeCount      55  non-null values\n",
        "favoriteCount     55  non-null values\n",
        "likeCount         55  non-null values\n",
        "viewCount         55  non-null values\n",
        "like_dislike_r    55  non-null values\n",
        "views_share       55  non-null values\n",
        "msgs_share        55  non-null values\n",
        "likes_share       55  non-null values\n",
        "dislikes_share    55  non-null values\n",
        "state             55  non-null values\n",
        "party             55  non-null values\n",
        "donations         55  non-null values\n",
        "dtypes: float64(6), int64(5), object(2)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Index: 55 entries, Dan Sullivan to Michael Enzi\n",
        "Data columns (total 13 columns):\n",
        "commentCount      55  non-null values\n",
        "dislikeCount      55  non-null values\n",
        "favoriteCount     55  non-null values\n",
        "likeCount         55  non-null values\n",
        "viewCount         55  non-null values\n",
        "like_dislike_r    55  non-null values\n",
        "views_share       55  non-null values\n",
        "msgs_share        55  non-null values\n",
        "likes_share       55  non-null values\n",
        "dislikes_share    55  non-null values\n",
        "state             55  non-null values\n",
        "party             55  non-null values\n",
        "donations         55  non-null values\n",
        "dtypes: float64(6), int64(5), object(2)"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class_2_senators[\"state\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "4     AK\n",
        "84    AL\n",
        "73    AR\n",
        "91    CO\n",
        "22    DE\n",
        "17    GA\n",
        "38    IA\n",
        "76    ID\n",
        "28    IL\n",
        "77    KS\n",
        "62    KY\n",
        "54    LA\n",
        "59    MA\n",
        "21    ME\n",
        "57    MI\n",
        "33    MN\n",
        "20    MS\n",
        "94    MT\n",
        "37    NC\n",
        "47    NE\n",
        "85    NH\n",
        "8     NJ\n",
        "92    NM\n",
        "45    OK\n",
        "64    OR\n",
        "74    RI\n",
        "35    SC\n",
        "49    SD\n",
        "0     TN\n",
        "24    TX\n",
        "95    VA\n",
        "78    WV\n",
        "29    WY\n",
        "Name: state, dtype: object"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_column = \"views_share\"\n",
      "y_column = \"viewCount\"\n",
      "s_column = \"donations\"\n",
      "\n",
      "color_dict = {\"DEM\": \"b\", \"REP\": \"r\", \"IND\":\"g\", \"NPA\": \"g\", \"DFL\": \"g\"}\n",
      "            \n",
      "plt.figure(figsize=(18,12))\n",
      "\n",
      "for party in sentate_2014[\"party\"].unique():\n",
      "    cands = sentate_2014[sentate_2014[\"party\"]==party]\n",
      "    x = cands[x_column]\n",
      "    y = cands[y_column]\n",
      "    size = sentate_2014[sentate_2014[\"party\"]==party][s_column] / 3000000\n",
      "    plt.scatter(x,y, s=(np.array(size)) * 1000, c=color_dict[party], alpha=0.5)\n",
      "\n",
      "\n",
      "    \n",
      "print plt.ylim()[1]\n",
      "plt.vlines(0.5, ymin=1, ymax=plt.ylim()[1]*0.9)\n",
      "\n",
      "prejected_winners = sentate_2014[sentate_2014[x_column]>0.5][\"party\"].value_counts()\n",
      "\n",
      "result_text = []\n",
      "for item in sentate_2014.iterrows():#[sentate_2014[x_column]>0.5].iterrows():\n",
      "    plt.annotate(item[1][\"state\"], xy=(item[1][x_column], item[1][y_column]))\n",
      "\n",
      "for item in sentate_2014[sentate_2014[x_column]>0.5].iterrows():\n",
      "    result_text += [\"%s: %s (%s) - %0.1f%%\" % (item[1][\"state\"], item[0], item[1][\"party\"], item[1][\"views_share\"] * 100.)]\n",
      "result_text = \"\\n\".join(result_text)\n",
      "prejected_winners = \"\\n\".join([\"%s:%s\" % (party, value) for party, value in zip(prejected_winners.index, prejected_winners.values)])\n",
      "\n",
      "plt.annotate(prejected_winners, xy=(.65,plt.ylim()[1]*0.8))\n",
      "\n",
      "plt.annotate(result_text, xy=(.8, 1.5))\n",
      "\n",
      "plt.xlabel(x_column)\n",
      "plt.ylabel(y_column + \" (Log Scale)\")\n",
      "plt.grid()\n",
      "plt.yscale(\"log\")\n",
      "#plt.axis(\"tight\")\n",
      "plt.title(\"Senate 2014 Elections Forecast (Size is relative and represents the amount of donations)\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3500000.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Iiorq8e1Tqi94/cUXCV++nAuyskJdlS7JP3aM/3M4uPGuu/R4HcB0zAmllFJK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\n3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Xf3+fzhD8vZvHkft956leIGdxYQFxfHgw/eypNP/oX9+1eQnb20T4bv0aatrZJQ6O/8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Hj6YJjEY9qampZGdnk5SUFPPGbWr+KOFScyd8NTU1vHj//XwjJydqjTGh68v+v/cc4pXdKRQk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t6e0NBQZs2aRVhYGCqVSqlnThpbW1u2b9+uvE5ubm4MHDiQVq1a8f777xda7z179tC5c+dC\nj7311luEhYUVuM6irh+gQ4cOhIaGAvDmm28SERFBfHx8oWme5dq1a8TGxuLi4gJkj4zI+X5u27Zt\nnk4rKPjZkz+9LMs8fvyYlJQUqlWrxq+//kqPHj2oU6dOmeonvPzc3Nxez84JQXjZ6erq0tfTk37z\n5xPRrh3/jYlhd2QkZ6KjiU5M5HFGBlmyTEZWFg9TU7kUF8ehyEhWRUZyoF493pw4kbFeXuKJ9CvG\nzs6WGjUiePy44oasVhVZziIz8wyurm2quiqCIAivpYiICBwcHIo9x9vbm0GDBvHuu++yf/9+Mp+O\n6PT19eXzzz8vNu2qVavQ1NQkODgYb29vhg8fzuPHj5FlmXPnzrF9+3YuXLjAtm3buH37NgApKSm0\na9eOc+fO4erqyvr16wvkK8syW7duRa1W4+Pjg4bG/25Zcj+MCQ0NZdKkSVy8eJE6deqwc+dOAEaO\nHMn69etRq9VoaWkpadasWcOUKVNQq9WcOXOGRo0a8fXXX9OsWTPUajVff/01O3fu5Pz58wQHB3Po\n0CFmzJjBnTt3ADh37hzLly/n0qVL3Lhxg5MnTxao+8mTJ2ndunWB6wE4cOAA1tbWefa7u7srUyaW\nL19eID9fX19sbf8X7k+lUnH69Oli35ei+Pj4MHjw4EKPbdiwgR49epQq/aRJk2jXrh1RUVE4Ozuz\nceNGJk6cWKa6CQKAVlVXQBCepxdxtE2TJk1oMmoUiQMHcvnyZW6HhRF45QoP7twh48kTNDQ1qVm7\nNkbW1hi1bEnv5s0xNTUVIyWqwPNoP9ra2nTpYoOf31lMTSs3Gndlu3//OhYWehgbi2CYL+Jnj/Dy\nEO1HKKsWLVoQHv6/qYKTJk3ixIkTaGtrExAQwJMnT/jjjz9YtmwZNWvWpG3bthw4cICePXvy7rvv\n8u677xab/8mTJ5k8eTIALVu2pEmTJly7dg1JkujUqRP6+voAWFpaEhkZiYmJCdra2vTs2RMAR0dH\n/vrrrwL5SpKEg4MDV69e5d9//6V9+/aFlm9ubq7cuDs6OhIREUFCQgJJSUm0bdsWgCFDhuDn5wdA\n+/bt+fLLL7l16xb9+/enefPmBUYunDx5kiFDhiBJEoaGhnTs2JHAwEBq1aqFk5OT8p1mb29PREQE\nzs7OedJHRkZiZPS/OEuyLDN06FCePHnCw4cPuXDhQp7r9Pf3p169ennykGWZGTNmsHDhQgwNDdmw\nYYNyzNjYmIiIiEJfj2fZtm0bv/76a4H9v/76K0FBQfz3v//Nsz//Z0/+9O+//74yguSLL75gypQp\n7Nu3j19++QVTU1OWLl0qfq8KpSI6JwThBaGvr4+TkxM4OVV1VYQq5uLShr17N5GV5YqGhmZVV6fM\nkpICeecdMWpCEAShqlhZWSmjCQBWrlzJ/fv3lSf7Bw8eJD4+Xnman5KSgo6OjtJ5UBJFTUuoXr26\n8m9NTU0ynk5frVatmrJfQ0ND2Z+fhYUFX3zxBe+99x4HDx7E0tLymWWkpqYWWz9PT0/eeust/Pz8\n6NGjB2vXrsXc3PyZ15Rzg13UNeWXlZWVJ+3WrVtxcHBgxowZfPvtt4WOkMhf3pIlS+jfv3+hdct/\nwx8VFUXv3r2B7LgUuafx5Dh//jwZGRmoVKo8+w8dOsRXX33FsWPH8rw3JU0P2VN4AgMDmTt3Lm5u\nbhw5coQFCxZw+PDhIqe4CEJhxLQO4bUi5u0K5fG82o+BgQHOzo24fbt0EblfJPfvX8PY+D5WVlZV\nXZUXgvjsEcpDtB+hrDQ0NEhLS2PNmjXKvuTkZOXm1tvbmw0bNhAeHq78/fXXX4Xe5BemQ4cObNmy\nBciOR3Dz5k0sLCzKvepUTvp27dqxevVqevXqRVRUVInS1q5dG319fQICAoDsqQg5bty4gbm5OR9/\n/DF9+vThwoUL1KpVi8TExDzXtG3bNrKysoiLi+PYsWM4OTmV+JqaNGmiTAPJfz0LFixg9+7deVbF\neFbMifxiYmIwMzPLs8/U1FQJWFlYxwRkv9dDhgzJs0+tVjNu3Dh8fX154403CqTJ/dlTWPocn332\nGQsWLAAgNTVV6UApaTsShByic0IQipCenk5UVBQBAQH4+/tz+M8/OfL335w8eZLQ0FCSk5OruorC\nK2zQoJ7UqBFIUtKdZ5/8gklPTyUx0Y8xY/oU+xRGEARBqHy7d+/m6NGjNG3alLZt2zJixAi+/vpr\nUlJSOHjwYJ5REjVq1MDFxQVfX98iY05kZGQoIwgmTJhAVlYWtra2DB48mE2bNlGtWrU8q23kl3t/\nUefl3t+rVy/mzp1L9+7deZBvGfb8aXO2N2zYwOjRo1GpVKSkpFC7dm0Atm/fjrW1NSqVipCQEIYN\nG0a9evVwdnbGxsaG//znP/Tr1w9bW1vs7Ozo1KkT3377LYaGhoXWtbC6u7i4cObMmULP09HRYcqU\nKSxatEg5ljvmxIgRI4rNG7I7FNq1a1foseL89ttveHp65tk3c+ZMkpOTGTBgACqVir59+yrH8o+Q\nKCw9ZMfh0NDQwN7eHsieRmNra8vp06d55513Sl1P4fUmlbdnsypIkiS/jPUWXnyJiYmcDQjg8vHj\nPIiK4g1ZxhjQy8pCS5LIlGUeSxJ3JIkYWaZ6/fo0bd2a1h06YGJiUtXVF14x586d57vvTmFmNual\nmt4REbGbPn206dev+MBagpBDkqRyP2kVBOH52LNnD97e3nlGJLxokpOTqVmzJgCLFy/m7t27BeIp\nVJakpCTc3d0JDKz4lbeuXbvG9OnT2bt3b4XnLQgV6en3eqkDjoiYE4IA3Lp1i1N//kn4yZNYZ2Xx\nbt26NDQxQUuj6MFFsizzMC2Ny4cO8duBA9Ro0YK2PXtia2srgv8IFcLOzhYXlxD+/ffYSxMc8/79\na5iYRNKz5/iqroogCIJQwebOncvevXvZtGlTVVelWPv27WPRokVkZGRgZmbGxo0bn1vZenp6uLu7\nc+TIEdzdK/a7e82aNcycObNC8xSEF4kYOSG8VnLWqM6Rnp7O4T/+IOT33+mgpYVdgwZU1yp9n12W\nLBP24AFHExLQcnSkz7Bh1K1btwJrLrwI8ref5yExMREvrzWAB3XrNn2uZZdWWlo8d+9u4PPPPQrM\nh33dVUXbeZmIkRPFE+1HKCvRdoTyEO1HKKuyjpwQMSeE11ZUVBSrv/iC5F27GG9sjJOJSZk6JgA0\nJIk369dnlLk5LS5eZP3s2QT++6/4sS2Um76+PlOnDiQ1dQePHt2q6uoU6fHjRKKjNzNmTAfRMSEI\ngvAC0dPTy7O9bNkydHV1efToUYnSa2ho8MEHHyjbGRkZGBgYPHOZ0fz8/f2fmaYk55SWmZlZgVgV\nJRUbG6vE5PD396d27dqoVCosLS3x8vJSztu4cSMGBgZK7AiVSsWVK1eIiIhAV1cXlUqFlZUV48eP\nR5Zl7t69S48epZ/6+PDhQ/r164ednR1t27YlJCQkz/HMzExUKlWRr+GePXuws7NDpVLh6OjI33//\nDUBcXBwuLi7Y2NiwZ88e5XwvL68CwT0FoTKJzgnhtZLT+3v16lW8v/iCLg8e4GFmRo0KCtqnIUm0\nNzFhVK1anFm5koO+vqKD4hVSVU8PzMzMmD69LwkJ3jx6dLtK6lCcx48TuX37F0aMsKddO7EUbmHE\nkyehPET7EcrKzc2twFRTb29vunTpwq5du0qUR82aNQkJCSEtLQ2Av/76i0aNGpVqCmtRS24+D+UZ\nmbVy5co8QSpdXV1Rq9UEBQWxc+dOzp49q5Th6emprJihVquxsLAAoHnz5qjVaoKDg7l06RK7d++m\nQYMG1K1bl6CgoFLV56uvvsLBwYHz58+zefNmpkyZkuf48uXLsbS0LPK96dy5M+fPn0etVrNx40Zl\nZQ9vb28mTJhAQEAAy5YtA8DX15euXbvSsGHDUtVREMrjpe2cmDdvnlhaSyiTq1evsvebbxiqr08r\nA4NKKeONGjUY0bgxt3/7jT927xYdFEK5tWjRgpkze5OUtJX4+Iiqro4iNfUht2//xMiRNri7d6jq\n6giCIAjFCAsLIz09nTlz5uDt7V3idD169GDfvn1A9o2sp6en8tsmICCA9u3b4+DggLOzM9euXQOy\nRxP07t2bTp060blz5zw3zIGBgTg4OBAeHl5kmUXlm5mZyfTp07GxscHOzo6VK1cCcPjwYRwcHLC1\nteXDDz/kyZMnSl7ffPMNtra2tG3blrCwMCB79QkbGxvs7e3p2LFjoXXYsWNHntVMcujo6GBvb8+N\nGzeA7Dhkz/qtp6mpSfv27QkNDQWgd+/epXoPAC5fvqzEsWjZsiURERHExcUB2fHT9u/fz0cffVRk\nXXKChEJ24M6c5UO1tbVJTk4mLS0NTU1NMjMzWb58uYhvIZSav78/8+bNK3P6l7pzQjxJEErrt99+\nY+/SpQypXRuTWrUqtSzdatUY2qQJUbt2cUJ0pL0SqrpDtGXLlsyZM5CMjN+4ffsEspxVpfWJi7tE\nXNwGxo9vh7t7BxEIthhV3XaEl5toP0JZ5W87Pj4+vPfee7z11luEhoYSGxsLwNmzZxk9enSR+Qwa\nNAgfHx8eP37MhQsXaNu2rXKsVatWHD9+nKCgIObPn8+cOXOUY2q1mp07d+Lv76/cMJ86dYrx48ez\nd+9ezM3NiyyzqHzXrVvHzZs3OX/+POfPn2fo0KGkpaUxcuRItm/fTnBwMBkZGaxevVrJq06dOgQH\nBzNp0iQ++eQTABYsWMCff/7JuXPn8PX1LVD+nTt30NTUpEaNGgWOPXjwgICAACwtLZV927ZtU6Z0\nODg48Pjx4zxpUlJSOHz4MLa2tgC0adOGY8eOFXn9hbGzs1NGvAQEBBAZGcmtW9lTPqdOncq3336L\nRjHB3CF7adlWrVrRvXt3vv/+eyB7+c89e/bQtWtXPv30U1atWsWwYcP4559/SlU/QXBzc3s9OycE\nobTS09M54edHDy2tSu+YyKGjpcWQRo3459dfiYmJeS5lCq82MzMzFi4cja1tGOHhG0hOjnvudUhP\nTyEiYgf16x9m/vxBYiqHIAjCS8LHx4eBAwcC0LdvX3777TcAHB0dWb9+fZHpbGxsiIiIwNvbu8BI\ngvj4eAYMGICNjQ3Tpk3j0qVLyrGuXbtSp04dZfvy5cuMHTsWPz8/GjVqVGxdi8r38OHDjB07VrkJ\nr1u3LlevXsXc3JzmzZsDMHz48Dw3/p6engAMHjyY06dPA+Ds7Mzw4cP58ccfC512EhkZiZGRUZ59\nx48fx97eHlNTU/r27YuVlRWQPa1j8ODBypSOoKAgqlevDmSPVlGpVLi4uNCrVy+6desGgLGxMRER\nEcW+BvnNmjWL+Ph4VCoVK1euRKVSoaGhgZ+fH4aGhqhUqmeO4Ojbty+XL1/G19dXiSVSq1Yt/Pz8\nCAwMxN7eHj8/Pzw8PFiyZAkDBw4UnRTCcyM6J4TXxpGDB3EBrAwNn2u5+tWr01Vbm90bNpCZmflc\nyxYq1osyWqtOnTpMmjSMiRPtSUr6+bmOooiLu0R09A8MGFCLzz4bh6mp6XMp92X3orQd4eUk2o9Q\nVrnbzoULF7h+/TqdO3fG3NwcHx+fUk0r6N27N9OnT88zpQPgs88+o1OnTly4cAFfX19SU1OVY7lH\nHUiShJGREbq6uiWKtZA737179+bJN/8NeP6Re7IsFzmaL2f/6tWrWbhwIVFRUTg6OhYaNDN/OR06\ndODcuXOEhISwa9cuoqKiijw3R7NmzZQOi7lz5z6zjl5eXsroi/z09fX56aefUKvVbN68mbi4OJo2\nbcqpU6eUkSienp78/fffDBs2rND65L6WjIwM7t+/n2f/ggUL8PLyYuvWrQwaNIhNmzaV60m4IJSG\n6JwQXgvR0dEE79pFDxOTKinf1tCQ2teucfLo0SopX3j1SJJE27ZtWLRoDHZ2N4iMXMXt26dJT099\nduJSysrK4O7dC0RG/kT9+of54otB9OrVlWoVFEhWEARBqHze3t7Mnz+f8PBwwsPDuX37NtHR0dy8\nebNE6UeNGsW8efOU0QI5Hj16hLGxMQA///xzkellWaZOnTr4+fkxe/Zsjj7jN1HufDdu3Kjs79Kl\nC2vXrlUe+Dx8+JAWLVoQERGhxJP45ZdflDgSsiyzbds2IHvqRfv27YHsEQ1OTk7Mnz8fAwMDZXpE\njiZNmhS5UoWZmRlTpkxhwYIFShmlFRMTQ5MmTQrsX7hwodKZkV9CQoISS2P9+vV07NgRfX19vvrq\nK6KioggPD8fHx4e3336bzZs3F0gfFham1DUn//r16yvHr1+/TnR0NK6urqSmpiqdJ7k7hgShMonO\nCeG18M/ff+OsqUlgdHSVlC9JEl0bNiRg794qjVgtlM+LOO+7Tp06TJz4AV980YcOHaKJjV1ORMQe\nEhPLP40oLS2eqKjDREX9l1at1Myc2Y558yaK0RJl8CK2HeHlIdqPUFb+/v7KDea2bdvo169fnuP9\n+vXDx8en2JgTOelNTEyYNGmSsi9n/8yZM5k9ezYODg5kZmYq+3Ofk3vb0NAQPz8/Jk6cSGBgYJ6y\nMjIylOkQReX70Ucf0bhxY2xtbbG3t8fb2xsdHR1+/vlnBg4ciK2tLVpaWowbN04p9+HDh9jZ2bFi\nxQr++9//Kvnb2tpiY2ODs7OzEgsiR8OGDcnIyCAlJaXQ6xk3bhwHDhzg1q1bSJKUJ+aESqVSpkIU\nNYIjICAAV1fXQo8V5fLly9jY2GBhYcHBgwdZvnx5oeflLnPt2rWsXbsWgJ07d2JjY4NKpWLKlCn4\n+PjkSefl5cWXX34JZE+FWbx4MU5OTkqcDkGobNLLuIqAJEnyy1hvoWokJyez4pNPmGJoyL+3b+Nm\nZlZlddkcGYlq2jRsbGyqrA5C2fn7+7/ww6uTkpI4c0bN/v1nuHdPEzBGkozR0zNCX98ILS2dQtNl\nZWWQlHSXxMRoMjNjgGh0dRPo1s2O9u1bKxG9hbJ5GdpOVSrPUn+vA9F+hLJ62drO8uXLiYmJYfHi\nxVVdFSA7AH+rVsodVNUAACAASURBVK0YNGhQhec9dOhQpk+fjkqlqvC8K8rL1n6EF8fT7/VSR0oX\nnRPCK+/E0aPc//ln+lRAp8TbmzYxy8WFrs2aKfuW/fMP1+7f5wt3d4yWLmVl9+6Mbd260PSX4+I4\nbW7OqOnTy10XQShOVlYW9+7dIzo6msjIGC5fjiY8/C7p6bpIkjagBUhABrKcjoZGEqam9WnZ0ohm\nzYwxMjKiYcOGaGlpVfGVCK8D0TkhCMKHH37IpUuX2L59+wszQi8uLo7hw4ezf//+Cs03NjaWkSNH\nKsuzCsKrRnROCEIRfly0iM5372KWK1p0Wa0/e5bTt27xU58+yr52GzbwbZcuXLh7l/2hoSQ+foz/\niBGFps+SZb6+eZMpq1YVujSVIFSmrKwsEhISSE9PJyMjA1mW0dLSQktLi9q1a4uOCKHKiM4JQRAE\nQXh1lLVzQsScEF5pmZmZ3A0Px1hfHwD/Ui7ZlJ+HpSX7rl8nIyt7ZYSI+HiiExNxadwYn5AQFrq7\nE5uczO1HjwpNryFJGEkS0VUU+0Ion5d93reGhgZ169bF0NAQY2NjTExMaNCgAfXr1xcdE5XsZW87\nQtUS7UcoK9F2hPIQ7Ud43kTnhPBKi4uLo3ZGBtqamhWSXz1dXZxMTNh//ToAPhcvMsjKiqiEBGKT\nk7Fr2JABlpZsCwkpMg9jWSZGdE4IgiAIgvAc6OnpKf/ev38/LVu2JCoqiqtXr+Lm5oZKpcLS0pKx\nY8c+My8zMzNsbW2xs7OjW7du3L17t8hzz58/zx9//FHoMX9/f2rXrq2U7eXlVarrKIujR49y+vTp\nUqWJjY2lZ8+eQPF13rhxIwYGBnkCYl65coWIiAh0dXVRqVRYWVkxfvx4ZFnm7t279OjRo9TX8PDh\nQ/r164ednR1t27YlJNfvzfj4eAYMGECrVq2wtLRUAnLmN3nyZN58803s7OxQq9VA9u9lFxcXbGxs\n2LNnj3Kul5dXkSuWCEJlEJ0Twivtzp07GOXarohgmJ7W1vhcvAjAtpAQPK2t2RYSwoBWrQAYaGmJ\n99PjhTHS0SHm2rVy10N4/kRQKKGsRNsRykO0H6Gs3NzclJUbDh8+zJQpUzhw4ACmpqZMnjyZ//u/\n/0OtVnPp0iU+/vjjZ+YnSRL+/v6cP3+e1q1b89VXXxV5rlqtLjZWg6urq7Jk5s6dOzl79uwzyy6r\njIwMjhw5wqlTp0qVbuXKlYzINVW3qDpLkoSnpydqtVr5s7CwAKB58+ao1WqCg4O5dOkSu3fvpkGD\nBtStW7fQ5UKL89VXX+Hg4MD58+fZvHkzU6ZMUY5NmTKFHj16cPnyZYKDg2n19Hdpbvv37yc0NJTr\n16+zbt06xo8fD2QvMzthwgQCAgJYtmwZAL6+vnTt2pWGDRuWqo6CUB6ic0J4paWmpqJXwfOYe7ds\nyeHwcNQxMaSkp6MyMsL74kV+PncO8+XL6e3jw4W7dwl98KDQ9DW1tUlNSKjQOgmCIAiCIBTl2LFj\njBkzhn379mFubg5kP8AxMTFRzrG2ti5Vnh06dCA0NJTAwEDat2+Pg4MDzs7OXLt2jSdPnjB37lxl\nec3ffvutyHx0dHSwt7fnxo0bQPaNcs4Sn7Nmzcpz7rRp07C2tqZz587cu3cPgLCwMLp3707r1q1x\ndXXl6tWrAIwYMYJx48bx1ltvMWjQINauXct///tfVCoVJ06c4LfffsPGxgZ7e3s6duxYaN127Nih\njJwors6yLD8zbo6mpibt27cnNDQUgN69e+Pt7V1smvwuX76Mu7s7AC1btiQiIoK4uDgSEhI4fvw4\no0aNAlBiSeW3d+9ehg8fDkDbtm2Jj4/nzp07aGtrk5ycTFpaGpqammRmZrJ8+XJmzpxZqvoJQnmJ\nzgnhlZaRkUHuCR3ljTkBoKetjbuZGSP37GGItTXX7t8n+ckTbk2bRviUKYRPmcIsFxe8L1woNL2W\nhgYZ6enlrofw/Im5l0JZibYjlIdoP0JZ+fv7k5aWRr9+/dizZw8tWrRQjk2dOpW3336bHj16sGzZ\nMhKePjiJjo4u9IY8R85NuJ+fH7a2tlhYWHD8+HGCgoKYP38+c+bMQVtbmwULFjB48GDUajUDBw4s\nMr8HDx4QEBCApaUl0dHRzJo1iyNHjnDu3DkCAwOVaQbJycm0adOGixcv0rFjR+bPnw/AmDFjWLFi\nBWfOnOHbb79lwoQJSt7R0dGcPn2anTt3Mm7cOKZNm4ZarcbFxYUFCxbw559/cu7cOXx9fQvU686d\nO2hqahYawDx3nXPkdMSoVCocHBx4/PhxnjQpKSkcPnwYW1tbANq0acOxY8eKfF0KY2dnx65duwAI\nCAggMjKSW7duER4ejoGBASNHjsTBwYHRo0eTkpJSIP3t27fzrITSqFEjoqOjGTJkCHv27KFr1658\n+umnrFq1imHDhhU5NUQQKovonBBeaZqammRVQr6e1tZciI3F08YGn4sX6Z9v6JxHq1b4FBF3IjMr\nC00RfFAQBEEQhOdAW1sbZ2dnfvzxxzz7R4wYweXLlxk4cCD+/v689dZbPHnyBGNj42KXuHR3d0el\nUpGUlMTs2bOVWAc2NjZMmzaNS5cuAc8eTXD8+HHs7e0xNTWlb9++WFlZERgYiLu7O/Xr10dTU5Oh\nQ4cqN/AaGhoMGjQIgPfff58TJ06QnJzMqVOnGDhwICqVinHjxikxEiRJYuDAgXmmg+Suj7OzM8OH\nD+fHH38kIyOjQP0iIyMxMjLKs6+wOueUldMRkzPto3r16kD2yA6VSoWLiwu9evWiW7duABgbGxNR\nyodms2bNIj4+HpVKxcqVK1GpVGhqapKRkUFQUBATJkwgKCiImjVrsnjx4kLzKOw9qVWrFn5+fgQG\nBmJvb4+fnx8eHh4sWbKEgQMHik4K4bkRd0jCK6169erE5fpSqoiYEwB9LCzInDsXgLmFDAW0adCA\nkFw997mlZWRQvWbNCqmH8HyJed9CWYm2I5SHaD9CWbm5uaGhocH27dt5++23WbRoEbNnz1aOGxkZ\nMXLkSEaOHImNjQ0hISGoVKpi8/T396devXrK9uTJk+nUqRO///47kZGRJW6vHTp0wNfXl4iICNzd\n3fnkk08KLCssy3KhsSZy9mdlZVG3bl0lsGN+xS3bvnr1agICAti3bx+Ojo6cPXs2z3XllPOsOueM\nRCiqI6ZZs2aF1q+oa/Py8mLfvn1IklQgJoW+vj4//fSTsm1ubk7Tpk1JSkqiUaNGtGnTBoABAwYU\n2jlhYmJCVFSUsn3r1q08U3sAFixYgJeXF1u3bmXQoEF4eHjQv39/Dhw4UOj1CUJFEiMnhFeaoaEh\nL1qM4TspKTRo3ryqqyEIgiAIwmtCR0eHffv2sWXLFuXm9sCBA6Q/nWZ6584d7t+/X+BGtSQePXqE\nsbExAD///LOyv1atWiQmJj4zvZmZGVOmTGHBggU4OTlx9OhR7t+/T2ZmJj4+Pko8iKysLCV2xdat\nW+nQoQP6+vqYm5uzY8cOIPuGPzg4uNBy9PX189QnLCwMJycn5s+fj4GBAbdu3cpzfpMmTYpcqSJ3\nnXPKLa2YmBiaNGlSYP/ChQuV0Rf5JSQk8OTJE4D/Z+/Ow6qq1geOf/c5zDMqiqICag7IdEBwRrAc\nE8PMAb0WWGhO2S2zyV9iannTblqZmWVmKTjPpZZ51DRC5CCKE5o4oSgOqMzD/v1B7MthnpzX53l6\nHve01tqHFZz97rXexZIlS+jRowcWFhbY29vTrFkzTv2TcP23335TRnUUN3DgQJYvXw5AVFQUNjY2\nNGrUSDmemJhIcnIyfn5+ZGZmKsGTzMzMat+fINTEIxucCA8PF3MwhUrZ29uTKknkFRRO7qiLnBO1\nlSxJNG7a9EE3Q6gB8TtHqCnRd4TaEP1HqCmtVqs8YNra2rJ9+3ZmzZrFli1b+PXXX5WEkH379mXe\nvHk0bNiwwpwTZb3pnzp1Ku+++y5eXl7k5+cr5wQEBHDs2LEyE2JKkqRX1quvvsr27dvJy8tjzpw5\nBAQE4OnpSYcOHQgMDATA3Nyc6Oho3Nzc0Gq1fPDPCNYVK1bw3Xff4enpiaurK5s3by6zvYGBgWzY\nsAEvLy/++OMPpk6dqiTe7Nq1q5ILooi9vT15eXlK7oby2nzx4kUkSdLLOaHRaJSpEOWtMhIdHY2f\nn1+Zx8pz/Phx3NzcaNu2LTt27GDBggXKsS+++IKRI0fi4eFBfHw87733HgCLFy9m8eLFAPTv358W\nLVrQqlUrxo4dy1dffaVX/rRp05g9ezYAwcHBzJkzB19fX15//fVqtVN4cmm1WsLDw2t8vVSTSN+D\nJkmS/Ci2W3gwvvrgA4LS02liaYk2KanOpnbUhCzLfHrhAmGffVZmFmXh4abVasXwaqFGRN+pWMmh\n3II+0X+EmhJ9p3bCw8Np166dkuuiLo0cOZIpU6ZUOo3mQRL9R6ipf/6uV3v9XxGcEB57O7duRbV+\nPc80b/6gm8K5W7fYYmnJhA8/rNV63YIgCI8TEZwQBOFhdO3aNV566SV+/vnnOi336tWrhIaGVph4\nVBAeZTUNTjyy0zoEoaq8O3dGB8rUjgfp4K1b+Dz7rAhMCIIgCIIgPOTs7OzqPDABhTnRRGBCEEoT\nwQnhsVe/fn3svbw4du3aA805cTcnh9PGxnh4ej6wNgi1I+Z9CzUl+o5QG6L/CDUl+o5QG6L/CPeb\nCE4IT4SOvXuzNyOD/Ac4emLv5cu49emDiYnJA2uDIAiCIAhPFgsLC+XfP//8M23atKFnz558/fXX\nyv6//voLDw8P8vPzyyzDycmpVPJGT09P3NzcAIiJiWHy5MkA7Nmzhz///LPCNiUlJSnXViQ8PByV\nSsWZM2eUffPnz0elUpW5msXDoOjzruger169qiQd1Wq1WFtbo9FocHFxYdq0acp5y5Ytw87OTi/R\n5okTJ0hKSsLU1BSNRkP79u0ZN24csiyTkpJC//79q93m4m3QaDTMmjVLOXbr1i1eeOEF2rVrh4uL\ni5Los7h58+Yp17q5uWFgYMCtW7e4du0a3bp1w83NjU2bNinnBwUFlbsSivBkE8EJ4Ynw1FNPYdej\nB7Kh4QOpP+nWLY7Xq0fPfv0eSP1C3RBJoYSaEn1HqA3Rf4Sa8vf3V6aS7tq1i8mTJ7N9+3YiIyOZ\nO3cuqampFBQUMGnSJBYtWoRarS63rLt37yrLbR4/flxv9YoOHTooK0fs3r2bAwcO1En7JUnCzc2N\nyMhIZd+aNWtwdXWtk/LvhapM3f3yyy8JCQlRtv38/JTlQ9etW8ehQ4eUsoKDg9HpdMp/bdu2BaBV\nq1bodDri4+M5duwYGzdupFGjRtja2tYocNOjRw+ljqIAib+/P5MnT6Z///4cP36c+Ph42rVrV+ra\nKVOmKNd+/PHH+Pv7Y2NjQ0REBOPHjyc6Opr58+cDsGXLFry8vLC3t692G4XHnwhOCE8ESZJ4duhQ\n4iwsuHT79n2tOyc/n803bzJgzBhMTU3va92CIAiCIAh79+5lzJgxbNu2DWdnZxo2bMiUKVOYOnUq\nX3/9NR4eHnTp0qXc6yVJYujQoaxatQqAiIgIgoODlUS2Wq2WwMBAzp07x+LFi/nss8/QaDTs37+f\nlJQUBg0ahKenJ56ensqb9/z8fMaMGYOrqyt9+vQhKyurzLqDgoKUt+5nzpzBxsaG+vXrK8e3b9+O\nt7c3np6ePPPMM0BhICU0NBR3d3c8PDzYsGGD0u6i5UPfeecdpQwLCwumTZuGp6cnnTt35urVqwCE\nhIQwefJkunbtSsuWLVm3bp1yzdy5c/H19cXDw6PaSyeuXbu2zOVaTUxM8PT05O+//wYKV3mrLFmw\nWq2mS5cunD59GoCBAwcSERFRrfYU1VVSWloa+/btY/To0QAYGBhUutrcypUrCQ4OBsDIyIj09HSy\nsrJQq9Xk5+ezYMECpk6dWu32CU8GEZwQnhgWFhbU9/Rk7fXr3MnOvi91Fsgym86fp3nfvrRp0+a+\n1CncO2LupVBTou8ItSH6j1BTWq2WrKwsBg0axKZNm2jdurVy7NVXX+XYsWPMmzePTz75hOTk5DIf\nmIs8//zzrF+/HoCtW7cSGBhY6hxHR0deffVV3njjDXQ6HV27duW1114jICCAuLg4YmNjcXFxASAx\nMZGJEydy9OhRbGxs9B78i7OysqJ58+YkJCSwatUqvWU9r127xpgxY1i/fj1xcXGsXbsWgJkzZ2Jr\na0t8fDyHDx8mICCA5ORk3nnnHXbv3k1cXBwHDx5Ugh4ZGRl07tyZuLg4/Pz8WLJkiVLHlStX2L9/\nP1u3blUCGjt37uT06dNER0ej0+mIiYlh3759VfqZXLlyBbVajZmZWaljN27cIDo6WvmMAFatWqVM\nmfDy8iK7xHfYjIwMdu3ahbu7OwA+Pj7s3bu3Sm0pIkkSBw4cwMPDg/79+3Ps2DGgcJSKnZ0doaGh\neHl5ERYWRkZGRrnlZGRksGPHDgYPHgzAiBEj2LRpE7179+b9999n4cKFvPjii2KKs1AuEZwQnijO\nzs54h4ay/OLFex6gKJBlNiclkenry4AhQ+5pXYIgCIIgCGUxMjKia9eufPvtt3r7JUli7Nix9O/f\nH1tbW5o0aVLhChL169fH1taWyMhIXFxcyny4LlL8Lfzu3bsZN24cACqVCisrK6DwO1nRA7W3tzdJ\nFSQtHzZsGBEREWzcuJFBgwYpdURFReHn54ejoyMANjY2QOEUlgkTJijX29jYcPDgQQICAqhfvz5q\ntZqRI0cqD/FGRkZKYKZ4WyRJIigoCIB27dqRkpICFAYndu7ciUajwdvbm1OnTikjFypz7tw5Gjdu\nrLdv3759eHp60qxZM4KCgmjfvr1S//Dhw5UpE7GxsRgbGwOFo0g0Gg3dunVjwIAB9OnTB4AmTZpU\n+FmWxcvLiwsXLnD48GEmTZqk3HN+fj6xsbGMHz+e2NhYzM3NmTNnTrnlbNmyhW7duik/BysrK7Zu\n3crBgwfx9PRk69atDB48mLCwMIYMGVJm/grhySaCE8ITxd/fn27+/riFhvL9pUukVhD9rY3svDzW\nJiVx29eX4WFhGBgY3JN6hPtLzPsWakr0HaE2RP8Rasrf3x+VSsXq1auJjo7m448/1juuUqmqvLy5\nJEkMGzaMiRMn6k3pqIqyzi16yIbCqQl5eXnl1jtgwAB++uknHB0dsbS01DtW1TolSdLbJ8uycr1h\nsZxkKpVKry1GRkZllvnuu+8qQYNTp04RGhpablsqa1v37t2Ji4sjISGB9evXc+HChXLPLdKyZUsl\nYPHBBx+UeV/FTZs2TRl9UZKlpaUSbOrXrx+5ubncuHGD5557jqZNm+Lj4wPACy+8UGE+i8jISGVK\nR0kzZ85k2rRprFy5Ej8/P3744YdqT4cRHn8iOCE8kfx69qTba6+x9PZtDly6REE1/sBW5uzNmyy6\ncAGT/v0ZMXas3h81QRAEQRCE+83ExIRt27axYsUKli5dquyvToABYNCgQbz99tvKW/qyWFpacufO\nHWX76aefZtGiRUDhm/jb1cz9Jcsypqam/Oc//+H9999X9kuSRKdOndi7d68yUuDGjRsA9OrVi4UL\nFyrn3rp1C19fX/bs2cP169fJz88nMjKSHj16VKstRfr06cPSpUtJT08H4NKlS1y7dq1K1zo6Opa7\nUoWTkxOTJ09m5syZQPV/PgCXL19WRpIUN2vWLCWYUVJKSopSV3R0NLIsU69ePezt7WnWrBmnTp0C\n4LffflNGdZSUlpbG3r17ee6550odS0xMJDk5GT8/PzIzM5XgSWZmZrXvT3i8ide5wmMtLS2Nixcv\ncv58MidOXCY6Ohp7e2ckScLY2JAGT3Vg09+H2X34MEObN6eVrW2V3yCUdDs7m71XrnCqQQMCp03j\nqaeequO7ER40rVYr3mAKNSL6jlAbov8INaXVapXvNba2tmzfvh0/Pz8aNmzIgAED9FbcSE5OJiws\nrMypHUXnWFhY8NZbb5XaX/zfgYGBvPDCC2zatIkvv/ySBQsWMGbMGL777jvUajVff/01jRo1KvV9\nq7zvX0X7i+eaKNKgQQO++eYbnn/+eQoKCmjUqBE7duxg2rRpTJgwATc3N9RqNeHh4QQFBTFnzhwC\nAgKQZZkBAwYoeTNK3kdZ91X837169eL48eN07txZ+VxWrFiBnZ1dudcWsbe3Jy8vj4yMDMzMzErV\n9+qrr9K6dWsuXryIJEmsWrWKP/74Qzm+aNEi7O3ty/28oqOjSy37Wpm1a9eyaNEiDAwMMDMzU1ZH\n0Wq1fPHFF4wcOZKcnBxatmzJ999/D8DixYsBGDt2LAAbN26kT58+ZSZ/nzZtGh999BEAwcHBys+i\nKAgjCEWkmkTkHjRJkuRHsd3C/VFQUEBiYiK7dh0kJiYZaA40xty8CdeuHcPZufCPUn5+DhkZ10hP\nv8SNG/GkXthPO6sMhjRrQE9nZ4wqWE6riCzLnL11i4O3b5NkaopHnz749+kjEv08psQDglBTou9U\nrORwa0Gf6D9CTYm+83AKDw+nXbt2ZQZcamvkyJFMmTIFjUZT67JE/xFq6p+/69V+4yuCE8Jj5dSp\nU3z//c9cuWKBsbEPdnYuqNWGlV8I5OSkk5i4h6uXtmKWd4aeLevRxsSEJoaGWBgZoZYkCmSZrLw8\nUrKzSZYkrgA2zs506NsXdw8PMYVDEAShBkRwQhCEJ8m1a9d46aWX+Pnnn+u03KtXrxIaGlphYlNB\nuB9EcEJ4omVmZrJx4w62bz+HjU0gtrYtalyWLMskJx/k7t2tdO/ugL2NJdlpaeTl5KA2MMDIzIxG\nLVvSxMGBxo0bV5itWhAEQaicCE4IgiAIwuOjpsEJkRBTeORdvXqVGTO+ZudOQ5o3H1dhYCIpSVtp\neZIk4eDgi6Pjm0RFmZF0JZOgkBBGTZ7MiAkTeCE0lO5+frRs2VIEJp4wWq32QTdBeESJviPUhug/\nQk1ptVosLCzKPf7666/TtGnTKgUHk5KSUKlU/N///Z+yLzU1FUNDQyZNmlQn7S1Oq9UqOSHqkr+/\nP4cOHSrzWFhYGMePH6+Teo4cOcLo0aMBWLZsGXZ2dnh5edG6dWv69u3Ln3/+qZwbEhJCixYt0Gg0\nyvKgRdepVCp27dqlnLtx40ZUKhXr168HYOjQoZw9e7ba7VuwYAFubm64urqyYMECZX90dDS+vr5o\nNBratm3LwYMHy7zeyckJd3d3NBoNvr6+yv63334bDw8PXnrpJWXfTz/9pFeHIJRHBCeER9rly5eZ\nPXs5t28/g6Pjs6jVdTetwsTEGmfnf3H0aDM+/XQZGfdo2VFBEARBEIR7pbzEiQUFBWzevBkXFxf2\n7NlTpbKcnZ31piKsWbMGV1fXaiUTL2/J0PulZALKIgUFBSxZsoR27drVST1z585l3LhxSp3BwcHE\nxsZy6tQp3nnnHZ5//nlOnDihHJ83b56yNGlRAkxJknBzc1MSVAJERETg6empbIeFhfHZZ59Vq21H\njx7l22+/5eDBgxw+fJitW7dy5swZAKZOncrMmTPR6XSEhoYyderUMsuQJAmtVotOpyM6OhooTESv\n0+k4fPgwRkZGHD16lMzMTJYtW8bEiROr1UbhySSCE8Ij6/r163zyyQry8vrTsKFbla5xcvKvVh2S\nJNGsWS/Onm3NF1/8RHZ2dg1aKjwuRFIooaZE3xFqQ/QfoaYq6jtarRYPDw9Gjx5NRERElcozMzOj\nXbt2ysiD1atXM3ToUGXkxZYtW+jUqRNeXl706tWLq1evAoUJIEeNGkW3bt148cUX6dGjB4cPH1bK\n7datG0eOHKlSG2bOnImvry9ubm7KShFF9/rOO+/QsWNH2rRpozzgZ2ZmMnz4cFxcXHj++ef1lq+0\nsLBgypQpeHp68ueff+qNqti5cyddunTB29uboUOHKsuGOjk5KUuWxsTEEBAQUKqN2dnZREVF4ePj\no+wrPjrF39+fMWPG8M0335R5vLju3bsTHR1NXl4ed+/e5cyZM3h4eCjn+/v7Vzt3xYkTJ+jYsSMm\nJiao1Wp69OihjMRo3LgxaWlpADRv3hwHB4dyyynZZpVKRW5uLrIsk5GRgaGhIfPmzeO1115DXYVE\n84IgghPCIyk/P59vvllLRkYP7Oxc7mldkiTRtGlPjh1ryJYtO+9pXYIgCIIgCPdDREQEw4YNIzAw\nkJ9//pn8/HygMMAwffr0cq8bPnw4kZGRXLx4EbVaTZMmTZRj3bt3JyoqitjYWIYNG8Ynn3yiHDtx\n4gS7du1i5cqVvPzyyyxbtgwoTGaenZ2Nm1vVXjRNnDiR6Ohojhw5QmZmJlu3bgUKv6/l5+fz119/\nMX/+fGbMmAEULr1pYWHBsWPHmDFjht6UjoyMDDp16kRcXBxdu3ZVRlWkpqYye/Zsdu3axaFDh/D2\n9ua///2vUk9ldDodbdq0qfAcjUajjJyQZZm33npLmdYxatQo5TxJkujVqxc7duxg8+bNDBw4UK8c\nQ0NDHBwcqjUdxdXVlX379nHjxg0yMjLYtm0bFy9eBGDOnDm8+eabNG/enLfeeouPP/64zDIkSeKZ\nZ56hQ4cOLFmyBABLS0v69++Pl5cXTZo0wcrKiujo6FJtFoTyiOCE8Ejas+cPTpwwx96+Q7Wuq0rO\nibIUjqDox5Ytp5Vhb8KTR8z7FmpK9B2hNkT/EWqqvL6Tk5PDL7/8QmBgIObm5nTs2JHt27cDEBgY\nqDzYl6VPnz78+uuvREZGlloK88KFC/Tu3Rt3d3fmzZvHsWPHgMLvUQMHDsTY2BiAF154ga1bt5KX\nl8fSpUsJDQ2t8j39/vvvdOrUCXd3d37//XelDoDnn38eAC8vL5KSkgDYt28f//rXvwBwc3PD3d1d\nOV+tVjN48GC98mVZJioqimPHjtGlSxc0Gg3Lly/n/PnzVW7juXPnaNy4cYXnFB91UHJax48//qh3\n7rBhw4iIOiVrxgAAIABJREFUiCAyMpLg4OBSZTVp0kS536po27Ytb7/9Nr1796Zfv35oNBplZMPL\nL7/M559/zvnz53nllVeUvBkl7d+/H51Oxy+//MLChQvZt28fAG+99RY6nY65c+fywQcfMHPmTL79\n9luGDRvG7Nmzq9xG4ckkghPCIyclJYWVK/+iSZOB1ZrjWFsGBsZYWw/km282i+kdgiAIgiA8snbs\n2MGtW7dwdXXF2dmZffv2VXlqh6GhoTKSYMiQIXoP2ZMmTeK1114jPj6exYsX602hKJ5E3MzMjF69\nerFx40bWrFnDyJEjq1R3VlYWEyZMYN26dcTHxxMWFkZWVpZyvCj4oVar9XJblDdlwsTEpNzvkr16\n9VKCBQkJCcroAAMDAwoKCpT2lKUqKxDpdDpcXKo2+tfHx4ejR49y/fp1nnrqqVLHZVlGpdJ/rIuO\njlZGYhSNLilu9OjRxMTEsGfPHmxsbGjdurVy3aBBgwDo0aOHkk+ipKLgi52dHYMGDSp1nk6nA6B1\n69asXbuWVatWcebMGU6fPl2lexaeTCI4ITxyduz4A+iGsbFVta+tbs6JkurVa8mVKw7odHG1Kkd4\nNIl530JNib4j1IboP0JNldd3IiIi+O677zh79qzy36+//qoXTKjIm2++yX/+8x9sbGz09t++fVuZ\n5lE0bQPKDg688sorvPbaa/j6+mJtbV2leouCAfXr1+fu3busWbOm0mv8/PxYuXIlUJgIMj4+vsLz\nJUmiU6dO7N+/Xxktm56eTmJiIlCYcyImJgaAdevWlVmGo6MjV65cUbZL3v+ePXtYsmQJYWFh5Z5T\nct+cOXP46KOPyqzv8uXLODo66u3z9fVVgisDBgwodU1RPpDz58+zYcMGRowYAUCrVq2UBKkFBQVK\n0KK4jIwM7ty5AxR+Njt37iw1Lado1EROTo4yZUilUlW5jwlPJoMH3QBBqI709HT27UukUaP+tSpn\nxgwVnTu/Qe/e8wA4cGAeOTnp+PtPR6sNx8jIki5d3izzWlvbjvz88xY6dvS9ryM3BEEQBEEQqisj\nI4NmzZop2+PHj2fnzp16yRjNzMzo1q0bW7ZswdTUlJiYmDKndhR973FxcVHe+hdf/SI8PJwhQ4Zg\na2tLz549OXfuXKlzinh5eWFtbV3ulA5Jkti1a5de29esWUNYWBiurq7Y29vTsWPHcu+7qL5x48YR\nGhqKi4sL7dq1o0OHDqXOKalBgwYsW7aM4OBgZbTs7Nmzeeqpp5g+fTovv/wyVlZW+Pv7l1mGh4cH\nJ0+e1Ktn1apV/PHHH2RkZNCiRQvWr1+vl5firbfeYtasWcr5f/31l97n1rdv3zLvLzc3l4sXL9K2\nbdtyP4uyvPDCC1y/fh1DQ0O++uorrKwKX/p98803TJgwgezsbExNTZV+kpycTFhYGNu2bePKlSvK\nFJq8vDxGjhxJ7969lbI3bdqEj48P9vb2AHh6euLu7o6Hh0eVc4sITyapKusaP2wkSZIfxXYLtafV\n7uOHH27i6FizxDpJSVqcnPyZNcsEKysHXnklGjOz+hw48Ck5OXf/CU7MwMjIotzghCzLnDu3iOnT\n++Hs7Fyb2xEeMVqtVrzBFGpE9J2KVWUI9JNM9B+hph7mvpOcnExAQIDeQ/zjJCQkhHHjxlUYQKkL\nO3fuZNu2bSxYsKDOy36Y+4/wcPvn73q13+KKaR3CI2XfvmPY2HhWfmIl1GpDvLzGEBVVvXWhofB/\nNrXak9jYhFq3QxAEQRAE4UmzfPlyOnXqVO40hcfBlClT+Prrr+95Pd9++y3//ve/73k9gnA/iJET\nwiMjLy+PV1/9D40bT0WtNqz29bIsk5V1k6ysWyxa5MZLL2lZtWoQwcFbOHZsLSDTs+ds9uz5sMKR\nEwBpaedp0GAH778fVu45giAIQtWIkROCIAiC8Pio6cgJkXNCeGSkpKRQUFCvyoGJrKw0bt++QFra\nZVJTk7lx4zJ5eUZAPfLzC4iJ+Rszs05s3jwblUpNfv4NcnM/5fr1eKytG5KaehIrq6YYGZmXKtvC\nwp6kpKvk5+crSy8JgiAIgiAIgiAINSOmdQiPjMKsx5WtGV1AauoJoqN/ZPv2xfz551GOHTPm5s0u\nmJhMQpa9sLYOQZIMsLYeRsuWS7h16xCGhu0xMemKgUEYGRmNuXxZZv/+g2zf/gVxcWu5deuc3ls9\ntdqI/HxrUlNT7/FdCw+T8taLF4TKiL4j1IboP0JNabVaVCoVo0aNUvbl5eVhZ2dHYGCg3rlBQUF0\n7ty5ymVHR0fj7+9P69at8fb2ZsCAARw9erTCa8LDw/n000+rVH7Xrl0rPcfCwqLM/SkpKYwYMYKW\nLVvSoUMHunTpwsaNG6tU77105MgRRo8eDRSuZmJnZ4dGo8HFxYWvvvpKOS88PJymTZsqS4FqNBrS\n0tLQarVYW1sr13z44YcAHD58mJdffrna7Tl37hxPP/00Hh4eBAQEcOnSJeXY22+/TYsWLXBzc2P1\n6tXllrF69Wrat2+Pq6ursiTsyZMn8fb2xsPDg6ioKKCw3/Xq1avc5VcFAR7hkRPh4eH4+/uLJC1P\nkKysLGS59CgGgJycu1y6FEti4iEyMqwwMPDB2joYSaq4ixsa2mJnN5QrV77D3v5lDAysMTS0Q622\nwNr6XxQUZHH+/GGSkrZgY6OiVSsf7O3dMTAwRqUyF79gBUEQBEF4qJmbm5OQkEBWVhYmJib8+uuv\nNG3aVG+ViVu3bnH06FGsra05e/ZspQm/U1JSGDZsGBEREXTq1AlAWXrT1dW13OuqsspZXl4eBgYG\n7N+/v9JzyypPlmWCgoIIDQ1VlhA9f/48mzdvrrS8e23u3LlMmjQJKGx7cHAwn3/+OTdu3KBdu3YM\nGTIEOzs7JEnijTfe4I033ihVhp+fH1u2bCEjIwNPT08GDhyIp6cnZ86c4erVqzRs2LDK7ZkyZQoh\nISGMGjWK3bt38+6777J8+XK2bduGTqfj22+/pVOnTvj7+9OvXz8sLS31rk9MTGTOnDkcOHAAa+v/\nvbT75ptv+OKLL3B0dGTy5MmsXbuWRYsWMWrUKExMTGrxCQoPO61WW6uA+iM7cqIoOCE8OfLz85Fl\n/SkUslzAuXMH2LlzIYcPpyHLw7GxeRkLC/cyAxM2Nv7//Ot/f8yaNn2T3Nz/jYCQ5TxUKmMAVCoT\nrKw6Ym09gczMfsTEnOXXXz/nypUjyLKKvLy8Or9P4eElfucINSX6jlAbov8INVXUd/r378+2bdsA\niIiIIDg4WG9E6Pr16wkMDGTIkCFERkZWWu6XX35JSEiIEpiAwpEOzz33HABbtmyhU6dOeHl50atX\nL65evaqcd/jwYbp06ULr1q359ttvgcIHmu7du/Pcc88pwY3ioyLmzp2Lr68vHh4ehIeHV9i233//\nHWNjY8aMGaPsa968ORMnTgQKX3aFhobi7u6Ol5eX8iC1bNkygoKC6N27N87Oznz55ZfMmzcPLy8v\nOnfuzM2bNwE4c+YM/fr1o0OHDvj5+SmrjRQf5WBmZsa+ffv02pWdnU1UVBQ+Pj7KvqKfQb169WjR\nogVJSUmljpXHzMwMb29vTp8+DUC/fv1Ys2ZNhdeUdPz4cXr27AkU9pVNmzYp+/38/OjZsydmZma4\nu7uzffv2UtcvWbKEiRMnYm1tDRQuwQpgaGhIeno66enpGBkZkZaWxtatW3nxxRer1T7h0ePv71/p\n/6MVeWSDE8KTR61WI0n5ynZGRip//rmU2NhTGBuPwcYmEGPjiqd9FOnW7bbybyOjhnTvno6T0wf/\nlJuAqWkrvfMlScLU1Bkbm6GoVCM4cGAvp07FKGtfC4IgCIIgPKyGDRtGZGQk2dnZHDlypNTylpGR\nkQwbNoyhQ4cSERGh7F+8eDGLFy8uVd6xY8fw8vIqt77u3bsTFRVFbGwsw4YN45NPPgEKH7jj4+PZ\nvXs3f/75Jx9++CGXL18GQKfT8fnnn3PixAngf6Midu7cyenTp4mOjkan0xETE1Pqwb+4hISECtu2\ncOFC1Go18fHxRERE8NJLLynf5xISEtiwYQMHDx7k/fffx8rKitjYWDp37szy5csBGDNmDF988QUx\nMTHMnTuX8ePHK+3X6XR8+OGH+Pj40KVLF716dTodbdq0KbNN586d4++//6Zly5bK5/TZZ58pwY6n\nn3661DXXr18nKiqK9u3bA+Dr68vevXvLve+yeHh4sG7dOgA2bNjAnTt3uHnzJh4eHmzfvp3MzExS\nU1PZvXs3Fy9eLHV9YmIiJ0+epFu3bnTu3JkdO3YAMGHCBD766CNCQ0N59913+fDDD3n//fer1Tbh\nyfTITusQnjympqbAVWS5gPPno4iP/wPwx8bGp0rDBAFu3dIWGz1RWkyMO6ambbC17V3uOcbGDhgZ\njeXy5a/473/X8Nprz+Pm5lrlNgiPLrHet1BTou8ItSH6j1BTRaMC3NzcSEpKIiIigmeffVbvnJSU\nFE6fPq2MgjAyMiIhIYH27dszduzYcssu/ma/Y8eO3Llzh969ezN//nwuXLjA0KFDuXLlCjk5ObRo\n0QIoDDgEBQVhbGyMsbExAQEBREdHY2Njg6+vL46OjqXq2blzJzt37kSj0QCQnp7O6dOn6d69e5nt\nKvl9bOLEifzxxx8YGRkRHR3N/v37ee211wBo06YNjo6OnDp1CkmSCAgIwNzcHHNzc2xsbJS8HG5u\nbsTHx5Oens6BAwcYMmSIUn5OTo7y78TERKZOnYpWqy2VMP3cuXM0bvy/l2iyLLNq1Sr27t3LiRMn\nmDdvHvXq1VPuobxpHfv27cPLywuVSsW7775Lu3btAGjcuLHeyIuqmDdvHhMnTmTZsmX4+fnh4OCA\nWq2mV69eHDx4EHd3d5ydnencuTMqVel32nl5eZw+fZo9e/Zw4cIF/Pz8OHLkCM2aNWP37t0AnD59\nmkuXLtG2bVtGjRpFbm4uM2fO5KmnnqpWW4UngwhOCI8Me3t7Cgr+ICZmBRcv5mNhEYahoW2d1tGh\nQ3yVzpNlsLT0xNKyBfPmbSIwMInBg58t8xe3IAiCIAjCgzZw4ECmTJnCnj17uHbtmrJ/9erV3Lhx\nQ8kzcefOHSIiIpg1a1a5ZbVv357Y2FgGDhwIwF9//cW6devYunUrAJMmTWLKlCkMGDCAPXv2VDjM\nu+i7k7l52XnFAN599129aRoVad++vTIaAAqnoFy/fp0OHToo+8qbMmFsbKzXrqJtlapwKm9BQQG2\ntrbodLpS1969e5dhw4bx7bff0qhRo1LHSy6ZLEkSw4cP5/PPP+fQoUMMHTqU0NBQZTpLeW3s3r07\nW7ZsKbVfluUyX5SNHj0anU6Hg4OD8vMp0rhxY+Wzunv3LuvWrcPKygqA9957jy5duuDv78/IkSPL\nHPXRtGlTOnbsiFqtxsnJidatW3P69Gm8vb2Vc6ZNm8bs2bNZsGABY8aMwdHRkffee4+ffvqpzPsT\nnmziSUp4ZFhaWnLixAEuXLDExubFGgUmKho1UR05OXexsbHA2ropjo5hbN58kx9/XEd+fn7lFwuP\nLPHmUqgp0XeE2hD9R6ip4n1n9OjRhIeHK9MAikRERLBjxw7Onj3L2bNniYmJqTTvxIQJE1i2bBl/\n/vmnsi89PV15OL59+zZNmjQBCnM5FJFlmU2bNpGdnc3169fRarX4+PhUmF+hT58+LF26lPT0dAAu\nXbqkF1wpqWfPnmRlZfH111/rta1I9+7dWbFiBQCnTp3i/PnztG3btsI2FB2ztLTE2dmZtWvXKvvj\n4wtfbI0ePZrQ0NByVxlxdHT8Z+W5/5VZVK63tzeBgYF8/vnn5bahMpcvXy5z5MnSpUvR6XSlAhNQ\nODWkoKAAgI8//lhZ8aOgoIDr16/j7+9PfHw88fHx9O5delRxUFCQMjonNTWVU6dOKaNkAPbs2YOD\ngwMtW7YkMzMTSZKQJImMjIwa36fweBMjJ4RHQlZWFgsXriQvrwumpj2QpAcbV8vOvo2jY2HGYrXa\nCGfnEezatZbc3NWEhg4tNZRPEARBEAThQSgKGDg4OChJIYseEs+dO8eFCxf0clA4OTlhY2Oj5HgA\nSk3vaNSoEatWreLtt9/m0qVLNGzYEDs7Oz74oDB/V3h4OEOGDMHW1paePXty7tw5pV53d3cCAgJI\nTU3lgw8+wN7enpMnT5Z661+03atXL44fP64sc2phYcGKFSuUVS3KsnHjRv7973/zySefYGdnh7m5\nuZL3Yvz48YwbNw53d3cMDAz44YcfMDQ0VD6TkvUX/7wAVqxYwbhx45g1axa5ubkEBwdjY2PDunXr\nSExMZOnSpQB89913erkvPDw8lOSZJcuEwqU7O3bsyOTJkwH47LPP9EYXbNy4sdQ1xUVHR+Pn51fm\nsfJotVreffddJEmiR48eLFy4ECicqlJUlrW1NStWrFBGuEyfPp0OHToQGBhInz592LlzJ+3bt0et\nVjNv3jxsbQtfHsqyzOzZs5VlSMeMGcPIkSPJz89n0aJF1Wqn8OSQKssE+zCSJEl+FNst1Exubi5f\nfvkj8fGNkWUX4uIysLFpV6OyKss5URWyLJOWFk1AQFslOzFAQUE+SUmR9OljyogRg0QOiseQmPct\n1JToOxUrOdxZ0Cf6j1BTou88XEJCQhg3blyphKR1wd/fn9WrV1drKdHKiP4j1NQ/f9er/TAkpnUI\nD72IiE3ExdnSvHlfHByaoFZfJz8/94G1JyvrFjY2KmVOXhGVSo2j41C2b7/Frl17HlDrBEEQBEEQ\nhIfRlClT9Kab1JX4+HhatWpVp4EJQXgQxMgJ4aGWkJDAf/6zG0fHsajVhgAcOXKcv/82w9q69Ly6\n++HWraP4+Nji4OBQ5vHs7Ntcu7aYWbNGYW9vf59bJwiC8OgRIycEQRAE4fEhRk4Ij5309HSWLPmF\nBg2ClMAEQMuWjqhUF8nNzbzvbcrISMXS8m6FQQdjYysMDHrx3XcbRYJMQRAEQRAeKJVKxahRo5Tt\nvLw87OzslGUyly1bhp2dHRqNRvnvxIkTFZa5detWvLy88PT0pH379nzzzTf39B7KU5RnoW3btnh5\neREWFkZmZvnfD5ctW8akSZOAwrwYn376KVCYR2HXrl21bk92djY9evRAlmWSkpIwNTVFo9Hg6urK\nK6+8oiSf1Gq1WFtb633mv//+OwBqtRqNRoObmxtDhw4lMzOT7Oxs/Pz8lOurKisri+DgYNzd3XFx\ncWHOnDnKsb59+yo/v5dffpnc3NKjkleuXKnXRrVaTXx8PNnZ2fTt2xc3Nze9/BFjxowpcyUTQagq\nEZwQHlpr1/7M7dvuWFk11dtvZmaGu3sz7t49We03bbduaWvcnvz8XHJyEvH2bltpwstGjTw4edKK\n3bv31bg+4eFTlJFaEKpL9B2hNkT/EWpKq9Vibm5OQkICWVlZAPz66680bdpUyY0lSRLBwcHodDrl\nv7Zt25ZbZm5uLmPHjmXr1q3ExcURFxf3QPISpKSkMHToUObOncuJEyeIjY2lb9++3Llzp9xrykt4\nOWPGDJ5++ulat2nFihUMGDBAKbtVq1bodDri4+M5e/YsGzZsUM7t0aOH3mfes2dPoPB7rk6n48iR\nIxgZGfH1119jbGxM9+7d2bhxY7XaU7TqSnx8PIcOHWLx4sWcP38egDVr1hAXF0dCQgJpaWmsWrWq\n1PVNmjRR2vfjjz/SokUL3N3d2bFjB35+fsTHx/Pjjz8CcPjwYWRZRqPRVP+DE4R/iOCE8FA6duwY\nu3al4OAQUObx5s2b0ahRAXfuXLwv7ZFlmdu3E2nTpgE2NjaVni9JEg4OgURGHtRbNkoQBEEQBOF+\n69+/P9u2bQMKlw4NDg5WXvAUX9KyKu7cuUNeXh716tUDwNDQkNatWwOFCR/XrVunnGthYQEULnPp\n5+enjAjYv38/ULhyho+PD66uroSHhyvXOTk5ER4ejre3N+7u7nqrXBRZuHAhISEhesklBw8eTMOG\nDblx4wZBQUF4eHjQuXNnjhw5UuE9FW93eXXv2bNHGUHg5eXF3bt3S5UTERHBc889V2q/SqXC19eX\nM2fOKPuq8pl369aN06dPAzBw4EAiIiIqvaa4xo0bk56eTn5+Punp6RgZGSk50ywtC1edy83NJScn\nhwYNGlRY1sqVKxk+fDgARkZGpKenk5OTo9zHBx98wMyZM6vVPkEoSQQnhIeOLMusXLkLW9tn9aZz\nFCdJEhpNO0xMLnL3bkqVy67JSh2Fq3Ocwd4+i9atW1R+wT+MjS1RqfzZskVb7TqFh5PIWC3UlOg7\nQm2I/iPUVFHfGTZsGJGRkWRnZ3PkyJFSq0WsWrVKb/h+dnY2QJlvwevVq8fAgQNxdHRkxIgRrFy5\nUnlALW850JUrV9K3b19lFIGHhwcAs2fP5uDBgxw+fJg9e/Zw9OhR5To7OzsOHTrEuHHjmDdvXql2\nJCQk4O3tXeZ9T58+HW9vbw4fPsxHH33Eiy++CJQfECi+RGd5dX/66ad89dVX6HQ6/vjjD0xNTfXK\nyM/P5+jRo0qgprisrCz27NmDq6ursm/fvn16n/nZs2f1rsnLy+OXX37B3d0dAE9PTw4cOFBm+8vT\np08frKysaNy4MU5OTrz11lt6L9n69OlDo0aNMDU1pW/fvqWuL/67Z/Xq1QQHBwOFy7smJSXRuXNn\nJk+ezObNm/H29ha51oRaE8EJ4aFz9uxZLl40wMbGqcLzTE1N6drVHUPDM9y9e/metKUwMJFIgwZp\n+Pi4Vzqdo6RGjTz4889zpKWl3ZP2CYIgCIIgVMbNzY2kpCQiIiJ49tlnSx0fPny43hQDY2NjgHLz\nByxZsoRdu3bh6+vLvHnzGD16dIX1+/r68v333zNjxgzi4+OVERWrVq3C29sbLy8vEhISOHbsmHLN\n888/D4CXlxdJSUlllltesGH//v1Kno2AgACuX79e4XSPksqqu2vXrvz73//miy++4ObNm6W+E6am\npiqjEYqcOXMGjUaDvb09jRs3pn///sqx7t27633mzs7OAGRmZqLRaPDx8cHJyYmXX34ZAGNjYwoK\nCpTpOVXx008/kZmZyeXLlzl79izz5s3TC4Ls2LGDy5cvk52dzQ8//FBuOX/99RdmZma4uLgAhXkx\nVqxYQWxsLIMHD2bBggW88cYbvPHGGwwZMoQtW7ZUuY2CUJwITggPnd27D2Jk5FMq+l4Wc3Nzunf3\nxMzsPLdunah0idHq5JzIzc3g1q04mjTJpFMnDwwMDKp8bRG12ghZduevvw5V+1rh4SPmfQs1JfqO\nUBui/wg1VbzvDBw4kClTpuhN6ShSk9VyXF1def311/n111+VKREGBgZK0saCggJycnKAwgfxffv2\n4eDgQEhICD/++CNnz57l008/5ffff+fw4cM8++yzeg/eRQEStVpNXl5eqfrbt2/PoUPlf78qeU9l\nfa8s77tmWXW//fbbfPfdd2RmZtK1a9cyp5qUrLNly5bodDrOnDnDiRMniImJKbe9RUxNTZWAxYIF\nC/S+f8qyXKrNX331lTLVpORU4gMHDjBo0CDUajV2dnZ07dq1VBuMjY0ZPHgwBw8eLNWWov4TGRnJ\niBEjymzvV199xUsvvURUVBQ2NjasWrVKSTQqCNUlghPCQ+X27dscOJBEw4ZuVb7GzMyMHj060KaN\nijt3YsjISK3VknSyXJjLIitLh7e3HT4+7jUKTBSxs/Ph559jxcodgiAIgiA8MKNHjyY8PJz27dvX\nqpz09HS9oIdOp8PJyQkozNdQFDDYvHmzsgLE+fPnsbOz45VXXuGVV15Bp9Nx584dzM3NsbKyIiUl\nhV9++aVa7Zg4cSI//PAD0dHRyr4NGzZw9epVunfvzooVK4DCB2w7OztltEaR6ubaOHPmDO3bt2fq\n1Kn4+PiUCk40aNCgzDwUAPXr12f27Nm89957Va6vpOzsbNRqtRI4KTJ+/Hh0Oh2xsbGlplW0bdtW\nWQUkPT2dqKgo2rVrR3p6OpcvF446zsvLY+vWreUmsiwoKGDNmjVKvonibt68ybZt23jxxRfJyMhA\npSp8tKxoxRRBqIgITggPlb/+OkRBgRsGBsaVn1yMWq2mXbvW9OjRFjOzv0lLi+HOnUsUFOhH2ivK\nOZGXl01a2lnS0qJo2PA6Tz+toXnzplUawVERM7MG3Lplx/Hjx2tVjvDgiXnfQk2JviPUhug/Qk35\n+/sr32McHByYOHEiUDrHQsmcE1FRUUDZOSdkWWbu3Lm0bdsWjUbDjBkzWLZsGQBhYWHs2bMHT09P\noqKilIDA7t278fT0xMvLi9WrVzN58mTc3d3RaDS0bduWkSNH0q1btzLvoXhbi2vYsCGRkZFMmTKF\ntm3b4uLiws6dO7G0tCQ8PJxDhw7h4eHBe++9p0xZKHnfVfmOV3TOggULcHNzw8PDAyMjI/r166d3\nnlqtxtXVVS9oUbz8oKAgrl69SnR0NJIklco5sX79+lLXFKfT6ejcuXOl7S1u7Nix5OTk4Obmhq+v\nL6NHj8bV1ZW7d+/y3HPP4eHhgZeXF82bN1em5mzZsoXp06cDhf1n7969NG/eXAlAFTdz5kymTZsG\nFOav2LdvH+7u7kqOD0GoLqk2b5gfFEmS5Eex3ULl3n//S7KynsfSskmNy5BlmVu3bpGUlMyFCzeR\nZUtk2RJDQwsMDc2QJNU/0fICcnPTycu7gyTdwcAggxYtGtG8eRPMzc3r8K4gJSUeL6/jhIUNq9Ny\nBUEQHgeSJNVqxJsgCMLDYNmyZaSkpPD222/XednvvfcePj4+DBo0qM7LFoS69s/f9Wq/4RUjJ4SH\nRnZ2NsnJaVhY1C7TryRJ2NraotG0p29fX7p1a4qHh4pGjVJIT/8JSTqCSnUUE5OTNGz4NxcuDOLS\npRBOnBjCL78E8NNP3fj881YsWNCCzMybAGRm3mTBghakpZ2vUZusrJpy8mRyre5LePDEvG+hpkTf\nEWrNbk9AAAAgAElEQVRD9B+hpkTfub9GjBjBtm3b6jzYmp2dzR9//EFQUFCdllsZ0X+E+63mE+kF\noY5duXIFSWqEJNVdzMzIyIj69etTv359nJ2hQYPrODnpL5/l61u4bJVWOwNjY0s6d34DgP375/Lb\nb+8QGLiY3357B2/vsVhbN69RO0xMbDl/Ppv09PQ6H5UhCIIgCIIgPHhGRkbs3bu3zss1Nja+J+UK\nwsNGjJwQHhrJycnIcuN7WoeTk3+Fx4tHujt3/jeXLkURFTWfixcP0KXLlBrXWzivsbGSfEh4NIl5\n30JNib4j1IboP0JNib4j1IboP8L9JoITwkPj1KlkjIxqnmuirqlUBjzzzCfs2PEGffrMR6VSV35R\nBWS5CRcviqkdgiAIgiDcPyqVilGjRinbeXl52NnZERgYCBTmSZg0aVK51x85ckRJ2li/fn1atGiB\nRqOhd+/eNWrP9OnT2bVrV6n9ISEhypKkdSUpKQk3t7JXgLt69SrPPvssUDh9wdraGo1Gg4uLi5Lk\nEQo/Hzs7O73klSdOnCApKQlTU1M0Gg3t27dn3LhxyLJMSkoK/fv3r3Zbb968yaBBg/Dw8KBjx44k\nJCQoxz7++GPat2+Pm5sbI0aMIDs7u9T1qamp9O3bF09PT1xdXZUkpdeuXaNbt264ubmxadMm5fyg\noKBSS48KwoMmghPCQyMx8QqWlvd25ERSkrZa558+/QuWlk24evVIres2NW1MYqIYOfEoE3MvhZoS\nfUeoDdF/hJrSarWYm5uTkJBAVlYWAL/++itNmzbVW7WiIm5ubuh0OnQ6HQMHDmTevHnodDp27txZ\naf0FBQWltmfMmMHTTz9d6tyqrp5RV7788ktCQkKUbT8/P2VJznXr1ilLokqSRHBwsPIZ6HQ62rZt\nC0CrVq3Q6XTEx8dz7NgxNm7cSKNGjbC1tSU2NrZa7fnoo4/w8vLi8OHDLF++nMmTJwOFAZYlS5YQ\nGxvLkSNHyM/PJzIyssz70Wg0xMXFodVqefPNN8nNzSUiIoLx48cTHR3N/PnzgcIVOby8vEotPVqS\n+N0j3G8PXXBCkqTnJEn6RpKkSEmSej3o9gj3z507mRgaPjz5GK5ciePvv3/j5Zf/JCrqM+7erV10\n2dDQnNu3s+qodYIgCIIgCFXTv39/tm3bBkBERATBwcHKVNbqJm8sOn/nzp106dIFb29vhg4dSnp6\nOgBOTk688847eHt7s2bNmlLbFY2QKNmW9PR0nnnmGby9vXF3d2fz5s1A4QN7u3btGDNmDK6urvTp\n00cJvhQtIerp6clXX31V7n2sXbtWGTlRnImJCZ6envz9999Kmyr7jNRqNV26dOH06dMADBw4kIiI\niAqvKen48eMEBAQA0KZNG5KSkrh27RpWVlYYGhqSkZFBXl4eGRkZODg4lLq+cePG3L59G4Dbt29T\nv359DAwMMDIyIj09naysLNRqNfn5+SxYsICpU6dWq32CcD88dMEJWZY3ybI8BngVEOsuPkFycnJR\nqw3vaR2V5ZwoIssy27aNo2/fBVhbN6NLl7fYubPmOSegcJpIdnZurcoQHiwx91KoKdF3hNoQ/Ueo\nqaK+M2zYMCIjI8nOzubIkSN07NixzPO3bNnC9OnTKyxTkiRSU1OZPXs2u3bt4tChQ3h7e/Pf//5X\nOd6gQQMOHTrEsGHDytyu6ggJExMTNmzYwKFDh/j999958803lWOnT59m4sSJHD16FBsbGyXgERoa\nysKFC4mLiyu33CtXrqBWqzEzMyt17MaNG0RHR+Pi4qLsW7VqlTKlw8vLq9S0ioyMDHbt2oW7uzsA\nPj4+1U5g6eHhwfr16wGIjo7m3LlzXLx4kXr16vHmm2/SvHlzmjRpgo2NDc8880yp68PCwkhISKBJ\nkyZ4eHiwYMECJElixIgRbNq0id69e/P++++zcOFCXnzxRUxMTCptk/jdI9xv9yU4IUnSUkmSUiRJ\nOlJif19Jkk5IkpQoSVLJBYGnAV/ej/YJD4e8vHwkqXZ5HWqr6I9lbOwSbGycaNGicNihj894UlOP\nc+7cvhqXrVKpyc3Nr5N2CoIgCIIgVJWbmxtJSUlERESUOVqgSGBgIDNmzKiwLFmWiYqK4tixY3Tp\n0gWNRsPy5cs5f/5/y60PG6b/frHkdlVHaxQUFPDuu+/i4eFBr169SE5O5urVqwA4OzsrwQBvb2+S\nkpJIS0sjLS2Nbt26Aejl2iju3LlzNG6sP5V43759eHp60qxZM4KCgmjfvj1Q+N1w+PDhypSO2NhY\njI2NAThz5gwajYZu3boxYMAA+vTpA0CTJk1ISkqq0j0Weeedd7h16xYajUaZoqFWqzlz5gzz588n\nKSmJ5ORk7t69y4oVK0pd/9FHH+Hp6UlycjJxcXFMmDCBO3fuYGVlxdatWzl48CCenp5s3bqVwYMH\nExYWxpAhQ4iKiqpWOwXhXrpfIye+B/oW3yEVPoV++c9+FyBYkqR2UqH/AL/Islx+yFN47BgYqJHl\ne/vwXlHOCX//6coyot7eYxg8+H/D8SRJxZgxh3B07F7jugsK8jE0fLDBF6F2xNxLoaZE3xFqQ/Qf\noaaK952BAwcyZcoUvSkdtdGrVy/lgT0hIYElS5Yox0oum17VZdRLjqhYsWIFqampxMbGotPpaNiw\noTJ9oyhAAIXTKvLy8kqVV9F9ljzWvXt34uLiSEhIYP369Vy4cKHSclq2bKkELD744AO988saHTJt\n2jRl9EVJlpaWLF26FJ1Ox/Lly7l27RotWrQgJiaGLl26KNM0nn/+eQ4cOFDq+gMHDjBkyBClXc7O\nzpw8eVLvnJkzZzJt2jRWrlyJn58fP/zwA+Hh4eV+RuJ3j3C/3ZfghCzL+4CbJXb7AqdlWU6SZTkX\niASeAyYCTwMvSJI09n60T3g4GBoaUFBQ+g/L46KgIA8jI4MH3QxBEARBEJ5Ao0ePJjw8XBkRUFOS\nJNGpUyf279/PmTNngMLcEImJibVuY8kgwO3bt2nYsCFqtZrdu3dz7ty5Cq+3trbGxsaG/fv3A5Q5\nwgDA0dGx3JUqnJycmDx5MjNnziyzTVVx+fJlHB0dS+2fNWuWEswoKS0tjZycHACWLFlCjx49sLCw\noE2bNkRFRZGZmYksy/z22296U06KtG3blt9++w2AlJQUTp48SYsWLZTjiYmJJCcn4+fnR2ZmphI8\nyczMrPb9CcK98iCflByAC8W2LwIdZVmeBHxR2cUhISE4OTkBYGNjg6enpzIvqijKJ7YfrW1zcxNy\nczNITo4B/pcfomi0Q11sOzn512l51dk2N2+IpaXJQ/N5i+3qb/v7+z9U7RHbYvtx2S7ysLRHbIvt\nx2m76CE0MTERV1dXZd+NGzfQarVKDgitVsuBAwfIzs5mxowZZZZX9EDfoEEDXn/9dQYMGICRkREA\nw4cPp3PnzhQpWX/x/9+L6ite/pUrVxg9ejSvv/46UBhomD17NpGRkbi7u+Pg4EDz5s2VMjIyMtBq\ntcr1SUlJaLVavv/+e0aPHk16ejodOnQoVb+/vz/29vbcvn2b7du307dvX73Pw9/fn1dffRVHR0d6\n9eqFJEmsWrWK7du3A2BhYcGiRYtISkoiIyOj1P36+/sTHR2Ns7OzXvsq+3mtWLGCOXPmYGZmhqur\nKyEhIcr1L774Iu3atUOlUtG9e3fGjBmDVqtl8+bNtGnThrFjx9KjRw/+85//sH79egoKCggJCSE+\nPl4pf8yYMbzyyisABAcHExAQwP/93/8puULKa19Z9ye2xXbJ7fnz5xMXF6c8n9eUVBfDuqpUkSQ5\nAVtkWXb7Z3sw0FeW5bB/tv/F/4ITlZUl3692C/fPd9+tISamDY0auT/optwT587tYuRIFc88E/Cg\nmyIIgvBQkSSpToaZC4IgVFV4eDjt2rUrlQ+jLowcOZIpU6ag0WjqvGxBeBT883e92msDq+5FY6ro\nEtCs2HYzCkdPCE+o1q0bk5WVfE/rqCjnxL2mUl2mWbMmD6x+ofZKvkUQhKoSfUeoDdF/hJoSfad8\nEyZM4Icffqjzcq9evaoktnzUif4j3G8PMjgRAzwlSZKTJElGFC4buvkBtkd4wBwcmiBJlx90M+4J\nWZYpKEimSRMRnBAEQRAEQXjQ7Ozs+Pnnn+u83IYNG7Jt27Y6L1cQngT3ZVqHJEkRQA+gPnAV+ECW\n5e8lSeoHzAfUwHeyLH9cxfLEtI7HUGZmJuPHf0bz5u8gSQ8yblb3srLSyMpawvz5Ux50UwRBEB46\nYlqHIAiCIDw+HuppHbIsB8uy3ESWZWNZlpvJsvz9P/t/kWW5jSzLraoamBAeX6ampjRsaE5GRuqD\nbkqdu3PnEq1bi1ETgiAIgiDcXyqVilGjRinbeXl52NnZERgYCMCyZctQq9UcOXJEOcfV1ZXz589X\nqXxPT0+Cg4P19oWEhLBu3bpS54aEhNCiRQs0Gg0ajYYvv/yyJrekJykpCTc3t1qXU1J2djY9evRA\nlmWSkpIwNTXFy8sLFxcXOnbsqDclZNmyZdjZ2Sn3FRISApT9OaSkpNC/f/9qtyc1NZW+ffvi6emJ\nq6sry5YtU44tWLAANzc3XF1dWbBgQYXlHDx4EAMDA9b/P3t3HhZluT5w/DuM4BaGmQu44Jagsg24\noAJCCu5o5m6u5VouIW4d82AueRRzqzyWdtSj4o6mlmkm4s7iAEooiaIm7oYooCLM7w8O749xBoTB\nMvX+XFfXNfMuz/u8wx34PvM8971tGwA3b97Ew8MDR0dHduzYoRzXrVu3AiuaCPFnebm+nhYvPE9P\ne27fjvvT2n9eOSfS02Nxd7d/LtcWz46svRSmktgRJSHxI0wVFhZG+fLliY+P58GDBwDs27ePGjVq\nKFUsAGrUqMHs2bOV9/n3FSYhIYEyZcpw4sQJvcoVeRVAnqRSqQgODkar1aLVavnoo4/09mdnZxfr\n/v5M69ato3Pnzsp91K9fn5MnT/Lrr7+yYcMGFi1apAwQqFQq+vbtq9xX/u1Pfg5Vq1alYsWKRsuJ\nFubLL79Eo9EQExNDWFgYEyZM4PHjx5w+fZoVK1YQGRlJbGwsu3btUkq8Pik7O5vJkyfTvn17ZVtI\nSAijR48mIiKCRYsWAbBz505cXV05c+ZMsfooREnJ4IT4W2nRogk6nZacnMfPuyvPzIMHqbz22uU/\nZVRfCCGEEOJpOnbsqORBCAkJoW/fvspSKpVKRefOnYmPjycxMbFY7ea15efnp/etO1DgUq0nt3t7\ne/Pxxx/TtGlTFi9ezK5du3B3d8fV1RVfX19u3LgB5FbXGDp0KD4+PtSrV4+lS5catH3+/HlcXV2J\njo4mKSmJDh060KRJE7y8vDh79iyQO5th3LhxtGrVinr16hmd4ZF3b127djW6r06dOnzxxRcsWbJE\nuaei3i+Av78/ISEhRo8viLW1NWlpaQCkpaVRqVIl1Go1CQkJNG/enDJlyqBWq2ndurUyK+JJS5cu\npUePHlSuXFnpl4WFBenp6Tx48AC1Wk12djaLFy9m0qRJxeqfEM+CDE6Iv5VKlSrh6lqNGzfi/5T2\na9f2/lPaLcyNG9H4+Tlhbm7+l19bPFt5tZyFKC6JHVESEj/CVHmx07t3bzZs2MDDhw85deoUzZs3\n1zvOzMyMSZMmMWfOHIM2hg0bRnR0tNH2N23aRK9evejVq1eRHrZ1Oh0TJ05Eo9Hg6urK6dOnUalU\nZGVlERkZSUBAAB4eHhw/fpyTJ0/Su3dv5s2bp5yfmJjI3r17iYiIYMaMGXozLc6ePUuPHj1YvXo1\nbm5uDB8+nKVLlxIVFcX8+fMZPXq0cuy1a9c4cuQIu3btYsqUKQb9zM7O5vTp0zRo0KDAe9FoNHoz\nCzZu3Kgs63haFZBmzZoRHh7+1M8rv2HDhhEfH4+NjQ3Ozs4sXrwYlUqFo6Mjhw4d4s6dO2RkZLB7\n925+/92wAOKVK1fYsWMHo0aNAv5/dky/fv3YsWMHfn5+/OMf/+Crr75i4MCBlClTRn73iL9cqefd\nAVMFBQXh7e0t/9O8hHx9mxIdfRhwft5dKbGcnMfodCdp2XLI8+6KEEIIIV5Rjo6OJCcnExISQqdO\nnfT25X2D3q9fP2bPnk1ycrLe/m+//dZom1FRUVSuXBlra2uqVKnC4MGDSU1NxcrKqsB+5C3r6N69\nu9723r17K68vX75Mr169uHbtGo8ePaJu3brKuZ06dcLc3JxKlSpRpUoVrl+/DuSW7+zWrRuhoaHY\n29tz//59jh07Rs+ePZV2Hz16pLTTrVs3ABo2bKi0kd+tW7ewtLQs8D7AcEZEnz59lJkUT2NtbW3w\nOT/NnDlzcHFxISwsjKSkJHx9fYmLi8Pe3p7Jkyfj5+dH+fLl0Wg0mJkZfv88fvx45s6dqyQgzut/\nhQoV2LVrFwB//PEHn3/+OaGhoQwbNozU1FQmTJiAu7t7sfoqXl1hYWElWor4ws6cyBucEC+fBg0a\nUKXKPe7dS3nmbf/VOSdu3IhHo6nKm2+++ZdeV/w5ZN23MJXEjigJiR9hqvyx4+/vT2BgoN6SjvzU\najUTJkxg7ty5RWo7JCSEhIQE6tSpQ/369UlLS2PLli1PPc/YtcuXL6+8HjNmDGPHjiUuLo7ly5eT\nmZmp7LOwsNDr7+PHucuArayssLW15dChQwDk5ORgZWWl5IDQarXEx8cbbac4yzHy02q1NGrU6KnH\nG8u9odPpjG6fNm2aMqvkSUePHlUGW+rVq0edOnWUmRtDhw4lKiqKgwcPYmVlhZ2dncH50dHR9OnT\nhzp16rB161ZGjx7N999/r3fMzJkzmTZtGuvXr8fLy4v333+foKCggj8EIZ7g7e1doph5YQcnxMvL\nzMyMnj29uHlzFzpdzvPujsmysjJ58OBnunZt/by7IoQQQohX3NChQwkKCqJx48YFHjN48GB+/vln\nbt68WWhbOTk5bN68mdOnT3PhwgUuXLjA9u3bi51HIU/+B/u0tDRsbHIrnOWvSFHYYIGFhQXbtm1j\nzZo1hISEUKFCBerUqaMMluh0OuLiip5w/c033+T+/fsF7k9OTmbixImMGTPmqW0Z6/fVq1extbU1\n2D5r1iy0Wq3RZJn29vb8/PPPQG7Fj7NnzyqzSvLycly6dInQ0FD69etncP758+eVn1WPHj1YtmwZ\n/v7+yv7ffvuNlJQUvLy8yMzMVAZP8g8OCfFnk8EJ8bfUpIkrzZqV4cqVI8+03b8y58SVK3vo1q2R\n0T8+4sUks7WEqSR2RElI/AhTeXt7Kw+Z1atXV6pj5K8ikf+1ubk548aN0xucMJZz4tChQ9SoUYNq\n1aop2zw9PUlISFDKTxZU8aOgKh55goKC6NmzJ02aNKFy5cpG+2ns/HLlyrFr1y4WLlzIrl27WLdu\nHStXrlRKb+afJZC/HWNtqtVqHBwclCSaAElJSUop0d69ezNu3DgGDRr01L6NGDGCmjVrUrNmTVq1\nagVAREQEXl5eRo8vyCeffEJUVBTOzs60bduWefPm8cYbbwDQo0cPGjdujL+/P19//TUVKlQAYPny\n5SxfvrxI7U+bNk2p2NK3b1+WLVtGYGAg48ePL1Y/hSgJ1dOmLP0dqVQq3YvYb1E8qampfPLJN5Qv\nP5jy5as87+4Uy61bZ3n99T3885+j9KYOCiGEMJS3BloIIf4uVq1axfXr15k8efIzb7t///4EBgai\n0WieedtC/B387+960WoS5yMzJ8TflpWVFUOGtOH69e3PbHnHX5FzIisrk/v3dzFsWFcZmHjJyLpv\nYSqJHVESEj/CVBI7puvXrx+7d+9+5gOnN27cIDU19YUYmJD4EX81GZwQf2tNmrji7l6Wy5d/fiG+\nVdPpcrh8eTvdujWidu3az7s7QgghhBDCBBYWFoSHhxe4XMNUVapUYffu3c+0TSFeFjI4If7WVCoV\ngwe/S50657hy5XCJ2/szc07odDqSk3fQsmU2Xbr4/WnXEc+PrPsWppLYESUh8SNM5e3tjZmZGQMG\nDFC2PX78mMqVK9OlSxcgN7li586dcXFxoXHjxgalRp/UvXt3NBoNb731FlZWVkp1iWPHjlG7dm3u\n3LlTov4+md8CICsriylTptCgQQPc3Nxo2bIle/bsKXb7QUFBLFiwwOT+FcepU6cYOnQokLtEpHLl\nymg0Gho1asTXX3+t16caNWqg0WiU/+7evUtYWBivv/66cs5nn30GQGxsLO+//36x+3Px4kXatGmD\ns7MzPj4+XLlyRdnu5uaGRqOhcePGLF68WDkn/++ec+fO4enpiUajwdnZmR9//BGAs2fP4ubmhrOz\nM8ePHwdyY8zX15cHDx4Uu5/i1VbqeXdAiKcpV64c48cPYO7c77hyRU316i2fd5cM6HQ5XLy4GxeX\nP3j//QGo1ern3SUhhBBCCMqXL098fDwPHjygTJky7Nu3jxo1aigzAqZPn067du2UyhOnT58utL1t\n27YBcPDgQYKDg9m5c6eyr6T5YwpKLPnpp59y/fp14uPjMTc358aNGxw8eNCk9p8mr/8lnTExf/58\n5TNVqVT07duXJUuWcOfOHRo2bEjPnj2VhJ8BAQEEBAQYtOHl5cXOnTvJyMjAxcUFf39/XFxcSEpK\n4saNG1SpUvScbIGBgQwePJgBAwZw4MABpk6dypo1a7CxseH48eOYm5uTnp5O48aNeffdd6lRo4be\n+bNmzeK9995jxIgRJCQk0LFjRy5cuMDy5ctZunQptra2jBs3ji1btrBs2TIGDBhAmTJlSvQZileP\nzJwQLwRLS0smTRqMjU00v/9+wOQ/fH9GzomcnGwuXNiGm9ttRo/uj7m5+TO/hvh7kLWXwlQSO6Ik\nJH6EqfJip2PHjspSgpCQEPr27av8W+ratWtUr15dOcfBwaFIbRf0b7GlS5fi5uaGk5OTUu0iIiKC\nli1b4urqSqtWrUhMTARyy1T26dOHRo0a0b17dzIzMw3azcjIYMWKFSxdulT5N1aVKlXo2bOncj9O\nTk44OjoyZcoU5bw9e/bg5uaGi4sLvr6+yvZff/0VHx8f6tWrx9KlS4Hc0qB2dnYMGjQIR0dHLl++\nzOjRo2natCkODg4EBQUp59euXZugoCCDe8zv4cOHHD9+nKZNmxp8Xm+88QZ169YlOTn5qZ9lnnLl\nyuHm5sa5c+cA6NChA5s3by70nCclJCTw9ttvA7kzInbs2AHkVmjJ+1wzMzMxNzenXLlygP7vHmtr\na+7evQvkJq3PixkLCwvS09NJT0/HwsKCu3fvsmvXLgYOHFis/gkBMjghXiCvv/46kyYNpV69RJKT\nt5KVlfG8u0Rm5h2Sk9fg5ZXFyJH9KV269PPukhBCCCGEnt69e7NhwwYePnzIqVOnaN68ubLvww8/\n5P333+ftt99mzpw5XL16VdlnStLGypUrEx0dzahRowgODgagYcOGHDp0iJMnTzJjxgw++eQTAJYt\nW8Zrr73Gr7/+yowZM4iOjjaYsXDu3Dlq1arFa6+9ZnCtlJQUpkyZwoEDB4iJiSEyMpIdO3Zw8+ZN\nhg8fzrZt24iJiVEe5HU6HWfOnGHv3r1EREQwY8YMsrOzlet8+OGHnD59mlq1ajF79mwiIyOJjY3l\n4MGDyowSlUpl9B7z02q12NnZGf18Ll68yPnz56lXr57Sp4ULFypLOtq0aWNwzu3btzl+/DiNGzcG\noFmzZoSHhz/9h5GPs7MzW7duBSA0NJR79+7xxx9/APD777/j5ORErVq1+Pjjj5USpflNnTqV1atX\nU7NmTTp16qQM7Hz44YfMmTOHIUOGMHXqVD777DP+8Y9/FKtvQuSRwQnxQilfvjyBgUPp0cOSlJRl\n3LyZUKzzn1XOCZ1OR0rKCf74YwXDhtkxdGhvmTHxCpB138JUEjuiJCR+hKnyYsfR0ZHk5GRCQkIM\nckr4+flx/vx5hg0bxpkzZ9BoNNy6dQvIfcguru7duwPg6uqqzA5ITU2lR48eODo6EhAQwK+//grA\noUOHeO+995Q+Ojk5FetakZGR+Pj4UKlSJdRqNf379yc8PJwTJ07g5eWFra0tkFsBDnIHFjp37oy5\nuTmVKlWiSpUqXL9+HQBbW1uaNWumtL1x40bc3NxwdXUlPj5e6XNB95jfxYsXsba2Vt7rdDo2btyI\ns7MzDRo0YNq0acoAQN6yDq1Wi1arZf/+/cp5hw4dwtXVlXbt2jF16lQaNmwI5M5iMHbdwgQHB3Pw\n4EFcXV0JDw+nevXqyjLkGjVqEBcXR1JSEosWLVJmaOT/3RMQEMAHH3zA5cuX+eGHH5SfW82aNTlw\n4ABHjhyhbNmyXLlyBXt7ewYMGECfPn347bffitVP8Wp7YQcngoKCZJrjK8rc3JwuXdoxY0ZP3njj\n5798FkVm5h0uXFiNnd1p5swZiqdnS8zMXtj/lYQQQgjxCvD39ycwMFBvSUeeihUr0rdvX9asWUPT\npk2L/a18fnmzSNVqNY8fPwZyc0a0adOGU6dO8f3335OZmakc/7QlDfXr1+fSpUvcu3fPYN+TOS6K\nsuw3f5n3/H0sX768sv3ChQssWLCAX375hdjYWDp16qSX3NHYPRbWL5VKRZ8+fYiNjeXo0aMsWrSI\n+/fvP7Xfnp6enDx5kqioKIYPH653vLGcGEOHDkWj0dC5c2eDfdbW1mzdupWTJ08ya9YsACpUqGBw\njKenJzExMQbnHz16lF69egHg7u7OgwcPlEGsPNOmTWP27NksXryY4cOHM2/ePGbMmGH03sTLKSws\nTG8ZVHG9sE9UQUFB8k3CK65WrVpMnz6Sd999jatXv+bSpQM8fJhW6DklyTmRnn6Tixd/JDV1BcOG\nNeDjj4fw5ptvmtyeePHIgKgwlcSOKAmJH2Gq/LEzdOhQgoKClKUBeQ4cOEBGRu6XPPfu3SMpKUmZ\ncfCspKWlYWNjA+RWrsjj5eXF+vXrgdxEnHFxcQbnlitXjvfff59x48aRlZUFwM2bN9myZQvNmjxa\nvlkAACAASURBVDXj4MGD3L59m+zsbDZs2IC3tzfu7u6Eh4crswuKW0EkLS2N8uXLU6FCBa5fv65U\npigqW1tbrl27przX6XTKAISbmxtdunRhyZIlxWozv6tXrxr9GX333XdotVp27dplsO/27dvk5OQA\n8PnnnysVP65cuaIMFv3xxx8cOXJEmcGSP37s7e35+eefgdz8FQ8ePND7d/DBgwepXr069erVIzMz\nU0lumhdb4tXg7e1dosEJqdYhXmh5syiaN9dw+HAk+/YtIzOzDq+/3hQrq9olzrSck5PN7dtnSU+P\nxMrqJn37utK8+Qhef/31Z3QHQgghhBB/nrx/C1WvXp2PPvpI2Za3PTo6mo8++ohSpUqRk5PDsGHD\ncHNzA3JzThS0tMNYZY387/PvnzRpEoMGDWLWrFl06tRJ2T5q1CiGDBlCo0aNaNiwIU2aNDF6rVmz\nZjFt2jQaNWpEmTJlKF++PDNnzqRatWrMnTsXHx8fdDodnTt3VkqkfvPNN3Tv3p2cnByqVq3KTz/9\nZNDHgvru7OyMRqPB3t6emjVr4uHhUeTPIO/8/Ikynzxu8uTJNG/enHHjxgGwcOFC1q5dq+zfvn17\ngW1DboJRLy8vo/sKEhYWxtSpU1GpVLRu3ZqvvvoKyB1omDBhgnK9Tz75hAYNGgDwn//8h3v37tGl\nSxfmz5/P+++/z8KFC1GpVKxevVppW6fTMXv2bDZt2gTA8OHD6d+/P9nZ2SxbtqxY/RSvNlVJyv08\nLyqVSvci9lv8+R4+fEhsbBw//BDJxYs5QB0sLGywtLSmXLnKmJkVXuIzOzuL9PTr3Lt3lcePU4Ak\nHBwq0q5dUxo2bCglQoUQ4k9Q0vKDQgjxdzN48GBGjRqll3z0WfH29mbTpk3FKiUqxF/pf3/Xi/0t\nsQxOiJeSTqfjypUrXL58md9+u8rZsymkpNxFpaqCSlUJna4UOl0pcgeks/73303MzO5Qs2Yl7O1t\nqFfPBltbWypXrvx8b0YIIV5yMjghhHjZnD59mgULFvCf//znmbYbFxfHkiVLWLFixTNtV4hnSQYn\nhHiKhw8fEhoaSsOGDXn8+DFZWVmoVCrMzc0pVaoUlSpVomrVqpQqJaudhHFhYWGS60aYRGKncDI4\nUTiJH2EqiR1REhI/wlSmDk7IU5h4ZZQuXZpq1arh7Oz8vLsihBBCCCGEECKfF7ZahxCmkNFfURIS\nP8JUEjuiJCR+hKm8vb0xMzMjMDBQ2RYcHKxX3nHNmjU4Ojri5OSEq6srCxYsKLC9n376CY1Gg0aj\nwdLSEnt7ezQaDYMGDWL16tWMGTPmT7uXU6dOKdeuVKkSdevWRaPR4Ovry86dO/nXv/4F5Fb0y7uH\nwYMHs3XrVgCGDRtGQkJCka/35ZdfKpVFBg8erFzP1dWVQ4cOKcd5e3srn4NGo1HKbQYFBVGjRg00\nGg2Ojo7s3LkTgCVLlvDf//632Pf/yy+/4ObmhqOjI4MHDyY7O1tvf2RkJKVKlWLbtm2FtjN27Fgs\nLS2V91u3bsXBwQEvLy+lqklSUhJ9+vSR3z3iLyeDE0IIIYQQQrykLCwsCA0N5fbt24B+VYoff/yR\nxYsXs2/fPuLi4jh+/HihFcnatWuHVqtFq9XSpEkT1q9fj1ar1avc8GdxdHRUru3v709wcDBarZZ9\n+/bRpUsXJk+erNxf3j3mf/3tt9/SsGHDIl1Lp9OxcuVK3nvvPaWdvOt98cUXjB49WjlWpVIpn4NW\nq1UqVqhUKgICAtBqtWzevJmhQ4cCMGTIEJYuXVqse8/JyWHw4MFs3LiRU6dOYWtrq/eZZ2dnM3ny\nZNq3b1/oErmoqChSU1P1YuDLL78kKiqKESNGKGVdP/30U2bPnl2sPgrxLMjghHilSK14URISP8JU\nEjuiJCR+hKnCwsIwNzdn+PDhLFy40GD/559/zoIFC6hWrRqQO5DxwQcfFLn9Jx+EU1JS6NChAw0a\nNFAGCwBCQkJwcnLC0dGRKVOmKNtfe+01Jk2ahIODA76+vhw/fpzWrVtTr149ZaZBUa69atUqvVkb\nxh7Qvb29OXnypPKgnzdbZNGiRQbHHjlyBHt7e708ZHlturu7k5SUVOjn8OT2vLZu3bqFpaUllSpV\nIj4+vtD7y+/27dtYWFhQv359ANq2bavMCAFYunQpPXr0KDSJe3Z2NpMmTWLevHl6/TUzM+PBgwek\np6djYWHBoUOHsLa2pl69evK7R/zlZHBCCCGEEEKIl9jo0aNZt24daWlpwP/PnoiPj8fNzc3oOcuX\nL2f58uWFtpv/G3idTkdMTAybNm3i1KlTbNy4kStXrpCSksKUKVM4cOAAMTExREZGsmPHDgAyMjJo\n06YNp0+fxtLSkunTp/PLL78QGhrK9OnTi3x/+fvxtGO0Wi0pKSmcOnWKuLg4hgwZYnDs4cOHadq0\nqdF29uzZg4ODg9599+/fX1nWkX9QJs+JEydQq9W8+eabADRr1ozw8PAi3RvAm2++yePHj4mOjgZg\ny5YtXL58GYArV66wY8cORo0apXefT/ryyy/p2rWrMhCVZ+rUqbRt25bdu3fTp08fZs2axaefflrk\nvgnxLElCTPFKkbVzoiQkfoSpJHZESUj8CFPlxY6lpSUDBw5kyZIllC1btkjVcUaMGFGsa6lUKtq0\naaPkM2jUqBHJycncunULb29vKlWqBED//v0JDw+na9euWFhY0K5dOyB32UaZMmVQq9U4ODiQnJxc\nrOsXVb169Th//jxjx46lU6dO+Pn5GRxz6dIlPDw8lPc6nY6JEyfyySefcPHiRY4cOaJ33+vXr8fV\n1VWvDZ1Ox8KFC1m7di2WlpZs3LhR2WdjY8P58+eL3GeVSsWGDRv4+OOPefjwIX5+fqjVagDGjx/P\n3LlzlapHxn62KSkpbNmyhbCwMIP9bdu2JSoqCsjNP9KpUyfOnDnDggULqFixIs2bN6ds2bJF7qsQ\nJSEzJ4QQQgghhHjJjR8/npUrV5Kenq5sa9y4sfJg+iyULl1aea1Wq3n8+LHBN/k6nU7ZZm5urmw3\nMzPDwsJCef348WOT+1HYTAorKytiY2Px9vbm3//+d4HLWPI/xOflnDh79izBwcF89tlnRepDXs6J\n8PBwWrVqpdf2k33Mzs5WZl8EBQUZtOfu7k54eDgnTpzA09MTOzs7AKKjo+nTpw916tRh69atjB49\nmu+//17v3JiYGM6dO0f9+vWpW7cuGRkZNGjQQO+YjIwMVq9ezejRowkKCmLNmjV4eHiwbt26p96r\nEM+KDE6IV4qsnRMlIfEjTCWxI0pC4keYKn/sVKxYkV69erFy5UrlwXjq1KlMnDiR69evA/Do0SNW\nrlxp0rWMfWOvUqlo1qwZBw8e5Pbt22RnZ7NhwwZat25t0jWKcu2CZg/k7cvrR/fu3Zk5cyYnT540\nOM7W1pZr164ZvcZHH33E5cuXOXbsmNHrF9Sv/K5evUrt2rX1tqnVaiWpprHBiZs3bwLw8OFD5s2b\nx8iRIwE4f/48Fy5c4MKFC/To0YNly5bh7++vd27Hjh25evWqcly5cuVITEzUO2b+/PmMGzeOUqVK\nkZmZCcCZM2eU10L8FWRwQgghhBBCiJdU/m/oJ0yYwK1bt5T3HTp04KOPPqJt27Y4ODjg5ubGvXv3\ngKLlnHjyOsZmLFSrVo25c+fi4+ODi4sLTZo0oUuXLgZ9e/L90/JIPHmssQodxs65cuUKPj4+aDQa\nBgwYwNy5cw2O8/DwMJhRkr/NadOm6c2eyJ9zIv8ykYL6ERERgaenZ6H396T58+fTqFEjnJ2d8ff3\nL9Jyr06dOhkMshjrV0pKCpGRkcqgxpgxY2jatCm7du2iX79+xeqnECWhKsqas78blUqlexH7LYQQ\nQghDeWulhRDi70Cn0+Hq6sqJEyeUpSbPSlpaGm3atCEyMvKZtivE38n//q4/PVPtE2TmhBBCCCGE\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CqukcOXJEeTD28fHh9u3b3Lt3D5VKhb+/P6VLlwagR48ezJw5k/nz5/Pdd98xZMgQo+15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\nNAC0Wi1btmzh3r17pKSkEB0dzdmzZwstY+HChWi1Wt544w1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A5PWFMWPG0K1bNyZOnFii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       "text": [
        "<matplotlib.figure.Figure at 0x7ff0758e6cd0>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sentate_2014[sentate_2014[x_column]>0.5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>commentCount</th>\n",
        "      <th>dislikeCount</th>\n",
        "      <th>favoriteCount</th>\n",
        "      <th>likeCount</th>\n",
        "      <th>viewCount</th>\n",
        "      <th>like_dislike_r</th>\n",
        "      <th>views_share</th>\n",
        "      <th>msgs_share</th>\n",
        "      <th>likes_share</th>\n",
        "      <th>dislikes_share</th>\n",
        "      <th>state</th>\n",
        "      <th>party</th>\n",
        "      <th>donations</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>candidate_name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Dan Sullivan</th>\n",
        "      <td>  360</td>\n",
        "      <td>   94</td>\n",
        "      <td> 0</td>\n",
        "      <td>   623</td>\n",
        "      <td>  165715</td>\n",
        "      <td> 0.868898</td>\n",
        "      <td> 0.613568</td>\n",
        "      <td> 0.814480</td>\n",
        "      <td> 0.774876</td>\n",
        "      <td> 0.494737</td>\n",
        "      <td> AK</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  6340422.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jeff Sessions</th>\n",
        "      <td>  142</td>\n",
        "      <td>   10</td>\n",
        "      <td> 0</td>\n",
        "      <td>    36</td>\n",
        "      <td>    5114</td>\n",
        "      <td> 0.782609</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> AL</td>\n",
        "      <td> REP</td>\n",
        "      <td>  1115688.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thomas Cotton</th>\n",
        "      <td>  100</td>\n",
        "      <td>   91</td>\n",
        "      <td> 0</td>\n",
        "      <td>   195</td>\n",
        "      <td>  797919</td>\n",
        "      <td> 0.681818</td>\n",
        "      <td> 0.814496</td>\n",
        "      <td> 0.434783</td>\n",
        "      <td> 0.537190</td>\n",
        "      <td> 0.722222</td>\n",
        "      <td> AR</td>\n",
        "      <td> REP</td>\n",
        "      <td>  7097224.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cory Gardner</th>\n",
        "      <td>  316</td>\n",
        "      <td>  167</td>\n",
        "      <td> 0</td>\n",
        "      <td>   450</td>\n",
        "      <td>  234223</td>\n",
        "      <td> 0.729335</td>\n",
        "      <td> 0.628876</td>\n",
        "      <td> 0.633267</td>\n",
        "      <td> 0.530660</td>\n",
        "      <td> 0.268058</td>\n",
        "      <td> CO</td>\n",
        "      <td> DEM</td>\n",
        "      <td> 10420571.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Christopher Coons</th>\n",
        "      <td>  190</td>\n",
        "      <td>   20</td>\n",
        "      <td> 0</td>\n",
        "      <td>   110</td>\n",
        "      <td>   19950</td>\n",
        "      <td> 0.846154</td>\n",
        "      <td> 0.559278</td>\n",
        "      <td> 0.627063</td>\n",
        "      <td> 0.379310</td>\n",
        "      <td> 0.400000</td>\n",
        "      <td> DE</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  4173447.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>John Kingston</th>\n",
        "      <td>   37</td>\n",
        "      <td>    2</td>\n",
        "      <td> 0</td>\n",
        "      <td>    17</td>\n",
        "      <td>     717</td>\n",
        "      <td> 0.894737</td>\n",
        "      <td> 0.540724</td>\n",
        "      <td> 0.451220</td>\n",
        "      <td> 0.447368</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> GA</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  9211931.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mark Jacobs</th>\n",
        "      <td> 7334</td>\n",
        "      <td> 1052</td>\n",
        "      <td> 0</td>\n",
        "      <td> 66064</td>\n",
        "      <td> 1464016</td>\n",
        "      <td> 0.984326</td>\n",
        "      <td> 0.943058</td>\n",
        "      <td> 0.959571</td>\n",
        "      <td> 0.995765</td>\n",
        "      <td> 0.932624</td>\n",
        "      <td> IA</td>\n",
        "      <td> REP</td>\n",
        "      <td>  4810813.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pat Roberts</th>\n",
        "      <td>  256</td>\n",
        "      <td>   83</td>\n",
        "      <td> 0</td>\n",
        "      <td>   374</td>\n",
        "      <td>   97678</td>\n",
        "      <td> 0.818381</td>\n",
        "      <td> 0.943867</td>\n",
        "      <td> 0.733524</td>\n",
        "      <td> 0.869767</td>\n",
        "      <td> 0.768519</td>\n",
        "      <td> KS</td>\n",
        "      <td> REP</td>\n",
        "      <td>  1068018.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mitch Mcconnell</th>\n",
        "      <td> 2197</td>\n",
        "      <td>  272</td>\n",
        "      <td> 0</td>\n",
        "      <td>  4760</td>\n",
        "      <td>  893077</td>\n",
        "      <td> 0.945946</td>\n",
        "      <td> 0.569208</td>\n",
        "      <td> 0.632230</td>\n",
        "      <td> 0.640387</td>\n",
        "      <td> 0.428346</td>\n",
        "      <td> KY</td>\n",
        "      <td> DEM</td>\n",
        "      <td> 11353760.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mary Landrieu</th>\n",
        "      <td>  745</td>\n",
        "      <td>   84</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2015</td>\n",
        "      <td>  395161</td>\n",
        "      <td> 0.959981</td>\n",
        "      <td> 0.986888</td>\n",
        "      <td> 0.937107</td>\n",
        "      <td> 0.971084</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> LA</td>\n",
        "      <td> DEM</td>\n",
        "      <td> 10190144.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Gabriel Gomez</th>\n",
        "      <td>  899</td>\n",
        "      <td>  514</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1239</td>\n",
        "      <td> 1855996</td>\n",
        "      <td> 0.706788</td>\n",
        "      <td> 0.995771</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.946524</td>\n",
        "      <td> 0.980916</td>\n",
        "      <td> MA</td>\n",
        "      <td> REP</td>\n",
        "      <td>  4755654.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Susan Collins</th>\n",
        "      <td>   81</td>\n",
        "      <td>    9</td>\n",
        "      <td> 0</td>\n",
        "      <td>   351</td>\n",
        "      <td>  291078</td>\n",
        "      <td> 0.975000</td>\n",
        "      <td> 0.834797</td>\n",
        "      <td> 0.658537</td>\n",
        "      <td> 0.983193</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> ME</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  1333016.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Terri Land</th>\n",
        "      <td>  110</td>\n",
        "      <td>  560</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1660</td>\n",
        "      <td> 3077280</td>\n",
        "      <td> 0.747748</td>\n",
        "      <td> 0.988351</td>\n",
        "      <td> 0.578947</td>\n",
        "      <td> 0.783019</td>\n",
        "      <td> 0.918033</td>\n",
        "      <td> MI</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  6994603.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Al Franken</th>\n",
        "      <td>  141</td>\n",
        "      <td>   71</td>\n",
        "      <td> 0</td>\n",
        "      <td>   208</td>\n",
        "      <td>  260800</td>\n",
        "      <td> 0.745520</td>\n",
        "      <td> 0.897435</td>\n",
        "      <td> 0.762162</td>\n",
        "      <td> 0.753623</td>\n",
        "      <td> 0.639640</td>\n",
        "      <td> MN</td>\n",
        "      <td> DFL</td>\n",
        "      <td> 15126268.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thad Cochran</th>\n",
        "      <td>   74</td>\n",
        "      <td>   10</td>\n",
        "      <td> 0</td>\n",
        "      <td>    35</td>\n",
        "      <td>   15199</td>\n",
        "      <td> 0.777778</td>\n",
        "      <td> 0.955972</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.897436</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> MS</td>\n",
        "      <td> REP</td>\n",
        "      <td>  2727209.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thom Tillis</th>\n",
        "      <td>  253</td>\n",
        "      <td>  194</td>\n",
        "      <td> 0</td>\n",
        "      <td>   586</td>\n",
        "      <td>  797943</td>\n",
        "      <td> 0.751282</td>\n",
        "      <td> 0.950310</td>\n",
        "      <td> 0.496078</td>\n",
        "      <td> 0.716381</td>\n",
        "      <td> 0.619808</td>\n",
        "      <td> NC</td>\n",
        "      <td> REP</td>\n",
        "      <td>  4764110.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Scott Brown</th>\n",
        "      <td>  708</td>\n",
        "      <td>   92</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2456</td>\n",
        "      <td>  388637</td>\n",
        "      <td> 0.963893</td>\n",
        "      <td> 0.916956</td>\n",
        "      <td> 0.964578</td>\n",
        "      <td> 0.967310</td>\n",
        "      <td> 0.736000</td>\n",
        "      <td> NH</td>\n",
        "      <td> REP</td>\n",
        "      <td>  3686708.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Cory Booker</th>\n",
        "      <td>  355</td>\n",
        "      <td>   60</td>\n",
        "      <td> 0</td>\n",
        "      <td>   894</td>\n",
        "      <td>  130143</td>\n",
        "      <td> 0.937107</td>\n",
        "      <td> 0.998527</td>\n",
        "      <td> 0.997191</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> NJ</td>\n",
        "      <td> DEM</td>\n",
        "      <td> 16167874.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Allen Weh</th>\n",
        "      <td>  821</td>\n",
        "      <td>   71</td>\n",
        "      <td> 0</td>\n",
        "      <td>   256</td>\n",
        "      <td> 1949518</td>\n",
        "      <td> 0.782875</td>\n",
        "      <td> 0.987909</td>\n",
        "      <td> 0.539776</td>\n",
        "      <td> 0.279476</td>\n",
        "      <td> 0.702970</td>\n",
        "      <td> NM</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  5050539.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>James Inhofe</th>\n",
        "      <td> 1905</td>\n",
        "      <td>  406</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2094</td>\n",
        "      <td>  170226</td>\n",
        "      <td> 0.837600</td>\n",
        "      <td> 0.981288</td>\n",
        "      <td> 0.968480</td>\n",
        "      <td> 0.984023</td>\n",
        "      <td> 0.966667</td>\n",
        "      <td> OK</td>\n",
        "      <td> REP</td>\n",
        "      <td>  2811701.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Monica Wehby</th>\n",
        "      <td>  115</td>\n",
        "      <td>  295</td>\n",
        "      <td> 0</td>\n",
        "      <td>   660</td>\n",
        "      <td>   95280</td>\n",
        "      <td> 0.691099</td>\n",
        "      <td> 0.702572</td>\n",
        "      <td> 0.481172</td>\n",
        "      <td> 0.662651</td>\n",
        "      <td> 0.891239</td>\n",
        "      <td> OR</td>\n",
        "      <td> REP</td>\n",
        "      <td>  2049732.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Jack Reed</th>\n",
        "      <td>   90</td>\n",
        "      <td>    4</td>\n",
        "      <td> 0</td>\n",
        "      <td>   128</td>\n",
        "      <td>   19412</td>\n",
        "      <td> 0.969697</td>\n",
        "      <td> 0.910763</td>\n",
        "      <td> 0.865385</td>\n",
        "      <td> 0.969697</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> RI</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  2833802.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lindsey Graham</th>\n",
        "      <td> 1089</td>\n",
        "      <td>  135</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1412</td>\n",
        "      <td>  119855</td>\n",
        "      <td> 0.912734</td>\n",
        "      <td> 0.891586</td>\n",
        "      <td> 0.840927</td>\n",
        "      <td> 0.811494</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> SC</td>\n",
        "      <td> REP</td>\n",
        "      <td>  6788544.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lamar Alexander</th>\n",
        "      <td>   34</td>\n",
        "      <td>    4</td>\n",
        "      <td> 0</td>\n",
        "      <td>    48</td>\n",
        "      <td>    6291</td>\n",
        "      <td> 0.923077</td>\n",
        "      <td> 0.590538</td>\n",
        "      <td> 0.161905</td>\n",
        "      <td> 0.578313</td>\n",
        "      <td> 0.173913</td>\n",
        "      <td> TN</td>\n",
        "      <td> REP</td>\n",
        "      <td>  1812250.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>John Cornyn</th>\n",
        "      <td>  166</td>\n",
        "      <td>   20</td>\n",
        "      <td> 0</td>\n",
        "      <td>   260</td>\n",
        "      <td>   21110</td>\n",
        "      <td> 0.928571</td>\n",
        "      <td> 0.995943</td>\n",
        "      <td> 0.982249</td>\n",
        "      <td> 0.996169</td>\n",
        "      <td> 0.909091</td>\n",
        "      <td> TX</td>\n",
        "      <td> DEM</td>\n",
        "      <td>  9673572.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Natalie Tennant</th>\n",
        "      <td>  210</td>\n",
        "      <td>  110</td>\n",
        "      <td> 0</td>\n",
        "      <td>   690</td>\n",
        "      <td>  159430</td>\n",
        "      <td> 0.862500</td>\n",
        "      <td> 0.969238</td>\n",
        "      <td> 0.860656</td>\n",
        "      <td> 0.873418</td>\n",
        "      <td> 0.873016</td>\n",
        "      <td> WV</td>\n",
        "      <td> REP</td>\n",
        "      <td>  5482547.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Elizabeth Cheney</th>\n",
        "      <td>   30</td>\n",
        "      <td>    0</td>\n",
        "      <td> 0</td>\n",
        "      <td>    20</td>\n",
        "      <td>    3900</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.722892</td>\n",
        "      <td> 0.545455</td>\n",
        "      <td> 0.222222</td>\n",
        "      <td>      inf</td>\n",
        "      <td> WY</td>\n",
        "      <td> REP</td>\n",
        "      <td>  3016825.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "                   commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                    \n",
        "Dan Sullivan                360            94              0        623     165715        0.868898     0.613568   \n",
        "Jeff Sessions               142            10              0         36       5114        0.782609     1.000000   \n",
        "Thomas Cotton               100            91              0        195     797919        0.681818     0.814496   \n",
        "Cory Gardner                316           167              0        450     234223        0.729335     0.628876   \n",
        "Christopher Coons           190            20              0        110      19950        0.846154     0.559278   \n",
        "John Kingston                37             2              0         17        717        0.894737     0.540724   \n",
        "Mark Jacobs                7334          1052              0      66064    1464016        0.984326     0.943058   \n",
        "Pat Roberts                 256            83              0        374      97678        0.818381     0.943867   \n",
        "Mitch Mcconnell            2197           272              0       4760     893077        0.945946     0.569208   \n",
        "Mary Landrieu               745            84              0       2015     395161        0.959981     0.986888   \n",
        "Gabriel Gomez               899           514              0       1239    1855996        0.706788     0.995771   \n",
        "Susan Collins                81             9              0        351     291078        0.975000     0.834797   \n",
        "Terri Land                  110           560              0       1660    3077280        0.747748     0.988351   \n",
        "Al Franken                  141            71              0        208     260800        0.745520     0.897435   \n",
        "Thad Cochran                 74            10              0         35      15199        0.777778     0.955972   \n",
        "Thom Tillis                 253           194              0        586     797943        0.751282     0.950310   \n",
        "Scott Brown                 708            92              0       2456     388637        0.963893     0.916956   \n",
        "Cory Booker                 355            60              0        894     130143        0.937107     0.998527   \n",
        "Allen Weh                   821            71              0        256    1949518        0.782875     0.987909   \n",
        "James Inhofe               1905           406              0       2094     170226        0.837600     0.981288   \n",
        "Monica Wehby                115           295              0        660      95280        0.691099     0.702572   \n",
        "Jack Reed                    90             4              0        128      19412        0.969697     0.910763   \n",
        "Lindsey Graham             1089           135              0       1412     119855        0.912734     0.891586   \n",
        "Lamar Alexander              34             4              0         48       6291        0.923077     0.590538   \n",
        "John Cornyn                 166            20              0        260      21110        0.928571     0.995943   \n",
        "Natalie Tennant             210           110              0        690     159430        0.862500     0.969238   \n",
        "Elizabeth Cheney             30             0              0         20       3900        1.000000     0.722892   \n",
        "\n",
        "                   msgs_share  likes_share  dislikes_share state party    donations  \n",
        "candidate_name                                                                       \n",
        "Dan Sullivan         0.814480     0.774876        0.494737    AK   DEM   6340422.00  \n",
        "Jeff Sessions        1.000000     1.000000        1.000000    AL   REP   1115688.00  \n",
        "Thomas Cotton        0.434783     0.537190        0.722222    AR   REP   7097224.06  \n",
        "Cory Gardner         0.633267     0.530660        0.268058    CO   DEM  10420571.00  \n",
        "Christopher Coons    0.627063     0.379310        0.400000    DE   DEM   4173447.00  \n",
        "John Kingston        0.451220     0.447368        1.000000    GA   DEM   9211931.00  \n",
        "Mark Jacobs          0.959571     0.995765        0.932624    IA   REP   4810813.00  \n",
        "Pat Roberts          0.733524     0.869767        0.768519    KS   REP   1068018.00  \n",
        "Mitch Mcconnell      0.632230     0.640387        0.428346    KY   DEM  11353760.00  \n",
        "Mary Landrieu        0.937107     0.971084        1.000000    LA   DEM  10190144.00  \n",
        "Gabriel Gomez        1.000000     0.946524        0.980916    MA   REP   4755654.00  \n",
        "Susan Collins        0.658537     0.983193        1.000000    ME   DEM   1333016.00  \n",
        "Terri Land           0.578947     0.783019        0.918033    MI   DEM   6994603.00  \n",
        "Al Franken           0.762162     0.753623        0.639640    MN   DFL  15126268.00  \n",
        "Thad Cochran         1.000000     0.897436        1.000000    MS   REP   2727209.00  \n",
        "Thom Tillis          0.496078     0.716381        0.619808    NC   REP   4764110.00  \n",
        "Scott Brown          0.964578     0.967310        0.736000    NH   REP   3686708.00  \n",
        "Cory Booker          0.997191     1.000000        1.000000    NJ   DEM  16167874.00  \n",
        "Allen Weh            0.539776     0.279476        0.702970    NM   DEM   5050539.00  \n",
        "James Inhofe         0.968480     0.984023        0.966667    OK   REP   2811701.00  \n",
        "Monica Wehby         0.481172     0.662651        0.891239    OR   REP   2049732.00  \n",
        "Jack Reed            0.865385     0.969697        1.000000    RI   DEM   2833802.97  \n",
        "Lindsey Graham       0.840927     0.811494        1.000000    SC   REP   6788544.00  \n",
        "Lamar Alexander      0.161905     0.578313        0.173913    TN   REP   1812250.00  \n",
        "John Cornyn          0.982249     0.996169        0.909091    TX   DEM   9673572.00  \n",
        "Natalie Tennant      0.860656     0.873418        0.873016    WV   REP   5482547.00  \n",
        "Elizabeth Cheney     0.545455     0.222222             inf    WY   REP   3016825.00  "
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "commentCount            360\n",
        "dislikeCount             94\n",
        "favoriteCount             0\n",
        "likeCount               623\n",
        "viewCount            165715\n",
        "like_dislike_r    0.8688982\n",
        "views_share       0.6135684\n",
        "msgs_share        0.8144796\n",
        "likes_share       0.7748756\n",
        "dislikes_share    0.4947368\n",
        "state                    AK\n",
        "party                   DEM\n",
        "donations           6340422\n",
        "Name: Dan Sullivan, dtype: object\n",
        "commentCount            142\n",
        "dislikeCount             10\n",
        "favoriteCount             0\n",
        "likeCount                36\n",
        "viewCount              5114\n",
        "like_dislike_r    0.7826087\n",
        "views_share               1\n",
        "msgs_share                1\n",
        "likes_share               1\n",
        "dislikes_share            1\n",
        "state                    AL\n",
        "party                   REP\n",
        "donations           1115688\n",
        "Name: Jeff Sessions, dtype: object\n",
        "commentCount            100\n",
        "dislikeCount             91\n",
        "favoriteCount             0\n",
        "likeCount               195\n",
        "viewCount            797919\n",
        "like_dislike_r    0.6818182\n",
        "views_share       0.8144956\n",
        "msgs_share        0.4347826\n",
        "likes_share       0.5371901\n",
        "dislikes_share    0.7222222\n",
        "state                    AR\n",
        "party                   REP\n",
        "donations           7097224\n",
        "Name: Thomas Cotton, dtype: object\n",
        "commentCount               316\n",
        "dislikeCount               167\n",
        "favoriteCount                0\n",
        "likeCount                  450\n",
        "viewCount               234223\n",
        "like_dislike_r       0.7293355\n",
        "views_share          0.6288761\n",
        "msgs_share           0.6332665\n",
        "likes_share          0.5306604\n",
        "dislikes_share       0.2680578\n",
        "state                       CO\n",
        "party                      DEM\n",
        "donations         1.042057e+07\n",
        "Name: Cory Gardner, dtype: object\n",
        "commentCount            190\n",
        "dislikeCount             20\n",
        "favoriteCount             0\n",
        "likeCount               110\n",
        "viewCount             19950\n",
        "like_dislike_r    0.8461538\n",
        "views_share       0.5592778\n",
        "msgs_share        0.6270627\n",
        "likes_share       0.3793103\n",
        "dislikes_share          0.4\n",
        "state                    DE\n",
        "party                   DEM\n",
        "donations           4173447\n",
        "Name: Christopher Coons, dtype: object\n",
        "commentCount             37\n",
        "dislikeCount              2\n",
        "favoriteCount             0\n",
        "likeCount                17\n",
        "viewCount               717\n",
        "like_dislike_r    0.8947368\n",
        "views_share        0.540724\n",
        "msgs_share        0.4512195\n",
        "likes_share       0.4473684\n",
        "dislikes_share            1\n",
        "state                    GA\n",
        "party                   DEM\n",
        "donations           9211931\n",
        "Name: John Kingston, dtype: object\n",
        "commentCount           7334\n",
        "dislikeCount           1052\n",
        "favoriteCount             0\n",
        "likeCount             66064\n",
        "viewCount           1464016\n",
        "like_dislike_r    0.9843256\n",
        "views_share       0.9430583\n",
        "msgs_share        0.9595708\n",
        "likes_share       0.9957646\n",
        "dislikes_share    0.9326241\n",
        "state                    IA\n",
        "party                   REP\n",
        "donations           4810813\n",
        "Name: Mark Jacobs, dtype: object\n",
        "commentCount            256\n",
        "dislikeCount             83\n",
        "favoriteCount             0\n",
        "likeCount               374\n",
        "viewCount             97678\n",
        "like_dislike_r    0.8183807\n",
        "views_share       0.9438673\n",
        "msgs_share        0.7335244\n",
        "likes_share       0.8697674\n",
        "dislikes_share    0.7685185\n",
        "state                    KS\n",
        "party                   REP\n",
        "donations           1068018\n",
        "Name: Pat Roberts, dtype: object\n",
        "commentCount              2197\n",
        "dislikeCount               272\n",
        "favoriteCount                0\n",
        "likeCount                 4760\n",
        "viewCount               893077\n",
        "like_dislike_r       0.9459459\n",
        "views_share          0.5692079\n",
        "msgs_share           0.6322302\n",
        "likes_share          0.6403875\n",
        "dislikes_share       0.4283465\n",
        "state                       KY\n",
        "party                      DEM\n",
        "donations         1.135376e+07\n",
        "Name: Mitch Mcconnell, dtype: object\n",
        "commentCount               745\n",
        "dislikeCount                84\n",
        "favoriteCount                0\n",
        "likeCount                 2015\n",
        "viewCount               395161\n",
        "like_dislike_r       0.9599809\n",
        "views_share          0.9868885\n",
        "msgs_share           0.9371069\n",
        "likes_share          0.9710843\n",
        "dislikes_share               1\n",
        "state                       LA\n",
        "party                      DEM\n",
        "donations         1.019014e+07\n",
        "Name: Mary Landrieu, dtype: object\n",
        "commentCount            899\n",
        "dislikeCount            514\n",
        "favoriteCount             0\n",
        "likeCount              1239\n",
        "viewCount           1855996\n",
        "like_dislike_r    0.7067884\n",
        "views_share       0.9957712\n",
        "msgs_share                1\n",
        "likes_share       0.9465241\n",
        "dislikes_share     0.980916\n",
        "state                    MA\n",
        "party                   REP\n",
        "donations           4755654\n",
        "Name: Gabriel Gomez, dtype: object\n",
        "commentCount             81\n",
        "dislikeCount              9\n",
        "favoriteCount             0\n",
        "likeCount               351\n",
        "viewCount            291078\n",
        "like_dislike_r        0.975\n",
        "views_share       0.8347974\n",
        "msgs_share        0.6585366\n",
        "likes_share       0.9831933\n",
        "dislikes_share            1\n",
        "state                    ME\n",
        "party                   DEM\n",
        "donations           1333016\n",
        "Name: Susan Collins, dtype: object\n",
        "commentCount            110\n",
        "dislikeCount            560\n",
        "favoriteCount             0\n",
        "likeCount              1660\n",
        "viewCount           3077280\n",
        "like_dislike_r    0.7477477\n",
        "views_share       0.9883509\n",
        "msgs_share        0.5789474\n",
        "likes_share       0.7830189\n",
        "dislikes_share    0.9180328\n",
        "state                    MI\n",
        "party                   DEM\n",
        "donations           6994603\n",
        "Name: Terri Land, dtype: object\n",
        "commentCount               141\n",
        "dislikeCount                71\n",
        "favoriteCount                0\n",
        "likeCount                  208\n",
        "viewCount               260800\n",
        "like_dislike_r       0.7455197\n",
        "views_share           0.897435\n",
        "msgs_share           0.7621622\n",
        "likes_share          0.7536232\n",
        "dislikes_share       0.6396396\n",
        "state                       MN\n",
        "party                      DFL\n",
        "donations         1.512627e+07\n",
        "Name: Al Franken, dtype: object\n",
        "commentCount             74\n",
        "dislikeCount             10\n",
        "favoriteCount             0\n",
        "likeCount                35\n",
        "viewCount             15199\n",
        "like_dislike_r    0.7777778\n",
        "views_share       0.9559721\n",
        "msgs_share                1\n",
        "likes_share       0.8974359\n",
        "dislikes_share            1\n",
        "state                    MS\n",
        "party                   REP\n",
        "donations           2727209\n",
        "Name: Thad Cochran, dtype: object\n",
        "commentCount            253\n",
        "dislikeCount            194\n",
        "favoriteCount             0\n",
        "likeCount               586\n",
        "viewCount            797943\n",
        "like_dislike_r    0.7512821\n",
        "views_share         0.95031\n",
        "msgs_share        0.4960784\n",
        "likes_share       0.7163814\n",
        "dislikes_share    0.6198083\n",
        "state                    NC\n",
        "party                   REP\n",
        "donations           4764110\n",
        "Name: Thom Tillis, dtype: object\n",
        "commentCount            708\n",
        "dislikeCount             92\n",
        "favoriteCount             0\n",
        "likeCount              2456\n",
        "viewCount            388637\n",
        "like_dislike_r    0.9638932\n",
        "views_share       0.9169557\n",
        "msgs_share        0.9645777\n",
        "likes_share         0.96731\n",
        "dislikes_share        0.736\n",
        "state                    NH\n",
        "party                   REP\n",
        "donations           3686708\n",
        "Name: Scott Brown, dtype: object\n",
        "commentCount               355\n",
        "dislikeCount                60\n",
        "favoriteCount                0\n",
        "likeCount                  894\n",
        "viewCount               130143\n",
        "like_dislike_r       0.9371069\n",
        "views_share          0.9985269\n",
        "msgs_share            0.997191\n",
        "likes_share                  1\n",
        "dislikes_share               1\n",
        "state                       NJ\n",
        "party                      DEM\n",
        "donations         1.616787e+07\n",
        "Name: Cory Booker, dtype: object\n",
        "commentCount            821\n",
        "dislikeCount             71\n",
        "favoriteCount             0\n",
        "likeCount               256\n",
        "viewCount           1949518\n",
        "like_dislike_r    0.7828746\n",
        "views_share       0.9879091\n",
        "msgs_share        0.5397765\n",
        "likes_share        0.279476\n",
        "dislikes_share    0.7029703\n",
        "state                    NM\n",
        "party                   DEM\n",
        "donations           5050539\n",
        "Name: Allen Weh, dtype: object\n",
        "commentCount           1905\n",
        "dislikeCount            406\n",
        "favoriteCount             0\n",
        "likeCount              2094\n",
        "viewCount            170226\n",
        "like_dislike_r       0.8376\n",
        "views_share        0.981288\n",
        "msgs_share        0.9684799\n",
        "likes_share       0.9840226\n",
        "dislikes_share    0.9666667\n",
        "state                    OK\n",
        "party                   REP\n",
        "donations           2811701\n",
        "Name: James Inhofe, dtype: object\n",
        "commentCount            115\n",
        "dislikeCount            295\n",
        "favoriteCount             0\n",
        "likeCount               660\n",
        "viewCount             95280\n",
        "like_dislike_r    0.6910995\n",
        "views_share        0.702572\n",
        "msgs_share        0.4811715\n",
        "likes_share       0.6626506\n",
        "dislikes_share    0.8912387\n",
        "state                    OR\n",
        "party                   REP\n",
        "donations           2049732\n",
        "Name: Monica Wehby, dtype: object\n",
        "commentCount             90\n",
        "dislikeCount              4\n",
        "favoriteCount             0\n",
        "likeCount               128\n",
        "viewCount             19412\n",
        "like_dislike_r     0.969697\n",
        "views_share       0.9107629\n",
        "msgs_share        0.8653846\n",
        "likes_share        0.969697\n",
        "dislikes_share            1\n",
        "state                    RI\n",
        "party                   DEM\n",
        "donations           2833803\n",
        "Name: Jack Reed, dtype: object\n",
        "commentCount           1089\n",
        "dislikeCount            135\n",
        "favoriteCount             0\n",
        "likeCount              1412\n",
        "viewCount            119855\n",
        "like_dislike_r    0.9127343\n",
        "views_share       0.8915859\n",
        "msgs_share        0.8409266\n",
        "likes_share       0.8114943\n",
        "dislikes_share            1\n",
        "state                    SC\n",
        "party                   REP\n",
        "donations           6788544\n",
        "Name: Lindsey Graham, dtype: object\n",
        "commentCount             34\n",
        "dislikeCount              4\n",
        "favoriteCount             0\n",
        "likeCount                48\n",
        "viewCount              6291\n",
        "like_dislike_r    0.9230769\n",
        "views_share       0.5905379\n",
        "msgs_share        0.1619048\n",
        "likes_share       0.5783133\n",
        "dislikes_share     0.173913\n",
        "state                    TN\n",
        "party                   REP\n",
        "donations           1812250\n",
        "Name: Lamar Alexander, dtype: object\n",
        "commentCount            166\n",
        "dislikeCount             20\n",
        "favoriteCount             0\n",
        "likeCount               260\n",
        "viewCount             21110\n",
        "like_dislike_r    0.9285714\n",
        "views_share       0.9959426\n",
        "msgs_share        0.9822485\n",
        "likes_share       0.9961686\n",
        "dislikes_share    0.9090909\n",
        "state                    TX\n",
        "party                   DEM\n",
        "donations           9673572\n",
        "Name: John Cornyn, dtype: object\n",
        "commentCount            210\n",
        "dislikeCount            110\n",
        "favoriteCount             0\n",
        "likeCount               690\n",
        "viewCount            159430\n",
        "like_dislike_r       0.8625\n",
        "views_share       0.9692383\n",
        "msgs_share        0.8606557\n",
        "likes_share       0.8734177\n",
        "dislikes_share    0.8730159\n",
        "state                    WV\n",
        "party                   REP\n",
        "donations           5482547\n",
        "Name: Natalie Tennant, dtype: object\n",
        "commentCount             30\n",
        "dislikeCount              0\n",
        "favoriteCount             0\n",
        "likeCount                20\n",
        "viewCount              3900\n",
        "like_dislike_r            1\n",
        "views_share       0.7228916\n",
        "msgs_share        0.5454545\n",
        "likes_share       0.2222222\n",
        "dislikes_share          inf\n",
        "state                    WY\n",
        "party                   REP\n",
        "donations           3016825\n",
        "Name: Elizabeth Cheney, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(sentate_2014[\"state\"].unique())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "27"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Check 2012 Senate Elections"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_state_data(candidates):\n",
      "    data_set = get_2012_data(candidates)\n",
      "    t_ds = pd.pivot_table(data_set, values=[\"commentCount\", \"favoriteCount\", \"dislikeCount\", \"likeCount\", \"viewCount\"],\n",
      "                   aggfunc='sum', rows=\"candidate_name\")\n",
      "    t_ds[\"like_dislike_r\"] = t_ds[\"likeCount\"] / (t_ds[\"dislikeCount\"] + t_ds[\"likeCount\"])\n",
      "    t_ds[\"views_share\"] = t_ds[\"viewCount\"] / t_ds[\"viewCount\"].sum()\n",
      "    t_ds[\"msgs_share\"] = t_ds[\"commentCount\"] / t_ds[\"commentCount\"].sum()\n",
      "    t_ds[\"likes_share\"] = t_ds[\"likeCount\"] / t_ds[\"likeCount\"].sum()\n",
      "    t_ds[\"dislikes_share\"] = t_ds[\"dislikeCount\"] / t_ds[\"dislikeCount\"].sum()\n",
      "    # Sentemate Analysis of the title\n",
      "    t_ds[\"sentiment\"] = pd.Series()\n",
      "    for cand in candidates:\n",
      "        t_ds[\"sentiment\"][cand] = np.mean(\n",
      "                                    [TextBlob(title).polarity for title in data_set[data_set[\"candidate_name\"]==cand][\"title\"]]\n",
      "                                         )\n",
      "    \n",
      "    print t_ds\n",
      "    return t_ds"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "senate_2012 = pd.read_csv(\"data/2012_senate_results.csv\")\n",
      "senate_2012[\"Full Name\"] = senate_2012[\"First Name\"] + \" \"  + senate_2012[\"Last Name\"]\n",
      "senate_2012"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 126 entries, 0 to 125\n",
        "Data columns (total 9 columns):\n",
        "State Postal    126  non-null values\n",
        "County Name     126  non-null values\n",
        "Party           126  non-null values\n",
        "First Name      126  non-null values\n",
        "Last Name       126  non-null values\n",
        "Incumbent       126  non-null values\n",
        "Vote Count      126  non-null values\n",
        "Winner          33  non-null values\n",
        "Full Name       126  non-null values\n",
        "dtypes: int64(2), object(7)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 126 entries, 0 to 125\n",
        "Data columns (total 9 columns):\n",
        "State Postal    126  non-null values\n",
        "County Name     126  non-null values\n",
        "Party           126  non-null values\n",
        "First Name      126  non-null values\n",
        "Last Name       126  non-null values\n",
        "Incumbent       126  non-null values\n",
        "Vote Count      126  non-null values\n",
        "Winner          33  non-null values\n",
        "Full Name       126  non-null values\n",
        "dtypes: int64(2), object(7)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "senate_2012[\"commentCount\"] = pd.Series()\n",
      "senate_2012[\"dislikeCount\"] = pd.Series()\n",
      "senate_2012[\"favoriteCount\"] = pd.Series()\n",
      "senate_2012[\"likeCount\"] = pd.Series()\n",
      "senate_2012[\"viewCount\"] = pd.Series()\n",
      "senate_2012[\"like_dislike_r\"] = pd.Series()\n",
      "senate_2012[\"views_share\"] = pd.Series()\n",
      "senate_2012[\"msgs_share\"] = pd.Series()\n",
      "senate_2012[\"likes_share\"] = pd.Series()\n",
      "senate_2012[\"dislikes_share\"] = pd.Series()\n",
      "senate_2012[\"sentiment\"] = pd.Series()\n",
      "\n",
      "for state in np.unique(senate_2012[\"State Postal\"]):\n",
      "    print state + \":\"\n",
      "    cands = senate_2012[senate_2012[\"State Postal\"] == state]\n",
      "    top_cands = cands.sort(\"Vote Count\",ascending=False)[:2]\n",
      "    #print top_cands\n",
      "    try:\n",
      "        youtube_stats = get_state_data(top_cands[\"Full Name\"].values)\n",
      "        #print youtube_stats\n",
      "        # Store Data Back\n",
      "\n",
      "        for item in youtube_stats.iterrows():\n",
      "            cand = item[0]\n",
      "            stats = item[1]\n",
      "            index = int(senate_2012[senate_2012[\"Full Name\"] == cand].index)\n",
      "            senate_2012[\"commentCount\"][index] = stats[\"commentCount\"]\n",
      "            senate_2012[\"dislikeCount\"][index] = stats[\"dislikeCount\"]\n",
      "            senate_2012[\"favoriteCount\"][index] = stats[\"favoriteCount\"]\n",
      "            senate_2012[\"likeCount\"][index] = stats[\"likeCount\"]\n",
      "            senate_2012[\"viewCount\"][index] = stats[\"viewCount\"]\n",
      "            senate_2012[\"like_dislike_r\"][index] = stats[\"like_dislike_r\"]\n",
      "            senate_2012[\"views_share\"][index] = stats[\"views_share\"]\n",
      "            senate_2012[\"msgs_share\"][index] = stats[\"msgs_share\"]\n",
      "            senate_2012[\"likes_share\"][index] = stats[\"likes_share\"]\n",
      "            senate_2012[\"dislikes_share\"][index] = stats[\"dislikes_share\"]\n",
      "            senate_2012[\"sentiment\"][index] = stats[\"sentiment\"]\n",
      "    except:\n",
      "        pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "AZ:\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Jeff Flake                542          1240              0       2927    1829090        0.702424      0.58453   \n",
        "Richard Carmona           513          2112              0       4590    1300075        0.684870      0.41547   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                       \n",
        "Jeff Flake         0.513744     0.389384        0.369928   0.016247  \n",
        "Richard Carmona    0.486256     0.610616        0.630072   0.044413  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CA:\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Dianne Feinstein         12130          1690              0      16760    3165370        0.908401       0.7182   \n",
        "Elizabeth Emken           4492           437              0       6777    1241994        0.939423       0.2818   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                        \n",
        "Dianne Feinstein    0.729756      0.71207        0.794546  -0.000463  \n",
        "Elizabeth Emken     0.270244      0.28793        0.205454  -0.006897  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CT:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Chris Murphy             233            88              0        447     159171        0.835514     0.371103   \n",
        "Linda McMahon           3962           418              0       4062     269742        0.906696     0.628897   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Chris Murphy      0.055542     0.099135        0.173913   0.004893  \n",
        "Linda McMahon     0.944458     0.900865        0.826087   0.029649  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "DE:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Kevin Wade                60            10              0         70      11400           0.875     0.276123   \n",
        "Thomas Carper             28            10              0         70      29886           0.875     0.723877   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Kevin Wade        0.681818          0.5             0.5  -0.031250  \n",
        "Thomas Carper     0.318182          0.5             0.5   0.018583  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "FL:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bill Nelson              376            59              0        937     511466        0.940763     0.865892   \n",
        "Connie Mack              164            67              0        249      79215        0.787975     0.134108   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Bill Nelson       0.696296     0.790051        0.468254   0.021819  \n",
        "Connie Mack       0.303704     0.209949        0.531746   0.015071  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "HI:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Linda Lingle             343           451              0        672     480724        0.598397     0.476687   \n",
        "Mazie Hirono             367           577              0        924     527744        0.615590     0.523313   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Linda Lingle      0.483099     0.421053        0.438716   0.023237  \n",
        "Mazie Hirono      0.516901     0.578947        0.561284   0.065901  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "IN:\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Joe Donnelly              3011           931              0       1203    2646352        0.563730     0.756489   \n",
        "Richard Mourdock          7869          1467              0       8777     851850        0.856794     0.243511   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                        \n",
        "Joe Donnelly        0.276746     0.120541         0.38824  -0.009662  \n",
        "Richard Mourdock    0.723254     0.879459         0.61176   0.043522  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MA:\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Elizabeth Warren         10007          2208              0      15744    2458138        0.877005     0.718429   \n",
        "Scott Brown               5226          1259              0       6490     963410        0.837527     0.281571   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                        \n",
        "Elizabeth Warren    0.656929     0.708105        0.636862   0.014748  \n",
        "Scott Brown         0.343071     0.291895        0.363138   0.019217  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MD:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Ben Cardin               301            90              0        386      61822        0.810924      0.82694   \n",
        "Daniel Bongino           162            10              0        162      12938        0.941860      0.17306   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Ben Cardin        0.650108      0.70438             0.9   0.008083  \n",
        "Daniel Bongino    0.349892      0.29562             0.1  -0.018750  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ME:\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Angus King                 91           133              0        385      39436        0.743243     0.911689   \n",
        "Charles Summers             0            10              0         40       3820        0.800000     0.088311   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                       \n",
        "Angus King                1     0.905882         0.93007   0.053315  \n",
        "Charles Summers           0     0.094118         0.06993   0.016667  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MI:\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Debbie Stabenow            63            83              0        595     428223        0.877581     0.870664   \n",
        "Pete Hoekstra              57           187              0        398      63612        0.680342     0.129336   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                       \n",
        "Debbie Stabenow       0.525     0.599194        0.307407   0.062909  \n",
        "Pete Hoekstra         0.475     0.400806        0.692593   0.121008  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MN:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Amy Klobuchar            264           242              0        430      97354        0.639881     0.659651   \n",
        "Kurt Bills               300           100              0        460      50230        0.821429     0.340349   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Amy Klobuchar     0.468085     0.483146        0.707602   0.030468  \n",
        "Kurt Bills        0.531915     0.516854        0.292398   0.160000  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MO:\n",
        "                  commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                   \n",
        "Claire McCaskill           525           438              0        765     141427        0.635910     0.036271   \n",
        "Todd Akin                44285          5817              0      66830    3757741        0.919928     0.963729   \n",
        "\n",
        "                  msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                        \n",
        "Claire McCaskill    0.011716     0.011317        0.070024   0.009500  \n",
        "Todd Akin           0.988284     0.988683        0.929976  -0.016794  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MS:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Albert Gore              370             0              0        260      75470        1.000000      0.70684   \n",
        "Roger Wicker             120            93              0        203      31301        0.685811      0.29316   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Albert Gore       0.755102     0.561555               0  -0.050000  \n",
        "Roger Wicker      0.244898     0.438445               1   0.029821  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "MT:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Denny Rehberg           4500           351              0      10651     390033        0.968097     0.807661   \n",
        "Jon Tester               480            72              0       1172      92884        0.942122     0.192339   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Denny Rehberg     0.903614     0.900871        0.829787   0.003883  \n",
        "Jon Tester        0.096386     0.099129        0.170213   0.023900  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "ND:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Heidi Heitkamp           315           289              0        726     773197        0.715271     0.574998   \n",
        "Rick Berg                331           200              0        573     571499        0.741268     0.425002   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Heidi Heitkamp    0.487616     0.558891        0.591002   0.007323  \n",
        "Rick Berg         0.512384     0.441109        0.408998   0.033317  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NE:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bob Kerrey               328           179              0       1526     526859        0.895015     0.616558   \n",
        "Deb Fischer              441           272              0       2076     327657        0.884157     0.383442   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Bob Kerrey        0.426528     0.423654        0.396896   0.015867  \n",
        "Deb Fischer       0.573472     0.576346        0.603104   0.011278  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NJ:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bob Menendez             601           232              0        819     132137        0.779258     0.777057   \n",
        "Joe Kyrillos              87           189              0        198      37911        0.511628     0.222943   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Bob Menendez      0.873547      0.80531        0.551069  -0.017692  \n",
        "Joe Kyrillos      0.126453      0.19469        0.448931   0.083939  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NM:\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Heather Wilson            170           330              0        460     112560        0.582278     0.116505   \n",
        "Martin Heinrich           380           790              0       2580     853580        0.765579     0.883495   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                       \n",
        "Heather Wilson     0.309091     0.151316        0.294643   0.064444  \n",
        "Martin Heinrich    0.690909     0.848684        0.705357   0.058437  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NV:\n",
        "                 commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                  \n",
        "Dean Heller               286           748              0       1077    1014221        0.590137     0.429432   \n",
        "Shelley Berkley           434           402              0        884    1347552        0.687403     0.570568   \n",
        "\n",
        "                 msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                       \n",
        "Dean Heller        0.397222      0.54921        0.650435  -0.003463  \n",
        "Shelley Berkley    0.602778      0.45079        0.349565   0.005413  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "NY:\n",
        "                    commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                     \n",
        "Kirsten Gillibrand           684           167              0       1452     399903        0.896850     0.638157   \n",
        "Wendy Long                   494            83              0        740     226750        0.899149     0.361843   \n",
        "\n",
        "                    msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                          \n",
        "Kirsten Gillibrand    0.580645     0.662409           0.668   0.030444  \n",
        "Wendy Long            0.419355     0.337591           0.332   0.074461  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "OH:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Josh Mandel              484           311              0        423     163496        0.576294     0.317986   \n",
        "Sherrod Brown            292            70              0        331     350665        0.825436     0.682014   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Josh Mandel       0.623711     0.561008        0.816273   0.002392  \n",
        "Sherrod Brown     0.376289     0.438992        0.183727   0.034265  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "PA:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bob Casey                117           139              0        316      57281        0.694505     0.203635   \n",
        "Tom Smith                793           209              0       1648     224012        0.887453     0.796365   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Bob Casey         0.128571     0.160896        0.399425   0.014105  \n",
        "Tom Smith         0.871429     0.839104        0.600575   0.048067  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "RI:\n",
        "                    commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                     \n",
        "Barry Hinckley                12            12              0        138      21111        0.920000     0.404712   \n",
        "Sheldon Whitehouse            10            10              0        134      31052        0.930556     0.595288   \n",
        "\n",
        "                    msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                          \n",
        "Barry Hinckley        0.545455     0.507353        0.545455   0.138462  \n",
        "Sheldon Whitehouse    0.454545     0.492647        0.454545   0.033625  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "TN:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bob Corker               155            20              0        162      17499        0.890110     0.258238   \n",
        "Mark Clayton             230            50              0        230      50264        0.821429     0.741762   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Bob Corker        0.402597     0.413265        0.285714   0.144643  \n",
        "Mark Clayton      0.597403     0.586735        0.714286   0.100538  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "TX:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Paul Sadler              840           257              0       1937     250148        0.882862     0.462666   \n",
        "Ted Cruz                1676           255              0       2410     290519        0.904315     0.537334   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Paul Sadler       0.333863     0.445595        0.501953   0.015821  \n",
        "Ted Cruz          0.666137     0.554405        0.498047   0.009659  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "UT:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Orrin Hatch              495           336              0        284      38701        0.458065     0.511722   \n",
        "Scott Howell              19            13              0        175      36928        0.930851     0.488278   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Orrin Hatch       0.963035     0.618736        0.962751   0.084909  \n",
        "Scott Howell      0.036965     0.381264        0.037249   0.050492  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "VA:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "George Allen             324           443              0        500     206940        0.530223     0.718385   \n",
        "Timothy Kaine            174            90              0        272      81123        0.751381     0.281615   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "George Allen      0.650602     0.647668        0.831144   0.023067  \n",
        "Timothy Kaine     0.349398     0.352332        0.168856   0.022738  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "VT:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Bernie Sanders          2131           129              0       4878     223002        0.974236     0.992571   \n",
        "John MacGovern             6             0              0          2       1669        1.000000     0.007429   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Bernie Sanders    0.997192      0.99959               1   0.011692  \n",
        "John MacGovern    0.002808      0.00041               0   0.000000  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "WA:\n",
        "                     commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                      \n",
        "Maria Cantwell                234            52              0        244      62934        0.824324     0.736725   \n",
        "Michael Baumgartner           112            70              0        137      22490        0.661836     0.263275   \n",
        "\n",
        "                     msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                           \n",
        "Maria Cantwell         0.676301      0.64042         0.42623   0.107853  \n",
        "Michael Baumgartner    0.323699      0.35958         0.57377   0.031234  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "WI:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Tammy Baldwin            393           169              0        944     488400        0.848158     0.602401   \n",
        "Tommy Thompson          1494           550              0       2332     322355        0.809160     0.397599   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Tammy Baldwin     0.208267     0.288156        0.235049   0.022898  \n",
        "Tommy Thompson    0.791733     0.711844        0.764951   0.010391  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "WV:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "Joe Manchin              675           545              0       3005     604285        0.846479     0.964792   \n",
        "John Raese                74            68              0        118      22052        0.634409     0.035208   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "Joe Manchin       0.901202     0.962216         0.88907   0.006897  \n",
        "John Raese        0.098798     0.037784         0.11093  -0.057143  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "WY:\n",
        "                commentCount  dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  \\\n",
        "candidate_name                                                                                                 \n",
        "John Barrasso            630           290              0        380      74030        0.567164     0.996943   \n",
        "Tim Chesnut                4             2              0          2        227        0.500000     0.003057   \n",
        "\n",
        "                msgs_share  likes_share  dislikes_share  sentiment  \n",
        "candidate_name                                                      \n",
        "John Barrasso     0.993691     0.994764        0.993151       0.09  \n",
        "Tim Chesnut       0.006309     0.005236        0.006849       0.00  "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cands_with_stats = senate_2012[~senate_2012[\"viewCount\"].isnull()]\n",
      "cands_with_stats[\"VotesShare\"] = cands_with_stats[[\"Vote Count\", \"State Postal\"]].apply(lambda x:x[0]/senate_2012[senate_2012[\"State Postal\"]==x[1]][\"Vote Count\"].sum(), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x_col = \"views_share\"\n",
      "y_col = \"VotesShare\"\n",
      "\n",
      "plt.figure(figsize=(15,10))\n",
      "color_dict = {\"Dem\": \"b\", \"GOP\": \"r\", \"Ind\":\"g\", \"NPA\": \"orange\"}\n",
      "shape_dict = {\"X\": \"*\", \"nan\": \".\"}\n",
      "\n",
      "wl_dp = [len(cands_with_stats[(cands_with_stats[x_col]>=0.5) &\n",
      "                          (cands_with_stats[\"Winner\"]==\"X\")]),\n",
      "                    len(cands_with_stats[(cands_with_stats[x_col]>=0.5)])]\n",
      "wl_dm = [len(cands_with_stats[(cands_with_stats[x_col]<0.5) &\n",
      "                          (cands_with_stats[\"Winner\"]==\"X\")]),\n",
      "                    len(cands_with_stats[(cands_with_stats[x_col]<0.5)])]\n",
      "\n",
      "wl_50p = \"Winning Ratio %s/%s ($%0.1f \\%%$)\" % (wl_dp[0], wl_dp[1], wl_dp[0]/wl_dp[1]*100)\n",
      "wl_50m = \"Winning Ratio %s/%s ($%0.1f \\%%$)\" % (wl_dm[0], wl_dm[1], wl_dm[0]/wl_dm[1]*100)\n",
      "\n",
      "for cand in cands_with_stats.iterrows():\n",
      "    stats = cand[1]\n",
      "    x = stats[x_col]\n",
      "    y = stats[y_col]\n",
      "    c = color_dict[stats[\"Party\"]]\n",
      "    m = shape_dict[str(stats[\"Winner\"])]\n",
      "    plt.scatter(x, y, c=c, marker=m, s=500, alpha=0.5)\n",
      "    if stats[x_col] > 0.9:\n",
      "        plt.annotate(stats[\"Full Name\"],xytext=(8,20), xy=(x,y),\n",
      "                     textcoords='offset points', arrowprops=dict(arrowstyle='-|>'))\n",
      "\n",
      "plt.xlabel(\"Youtube \" + x_col + \" Between Competing Candidates in a State Race\")\n",
      "plt.ylabel(\"Actual \" + y_col)\n",
      "plt.vlines(.5, ymin=0, ymax=1)\n",
      "\n",
      "plt.annotate(s=wl_50p, xy=(0.7, 1))\n",
      "plt.annotate(s=wl_50m, xy=(0.2, 1))\n",
      "plt.title(\"Youtube Video Views for Candidate from 2012-08-04 to 2012-11-04 and Actual Votes\")\n",
      "plt.annotate(\"Start Represent Winning Candidates\\nCircles Represent Loosing Candidate\", xy=(0.03, 0.85))\n",
      "plt.annotate(\"Red: GOP\\nBlue: Dem\\nGreen: Ind\\nYellow: NPA\", xy=(0.03, 0.7))\n",
      "axis(\"tight\")\n",
      "plt.box(on=\"off\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DRuvdYV0kDaXc63pwRNwwK2PELZXtsnGT8pqZYzkRcWilvBMi4hXK96dZ3d8H7Jnb9WlK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\nNoqN8Opk9wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff080672950>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cands_with_stats[cands_with_stats[\"State Postal\"]==\"MO\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>State Postal</th>\n",
        "      <th>County Name</th>\n",
        "      <th>Party</th>\n",
        "      <th>First Name</th>\n",
        "      <th>Last Name</th>\n",
        "      <th>Incumbent</th>\n",
        "      <th>Vote Count</th>\n",
        "      <th>Winner</th>\n",
        "      <th>Full Name</th>\n",
        "      <th>commentCount</th>\n",
        "      <th>dislikeCount</th>\n",
        "      <th>favoriteCount</th>\n",
        "      <th>likeCount</th>\n",
        "      <th>viewCount</th>\n",
        "      <th>like_dislike_r</th>\n",
        "      <th>views_share</th>\n",
        "      <th>msgs_share</th>\n",
        "      <th>likes_share</th>\n",
        "      <th>dislikes_share</th>\n",
        "      <th>sentiment</th>\n",
        "      <th>VotesShare</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> MO</td>\n",
        "      <td> Missouri</td>\n",
        "      <td> Dem</td>\n",
        "      <td> Claire</td>\n",
        "      <td> McCaskill</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1484683</td>\n",
        "      <td>   X</td>\n",
        "      <td> Claire McCaskill</td>\n",
        "      <td>   525</td>\n",
        "      <td>  438</td>\n",
        "      <td> 0</td>\n",
        "      <td>   765</td>\n",
        "      <td>  141427</td>\n",
        "      <td> 0.635910</td>\n",
        "      <td> 0.036271</td>\n",
        "      <td> 0.011716</td>\n",
        "      <td> 0.011317</td>\n",
        "      <td> 0.070024</td>\n",
        "      <td> 0.009500</td>\n",
        "      <td> 0.547173</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> MO</td>\n",
        "      <td> Missouri</td>\n",
        "      <td> GOP</td>\n",
        "      <td>   Todd</td>\n",
        "      <td>      Akin</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1063698</td>\n",
        "      <td> NaN</td>\n",
        "      <td>        Todd Akin</td>\n",
        "      <td> 44285</td>\n",
        "      <td> 5817</td>\n",
        "      <td> 0</td>\n",
        "      <td> 66830</td>\n",
        "      <td> 3757741</td>\n",
        "      <td> 0.919928</td>\n",
        "      <td> 0.963729</td>\n",
        "      <td> 0.988284</td>\n",
        "      <td> 0.988683</td>\n",
        "      <td> 0.929976</td>\n",
        "      <td>-0.016794</td>\n",
        "      <td> 0.392021</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "   State Postal County Name Party First Name  Last Name  Incumbent  Vote Count Winner         Full Name  commentCount  \\\n",
        "13           MO    Missouri   Dem     Claire  McCaskill          1     1484683      X  Claire McCaskill           525   \n",
        "46           MO    Missouri   GOP       Todd       Akin          0     1063698    NaN         Todd Akin         44285   \n",
        "\n",
        "    dislikeCount  favoriteCount  likeCount  viewCount  like_dislike_r  views_share  msgs_share  likes_share  \\\n",
        "13           438              0        765     141427        0.635910     0.036271    0.011716     0.011317   \n",
        "46          5817              0      66830    3757741        0.919928     0.963729    0.988284     0.988683   \n",
        "\n",
        "    dislikes_share  sentiment  VotesShare  \n",
        "13        0.070024   0.009500    0.547173  \n",
        "46        0.929976  -0.016794    0.392021  "
       ]
      }
     ],
     "prompt_number": 40
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/data-mining/3. Twitter API.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:cf456697bea046551257aa9fb7eaf09f0280024ac5f7db881b965d58e72cda37"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will be using tweepy library to perform data mine on twitter.\n",
      "\n",
      "##Twitter API\n",
      "\n",
      "https://apps.twitter.com/\n",
      "\n",
      "###Topics:\n",
      "\n",
      "**Topic 1**: Register You App.\n",
      "\n",
      "**Topic 2**: Using REST API\n",
      "\n",
      "**Video Tutorial**:\n",
      "\n",
      "https://www.youtube.com/user/roshanRush"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import the library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import tweepy\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "pd.options.display.max_columns = 50\n",
      "pd.options.display.max_rows= 50\n",
      "pd.options.display.width= 120"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Authentication"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "consumer_key = \"\" # Use your own key. To get a key https://apps.twitter.com/\n",
      "consumer_secret = \"\"\n",
      "\n",
      "auth = tweepy.OAuthHandler(consumer_key=consumer_key, consumer_secret=consumer_secret)\n",
      "\n",
      "api = tweepy.API(auth)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "REST API"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Searching for Tweets"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###`API.search(q[, lang][, locale][, rpp][, page][, since_id][, geocode][, show_user])`\n",
      "```\n",
      "\n",
      "    Returns tweets that match a specified query.\n",
      "    Parameters:\t\n",
      "\n",
      "        q \u2013 the search query string\n",
      "        lang \u2013 Restricts tweets to the given language, given by an ISO 639-1 code.\n",
      "        locale \u2013 Specify the language of the query you are sending. This is intended for language-specific clients and the default should work in the majority of cases.\n",
      "        rpp \u2013 The number of tweets to return per page, up to a max of 100.\n",
      "        page \u2013 The page number (starting at 1) to return, up to a max of roughly 1500 results (based on rpp * page.\n",
      "        geocode \u2013 Returns tweets by users located within a given radius of the given latitude/longitude. The location is preferentially taking from the Geotagging API, but will fall back to their Twitter profile. The parameter value is specified by \u201clatitide,longitude,radius\u201d, where radius units must be specified as either \u201cmi\u201d (miles) or \u201ckm\u201d (kilometers). Note that you cannot use the near operator via the API to geocode arbitrary locations; however you can use this geocode parameter to search near geocodes directly.\n",
      "        show_user \u2013 When true, prepends \u201c<user>:\u201d to the beginning of the tweet. This is useful for readers that do not display Atom\u2019s author field. The default is false.\n",
      "\n",
      "    Return type:\t\n",
      "\n",
      "    list of SearchResult objects\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = api.search(q=\"IPython\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Inspecting Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "15"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_tweet(tweet):\n",
      "    print \"@%s - %s (%s)\" % (tweet.user.screen_name, tweet.user.name, tweet.created_at)\n",
      "    print tweet.text\n",
      "\n",
      "tweet=results[1]\n",
      "print_tweet(tweet)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "@josephmisiti - joseph misiti (2014-10-12 16:31:53)\n",
        "RT @datamusing: Easy sort and slice of pandas dataframe views.  http://t.co/68KyqDidkO\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Inspecting a Status Object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tweet=results[2]\n",
      "\n",
      "for param in dir(tweet):\n",
      "    if not param.startswith(\"_\"):\n",
      "        print \"%s : %s\" % (param, eval(\"tweet.\" + param))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "author : <tweepy.models.User object at 0x7f854492d650>\n",
        "contributors : None\n",
        "coordinates : None\n",
        "created_at : 2014-10-12 16:26:20\n",
        "destroy : <bound method Status.destroy of <tweepy.models.Status object at 0x7f854492d4d0>>\n",
        "entities : {'symbols': [], 'user_mentions': [{'id': 20167623, 'indices': [3, 13], 'id_str': '20167623', 'screen_name': 'kdnuggets', 'name': 'Gregory Piatetsky'}], 'hashtags': [], 'urls': [{'url': 'http://t.co/hVnBWL0R72', 'indices': [77, 99], 'expanded_url': 'http://buff.ly/1s5OuRe', 'display_url': 'buff.ly/1s5OuRe'}]}\n",
        "favorite : <bound method Status.favorite of <tweepy.models.Status object at 0x7f854492d4d0>>\n",
        "favorite_count : 0\n",
        "favorited : False\n",
        "geo : None\n",
        "id : 521336071288680449\n",
        "id_str : 521336071288680449\n",
        "in_reply_to_screen_name : None\n",
        "in_reply_to_status_id : None\n",
        "in_reply_to_status_id_str : None\n",
        "in_reply_to_user_id : None\n",
        "in_reply_to_user_id_str : None\n",
        "lang : en\n",
        "metadata : {'iso_language_code': 'en', 'result_type': 'recent'}\n",
        "parse : <bound method type.parse of <class 'tweepy.models.Status'>>\n",
        "parse_list : <bound method type.parse_list of <class 'tweepy.models.Status'>>\n",
        "place : None\n",
        "possibly_sensitive : False\n",
        "retweet : <bound method Status.retweet of <tweepy.models.Status object at 0x7f854492d4d0>>\n",
        "retweet_count : 6\n",
        "retweeted : False\n",
        "retweeted_status : <tweepy.models.Status object at 0x7f854492d610>\n",
        "retweets : <bound method Status.retweets of <tweepy.models.Status object at 0x7f854492d4d0>>\n",
        "source : Twitter for iPhone\n",
        "source_url : http://twitter.com/download/iphone\n",
        "text : RT @kdnuggets: Using Python to detect Algorithmically Generated Domain Names http://t.co/hVnBWL0R72\n",
        "truncated : False\n",
        "user : <tweepy.models.User object at 0x7f854492d650>\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Inspecting a User Object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "user=tweet.author\n",
      "\n",
      "for param in dir(user):\n",
      "    if not param.startswith(\"_\"):\n",
      "        print \"%s : %s\" % (param, eval(\"user.\" + param))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "contributors_enabled : False\n",
        "created_at : 2010-05-22 09:03:02\n",
        "default_profile : True\n",
        "default_profile_image : False\n",
        "description : Data science, javascript, nodejs, iOS & Sencha touch\n",
        "entities : {'url': {'urls': [{'url': 'http://t.co/gzMlBYVCcv', 'indices': [0, 22], 'expanded_url': 'http://in.linkedin.com/in/sandeepu', 'display_url': 'in.linkedin.com/in/sandeepu'}]}, 'description': {'urls': []}}\n",
        "favourites_count : 141\n",
        "follow : <bound method User.follow of <tweepy.models.User object at 0x7f854492d650>>\n",
        "follow_request_sent : None\n",
        "followers : <bound method User.followers of <tweepy.models.User object at 0x7f854492d650>>\n",
        "followers_count : 67\n",
        "followers_ids : <bound method User.followers_ids of <tweepy.models.User object at 0x7f854492d650>>\n",
        "following : False\n",
        "friends : <bound method User.friends of <tweepy.models.User object at 0x7f854492d650>>\n",
        "friends_count : 297\n",
        "geo_enabled : True\n",
        "id : 146775585\n",
        "id_str : 146775585\n",
        "is_translation_enabled : False\n",
        "is_translator : False\n",
        "lang : en\n",
        "listed_count : 4\n",
        "lists : <bound method User.lists of <tweepy.models.User object at 0x7f854492d650>>\n",
        "lists_memberships : <bound method User.lists_memberships of <tweepy.models.User object at 0x7f854492d650>>\n",
        "lists_subscriptions : <bound method User.lists_subscriptions of <tweepy.models.User object at 0x7f854492d650>>\n",
        "location : Cochin, Kerala, India\n",
        "name : Sandeep Unni\n",
        "notifications : None\n",
        "parse : <bound method type.parse of <class 'tweepy.models.User'>>\n",
        "parse_list : <bound method type.parse_list of <class 'tweepy.models.User'>>\n",
        "profile_background_color : C0DEED\n",
        "profile_background_image_url : http://abs.twimg.com/images/themes/theme1/bg.png\n",
        "profile_background_image_url_https : https://abs.twimg.com/images/themes/theme1/bg.png\n",
        "profile_background_tile : False\n",
        "profile_image_url : http://pbs.twimg.com/profile_images/378800000096874830/9122fec9b60fcaf9b826731473bf4b17_normal.jpeg\n",
        "profile_image_url_https : https://pbs.twimg.com/profile_images/378800000096874830/9122fec9b60fcaf9b826731473bf4b17_normal.jpeg\n",
        "profile_link_color : 0084B4\n",
        "profile_sidebar_border_color : C0DEED\n",
        "profile_sidebar_fill_color : DDEEF6\n",
        "profile_text_color : 333333\n",
        "profile_use_background_image : True\n",
        "protected : False\n",
        "screen_name : psandeepu\n",
        "statuses_count : 358\n",
        "time_zone : None\n",
        "timeline : <bound method User.timeline of <tweepy.models.User object at 0x7f854492d650>>\n",
        "unfollow : <bound method User.unfollow of <tweepy.models.User object at 0x7f854492d650>>\n",
        "url : http://t.co/gzMlBYVCcv\n",
        "utc_offset : None\n",
        "verified : False\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using Cursor for Pagination"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For data mining you will be dealing with a large amount of results. Cursor is a simple way to handle interation and results pages."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = []\n",
      "for tweet in tweepy.Cursor(api.search, q=\"IPython\").items(100):\n",
      "    results.append(tweet)\n",
      "\n",
      "print len(results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "100\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Store Results in a Data Frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def process_results(results):\n",
      "    id_list = [tweet.id for tweet in results]\n",
      "    data_set = pd.DataFrame(id_list, columns=[\"id\"])\n",
      "\n",
      "    # Processing Tweet Data\n",
      "\n",
      "    data_set[\"text\"] = [tweet.text for tweet in results]\n",
      "    data_set[\"created_at\"] = [tweet.created_at for tweet in results]\n",
      "    data_set[\"retweet_count\"] = [tweet.retweet_count for tweet in results]\n",
      "    data_set[\"favorite_count\"] = [tweet.favorite_count for tweet in results]\n",
      "    data_set[\"source\"] = [tweet.source for tweet in results]\n",
      "\n",
      "    # Processing User Data\n",
      "    data_set[\"user_id\"] = [tweet.author.id for tweet in results]\n",
      "    data_set[\"user_screen_name\"] = [tweet.author.screen_name for tweet in results]\n",
      "    data_set[\"user_name\"] = [tweet.author.name for tweet in results]\n",
      "    data_set[\"user_created_at\"] = [tweet.author.created_at for tweet in results]\n",
      "    data_set[\"user_description\"] = [tweet.author.description for tweet in results]\n",
      "    data_set[\"user_followers_count\"] = [tweet.author.followers_count for tweet in results]\n",
      "    data_set[\"user_friends_count\"] = [tweet.author.friends_count for tweet in results]\n",
      "    data_set[\"user_location\"] = [tweet.author.location for tweet in results]\n",
      "\n",
      "    return data_set\n",
      "data_set = process_results(results)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Looking at the Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_set.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>text</th>\n",
        "      <th>created_at</th>\n",
        "      <th>retweet_count</th>\n",
        "      <th>favorite_count</th>\n",
        "      <th>source</th>\n",
        "      <th>user_id</th>\n",
        "      <th>user_screen_name</th>\n",
        "      <th>user_name</th>\n",
        "      <th>user_created_at</th>\n",
        "      <th>user_description</th>\n",
        "      <th>user_followers_count</th>\n",
        "      <th>user_friends_count</th>\n",
        "      <th>user_location</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 521338248673501184</td>\n",
        "      <td> RT @kdnuggets: Using Python to detect Algorith...</td>\n",
        "      <td>2014-10-12 16:34:59</td>\n",
        "      <td> 6</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Twitter for iPhone</td>\n",
        "      <td>    7534972</td>\n",
        "      <td>          riyaaz</td>\n",
        "      <td>             Riyaz</td>\n",
        "      <td>2007-07-17 15:59:49</td>\n",
        "      <td> Professional Heretic preaching how to OR how n...</td>\n",
        "      <td>  215</td>\n",
        "      <td> 478</td>\n",
        "      <td>               Chennai, India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 521337469640249345</td>\n",
        "      <td> RT @datamusing: Easy sort and slice of pandas ...</td>\n",
        "      <td>2014-10-12 16:31:53</td>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Twitter Web Client</td>\n",
        "      <td>   58631564</td>\n",
        "      <td>    josephmisiti</td>\n",
        "      <td>     joseph misiti</td>\n",
        "      <td>2009-07-20 23:42:13</td>\n",
        "      <td> I write about Bayesian Inference, Django, Scal...</td>\n",
        "      <td> 2092</td>\n",
        "      <td> 306</td>\n",
        "      <td>    Lower East Side, New York</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 521336071288680449</td>\n",
        "      <td> RT @kdnuggets: Using Python to detect Algorith...</td>\n",
        "      <td>2014-10-12 16:26:20</td>\n",
        "      <td> 6</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Twitter for iPhone</td>\n",
        "      <td>  146775585</td>\n",
        "      <td>       psandeepu</td>\n",
        "      <td>      Sandeep Unni</td>\n",
        "      <td>2010-05-22 09:03:02</td>\n",
        "      <td> Data science, javascript, nodejs, iOS &amp; Sencha...</td>\n",
        "      <td>   67</td>\n",
        "      <td> 297</td>\n",
        "      <td>        Cochin, Kerala, India</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 521335988548022272</td>\n",
        "      <td> RT @kdnuggets: Using Python to detect Algorith...</td>\n",
        "      <td>2014-10-12 16:26:00</td>\n",
        "      <td> 6</td>\n",
        "      <td> 0</td>\n",
        "      <td>   Twitter for iPhone</td>\n",
        "      <td> 1318985240</td>\n",
        "      <td> DataScienceDojo</td>\n",
        "      <td> Data Science Dojo</td>\n",
        "      <td>2013-03-31 19:25:57</td>\n",
        "      <td> Visit Our Website: http://t.co/mRpEkKrS55\\r\\nF...</td>\n",
        "      <td>  511</td>\n",
        "      <td> 232</td>\n",
        "      <td> Seattle, WA and Stanford, CA</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 521335809987723265</td>\n",
        "      <td> @davidwhogg @exoplaneteer @kylebarbary http://...</td>\n",
        "      <td>2014-10-12 16:25:17</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> Twitter for Websites</td>\n",
        "      <td>  244991150</td>\n",
        "      <td>      fperez_org</td>\n",
        "      <td>    Fernando Perez</td>\n",
        "      <td>2011-01-30 16:28:09</td>\n",
        "      <td> Physicist, applied mathematician, scientific P...</td>\n",
        "      <td> 5174</td>\n",
        "      <td> 335</td>\n",
        "      <td>                 Berkeley, CA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                   id                                               text          created_at  retweet_count  \\\n",
        "0  521338248673501184  RT @kdnuggets: Using Python to detect Algorith... 2014-10-12 16:34:59              6   \n",
        "1  521337469640249345  RT @datamusing: Easy sort and slice of pandas ... 2014-10-12 16:31:53              2   \n",
        "2  521336071288680449  RT @kdnuggets: Using Python to detect Algorith... 2014-10-12 16:26:20              6   \n",
        "3  521335988548022272  RT @kdnuggets: Using Python to detect Algorith... 2014-10-12 16:26:00              6   \n",
        "4  521335809987723265  @davidwhogg @exoplaneteer @kylebarbary http://... 2014-10-12 16:25:17              0   \n",
        "\n",
        "   favorite_count                source     user_id user_screen_name          user_name     user_created_at  \\\n",
        "0               0    Twitter for iPhone     7534972           riyaaz              Riyaz 2007-07-17 15:59:49   \n",
        "1               0    Twitter Web Client    58631564     josephmisiti      joseph misiti 2009-07-20 23:42:13   \n",
        "2               0    Twitter for iPhone   146775585        psandeepu       Sandeep Unni 2010-05-22 09:03:02   \n",
        "3               0    Twitter for iPhone  1318985240  DataScienceDojo  Data Science Dojo 2013-03-31 19:25:57   \n",
        "4               0  Twitter for Websites   244991150       fperez_org     Fernando Perez 2011-01-30 16:28:09   \n",
        "\n",
        "                                    user_description  user_followers_count  user_friends_count  \\\n",
        "0  Professional Heretic preaching how to OR how n...                   215                 478   \n",
        "1  I write about Bayesian Inference, Django, Scal...                  2092                 306   \n",
        "2  Data science, javascript, nodejs, iOS & Sencha...                    67                 297   \n",
        "3  Visit Our Website: http://t.co/mRpEkKrS55\\r\\nF...                   511                 232   \n",
        "4  Physicist, applied mathematician, scientific P...                  5174                 335   \n",
        "\n",
        "                  user_location  \n",
        "0                Chennai, India  \n",
        "1     Lower East Side, New York  \n",
        "2         Cochin, Kerala, India  \n",
        "3  Seattle, WA and Stanford, CA  \n",
        "4                  Berkeley, CA  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data_set.tail(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>id</th>\n",
        "      <th>text</th>\n",
        "      <th>created_at</th>\n",
        "      <th>retweet_count</th>\n",
        "      <th>favorite_count</th>\n",
        "      <th>source</th>\n",
        "      <th>user_id</th>\n",
        "      <th>user_screen_name</th>\n",
        "      <th>user_name</th>\n",
        "      <th>user_created_at</th>\n",
        "      <th>user_description</th>\n",
        "      <th>user_followers_count</th>\n",
        "      <th>user_friends_count</th>\n",
        "      <th>user_location</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>95</th>\n",
        "      <td> 521104303117717504</td>\n",
        "      <td> Clare Slogett: NBViewer great for sharing iPyt...</td>\n",
        "      <td>2014-10-12 01:05:22</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Twitter for iPhone</td>\n",
        "      <td> 2704427049</td>\n",
        "      <td>   mritchieau</td>\n",
        "      <td>           Matt</td>\n",
        "      <td>2014-07-12 19:59:01</td>\n",
        "      <td> I'm a medical researcher from the Walter and E...</td>\n",
        "      <td>   65</td>\n",
        "      <td>   81</td>\n",
        "      <td>                          </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
        "      <td> 521104249279635456</td>\n",
        "      <td> Q for CS: Does iPython support version control...</td>\n",
        "      <td>2014-10-12 01:05:09</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> Twitter Web Client</td>\n",
        "      <td>   50933459</td>\n",
        "      <td>     ncloonan</td>\n",
        "      <td> Nicole Cloonan</td>\n",
        "      <td>2009-06-26 05:15:46</td>\n",
        "      <td> New lab head at  QIMR Berghofer Medical Resear...</td>\n",
        "      <td>  841</td>\n",
        "      <td> 1232</td>\n",
        "      <td> Brisbane, QLD, Australia </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
        "      <td> 521103719120011264</td>\n",
        "      <td> RT @ramialison_lab: Clare Sloggett iPython liv...</td>\n",
        "      <td>2014-10-12 01:03:02</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "      <td>    Twitter for Mac</td>\n",
        "      <td>  369758524</td>\n",
        "      <td> LorenaABarba</td>\n",
        "      <td>   Lorena Barba</td>\n",
        "      <td>2011-09-07 22:10:00</td>\n",
        "      <td> Engineering professor, computational scientist...</td>\n",
        "      <td> 1790</td>\n",
        "      <td>  656</td>\n",
        "      <td>            Washington, DC</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
        "      <td> 521103688673550338</td>\n",
        "      <td> RT @ctitusbrown: People interested in Project ...</td>\n",
        "      <td>2014-10-12 01:02:55</td>\n",
        "      <td> 16</td>\n",
        "      <td> 0</td>\n",
        "      <td> Twitter for iPhone</td>\n",
        "      <td>   34540379</td>\n",
        "      <td>   ethanwhite</td>\n",
        "      <td>    Ethan White</td>\n",
        "      <td>2009-04-23 05:10:52</td>\n",
        "      <td> I'm a happily married dad and a scientist. I l...</td>\n",
        "      <td> 2031</td>\n",
        "      <td>  216</td>\n",
        "      <td>                 Logan, UT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
        "      <td> 521103611179597824</td>\n",
        "      <td> RT @ramialison_lab: Clare Sloggett iPython liv...</td>\n",
        "      <td>2014-10-12 01:02:37</td>\n",
        "      <td>  5</td>\n",
        "      <td> 0</td>\n",
        "      <td>          TweetDeck</td>\n",
        "      <td>   26616462</td>\n",
        "      <td>  ctitusbrown</td>\n",
        "      <td>    Titus Brown</td>\n",
        "      <td>2009-03-25 22:52:41</td>\n",
        "      <td> So interdisciplinary I occasionally risk being...</td>\n",
        "      <td> 4663</td>\n",
        "      <td>  101</td>\n",
        "      <td>  A secure, hidden bunker.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "                    id                                               text          created_at  retweet_count  \\\n",
        "95  521104303117717504  Clare Slogett: NBViewer great for sharing iPyt... 2014-10-12 01:05:22              1   \n",
        "96  521104249279635456  Q for CS: Does iPython support version control... 2014-10-12 01:05:09              1   \n",
        "97  521103719120011264  RT @ramialison_lab: Clare Sloggett iPython liv... 2014-10-12 01:03:02              5   \n",
        "98  521103688673550338  RT @ctitusbrown: People interested in Project ... 2014-10-12 01:02:55             16   \n",
        "99  521103611179597824  RT @ramialison_lab: Clare Sloggett iPython liv... 2014-10-12 01:02:37              5   \n",
        "\n",
        "    favorite_count              source     user_id user_screen_name       user_name     user_created_at  \\\n",
        "95               1  Twitter for iPhone  2704427049       mritchieau            Matt 2014-07-12 19:59:01   \n",
        "96               0  Twitter Web Client    50933459         ncloonan  Nicole Cloonan 2009-06-26 05:15:46   \n",
        "97               0     Twitter for Mac   369758524     LorenaABarba    Lorena Barba 2011-09-07 22:10:00   \n",
        "98               0  Twitter for iPhone    34540379       ethanwhite     Ethan White 2009-04-23 05:10:52   \n",
        "99               0           TweetDeck    26616462      ctitusbrown     Titus Brown 2009-03-25 22:52:41   \n",
        "\n",
        "                                     user_description  user_followers_count  user_friends_count  \\\n",
        "95  I'm a medical researcher from the Walter and E...                    65                  81   \n",
        "96  New lab head at  QIMR Berghofer Medical Resear...                   841                1232   \n",
        "97  Engineering professor, computational scientist...                  1790                 656   \n",
        "98  I'm a happily married dad and a scientist. I l...                  2031                 216   \n",
        "99  So interdisciplinary I occasionally risk being...                  4663                 101   \n",
        "\n",
        "                user_location  \n",
        "95                             \n",
        "96  Brisbane, QLD, Australia   \n",
        "97             Washington, DC  \n",
        "98                  Logan, UT  \n",
        "99   A secure, hidden bunker.  "
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Visualizing Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sources = data_set[\"source\"].value_counts()[:5][::-1]\n",
      "\n",
      "plt.barh(xrange(len(sources)), sources.values)\n",
      "plt.yticks(np.arange(len(sources)) + 0.4, sources.index)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f85441f5690>"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/data-mining/4. Google Custom Search.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:165cb1198a163a54ba91b1e493f98feb89ae0ae4d3d8bd78adcf43efaf80056c"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this tutorial we will cover the basics of using Google Custom Search to search the Interner.\n",
      "\n",
      "Links:\n",
      "\n",
      "- https://www.google.com/cse/all\n",
      "- https://console.developers.google.com/\n",
      "- https://developers.google.com/custom-search/json-api/v1/reference/cse/list\n",
      "\n",
      "Video Tutorial:\n",
      "\n",
      "http://youtu.be/ewPJs4N8d8M"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import requests\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Understanding Google Custom Search"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Google Custom Search replaces the depreciated Google Search API. It is designed to search one or more website and to embedded with in the website.\n",
      "\n",
      "There is still an options to search the complete web. This options combined with no specified website to search retuen results which are very close to what you get when you search Google. The difference in results is due to personalized and localized search results that Google search returns."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "key = \"\"\n",
      "cx = \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table class=\"matchpre\">\n",
      "    <thead>\n",
      "      <tr>\n",
      "        <th>Parameter name</th>\n",
      "        <th>Value</th>\n",
      "        <th>Description</th>\n",
      "      </tr>\n",
      "    </thead>\n",
      "    <tbody>\n",
      "    <tr id=\"required-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Required parameters</b></td>\n",
      "    </tr>\n",
      "      <tr id=\"q\">\n",
      "        <td><code>q</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          The search expression.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "\n",
      "    <tr id=\"optional-parameters\" class=\"alt\">\n",
      "      <td colspan=\"3\"><b>Optional parameters</b></td>\n",
      "    </tr>\n",
      "      <tr id=\"c2coff\">\n",
      "        <td><code>c2coff</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>Enables or disables&nbsp;</span><a href=\"https://developers.google.com/custom-search/docs/xml_results#chineseSearch\">Simplified and Traditional Chinese Search</a>.&nbsp;<ul><li>The default value for this parameter is&nbsp;<code>0</code>&nbsp;(zero), meaning that the feature is enabled. Supported values are:<ul><li><code>1</code>: Disabled</li><li><code>0</code>: Enabled (default)</li></ul></li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"cr\">\n",
      "        <td><code>cr</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>R</span><span>estricts search results to documents originating in a particular country. <br></span><ul><li><span>You may use&nbsp;</span><a href=\"https://developers.google.com/custom-search/docs/xml_results#booleanOperators\">Boolean operators</a><span>&nbsp;</span><span>in the</span><span>&nbsp;</span><code>cr</code><span>&nbsp;</span><span>parameter's value.</span><br></li><li>Google Search determines the country of a document by analyzing:<ul><li>the top-level domain (TLD) of the document's URL</li><li>the geographic location of the Web server's IP address</li></ul></li><li>See the&nbsp;<a href=\"https://developers.google.com/custom-search/docs/xml_results#countryCollections\">Country Parameter Values</a>&nbsp;page for a list of valid values for this parameter.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"cref\">\n",
      "        <td><code>cref</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>The URL of a&nbsp;</span><a href=\"http://www.google.com/cse/docs/cref.html\">linked</a><span><font color=\"#222222\">&nbsp;custom search engine specification to use for this request.&nbsp;<br><ul><li><span>Does not apply for Google Site Search</span></li><li><font color=\"#222222\" face=\"Arial\"><span>If both&nbsp;</span></font><code>cx</code><span>&nbsp;and&nbsp;</span><code>cref</code><span>&nbsp;are specified, the&nbsp;</span><code>cx</code><span>&nbsp;value is used</span><br></li></ul></font></span>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"cx\">\n",
      "        <td><code>cx</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          The custom search engine ID to use for this request.<br><ul><li>If both&nbsp;<code>cx</code>&nbsp;and&nbsp;<code>cref</code>&nbsp;are specified, the&nbsp;<code>cx</code>&nbsp;value is used.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"dateRestrict\">\n",
      "        <td><code>dateRestrict</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>Restricts results to URLs based on date. Supported values include:</span><ul><li><code>d[number]</code>: requests results from the specified number of past days.</li><li><code>w[number]</code>: requests results from the specified number of past weeks.</li><li><code>m[number]</code>: requests results from the specified number of past months.</li><li><code>y[number]</code>: requests results from the specified number of past years.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"exactTerms\">\n",
      "        <td><code>exactTerms</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Identifies a phrase that all documents in the search results must contain.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"excludeTerms\">\n",
      "        <td><code>excludeTerms</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Identifies a word or phrase that should not appear in any documents in the search results.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"fileType\">\n",
      "        <td><code>fileType</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Restricts results to files of a specified extension. A list of file types indexable by Google<span>&nbsp;can be found in Webmaster Tools <a href=\"https://support.google.com/webmasters/answer/35287?hl=en\">Help Center</a>.</span><span><br></span>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"filter\">\n",
      "        <td><code>filter</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Controls turning on or off the duplicate content filter.<ul><li>See&nbsp;<a href=\"https://developers.google.com/custom-search/docs/xml_results#automaticFiltering\">Automatic Filtering</a>&nbsp;for more information about Google's search results filters. Note that host crowding filtering applies only to multi-site searches.</li><li>By default, Google applies filtering to all search results to improve the quality of those results.</li></ul>\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>0</code>\": Turns off duplicate content filter.\n",
      "            </li>\n",
      "            <li>\"<code>1</code>\": Turns on duplicate content filter.\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"gl\">\n",
      "        <td><code>gl</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Geolocation of end user.&nbsp;<ul><li>The&nbsp;<code>gl</code>&nbsp;parameter value is a two-letter country code. The&nbsp;<code>gl</code>&nbsp;parameter boosts search results whose country of origin matches the parameter value. See the&nbsp;<a href=\"https://developers.google.com/custom-search/docs/xml_results#countryCodes\">Country Codes</a>&nbsp;page for a list of valid values.</li><li>Specifying a&nbsp;<code>gl</code>&nbsp;parameter value should lead to more relevant results. This is particularly true for international customers and, even more specifically, for customers in English- speaking countries other than the United States.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"googlehost\">\n",
      "        <td><code>googlehost</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          The local Google domain&nbsp;<span>(for example, google.com, google.de, or google.fr) </span>to use to perform the search.&nbsp;\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"highRange\">\n",
      "        <td><code>highRange</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <ul><li><span>Specifies the ending value for a search range.</span><br></li><li>Use&nbsp;<code>lowRange</code>&nbsp;and&nbsp;<code>highRange</code>&nbsp;to append an inclusive search range of&nbsp;<code>lowRange...highRange&nbsp;</code>&nbsp;to the query.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"hl\">\n",
      "        <td><code>hl</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Sets the user interface language.&nbsp;<ul><li>Explicitly setting this parameter improves the performance and the quality of your search results.</li><li>See the&nbsp;<a href=\"https://developers.google.com/custom-search/docs/xml_results#wsInterfaceLanguages\">Interface Languages</a>&nbsp;section of&nbsp;<a href=\"https://developers.google.com/custom-search/docs/xml_results#wsInternationalizing\">Internationalizing Queries and Results Presentation</a>&nbsp;for more information, and&nbsp;<a href=\"https://developers.google.com/custom-search/docs/xml_results#interfaceLanguages\">Supported Interface Languages</a>&nbsp;for a list of supported languages.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"hq\">\n",
      "        <td><code>hq</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>Appends the specified query terms to the query, as if they were combined with a logical&nbsp;</span><code>AND</code><span>&nbsp;</span><span>operator.</span>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"imgColorType\">\n",
      "        <td><code>imgColorType</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Returns black and white, grayscale, or color images: <code>mono</code>, <code>gray</code>, and <code>color</code>.\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>color</code>\": color\n",
      "            </li>\n",
      "            <li>\"<code>gray</code>\": gray\n",
      "            </li>\n",
      "            <li>\"<code>mono</code>\": mono\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"imgDominantColor\">\n",
      "        <td><code>imgDominantColor</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Returns images of a specific dominant color.\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>black</code>\": black\n",
      "            </li>\n",
      "            <li>\"<code>blue</code>\": blue\n",
      "            </li>\n",
      "            <li>\"<code>brown</code>\": brown\n",
      "            </li>\n",
      "            <li>\"<code>gray</code>\": gray\n",
      "            </li>\n",
      "            <li>\"<code>green</code>\": green\n",
      "            </li>\n",
      "            <li>\"<code>pink</code>\": pink\n",
      "            </li>\n",
      "            <li>\"<code>purple</code>\": purple\n",
      "            </li>\n",
      "            <li>\"<code>teal</code>\": teal\n",
      "            </li>\n",
      "            <li>\"<code>white</code>\": white\n",
      "            </li>\n",
      "            <li>\"<code>yellow</code>\": yellow\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"imgSize\">\n",
      "        <td><code>imgSize</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Returns images of a specified size.\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>huge</code>\": huge\n",
      "            </li>\n",
      "            <li>\"<code>icon</code>\": icon\n",
      "            </li>\n",
      "            <li>\"<code>large</code>\": large\n",
      "            </li>\n",
      "            <li>\"<code>medium</code>\": medium\n",
      "            </li>\n",
      "            <li>\"<code>small</code>\": small\n",
      "            </li>\n",
      "            <li>\"<code>xlarge</code>\": xlarge\n",
      "            </li>\n",
      "            <li>\"<code>xxlarge</code>\": xxlarge\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"imgType\">\n",
      "        <td><code>imgType</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Returns images of a type.<code></code>\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>clipart</code>\": clipart\n",
      "            </li>\n",
      "            <li>\"<code>face</code>\": face\n",
      "            </li>\n",
      "            <li>\"<code>lineart</code>\": lineart\n",
      "            </li>\n",
      "            <li>\"<code>news</code>\": news\n",
      "            </li>\n",
      "            <li>\"<code>photo</code>\": photo\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"linkSite\">\n",
      "        <td><code>linkSite</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Specifies that all search results should contain a link to a particular URL\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"lowRange\">\n",
      "        <td><code>lowRange</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>Specifies the starting value for a search range. <br></span><span>Use&nbsp;</span><code>lowRange&nbsp;</code><span>and</span><span>&nbsp;</span><code>highRange</code><span>&nbsp;</span><span>to append an inclusive search range of&nbsp;</span><code>lowRange...highRange</code><span>&nbsp;to the query.</span><span><br></span>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"lr\">\n",
      "        <td><code>lr</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          <span>Restricts the search to documents written in a particular language (e.g.,&nbsp;</span><code>lr=lang_ja</code><span>).</span>\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>lang_ar</code>\": Arabic\n",
      "            </li>\n",
      "            <li>\"<code>lang_bg</code>\": Bulgarian\n",
      "            </li>\n",
      "            <li>\"<code>lang_ca</code>\": Catalan\n",
      "            </li>\n",
      "            <li>\"<code>lang_cs</code>\": Czech\n",
      "            </li>\n",
      "            <li>\"<code>lang_da</code>\": Danish\n",
      "            </li>\n",
      "            <li>\"<code>lang_de</code>\": German\n",
      "            </li>\n",
      "            <li>\"<code>lang_el</code>\": Greek\n",
      "            </li>\n",
      "            <li>\"<code>lang_en</code>\": English\n",
      "            </li>\n",
      "            <li>\"<code>lang_es</code>\": Spanish\n",
      "            </li>\n",
      "            <li>\"<code>lang_et</code>\": Estonian\n",
      "            </li>\n",
      "            <li>\"<code>lang_fi</code>\": Finnish\n",
      "            </li>\n",
      "            <li>\"<code>lang_fr</code>\": French\n",
      "            </li>\n",
      "            <li>\"<code>lang_hr</code>\": Croatian\n",
      "            </li>\n",
      "            <li>\"<code>lang_hu</code>\": Hungarian\n",
      "            </li>\n",
      "            <li>\"<code>lang_id</code>\": Indonesian\n",
      "            </li>\n",
      "            <li>\"<code>lang_is</code>\": Icelandic\n",
      "            </li>\n",
      "            <li>\"<code>lang_it</code>\": Italian\n",
      "            </li>\n",
      "            <li>\"<code>lang_iw</code>\": Hebrew\n",
      "            </li>\n",
      "            <li>\"<code>lang_ja</code>\": Japanese\n",
      "            </li>\n",
      "            <li>\"<code>lang_ko</code>\": Korean\n",
      "            </li>\n",
      "            <li>\"<code>lang_lt</code>\": Lithuanian\n",
      "            </li>\n",
      "            <li>\"<code>lang_lv</code>\": Latvian\n",
      "            </li>\n",
      "            <li>\"<code>lang_nl</code>\": Dutch\n",
      "            </li>\n",
      "            <li>\"<code>lang_no</code>\": Norwegian\n",
      "            </li>\n",
      "            <li>\"<code>lang_pl</code>\": Polish\n",
      "            </li>\n",
      "            <li>\"<code>lang_pt</code>\": Portuguese\n",
      "            </li>\n",
      "            <li>\"<code>lang_ro</code>\": Romanian\n",
      "            </li>\n",
      "            <li>\"<code>lang_ru</code>\": Russian\n",
      "            </li>\n",
      "            <li>\"<code>lang_sk</code>\": Slovak\n",
      "            </li>\n",
      "            <li>\"<code>lang_sl</code>\": Slovenian\n",
      "            </li>\n",
      "            <li>\"<code>lang_sr</code>\": Serbian\n",
      "            </li>\n",
      "            <li>\"<code>lang_sv</code>\": Swedish\n",
      "            </li>\n",
      "            <li>\"<code>lang_tr</code>\": Turkish\n",
      "            </li>\n",
      "            <li>\"<code>lang_zh-CN</code>\": Chinese (Simplified)\n",
      "            </li>\n",
      "            <li>\"<code>lang_zh-TW</code>\": Chinese (Traditional)\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"num\">\n",
      "        <td><code>num</code></td>\n",
      "        <td><code class=\"apitype\">unsigned integer</code></td>\n",
      "        <td>\n",
      "          Number of search results to return.<br><ul><li>Valid values are integers between 1 and 10, inclusive.</li></ul>\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"orTerms\">\n",
      "        <td><code>orTerms</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Provides additional search terms to check for in a document, where each document in the search results must contain at least one of the additional search terms.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"relatedSite\">\n",
      "        <td><code>relatedSite</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Specifies that all search results should be pages that are related to the specified URL.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"rights\">\n",
      "        <td><code>rights</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Filters based on licensing. Supported values include: <code>cc_publicdomain</code>, <code>cc_attribute</code>, <code>cc_sharealike</code>, <code>cc_noncommercial</code>, <code>cc_nonderived,</code> and combinations of these.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"safe\">\n",
      "        <td><code>safe</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Search safety level.\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>high</code>\": Enables highest level of SafeSearch filtering.\n",
      "            </li>\n",
      "            <li>\"<code>medium</code>\": Enables moderate SafeSearch filtering.\n",
      "            </li>\n",
      "            <li>\"<code>off</code>\": Disables SafeSearch filtering.\n",
      "              (default)\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"searchType\">\n",
      "        <td><code>searchType</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Specifies the search type: <code>image</code>.&nbsp;<span>&nbsp;If unspecified, results are limited to webpages.</span>\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>image</code>\": custom image search.\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"siteSearch\">\n",
      "        <td><code>siteSearch</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Specifies all search results should be pages from a given site.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"siteSearchFilter\">\n",
      "        <td><code>siteSearchFilter</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          Controls whether to include or exclude results from the site named in the <code>siteSearch</code> parameter.\n",
      "\n",
      "          <br><br>Acceptable values are:\n",
      "          <ul>\n",
      "            <li>\"<code>e</code>\": exclude\n",
      "            </li>\n",
      "            <li>\"<code>i</code>\": include\n",
      "            </li>\n",
      "          </ul>\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"sort\">\n",
      "        <td><code>sort</code></td>\n",
      "        <td><code class=\"apitype\">string</code></td>\n",
      "        <td>\n",
      "          The sort expression to apply to the results.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "      <tr id=\"start\">\n",
      "        <td><code>start</code></td>\n",
      "        <td><code class=\"apitype\">unsigned integer</code></td>\n",
      "        <td>\n",
      "          The index of the first result to return.\n",
      "\n",
      "\n",
      "        </td>\n",
      "      </tr>\n",
      "    </tbody>\n",
      "  </table>"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Prepare The request"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"https://www.googleapis.com/customsearch/v1\"\n",
      "parameters = {\"q\": \"halloween\",\n",
      "              \"cx\": cx,\n",
      "              \"key\": key,\n",
      "              }"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Make the request"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "page = requests.request(\"GET\", url, params=parameters)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Process Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = json.loads(page.text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Inspecting Results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[u'kind', u'url', u'items', u'context', u'queries', u'searchInformation']"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Inspecting Search Meta Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results[\"kind\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "u'customsearch#search'"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results[\"url\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "{u'template': u'https://www.googleapis.com/customsearch/v1?q={searchTerms}&num={count?}&start={startIndex?}&lr={language?}&safe={safe?}&cx={cx?}&cref={cref?}&sort={sort?}&filter={filter?}&gl={gl?}&cr={cr?}&googlehost={googleHost?}&c2coff={disableCnTwTranslation?}&hq={hq?}&hl={hl?}&siteSearch={siteSearch?}&siteSearchFilter={siteSearchFilter?}&exactTerms={exactTerms?}&excludeTerms={excludeTerms?}&linkSite={linkSite?}&orTerms={orTerms?}&relatedSite={relatedSite?}&dateRestrict={dateRestrict?}&lowRange={lowRange?}&highRange={highRange?}&searchType={searchType}&fileType={fileType?}&rights={rights?}&imgSize={imgSize?}&imgType={imgType?}&imgColorType={imgColorType?}&imgDominantColor={imgDominantColor?}&alt=json',\n",
        " u'type': u'application/json'}"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(results[\"items\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "10"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results[\"queries\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "{u'nextPage': [{u'count': 10,\n",
        "   u'cx': u'013297447421619698040:gnhhn4xeyns',\n",
        "   u'inputEncoding': u'utf8',\n",
        "   u'outputEncoding': u'utf8',\n",
        "   u'safe': u'off',\n",
        "   u'searchTerms': u'halloween',\n",
        "   u'startIndex': 11,\n",
        "   u'title': u'Google Custom Search - halloween',\n",
        "   u'totalResults': u'117000000'}],\n",
        " u'request': [{u'count': 10,\n",
        "   u'cx': u'013297447421619698040:gnhhn4xeyns',\n",
        "   u'inputEncoding': u'utf8',\n",
        "   u'outputEncoding': u'utf8',\n",
        "   u'safe': u'off',\n",
        "   u'searchTerms': u'halloween',\n",
        "   u'startIndex': 1,\n",
        "   u'title': u'Google Custom Search - halloween',\n",
        "   u'totalResults': u'117000000'}]}"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results[\"searchInformation\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "{u'formattedSearchTime': u'0.53',\n",
        " u'formattedTotalResults': u'117,000,000',\n",
        " u'searchTime': 0.531343,\n",
        " u'totalResults': u'117000000'}"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Inspecting a single result"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results[\"items\"][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "{u'cacheId': u'0b5Oki2-f4EJ',\n",
        " u'displayLink': u'en.wikipedia.org',\n",
        " u'formattedUrl': u'en.wikipedia.org/wiki/Halloween',\n",
        " u'htmlFormattedUrl': u'en.wikipedia.org/wiki/<b>Halloween</b>',\n",
        " u'htmlSnippet': u'<b>Halloween</b> or Hallowe&#39;en <sup>6]</sup> also known as Allhalloween, All Hallows&#39; Eve, or All <br>\\nSaints&#39; Eve, is a yearly celebration observed in a number of countries on 31&nbsp;...',\n",
        " u'htmlTitle': u'<b>Halloween</b> - Wikipedia, the free encyclopedia',\n",
        " u'kind': u'customsearch#result',\n",
        " u'link': u'http://en.wikipedia.org/wiki/Halloween',\n",
        " u'pagemap': {u'cse_image': [{u'src': u\"http://upload.wikimedia.org/wikipedia/commons/thumb/a/a2/Jack-o'-Lantern_2003-10-31.jpg/240px-Jack-o'-Lantern_2003-10-31.jpg\"}],\n",
        "  u'cse_thumbnail': [{u'height': u'188',\n",
        "    u'src': u'https://encrypted-tbn1.gstatic.com/images?q=tbn:ANd9GcQDphQXJ1JeGZS30rhMAfcAhklES25AKZ-9TS9YwUdycnIqLa7W96Maxjg',\n",
        "    u'width': u'192'}],\n",
        "  u'event': [{u'dtstart': u'2014-10-31',\n",
        "    u'summary': u'First day of Allhallowtide'}],\n",
        "  u'hcalendar': [{u'dtstart': u'2014-10-31',\n",
        "    u'summary': u'First day of Allhallowtide'}]},\n",
        " u'snippet': u\"Halloween or Hallowe'en 6] also known as Allhalloween, All Hallows' Eve, or All \\nSaints' Eve, is a yearly celebration observed in a number of countries on 31\\xa0...\",\n",
        " u'title': u'Halloween - Wikipedia, the free encyclopedia'}"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Process Results Into a Pandas Data Frame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def process_search(results):\n",
      "    link_list = [item[\"link\"] for item in results[\"items\"]]\n",
      "    df = pd.DataFrame(link_list, columns=[\"link\"])\n",
      "    df[\"title\"] = [item[\"title\"] for item in results[\"items\"]]\n",
      "    df[\"snippet\"] = [item[\"snippet\"] for item in results[\"items\"]]\n",
      "    return df\n",
      "df = process_search(results)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>link</th>\n",
        "      <th>title</th>\n",
        "      <th>snippet</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>            http://en.wikipedia.org/wiki/Halloween</td>\n",
        "      <td>      Halloween - Wikipedia, the free encyclopedia</td>\n",
        "      <td> Halloween or Hallowe'en 6] also known as Allha...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>           http://www.history.com/topics/halloween</td>\n",
        "      <td> Halloween - Videos, Facts, Origin &amp; Meaning - ...</td>\n",
        "      <td> Find out more about the history of Halloween, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>              http://www.imdb.com/title/tt0077651/</td>\n",
        "      <td>                           Halloween (1978) - IMDb</td>\n",
        "      <td> Halloween II. A Nightmare on Elm Street. Hallo...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>                         http://www.halloween.com/</td>\n",
        "      <td>                    Halloween 2014 | Halloween.com</td>\n",
        "      <td> Halloween fun on the internet, the one source ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>                       http://halloweenmovies.com/</td>\n",
        "      <td>                                  HalloweenMovies\u2122</td>\n",
        "      <td> Tickets now on sale for Halloween - ODEON Cine...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>        http://www.loc.gov/folklife/halloween.html</td>\n",
        "      <td> Halloween: The Fantasy and Folklore of All Hal...</td>\n",
        "      <td> Beginnings of Halloween celebration; First ori...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>                   http://www.spirithalloween.com/</td>\n",
        "      <td> Halloween Costumes - Childrens &amp; Adult Hallowe...</td>\n",
        "      <td> Spirit Halloween - Halloween Stores nationwide...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>              http://www.cdc.gov/family/halloween/</td>\n",
        "      <td> CDC - Halloween Health and Safety - Family Health</td>\n",
        "      <td> 5 days ago ... Fall celebrations like Hallowee...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td> http://www.partycity.com/category/halloween+co...</td>\n",
        "      <td> Halloween Costumes for Kids &amp; Adults - Costume...</td>\n",
        "      <td> Halloween costumes for all ages and sizes. Sho...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>        http://www.overkillsoftware.com/halloween/</td>\n",
        "      <td>                     PAYDAY HALLOWEEN SPECIAL 2014</td>\n",
        "      <td>                                                  </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                                                link  \\\n",
        "0             http://en.wikipedia.org/wiki/Halloween   \n",
        "1            http://www.history.com/topics/halloween   \n",
        "2               http://www.imdb.com/title/tt0077651/   \n",
        "3                          http://www.halloween.com/   \n",
        "4                        http://halloweenmovies.com/   \n",
        "5         http://www.loc.gov/folklife/halloween.html   \n",
        "6                    http://www.spirithalloween.com/   \n",
        "7               http://www.cdc.gov/family/halloween/   \n",
        "8  http://www.partycity.com/category/halloween+co...   \n",
        "9         http://www.overkillsoftware.com/halloween/   \n",
        "\n",
        "                                               title  \\\n",
        "0       Halloween - Wikipedia, the free encyclopedia   \n",
        "1  Halloween - Videos, Facts, Origin & Meaning - ...   \n",
        "2                            Halloween (1978) - IMDb   \n",
        "3                     Halloween 2014 | Halloween.com   \n",
        "4                                   HalloweenMovies\u2122   \n",
        "5  Halloween: The Fantasy and Folklore of All Hal...   \n",
        "6  Halloween Costumes - Childrens & Adult Hallowe...   \n",
        "7  CDC - Halloween Health and Safety - Family Health   \n",
        "8  Halloween Costumes for Kids & Adults - Costume...   \n",
        "9                      PAYDAY HALLOWEEN SPECIAL 2014   \n",
        "\n",
        "                                             snippet  \n",
        "0  Halloween or Hallowe'en 6] also known as Allha...  \n",
        "1  Find out more about the history of Halloween, ...  \n",
        "2  Halloween II. A Nightmare on Elm Street. Hallo...  \n",
        "3  Halloween fun on the internet, the one source ...  \n",
        "4  Tickets now on sale for Halloween - ODEON Cine...  \n",
        "5  Beginnings of Halloween celebration; First ori...  \n",
        "6  Spirit Halloween - Halloween Stores nationwide...  \n",
        "7  5 days ago ... Fall celebrations like Hallowee...  \n",
        "8  Halloween costumes for all ages and sizes. Sho...  \n",
        "9                                                     "
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Getting Results from more pages"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use `\"start\"` parameter to skip results from previous pages. To get the next `\"start\"` index look it up in `\"queries.nextPage[0].startIndex\"`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "next_index = results[\"queries\"][\"nextPage\"][0][\"startIndex\"]\n",
      "search_terms = results[\"queries\"][\"nextPage\"][0][\"searchTerms\"]\n",
      "\n",
      "url = \"https://www.googleapis.com/customsearch/v1\"\n",
      "parameters = {\"q\": search_terms,\n",
      "              \"cx\": cx,\n",
      "              \"key\": key,\n",
      "              \"start\": next_index\n",
      "              }"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "page = requests.request(\"GET\", url, params=parameters)\n",
      "results = json.loads(page.text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def process_search(results):\n",
      "    link_list = [item[\"link\"] for item in results[\"items\"]]\n",
      "    df = pd.DataFrame(link_list, columns=[\"link\"])\n",
      "    df[\"title\"] = [item[\"title\"] for item in results[\"items\"]]\n",
      "    df[\"snippet\"] = [item[\"snippet\"] for item in results[\"items\"]]\n",
      "    return df\n",
      "temp_df = process_search(results)\n",
      "df = pd.concat([df, temp_df], ignore_index=True)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>link</th>\n",
        "      <th>title</th>\n",
        "      <th>snippet</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>            http://en.wikipedia.org/wiki/Halloween</td>\n",
        "      <td>      Halloween - Wikipedia, the free encyclopedia</td>\n",
        "      <td> Halloween or Hallowe'en 6] also known as Allha...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>           http://www.history.com/topics/halloween</td>\n",
        "      <td> Halloween - Videos, Facts, Origin &amp; Meaning - ...</td>\n",
        "      <td> Find out more about the history of Halloween, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>              http://www.imdb.com/title/tt0077651/</td>\n",
        "      <td>                           Halloween (1978) - IMDb</td>\n",
        "      <td> Halloween II. A Nightmare on Elm Street. Hallo...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>                         http://www.halloween.com/</td>\n",
        "      <td>                    Halloween 2014 | Halloween.com</td>\n",
        "      <td> Halloween fun on the internet, the one source ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>                       http://halloweenmovies.com/</td>\n",
        "      <td>                                  HalloweenMovies\u2122</td>\n",
        "      <td> Tickets now on sale for Halloween - ODEON Cine...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>        http://www.loc.gov/folklife/halloween.html</td>\n",
        "      <td> Halloween: The Fantasy and Folklore of All Hal...</td>\n",
        "      <td> Beginnings of Halloween celebration; First ori...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>                   http://www.spirithalloween.com/</td>\n",
        "      <td> Halloween Costumes - Childrens &amp; Adult Hallowe...</td>\n",
        "      <td> Spirit Halloween - Halloween Stores nationwide...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>              http://www.cdc.gov/family/halloween/</td>\n",
        "      <td> CDC - Halloween Health and Safety - Family Health</td>\n",
        "      <td> 5 days ago ... Fall celebrations like Hallowee...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> http://www.partycity.com/category/halloween+co...</td>\n",
        "      <td> Halloween Costumes for Kids &amp; Adults - Costume...</td>\n",
        "      <td> Halloween costumes for all ages and sizes. Sho...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>        http://www.overkillsoftware.com/halloween/</td>\n",
        "      <td>                     PAYDAY HALLOWEEN SPECIAL 2014</td>\n",
        "      <td>                                                  </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> http://www.popsugar.com/moms/Ultimate-Hallowee...</td>\n",
        "      <td>          Ultimate Halloween Guide | POPSUGAR Moms</td>\n",
        "      <td> Download our Halloween app! ... Felicity to Sc...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> http://www.yandy.com/Shopping/products/categor...</td>\n",
        "      <td>                           Sexy Halloween Costumes</td>\n",
        "      <td> Sexy Halloween costumes up to 75% off! Free sh...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>       http://www.grandinroad.com/halloween-haven/</td>\n",
        "      <td> Halloween Decorations - Halloween Decor - Gran...</td>\n",
        "      <td> Shop Grandin Road's collection of outdoor Hall...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> http://www.amazon.com/Halloween-Donald-Pleasen...</td>\n",
        "      <td> Amazon.com: Halloween: Donald Pleasence, Jamie...</td>\n",
        "      <td> Halloween stars Jamie Lee Curtis (A Fish Calle...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>            http://www.instructables.com/halloween</td>\n",
        "      <td>                           Halloween Instructables</td>\n",
        "      <td> DIY Halloween costumes for adults, kids and pe...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> http://www.huffingtonpost.com/2014/10/27/hallo...</td>\n",
        "      <td> These Are The Most Googled Halloween Costumes ...</td>\n",
        "      <td> 1 day ago ... Some states are making a topical...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>       http://www.wilstar.com/holidays/hallown.htm</td>\n",
        "      <td> Halloween - The History, Traditions, and Custo...</td>\n",
        "      <td> The history of Halloween and its customs start...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>             https://www.etsy.com/browse/halloween</td>\n",
        "      <td>        Halloween Costumes, Decor &amp; Treats on Etsy</td>\n",
        "      <td> Find one-of-a-kind costumes for kids, adults a...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> https://nrf.com/media/press-releases/record-nu...</td>\n",
        "      <td> Record Number of Americans to Buy Halloween Co...</td>\n",
        "      <td> Sep 24, 2014 ... More than two-thirds (67.4%) ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> http://www.accuweather.com/en/weather-news/eas...</td>\n",
        "      <td> Halloween Forecast: Snow, Cold to Blast Northe...</td>\n",
        "      <td> 1 day ago ... A shocking blast of cold air and...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "                                                 link  \\\n",
        "0              http://en.wikipedia.org/wiki/Halloween   \n",
        "1             http://www.history.com/topics/halloween   \n",
        "2                http://www.imdb.com/title/tt0077651/   \n",
        "3                           http://www.halloween.com/   \n",
        "4                         http://halloweenmovies.com/   \n",
        "5          http://www.loc.gov/folklife/halloween.html   \n",
        "6                     http://www.spirithalloween.com/   \n",
        "7                http://www.cdc.gov/family/halloween/   \n",
        "8   http://www.partycity.com/category/halloween+co...   \n",
        "9          http://www.overkillsoftware.com/halloween/   \n",
        "10  http://www.popsugar.com/moms/Ultimate-Hallowee...   \n",
        "11  http://www.yandy.com/Shopping/products/categor...   \n",
        "12        http://www.grandinroad.com/halloween-haven/   \n",
        "13  http://www.amazon.com/Halloween-Donald-Pleasen...   \n",
        "14             http://www.instructables.com/halloween   \n",
        "15  http://www.huffingtonpost.com/2014/10/27/hallo...   \n",
        "16        http://www.wilstar.com/holidays/hallown.htm   \n",
        "17              https://www.etsy.com/browse/halloween   \n",
        "18  https://nrf.com/media/press-releases/record-nu...   \n",
        "19  http://www.accuweather.com/en/weather-news/eas...   \n",
        "\n",
        "                                                title  \\\n",
        "0        Halloween - Wikipedia, the free encyclopedia   \n",
        "1   Halloween - Videos, Facts, Origin & Meaning - ...   \n",
        "2                             Halloween (1978) - IMDb   \n",
        "3                      Halloween 2014 | Halloween.com   \n",
        "4                                    HalloweenMovies\u2122   \n",
        "5   Halloween: The Fantasy and Folklore of All Hal...   \n",
        "6   Halloween Costumes - Childrens & Adult Hallowe...   \n",
        "7   CDC - Halloween Health and Safety - Family Health   \n",
        "8   Halloween Costumes for Kids & Adults - Costume...   \n",
        "9                       PAYDAY HALLOWEEN SPECIAL 2014   \n",
        "10           Ultimate Halloween Guide | POPSUGAR Moms   \n",
        "11                            Sexy Halloween Costumes   \n",
        "12  Halloween Decorations - Halloween Decor - Gran...   \n",
        "13  Amazon.com: Halloween: Donald Pleasence, Jamie...   \n",
        "14                            Halloween Instructables   \n",
        "15  These Are The Most Googled Halloween Costumes ...   \n",
        "16  Halloween - The History, Traditions, and Custo...   \n",
        "17         Halloween Costumes, Decor & Treats on Etsy   \n",
        "18  Record Number of Americans to Buy Halloween Co...   \n",
        "19  Halloween Forecast: Snow, Cold to Blast Northe...   \n",
        "\n",
        "                                              snippet  \n",
        "0   Halloween or Hallowe'en 6] also known as Allha...  \n",
        "1   Find out more about the history of Halloween, ...  \n",
        "2   Halloween II. A Nightmare on Elm Street. Hallo...  \n",
        "3   Halloween fun on the internet, the one source ...  \n",
        "4   Tickets now on sale for Halloween - ODEON Cine...  \n",
        "5   Beginnings of Halloween celebration; First ori...  \n",
        "6   Spirit Halloween - Halloween Stores nationwide...  \n",
        "7   5 days ago ... Fall celebrations like Hallowee...  \n",
        "8   Halloween costumes for all ages and sizes. Sho...  \n",
        "9                                                      \n",
        "10  Download our Halloween app! ... Felicity to Sc...  \n",
        "11  Sexy Halloween costumes up to 75% off! Free sh...  \n",
        "12  Shop Grandin Road's collection of outdoor Hall...  \n",
        "13  Halloween stars Jamie Lee Curtis (A Fish Calle...  \n",
        "14  DIY Halloween costumes for adults, kids and pe...  \n",
        "15  1 day ago ... Some states are making a topical...  \n",
        "16  The history of Halloween and its customs start...  \n",
        "17  Find one-of-a-kind costumes for kids, adults a...  \n",
        "18  Sep 24, 2014 ... More than two-thirds (67.4%) ...  \n",
        "19  1 day ago ... A shocking blast of cold air and...  "
       ]
      }
     ],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/data-mining/5. MongoDB.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Video Tutorial: http://youtu.be/2b32-KVzBXQ\n",
      "\n",
      "In many cases you don't have to go and collect the data because you have data in a local NoSQL DB that requires analysis.This could be a URL audit-trail for your website where you want to study visitors and trends. One common NoSQL Database is MongoDB which is the subject of this tutorial.\n",
      "\n",
      "- [Why to use NoSQL?](#Why-to-use-NoSQL?)\n",
      "- [Common NoSQL Databases](#Common-NoSQL-Databses)\n",
      "- [PyMongo](#PyMongo)\n",
      "- [Single document with nested documents](#Finding-a-Single-Document)\n",
      "- [Working with multiple documents](#Finding-Multiple-Documents)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Why to use NoSQL?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "NoSQL was designed to deal with the problems that came up when developers started dealing with large amount of data using the existing Relational Databases (also referred to as SQL Databases). The three main issues with SQL Databases were:\n",
      "\n",
      "###Changing Schema of Data\n",
      "SQL Databases had fixed schema of tables which limited the ability to store new fields in your table. You have to change the table schema when ever you need a new field.\n",
      "\n",
      "###The Volume of Data in Storage\n",
      "SQL Databases can store virtually billions of records in a single table but the problem was the overhead cost to your CPU and memory to be able to access this data efficiently.\n",
      "\n",
      "###The Frequency of Adding New Data\n",
      "SQL databases are not designed to insert large amount of records in short time specially to large tables.\n",
      "\n",
      "##What did NoSQL Do?\n",
      "- Document based archives use a JSON-like format to store rows in a collection. This allows the collection to store data in any schema without changing the database.\n",
      "- NoSQL databases use different distributed clustering systems to store data in multiple machines and distribute the processing power over multiple machines. MongoDB uses GridFS and some other databases use Hadoop.\n",
      "- NoSQL dropped the support for relational data which required multiple read operations to ensure PK integrity in a relation. This reduced the cost of storing new data to simply storing the data on disk and generating PK in some systems."
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Common NoSQL Databases"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##MongoDB\n",
      "\n",
      "![MongoDB](http://www.mongodb.com/sites/mongodb.com/files/media/mongodb-logo-web.png)\n",
      "\n",
      "> <p><b>MongoDB</b> (from \"hu<b>mongo</b>us\") is a cross-platform <a href=\"http://en.wikipedia.org/wiki/Document-oriented_database\" title=\"Document-oriented database\">document-oriented database</a>. Classified as a <a href=\"http://en.wikipedia.org/wiki/NoSQL\" title=\"NoSQL\">NoSQL</a> database, MongoDB eschews the traditional table-based <a href=\"http://en.wikipedia.org/wiki/Relational_database\" title=\"Relational database\">relational database</a> structure in favor of <a href=\"http://en.wikipedia.org/wiki/JSON\" title=\"JSON\">JSON</a>-like documents with dynamic <a href=\"http://en.wikipedia.org/wiki/Database_schema\" title=\"Database schema\">schemas</a> (MongoDB calls the format <a href=\"http://en.wikipedia.org/wiki/BSON\" title=\"BSON\">BSON</a>), making the integration of data in certain types of applications easier and faster. Released under a combination of the <a href=\"http://en.wikipedia.org/wiki/GNU_Affero_General_Public_License\" title=\"GNU Affero General Public License\" class=\"mw-redirect\">GNU Affero General Public License</a> and the <a href=\"http://en.wikipedia.org/wiki/Apache_License\" title=\"Apache License\">Apache License</a>, MongoDB is <a href=\"http://en.wikipedia.org/wiki/Free_and_open_source_software\" title=\"Free and open source software\" class=\"mw-redirect\">free and open-source software</a>.</p>\n",
      "> ####from [Wikipedia](http://en.wikipedia.org/wiki/MongoDB)\n",
      "\n",
      "##Apache Casandra\n",
      "\n",
      "<img alt=\"Cassandra logo\" src=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/220px-Cassandra_logo.svg.png\" srcset=\"//upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/330px-Cassandra_logo.svg.png 1.5x, //upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Cassandra_logo.svg/440px-Cassandra_logo.svg.png 2x\" data-file-width=\"279\" data-file-height=\"187\" height=\"147\" width=\"220\">\n",
      "\n",
      "> <p><b>Apache Cassandra</b> is an <a href=\"http://en.wikipedia.org/wiki/Open_source_software\" title=\"Open source software\" class=\"mw-redirect\">open source</a> <a href=\"http://en.wikipedia.org/wiki/Distributed_database\" title=\"Distributed database\">distributed</a> <a href=\"http://en.wikipedia.org/wiki/Database_management_system\" title=\"Database management system\" class=\"mw-redirect\">database management system</a> designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters,<sup id=\"cite_ref-1\" class=\"reference\"><a href=\"http://en.wikipedia.org/wiki/Apache_Cassandra#cite_note-1\"><span>[</span>1<span>]</span></a></sup> with asynchronous masterless replication allowing low latency operations for all clients.</p>\n",
      "> ####from [Wikipedia](http://en.wikipedia.org/wiki/Apache_Cassandra)\n",
      "\n",
      "##Apache HBase\n",
      "\n",
      "![HBase](http://upload.wikimedia.org/wikipedia/en/e/e7/HBase_Logo.png)\n",
      "\n",
      "><p><b>HBase</b> is an <a href=\"http://en.wikipedia.org/wiki/Open_source\" title=\"Open source\">open source</a>, <a href=\"http://en.wikipedia.org/wiki/Non-relational_database\" title=\"Non-relational database\" class=\"mw-redirect\">non-relational</a>, <a href=\"http://en.wikipedia.org/wiki/Distributed_database\" title=\"Distributed database\">distributed database</a> modeled after <a href=\"http://en.wikipedia.org/wiki/Google\" title=\"Google\">Google's</a> <a href=\"http://en.wikipedia.org/wiki/BigTable\" title=\"BigTable\">BigTable</a> and written in <a href=\"http://en.wikipedia.org/wiki/Java_(programming_language)\" title=\"Java (programming language)\">Java</a>. It is developed as part of <a href=\"http://en.wikipedia.org/wiki/Apache_Software_Foundation\" title=\"Apache Software Foundation\">Apache Software Foundation</a>'s <a href=\"http://en.wikipedia.org/wiki/Hadoop\" title=\"Hadoop\" class=\"mw-redirect\">Apache Hadoop</a> project and runs on top of <a href=\"http://en.wikipedia.org/wiki/Hadoop_Distributed_Filesystem\" title=\"Hadoop Distributed Filesystem\" class=\"mw-redirect\">HDFS (Hadoop Distributed Filesystem)</a>, providing BigTable-like capabilities for Hadoop. That is, it provides a <a href=\"http://en.wikipedia.org/wiki/Fault-tolerant\" title=\"Fault-tolerant\" class=\"mw-redirect\">fault-tolerant</a> way of storing large quantities of <a href=\"http://en.wikipedia.org/wiki/Sparse_file\" title=\"Sparse file\">sparse</a> data (small amounts of information caught within a large collection of empty or unimportant data, such as finding the 50 largest items in a group of 2 billion records, or finding the non-zero items representing less than 0.1% of a huge collection).</p>\n",
      "> ####from [Wikipedia](http://en.wikipedia.org/wiki/Apache_HBase)\n",
      "\n",
      "##CouchDB\n",
      "\n",
      "![CouchDB](http://couchdb.apache.org/image/couch.png)\n",
      "\n",
      "><p><b>Apache CouchDB</b>, commonly referred to as <b>CouchDB</b>, is an <a href=\"http://en.wikipedia.org/wiki/Open-source_software\" title=\"Open-source software\">open source</a> database that focuses on ease of use and on being \"a database that completely embraces the web\".<sup id=\"cite_ref-official-website_1-0\" class=\"reference\"><a href=\"http://en.wikipedia.org/wiki/CouchDB#cite_note-official-website-1\"><span>[</span>1<span>]</span></a></sup> It is a <a href=\"http://en.wikipedia.org/wiki/NoSQL\" title=\"NoSQL\">NoSQL</a> database that uses <a href=\"http://en.wikipedia.org/wiki/JSON\" title=\"JSON\">JSON</a> to store data, <a href=\"http://en.wikipedia.org/wiki/JavaScript\" title=\"JavaScript\">JavaScript</a> as its query language using <a href=\"http://en.wikipedia.org/wiki/MapReduce\" title=\"MapReduce\">MapReduce</a>, and <a href=\"http://en.wikipedia.org/wiki/HTTP\" title=\"HTTP\" class=\"mw-redirect\">HTTP</a> for an <a href=\"http://en.wikipedia.org/wiki/API\" title=\"API\" class=\"mw-redirect\">API</a>.<sup id=\"cite_ref-official-website_1-1\" class=\"reference\"><a href=\"http://en.wikipedia.org/wiki/CouchDB#cite_note-official-website-1\"><span>[</span>1<span>]</span></a></sup> One of its distinguishing features is <a href=\"http://en.wikipedia.org/wiki/Multi-master_replication\" title=\"Multi-master replication\">multi-master replication</a>. CouchDB was first released in 2005 and later became an <a href=\"http://en.wikipedia.org/wiki/Apache_Software_Foundation\" title=\"Apache Software Foundation\">Apache</a> project in 2008.</p>\n",
      ">#### from [Wikipedia](http://en.wikipedia.org/wiki/CouchDB)\n",
      "\n",
      "##Cloud Only NoSQL (Managed Services)\n",
      "\n",
      "- [Amazon Dynamo DB](http://aws.amazon.com/dynamodb/)\n",
      "- [Google DataStore](https://cloud.google.com/datastore/)\n",
      "\n",
      "##Other Options:\n",
      "\n",
      "- [MySQL Cluter (Suports both SQL and NoSQL)](http://www.mysql.com/products/cluster/nosql.html)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "PyMongo"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Documentation: http://api.mongodb.org/python/current/"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Importing the Library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pymongo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Creating a Connection"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "client_con = pymongo.MongoClient()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Exploring MongoDB"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Listing Available Databases"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "client_con.database_names()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[u'mydb', u'local', u'roshan', u'admin']"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Listing Available Collections"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "roshan_db = client_con[\"roshan\"]\n",
      "roshan_db.collection_names()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[u'system.indexes',\n",
        " u'Twitter',\n",
        " u'SearchTweets',\n",
        " u'TwitterFollowers',\n",
        " u'TwitterUserSearch',\n",
        " u'TCelebrities',\n",
        " u'TwitterFriendsIds',\n",
        " u'TwitterFollowersIds',\n",
        " u'TwitterIds',\n",
        " u'TwiiterTemp',\n",
        " u'test youtube']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Connecting to a Collection"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "twitter_col = roshan_db[\"Twitter\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Counting Documents in a Collection"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "twitter_col.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "1045260"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Finding a Single Document"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doc = twitter_col.find_one()\n",
      "doc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "{u'_id': ObjectId('543d819c105f1913c74ab20b'),\n",
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        " u'coordinates': None,\n",
        " u'created_at': u'Tue Oct 14 20:04:05 +0000 2014',\n",
        " u'entities': {u'hashtags': [],\n",
        "  u'symbols': [],\n",
        "  u'trends': [],\n",
        "  u'urls': [],\n",
        "  u'user_mentions': []},\n",
        " u'favorite_count': 0,\n",
        " u'favorited': False,\n",
        " u'filter_level': u'medium',\n",
        " u'geo': None,\n",
        " u'id': 522115647623692288L,\n",
        " u'id_str': u'522115647623692288',\n",
        " u'in_reply_to_screen_name': None,\n",
        " u'in_reply_to_status_id': None,\n",
        " u'in_reply_to_status_id_str': None,\n",
        " u'in_reply_to_user_id': None,\n",
        " u'in_reply_to_user_id_str': None,\n",
        " u'lang': u'ar',\n",
        " u'place': None,\n",
        " u'possibly_sensitive': False,\n",
        " u'retweet_count': 0,\n",
        " u'retweeted': False,\n",
        " u'source': u'<a href=\"http://w.drr-alkalam.com/\" rel=\"nofollow\">\\u062f\\u0631\\u0631 \\u0627\\u0644\\u0643\\u0644\\u0627\\u0645 3</a>',\n",
        " u'text': u'\\u0627\\u0644\\u0632\\u0646\\u0643 \\u064a\\u0648\\u062c\\u062f \\u0628\\u0648\\u0641\\u0631\\u0629 \\u0627\\u0644\\u0644\\u062d\\u0645 \\u0648\\u0627\\u0644\\u0633\\u0645\\u0643 \\u0648\\u0627\\u0644\\u062d\\u0644\\u064a\\u0628 \\u0627\\u0644\\u0637\\u0627\\u0632\\u062c \\u0648 \\u0627\\u0644\\u062c\\u0628\\u0646 \\u0627\\u0644\\u0637\\u0627\\u0632\\u062c \\u0648 \\u0627\\u0644\\u0628\\u0642\\u0648\\u0644\\u064a\\u0627\\u062a \\u0648 \\u0627\\u0644\\u0644\\u0628\\u0646, \\u0643\\u0645\\u0627 \\u064a\\u0645\\u0643\\u0646 \\u062a\\u0646\\u0627\\u0648\\u0644\\u0647 \\u0643\\u0645\\u0643\\u0645\\u0644\\u0627\\u062a \\u063a\\u0630\\u0627\\u0626\\u064a\\u0629.',\n",
        " u'timestamp_ms': u'1413317045494',\n",
        " u'truncated': False,\n",
        " u'user': {u'contributors_enabled': False,\n",
        "  u'created_at': u'Mon Sep 15 09:21:34 +0000 2014',\n",
        "  u'default_profile': True,\n",
        "  u'default_profile_image': False,\n",
        "  u'description': u'\\u062a\\u0631\\u0628\\u064a\\u0647\\u06c1 \\u0629 \\u0634\\u0627\\u064a\\u0628 \\u0648\\u0648\\u0644\\u0648 \\ufe8e\\u0646\\u0646\\u064a\\u064a\\u064e \\u0641\\u0642\\u064a\\u0631 \\ufe8e\\u0639\\u0637\\u064a\\u064a\\u0621 \\ufe8e\\u0644\\u062d\\u0627\\u062c\\u0647\\u06c1 \\u0648\\ufe8e\\u0646\\u0627 \\u0645\\u062d\\u062a\\u0627\\u062c\\u0647\\u0627\\u0627 \\u060c PIN:264400B1',\n",
        "  u'favourites_count': 0,\n",
        "  u'follow_request_sent': None,\n",
        "  u'followers_count': 133,\n",
        "  u'following': None,\n",
        "  u'friends_count': 119,\n",
        "  u'geo_enabled': False,\n",
        "  u'id': 2811023737L,\n",
        "  u'id_str': u'2811023737',\n",
        "  u'is_translator': False,\n",
        "  u'lang': u'en',\n",
        "  u'listed_count': 0,\n",
        "  u'location': u'',\n",
        "  u'name': u'\\u0639\\u0640\\u0627\\u0645\\u0640\\u062f \\u0627\\u0644\\u0640\\u0639\\u0640\\u0637\\u0640\\u0627\\u0648\\u064a',\n",
        "  u'notifications': None,\n",
        "  u'profile_background_color': u'C0DEED',\n",
        "  u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
        "  u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
        "  u'profile_background_tile': False,\n",
        "  u'profile_image_url': u'http://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\n",
        "  u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\n",
        "  u'profile_link_color': u'0084B4',\n",
        "  u'profile_sidebar_border_color': u'C0DEED',\n",
        "  u'profile_sidebar_fill_color': u'DDEEF6',\n",
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        "  u'profile_use_background_image': True,\n",
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        "  u'screen_name': u'Aamdaladawe_',\n",
        "  u'statuses_count': 209,\n",
        "  u'time_zone': None,\n",
        "  u'url': None,\n",
        "  u'utc_offset': None,\n",
        "  u'verified': False}}"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Examining a Document"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "ObjectId"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Documentation: http://docs.mongodb.org/manual/reference/object-id/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "document_id = doc[\"_id\"]\n",
      "print type(document_id)\n",
      "document_id"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'bson.objectid.ObjectId'>\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "ObjectId('543d819c105f1913c74ab20b')"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print document_id.generation_time.strftime(\"%Y-%m-%d %H:%M:%SZ%z\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2014-10-14 20:03:40Z+0000\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Other Fields"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doc[\"created_at\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "u'Tue Oct 14 20:04:05 +0000 2014'"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doc[\"text\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "u'\\u0627\\u0644\\u0632\\u0646\\u0643 \\u064a\\u0648\\u062c\\u062f \\u0628\\u0648\\u0641\\u0631\\u0629 \\u0627\\u0644\\u0644\\u062d\\u0645 \\u0648\\u0627\\u0644\\u0633\\u0645\\u0643 \\u0648\\u0627\\u0644\\u062d\\u0644\\u064a\\u0628 \\u0627\\u0644\\u0637\\u0627\\u0632\\u062c \\u0648 \\u0627\\u0644\\u062c\\u0628\\u0646 \\u0627\\u0644\\u0637\\u0627\\u0632\\u062c \\u0648 \\u0627\\u0644\\u0628\\u0642\\u0648\\u0644\\u064a\\u0627\\u062a \\u0648 \\u0627\\u0644\\u0644\\u0628\\u0646, \\u0643\\u0645\\u0627 \\u064a\\u0645\\u0643\\u0646 \\u062a\\u0646\\u0627\\u0648\\u0644\\u0647 \\u0643\\u0645\\u0643\\u0645\\u0644\\u0627\\u062a \\u063a\\u0630\\u0627\\u0626\\u064a\\u0629.'"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Unicode Trick"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print doc[\"text\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u0627\u0644\u0632\u0646\u0643 \u064a\u0648\u062c\u062f \u0628\u0648\u0641\u0631\u0629 \u0627\u0644\u0644\u062d\u0645 \u0648\u0627\u0644\u0633\u0645\u0643 \u0648\u0627\u0644\u062d\u0644\u064a\u0628 \u0627\u0644\u0637\u0627\u0632\u062c \u0648 \u0627\u0644\u062c\u0628\u0646 \u0627\u0644\u0637\u0627\u0632\u062c \u0648 \u0627\u0644\u0628\u0642\u0648\u0644\u064a\u0627\u062a \u0648 \u0627\u0644\u0644\u0628\u0646, \u0643\u0645\u0627 \u064a\u0645\u0643\u0646 \u062a\u0646\u0627\u0648\u0644\u0647 \u0643\u0645\u0643\u0645\u0644\u0627\u062a \u063a\u0630\u0627\u0626\u064a\u0629.\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Nested Documents"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doc[\"user\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "{u'contributors_enabled': False,\n",
        " u'created_at': u'Mon Sep 15 09:21:34 +0000 2014',\n",
        " u'default_profile': True,\n",
        " u'default_profile_image': False,\n",
        " u'description': u'\\u062a\\u0631\\u0628\\u064a\\u0647\\u06c1 \\u0629 \\u0634\\u0627\\u064a\\u0628 \\u0648\\u0648\\u0644\\u0648 \\ufe8e\\u0646\\u0646\\u064a\\u064a\\u064e \\u0641\\u0642\\u064a\\u0631 \\ufe8e\\u0639\\u0637\\u064a\\u064a\\u0621 \\ufe8e\\u0644\\u062d\\u0627\\u062c\\u0647\\u06c1 \\u0648\\ufe8e\\u0646\\u0627 \\u0645\\u062d\\u062a\\u0627\\u062c\\u0647\\u0627\\u0627 \\u060c PIN:264400B1',\n",
        " u'favourites_count': 0,\n",
        " u'follow_request_sent': None,\n",
        " u'followers_count': 133,\n",
        " u'following': None,\n",
        " u'friends_count': 119,\n",
        " u'geo_enabled': False,\n",
        " u'id': 2811023737L,\n",
        " u'id_str': u'2811023737',\n",
        " u'is_translator': False,\n",
        " u'lang': u'en',\n",
        " u'listed_count': 0,\n",
        " u'location': u'',\n",
        " u'name': u'\\u0639\\u0640\\u0627\\u0645\\u0640\\u062f \\u0627\\u0644\\u0640\\u0639\\u0640\\u0637\\u0640\\u0627\\u0648\\u064a',\n",
        " u'notifications': None,\n",
        " u'profile_background_color': u'C0DEED',\n",
        " u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
        " u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
        " u'profile_background_tile': False,\n",
        " u'profile_image_url': u'http://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\n",
        " u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png',\n",
        " u'profile_link_color': u'0084B4',\n",
        " u'profile_sidebar_border_color': u'C0DEED',\n",
        " u'profile_sidebar_fill_color': u'DDEEF6',\n",
        " u'profile_text_color': u'333333',\n",
        " u'profile_use_background_image': True,\n",
        " u'protected': False,\n",
        " u'screen_name': u'Aamdaladawe_',\n",
        " u'statuses_count': 209,\n",
        " u'time_zone': None,\n",
        " u'url': None,\n",
        " u'utc_offset': None,\n",
        " u'verified': False}"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "doc[\"user\"][\"verified\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Unicode Trick for dict"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for k,v in doc[\"user\"].iteritems():\n",
      "    print \"%s: %s\" % (k,v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "follow_request_sent: None\n",
        "profile_use_background_image: True\n",
        "id: 2811023737\n",
        "verified: False\n",
        "profile_image_url_https: https://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png\n",
        "profile_sidebar_fill_color: DDEEF6\n",
        "is_translator: False\n",
        "geo_enabled: False\n",
        "profile_text_color: 333333\n",
        "followers_count: 133\n",
        "protected: False\n",
        "location: \n",
        "default_profile_image: False\n",
        "id_str: 2811023737\n",
        "utc_offset: None\n",
        "statuses_count: 209\n",
        "description: \u062a\u0631\u0628\u064a\u0647\u06c1 \u0629 \u0634\u0627\u064a\u0628 \u0648\u0648\u0644\u0648 \ufe8e\u0646\u0646\u064a\u064a\u064e \u0641\u0642\u064a\u0631 \ufe8e\u0639\u0637\u064a\u064a\u0621 \ufe8e\u0644\u062d\u0627\u062c\u0647\u06c1 \u0648\ufe8e\u0646\u0627 \u0645\u062d\u062a\u0627\u062c\u0647\u0627\u0627 \u060c PIN:264400B1\n",
        "friends_count: 119\n",
        "profile_link_color: 0084B4\n",
        "profile_image_url: http://pbs.twimg.com/profile_images/511445636961419264/9enIvHyS_normal.png\n",
        "notifications: None\n",
        "profile_background_image_url_https: https://abs.twimg.com/images/themes/theme1/bg.png\n",
        "profile_background_color: C0DEED\n",
        "profile_background_image_url: http://abs.twimg.com/images/themes/theme1/bg.png\n",
        "screen_name: Aamdaladawe_\n",
        "lang: en\n",
        "profile_background_tile: False\n",
        "favourites_count: 0\n",
        "name: \u0639\u0640\u0627\u0645\u0640\u062f \u0627\u0644\u0640\u0639\u0640\u0637\u0640\u0627\u0648\u064a\n",
        "url: None\n",
        "created_at: Mon Sep 15 09:21:34 +0000 2014\n",
        "contributors_enabled: False\n",
        "time_zone: None\n",
        "profile_sidebar_border_color: C0DEED\n",
        "default_profile: True\n",
        "following: None\n",
        "listed_count: 0\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Finding Multiple Documents"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "verified_users = twitter_col.find({\"user.verified\":True})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "verified_users.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "672"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas\n",
      "\n",
      "users = pandas.DataFrame([msg[\"user\"] for msg in verified_users])\n",
      "users"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<pre>\n",
        "&lt;class 'pandas.core.frame.DataFrame'&gt;\n",
        "Int64Index: 672 entries, 0 to 671\n",
        "Data columns (total 38 columns):\n",
        "contributors_enabled                  672  non-null values\n",
        "created_at                            672  non-null values\n",
        "default_profile                       672  non-null values\n",
        "default_profile_image                 672  non-null values\n",
        "description                           672  non-null values\n",
        "favourites_count                      672  non-null values\n",
        "follow_request_sent                   0  non-null values\n",
        "followers_count                       672  non-null values\n",
        "following                             0  non-null values\n",
        "friends_count                         672  non-null values\n",
        "geo_enabled                           672  non-null values\n",
        "id                                    672  non-null values\n",
        "id_str                                672  non-null values\n",
        "is_translator                         672  non-null values\n",
        "lang                                  672  non-null values\n",
        "listed_count                          672  non-null values\n",
        "location                              672  non-null values\n",
        "name                                  672  non-null values\n",
        "notifications                         0  non-null values\n",
        "profile_background_color              672  non-null values\n",
        "profile_background_image_url          672  non-null values\n",
        "profile_background_image_url_https    672  non-null values\n",
        "profile_background_tile               672  non-null values\n",
        "profile_banner_url                    578  non-null values\n",
        "profile_image_url                     672  non-null values\n",
        "profile_image_url_https               672  non-null values\n",
        "profile_link_color                    672  non-null values\n",
        "profile_sidebar_border_color          672  non-null values\n",
        "profile_sidebar_fill_color            672  non-null values\n",
        "profile_text_color                    672  non-null values\n",
        "profile_use_background_image          672  non-null values\n",
        "protected                             672  non-null values\n",
        "screen_name                           672  non-null values\n",
        "statuses_count                        672  non-null values\n",
        "time_zone                             656  non-null values\n",
        "url                                   631  non-null values\n",
        "utc_offset                            656  non-null values\n",
        "verified                              672  non-null values\n",
        "dtypes: bool(9), float64(1), int64(6), object(22)\n",
        "</pre>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 672 entries, 0 to 671\n",
        "Data columns (total 38 columns):\n",
        "contributors_enabled                  672  non-null values\n",
        "created_at                            672  non-null values\n",
        "default_profile                       672  non-null values\n",
        "default_profile_image                 672  non-null values\n",
        "description                           672  non-null values\n",
        "favourites_count                      672  non-null values\n",
        "follow_request_sent                   0  non-null values\n",
        "followers_count                       672  non-null values\n",
        "following                             0  non-null values\n",
        "friends_count                         672  non-null values\n",
        "geo_enabled                           672  non-null values\n",
        "id                                    672  non-null values\n",
        "id_str                                672  non-null values\n",
        "is_translator                         672  non-null values\n",
        "lang                                  672  non-null values\n",
        "listed_count                          672  non-null values\n",
        "location                              672  non-null values\n",
        "name                                  672  non-null values\n",
        "notifications                         0  non-null values\n",
        "profile_background_color              672  non-null values\n",
        "profile_background_image_url          672  non-null values\n",
        "profile_background_image_url_https    672  non-null values\n",
        "profile_background_tile               672  non-null values\n",
        "profile_banner_url                    578  non-null values\n",
        "profile_image_url                     672  non-null values\n",
        "profile_image_url_https               672  non-null values\n",
        "profile_link_color                    672  non-null values\n",
        "profile_sidebar_border_color          672  non-null values\n",
        "profile_sidebar_fill_color            672  non-null values\n",
        "profile_text_color                    672  non-null values\n",
        "profile_use_background_image          672  non-null values\n",
        "protected                             672  non-null values\n",
        "screen_name                           672  non-null values\n",
        "statuses_count                        672  non-null values\n",
        "time_zone                             656  non-null values\n",
        "url                                   631  non-null values\n",
        "utc_offset                            656  non-null values\n",
        "verified                              672  non-null values\n",
        "dtypes: bool(9), float64(1), int64(6), object(22)"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Filtering out duplicates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "users_unique = users.drop_duplicates(cols=[\"id\"])\n",
      "print len(users_unique)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "222\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Visualizing the realtion between number of tweets and the number of followers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = users_unique[\"followers_count\"]\n",
      "y = users_unique[\"statuses_count\"]\n",
      "z = users_unique[\"friends_count\"]\n",
      "plt.scatter(x, y, c=z, alpha=0.4, s=200, cmap=plt.cm.Accent)\n",
      "plt.yscale(\"symlog\")\n",
      "plt.xscale(\"symlog\")\n",
      "plt.ylim(y.min(),y.max())\n",
      "plt.xlim(x.min(),x.max())\n",
      "plt.grid()\n",
      "plt.xlabel(\"followers_count\")\n",
      "plt.ylabel(\"statuses_count\")\n",
      "plt.colorbar()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wMDU2hXvaTXFeMRkZGQQCAS6fuMwTO56gsLBwXZ/xVmhpaeFi50UqdixSda+I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9VMhgzD54Y5euQo0XCUWCiGs9BBKLC9L45Cp+JOS2aIRxLotDp0Oh1CI1gNrhKJRahprqGsroz1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       "text": [
        "<matplotlib.figure.Figure at 0x7f5c26c91a90>"
       ]
      }
     ],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/data-mining/6. Twitter Streaming API.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial  Outline\n",
    "\n",
    "- [Introduction](#Introduction)\n",
    "- [Preprerequisites](#Preprerequisites)\n",
    "- [How does it work?](#How-does-it-work?)\n",
    "- [Authentication](#Authentication)\n",
    " - [Authentication keys](#Authentication-keys)\n",
    "- [MongoDB Collection](#MongoDB-Collection)\n",
    "- [Starting a Stream](#Starting-a-Stream)\n",
    " - [Stream Listener](#Stream-Listener)\n",
    " - [Connect to a streaming API](#Connect-to-a-streaming-API)\n",
    "- [Data Access and Analysis](#Data-Access-and-Analysis)\n",
    " - [Load results to a DataFrame](#Load-results-to-a-DataFrame)\n",
    "- [Visualization](#Visualization)\n",
    "\n",
    "# Introduction\n",
    "\n",
    "Twitter provides two types of API to access their data:\n",
    "\n",
    "- RESTful API: Used to get data about existing data objects like statuses \"tweets\", user, ... etc\n",
    "- Streaming API: Used to get live statuses \"tweets\" as they are sent\n",
    "\n",
    "The reason why you would like to use streaming API:\n",
    "\n",
    "- Capture large amount of data because RESTful API has limited access to older data\n",
    "- Real-time analysis like monitoring social discussion about a live event\n",
    "- In house archive like archiving social discussion about your brand(s)\n",
    "- AI response system for a twitter account like automated reply and filing questions or providing answers\n",
    "\n",
    "# Preprerequisites\n",
    "\n",
    "- Python 2 or 3\n",
    "- Jupyter /w IPyWidgets\n",
    "- Pandas\n",
    "- Numpy\n",
    "- Matplotlib\n",
    "- MogoDB Installtion\n",
    "- Pymongo\n",
    "- Scikit-learn\n",
    "- Tweepy\n",
    "- Twitter account\n",
    "\n",
    "\n",
    "# How does it work?\n",
    "\n",
    "Twitter streaming API can provide data through a streaming HTTP response. This is very similar to downloading a file where you read a number of bytes and store it to disk and repeat until the end of file. The only difference is this stream is endless. The only things that could stop this stream are:\n",
    "\n",
    "- If you closed your connection to the streaming response\n",
    "- If your connection speed is not capable of receiving data and the servers buffer is filling up\n",
    "\n",
    "This means that this process will be using the thread that it was launched from until it is stopped. In production, you should always start this in a different thread or process to make sure your software doesn't freeze until you stop the stream.\n",
    "\n",
    "# Authentication\n",
    "\n",
    "You will need four numbers from twitter development to start using streaming API. First, let's import some important libraries for dealing with twitter API, data analysis, data storage ... etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tweepy\n",
    "import matplotlib.pyplot as plt\n",
    "import pymongo\n",
    "import ipywidgets as wgt\n",
    "from IPython.display import display\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import re\n",
    "from datetime import datetime\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authentication keys\n",
    "\n",
    "1. Go to https://apps.twitter.com/\n",
    "2. Create an App (if you don't have one yet)\n",
    "3. Grant read-only access to your account\n",
    "4. Copy the four keys and paste them here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "api_key = \"yP0yoCitoUNgD63ebMerGyJaE\" # <---- Add your API Key\n",
    "api_secret = \"kLO5YUtlth3cd4lOHLy8nlLHW5npVQgUfO4FhsyCn6wCMIz5E6\" # <---- Add your API Secret\n",
    "access_token = \"259862037-iMXNjfL8JBApm4LVcdfwc3FcMm7Xta4TKg5cd44K\" # <---- Add your access token\n",
    "access_token_secret = \"UIgh08dtmavzlvlWWukIXwN5HDIQD0wNwyn5sPzhrynBf\" # <---- Add your access token secret\n",
    "\n",
    "auth = tweepy.OAuthHandler(api_key, api_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "\n",
    "api = tweepy.API(auth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MongoDB Collection\n",
    "\n",
    "Connect to MongoDB and create/get a collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2251"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = pymongo.MongoClient()[\"tweets\"][\"StreamingTutorial\"]\n",
    "col.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starting a Stream\n",
    "\n",
    "We need a listener which should extend `tweepy.StreamListener` class. There is a number of methods that you can extend to instruct the listener class to perform functionality. Some of the important methods are:\n",
    "\n",
    "- `on_status(self, status)`: This will pass a status \"tweet\" object when a tweet is received\n",
    "- `on_data(self, raw_data)`: Called when any any data is received and the raw data will be passed\n",
    "- `on_error(self, status_code)`: Called when you get a response with code other than 200 (ok)\n",
    "\n",
    "## Stream Listener"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class MyStreamListener(tweepy.StreamListener):\n",
    "    \n",
    "    counter = 0\n",
    "    \n",
    "    def __init__(self, max_tweets=1000, *args, **kwargs):\n",
    "        self.max_tweets = max_tweets\n",
    "        self.counter = 0\n",
    "        super().__init__(*args, **kwargs)\n",
    "    \n",
    "    def on_connect(self):\n",
    "        self.counter = 0\n",
    "        self.start_time = datetime.now()\n",
    "    \n",
    "    def on_status(self, status):\n",
    "        # Increment counter\n",
    "        self.counter += 1\n",
    "        \n",
    "        # Store tweet to MongoDB\n",
    "        col.insert_one(status._json)\n",
    "        \n",
    "        \n",
    "        if self.counter % 1 == 0:\n",
    "            value = int(100.00 * self.counter / self.max_tweets)\n",
    "            mining_time = datetime.now() - self.start_time\n",
    "            progress_bar.value = value\n",
    "            html_value = \"\"\"<span class=\"label label-primary\">Tweets/Sec: %.1f</span>\"\"\" % (self.counter / max([1,mining_time.seconds]))\n",
    "            html_value += \"\"\" <span class=\"label label-success\">Progress: %.1f%%</span>\"\"\" % (self.counter / self.max_tweets * 100.0)\n",
    "            html_value += \"\"\" <span class=\"label label-info\">ETA: %.1f Sec</span>\"\"\" % ((self.max_tweets - self.counter) / (self.counter / max([1,mining_time.seconds])))\n",
    "            wgt_status.value = html_value\n",
    "            #print(\"%s/%s\" % (self.counter, self.max_tweets))\n",
    "            if self.counter >= self.max_tweets:\n",
    "                myStream.disconnect()\n",
    "                print(\"Finished\")\n",
    "                print(\"Total Mining Time: %s\" % (mining_time))\n",
    "                print(\"Tweets/Sec: %.1f\" % (self.max_tweets / mining_time.seconds))\n",
    "                progress_bar.value = 0\n",
    "                \n",
    "    \n",
    "myStreamListener = MyStreamListener(max_tweets=100)\n",
    "myStream = tweepy.Stream(auth = api.auth, listener=myStreamListener)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connect to a streaming API\n",
    "\n",
    "There are two methods to connect to a stream:\n",
    "\n",
    "- `filter(follow=None, track=None, async=False, locations=None, stall_warnings=False, languages=None, encoding='utf8', filter_level=None)`\n",
    "- `firehose(count=None, async=False)`\n",
    "\n",
    "Firehose captures everything. You should make sure that you have connection speed that can handle the stream and you have the storage capacity that can store these tweets at the same rate. We cannot really use firehose for this tutorial but we'll be using `filter`.\n",
    "\n",
    "You have to specify one of two things to filter:\n",
    "\n",
    "- `follow`: A list of user ID to follow. This will stream all their tweets, retweets, and others retweeting their tweets. This doesn't include mentions and manual retweets where the user doesn't press the retweet button.\n",
    "- `track`: A string or list of string to be used for filtering. If you use multiple words separated by spaces, this will be used for AND operator. If you use multiple words in a string separated by commas or pass a list of words this will be treated as OR operator.\n",
    "\n",
    "**Note**: `track` is case insensitive.\n",
    "\n",
    "### What to track?\n",
    "I want to collect all tweets that contains any of these words:\n",
    "\n",
    "- Jupyter\n",
    "- Python\n",
    "- Data Mining\n",
    "- Machine Learning\n",
    "- Data Science\n",
    "- Big Data\n",
    "- IoT\n",
    "- #R\n",
    "\n",
    "This could be done with a string or a list. It is easier to to it with a list to make your code clear to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished\n",
      "Total Mining Time: 0:01:21.477351\n",
      "Tweets/Sec: 1.2\n",
      "Tweets collected: 100\n",
      "Total tweets in collection: 2351\n"
     ]
    }
   ],
   "source": [
    "keywords = [\"Jupyter\",\n",
    "            \"Python\",\n",
    "            \"Data Mining\",\n",
    "            \"Machine Learning\",\n",
    "            \"Data Science\",\n",
    "            \"Big Data\",\n",
    "            \"DataMining\",\n",
    "            \"MachineLearning\",\n",
    "            \"DataScience\",\n",
    "            \"BigData\",\n",
    "            \"IoT\",\n",
    "            \"#R\",\n",
    "           ]\n",
    "\n",
    "# Visualize a progress bar to track progress\n",
    "progress_bar = wgt.IntProgress(value=0)\n",
    "display(progress_bar)\n",
    "wgt_status = wgt.HTML(value=\"\"\"<span class=\"label label-primary\">Tweets/Sec: 0.0</span>\"\"\")\n",
    "display(wgt_status)\n",
    "\n",
    "# Start a filter with an error counter of 20\n",
    "for error_counter in range(20):\n",
    "    try:\n",
    "        myStream.filter(track=keywords)\n",
    "        print(\"Tweets collected: %s\" % myStream.listener.counter)\n",
    "        print(\"Total tweets in collection: %s\" % col.count())\n",
    "        break\n",
    "    except:\n",
    "        print(\"ERROR# %s\" % (error_counter + 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Access and Analysis\n",
    "\n",
    "Now that we have stored all these tweets in a MongoDB collection, let's take a look at one of these tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': ObjectId('56937d2e105f1970314720e2'),\n",
       " 'contributors': None,\n",
       " 'coordinates': None,\n",
       " 'created_at': 'Mon Jan 11 10:00:14 +0000 2016',\n",
       " 'entities': {'hashtags': [{'indices': [22, 27], 'text': 'Rの法則'}],\n",
       "  'symbols': [],\n",
       "  'urls': [],\n",
       "  'user_mentions': []},\n",
       " 'favorite_count': 0,\n",
       " 'favorited': False,\n",
       " 'filter_level': 'low',\n",
       " 'geo': None,\n",
       " 'id': 686487772970942466,\n",
       " 'id_str': '686487772970942466',\n",
       " 'in_reply_to_screen_name': None,\n",
       " 'in_reply_to_status_id': None,\n",
       " 'in_reply_to_status_id_str': None,\n",
       " 'in_reply_to_user_id': None,\n",
       " 'in_reply_to_user_id_str': None,\n",
       " 'is_quote_status': False,\n",
       " 'lang': 'ja',\n",
       " 'place': None,\n",
       " 'retweet_count': 0,\n",
       " 'retweeted': False,\n",
       " 'source': '<a href=\"http://twitter.com/download/iphone\" rel=\"nofollow\">Twitter for iPhone</a>',\n",
       " 'text': '体力落ちてきておばさんみたいになってきた。\\n#Rの法則',\n",
       " 'timestamp_ms': '1452506414059',\n",
       " 'truncated': False,\n",
       " 'user': {'contributors_enabled': False,\n",
       "  'created_at': 'Tue Aug 18 16:19:16 +0000 2015',\n",
       "  'default_profile': True,\n",
       "  'default_profile_image': False,\n",
       "  'description': '☮ 関ジャニ∞ ＆ 山田涼介 ＆ Justin Bieber ＆ Benjamin Lasnier ＆ Selena Gomez ☮',\n",
       "  'favourites_count': 1121,\n",
       "  'follow_request_sent': None,\n",
       "  'followers_count': 121,\n",
       "  'following': None,\n",
       "  'friends_count': 92,\n",
       "  'geo_enabled': True,\n",
       "  'id': 3318871652,\n",
       "  'id_str': '3318871652',\n",
       "  'is_translator': False,\n",
       "  'lang': 'en',\n",
       "  'listed_count': 0,\n",
       "  'location': 'The land of dreams',\n",
       "  'name': 'rena',\n",
       "  'notifications': None,\n",
       "  'profile_background_color': 'C0DEED',\n",
       "  'profile_background_image_url': 'http://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_image_url_https': 'https://abs.twimg.com/images/themes/theme1/bg.png',\n",
       "  'profile_background_tile': False,\n",
       "  'profile_banner_url': 'https://pbs.twimg.com/profile_banners/3318871652/1452436374',\n",
       "  'profile_image_url': 'http://pbs.twimg.com/profile_images/683964013558931456/Q1rx1s5b_normal.jpg',\n",
       "  'profile_image_url_https': 'https://pbs.twimg.com/profile_images/683964013558931456/Q1rx1s5b_normal.jpg',\n",
       "  'profile_link_color': '0084B4',\n",
       "  'profile_sidebar_border_color': 'C0DEED',\n",
       "  'profile_sidebar_fill_color': 'DDEEF6',\n",
       "  'profile_text_color': '333333',\n",
       "  'profile_use_background_image': True,\n",
       "  'protected': False,\n",
       "  'screen_name': 'Q2HpiJwCX1huBwf',\n",
       "  'statuses_count': 497,\n",
       "  'time_zone': None,\n",
       "  'url': None,\n",
       "  'utc_offset': None,\n",
       "  'verified': False}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.find_one()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load results to a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_at</th>\n",
       "      <th>source</th>\n",
       "      <th>text</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mon Jan 11 10:00:14 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>体力落ちてきておばさんみたいになってきた。\\n#Rの法則</td>\n",
       "      <td>@Q2HpiJwCX1huBwf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mon Jan 11 10:09:26 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>皆におばさんと言われてうれしがってる #Rの法則</td>\n",
       "      <td>@Tamutamu1017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mon Jan 11 10:00:10 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://trendkeyword.blog.jp/\" rel=\"no...</td>\n",
       "      <td>【R.I.P】急上昇ワード「R.I.Pã€ã®ã¾ã¨ã‚é€Ÿå ± https://t.co/yi1yfC...</td>\n",
       "      <td>@pickword_matome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mon Jan 11 10:00:10 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>#Rの法則 \\nどれもおばさん臭いけれどやっぱり黄色が一番だなぁ</td>\n",
       "      <td>@kakinotise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mon Jan 11 10:00:10 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://bufferapp.com\" rel=\"nofollow\"&gt;...</td>\n",
       "      <td>The New Best Thing HP ATP - Vertica Big Data S...</td>\n",
       "      <td>@DataCentreNews1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mon Jan 11 10:00:11 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>IoT Now: l’Internet of Things è qui, ora https...</td>\n",
       "      <td>@datamanager_it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mon Jan 11 10:00:11 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://trendkeyword.doorblog.jp/\" rel...</td>\n",
       "      <td>今話題の「R.I.P」まとめ https://t.co/VOc5cwK5hg #R.I.P ...</td>\n",
       "      <td>@buzz_wadai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mon Jan 11 10:00:11 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitterfeed.com\" rel=\"nofollow...</td>\n",
       "      <td>#oldham #stockport VIDEO: Snake thief hides py...</td>\n",
       "      <td>@Labour_is_PIE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mon Jan 11 10:00:11 +0000 2016</td>\n",
       "      <td>&lt;a href=\"https://about.twitter.com/products/tw...</td>\n",
       "      <td>Las #startup pioneras de #machinelearning ofre...</td>\n",
       "      <td>@techreview_es</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mon Jan 11 10:00:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.linkedin.com/\" rel=\"nofoll...</td>\n",
       "      <td>Lets talk about how to harness the power of ma...</td>\n",
       "      <td>@jansmit1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mon Jan 11 10:00:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://catalystfive.com\" rel=\"nofollo...</td>\n",
       "      <td>Business Intelligence and Big Data Consulting ...</td>\n",
       "      <td>@Catalyst5Jobs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mon Jan 11 10:00:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/NewsICT\" rel=\"nofo...</td>\n",
       "      <td>[æƒ…å ±é€šä¿¡]2016年台北国際コンピューター見本市が新しい位置づけと新しい展示で装い新たに！...</td>\n",
       "      <td>@NewsICT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mon Jan 11 10:02:10 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>#bonplan Parties de Laser Quest entre amis à 2...</td>\n",
       "      <td>@Bons_Plans_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Mon Jan 11 10:02:10 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>Parties de Laser Quest entre amis à 22.00€ au ...</td>\n",
       "      <td>@keepmymindfree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mon Jan 11 10:02:10 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @jose_garde: Why a Simple Data Analytics St...</td>\n",
       "      <td>@martingeldish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Mon Jan 11 10:02:11 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>芸能人の人たくさん手たたき笑いしてるからおばさんたくさんになっちゃうよwww\\n\\n#Rの法則</td>\n",
       "      <td>@YK__0704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mon Jan 11 10:02:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>Découvrez le jeu Pure Mission entre amis à 22....</td>\n",
       "      <td>@keepmymindfree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mon Jan 11 10:02:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>#bonplan Parties de bowling pour 4 Ã  #POINCY :...</td>\n",
       "      <td>@Bons_Plans_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Mon Jan 11 10:02:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>20 min de vol découverte ULM pour 1 ou 2 à 79....</td>\n",
       "      <td>@CrationSiteWeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Mon Jan 11 10:02:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @hortonworks: Paris is the city of love but...</td>\n",
       "      <td>@bigdataparis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mon Jan 11 10:02:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @hynek: So #emacs / @spacemacs nerds: is th...</td>\n",
       "      <td>@fdiesch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mon Jan 11 10:02:12 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.hootsuite.com\" rel=\"nofoll...</td>\n",
       "      <td>.@QonexCyber founder member of @IoT_SF is orga...</td>\n",
       "      <td>@QonexCyber</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Mon Jan 11 10:02:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>Parties de bowling pour 4 à #POINCY : 35.00€ a...</td>\n",
       "      <td>@keepmymindfree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Mon Jan 11 10:02:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>#bonplan 30 séances de Squash à #LISSES : 39.9...</td>\n",
       "      <td>@Bons_Plans_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mon Jan 11 10:02:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>App: ExZeus 2 – free to play   https://t.co/ZT...</td>\n",
       "      <td>@UniversalConsol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mon Jan 11 10:02:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://dlvr.it\" rel=\"nofollow\"&gt;dlvr.i...</td>\n",
       "      <td>#discount Parties de bowling pour 4 Ã  #POINCY ...</td>\n",
       "      <td>@PromosPromos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mon Jan 11 10:02:13 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.google.com/\" rel=\"nofollow...</td>\n",
       "      <td>spiegel.de :  Tier macht Sachen: Python beißt ...</td>\n",
       "      <td>@arminfischer_de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mon Jan 11 10:09:02 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>若いっていいねえ…ってよく言うw #Rの法則</td>\n",
       "      <td>@naco75x</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mon Jan 11 10:09:17 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>最近の若い子は最近使った\\n#Rの法則</td>\n",
       "      <td>@K1224West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mon Jan 11 10:09:19 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>#Rの法則\\n自分も若者なのに笑</td>\n",
       "      <td>@V6ZRRT7Q22BZ1cF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>Mon Jan 11 10:29:40 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://201512291327-7430af.bitnamiapp...</td>\n",
       "      <td>https://t.co/BTAAq6HuuJ - pcgamer - #machinele...</td>\n",
       "      <td>@vinceyue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>Mon Jan 11 10:29:40 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @jose_garde: The Big Data Analytics Softwar...</td>\n",
       "      <td>@LJ_Blanchard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>Mon Jan 11 10:29:41 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.linkedin.com/\" rel=\"nofoll...</td>\n",
       "      <td>What is data mining? Do you have to be a mathe...</td>\n",
       "      <td>@ednuwan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>Mon Jan 11 10:29:41 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @AgroKnow: A #BigData platform for the futu...</td>\n",
       "      <td>@albertspijkers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2225</th>\n",
       "      <td>Mon Jan 11 10:29:42 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.linkedin.com/\" rel=\"nofoll...</td>\n",
       "      <td>Big Data: Is It A Tsunami, The New Oil, Or Sim...</td>\n",
       "      <td>@Summerlovegrove</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2226</th>\n",
       "      <td>Mon Jan 11 10:29:43 +0000 2016</td>\n",
       "      <td>&lt;a href=\"https://www.jobfindly.com/php-jobs.ht...</td>\n",
       "      <td>Sr Software Engineer C Php Python Linux Jobs i...</td>\n",
       "      <td>@jobfindlyphpdev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2227</th>\n",
       "      <td>Mon Jan 11 10:29:43 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @bigdataparis: #Bigdata bang : un marché en...</td>\n",
       "      <td>@LifeIsWeb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2228</th>\n",
       "      <td>Mon Jan 11 10:29:43 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>Learn from the best professors in India .. cou...</td>\n",
       "      <td>@ashwaniapex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2229</th>\n",
       "      <td>Mon Jan 11 10:29:45 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://getsmoup.com\" rel=\"nofollow\"&gt;S...</td>\n",
       "      <td>RT @rebrandtoday: #startup or #rebrand -Buy Cr...</td>\n",
       "      <td>@SmartData_Fr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2230</th>\n",
       "      <td>Mon Jan 11 10:29:45 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.ajaymatharu.com/\" rel=\"nof...</td>\n",
       "      <td>¿Cómo será el futuro del Big Data? https://t.c...</td>\n",
       "      <td>@eduardogarsanch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2231</th>\n",
       "      <td>Mon Jan 11 10:29:46 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @jose_garde: 3 Ways to Transform Your Compa...</td>\n",
       "      <td>@LJ_Blanchard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232</th>\n",
       "      <td>Mon Jan 11 10:29:46 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://publicize.wp.com/\" rel=\"nofoll...</td>\n",
       "      <td>Woman Tries To Kiss Python, Gets Bitten In The...</td>\n",
       "      <td>@NAIJA_VIBEZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2233</th>\n",
       "      <td>Mon Jan 11 10:29:46 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.itknowingness.com\" rel=\"no...</td>\n",
       "      <td>RT @jose_garde: 3 Ways to Transform Your Compa...</td>\n",
       "      <td>@itknowingness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>Mon Jan 11 10:29:48 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/iphone\" r...</td>\n",
       "      <td>RT @ErikaPauwels: Building a #BigData platform...</td>\n",
       "      <td>@impulsater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>Mon Jan 11 10:29:49 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com/download/android\" ...</td>\n",
       "      <td>En 2016 j'aimerais moins râler. #résolution. S...</td>\n",
       "      <td>@ce1ce2makarenko</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>Mon Jan 11 10:29:51 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @jose_garde: How Marketing Can Be Better Au...</td>\n",
       "      <td>@LJ_Blanchard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>Mon Jan 11 10:29:51 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>Hey @Pontifex accueille ces réfugiées dans ta ...</td>\n",
       "      <td>@Atmosfive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>Mon Jan 11 10:29:51 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @Ubixr: .@GroupeLaPoste choisit le toulousa...</td>\n",
       "      <td>@The_Nextwork</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>Mon Jan 11 10:29:56 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitterfeed.com\" rel=\"nofollow...</td>\n",
       "      <td>Thanks @hackplayers Blade: un webshell en Pyth...</td>\n",
       "      <td>@Navarmedia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2240</th>\n",
       "      <td>Mon Jan 11 10:29:56 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @jose_garde: Which big data personality are...</td>\n",
       "      <td>@LJ_Blanchard</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2241</th>\n",
       "      <td>Mon Jan 11 10:29:56 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://ifttt.com\" rel=\"nofollow\"&gt;IFTT...</td>\n",
       "      <td>Cybersecurity Forum tackles challenges with th...</td>\n",
       "      <td>@wulfsec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2242</th>\n",
       "      <td>Mon Jan 11 10:29:56 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://publicize.wp.com/\" rel=\"nofoll...</td>\n",
       "      <td>Woman Tries To Kiss Python, Gets Bitten In The...</td>\n",
       "      <td>@Lola2Records</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2243</th>\n",
       "      <td>Mon Jan 11 10:29:56 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @TeamAnodot: Join David Drai, CEO of Anodot...</td>\n",
       "      <td>@iottechexpo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244</th>\n",
       "      <td>Mon Jan 11 10:29:57 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://getsmoup.com\" rel=\"nofollow\"&gt;A...</td>\n",
       "      <td>RT @rebrandtoday: #startup or #rebrand -Buy Cr...</td>\n",
       "      <td>@AI__news</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2245</th>\n",
       "      <td>Mon Jan 11 10:29:57 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.twitter.com\" rel=\"nofollow...</td>\n",
       "      <td>RT @Matthis__VERNON: \"@Fred_Poquet Sans #confi...</td>\n",
       "      <td>@sibueta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2246</th>\n",
       "      <td>Mon Jan 11 10:29:57 +0000 2016</td>\n",
       "      <td>&lt;a href=\"https://social.zoho.com\" rel=\"nofollo...</td>\n",
       "      <td>The right place for #BigData is #Cloud #Storag...</td>\n",
       "      <td>@TyroneSystems</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2247</th>\n",
       "      <td>Mon Jan 11 10:29:59 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @PEBlanrue: Il a toujours le mot pour rire,...</td>\n",
       "      <td>@lesroisduring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2248</th>\n",
       "      <td>Mon Jan 11 10:29:58 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>RT @DigitalAgendaEU: €15 million for a #IoT so...</td>\n",
       "      <td>@ImproveNPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2249</th>\n",
       "      <td>Mon Jan 11 10:29:59 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://twitter.com\" rel=\"nofollow\"&gt;Tw...</td>\n",
       "      <td>Neat IoT innovation https://t.co/atARX0m5Bj</td>\n",
       "      <td>@sherwinnovator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2250</th>\n",
       "      <td>Mon Jan 11 10:30:00 +0000 2016</td>\n",
       "      <td>&lt;a href=\"http://www.hubspot.com/\" rel=\"nofollo...</td>\n",
       "      <td>Check out our #Mobile App Predicitions for 201...</td>\n",
       "      <td>@B60uk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2251 rows × 4 columns</p>\n",
       "</div>"
      ],
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       "                          created_at  \\\n",
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       "3     Mon Jan 11 10:00:10 +0000 2016   \n",
       "4     Mon Jan 11 10:00:10 +0000 2016   \n",
       "5     Mon Jan 11 10:00:11 +0000 2016   \n",
       "6     Mon Jan 11 10:00:11 +0000 2016   \n",
       "7     Mon Jan 11 10:00:11 +0000 2016   \n",
       "8     Mon Jan 11 10:00:11 +0000 2016   \n",
       "9     Mon Jan 11 10:00:12 +0000 2016   \n",
       "10    Mon Jan 11 10:00:13 +0000 2016   \n",
       "11    Mon Jan 11 10:00:13 +0000 2016   \n",
       "12    Mon Jan 11 10:02:10 +0000 2016   \n",
       "13    Mon Jan 11 10:02:10 +0000 2016   \n",
       "14    Mon Jan 11 10:02:10 +0000 2016   \n",
       "15    Mon Jan 11 10:02:11 +0000 2016   \n",
       "16    Mon Jan 11 10:02:12 +0000 2016   \n",
       "17    Mon Jan 11 10:02:12 +0000 2016   \n",
       "18    Mon Jan 11 10:02:12 +0000 2016   \n",
       "19    Mon Jan 11 10:02:12 +0000 2016   \n",
       "20    Mon Jan 11 10:02:12 +0000 2016   \n",
       "21    Mon Jan 11 10:02:12 +0000 2016   \n",
       "22    Mon Jan 11 10:02:13 +0000 2016   \n",
       "23    Mon Jan 11 10:02:13 +0000 2016   \n",
       "24    Mon Jan 11 10:02:13 +0000 2016   \n",
       "25    Mon Jan 11 10:02:13 +0000 2016   \n",
       "26    Mon Jan 11 10:02:13 +0000 2016   \n",
       "27    Mon Jan 11 10:09:02 +0000 2016   \n",
       "28    Mon Jan 11 10:09:17 +0000 2016   \n",
       "29    Mon Jan 11 10:09:19 +0000 2016   \n",
       "...                              ...   \n",
       "2221  Mon Jan 11 10:29:40 +0000 2016   \n",
       "2222  Mon Jan 11 10:29:40 +0000 2016   \n",
       "2223  Mon Jan 11 10:29:41 +0000 2016   \n",
       "2224  Mon Jan 11 10:29:41 +0000 2016   \n",
       "2225  Mon Jan 11 10:29:42 +0000 2016   \n",
       "2226  Mon Jan 11 10:29:43 +0000 2016   \n",
       "2227  Mon Jan 11 10:29:43 +0000 2016   \n",
       "2228  Mon Jan 11 10:29:43 +0000 2016   \n",
       "2229  Mon Jan 11 10:29:45 +0000 2016   \n",
       "2230  Mon Jan 11 10:29:45 +0000 2016   \n",
       "2231  Mon Jan 11 10:29:46 +0000 2016   \n",
       "2232  Mon Jan 11 10:29:46 +0000 2016   \n",
       "2233  Mon Jan 11 10:29:46 +0000 2016   \n",
       "2234  Mon Jan 11 10:29:48 +0000 2016   \n",
       "2235  Mon Jan 11 10:29:49 +0000 2016   \n",
       "2236  Mon Jan 11 10:29:51 +0000 2016   \n",
       "2237  Mon Jan 11 10:29:51 +0000 2016   \n",
       "2238  Mon Jan 11 10:29:51 +0000 2016   \n",
       "2239  Mon Jan 11 10:29:56 +0000 2016   \n",
       "2240  Mon Jan 11 10:29:56 +0000 2016   \n",
       "2241  Mon Jan 11 10:29:56 +0000 2016   \n",
       "2242  Mon Jan 11 10:29:56 +0000 2016   \n",
       "2243  Mon Jan 11 10:29:56 +0000 2016   \n",
       "2244  Mon Jan 11 10:29:57 +0000 2016   \n",
       "2245  Mon Jan 11 10:29:57 +0000 2016   \n",
       "2246  Mon Jan 11 10:29:57 +0000 2016   \n",
       "2247  Mon Jan 11 10:29:59 +0000 2016   \n",
       "2248  Mon Jan 11 10:29:58 +0000 2016   \n",
       "2249  Mon Jan 11 10:29:59 +0000 2016   \n",
       "2250  Mon Jan 11 10:30:00 +0000 2016   \n",
       "\n",
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       "3     <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "4     <a href=\"http://bufferapp.com\" rel=\"nofollow\">...   \n",
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       "7     <a href=\"http://twitterfeed.com\" rel=\"nofollow...   \n",
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       "9     <a href=\"http://www.linkedin.com/\" rel=\"nofoll...   \n",
       "10    <a href=\"http://catalystfive.com\" rel=\"nofollo...   \n",
       "11    <a href=\"http://twitter.com/NewsICT\" rel=\"nofo...   \n",
       "12    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "13    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "14    <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "15    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "16    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "17    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "18    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "19    <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "20    <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "21    <a href=\"http://www.hootsuite.com\" rel=\"nofoll...   \n",
       "22    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "23    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "24    <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "25    <a href=\"http://dlvr.it\" rel=\"nofollow\">dlvr.i...   \n",
       "26    <a href=\"http://www.google.com/\" rel=\"nofollow...   \n",
       "27    <a href=\"http://twitter.com/download/android\" ...   \n",
       "28    <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "29    <a href=\"http://twitter.com/download/android\" ...   \n",
       "...                                                 ...   \n",
       "2221  <a href=\"http://201512291327-7430af.bitnamiapp...   \n",
       "2222  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2223  <a href=\"http://www.linkedin.com/\" rel=\"nofoll...   \n",
       "2224  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2225  <a href=\"http://www.linkedin.com/\" rel=\"nofoll...   \n",
       "2226  <a href=\"https://www.jobfindly.com/php-jobs.ht...   \n",
       "2227  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2228  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2229  <a href=\"http://getsmoup.com\" rel=\"nofollow\">S...   \n",
       "2230  <a href=\"http://www.ajaymatharu.com/\" rel=\"nof...   \n",
       "2231  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2232  <a href=\"http://publicize.wp.com/\" rel=\"nofoll...   \n",
       "2233  <a href=\"http://www.itknowingness.com\" rel=\"no...   \n",
       "2234  <a href=\"http://twitter.com/download/iphone\" r...   \n",
       "2235  <a href=\"http://twitter.com/download/android\" ...   \n",
       "2236  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2237  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2238  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2239  <a href=\"http://twitterfeed.com\" rel=\"nofollow...   \n",
       "2240  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2241  <a href=\"http://ifttt.com\" rel=\"nofollow\">IFTT...   \n",
       "2242  <a href=\"http://publicize.wp.com/\" rel=\"nofoll...   \n",
       "2243  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2244  <a href=\"http://getsmoup.com\" rel=\"nofollow\">A...   \n",
       "2245  <a href=\"http://www.twitter.com\" rel=\"nofollow...   \n",
       "2246  <a href=\"https://social.zoho.com\" rel=\"nofollo...   \n",
       "2247  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2248  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2249  <a href=\"http://twitter.com\" rel=\"nofollow\">Tw...   \n",
       "2250  <a href=\"http://www.hubspot.com/\" rel=\"nofollo...   \n",
       "\n",
       "                                                   text              user  \n",
       "0                          体力落ちてきておばさんみたいになってきた。\\n#Rの法則  @Q2HpiJwCX1huBwf  \n",
       "1                              皆におばさんと言われてうれしがってる #Rの法則     @Tamutamu1017  \n",
       "2     【R.I.P】急上昇ワード「R.I.Pã€ã®ã¾ã¨ã‚é€Ÿå ± https://t.co/yi1yfC...  @pickword_matome  \n",
       "3                      #Rの法則 \\nどれもおばさん臭いけれどやっぱり黄色が一番だなぁ       @kakinotise  \n",
       "4     The New Best Thing HP ATP - Vertica Big Data S...  @DataCentreNews1  \n",
       "5     IoT Now: l’Internet of Things è qui, ora https...   @datamanager_it  \n",
       "6     今話題の「R.I.P」まとめ https://t.co/VOc5cwK5hg #R.I.P ...       @buzz_wadai  \n",
       "7     #oldham #stockport VIDEO: Snake thief hides py...    @Labour_is_PIE  \n",
       "8     Las #startup pioneras de #machinelearning ofre...    @techreview_es  \n",
       "9     Lets talk about how to harness the power of ma...         @jansmit1  \n",
       "10    Business Intelligence and Big Data Consulting ...    @Catalyst5Jobs  \n",
       "11    [æƒ…å ±é€šä¿¡]2016年台北国際コンピューター見本市が新しい位置づけと新しい展示で装い新たに！...          @NewsICT  \n",
       "12    #bonplan Parties de Laser Quest entre amis à 2...      @Bons_Plans_  \n",
       "13    Parties de Laser Quest entre amis à 22.00€ au ...   @keepmymindfree  \n",
       "14    RT @jose_garde: Why a Simple Data Analytics St...    @martingeldish  \n",
       "15      芸能人の人たくさん手たたき笑いしてるからおばさんたくさんになっちゃうよwww\\n\\n#Rの法則         @YK__0704  \n",
       "16    Découvrez le jeu Pure Mission entre amis à 22....   @keepmymindfree  \n",
       "17    #bonplan Parties de bowling pour 4 Ã  #POINCY :...      @Bons_Plans_  \n",
       "18    20 min de vol découverte ULM pour 1 ou 2 à 79....   @CrationSiteWeb  \n",
       "19    RT @hortonworks: Paris is the city of love but...     @bigdataparis  \n",
       "20    RT @hynek: So #emacs / @spacemacs nerds: is th...          @fdiesch  \n",
       "21    .@QonexCyber founder member of @IoT_SF is orga...       @QonexCyber  \n",
       "22    Parties de bowling pour 4 à #POINCY : 35.00€ a...   @keepmymindfree  \n",
       "23    #bonplan 30 séances de Squash à #LISSES : 39.9...      @Bons_Plans_  \n",
       "24    App: ExZeus 2 – free to play   https://t.co/ZT...  @UniversalConsol  \n",
       "25    #discount Parties de bowling pour 4 Ã  #POINCY ...     @PromosPromos  \n",
       "26    spiegel.de :  Tier macht Sachen: Python beißt ...  @arminfischer_de  \n",
       "27                               若いっていいねえ…ってよく言うw #Rの法則          @naco75x  \n",
       "28                                  最近の若い子は最近使った\\n#Rの法則        @K1224West  \n",
       "29                                     #Rの法則\\n自分も若者なのに笑  @V6ZRRT7Q22BZ1cF  \n",
       "...                                                 ...               ...  \n",
       "2221  https://t.co/BTAAq6HuuJ - pcgamer - #machinele...         @vinceyue  \n",
       "2222  RT @jose_garde: The Big Data Analytics Softwar...     @LJ_Blanchard  \n",
       "2223  What is data mining? Do you have to be a mathe...          @ednuwan  \n",
       "2224  RT @AgroKnow: A #BigData platform for the futu...   @albertspijkers  \n",
       "2225  Big Data: Is It A Tsunami, The New Oil, Or Sim...  @Summerlovegrove  \n",
       "2226  Sr Software Engineer C Php Python Linux Jobs i...  @jobfindlyphpdev  \n",
       "2227  RT @bigdataparis: #Bigdata bang : un marché en...        @LifeIsWeb  \n",
       "2228  Learn from the best professors in India .. cou...      @ashwaniapex  \n",
       "2229  RT @rebrandtoday: #startup or #rebrand -Buy Cr...     @SmartData_Fr  \n",
       "2230  ¿Cómo será el futuro del Big Data? https://t.c...  @eduardogarsanch  \n",
       "2231  RT @jose_garde: 3 Ways to Transform Your Compa...     @LJ_Blanchard  \n",
       "2232  Woman Tries To Kiss Python, Gets Bitten In The...      @NAIJA_VIBEZ  \n",
       "2233  RT @jose_garde: 3 Ways to Transform Your Compa...    @itknowingness  \n",
       "2234  RT @ErikaPauwels: Building a #BigData platform...       @impulsater  \n",
       "2235  En 2016 j'aimerais moins râler. #résolution. S...  @ce1ce2makarenko  \n",
       "2236  RT @jose_garde: How Marketing Can Be Better Au...     @LJ_Blanchard  \n",
       "2237  Hey @Pontifex accueille ces réfugiées dans ta ...        @Atmosfive  \n",
       "2238  RT @Ubixr: .@GroupeLaPoste choisit le toulousa...     @The_Nextwork  \n",
       "2239  Thanks @hackplayers Blade: un webshell en Pyth...       @Navarmedia  \n",
       "2240  RT @jose_garde: Which big data personality are...     @LJ_Blanchard  \n",
       "2241  Cybersecurity Forum tackles challenges with th...          @wulfsec  \n",
       "2242  Woman Tries To Kiss Python, Gets Bitten In The...     @Lola2Records  \n",
       "2243  RT @TeamAnodot: Join David Drai, CEO of Anodot...      @iottechexpo  \n",
       "2244  RT @rebrandtoday: #startup or #rebrand -Buy Cr...         @AI__news  \n",
       "2245  RT @Matthis__VERNON: \"@Fred_Poquet Sans #confi...          @sibueta  \n",
       "2246  The right place for #BigData is #Cloud #Storag...    @TyroneSystems  \n",
       "2247  RT @PEBlanrue: Il a toujours le mot pour rire,...    @lesroisduring  \n",
       "2248  RT @DigitalAgendaEU: €15 million for a #IoT so...       @ImproveNPA  \n",
       "2249        Neat IoT innovation https://t.co/atARX0m5Bj   @sherwinnovator  \n",
       "2250  Check out our #Mobile App Predicitions for 201...            @B60uk  \n",
       "\n",
       "[2251 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = [{\"created_at\": item[\"created_at\"],\n",
    "            \"text\": item[\"text\"],\n",
    "            \"user\": \"@%s\" % item[\"user\"][\"screen_name\"],\n",
    "            \"source\": item[\"source\"],\n",
    "           } for item in col.find()]\n",
    "\n",
    "dataset = pd.DataFrame(dataset)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the highest used words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>co</td>\n",
       "      <td>1907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rt</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>de</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>rの法則</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>iot</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>the</td>\n",
       "      <td>358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>bigdata</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>00</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>data</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>in</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>python</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>to</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>au</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>of</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>lieu</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>réduction</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>big</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>on</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>is</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>and</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>for</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>analytics</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>le</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>via</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>you</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>thingsexpo</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>by</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>snake</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>en</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>bowie</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>la</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>thief</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>video</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>m2m</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>jose_garde</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>david</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>with</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>how</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>it</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>will</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>un</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>amp</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>des</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>réparation</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>new</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>39</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>at</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          word  count\n",
       "0        https   1986\n",
       "1           co   1907\n",
       "2           rt    804\n",
       "3           de    550\n",
       "4         rの法則    408\n",
       "5          iot    374\n",
       "6          the    358\n",
       "7      bigdata    293\n",
       "8           00    275\n",
       "9         data    250\n",
       "10          in    234\n",
       "11      python    219\n",
       "12          to    212\n",
       "13          au    199\n",
       "14          of    188\n",
       "15        lieu    168\n",
       "16   réduction    166\n",
       "17         big    157\n",
       "18          on    143\n",
       "19          is    142\n",
       "20         and    140\n",
       "21         for    136\n",
       "22   analytics    107\n",
       "23          le     89\n",
       "24         via     86\n",
       "25         you     86\n",
       "26  thingsexpo     86\n",
       "27          by     85\n",
       "28        2016     84\n",
       "29       snake     80\n",
       "30          en     76\n",
       "31       bowie     75\n",
       "32          la     74\n",
       "33       thief     74\n",
       "34       video     73\n",
       "35         m2m     70\n",
       "36  jose_garde     68\n",
       "37          19     67\n",
       "38       david     66\n",
       "39        with     63\n",
       "40         how     61\n",
       "41          it     60\n",
       "42        will     55\n",
       "43          un     54\n",
       "44         amp     53\n",
       "45         des     53\n",
       "46  réparation     53\n",
       "47         new     52\n",
       "48          39     52\n",
       "49          at     51"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer()\n",
    "count_matrix = cv.fit_transform(dataset.text)\n",
    "\n",
    "word_count = pd.DataFrame(cv.get_feature_names(), columns=[\"word\"])\n",
    "word_count[\"count\"] = count_matrix.sum(axis=0).tolist()[0]\n",
    "word_count = word_count.sort_values(\"count\", ascending=False).reset_index(drop=True)\n",
    "word_count[:50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_source_name(x):\n",
    "    value = re.findall(pattern=\"<[^>]+>([^<]+)</a>\", string=x)\n",
    "    if len(value) > 0:\n",
    "        return value[0]\n",
    "    else:\n",
    "        return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Facebook                25\n",
       "TweetDeck               37\n",
       "RoundTeam               41\n",
       "Hootsuite               46\n",
       "twitterfeed             81\n",
       "IFTTT                  134\n",
       "dlvr.it                200\n",
       "Twitter Web Client     388\n",
       "Twitter for Android    392\n",
       "Twitter for iPhone     515\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TSVMk3S7pNknH9ZP3y5J+X55HPa6kbSTpN+W52fvX8l4k6Xnt9ikiIobOsArQ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\nOlACdERERAdKgI6IiOhA/z8aaHKp8XyaNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec53d81550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.source_name = dataset.source.apply(get_source_name)\n",
    "\n",
    "source_counts = dataset.source_name.value_counts().sort_values()[-10:]\n",
    "\n",
    "bottom = [index for index, item in enumerate(source_counts.index)]\n",
    "plt.barh(bottom, width=source_counts, color=\"orange\", linewidth=0)\n",
    "\n",
    "y_labels = [\"%s %.1f%%\" % (item, 100.0*source_counts[item]/len(dataset)) for index,item in enumerate(source_counts.index)]\n",
    "plt.yticks(np.array(bottom)+0.4, y_labels)\n",
    "\n",
    "source_counts"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: notebooks/ipython_widgets/2. Events.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:c95d641333791fd278e7097c70c834da61dbd26a43aa874668dfcdd14869cd67"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Handling the wivents of widgets using:\n",
      "\n",
      "- on_click()\n",
      "- on_trait_change()\n",
      "\n",
      "**Video Tutorial**: http://youtu.be/hXS4xhyx0a8"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widgets.on_click()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def print_msg(widget):\n",
      "    print \"You have clicked (%s)\" % widget.description\n",
      "\n",
      "container = widgets.ContainerWidget()\n",
      "\n",
      "button_1 = widgets.ButtonWidget(description=\"Click Me\")\n",
      "button_2 = widgets.ButtonWidget(description=\"Don't Click Me\")\n",
      "\n",
      "button_1.on_click(print_msg)\n",
      "button_2.on_click(print_msg)\n",
      "\n",
      "container.children = [button_1, button_2]\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "You have clicked (Click Me)\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "You have clicked (Don't Click Me)\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widget.on_trait_change()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()\n",
      "\n",
      "control_1 = widgets.FloatTextWidget(description=\"X: \")\n",
      "control_2 = widgets.FloatTextWidget(description=\"Y: \")\n",
      "control_3 = widgets.SelectWidget(description= \"Operation\", values=[\"+\",\"-\",\"*\",\"/\"])\n",
      "control_4 = widgets.LatexWidget(value=\"Formula: NA\")\n",
      "control_5 = widgets.LatexWidget(value=\"Result: 0.0\")\n",
      "\n",
      "container.children = [control_1, control_2, control_3, control_4, control_5]\n",
      "\n",
      "def calculate_form(name):\n",
      "    # Retreive values from form\n",
      "    form = container.children\n",
      "    X = form[0].value\n",
      "    Y = form[1].value\n",
      "    operation = form[2].value\n",
      "    \n",
      "    # Build Fomula\n",
      "    formula = str(X) + operation + str(Y)\n",
      "    form[3].value = \"Formula: %s\" % formula\n",
      "    \n",
      "    # Check if Divive by Zero\n",
      "    if Y == 0.0 and operation == \"/\":\n",
      "        form[4].value = \"Div by Zero\"\n",
      "    else:\n",
      "        # Evaluate fumula\n",
      "        form[4].value = str(eval(formula))\n",
      "    print \"%s = %s\" % (formula, control_5.value)\n",
      "\n",
      "# Add Handlers\n",
      "control_1.on_trait_change(calculate_form, \"value\")\n",
      "control_2.on_trait_change(calculate_form, \"value\")\n",
      "control_3.on_trait_change(calculate_form, \"value\")\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0-0.0 = 0.0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0*0.0 = 0.0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0/0.0 = Div by Zero\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0/4.0 = 0.0\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.0/4.0 = 0.5\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.0+4.0 = 6.0\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.0/4.0 = 0.5\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.2/4.0 = 0.55\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25/4.0 = 0.5625\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.254/4.0 = 0.5635\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.2547/4.0 = 0.563675\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478/4.0 = 0.563695\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.254785/4.0 = 0.56369625\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.2547854/4.0 = 0.56369635\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546/4.0 = 0.563696365\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.254785465/4.0 = 0.56369636625\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.2547854658/4.0 = 0.56369636645\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546587/4.0 = 0.563696366467\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.0 = 0.56369636647\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.0 = 0.56369636647\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.0 = 0.56369636647\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.0 = 0.56369636647\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.4 = 0.512451242245\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.45 = 0.506693363119\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.458 = 0.505784088354\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.4585 = 0.505727367025\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.45854 = 0.505722829868\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.458542 = 0.505722603012\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.4585426 = 0.505722534956\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.45854264 = 0.505722530419\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.458542648 = 0.505722529511\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2.25478546588/4.4585426481 = 0.5057225295\n"
       ]
      }
     ],
     "prompt_number": 4
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/ipython_widgets/3. Styling Widgets.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Controlling the style of your widgets using the following:\n",
      "\n",
      "- widget.visible()\n",
      "- widget.set_css()\n",
      "- widget.add_class()\n",
      "\n",
      "**Video Tutorial:**\n",
      "\n",
      "http://youtu.be/sER3W_ChuNQ"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets\n",
      "import re"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widget.visible()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()\n",
      "\n",
      "widget_1 = widgets.TextWidget(description=\"Username: \")\n",
      "widget_2 = widgets.ButtonWidget(description=\"Hide\")\n",
      "widget_3 = widgets.ButtonWidget(description=\"Show\")\n",
      "\n",
      "container.children = (widget_1, widget_2, widget_3)\n",
      "\n",
      "def hide_user(widget):\n",
      "    widget_1.visible = False\n",
      "\n",
      "def show_user(widget):\n",
      "    widget_1.visible = True\n",
      "    \n",
      "widget_2.on_click(hide_user)\n",
      "widget_3.on_click(show_user)\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widget.set_css(self, dict_or_key, value=None, selector='')`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()\n",
      "\n",
      "widget_1 = widgets.TextWidget(description=\"Name\")\n",
      "widget_2 = widgets.TextWidget(description=\"Email\")\n",
      "\n",
      "container.children = (widget_1, widget_2)\n",
      "\n",
      "def check_email(name, old, new):\n",
      "    new_check = re.match(r\"[^@]+@[^@]+\\.[^@]+\", new)\n",
      "    old_check = re.match(r\"[^@]+@[^@]+\\.[^@]+\", old)\n",
      "    \n",
      "    if new_check <> old_check:\n",
      "        style_email()\n",
      "\n",
      "def style_email(*args):\n",
      "    new_check = re.match(r\"[^@]+@[^@]+\\.[^@]+\", widget_2.value)\n",
      "    if new_check:\n",
      "        widget_2.set_css(\"color\", \"#228822\")\n",
      "        widget_2.set_css(\"background-color\", \"#fff\")\n",
      "        widget_2.set_css(\"font-weight\", \"400\")\n",
      "    else:\n",
      "        widget_2.set_css(\"color\", \"#ff0000\")\n",
      "        widget_2.set_css(\"background-color\", \"#ffbbbb\")\n",
      "        widget_2.set_css(\"font-weight\", \"800\")\n",
      "            \n",
      "widget_2.on_trait_change(check_email, \"value\")\n",
      "widget_2.on_displayed(style_email)\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widget.add_class(self, class_names, selector='')`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can define your own CSS calsses or use the Bootstrap library that is available"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Bootstrap Classes"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Button Style"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We will add a list of buttons"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()\n",
      "\n",
      "widget_1 = widgets.ButtonWidget(description=\"btn-default\")\n",
      "widget_2 = widgets.ButtonWidget(description=\"btn-primary\")\n",
      "widget_3 = widgets.ButtonWidget(description=\"btn-success\")\n",
      "widget_4 = widgets.ButtonWidget(description=\"btn-info\")\n",
      "widget_5 = widgets.ButtonWidget(description=\"btn-warning\")\n",
      "widget_6 = widgets.ButtonWidget(description=\"btn-danger\")\n",
      "widget_7 = widgets.ButtonWidget(description=\"btn-link\")\n",
      "\n",
      "container.children = (widget_1, widget_2, widget_3, widget_4, widget_5,\n",
      "                       widget_6, widget_7)\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Works for already displayed widgets\n",
      "widget_1.add_class(\"btn-default\")\n",
      "widget_2.add_class(\"btn-primary\")\n",
      "widget_3.add_class(\"btn-success\")\n",
      "widget_4.add_class(\"btn-info\")\n",
      "widget_5.add_class(\"btn-warning\")\n",
      "widget_6.add_class(\"btn-danger\")\n",
      "widget_7.add_class(\"btn-link\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Block Button"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()\n",
      "\n",
      "widget_1 = widgets.TextWidget(description=\"Name\")\n",
      "widget_2 = widgets.ButtonWidget(description=\"btn-block\")\n",
      "\n",
      "container.children = (widget_1, widget_2)\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Works for already displayed widgets\n",
      "widget_2.add_class(\"btn-primary\")\n",
      "widget_2.add_class(\"btn-block\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "HTMLWidget"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Labels"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%html\n",
      "<span class=\"label label-default\">Default</span>\n",
      "<span class=\"label label-success\">Success</span>\n",
      "<span class=\"label label-info\">Info</span>\n",
      "<span class=\"label label-warning\">Warning</span>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<span class=\"label label-default\">Default</span>\n",
        "<span class=\"label label-success\">Success</span>\n",
        "<span class=\"label label-info\">Info</span>\n",
        "<span class=\"label label-warning\">Warning</span>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7fa25c094650>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "html_code = \"\"\"\n",
      "<span class=\"label label-default\">Default</span>\n",
      "<span class=\"label label-success\">Success</span>\n",
      "<span class=\"label label-info\">Info</span>\n",
      "<span class=\"label label-warning\">Warning</span>\n",
      "\"\"\"\n",
      "\n",
      "container = widgets.ContainerWidget()\n",
      "\n",
      "widget_1 = widgets.HTMLWidget(value = html_code)\n",
      "\n",
      "container.children = (widget_1,)\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Alerts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%html\n",
      "<div class=\"alert alert-success alert-dismissible\" role=\"alert\">\n",
      "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
      "    <span aria-hidden=\"true\">&times;</span>\n",
      "  </button>\n",
      "  <strong>Calss: </strong>alert-success\n",
      "</div>\n",
      "<div class=\"alert alert-info\" role=\"alert\">\n",
      "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
      "    <span aria-hidden=\"true\">&times;</span>\n",
      "  </button>\n",
      "  <strong>Calss: </strong>alert-info\n",
      "</div>\n",
      "<div class=\"alert alert-warning\" role=\"alert\">\n",
      "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
      "    <span aria-hidden=\"true\">&times;</span>\n",
      "  </button>\n",
      "  <strong>Calss: </strong>alert-warning\n",
      "</div>\n",
      "<div class=\"alert alert-danger\" role=\"alert\">\n",
      "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
      "    <span aria-hidden=\"true\">&times;</span>\n",
      "  </button>\n",
      "  <strong>Calss: </strong>alert-danger\n",
      "</div>"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div class=\"alert alert-success alert-dismissible\" role=\"alert\">\n",
        "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
        "    <span aria-hidden=\"true\">&times;</span>\n",
        "  </button>\n",
        "  <strong>Calss: </strong>alert-success\n",
        "</div>\n",
        "<div class=\"alert alert-info\" role=\"alert\">\n",
        "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
        "    <span aria-hidden=\"true\">&times;</span>\n",
        "  </button>\n",
        "  <strong>Calss: </strong>alert-info\n",
        "</div>\n",
        "<div class=\"alert alert-warning\" role=\"alert\">\n",
        "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
        "    <span aria-hidden=\"true\">&times;</span>\n",
        "  </button>\n",
        "  <strong>Calss: </strong>alert-warning\n",
        "</div>\n",
        "<div class=\"alert alert-danger\" role=\"alert\">\n",
        "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
        "    <span aria-hidden=\"true\">&times;</span>\n",
        "  </button>\n",
        "  <strong>Calss: </strong>alert-danger\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x7fa25c0946d0>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example Using HTML Widget"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()\n",
      "\n",
      "alert_code = \"\"\"\n",
      "<div class=\"alert alert-{type} alert-dismissible\" role=\"alert\">\n",
      "  <button type=\"button\" class=\"close\" data-dismiss=\"alert\">\n",
      "    <span aria-hidden=\"true\">&times;</span>\n",
      "  </button>\n",
      "  <strong>{title}: </strong>{message}\n",
      "</div>\n",
      "\"\"\"\n",
      "\n",
      "widget_1 = widgets.HTMLWidget()\n",
      "widget_2 = widgets.TextWidget(description=\"Name\")\n",
      "widget_3 = widgets.ButtonWidget(description=\"Save\")\n",
      "\n",
      "\n",
      "def save_form(widget):\n",
      "    widget_1.value = alert_code.format(type=\"success\",\n",
      "                                       title=\"Saved\",\n",
      "                                       message=\"Your form has been saved\",\n",
      "                                        )\n",
      "\n",
      "widget_3.on_click(save_form)\n",
      "\n",
      "container.children = (widget_1, widget_2, widget_3)\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/ipython_widgets/4. Widges Layout.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This tutorial covers the basics of contrlling the layout of IPython widgets.\n",
      "\n",
      "We will be using the following functions to do that.\n",
      "\n",
      "- display()\n",
      "- widget.add_class()\n",
      "- widget.set_css()\n",
      "\n",
      "Video Tutorial:\n",
      "\n",
      "https://www.youtube.com/watch?v=8v7gVQlHJ-g"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import the library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets\n",
      "from IPython.display import display"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Default Layout `\"vbox\"` class"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By default a container will have a `\"vbox\"` css class added to it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v_container = widgets.ContainerWidget()\n",
      "\n",
      "# Define Widgets\n",
      "for counter in range(5):\n",
      "    wgt_check = widgets.CheckboxWidget()\n",
      "    wgt_text = widgets.TextWidget(description=\"Name\")\n",
      "    wgt_select = widgets.DropdownWidget(values=[\"Guest\", \"User\", \"Admin\"])\n",
      "\n",
      "    # Add Widgets\n",
      "    v_container.children += (wgt_check, wgt_text, wgt_select)\n",
      "\n",
      "wgt_button = widgets.ButtonWidget(description=\"Submit\")\n",
      "v_container.children += (wgt_button,)\n",
      "\n",
      "# Display the container\n",
      "display(v_container)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Horizontal Layout with `\"hbox\"` class"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v_container = widgets.ContainerWidget()\n",
      "\n",
      "# Display the container\n",
      "display(v_container)\n",
      "\n",
      "# Define Widgets\n",
      "for counter in range(5):\n",
      "    wgt_check = widgets.CheckboxWidget()\n",
      "    wgt_text = widgets.TextWidget(description=\"Name\")\n",
      "    wgt_select = widgets.DropdownWidget(values=[\"Guest\", \"User\", \"Admin\"])\n",
      "\n",
      "    # Add Widgets\n",
      "    h_container = widgets.ContainerWidget()\n",
      "    h_container.children += (wgt_check, wgt_text, wgt_select)\n",
      "    v_container.children += (h_container,)\n",
      "    \n",
      "    # Change css class of the container\n",
      "    h_container.remove_class('vbox')\n",
      "    h_container.add_class('hbox')\n",
      "\n",
      "wgt_button = widgets.ButtonWidget(description=\"Submit\")\n",
      "v_container.children += (wgt_button,)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/ipython_widgets/Widgets Containers.ipynb
================================================
{
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this tutorial we will cover the following containers and how to use them:\n",
      "\n",
      "- ContainerWidget\n",
      "- AccordionWidget\n",
      "- TabWidget\n",
      "- PopupWidget"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import the Library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html import widgets"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widgets.ContainerWidget`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container = widgets.ContainerWidget()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "control_1 = widgets.TextWidget(description=\"Name: \")\n",
      "control_2 = widgets.TextWidget(description=\"Email: \")\n",
      "control_3 = widgets.ButtonWidget(description=\"Submit\")\n",
      "\n",
      "container.children = [control_1, control_2, control_3]\n",
      "\n",
      "container"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widgets.AccordionWidget`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "container_1 = container\n",
      "\n",
      "container_2 = widgets.ContainerWidget()\n",
      "\n",
      "control_1 = widgets.TextWidget(description=\"Country: \")\n",
      "control_2 = widgets.TextWidget(description=\"State: \")\n",
      "control_3 = widgets.TextWidget(description=\"City: \")\n",
      "control_4 = widgets.ButtonWidget(description=\"Save\")\n",
      "\n",
      "container_2.children = [control_1, control_2, control_3, control_4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "accordion = widgets.AccordionWidget()\n",
      "accordion.children = [container_1, container_2]\n",
      "accordion"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "accordion.set_title(0, \"Personal Information\")\n",
      "accordion.set_title(1, \"Location\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widgets.TabWidget`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tabs = widgets.TabWidget()\n",
      "tabs.children = [container_1, container_2]\n",
      "\n",
      "tabs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tabs.set_title(0, \"Personal Information\")\n",
      "tabs.set_title(1, \"Location\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "`widgets.PopupWidget`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "popup = widgets.PopupWidget(description=\"Register Now!\")\n",
      "popup.children = [accordion]\n",
      "popup"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/jupyter/1. Introduction.ipynb
================================================
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPython 3 (jupyter)\n",
    "\n",
    "**Video Toturial:** https://www.youtube.com/user/roshanRush\n",
    "\n",
    "Jupyter is an web-based interactive development environment. It support multiple programming languages like Julia, Octave, Python and R (In alphabetical order).\n",
    "\n",
    "## Upgrade to the new version\n",
    "\n",
    "```\n",
    "sudo pip install --upgrade \"ipython[all]\"\n",
    "```\n",
    "If you use `sudo pip install --upgrade ipython`, you might get **\"Terminal Unavailable\"** on **`New`** menu.\n",
    "\n",
    "## Enabling Python3\n",
    "\n",
    "You can add Python 3 kernel to jupyter by using this command:\n",
    "\n",
    "```\n",
    "sudo ipython3 kernelspec install-self\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why is it called jupyter?\n",
    "\n",
    "<img src=\"images/jupyter-main-logo.svg\" style=\"width: 400px\"> \n",
    "\n",
    "The name represents the new direction of IPython development. They are moving away from linking the IPython platform with a single language. The way it currently works is as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/kernel.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$Julia + Python + R = jupyter$$\n",
    "\n",
    "This is not to indicate that jupyter only supports these languages. But it is a reference to a [talk](http://youtu.be/JDrhn0-r9Eg?t=6m14s) by [Fernando Perez](https://twitter.com/fperez_org). The complete list is available [here](https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages).\n",
    "\n",
    "\n",
    "### But there is another reason\n",
    "\n",
    "Galileo documented his observations about Jupiter's moons and published them in the Sidereus Nuncius (Starry Messenger) back in 1610 which was a pamphlet that he published. This was the first paper ever published about astronomy that used observations using a telescope. He proved that the Earth orbits the Sun like Jupiter's moons orbit Jupiter.\n",
    "\n",
    "> \"I therefore concluded and decided unhesitatingly, that there are three stars in the heavens moving about Jupiter, as Venus and Mercury round the Sun; which at length was established as clear as daylight by numerous subsequent observations. These observations also established that there are not only three, but four, erratic sidereal bodies performing their revolutions round Jupiter...the revolutions are so swift that an observer may generally get differences of position every hour.\"\n",
    ">\n",
    "> Galileo trans Carlos, 1880, p47. \n",
    "\n",
    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/d/d0/Sidereus_Nuncius_Medicean_Stars.jpg\">\n",
    "<center>Image(s) courtesy of the History of Science Collections, University of Oklahoma Libraries.</center>\n",
    "\n",
    "Sharing these observations and conclusions in a way that can be replicated and verified inspired the modern scientific method. This is what IPython Notebooks are used for; sharing the complete process with others to verify your process and replicate the results.\n",
    "\n",
    "\n",
    "# So what's new?\n",
    "\n",
    "http://ipython.org/ipython-doc/3/whatsnew/version3.html\n",
    "\n",
    "> 3.x will be the last monolithic release of IPython, as the next release cycle will see the growing project split into its Python-specific and language-agnostic components.\n",
    ">\n",
    "> **http://ipython.org/ipython-doc/3/whatsnew/version3.html**\n",
    "\n",
    "## 1. Notebooks with different kernels\n",
    "https://github.com/ipython/ipython/wiki/IPython-kernels-for-other-languages\n",
    "\n",
    "![](http://ipython.org/ipython-doc/3/_images/kernel_selector_screenshot.png)\n",
    "## 2. Unicode Identifiers\n",
    "**Works with Python3 and Julia**\n",
    "\n",
    "To get `α` variable, type \\alpha Then Press Tab."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "α = 1 #\\alpha\n",
    "β = 2 #\\beta\n",
    "λ = 0 #\\lambda\n",
    "Λ = 0 #\\Lambda\n",
    "𝔞 = 0 #\\mfraka - Notice it is not monospaced - Thinner\n",
    "𝖰 = 0 #\\msansQ - Notice it is not monospaced - Wider\n",
    "α / β"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## 3. Widgets are **NOT** Backwards Compatible\n",
    "\n",
    "http://ipython.org/ipython-doc/3/whatsnew/version3_widget_migration.html\n",
    "\n",
    "## 4. No Header Cells\n",
    "You can now use heading inside a markdown cell and you will get a bookmark link on them.\n",
    "\n",
    "## 5. Text Editor\n",
    "A multilingual text editor has been added with highlighting for many programming and scripting languages.\n",
    "\n",
    "## 6. Static File Server\n",
    "You can serve static files using https://url:port/files/%FILE_PATH% like https://0.0.0.0:8888/files/myproject/README.html\n",
    "\n",
    "**<span style=\"color:red;\">Security Warning:</span>** Because Jupyter uses a static file server, it is advised that you use https even if you are running on localhost.\n",
    "\n",
    "But on the upside, you can start with exploring the data and end up with a running web-based application without leaving jupyter. You might not want to use this setup for production but it is perfect for development.\n",
    "\n",
    "## 7. Build-it Terminal\n",
    "You can start a terminal from jupyter to access your machine. Thanks to the development team for making it black background with white text.\n",
    "\n",
    "## 8. Jupyter Drive\n",
    "You can share jupyter notebooks from your Google Drive using the new [Jupyter Drive](https://github.com/jupyter/jupyter-drive). This allows you share jupyter notebooks like NBViewr with all the access control that Google Drive provides.\n",
    "\n",
    "You can also install [Jupyter Drive](https://github.com/jupyter/jupyter-drive) locally. This allows you to access Notebooks on your Google Drive or other Notebooks shared with you. It also allows you to access notebooks on you local machines."
   ]
  }
 ],
 "metadata": {
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   "language": "python",
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================================================
FILE: notebooks/jupyter/2. Markdown & LaTeX.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Tutorial Brief\n",
    "\n",
    "**Video Tutorial:** https://www.youtube.com/user/roshanRush\n",
    "\n",
    "Jupyter has an implementation of markdown language that can be used in in markdown cells to create formatted text and media documentation in your notebook. LaTeX is also implemented to create high quality mathematical typeset.\n",
    "\n",
    "#Markdown\n",
    "\n",
    "##Headings\n",
    "\n",
    "#H1\n",
    "##H2\n",
    "###H3\n",
    "####H4\n",
    "#####H5\n",
    "######H6\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "#H1\n",
    "##H2\n",
    "###H3\n",
    "####H4\n",
    "#####H5\n",
    "######H6\n",
    "```\n",
    "\n",
    "##Alternative Headings\n",
    "\n",
    "Heading 1\n",
    "=========\n",
    "\n",
    "Heading 2\n",
    "----------\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "Heading 1\n",
    "=========\n",
    "\n",
    "Heading 2\n",
    "---------\n",
    "```\n",
    "\n",
    "##Font Styles\n",
    "\n",
    "**Bold Font** or __Bold Font__\n",
    "\n",
    "*Italic* or _Italic Font_\n",
    "\n",
    "~~Scratched Text~~\n",
    "\n",
    "Markdown doesn't support underline. but you can do that using HTML <u>Text</u>\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "**Bold Font** or __Bold Font__\n",
    "\n",
    "*Italic* or _Italic Font_\n",
    "\n",
    "~~Scratched Text~~\n",
    "\n",
    "Markdown doesn't support underline. but you can do that using HTML <u>Text</u>\n",
    "```\n",
    "\n",
    "##Lists\n",
    "\n",
    "- item\n",
    "- item\n",
    " - subitem\n",
    " - subitem\n",
    "- item\n",
    "\n",
    "\n",
    "1. item\n",
    "2. item\n",
    " 1. sub item\n",
    " 2. sub item\n",
    "3. item\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "- item\n",
    "- item\n",
    " - subitem\n",
    " - subitem\n",
    "- item\n",
    "\n",
    "\n",
    "1. item\n",
    "2. item\n",
    " 1. sub item\n",
    " 2. sub item\n",
    "3. item\n",
    "```\n",
    "\n",
    "##Links\n",
    "\n",
    "http://www.github.com/\n",
    "\n",
    "[Github](http://www.github.com/)\n",
    "\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "http://www.github.com/\n",
    "\n",
    "[Github](http://www.github.com/)\n",
    "```\n",
    "\n",
    "##Images\n",
    "![Turing's Device](http://www.google.com/logos/2012/turing-doodle-static.jpg \"Alan Turing's 100th Birthday\")\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "![Turing's Device](http://www.google.com/logos/2012/turing-doodle-static.jpg \"Alan Turing's 100th Birthday\")\n",
    "```\n",
    "\n",
    "##Quotes\n",
    "\n",
    "> Why, oh why, Javascript??? Wars, famine, planetary destruction... I guess as a species, we deserve this abomination...\n",
    ">\n",
    "> [Fernando Perez](https://twitter.com/fperez_org)\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "> Why, oh why, Javascript??? Wars, famine, planetary destruction... I guess as a species, we deserve this abomination...\n",
    ">\n",
    "> [Fernando Perez](https://twitter.com/fperez_org)\n",
    "```\n",
    "\n",
    "##Horizontal Line\n",
    "\n",
    "---\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "---\n",
    "```\n",
    "\n",
    "##Tables\n",
    "\n",
    "| Tables        | Are           | Cool  |\n",
    "| ------------- |:-------------:| -----:|\n",
    "| col 3 is      | right-aligned |  1600 |\n",
    "| col 2 is      | centered      |    12 |\n",
    "| zebra stripes | are neat      |     1 |\n",
    "\n",
    "**Code:**\n",
    "\n",
    "```\n",
    "| Tables        | Are           | Cool  |\n",
    "| ------------- |:-------------:| -----:|\n",
    "| col 3 is      | right-aligned |  1600 |\n",
    "| col 2 is      | centered      |    12 |\n",
    "| zebra stripes | are neat      |     1 |\n",
    "```\n",
    "\n",
    "##HTML\n",
    "\n",
    "<b>You</b> can <i>render</i> almost any <span style=\"color:red;\">HTML</span> code you <u>like</u>.\n",
    "\n",
    "**Code:**\n",
    "\n",
    "```\n",
    "<b>You</b> can <i>render</i> almost any <span style=\"color:red;\">HTML</span> code you <u>like</u>.\n",
    "```\n",
    "\n",
    "##Code\n",
    "\n",
    "You can add in line code like this `import numpy as np`\n",
    "\n",
    "Or block code:\n",
    "\n",
    "Python Code:\n",
    "```python\n",
    "x = 5\n",
    "print \"%.2f\" % x\n",
    "```\n",
    "\n",
    "Java Script Code:\n",
    "```javascript\n",
    "x = 5\n",
    "alert(x);\n",
    "```\n",
    "\n",
    "**Code:**\n",
    "\n",
    "<pre>\n",
    "Python Code:\n",
    "```python\n",
    "x = 5\n",
    "print \"%.2f\" % x\n",
    "```\n",
    "\n",
    "Java Script Code:\n",
    "```javascript\n",
    "x = 5\n",
    "alert(x);\n",
    "```\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#LaTeX\n",
    "\n",
    "**References:**\n",
    "\n",
    "- [LaTeX Wiki](http://en.wikibooks.org/wiki/LaTeX/Mathematics)\n",
    "- [Duke University, Department of  Statistical Science](https://stat.duke.edu/resources/computing/latex)\n",
    "- [Equation Sheet](http://www.equationsheet.com/)\n",
    "\n",
    "LaTeX is a large typeset system for scientific documentation which symbols for mathematics, statistics, physics, quantum mechanics, and computer science. It is beyond the scope of this tutorial to cover everything, but we will go over the basics of writing high quality mathematical equations using LaTeX.\n",
    "\n",
    "You can use LaTeX in line by like this $y = x^2$ or in block like this $$y = x^2$$\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "You can use LaTeX in line by like this $y = x^2$ or in block like this $$y = x^2$$\n",
    "```\n",
    "\n",
    "##Operators:\n",
    "\n",
    "- Add:\n",
    " - $x + y$\n",
    "- Subtract:\n",
    " - $x - y$\n",
    "- Multiply\n",
    " - $x * y$\n",
    " - $x \\times y$ \n",
    " - $x . y$ \n",
    "- Divide\n",
    " - $x / y$\n",
    " - $x \\div y$\n",
    " - $\\frac{x}{y}$\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "- Add:\n",
    " - $x + y$\n",
    "- Subtract:\n",
    " - $x - y$\n",
    "- Multiply\n",
    " - $x * y$\n",
    " - $x \\times y$ \n",
    " - $x . y$ \n",
    "- Divide\n",
    " - $x / y$\n",
    " - $x \\div y$\n",
    " - $\\frac{x}{y}$\n",
    "```\n",
    "\n",
    "##Relations\n",
    "\n",
    "- $\\pi \\approx 3.14159$\n",
    "- ${1 \\over 0} \\neq \\inf$\n",
    "- $0 < x > 1$\n",
    "- $0 \\leq x \\geq 1$\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "- $\\pi \\approx 3.14159$\n",
    "- ${1 \\over 0} \\neq \\inf$\n",
    "- $0 < x > 1$\n",
    "- $0 \\leq x \\geq 1$\n",
    "```\n",
    "\n",
    "##Fractions\n",
    "\n",
    "- $^1/_2$\n",
    "- $\\frac{1}{2x}$\n",
    "- ${3 \\over 4}$\n",
    "\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "- $^1/_2$\n",
    "- $\\frac{1}{2x}$\n",
    "- ${3 \\over 4}$\n",
    "```\n",
    "\n",
    "##Greek Alphabet\n",
    "\n",
    "| Small Letter          | Capical Letter       | Alervative                  |\n",
    "| --------------------- | -------------------- | --------------------------- |\n",
    "| $\\alpha$   `\\alpha`   | $A$ `A`              |                             |\n",
    "| $\\beta$   `\\beta`     | $B$ `B`              |                             |\n",
    "| $\\gamma$   `\\gamma`   | $\\Gamma$ `\\Gamma`    |                             |\n",
    "| $\\delta$   `\\delta`   | $\\Delta$ `\\Delta`    |                             |\n",
    "| $\\epsilon$ `\\epsilon` | $E$ `E`              | $\\varepsilon$ `\\varepsilon` |\n",
    "| $\\zeta$   `\\zeta`     | $Z$ `Z`              |                             |\n",
    "| $\\eta$   `\\eta`       | $H$ `H`              |                             |\n",
    "| $\\theta$ `\\theta`     | $\\Theta$ `\\Theta`    | $\\vartheta$ `\\vartheta`     |\n",
    "| $\\iota$   `\\zeta`     | $I$ `I`              |                             |\n",
    "| $\\kappa$ `\\kappa`     | $K$ `K`              | $\\varkappa$ `\\varkappa`     |\n",
    "| $\\lambda$   `\\lambda` | $\\Lambda$ `\\Lambda`  |                             |\n",
    "| $\\mu$   `\\mu`         | $M$ `M`              |                             |\n",
    "| $\\nu$   `\\nu`         | $N$ `N`              |                             |\n",
    "| $\\xi$   `\\xi`         | $Xi$ `\\Xi`           |                             |\n",
    "| $\\omicron$ `\\omicron` | $O$ `O`              |                             |\n",
    "| $\\pi$ `\\pi`           | $\\Pi$ `\\Pi`          | $\\varpi$ `\\varpi`           |\n",
    "| $\\rho$ `\\rho`         | $P$ `P`              | $\\varrho$ `\\varrho`         |\n",
    "| $\\sigma$ `\\sigma`     | $\\Sigma$ `\\Sigma`    | $\\varsigma$ `\\varsigma`     |\n",
    "| $\\tau$   `\\tau`       | $T$ `T`              |                             |\n",
    "| $\\upsilon$ `\\upsilon` | $\\Upsilon$ `\\Upsilon`|                             |\n",
    "| $\\phi$ `\\phi`         | $\\Phi$ `\\Phi`        | $\\varphi$ `\\varphi`         |\n",
    "| $\\chi$   `\\chi`       | $X$ `X`              |                             |\n",
    "| $\\psi$ `\\psi`         | $\\Psi$ `\\Psi`        |                             |\n",
    "| $\\omega$   `\\omega`   | $\\Omega$ `\\Omega`    |                             |\n",
    "\n",
    "##Power & Index\n",
    "\n",
    "You can add power using the carrot `^` symbol. If you have more than one character you have to enclose them in a curly brackets.\n",
    "\n",
    " $$f(x) = x^2 - x^{1 \\over \\pi}$$\n",
    "\n",
    "For index you can use the underscore symbol:\n",
    "\n",
    "$$f(X,n) = X_n + X_{n-1}$$\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "$$f(x) = x^2 - x^{1 \\over \\pi}$$\n",
    "$$f(X,n) = X_n + X_{n-1}$$\n",
    "\n",
    "```\n",
    "\n",
    "##Roots & Log\n",
    "\n",
    "You can express a square root in LaTeX using the `\\sqrt` and to change the level of the root you can use `\\sqrt[n]` where `n` is the level of the root.\n",
    "\n",
    "$$f(x) = \\sqrt[3]{2x} + \\sqrt{x-2}$$\n",
    "\n",
    "To represent a log use `\\log[base]` where `base` is the base of the logarithmic term.\n",
    "\n",
    "$$\\log[x] x = 1$$\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "$$f(x) = \\sqrt[3]{2x} + \\sqrt{x-2}$$\n",
    "```\n",
    "\n",
    "##Sums & Products\n",
    "\n",
    "You can represent a sum with a sigma using `\\sum\\limits_{a}^{b}` where a and b are the lower and higher limits of the sum.\n",
    "\n",
    "$$\\sum\\limits_{x=1}^{\\infty} {1 \\over x} = 2$$\n",
    "\n",
    "Also you can represent a product with `\\prod\\limits_{a}^{a}` where a and b are the lower and higher limits.\n",
    "\n",
    "$$\\prod\\limits_{i=1}^{n} x_i - 1$$\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "$$\\sum\\limits_{x=1}^{\\infty} {1 \\over x} = 2$$\n",
    "$$\\prod\\limits_{i=1}^{n} x_i - 1$$\n",
    "```\n",
    "\n",
    "##Statistics\n",
    "\n",
    "To represent basic concepts in statistics about sample space `S`, you can represent a maximum:\n",
    "\n",
    "$$max(S) = \\max\\limits_{i: S_i \\in S} S_i$$\n",
    "\n",
    "In the same way you can get the minimum:\n",
    "\n",
    "$$min(S) = \\min\\limits_{i: S_i \\in S} S_i$$\n",
    "\n",
    "To represent a [binomial coefficient](http://en.wikipedia.org/wiki/Binomial_coefficient) with n choose k, use the following:\n",
    "\n",
    "$$\\frac{n!}{k!(n-k)!} = {n \\choose k}$$\n",
    "\n",
    "for :\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "$$max(S) = \\max\\limits_{i: x_i \\in \\{S\\}} x_i$$\n",
    "$$min (S) = \\min\\limits_{i: x_i \\in \\{S\\}} x_i$$\n",
    "$$\\frac{n!}{k!(n-k)!} = {n \\choose k}$$\n",
    "```\n",
    "\n",
    "##Calculus\n",
    "\n",
    "Limits are represented using `\\lim\\limits_{x \\to a}` as `x` approaches `a`.\n",
    "\n",
    "$$\\lim\\limits_{x \\to 0^+} {1 \\over 0} = \\infty$$\n",
    "\n",
    "For integral equations use `\\int\\limits_{a}^{b}` where `a` and `b` are the lower and higher limits.\n",
    "\n",
    "$$\\int\\limits_a^b 2x \\, dx$$\n",
    "\n",
    "\n",
    "**Code:**\n",
    "```markdown\n",
    "$$\\lim\\limits_{x \\to 0^+} {1 \\over 0} = \\inf$$\n",
    "$$\\int\\limits_a^b 2x \\, dx$$\n",
    "```\n",
    "\n",
    "##Function definition over periods\n",
    "\n",
    "Defining a function that is calculated differently over a number of period can done using LaTeX. There are a few tricks that we will use to do that:\n",
    "\n",
    "- The large curly bracket `\\left\\{ ... \\right.` Notice it you want to use `(` or `[` you don't have to add a back slash(`\\`). You can also place a right side matching bracket by replacing the `.` after `\\right` like this `.right}`\n",
    "- Array to hold the definitions in place. it has two columns with left alignment. `\\begin{array}{ll} ... \\end{array}`\n",
    "- Line Breaker `\\\\`\n",
    "- Text alignment box ` \\mbox{Text}`\n",
    "\n",
    "$f(x) =\\left\\{\\begin{array}{ll}0  & \\mbox{if } x = 0 \\\\{1 \\over x} & \\mbox{if } x \\neq 0\\end{array}\\right.$\n",
    "\n",
    "**Code:**\n",
    "```\n",
    "$f(x) =\n",
    "\\left\\{\n",
    "\t\\begin{array}{ll}\n",
    "\t\t0  & \\mbox{if } x = 0 \\\\\n",
    "\t\t{1 \\over x} & \\mbox{if } x \\neq 0\n",
    "\t\\end{array}\n",
    "\\right.$\n",
    "```\n",
    "\n",
    "**Note:** If you are planning to show your notebook in NBViewer write your latex code in one line. For example you can write the code above like this:\n",
    "\n",
    "```\n",
    "$f(x) =\\left\\{\\begin{array}{ll}0  & \\mbox{if } x = 0 \\\\{1 \\over x} & \\mbox{if } x \\neq 0\\end{array}\\right.$\n",
    "```\n",
    "\n",
    "#Quick Quiz (Normal Distribution)\n",
    "\n",
    "Try to replicate the [Normal Distribution](http://en.wikipedia.org/wiki/Normal_distribution) formula using LaTeX. If you solve it, leave the LaTeX code in the comments below. $Don't\\ cheat$.\n",
    "\n",
    "$$P(x,\\sigma,\\mu) = \\frac{1}{{\\sigma \\sqrt {2\\pi } }}e^{{-(x - \\mu)^2 } / {2\\sigma ^2}}$$\n",
    "\n",
    "Tips to help with the quiz:\n",
    "\n",
    "- $\\mu$ is `\\mu`\n",
    "- $\\sigma$ is `\\sigma`\n",
    "- $e$ is `e`"
   ]
  }
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================================================
FILE: notebooks/jupyter/3. Python Basics.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Tutorial Brief\n",
    "This tutorial is an introduction to Python 3. This should give you the set of `pythonic` skills that you will need to proceed with this tutorial series.\n",
    "\n",
    "If you don't have the Jupyter installed, shame on you. No just kidding you can follow this tutorial using an online jupyter service:\n",
    "\n",
    "https://try.jupyter.org/\n",
    "\n",
    "##Cell Input and Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1\n",
    "1+2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##The `print` function\n",
    "\n",
    "**Notice:** `print` is a function in Python 3. You should use parentheses around your parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(1+2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Variables\n",
    "\n",
    "There are many variable types in Python 3. Here is a list of the most common types:\n",
    "\n",
    "###Numerical Types:\n",
    "- bool (Boolean)\n",
    "- int (Integer/Long)\n",
    "- float\n",
    "- complex\n",
    "\n",
    "**Notice:** In Python 3 integer represents integer and long. Because there is no more long data type, you will not get `L` at the end of long integers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = 4\n",
    "b = 1.5\n",
    "c = 121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212121212\n",
    "d = 1j\n",
    "e = 1/3\n",
    "f = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848484848"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6+4j)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(b+d)*a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Other Value Types:\n",
    "- str (String)\n",
    "- list (Ordered Array)\n",
    "- tuple (Ordered Immutable Array)\n",
    "- dict (Unordered list of keys and values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Roshan\n"
     ]
    }
   ],
   "source": [
    "my_name = \"Roshan\"\n",
    "print(my_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1,2,3,4,5]\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list + [6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list += [6,7,8]\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list.append(9)\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tuple = (1,2,3)\n",
    "my_tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 4, 5, 6)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_tuple + (4,5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'credit': 100, 'name': 'Roshan'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict = {\"name\":\"Roshan\", \"credit\":100}\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Roshan'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict[\"name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'credit': 100, 'level': 4, 'name': 'Roshan'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict[\"level\"] = 4\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values([100, 'Roshan', 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['credit', 'name', 'level'])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_dict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Selecting / Slicing\n",
    "\n",
    "Use `len` function to measure the length of a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access a single value in a list use this syntax:\n",
    "```python\n",
    "list_name[index]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select multiple value from a list use this syntax:\n",
    "\n",
    "```python\n",
    "index[start:end:step]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice:** negative index selected from the end of the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use negative indexing in selecting multiple values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 9]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5, 6, 7, 8]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[3:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The third location in the index is the `step`. If the `step` is negative the the list is returned in descending order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 9]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 6, 8]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[3::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 8, 7, 6, 5, 4, 3, 2, 1]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Working with Strings\n",
    "\n",
    "You can select from a string like a list suing this syntax:\n",
    "\n",
    "```python\n",
    "my_string[star:end:step]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Roshan'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'R'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_name[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice:** You can also use negative indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ro'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_name[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Unicode\n",
    "\n",
    "**Notice:** You can use unicode inside your string variables. Unlike Python 2, no need to use `u\"\"` to use unicode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "आप शून्य से विभाजित नहीं किया जा सकता ≈\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorted by most spoken languages in order\n",
    "divide_by_zero = {\"zho\":\"你不能除以零\",\n",
    "                  \"eng\":\"You cannot divide by zero\",\n",
    "                  \"esp\":\"No se puede dividir por cero\",\n",
    "                  \"hin\":\"आप शून्य से विभाजित नहीं किया जा सकता \\u2248\",\n",
    "                  \"arb\":\"لا يمكن القسمة على صفر\"}\n",
    "\n",
    "print(divide_by_zero[\"hin\"])\n",
    "type(divide_by_zero[\"hin\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##String Formatting\n",
    "\n",
    "You can use this syntax to format a string:\n",
    "\n",
    "```python\n",
    "some_variable = 50\n",
    "x = \"Value: %s\" % some_variable\n",
    "print(x) # Value: 50\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rush, R.\n"
     ]
    }
   ],
   "source": [
    "first_name = \"Roshan\"\n",
    "last_name = \"Rush\"\n",
    "formatted_name = \"%s, %s.\" % (last_name, first_name[0])\n",
    "print(formatted_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other formatters could be used to format numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "π ≈ 3.14\n"
     ]
    }
   ],
   "source": [
    "print(\"π ≈ %.2f\" % 3.14159)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find unicode symbols:\n",
    "\n",
    "http://www.fileformat.info/info/unicode/char/search.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homeworks: 15.75\n",
      "Mid-term: 22.00\n",
      "Final: 51.00\n",
      "Total: 88.75/100\n"
     ]
    }
   ],
   "source": [
    "homeworks = 15.75\n",
    "midterm = 22\n",
    "final = 51\n",
    "total = homeworks + midterm + final\n",
    "\n",
    "print(\"Homeworks: %.2f\\nMid-term: %.2f\\nFinal: %.2f\\nTotal: %.2f/100\" % (homeworks, midterm, final, total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Using `format(*args, **kwargs)` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'http://en.wikipedia.org/'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"http://{language}.wikipedia.org/\"\n",
    "url = url.format(language=\"en\")\n",
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Mathematics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice:** The default behavior of division in Python 3 is float division. To use integer division like Python 2, use `//`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.8"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14/5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "14//5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2*5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To raise a number to any power use down asterisk `**`. To represent $a^{n}$:\n",
    "\n",
    "```python\n",
    "a**n\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To calculate the remainder (modulo operator) use `%`. To represent $a \\mod b = r$:\n",
    "\n",
    "```python\n",
    "a % b # Returns r\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 % 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the `math` library to access a varaity of tools for algebra and geometry. To import a library, you can use one of these syntaxes:\n",
    "\n",
    "```python\n",
    "import library_name\n",
    "import library_name as alias\n",
    "from module_name import some_class\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n=52\n",
    "k=1\n",
    "math.factorial(n) / (math.factorial(k) * math.factorial(n-k))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for counter in [1,2,3,4]:\n",
    "    print(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###range\n",
    "\n",
    "In Python 3 `range` is a data type that generates a list of numbers.\n",
    "\n",
    "```python\n",
    "range(stop)\n",
    "range(start,stop[ ,step])\n",
    "```\n",
    "\n",
    "**Notice:** In Python 2 `range` is a function that returns a list. In Python 3, `range` returns an iterable of type `range`. If you need to get a list you can use the `list()` function:\n",
    "\n",
    "```python\n",
    "list(range(start,stop[, step]))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for counter in range(5):\n",
    "    print(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice:** The list doesn't reach the `stop` value and stops one `step` before. The reason behind that is to make this syntax possible:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(range(1,10)) == list(range(1,5)) + list(range(5,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice:** In Python 3 use use `==` to check if two values are equal. To check if two values are not equal use `!=` and don't use `<>` from Python 2 because it is not supported any more in Python 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for counter in range(1,5):\n",
    "    print(counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for counter in range(2,10,2):\n",
    "    print(counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###While Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "counter =1\n",
    "while counter < 5:\n",
    "    print(counter)\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##If .. Else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "π is irrational\n"
     ]
    }
   ],
   "source": [
    "if math.pi == 3.2:\n",
    "    print(\"Edward J. Goodwin was right!\")\n",
    "else:\n",
    "    print(\"π is irrational\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Square root of 2 is irrational\n"
     ]
    }
   ],
   "source": [
    "if math.sqrt(2) == (10/7):\n",
    "    print(\"Edward J. Goodwin was right!\")\n",
    "elif math.sqrt(2) != (10/7):\n",
    "    print(\"Square root of 2 is irrational\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###If you like Math:\n",
    "Fun story about pi where it was *almost* set by law to be equal to 3.2!\n",
    "\n",
    "If you don't what is the \"pi bill\" you can read about it here:\n",
    "http://en.wikipedia.org/wiki/Indiana_Pi_Bill\n",
    "\n",
    "Or watch Numberphile video about it:\n",
    "https://www.youtube.com/watch?v=bFNjA9LOPsg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unusual\n"
     ]
    }
   ],
   "source": [
    "probability = 0.3\n",
    "\n",
    "if probability >= 0.75:\n",
    "    print(\"Sure thing\")\n",
    "elif probability >= 0.5:\n",
    "    print(\"Maybe\")\n",
    "elif probability >= 0.25:\n",
    "    print(\"Unusual\")\n",
    "else:\n",
    "    print(\"No way\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Functions\n",
    "\n",
    "Functions are defined in Python using `def` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_circumference(r):\n",
    "    return math.pi * r * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31.41592653589793"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_circumference(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1326.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def binomilal_coef(n,k):\n",
    "    \"\"\"\n",
    "    This function returns the binominal coef\n",
    "    \n",
    "    Parameters:\n",
    "    ===========\n",
    "    n, k int\n",
    "    \n",
    "    return n!/(k!*(n-k)!)\n",
    "    \"\"\"\n",
    "    value = math.factorial(n)/(math.factorial(k)*math.factorial(n-k))\n",
    "    return value\n",
    "\n",
    "\n",
    "binomilal_coef(52,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice:** Use can use SHIFT+TAB to show the docstring of a function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###If You Like Math\n",
    "\n",
    "This was the binomial function which tells you how many ways are there to select k number of samples from a sample space with n items if order didn't matter and chosen samples could not be repeated (chosen again).\n",
    "\n",
    "As an example, How many ways to select 2 card from a deck of 52 cards if order of selection didn't matter and you where not allowed to choose the same card twice?\n",
    "\n",
    "$${n \\choose k} = {n! \\over {k!(n-k)!}}$$\n",
    "\n",
    "$${52 \\choose 2} = {52! \\over {2!(52-2)!}} = {52! \\over {2!(50)!}} =  1326$$\n",
    "\n",
    "\n",
    "#Quick Quiz:\n",
    "\n",
    "How many ways to select 2 cards from a deck of 52 cards if order didn't matter and you could choose the same card twice?\n",
    "\n",
    "**hints:**\n",
    "\n",
    "- Read about k-combination with repetitions:\n",
    "http://en.wikipedia.org/wiki/Combination#Number_of_combinations_with_repetition\n",
    "\n",
    "- The equation\n",
    "$$\\left(\\!\\!\\!\\binom{n}{k}\\!\\!\\!\\right) = {{n+k-1} \\choose k}$$\n",
    "\n",
    "- To make your life easier:\n",
    "\n",
    "$$\\left(\\!\\!\\!\\binom{52}{2}\\!\\!\\!\\right) = {{52+2-1} \\choose 2} = {{53} \\choose 2} = \\frac{53!}{2!(51)!}$$\n",
    "\n",
    "- **If you got 1326 then you got it <span style=\"color:red\">WRONG</span>.**\n",
    "\n",
    "#Last quiz was solved by:\n",
    "[pacrii](https://www.youtube.com/user/pacrii)"
   ]
  }
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================================================
FILE: notebooks/jupyter/4. Numpy.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial Outline\n",
    "\n",
    "- [What is Numpy?](#What-is-Numpy)\n",
    "- [`ndarray` object](#ndarray-object)\n",
    "- [Mathematical & Logical Operation](#Mathematical-&-Logical-Operation)\n",
    "- Benchmarking Performance\n",
    "- [Multi-Dimensional Arrays Operations](#Multi-Dimensional-Arrays-Operations)\n",
    "- [Slicing](#Slicing)\n",
    "- [Filtering](#Filtering)\n",
    "- [Exploring `ndarray` object](#Exploring-ndarray-object)\n",
    "- [Array Creation](#Array-Creation)\n",
    "- [Numpy array as an image](#Numpy-array-as-an-image)\n",
    "\n",
    "# What is Numpy\n",
    "\n",
    "Numpy is a fundamental library for Python which is used for scientific computing and manipulation of large arrays of data. Numpy performs mathematical, statistical and data manipulation on arrays much faster that regular Python operations. This difference is very important when you are performing large amount of calculations of arrays.\n",
    "\n",
    "The major advantage of using Numpy for handling arrays, is that Numpy implements all the heavy lifting in C language which is much faster. This allows you to tap into the powerful C language performance from the comfort of Python.\n",
    "\n",
    "Numpy is a part of the Scipy ecosystem of libraries which is used for mathimatics, science and engineering. This ecosystem includes:\n",
    "\n",
    "- Scipy (Fundamental librariy for science, mathematics and engineering)\n",
    "- Numpy (Multi-dimensional array for effecient math and logic operations)\n",
    "- Pandas (Data analysis and DataFrame & Series objects)\n",
    "- Jupyter (Web-based interactive development environment)\n",
    "- Matplotlib (Charting library)\n",
    "- Sympy (Symbolic computation library)\n",
    "\n",
    "\n",
    "# `ndarray` object\n",
    "\n",
    "The `ndarray` (pronounced *N D array*) object is the main object for representing your array. This object can handle multi-dimensional array of any size that your memory can store. The biggest differences between a Python `list` and a `ndarray` object are these:\n",
    "\n",
    "- `ndarray` is a fixed size array while `list` has a dynamic size. When you reshare a `ndarray` a new object is created with the new shape and the old object is deleted from memory.\n",
    "- `ndarray` allows mathematical and logical operations on complete multi-dimensional arrays. With a Python `list` you will have to iterate over the sequence which take more time and code.\n",
    "- `ndarray` has homogeneous data type for the complete array while Python `list` can contain multiple data types within a single array.*\n",
    "\n",
    "**Note**: You could have multiple data types in a single object that has multiple dimensions.\n",
    "\n",
    "# Mathematical & Logical Operation\n",
    "\n",
    "## Import the library\n",
    "\n",
    "You will have to import Numpy in your code to use it. A common alias for Numpy is `np`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create arrays\n",
    "\n",
    "We will perform a number of mathematical operations on a large list with 5 millions variables to compare Python `list` to Numpy `ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#%%timeit\n",
    "python_list_1 = list(range(5000000))\n",
    "python_list_2 = list(range(5000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#%%timeit\n",
    "np_array_1 = np.arange(5000000)\n",
    "np_array_2 = np.arange(5000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding a fixed number to an array\n",
    "\n",
    "We will define a fixed number that we will add to every item in our array. In Numpy it is very simple to do that, just add the two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 11.4 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np_array_1 + 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In python it is much more complicated because you will have to do that manually with a loop. It is also much slower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 626 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = []\n",
    "for i in python_list_1:\n",
    "    python_output.append(i + 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 373 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = [i + 7 for i in python_list_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding two lists to each other\n",
    "\n",
    "You will see the same tpattern here. In it very simple and fast to add two arrays in Numpy, just add them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 14.3 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np_array_1 + np_array_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 1.16 s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = []\n",
    "for i in range(len(python_list_1)):\n",
    "    python_output.append(python_list_1[i] + python_list_2[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 874 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = [python_list_1[i] + python_list_2[i] for i in range(len(python_list_1))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiplying two lists\n",
    "\n",
    "This is pointwise product multiplication, meaning that we multiply each value in the fist array by the value of the second array. If we had two arrays $A$ and $B$. Both matricies should have the same size.\n",
    "\n",
    "$$ C = A \\circ B$$\n",
    "$$ C_{ij} = A_{ij} \\times B_{ij}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 14.7 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np_array_1 * np_array_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 1.18 s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = []\n",
    "for i in range(len(python_list_1)):\n",
    "    python_output.append(python_list_1[i] * python_list_2[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 914 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = [python_list_1[i] * python_list_2[i] for i in range(len(python_list_1))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Multiplication\n",
    "\n",
    "This is different that the previous pointwise product multiplication. If we had matrix $A$ and matrix $B$ and we wanted to multiply them the following must be true.\n",
    "\n",
    "$A$ has size of $m,n$ *(rows, columns)* and $B$ has a size of $o,p$. To multiply:\n",
    "\n",
    "$$A_{m,n} . B_{o,p} = C_{m,p}$$\n",
    "\n",
    "if $n=o$ *(the inner dimension)* resulting in a matrix of the size $(m,p)$ *(the outer dimension)*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A:\n",
      "==\n",
      "[[4]\n",
      " [3]\n",
      " [6]]\n",
      "B:\n",
      "==\n",
      "[[8 8 7]]\n"
     ]
    }
   ],
   "source": [
    "A = np.random.randint(1,10,(3,1))\n",
    "B = np.random.randint(1,10,(1,3))\n",
    "print(\"A:\\n==\")\n",
    "print(A)\n",
    "print(\"B:\\n==\")\n",
    "print(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[32 32 28]\n",
      " [24 24 21]\n",
      " [48 48 42]]\n"
     ]
    }
   ],
   "source": [
    "C = A.dot(B)\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[98]]\n"
     ]
    }
   ],
   "source": [
    "C = B.dot(A)\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Odd or even"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ..., False,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1 % 2 == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "python_output = [python_list_1[i] % 2 == 0 for i in python_list_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Dimensional Arrays Operations\n",
    "\n",
    "Starting with 2D array which is 2000 * 2000 = 4M with values like this:\n",
    "\n",
    "```\n",
    "0 0 0 ... 0\n",
    "1 1 1 ... 1\n",
    "...\n",
    "...\n",
    "1998 1998 1998 ... 1998\n",
    "1999 1999 1999 ... 1999\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "python_list_1 = [[i for l in range(2000)] for i in range(2000)]\n",
    "python_list_2 = [[i for l in range(2000)] for i in range(2000)]\n",
    "\n",
    "np_array_1 = np.array(python_list_1)\n",
    "np_array_2 = np.array(python_list_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   1,    1,    1, ...,    1,    1,    1],\n",
       "       [   2,    2,    2, ...,    2,    2,    2],\n",
       "       ..., \n",
       "       [1997, 1997, 1997, ..., 1997, 1997, 1997],\n",
       "       [1998, 1998, 1998, ..., 1998, 1998, 1998],\n",
       "       [1999, 1999, 1999, ..., 1999, 1999, 1999]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiply 2D Arrays\n",
    "\n",
    "This is pointwise product multiplication, meaning that we multiply each value in the fist array by the value of the second array. If we had two arrays $A$ and $B$. Both matricies should have the same size.\n",
    "\n",
    "$$ C = A \\circ B$$\n",
    "$$ C_{ij} = A_{ij} \\times B_{ij}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 8.55 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "np_array_1 * np_array_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 1.95 s per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "python_output = []\n",
    "for i in range(len(python_list_1)):\n",
    "    python_output.append([])\n",
    "    for l in range(len(python_list_2)):\n",
    "        python_output[-1:].append(python_list_1[i][l] * python_list_2[i][l])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexing\n",
    "\n",
    "## 1D Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np_array_1 = np.array(range(5000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999999"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[1:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[1:10:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    1,    2, ..., 1997, 1998, 1999],\n",
       "       [   1,    2,    3, ..., 1998, 1999, 2000],\n",
       "       [   2,    3,    4, ..., 1999, 2000, 2001],\n",
       "       ..., \n",
       "       [1997, 1998, 1999, ..., 3994, 3995, 3996],\n",
       "       [1998, 1999, 2000, ..., 3995, 3996, 3997],\n",
       "       [1999, 2000, 2001, ..., 3996, 3997, 3998]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "python_list_1 = [[i+l for l in range(2000)] for i in range(2000)]\n",
    "np_array_1 = np.array(python_list_1)\n",
    "np_array_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2,    3,    4, ..., 1999, 2000, 2001])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[2][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[2,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[1:5,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering\n",
    "\n",
    "You find find items in a numpy array using a mask of booleans. The mask \"or filter\" should be the same shape as data and returns values where the mask is equal to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img 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VxXrpi3lxcmfMXXSDso9TaiGiDRrAT0w5NwyjUtGv8DWn3vrPI69f+/g3+d35929s+dg78y4efO5Lc3u7yqLn9g6pjGk02be31bEnNuWkjM8iVZhHkozs/j2uyAGj23F568U4t2vS9jaNp3rxhfzr6ey2PT8Ih54rxVpncOxuWrJW59PfhUQlUzGPanEalYHkZNeQAeqt3Y3GzZsIbuRdVW7trJl37/q2F2rW9Cc1GzRDH3qHCzT1/LVinxWvJkP1mAS+6Ry2bTTOK1iJV8DdW43HpO9s0RYiD/nTCamtuXT2VtYu6aIDV8WAhDSKo7ThqcxNKMjHWJ0+zgRCfBADen5FFnmU/6uhvyegqIY+vIYhh5hs9CB5zDts8NvY4lJYOgD5x2irMH847PBjTxuENYplQv+nsoF/n4vROSEpjFUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqInISM4tmt/6DYZQahmEeerGbsR1O3zXir0+/vGhbTQ9z79QnJz6vUfDGgEsNwzCN019cv9tD+O/5bApUEREBILRt54K0tLQt+y9dOnfYlhTlLi/PWpP80Yvjx47s3HPNze/nj/EETKg2ptqyfGzc00bkZWWfVdLWV6UqUEVEBEj0Xj9//U2bN29O33/Z8tOOzrkOZ4uyDXNPueuM4M9ha9DMK2947t18T7q/a3zUXHm2b1eX9vF1sQpUERE5giAz5tTRm55+/5Wrzw0lj+oPo579KP+iQG2lmpWZkZ9vpauvy1WgiohIk1haDS6+pBtrALLX7j7FeUCGeIzS718/7f7RA9/sGGsrahh7DTOTTjsv87YZX99Z6Cb01yV6jYoNb3d9MGPQa11ahuT/PGYbHN+5bFDGxPmzf6zo790X2i5j+zPdbzQMwzTOnLu6xCT4wLLcRta0nmMPvR6gwvjoPPssS9yook9cJFA1L2ZolJFjGOHei5dWXnus709AT44vIiLHkddt1LuxAdhCbfW/NE9dRtbrV10y4Np5b+ZDcEy3P20dNSL50+CyzMgvPlo87PlbFj/z1oczz181/6ZLOgdT3rCPaVSunNhn4IAnv8iEiKj0wTtGntVmfrir1Ni57qseK96ZfPGKdxZc8OM33148ZUDUIt80hYPNjqPvXXxr9IehL85Zn+EkxXvhzRe93j44qvz09vb/HWvpClQREWkS9+7FSbM3cAaE0H1Q+++CwQvg2jYjNWPsvBfzibGPnLliwpwbT/lnmIEbwJn9Trvr+md8/NaHN5919f87666vb+/0iA28mEWWjx78552ZEJH+9++f/+6RPndFGLgAMCuta58+928D7vtm2tR75j48btkNnydbqTn2VxBM2tgn3n26l/unD+asvyg7om/9bU89+/ezI9nti/enSYHatm3bmpdeeomzzz7b2ZTtpWmcTmeIw+EI2Lueh9WaZv/6IOZf/qGOCx/bUlbew2Kx6Ca+coLwGhUb53Z5IOPWWd94iKPt9bvvOyt2kQEm1FjW/GvKtau8xAYPe+HrmTec8tzPYQpgTxmd88xzr93338sWL1j97EtjMv/65HM9QyjGVWTfvMvVDqLMnkPSVu4LUwAj0tP7jnkvLTtj54bgdmn5rSzU+fsdaIomBeru3bvDiouLmTBhQsB++Z+ISktLnV9//bW/q3HUDJfHWOxwMDc1VceFj11WXILX69U5DnIc7bG8clmfGV9EWCYf8LDpslQXZcVlFTlbARAzrPyfi568ZlCkkQdA/Y7gxUvyzwLodfVZH7T+VfhZzPjBV33fm8WOFVlLk1YWPZHcM9lajK21s3unkO1srRj07l0TH7zozUe3XnpqzHdWMAGwJ9X3+1PSsv0KOuFPgGpyl++IESMYPXq0v+vbrDgcjrr77ruv1t/1OBbt7HZGx8b6uxrNzqBevX70dx3k5FOdvSkpE5IaXdnizLLr7xo37dabL53Zs4U1b9/jzvzgzD0N13Jue/HmjIxFttN/ta+7wLYdwiDXuj63PoXksHUY8Z7zpz3/2IX9bxi0cP1z6Rndn1sVmtIvd9ifz1ly7vCRiy/48+mftAk1Kvz9nvwWGkMVEREg0Xvrqu2jpvUNff+XxzxG7lsXjEq7+qO51bWhtj9cc8l7B4QpgFlvVDsbzqgtXrGg1zzodejnqDFLq70RP//L3vH6HfN/6n3GO/96+ubpby64dsXOVV3ef3HV2PdffHzsLZYU97nj//XCCw+Pejg1BIe/352mUJeSiIgcgtVsc9m0D6cOsn1A7ZKI/7vlnWdyPYQdsIklxBsVQi1EmRmfV15lmqZx6KXC8s7ZEW8e8AyxvcqufGz25K93VKVX7vq+7aKXHv/L34anvR/izbZ8/OTFtw/460evFHsJaUptvV7Tr5mmQBURkUOzpdZfP/OxB3pCRdWHt/zxtvl7/nLAhA72NvU9ksiBCmPnzooU71GPdVrMiOQ+uSOuf/C1GR9lXrTro+vPSYT6wjeePO/jQm9HMDAsRsP4qtdj/PqMPTcVeY6W/nyrFKgiInJYwafctuU/93WaAhXGe3+747H3C72d9620p9QPG5b4JcCa1z65MO/gFixAzZbwhbM/Gr0ur7b9zxPre0rXxy565Zlrnn5ry5g6sB64g9VsOeiyNf1CKcQstuZWeGLAQlhsWMOYasWe8Ar3QRM3uPPsy5ZmD/lNL8z0Gr6c6F+BKiIiRxDu7XP/f2b+LYkfKZsbd9Pdi6f+0g0bava87e43+xg43Mtu73vD9B/uqjQbJn8AoD4rZN5tf5500VUj5va94s13ctwNd3zxln3V4pHr7p42/torX5y8vGTEgdMYuo3cJa8OWl5LAjE9HX0SbLvAYkZ3PWN7EtSz8d3UpXnuX0Idp5E9557zH1vH2Q3/NveeKtw4iy3UbQOT6qywbWUen7VqFagiInJERvTg8sdnXnFPC3AVv3n9yPs+Lb3k5+5de5fbts195bIbE6l0fvJ/vR5LTBu89YLLr3o346I/LeiW0CH7spez7yDugpIZs8bc2i6IKgBb6tisFx7s+YTFszb48cHxC5N6/HHjyEsz/psxeuS8P/VIWtv20rc/KKFF0IXPTnliSBS5AKGn3bzx1p4sx/tt8M0D//zRTQ899dQ/n350/P+N6jX3lBtzXrnu8T/8ywo/dwkfsuVpb9M/v1ckBbDGdnPf05cOHnzm8kumbbnfeYyZqEAVEZEmsJjxw//x5bQR4bOhwPLydROf+qrcTG5YZzdTr539bubaN86YMHrgm62Lvg3/YO7sS95Z+NWFOVGnu0bd/dK0bzfN731Dp+Dv9hVnRHr6Pfb1PzcteGL42KHdP7LtXJa4aP47o955d9GlX++OSD3jojs/nLli0znzrk35t23vjEwEn1J77ydfXDX1qn7/SXZ8HvyfSRPG3/PA8098Xj8s5oVvPx0+cUCbj8LAg7PG5vIeJt9izi775yvjbu0bz24KfmixfHXWGXVB1mPu+zVM0zziRjNmzHh7w4YNGdOnT/f3J9qsOByOipSUFLO8vDzG33U5GrvWrOl/5sCB32Z37+7vqjQ7Z3q9P0557rlxgwYNCtyZP0ROMmqhioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPNIPJ8b041r/N1CdeYM6S1exweIBQWncbyIgxd/Hg/w2nQ8gJf9efk5LpLmXQ+p1809QdwjtQkN6CVkf5fAUlm0nIqoawFHLS42mrw0JEfCjAA9XJzjeuYdCYueQBGDEkp7clrGIbWzI/4+X7PuON+Y+x4tMHOSNS354nMmuQjdZH+Igigq0Hz08mInLCCOhAdW37f1w5di55WOhx5wLen3w+KSEG4CL/i8e58M+Ps3r13xk77VLWPtAV3QX7RGXj5k6nMi1cIxAiErgCOFDr2fjys6z0AN2nMPfJkaTsS0wbCX+ayEv3zabHpO1kvvUB2fd2pbPtGJ5ORCRArFy58qUFCxZ083c9mpuYmJh1EyZMuOVQ6wM4UA2i+t7Gw+N3Y+l/Bam/an4GkzIwDTvbcZZmU+oCFKjNi+lmRfEephaX82WNk4bbUFhIDI3govhEHm4ZccRuZICK2lKm7ilibmU1W90NM4fZg4I5IzKWWxITyAi1HnT2nklpTSlP5xcxt6KGHR5z7/NGcknLBB6Kj6CVRhjEjwYMGHD9/fffT3R0tL+r0qxMmDChf3R09Iabb755RmPrAzhQ7XS46E4euehQ671U5ZfgBIhPJU5h2ryYLt7N2sToUhdgJT0immE2Cy53HcsrK5iRU8F/qzvyQ/sYEg4TbpXVuQzcnE8mEBUSwchIO+Gmm501Vawoy2dFWTk/pqczJfzneT5Nskp2MiCrjHwgJjSSUaF2gj21fOFw8PwuB285UljVMZ7OClXxk+joaPO+++4zFKi+lZ2dvexw6wM4UI/AuZW3pq0CoMeYkaQoUJuV2pp87ih1ASE8lJ7Oo+G/TGxdXbOHQZvy+KE0h2daRzE17FBjs24+yi0gE0hP7Mp3SWFE7FvnYW3+NgbkVjF1dxnjusSTbICrvoiMrDLysTIyJZ058SH7bv7odJZx3eYdvOXI5uqiSL5uFaxOEZGTSPM8C8SsZPXkDCasA1JuZdpfO+uLrZmx2FswI7U9s1Lbc3vYgXeJCA+LZ1wYgJPPqt14DlWI6WKz0wSs9IwM2S9MAaz0bp3Ksi5prG0fs7cL18uagnxWAcFR7Zi5X5gC2O2xPJMcTSiwuqCYTK+/3yUROZ6aXwvVW863Uy5g6KM/4A0exNT3n+LMKPW9ndhcTN+6gbmH/Zgs9G2bxoI4O1Yg2BbOyNjwfWudHjcFbg+1pgl4ce0tq9rtxQONX25j2OgeYoF6D+/m5HJRhyQuDd3v0hzDRr/I/X6KmfUsrnAB0KtFJK0bKTI+ogW9cbDCWcFKdxI97Tr2RE4WzStQ67bz1s1/5upXt0P0MP7x+Xzu6hF2zPe4k9+f1+Om8Ajb5Hhg/5sNVtaWMikvn1cdtRQe4i6Eh785YRDnJydzYXU2C2sLydhYSKg9nGFRUZwbHc0FUeG02b8Px+sisyFP2Va8iwxHI0eW6WI7AC7Wu0xQoIqcNJpNoHpLlzPpouE8vLwaUv/CW0tmcGXHEH9XS5rExq3pv+061KqaPQzZlMc6ICQkhnGxEXS1BxFrMTDw8v2ebJ6pO3I59uB45p8axjsFBUwvLWdFfTXvF1fzfvEebsHOuQnJvJAYQ6oFwKR6b0IXV5Uz77AleylVl6/ISaVZBKp7zwfcNvgC/t82iDprCh+/O54Bsc1zeFgA08nbuxrCNDyqPRs6xdHhgIagh+DipgUqgNUaxpVJHbgyCaqcNXxV4WBReSmvOur4OH87A1ydyGwfTbxhIcoCeKxkdOnB25E6xkTkFwH/jeAtX87EoQ1h2vaqN/nhowkK0+bOW8cntQBWRrSOOShMAW89q+uPrugIexgj4hOZ0ekUdnWKJxEoLMnnYxdg2OhhA/Cws96DGqAisr/ATh5PPu/99UKmboTYUa+z/OWr6BDs70rJ8eAGwCDG+usxypLKQmY5G/4+3Biqx13LopICni6t49eNWYOWEbH0szQ8W67HBMPOsKiGk5TWlFSQ11jh3joWljpY5/IeYfxWRJqbAA5Uk9Ild3PT3DJodR1zXrqK9pqs9+RgsdPPDuBmSVktVftWmJRU5XN5VsXeOZ2huN6F8xDFeD2VPJK1m/FZO5lcdfDlNSa5FSUs9wLWMPrYDMBCz5at6QO4q3K4oaiGygN2cTIvZysX7dxG3x2l5ChRRU4qgTuG6slh3qOzKQUo/5hxA0859J1Igk/h7vfe4cZUXY3aLBjBXJ0QxaSsCrIKttCjJpazgg2K6yr5sMpFt8QuvG3PpVt2FWUlWVxMDCNatSHjoGJswXG8kFjCwD01PL5lPTNDI+kbEkQ4Xgrqqvmy1g0EcWFyG4bsPbjsIa2Y276aQVllfJKzicTCCM4KsxNmuthQWclGDxAUw4z2cbTTCb4iJ5UADtQaisv3/u3MY9uWw23sZU+dmgvNh0FyXCorzFzuzC/ly8pSZlUatA6N4s4OnXioRSgRnrZMLtvBgxVOlpY6aB+X1Eg5VvolpbEp+DfG2gAAHb5JREFUrJAni0pZUlXJotqGNUHWYM6IieOG1gmMjQjab2IQg9S4DmSGxjA1v4i5FdV8UNbQRo60hzEqviXjE+LoH6Q0FTnZBG6g2tOZuNlkor/rIUfNCGrBij4tjnJvKz3j2/FFfLtDrA5nQufuTDjgwXTMuIM3tNAlJoGXYxJ+S82JDWvBlNQWTPHTeyciJ54AHkMVERE5cShQRUTk+HPuZvGDfyTWMDCMjjy44SivdTuBBG6Xr4iIBCCT2q3zGJ9xLc+vbeLsKwFCLVQRETlOnOx4Yyw90y7n+bUh/PGOuzi9GaVQM3opIiJyQjMr+f7F1/gpdiiPf76FpY8OpVUz6idtRi9FREROaIaN5EueZ9l7f+PMeCtU+LtCvqVAFRGR4ySKAXeM83clfjfq8hUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+IACVURExAeadB1qu3btaqdPn07//v09/q5wc+J0OsMdDof12EvyD6vFYnYLDaX/1q06LnxsU3V1D4vF4vV3PUSk6ZoUqLt27Qo1DINJkyYF7Jf/iai0tLT+8ssvD9j31OP1GosdDpZ27hywr+FEdWFOTr3X61UPkkgAafJMSYMGDWLo0KH+rm+z4nA46qOjowP6dgvt7HaGRkX5uxrNTu+uXbf4uw4ivubcMo1LRr/AVufeBzyOvX/v4N/nd+fd0L2/Ie2dGTdvPrel2f1d5d9EUw+KiMhx4a3dzYYNW8huZF3Vrq388iuyjt21gTfioUAVEZHjIqTnU2SZT/m7Gr8bjdGIiIj4gAJVRETEBxSoIiIiPqBAFRER8QEFqoiIiA/oLF8R+V158uYlXX/JE2/8UEucv+tysJDu4zfNe+XKG5ODqPJ3XSTwKVBF5Hdl1ucFr1u5/owfIdLfdfkV1+6QOhPN9CU+oS5fERERH1CgioiI+IACVURExAcUqCIiIj4Q+CclmXXs+nQGU/71Oh8s+4HcaoBQEk8dxIgxd3D/rcNJDTX8XUtphOkuZdD6nXzT1B3CO1CQ3oJWR/l8BSWbSciqhrAUctLjaavD4rgI6nD7zvXm7bolkTR7gR2o3lK+emAoQ59ahxsgKoWuPSJxF25l6/+W8tL4pbz6zkMs/+xR+kfr2/NEZg2y0foIH1FEsFWnY4rICSuAA9WkdMntXPLUOtykcM2rH/LCNd2ItAA42fPZ41w4fBLfrXmcvzx7Oesf7kawv6ssh2Dj5k6nMi1cIxAiErgCN1DNUr58di4lQOIts5l+bTci9q20k3j2g7x072xOm7yDLfOXkjuxG6mB+2pFRJps9OjRxk033VQVFBQUeDcVPYF9/fXXg7t37z7nUOsDOGIiGPjUUj4ZV05I79P3C9OfBdO2V1tgB9SUUuMJ7FcrjTDdrCjew9Ticr6scVIBgIXE0Aguik/k4ZYRR+xGBqioLWXqniLmVlaz1W0CYA8K5ozIWG5JTCAj1HrQ2XsmpTWlPJ1fxNyKGnZ4zL3PG8klLRN4KD6CVhphED966aWXuP/++yOio6P9XZVmZfbs2YddH7gRYwSTcNpgEk471AZVbPpiGwDh3fvRxu7vCotPmS7ezdrE6FIXYCU9IpphNgsudx3LKyuYkVPBf6s78kP7GBIOE26V1bkM3JxPJhAVEsHISDvhppudNVWsKMtnRVk5P6anMyXcSkMxJlklOxmQVUY+EBMayahQO8GeWr5wOHh+l4O3HCms6hhPZ4Wq+El0dLR53333GQpU38rOzl52uPWBG6iH4ancztJpt3HN9DywDeDhSWcTqy+3ZqW2Jp87Sl1ACA+lp/PovsCD6po9DNqUxw+lOTzTOoqpYYcam3XzUW4BmUB6Yle+Swrbr6fDw9r8bQzIrWLq7jLGdYkn2QBXfREZWWXkY2VkSjpz4kMI27uH01nGdZt38JYjm6uLIvm6VTA2f79RInLcNJuzQFw7X+S8jkm0jgkhKKoTwyeuJvHCCcxZv4R7uoX4u3riYxZ7C2aktmdWantuD/slTAHCw+IZFwbg5LNqN55DFWK62Ow0ASs9I0MOGjaw0rt1Ksu6pLG2fczeLlwvawryWQUER7Vj5n5hCmC3x/JMcjShwOqCYjI1eiVyUmk2LVTTWcLOHXso/PkBq4Grsojdu8twdo3QGb4nNBfTt25g7mF7ESz0bZvGgjg7ViDYFs7I2PB9a50eNwVuD7WmCXhx7S2r2u3FA41fbmPY6B5igXoP7+bkclGHJC4N3e/SHMNGv8j92phmPYsrXAD0ahFJ60aKjI9oQW8crHBWsNKdRE+7ukZEThbNJlDtafezybwfr7OSwh3r+Oytp7l/0izu+XwuS6av4f2bOytUT2Bej/uXH0OHkOMBc79/V9aWMikvn1cdtRSaje9jHrbEIM5PTubC6mwW1haSsbGQUHs4w6KiODc6mguiwmmzfx+O10VmQ56yrXgXGY5GwtJ0sR0AF+tdJihQRU4azSZQf2axR5KQPpirHv8DZ/W4nFMum8+Se+7mo9ELGRWvL7cTk41b03/bdahVNXsYsimPdUBISAzjYiPoag8i1mJg4OX7Pdk8U3fkcuzB8cw/NYx3CgqYXlrOivpq3i+u5v3iPdyCnXMTknkhMYZUC4BJ9d6ELq4qZ95hS/ZSqi5fkZNKswvUX1hJPPdmhkXOZ17lChZurGHU4PBjL1b8z3Ty9q6GMA2Pas+GTnF0OOC3kofg4qYFKoDVGsaVSR24MgmqnDV8VeFgUXkprzrq+Dh/OwNcnchsH028YSHKAnisZHTpwduRzeYUBBHxgcD9RqhcxeOXDWPQwFE8t9nZ+DYGe09WcVLtVHOh2fDW8UktgJURrWMOClPAW8/q+qMrOsIexoj4RGZ0OoVdneJJBApL8vnYBRg2etgAPOys96AjSkT2F7iBagum+rtPWfHtAp57cxONNUZqNy9mZSVAMn2SdaZvc+IGwCDG+utu/JLKQmbt/Y11uDFUj7uWRSUFPF1a18jxY9AyIpZ+loZny/WYYNgZFtVwktKakgryGivcW8fCUgfrXN4jjN+KSHMTuF2+Id244fYzmHrnd2x/YhR/6/I+z119akOXHF4qN77NnRn/ZBdgnH4Tl6bqisBmw2Knnx0W1rlZUlZLVXj43kteTEqqCrg8q4KUEIPiOpPiehdOQmhsXg+vp5JHsnazxiil0t6ZhyOC9jsb2CS3ooTlXsAaRh+bARj0bNmaPoW7WVOVww1FocxrFUbkvl2czMvZymXFToIiUtjeJZ52GrbHk/dO0rUXPv72D7XE+bsuBwvpPmHje29cfV1yEFX+rosEvsANVGx0umUOL389kLHzd/Latd15bVwyXVNjoXwnm3ZVNmwWO4Lpb/6NTsrT5sMI5uqEKCZlVZBVsIUeNbGcFWxQXFfJh1UuuiV24W17Lt2yqygryeJiYhjRqg0ZBxVjC47jhcQSBu6p4fEt65kZGknfkCDC8VJQV82XtW4giAuT2zBkb9LaQ1oxt301g7LK+CRnE4mFEZwVZifMdLGhspKNHiAohhnt4xSme5n1+cEbvs/slckvvz1OGGZeUJ2pmxiJbwRuly+APZW/zN3CD7MfJOMPnWhRn8OmH39k0y4n8V3+wOh7Z7HypwX8LU3dvc2LQXJcKitSWvLHYAs7K0uZVVzKSk8od3boyvKkCNJj2zI5yo4VJ0tLHWR6GuuAtdIvKY1NHdswNioUW30li8rKeKfMwddOK2fEtGZmWjfmxdn3m/HIIDWuA5ldOzAhNoLW7mo+KCvlnfJKcqxhjGqdwrfdUrkhWGkqcrIJ4BbqXpZoTrvicd6+4nF/10R+IyOoBSv6tDjKva30jG/HF/HtDrE6nAmduzPhgAfTMX/V6WihS0wCL8ck/JaaExvWgimpLZjip/dORE48gd1CFREROUEoUEVE5Lgwi2bzB8PAOMIy4I2CgLwsLfC7fEXkhBbU4fad683bo/xdD/E/T1Xx3tOp7SR16kBko6eDhZMeF0QgnoWgQBURkePCU1VENUDr65i/fgb9w461xBOLunxFROS42NdCjYgnvBlerKRAFRGR48JTXdQQqOHxRChQRUREjo67soQagPDm2ULVGKqIiBwHHqqLqzABW3QY5d/N5qU3FvHNpjyqjEgS0/oz/MrryBiY2OhUoYFAgSoiIseBh6qihnN8XUuuJO3jg25J8eki5rzwII+OeYMlL15Nx2B/1/e3U5eviIgcBw0tVAC8kZx110t8vqWYWlcdZTu/ZfYDw2gJ7Hj9Gs59cHXD2cABRi1UERE5DkLoes9SfvyLE2tsKunJEftadCHt+3PFE4s4vdUQut6xkm3/foCFdy/hyoTAavMFVm1FRCRAGYQlpdO9Rw9O2S9Mf2Gn0zX38+cQwLWahZk1/q7wb6ZAFRGRE4IRmkinFgBVFFW5/V2d30yBKiIix4e3jrI9uZQ5D7G+voisMoBokqIDb0SySTVOTk6u+de//kX//v09/q5wc+J0OsMrKioC9keN1TC87e12+m/dquPCxzKrq3tYrVa9r9J8VK/gpo6D+E9BMEPf2MHHVycddGd3LwWfzuTzWsDWmxFdA29ewiYFak5OTlhISAiTJk1qhpfi+k9paWn95ZdfHoAnhzfwmKZlWVUVSzt31nHhYxfm5NR7PB69r9J8hPfk+iva8Z9nd/HpTRlMSprD/Wcl7b3mtJ7dS6dwxbXvUwUkXf93RrQKvLZGk9vUgwYNYujQof6ub7PicDjqo6Oj6469JP9pZ7czNEo3EvG13l27bvF3HUR8K5y+j87j8dVDeOib5TxydhsmJ6bTLSmEmt2ZbClwARD5p6f4YOpgogLwdjOB9xNAREQCkhHVlwe/2MGXL9zG+b2TCS7YzLo1P7ClMJSUM0Zx90vfsn3JeHpHBmCaoutQRUTkeLInMuSW5xhyy3P+ronPqYUqIiLiAwpUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+EAznMvXpPyLcXQ/awa7MThrYRmfXRDt70pJI0x3KYPW7+Sbpu4Q3oGC9Ba0OsrnKyjZTEJWNYSlkJMeT9vAnH9bRE5QzS5QzfIveWDMDHb7uyLym1iDbLQ+QsBFBFvRDUJF5ETVvALVLOfLB8YwYzdEAFX+ro80kY2bO53KtHCNQIhI4GpGgWpS/uUDjJmxG0vfv/NQ0GPc1+S+RBGR5sPhcBj333//yqioqHp/16U5mTFjxpBZs2a9dqj1zSZQzfKvmHjtDHYbvZny8t/oPO4xf1dJfm+mmxXFe5haXM6XNU4qALCQGBrBRfGJPNwy4ojdyAAVtaVM3VPE3MpqtrpNAOxBwZwRGcstiQlkhFoPOnvPpLSmlKfzi5hbUcMOj7n3eSO5pGUCD8VH0Erjs+JHa9eu7b1kyZJz/F2P5mb69OnvXHfddS8fan3zCFSznK8mjmF6jkGvya9xR7dQFvu7TvL7Ml28m7WJ0aUuwEp6RDTDbBZc7jqWV1YwI6eC/1Z35If2MSQcJtwqq3MZuDmfTCAqJIKRkXbCTTc7a6pYUZbPirJyfkxPZ0q4lYZiTLJKdjIgq4x8ICY0klGhdoI9tXzhcPD8LgdvOVJY1TGezgpV8ZNevXqt69Wr1zp/1+Nk0wwC1aT8q4mMmZ6D0Wsyr995KiGU+7tS8jurrcnnjlIXEMJD6ek8ui/woLpmD4M25fFDaQ7PtI5iatihxmbdfJRbQCaQntiV75LCiNi3zsPa/G0MyK1i6u4yxnWJJ9kAV30RGVll5GNlZEo6c+JDCNu7h9NZxnWbd/CWI5uriyL5ulUwNn+/USJy3AT8WSCm4ysmjplOjtGLJ16/k1ND/F0jOR4s9hbMSG3PrNT23B72S5gChIfFMy4MwMln1W48hyrEdLHZaQJWekaG7BemAFZ6t05lWZc01raP2duF62VNQT6rgOCodszcL0wB7PZYnkmOJhRYXVBMptff75KIHE+B3UI1HSybOIbpOdBr8uvcqTQNUC6mb93A3MN2kVro2zaNBXF2rECwLZyRseH71jo9bgrcHmpNE/Di2ltWtduLBxq/3Maw0T3EAvUe3s3J5aIOSVwaut+lOYaNfpH7tTHNehZXuADo1SKS1o0UGR/Rgt44WOGsYKU7iZ529fuKnCwCOFBNHMsmMuaFHNjX1SuByutxU3iEbXI8YO7378raUibl5fOqo5ZCs/F9zMOWGMT5yclcWJ3NwtpCMjYWEmoPZ1hUFOdGR3NBVDht9u/D8brIbMhTthXvIsPRSFiaLrYD4GK9ywQFqshJI2AD1XQsY+KYF9hl6cOUN9TVG9hs3Jr+265DrarZw5BNeawDQkJiGBcbQVd7ELEWAwMv3+/J5pm6I5djD45n/qlhvFNQwPTSclbUV/N+cTXvF+/hFuycm5DMC4kxpFoATKr3JnRxVTnzDluyl1J1+YqcVAI0UN1kv3k3L+wCor18du/FLDtgvZPc7wBMVv39Us77f3aiBj3OKw/0JtTfVZdjZzp5e1dDmIZHtWdDpzg6HNAQ9BBc3LRABbBaw7gyqQNXJkGVs4avKhwsKi/lVUcdH+dvZ4CrE5nto4k3LERZAI+VjC49eDsy4E9BEBEfCtBANXFW1TT86VjHp4e5RqZ6/acsXg82xvEiKFCbA28dn9QCWBnROuagMAW89aw+ysvZI+xhjIgPY0R8Ao85dnHatmL2lOTzcZtorg6y0cMGcz0edtZ78EZaAv+sPhHxmQD9PrDR5b6NmKZ5iKWM/w4BMDhrYTmmaeL86Dyi/F1t8Rk3AAYx1l+PUZZUFjLL2fD34cZQPe5aFpUU8HRpHb9uzBq0jIiln6Xh2XI9Jhh2hkU1nKS0pqSCvMYK99axsNTBOpf3COO3ItLcBGigyknNYqefHcDNkrLa/eZsNimpyufyrApSQhqCtrjehfMQxXg9lTyStZvxWTuZXHXw5TUmuRUlLPcC1jD62AzAQs+WrekDuKtyuKGohsoDdnEyL2crF+3cRt8dpeQoUUVOKgHa5SsnNSOYqxOimJRVQVbBFnrUxHJWsEFxXSUfVrnoltiFt+25dMuuoqwki4uJYUSrNmQcVIwtOI4XEksYuKeGx7esZ2ZoJH1DggjHS0FdNV/WuoEgLkxuw5C919LYQ1oxt301g7LK+CRnE4mFEZwVZifMdLGhspKNHiAohhnt42inE3xFTioKVAlABslxqawwc7kzv5QvK0uZVWnQOjSKOzt04qEWoUR42jK5bAcPVjhZWuqgfVxSI+VY6ZeUxqawQp4sKmVJVSWLahvWBFmDOSMmjhtaJzA2Imi/GY8MUuM6kBkaw9T8IuZWVPNBWUMbOdIexqj4loxPiKN/kNJU5GTTTAM1hlFfmhrDOsEZQS1Y0afFUe5tpWd8O76Ib3eI1eFM6NydCQc8mI4Zd/CGFrrEJPByTMJvqTmxYS2YktqCKX5670TkxKMxVBERER9QoIqIiPiAAlVERMQHFKgiIiI+oEAVERHxAQWqiIiIDyhQRUREfECBKiIi4gMKVBERER9QoIqIiPiAAlVERMQHFKgiIiI+oEAVERHxAQWqiIiIDzTp9m0pKSm1kydPZtmyZV5/V7g5cTqdkQ6HI2BvnFkfZfPausTQpmSHjgsfK86v7GG1Wj3+roeINF2TAjU7Ozu0ffv2PPvss2rR+lBZWVntsGHDQv1dj6PlxrRs/18h42eM0HHhY7Me+rbc4/FY/V0PEWm6Jt9gvHv37vTp08ff9W1WHA6HKzo62unvehyL2FbhtOsSd+wFyQE6duqwy991EJHfRi0LERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfGBJt9t5kRkFs1mUKur+OYI2/V/PZ8V17TWr4cTmpfCOR/w2H8cv3G/GEa9fj5nt9GnKyL+FdCB6qkqpgoAO0mdOhDZ6N0jw0mPCyJg7+J90jCwRkYQH+fCdcDjHmpK6hseCw4hOuKg4AyOICxIn66I+F+AB2oR1QCtr2P++hn0D/N3jeToGcSNOJtHRhz0sLuCT29awIJsSLpuOPddGonuui0iJ6KA7ifb10KNiCdc37JyMNPEVe/F9Hc9ROSkENgt1OqihkANjydCgXoSM3EsXszEfxRjP3Moj46t5r0n1rJ6ez0hfzqHJx5sReW7i3hkRjmcOpinnm1PuHHg/iXvLeLh58saWW9Ss2UHn875ibVriymuMgEr0akJ9BzZnXNHtDrEUIOInGwCOlDdlSXUAISrhXpyM7CGNnS2uMpKWProOlbnR5DcLZqotrZjGD/3UrJkOf98KpsKILR9Aqf1CyeoqpyfVuXy1b9z+W5Vf+59pAstbf5+D0TE3wI4UD1UF1dhArboMMq/m81Lbyzim015VBmRJKb1Z/iV15ExMBG7v6sqvzurrSFQzc2bWNO1N+OfP5V2+8bUvUdVpid3C69MzaYCO93vHM7YEdHY96azpyCLN29dxncrV/HqB4ncdbHGdkVOdgEdqFVFDef4upZcSdrHdQeu/nQRc154kEfHvMGSF6+mY7C/6yu/KwMMwHRb6XNz1/3C9Gi52TX/f2SZENSnHxn7hSmAtXUKF9/ahh8eyyV7/lb2nN+btvrlJnJSC+CTkhpaqAB4Iznrrpf4fEsxta46ynZ+y+wHhtES2PH6NZz74OqGs4Gl+QtvxantfNBWdFWx8ftaAJKHJhD1q35jg4geHUgGyM8jy6FTn0ROdgHcQg2h6z1L+fEvTqyxqaQnR+z7dRDSvj9XPLGI01sNoesdK9n27wdYePcSrkwI4N8P0jQxUUT6YjzTVcuekoY/iz5cycsrGzl2PHUUA1DD7iIPtAzg/04icswC+BvAICwpne5Jh1pvp9M19/PnCRfyUd1qFmbWcGVChL8rLb+3oCAfHdQenO6Gv6r+l8O6I2xbU9eEIkWkWQvgQD0yIzSRTi2AvCqKqtz+ro6cwMyDe2wNKyF2wG2jzz9HM7Zns/6vIiI+ENh9oN46yvbkUuY8xPr6IrLKAKJJitYX4snLAGPvIKhpNjLRg5e64oMOIlsYbeIAXJTscWpyCBE5osAN1OoV3JQUSoukjlw2Nw/PrzbwUvDpTD6vBWy9GdFV8xKezOw/z75QU0vdwQeLp4Ztaw46bS0ogvQ+oQDsWpKHo7Erb+or+PGzXHaXeBS4IhLAgRrek+uvaAfU8+lNGUz6PI9f2hj17F76GJde+z5VQNL1f2dEq8B9qXKsDELbxRMNkJXN5pL9489L6RdrWLztl0ca1lppO6ob7Qzw/vgdby0spW7/3VxVrJu2lBcnf8bUSTso9zShGiLSrAVwP2g4fR+dx+Orh/DQN8t55Ow2TE5Mp1tSCDW7M9lS0HDPksg/PcUHUwc3ctmDnExsHdMY0mkz728rYs5tS8kZnkSrMA+lmVl8+10Qg8e24vNXCvHu1yVsbZvOdeOL+ddTWWx6fhEPvNeKtM7h2Fy15K3PJ78KiEom455UYjWrg8hJL4ADFYyovjz4xQ7OfGkK/5i1gK9+2My6PYARRcoZ53PpX8dz37X9dTWDgC2aoU+dg2X6Wr5akc+KN/PBGkxin1Qum3Yap1Ws5Gugzu3GY9IwSwQW4s85k4mpbfl09hbWriliw5eFAIS0iuO04WkMzehIhxj9WhORAA9UAOyJDLnlOYbc8py/ayK/h6Aohr48hqFH2Cx04DlM++zw21hiEhj6wHmHKGsw//hscCOPG4R1SuWCv6dygb/fCxE5oWlgUURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqIiLiAwpUERERH1CgioiI+IACVURExAcUqCIiIj6gQBUREfEBBaqIiIgPKFBFRER8QIEqIiLiA026fVvHjh3/e8stt2SsXr16k78r3JzU1NSEuN3uVv6ux9FqEd16V1lhNf+5e52OCx9b/8OGHvHx8cX+roeINJ1hmmaTNtywYUN3p9Np93eFm5t27drtatmyZZG/63G0du3a1a6oqKilv+vR3ERHRzs6deq0zd/1EJGma3KgioiIyKFpDFVERMQH/j/eBkKtn0onLwAAAABJRU5ErkJggg==\" alt=\"Array Filter\" />"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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\" alt=\"Array Filter\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "np_array_1 = np.array(range(10))\n",
    "print(np_array_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 9]\n"
     ]
    }
   ],
   "source": [
    "mask = np.array([True, False, False, False, False, False, False, False, False, True])\n",
    "print(np_array_1[mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[np_array_1 < 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[np_array_1 % 2 == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying a function\n",
    "\n",
    "To apply a function to a numpy array, you have to vectorize the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def isprime(n):\n",
    "    '''check if integer n is a prime'''\n",
    "    # make sure n is a positive integer\n",
    "    n = abs(int(n))\n",
    "    # 0 and 1 are not primes\n",
    "    if n < 2:\n",
    "        return False\n",
    "    # 2 is the only even prime number\n",
    "    if n == 2: \n",
    "        return True    \n",
    "    # all other even numbers are not primes\n",
    "    if not n & 1: \n",
    "        return False\n",
    "    # range starts with 3 and only needs to go up the squareroot of n\n",
    "    # for all odd numbers\n",
    "    for x in range(3, int(n**0.5)+1, 2):\n",
    "        if n % x == 0:\n",
    "            return False\n",
    "    return True\n",
    "\n",
    "visprime = np.vectorize(isprime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 5, 7])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1[visprime(np_array_1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring `ndarray` object\n",
    "\n",
    "## Exploring booleans with `all()` and `any()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 4, ..., 6, 8, 1])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1 = np.random.randint(1,10, size=(500000,))\n",
    "np_array_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True, False], dtype=bool)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1 % 2 == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np_array_1 % 2 == 0).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np_array_1 % 2 == 0).any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0037859999999998"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.580650240967187"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring data type shape and memory use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500000,)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.size * np_array_1.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'MB: 3.8'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"MB: %0.1f\" % (_ / 1024 / 1024)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manuplating data type and shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6.,  7.,  4., ...,  6.,  8.,  1.])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 7, 4, ..., 5, 8, 3],\n",
       "       [4, 7, 6, ..., 1, 6, 1],\n",
       "       [6, 6, 3, ..., 5, 5, 8],\n",
       "       ..., \n",
       "       [5, 1, 8, ..., 9, 2, 5],\n",
       "       [1, 9, 5, ..., 2, 8, 1],\n",
       "       [7, 8, 1, ..., 6, 8, 1]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.reshape(500, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[6, 7, 4, 2, 9],\n",
       "        [7, 9, 5, 7, 5],\n",
       "        [1, 2, 3, 5, 3],\n",
       "        ..., \n",
       "        [5, 7, 1, 9, 7],\n",
       "        [3, 9, 8, 5, 8],\n",
       "        [5, 6, 4, 4, 9]],\n",
       "\n",
       "       [[6, 4, 8, 7, 8],\n",
       "        [6, 3, 5, 2, 5],\n",
       "        [5, 5, 1, 4, 6],\n",
       "        ..., \n",
       "        [4, 4, 1, 5, 5],\n",
       "        [4, 3, 9, 6, 4],\n",
       "        [8, 1, 2, 1, 9]],\n",
       "\n",
       "       [[7, 2, 3, 1, 9],\n",
       "        [8, 7, 5, 2, 8],\n",
       "        [9, 3, 3, 7, 3],\n",
       "        ..., \n",
       "        [7, 4, 4, 8, 7],\n",
       "        [4, 6, 7, 4, 5],\n",
       "        [9, 9, 8, 9, 2]],\n",
       "\n",
       "       ..., \n",
       "       [[3, 4, 1, 7, 4],\n",
       "        [8, 9, 3, 4, 8],\n",
       "        [9, 8, 4, 1, 1],\n",
       "        ..., \n",
       "        [6, 6, 4, 5, 9],\n",
       "        [7, 1, 4, 1, 9],\n",
       "        [2, 1, 5, 5, 7]],\n",
       "\n",
       "       [[2, 9, 1, 8, 5],\n",
       "        [7, 2, 5, 5, 4],\n",
       "        [3, 4, 9, 6, 4],\n",
       "        ..., \n",
       "        [7, 9, 8, 2, 6],\n",
       "        [4, 6, 3, 3, 8],\n",
       "        [9, 2, 6, 9, 9]],\n",
       "\n",
       "       [[3, 8, 5, 5, 4],\n",
       "        [7, 9, 8, 3, 9],\n",
       "        [5, 1, 4, 4, 4],\n",
       "        ..., \n",
       "        [5, 5, 8, 9, 9],\n",
       "        [2, 3, 5, 5, 3],\n",
       "        [6, 4, 6, 8, 1]]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_array_1.reshape(100, 1000, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
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NuhY8dT6mdWvrklJZPFt2Rza9RKX9Wbky3u4VLWfX8jf4fMDl4TPUGZiD9MYJ+FRumKo73XPp5jTF5F25AIuOY/EKeml4yxdUf1H/vjmQdoVmfY6E1sfi0EHRGTOJ+JkHT4lZYreKY2jTELgPC9+7Wm65s7uLrEtt3KlNIcnf/hlEutP4cxe3ycLQRhoGcYniusIO372Gg69iN+tJyUvg7g7/4pHMy0ortO8995xZj28muhw8MmwfUvCdeyH/PbtSkSjij7nUT7+6Fo2PbAEkOmIbyGwXEdrfXfpCi0oaWjA2Es+DPieql6vRah463GYU1jU55BhWSYBkJvf4uVdEdz5Z98lziDRsu9ZTtd6KCqworgb6bKlYxxwyEnVMtYyCbp45j3wNfK2LCBCAKnmF/w0ajVJeh+151xkb4i5YdALT58EqX/zOaT7f8TnZptRO9/h6a99QOId3QsbdJZYIqWy7gKSYjjrAALFvPd2JPd/+8ekGWTa93yLt8/M59GldpSOcxwqm8XKjS3sqB9dM7TSFRofDcZa8qHv9yLmkZ8m0O4HURSQJStztm7rU6IjXMsk+Kl55330llm8++O9hKLWsv2Ru0ja8wo7Si0IchSLbs+6IcXNpGoZc5mEVBZsTe35XJCGfafJ2PAZ9F2XKPEVstE+MCSFq09EzDE62upcyLNNSK0N6HIW4Nn9PDuMJmQfJK+/t88KwnDVAYhWrGo9rV6ZNLtEoLOG0+/+iri6eCRDNosfuo3kjt+NshFa6QqNGcL0lK4Y5Ht71/VJ5z+AcC2ToDipOHGKloe+wZ8/lozv+D/z8zdn86XHPsvioX5qUrWMr0zCNfwlHClfzPpPGhEMC7h7sI344eoT9CTc9kVi//IR/t+uFDrKDNz14+fYkjLEcBe2OgBDDrd9YRmv/+//5YQxjlh7NNGzNrLlvkXXbnTka6WVru9TGrR8hVa6QmOmMD2lK0ZK5z+AMC6TIESvZsvq7uJ3tqLbidpxEY9SQMy0lOEYR5mEPvgvvU7ToidIGi4Tetj6xMfVX/8X6pd28K0iG0rLm/zif/5A0T88SsJgg3HY6uj+rjHzPj7+9fsAieZXHqdudt/il33p3qc0ZPkKrXSFxkeFsZYXGD6d/wwpkyDGkzfHwfFzTlRAdlzCHZdxQ2XgmVAm4foBOinb3UTRmrQb3yHNBJ8g4W0Xiesp8aKLyiQ62Iavz9PgzNDRH6n5Hd44uoR7VsSOek+SVrpC46PHWMsLjJjOf4aUScBIxiNfJ+vH3+AnB6IQpEQ2/+kS+hfPnSFlEnpQXYc55P4YH79h2eNM8YmNws89wMs/+XN+YrEhuSHn0W+RcW2gnyk6AF8xu3/9Mg3uDnzmZWz9+y+TM+o9HFrpirBEyxJ+I1rpCk2LpuMW0KGVrpiZaEFJQ0NDY3KZyKCkTd9paFnCNTRuCbQs4Rq3CFOXJVwh6LxC8alzlF/pZMHnnqKg70/KDkp276EBA4pkZc6WLWRGhGOEHE7HCBrDjuHaK+Mp/4D9p1tAlAgI6Sy/YwsZM80nqp+W07s43SAhhjwEo5ezaVO43nyNtv9IOA89wwtXNvDk44u6M3DcIlnCJ2z67pUDV/nJ62eR5PBa5aGoKqggCN0zqiqgquq1vzUAVUFFZEpOSW/aBlUFYeASNQUVocc3avdHwtVPw+oYxhaODNne7mvneseYwT7p9zkFVRDD913SMDpCoRB2awSf3iiwa38jkpTJlgeKsPQRIzsP8l5xLtvXT12+xbCbvnv1wwr+86Xj5GclMic7YYpOw+hodHgJSgqxtu7bja5ACE9XCJvVNM4j3zqo/jY6iCXGPFUDqEKw3YFiT8DcZ7Vd0NmGHJ2IRQQI4m7zERFvD+MpiMF0jMYWjoyivbIbp0tPTKwlfAf0EXXI+B0OFHsiEWE9T3SjjqraJmqbQjg7u6g60kDqqmW0Huqa7oZOOON2y6sfVvD0i8dYUZTJxiXZ063nBlRBxB+USUuMAsDlCeBw+UiMtU5306YN2VXMlZoARmsa6dlJ6NwdiNhJs03V8C/jDblQEqO5/pMSbr8LITEaqw4ggDMQwpIY3e8uMLwYTMdobOHICO1VfbTXtWPJSSPOFM5RdmgdquSitbYJX0Qys1KjCe+qKP11nC2toq65k8xUGw1Nboqt69ke7aH84rM8X+pDHWz13QxlXEEp3AOSxhBIfnQpC8jp2aE3vglXma6KI1T5o7DoAAQMcXmkxVsQUJF9rbTW1+Ns85O0dDVx/TZpemktu4IbERQjERYDFlUFBFBlJFU/hbu7x6pDRfa10NQ0hMYpZzgdve0dWkvIeYkrTW5AQRaiSSnIxy54aK1rgcRZJExpQBqrT2R8DRdo8SigBJDNWczKTcKot5OYHUWsu47GtkjSE8xT9MQ3jmsEOHepkksVrRTEOKhuVFBUgYXLUtALVdjnP8Fty8/w2wO3TpbwMb9Teu94Fd9/6RSyomIyhu+pkOXubdyi2JOJWlFRUBHDdV58KlAkZHToxJ5zoCooiIhjPCWqLIOoG/SdlKoqgACKDKL+2mdURQEUlN52qDKyDIJO7PaN2n3xif0apaIq3fPsk+G+seug+73LYLYh2zp5WobTMawWWe5+haTTdQ/WqoysCAiAIIoIwmDvlMLUJ33ao8oSiqhHd+3VmIKsCtf7fxjr8AckApLEgiw93nY3nV062kJG5sU6EZQOqq+GSMt1U+66i//zqQIMDP9OabKyhE/7O6VDxc38y6+PYTDqiY+xTZiwyUCSFFS41gEVVUVR1AGD3UcMJYSMAV1vn1RlFHTjCEohZFVAoPuiFnsHtb6fkUKgN4Aid6dcUdXui1HUI+pEBBQUSeke3AVAFRB0A15GqxKyIqLTTc7d+lh1dLe5e1ARhrAJ4tRpGY2OkbQIooigSkghtZ8fVIT+bQ5znwiiCFIItd+CgZnRt7zeLgIhGRAQO9uwZWXgK/MhKiorc22cuNJxk624RbOEHypu5us/2UdGSjTpKXHMzoybbg3D0uT0IkkK0T0LHXwBCY8vSFTkR3ehg+KtoVVNI6n75Q1KsIMOt4LOaMFqs9z0FIAS8iHpLBhFkLsaae60kZRs7XMcBV9jLXJiVs/7IgAZb10dpGYRKQJqEFeDA1NqCuZBg6OMr62FoC0Z+ySVDB2bjtHYplbLyDpG0V4lQGdTE2pc5jBtDG+fqFInzsYGPEIiqWmxDF2RPvx0VNe1cKbEhdmoQ5ZkOky55JklWno+bSvYypaCntbX/4bK/fCRzBLeG5ByMuJYMT+TqqbO6W6/xgQgGqOJHce9hWiwXMvVpbNEY3a6kFTr9emSQREQdArytUJrMjK6Yd4h6bDEpzCZW37GpmMsTK6W8epQZQ/tja0IcZnEDDtIh7dPBH0UcRlRRHvraXUGSIozDfEOKbx0VNQ0c/5SHRnJUXgDQdqdEknpUYg4R/iVWyNL+KiDUll9B1/54fvYbWbm5yZS09hOk8ODUR/e02BtLh+SrOL2+gDwB2V8/hCdEcZxHnnmonR56aCDkHcSpo6kTpztItGG9n53gX6nF0VtJ6LPHZrc5cNV3UasVYcadOLwRBCnb78+cCh+PC4felsM5ilevnszOka2TZ+WwXUM3V412EmH04cpNpEIVxutdSqWKNMM9UmvJh8Oj5OQZA57HR3t1dTWtTErNYrMlGiKrzaiAo6rF3CqPjo7ZRRjCh6FUW3+Vb0X2ffqCZLu+QyF4fx41IdRu+PUpRb0OpGclGiuVLXS6Q3gdAdQQtJ0axiWzq4giqLiMncvaQlJMoGQjNkY1psUJhU14MVDO74JmapQCHo6Ccjd0/iqKmKyRuFvdAIqUpebgKwgBwLg8KMT9JgiI9GLgKoj4KmmFQFUEZMVAo3X910ofgcdXhP2WHUSnlYmSscIGqdcy3A6GF4LAdyOdhSjBX1nFagyISWCKK/52hPsjPCJIBP0errLnaOgYsBsFWZE35J0fqIidCQmRXd/VTCg08mkx5mob/Pd9O/OxCzhox6ZfaEQq+en8fDHuh/+Zkpi00s1Tm2f0gC69ym5ru1TCs9nRj8tpypwL9pEbsxMv4G4VbRoOiab0xcqMJuEGzb+GuMLyI6H6DQ/Z0qbe56SrmcJFxZsvHHqW2nj/I56ln3xkySFl8xhmUFN1ZgodPZ5zCma7laMhJnEpR8jcbqboWnRdIQtRlLv/iZP3D2EWUxk3dPvs266m3mThO+6QA0NDQ2NjxxaUNLQ0NDQCBu0oKShoaGhETZo75RuEqlpF8fOdxERN4+5S2b32+g5nE1DQ2OmIOEueZ/LjR0oKbezrDBWu3ufQrSgdJOoQQ/6gjtZnGUehU3BX7OXs8VtCDoQ45azYEkO11dij2BXnNQcPYhDNaBIkWSsWk9ir3GstjEzXFsnSQcqkusqVaUXaaz2kH3/Y6SPOxHHOHSMWYuMr/ZDLpS2giATFFIpWL+ehBnnkwAdJXspb5UQJC+SbQkLl0/Ezdck6RiNHRn32V+yt3oN2+6bT3f30mMr3M6StGOcvBpiQgrOaYwaLShNJnIjZcVG5m77OHZRwVf+NqX1KSxM78nYPKxdxV+xh6ak7SzPtaK0H+LY6XpiV6ejH7NtkrRMig4AmYBbJnbhNqL87zAhtTTHrAMYqxbVi6PByOwtDxOjB9lxgCMn64hZmzHDfGLEmruFpYVGBCQ6Tv6Bq45s5sWPcxiZFB0j+asb1VNMWUc2yeFZhvYjieaJyUQwYlJddAW6M2JLQQ+tVQ6UUdmDuGpkklMjEQBdVA52dw0+ZTy2QZC7cLW24A4qw9sGttXnoqm0nM7BbEEPrZX1OFta8ErjaaueyPQCYi0TuPFvuHM+kr/Get6FKNJXrqF3S4xoioTABITYMWsZq08E9CZjTwZxH55OEctElLKYFB2jsftoPn+V2IV5YV5b6aOF9qQ0AsGy/+SVne1EZW5j0/3rMI3SBoAYT+6aTEoOv06dPpIIs77/7vFh7QpSSIeh9z9EEya6CKnjsd2IGnTS2tSGQWfHGts/N1h/W/+2WnQKSlcbTk+IqNhBdIRaaXHZyE2YuLZOCMOd85H8NQHnXZVaqTxeQdKi+8d/8Y1Zy/h0qF2Xubh3F3W6dayPmICgNCk6RrZLLceotqxhmc3D4dLfsLPUjzrna9y3NeOWqEs0U9GC0gioXQ5MG7/O3Yu7S3SEhrUpBC79hJdfrkMwqOgyH+H2+1axYOsCQMZz/te01P+BV/+jHvEGu4/KZx7kovcy/is9Vb7UeArlnqJ3wSrKjr1KoPEkeslOWko08aOxKQECRAyZIVmwpJNXlD6IRcZ76XXOfFCFYFA53a+tQZpf/yIHzp/FW/U2ZwfRcaFtDo6UBCoPCaPXofjxdlbS8Pt/oPpMG0u+/Qzzxp14Q8Zz5hne7dGhG0TH/vNHcJwU0d/gr19Rd/xbvHAhn7jeFyej0iLjPvsLDuwpJeLSDhSxkDWPP0WWoZqSvUcQFz/InNixVAIcq5Yx9i21i6YPfsjBQw5sZTtREx9k+0PrsEYUUHRXPvk173LqYgarFsbf5JTLJOlQAgQUL1d+94+0YUYJDXYtGHHvfJRfnHmQO9fYydmQiI4AtrmfYVnhWXYe82vvkKYZLShNJKFGKo9FcMc3niZRL+M+/B0+LC5kW1EUqruYkqvgLrez6a++Ooi9hIrOTFY89S1WxYuAiv/Kq5xv8JKVG0HngR9Tk/kVnnx8AUL9c/z+9WZyCrxkDGuzonRepcOaRUHfEjjBBirPV2Odt5JEyxDDSbCUIx9EsPErg2hxvMN7BxO47bvfY7Z5cB0rv/j0TeqwonSW0SrFkH/XI6R7v4t/FKd8RC1j1lFMSVU6OfG5pG36OvOjb0LLLC/V57pIueOfuW1jBtT8jJdeeo2WdDNJqx8m0+aiprSD9LnJ1+7Ip8InY+lb0XM3kBeRz6o1SbS9+n85XrOcLbNMgIA+MhK1IXDDID55PhlOhxWls5zmpvdpn/OPPLgqDnmQa6GdKq40rCDX7qJT0hE6/i51cictjUEksZOhZrlvut9pjBktKE0kohGD2khHl0yiTSLoqeXS/heIb4tHNmSSd9tGguUvDGFPJSNH4dRbP8AldhGyeFYK/gAAIABJREFULGbF3ZtJOruHs00KzYeCzP+z+RgAEleTGtpLZP1INgOKFMHslX0XOagEa97nQmUeW5YMczGJEVjUhsHbKhpISbCgBmUwTZSO7rYuuP9PSDT7qAQgSEfxPirbA3Q11KAc2UmLKY6spSuINQqj0zJmHZnk3b4Raeer7HntB9TfrJbszSztOe+KQcZ16RCdOVsxXNyNU3bh8C0h9VpQCm+f5K9MR684cDQryFd3c7oWVMlPkCTy16QOmOqaTJ8Mp8OAIonYvXri58YiAPobtKhY6svIuus+mt92sWjbFoSag1yuutkSPKP0l8aY0ILSTSIYrUjn3+NM2zxyBvRHwTKL9LlNHPvhX3I+ZRaxNjvWtLUs3FzUfcGjsvLRxez97TcpNsZh72dXCbbEEGcrIM4CvtIf8cofEnj4s/eSJXRSevwwuqieHxQjiRSDxK26lzTjcLZBBEj1lJ1qJWH5Qzemvpc6aKlzoE+aRawlu39bIyMw2bLIXHU7iRaRUJ6uv46UBSRnJWFLz8bSMRYdg51tI9HzbmfxkN7w0FTiIn3N0qHT+Ouzhz3nN+gY4K/gur/i9nFoUQMVnHmjkiVf+R6rM8xj1zEuLWPtW93/pbTv5/0fPs1F/RM8vuFuYoadeZxMnwynA6CT0tMfoOtdtTBAS/DqT3jL9RnuT/DS3P0BLFkbWNSTU1pueoHqY9B3nxIpswepwTRKf2mMCS0o3ST65NtZm9z978BZ6Dt3oU/exoZHtvX8JeHc+QVaZvV9ShEwpN3N7V+8exC7gDFxLr219ix5d5Cx8wM6pJUkG3QYLUG8QRUsAihevKodowgwnO1GlK4OpIQ5RJsGs9VRX1GNyZBCTFpEv7a2vfYpyixu2pwBEgbYnDu/QFOMh4bWBIqyxTHqGAs2su79EsPnqR/+nA9tG49Pes6n+zj7f/pbxHu/y4YhA9JodYxHy/h0iDEb+djfrmfNuX/jrV0Lefiu7GEWAkymT4bTAcNeC1IVZ971s+yJ2eg5O8J57t6ntKxwvP7SGAtanJ8k5Nb32HdmEesXxQxa7XIwu9onwCmdl2i1zMamB7CQVGSkvNSJCkgtR2iKW0z0iLae4/pKOPnqb6l0yYhR81m85S4K0yJuaNdQNrn1PQ6UruOexx4Z1LbvzHyKokTi8rOxCGPVMTr6aplInwxlG6sWqXEH7/x0F7Gf+nc2ZLdxfs/l/otkxqFjLFrG7xMRY0wcSqf3hvcuU+mToXUMr0XpvERT0EHxH/6Vnb/9NeWXXmbPztP4RrmqYbz+0hg92pPSROIv5dgrr9PqdREwLWHFX36RNNMo7UorZS//N8VOFZ1eRA7GUvSJPydSABCxrvgSuS/+NzvLLCiBaFZ+oneKaTgbgELX2f/i/ZNr+Pz9N7HQdbi23mD7SzJsunHqAPDTtPsZTtd76Cg5h/zC96iKzGThA4+SFiGMTctN6Rjgr7Fq8R3ljX/8d1wLtqH74w+ol5uo6XiQgi0FPVqnwicT0LfkJkrfeI6rLgU15MJHAWseLaT/7N0U+mRYHSNoid3OfV+9jc7Tz3L4TPPoz/l4/KUxJgRVVUd1r/Dse8VcvOr4yBX5G7gXqe8c8nC2sEFt5ex3n6DmgRe5Z5510Kc2pauOulYZgyWahER7+N6pjELLjOBW0XHLaAnS+sEPOXypHnmwfUpTqLG3yF9qQvc2k+KKNlwuP5tXdI+z7e7uIn9/8cm1g3xbwnFqJyeqnchZd7N9afyU7bfac7QMWZUoyIoF4GRpI40tXTxxT/ccaEu7j52Hqzj4P58Y8VhhO/6EC8b8r/Jo/s3bwgYhgUXfeptFw3xElSV0sRmk2CayC6sEWy9T6U4kLyf2+sUhuWltciIJoMgCEUlpxJh0gEqgpZzmoAmjIAACelsicVHG64PAKLRMPDero/s7SqADR5sbCRAMduL7Bvtw0jGcFrWL5qv1yJGW7nl+JYTPZyI1N/V67rlw0jKcT4b1l5GErX/NfVuH+LlJ0ziEDlXC3e5EEUAKBen/2KCiKgpvvfg8ol5Fl7SKjevy6d7DrCdu6b3cln2Q94onJCnXtKAFpQlEyxLeh1A7joANq6Hv46NKwNmCGpdNikUEuYP6Ohe2rNjujiiYsCdnYNeH0Ykbgw416KSlXUdcauaQm5bDQ8dIWvRExqdhiY5AB6iBZuo7bNOfkuemfTJCv7uBKcoSPoQOOeBHjUnGbhDQO5pRVRmJnsFa9aOqsOaRTxOvU/BceIWjlelszo2YoU+pN6IFpQnk5rKET1ZGagnXyd9yvjMBq1EARCwZayjIihpnp70ZLUvITTZiSYjF3yKD7KTm2EEcqh7ZGyRiUToJFiNqMIAsBqg7dJB29ITcASILRHxGBVVnJTYxGp2rlLKLV/GhQxcxm4Il+VjGJWSSdOgtCLKTmmMf0uzxEVCsdBTMx26JIi7Ox6W3PyCYlIheABQPjuY4lt2/GfuYH05vJgO9imLMJHdlEUKbPKD/DOeTvn0LAi4vJpOXS/vO0SUISAE9CctuJzvWGPZ960Z/mQhWf8DJksF+cyqyhEt42tzXdfRFBDmkoBq6ZxCAPue3e39ep18hPkImGHBTW9eGnJt5ywzmt4qO8GQ6MlID6OOZtepOMiIGHypkVzFXagIYrWmkZyeNbt75JrR0Ff+e0+3bWJMi4B/YVsde9h89irkwGdknYBPPU5O8neW5kcgt+zlarlKwJgPB20BdxRWcbVEUrL2XiUixNnk6ROLz4glVvk5T4hbyEutxG53U1+vInu+hyWEioWgrcbPTMAJKx2GOm7Oxjme29CYy0NsC9VSXHKOhNZ9kQX9j3xrSJ3361qoYOn1mhJp9KPMfZmm8ATVwiRP7L5N6exHmcUiZ/L41iL+yLZTvG+76m1wUXysufQKpBmVA5hIBncGCztOMo0tHMCgjCML1a1QwI4oCNbte4rLejHx5D5eq9vML+Tt8/u6sWyJnnxaUJpM+GY7tlp4Mx00OlPR0dMPZgGsZjlf3zXBciU9JxyYOZwNkJ7WnduMUgsjGTHKXFPWfEpP86FIWkBPfvY5qVItch2xvGrKzjk5XGVfOGomOTsHg9NJFF6pgBLmVigttCHllXLkgYomMwCqESMjKQ6zYy9lTLRgKK6i6LGCIzibGU4tPycAWYUOuuIQam07Zh2XI5hzyl83HphvnkDHhOgqwyg7qq0qovehAzK2iRdKTPD8fz6VKgpYi9M4gCbOTei42ic7yWuw5K8Y3gAynw1FKZW0Tna5KysuyybAoWE0+yqvbSZyVgBps5Or5QbRkJNJ2+jSVVa3oCyupuhzA3SES0VmD16MQsCYQ4xHxdvhR4/XIHgchU9L4p7YmwSeR/sucfLsFdc5g/nLSUNeGftjrbyJRCTpKqazzIIggRqYRpYeYNAuC4AW5nfqSCkKigKdTRY7QY01NId4o0OxqwusNcOnccUQRfPp0VEFk0wOPAjId8S0ElutorfejgBaUNEZgWjJS64jMv40iUzI2IwQb3+fYESur1ucwlhSgI2pROmhsiGHh7cupPFOGo6mELlVA1TfT1ODH3VKBV4giY84S0oR6Sk5dxSgGCKlg1kVg1llJyF9ApglQnPh7dKiSE2dbM4Hs+1i9zE6oZicnzkQxN9OCOTaByLG+d5oEHYScuFwSEZY4EuYsJqa9HKdP3+2ToA9JH3F94JaaqGlLJ3thkPYWJ8axahlOR52BjIWbyZ0Vx4UP3+GsbMduDaJITrxeqC+rJ2iMI33OElL6aTERk5GDp64B++wiMuRyyvQGRFcZHmcIa4oZq309Ea//kr2X7fhbRebcvwBvSwuhsPKJjL+pEVW0kjJnCZlif38JOiN6rGStyuRq72+aBPC10yWlYZvod5q9PlmwgghBJVh/nLLOeJSmOjoJ0NFSjZK8jIJMO65zxfi7/Aji9UKXqgrJ81cQrVNpqTxLiysGFVA6znGobBYrN7awo350TVH9dZw/VkHM0nVkhuVyYS0ojYsRS1cgoI9e0Cfr9PO4rL0vTYezAYjoDRKBQbN9D2cT0NtSsPUcxZhYRNy5UrxyDtHjuzUfvL2CjF7tQrIuZMHWefjqj1FcUow/JRYdMoIoogoSQUlFVroQIy0EGjtw1lcjtnsICj5ay84iWaKxmZ20dfmhvhofASKickmfFY0ImFLnYjxbSmPUYvITxjNoTLSOCjpa6xFt0Xhr22gvryc6Pw1zfTHVXX5oDZGcFnnNr1LLOTxpqzB562lwRZE9Zi3D6fASCEFE9HzmLOlC9OSR6XqNM4IVERD1IooQJCiryHIfLY0tmBUJs00PikxXk5PI+DRcVwQEJZokYwjnkX2o6/+UzclmVM95Tuw7gHf2MgrCyic16NwhMIuoiooc6muroksBc1I6kRGp17OP/+Ipymr30SH+PQ9OdOkKQd/jE5UIo4KMgizGEGsL4nQHEEQBKSChoqIqMqLeiOJqpV0UCUkKCKDKKugUlJAPRY7ij2/+DsmQzeKHbiO543ejbIiCr3wnB0rz+dS68AxIoAWlcTHa0hWRIiju85w5fJ5q9wUajANtxZRUpZAQ+BK/25VLrFkAdJjj44ls6Mlw7DxK6ZnDVDQdgFAs+cticDV4ycg10vTSX/BBeQE5DfsR0WNd8BnWLemeVlF9TXQaksjtc5WpIRdtl49hX7GGRIuIoNMjt9bR6Ou7T2nw8gKDaknezFy9hENnwiDXU3toB+2JrXRGCKBKBEIGXEf24DSYSc1t4WLZYVrcJai+CLJXJOOLyCUzrpbLV1sI6N3UfvArPKoOuStEfN0dJGVHoHoa6BLNpKbG91v9NXK25knSka/i6EjGvf9Zyhvb8FuKWL4hAX+wgcqKeqqf+w9q7XNId5dSLrdSUz6L+779OdTLnSQvs+JrbSIq5bqW8ekopqQqjYzUX/LKa1UgduE/uorF65cQZdIhBEo5tfs9amIc+PtoaT28h7YBPuntW011jSi+GOL077H3+EWi6g9w1iMSm5bNrPndCxsEkw3R58VyUzqmpm8RiCAmAS71asyXabhwgao/7Ed3w29eot52D1s/JVFcOrrSFTeVJVywkpIfT+2VUzhEE0aDDlEAfVQyyVGQnBxPzcnjlJ614HKZsSfFY4/rHlMa2tvQiTKdPd8NKnoEQWTLfZ+iN8Wi3NH7Q9f3KZE158Z2KB4qT7WTv20VMWE8z6cFpYlkYOmKD/+Ot/5Qx5wkCAoy7a5Cbv/aUzfaejNS7z7Ook/0lkkAZCfVR/dwtklPV+mbBNf8B3+yPh65/jle3pPCipw9nG0S6GhNZOmffoe1STpQ3TSe+ICTe0AUBRQ5koyVm/s8xakEG0tobFjI/J6qoWJEOpkD90APLC8wgpYdF1cwJwl8/gAxuYuYfc/XmR+t4r5yHGdcLpQexSk5OPfyG/hX/4g/2dCrI5UVOXu50KjiqjxFMxvZ/ulPYteDKjVx9cO3OFZpRlBsFGzZTEpE/+WzI2ZrnhQdTkoP60mPPcrF2V9iXVEzxtjz7NqfyopZldRUdKLk3sMdn/o0GRYBueHXvGZdQaxUyenAHIqsJiJs87BPlA5DJnmbkjj9s5p+9h0XIsmLbKXDNJ+suYtJ3jJan6SwLHU3FSE7lbs/IPrjv+SeAguqey+v/fQK1kO/p9VoQglAzPp7yI3SjV7HlPStGFyHv8OrJZvZlOCiU3JS+mEz7bVJbP7aNwc9f3Pve4TojpcoHtWFfrNZwgXEiHSy5qUDKv6aQ3SZe5+eZdwVpagFmymKNnDqXDEht5dQku36dgJBJGveMkCl+fIR6kRhiKn47n1K25cO0QwxinmP/y3zRqVx+tCC0gQimKPRufdy6sgmNq+dRTDoxRUoYP7mIgxSBQdP/O56uv6+NgCCNIdqudC3TMJ9d5G19l6y8FJxYTe2BXHXUvInO45h/+Tj5OqDNDe/zZ53/gvPte/dx4ohN0l5aK7oInrh4uEzUAwsLzAKLfNWx1Bd4kDvPcDR135AvejB47Ez97GVFK69lyy5jiMHdxE3iI4cuZyjZ/9IVFoxh3/9ISHrKtbesxJr1nIS0nOwD7o5ZhTZmidFh5PqC9V4jh4g75PJRDlqaDQsJdlxhqj71pLSEMPsOXHXFqy0HjlL0tI7cTR2MmfLXG5cFDlOHQBSxY12bwy2/ELmLszD++bzPSU4RuOTw+gWzaMwbykm/QH2Od0omJGcVQSiZpE0O5fojMF8Msrs2ZPetySILCTRVYV95ePkCk6qz57hauXh68f1ddDWEknmg9uJ69FxfcHP5GUJV/311LbFkr7wWu4k5KCAydyTclbQIaqBQes6qf56Gtvt6ARh1CsEVe9F9r16gqR7PkNhOD8e9UELShOIPu0xPv6VGPa++RzvXo7DbjNx7Z3pDen6+9gAMBAzsEzCi8d5+LMrMQsyAZ8Bc9+U/EIHAWWk7w3WShuZm7YiqabhO/Zw7e1rM0QiSn4kfykV1ctJKyrClH69PV3nvstvf/4b1DuL0EteZH00cSYB8NNeVY238xJXS87QLroJNFzGtfjvefiBJALn/pXXXnWTl7+IhdlDtXQU2ZonRQdYZ2Uh7jVgMOqJzJqHtfRDPJ2XKK9Yzpy5fXbnBy5zoWoB81dfoaY2gaJBtYxTR1/783/NSb8ek6mLkCyRVDQXqw6M60bjk56+pTbSHNrKUose4/rPE/Xtz/DsgRTcFTrWfPlxmtsUEnPGqGNK+pYBe14O+jde5krxKRyANXcd65N9Pb8Zi1UIIPlUjIPubp7gLOFyB40VNfhCQWRdHMk9PunGgD03g46y45Tp9Lg7BSKj7df3m6kyiiJTUXISWRdHfEEOzisto/xhBffx7/PywXX8w6MzIyCBFpQmmOkokzBSOv9B0JuRay5Q4e7ep2ScAC274m6joKAnm3Of9kQUPkL++x8Ql7+UZIMX/ZHXenSYiZmVRWRUATmFi0kQG3HZV7BsSQo6IKJgPfpXPiTyrgfGuVl2MnQAePFf84md5II8rFFt5M0vJKbPVRW4+hZt8z6O0Fh1LYv6xOvoY//zvlpWEN0z6BpH5ZOeviWkMX9BFkb81L3yDOrnXubJAhuK4w1+/8O9zPvyN8LQJ/11CEYrZvtc8uYtJaH3JEX3HtfDlZ9+murlK7BOxaYkXTQps6OHPhvmNGYtSAPAc6Hi+s0ngKBDFHXkFC4DunPfXSdIw47v8975WoQFG29cmq+0cX5HPcu++EmSZtBIH75LMGY4U1kmYfh0/jem3dfZ5zGnaAk5Qwak8WkZkw4xjvQ8B8Wl7aiA3F6FkrONgtj+V9NMKJNwvbFuaj5sYfbSbJKXfozcPtFqsnRMqE8I4XMJRMd2p7DRWbOJj0wgKWpidEysT0ZfFkVu3ceFti3csTZ+9NNgYVm6wkjq3d/kiW/8L18YbOOsmMi6p9/niUUzK1HuDIqfM4DpKJMwYjr/kdPuD5olfKrLJGAk6a6vkfLrf+KVYzYEKYFljy0aEDRnSJmEHlT3Uc55NnF73MC2TrCOSfOJjZyH7+X9336VV8xWZC+k3f83JBnGoWNa+tYozt+wzJTSFdOXJXwi0UpXjINbpXSF7K6iiQzSJj1LuIrkbqTJ4UcQQTDHk5gYdf3OSA3gamrCh4iq6IlKTiay747iaSmTcLM6VJSgmw5HO253iJjcPKIGntaw0TGyFsndRLPTD4KCTATxaclE9Ppk2spW3KxPZPzORpw+BUGRUAxxJCX3LdEyXaUrbtRx+kL3Jt9IkwgC1DhkvJ7QIKUrVuI89AwvXNnAk48v6jedKjsP8l5xLtvXp2ilKz5q3CqlKyaFwTIgq104HSIJWTmYBBWpo4Y2TwTJ1u58bJKrAU9EOml2A2qgmboWL5YU6/U55unQctM6QAqqWBLSMMm1g66iChsdI2oJ0eUVic3IxiKC6m+ktsVLeq9PZkzfEjHaU0iN7U5w6m+pwOmz9dlfNE2lKwbVIRMICiSlxGMQoN7RgDLIY4PqOs8JRy7ZUTPxWWh4wvHefsYiNe3i0K43OHPqCv7JSS08Q+jNgBw14E5NREeQkKwCKooSoqsz0LNZUSHgUbFG9rzuNtowB71I03oex6JDwGi1Ywmn8htD6hhJi5Go5CR6x25BpwdZuYnfnUotw/tE1OmufT8YEK6n6BriN9wl73Hyg5c4XuJkchQPpUNAREFWoTdDuKqqN2zorTpyhdRVc8a54CQ80Z6UJpCpK12hIrmuUlV6kcZqD9n3P0a6aTTf62ZMWcJvQosQlUNizjyyejMg9ykvoARErLKMzWxArxNRvZVcPVpOY7Wb5KXrSBF62imI0FVFxdEjtNYMonHMTI4OQVAJucqpueaTTxJz/Yzjq/2QC6WtIMgEhVQK1q8nwTSeEWX0pSvARyByBSvWxl3PSN2ndMXQPulz3lUf7Y3tqB1XOX1VRpC8SLYlLFw+EXXDJrtvdetI07lora3DJSSTdW0puIz77C/ZW72GbffN79lkPvmlK4bMEi6IGCx6gi4HfkGHAjdOF6oKxdb1bI/2UH7xWZ4v9aEu+Due0rKEa4zIpJWukAm4ZWIXbiPK/w79a0yOUNYCxpYlfMj2mgm1HObo4WZiC/OIsKZibj5Ak38+WYKK7K2gdN8JmoRCsrLnkGkv49RlA7mr01BaL9DUVIcQk47NdI6gqiCrPfn81BC+LohZuI3YwECNk+GTsepIRao/R1tHGyUXGrGv2kbBQJ+oXhwNRmZveZgYPciOAxw5WUfM2oyxX4DD6XCUUllTR1NZG7FLN5JkVNH7T3G5PpFkQSTYVkLFuQ9pvEFLCoGKw9TUdqCPjsdicOILqaDz0FrXAok5pCXNItNkRECi4+QfuOrIZl78OIeRCfdJEt7Lh6lp6sIUlUCExY2sgqC3k5gdRay7jsa2SNITzOAppqwjm+RJfSE8XJZwD7K3kivnQogieLtsGGPMRMV0pxmqa/WCICCg4Kv+kIuN6SiqwMJlKeiFKuzzn+C25Wf47YFbJ0u4Nn03mfRJyQ896fGrHN3TAcPZgGulK1L7lqeowacA6IlMLyDWMlgXHO57k6BF6aCxwUxcVBQJs5eSaaqhpctDa0kJDfVVtLZ5UYNWcletJctSR4Ocid1dQ5ffSVOjC+PsrRTNX0CyVcTt8eH2SgCoQS9C6jwSIvpolLtob2nBO545vUnQ0dxhJCl3EdkZaUQN5hMhivSVa67tXxJNkRBwj0/LcDrqDGQULSbZZsOuq6XF24WrrZXGkqu4PXVUVASJFKLJXz1Ai6+FBmck2evvYdGSFaTbwFFRTX1dO8aUbBIsEt4e/6D68HSKWPT+MPRJJ25dLoXr76Bo8RIS8NLu7b31EtAZ9KiSAvhoPn+V2IV5GAngah6njqHokyU8v2g5maY6Wrx+OpvqaKqvorXNjyW1kNyi5VhEH13B7otVlbqQFbG7rF+ggQZvPFZ995Pb5T1v8sEf91NVc5RDZ9t6pvtGRvXXcW7/AWo80z0NOzTak9I4GDFL+KSVrhiOsX5vBIZqr6BHj56UZRk0HH6d6oAbLxaMZj0COkSCBIMiRp2KLHlx1svQ1UBjSw4xOiOWWCMQQtSbsCh6jJ4Gmrp0qIqO2KRIxGvPHCqSu56WcWXWngwds8koKCRCL+MCQCbo76KjsR454EFtrMWrM2FPTMCiE1ClViqPVxCfW4TTpWIfq5bhdKheAkouuaszOH/wKK1KMikxhp7FCTp0ePAHROwDtbTOJmPRYiJ6g6egEupoJhCTgq6tHh9BuqR4Is2XKd27izrdOtYojTSFo09mpfUUhgwRlFVUVz2NfkCVkbEQn2JBbtlPtWUNy2weDhU/y9njIawrvskDU5klvLNbhySroCqoqkzA46GjXUIVTOj0egRBwlXdgi0rA1+ZD1FRWZlr48SVjptsiJYl/JZntFnCu9PjB2l+/YvsP38Ex0kRfT+bjOf8r6g7/i1euJBPXO8kvRpPYc+L22DDIYpP7MC5+zVWfPsZ5vVd0S7Vcv4Pv6RWNaOE7KSlRBM/aFmLwbmZLOGDaknezFx9ChGpBUQbOrn62ne4IFQSqDGgSh68/mikSyXIkQbEwFWqi/fQfuh9Ch96ivxrdWMEVNcZLl8upepMG0u+/Qwp/TaiSnidwX6ZtUfHZOgwYoy0IkvtNB/8b46faWPJt39EOgA6jGYTprZ3OHmwElUXJCAUsubxp8gyVFGy9wji4ntJ6arGd1NahtMh4zn/a1rq/8CrT9cj9MkSHj93CQYlA+v7T7DPuxq56bqWrNQSQv18sud635LdtLu7QLePC3+UEAIOggkPsv2hXPSinaK78smveYejZ+PJW5ETBj65UUehoYma0vM0KRGkpWSSYum+Stt2PM6Pn32QO9fYydmQiA4v5qy72VTUyqkLo8sSflMMmyU8icTYKGorL1PZbiIgC+gMZqJi4hCBBqcfVJUWYy55ZoneBEO2gq1sKeg5m/W/oXI/aFnCNW5kYJbww9/hw+JCthVFoTre4b2DCdz23e8x2zzA5i6mpCqdnPhc0jb1ZglX8V95lfMNXrJyjbirazGs/DLrKn91wzukrpM/pnz23/DAqjjk+uf4/evN5BT0lLzovEqHNYuCvqtOB5SuGFWW8BG0HFK/yra5NnyXXuJ0KJMVX/gWq+L76BBnkWA4RUtnK8aVX2Zd5XN06GWka+tdJbo6dKTd+TXSvN8dUCIaEAzYc/pm1u7RMlIJgUnRcQ4v9TRLMeTf9QjpA9ureGm+HMmqP/t3Uk0g1fyMl176/+2deWBV5Zn/P+eu2fc9ZCUQAoLsmwiIbCpuraKiHbXa6W47bX+ddmrbmXZsp9t0uljbam2L1naqHTcUCgUFWQQlrAkkgRCy78lNbnK3s/z+uCEmIdzc3CX3Bt/Pf/DknPN+z/ue89yRF3nJAAAgAElEQVRz3vc83/+jdUoE6cvuJjfWQm1lMlOG+KH7paO3jPLz0HsuntX/8qXB+AH1DlabTtGn1lLdk8viR8fqE/fY0lxt1JdXIkenkbvwNq5LikSHk+a/fYUjtYtYk28GJAzRsRiicsgYj44JGVtuHZI5g7y5iZian6OuwUp+UQxax04ONixmaryFHlmP68h26pUeuqxOdLU9Xq+0G5d1hccq4cNjXYffRzEZhsyrqKiqRvqUOHR0jnEcUSVcMBKdCaPW9EElYmsdZ/e+QEp7CorOSGZqJJpTAfOI2CXrijf/NlDJeaDa96YbSD++h+PNRlR5FgtX5NF3AVw46S57mwtdDvobq2mvqMWwsoouZxJJacvIcr1FdMOl7aKYtmRojbTLrStG1zKikrMXWpJborD0xFA4VaPstZ9iGaIj7f3XebdDIyppNguX5tF3QcKcCE1Hd1CvyPTVN9EbW0TkmRPoFDDipPvk3ymr68HV3YL+0Ju0mpPJW7CYpMGf5V5YCARNxxTm3PFR0iJsXADARW9DOXWtTajN3WjZM2gpPUzEgsUkGBUsZ9+hPXUJ2omddNJDh20+WSUZgx9++qXDmEvRulU4z70wPP727zBOiycio4ScQpWjY/YJOG0XKNt/gB5zHFp7Pc2HdtCZlEXevEI6WlSUqm3sP+1AH2kEXQbTl2cN+xDXK0uHoI+t4ddIX3sHvY5dHG+Lh4pD5NxyOy2vNpI1bSZRWiONDT3jvNDHa10xZMvLqoQPj9mcRuIThn+3pAEd50/Rqdno6VFQTZlYVbz6WF9UCf+QI0XmM6WkmcM/e4yTmfkkxcYTk30d194wGyMariL9kMrIQ2MAGs6R1b7/msrdD91G3uCrkf6BG6CJhFnrmQfAKs789D/RL19KkglQoonWOUleehvZoxa289K64rJKzlfQYoxGJ4MhIonYnEXMKMzD3FVCdmwxyaZuavb8mJd/3cWKW1awcHk+sUZpUIchdRap8jl60ROfPJ3kounEG21cOL0NG0ai05Mx2mYw/+ZpV/gewwsLgaDqAFCwWS20nqtAF5VBfEIGkVmzSC0oIMYgoTmqKX2lmms/dicmRzazF4+mxV8d7vEzvEq4ij56FvPvWE+iEZytiSSP1Seqg6ZjZ0nPmU2mQYLMdHqceZTkVLDvZ49w2vBxtsxMo6Y+1XcdEzG2NAVrzVk6DenEJ7mISprB7GlFRNb+mtfa/ok7UvtoctRR3z6TBYtXMbdooCebX+DiYQi4dYWnKuEjYhFxscNftUtG9HqFKclmGtptXhxsKKJK+IceQ8YGVm7eMPCvQFYJ93RUTxXERyOW3LW34Kit5MKFYFcJTyJ/9YMUn9xN8tQCYkfqkGJInT6X1FGP76Crvn+MytreWAgEW4eeyJh40ormMmNE5Sq19wh7f/M8utu+xYze41ivqMVfHUPiY1YJ96BFZyZr+U3MvKwCVwZr//V6lp/4Hi++Xsjyexb7oWMC+kTSE5M/h8Jh8641HN5uZ+HHp2HgCPY+J/nTrtQfAbau8FQlfESs9FT1qH9mSimmIAUSst1lhtzJUFQJF4yDwFYJ98TY1ZHDp0r4eIggbURl7dG0BLNP/NEhN23jjd/sJOn+H7KyoJdmSw65YVkl3Ft0mBLTMMbPIT9AOiayT9SeszQ7Oyj763/x5vN/prG9gupDJ7F5u5RaVAmfMCZR/pwEBK1KuJ3mXb+itMFKd/kJlBe+T010LtfeuYXsqLGqVYdTJWdPOhweYpLvWoJWJdyDFukwr/z7D7HM2YD+7z+lQWmmtvsjFK8pHuiXcKoS7kGHuYUzr/ye8xYVzWXBRjHLt8ychGNLQpe0kdu/tI6e0mc5eKzF+7b6o1PgE6JKuB9cLVXCR7WuCEdCVpVa6LiqdYSsSvjllJ6qJsIskZXq/sykrLodi8U+SpXw60bZ2pN1RXBtLUSV8DDhaqkSriky+qQcMkNqXaHhaD1Hi9OMSZIACUNsGslxpg9uAiGpSu2DBQcaqqObjvZeZEAyxpMyNNmHjY4xtGj9tJxvQImOdL/nV13YbGaypma569KFqkq4L30i99LW3IksgapIRKVnk2i+dBZCVCX8CjpUl52uDgdIICvKZdtoqsprf34OnUFDn76UVSumD3wo7F4Svq5gPzvKRhbm8hQLL0RSEgQHHywfkMzEZ+QQH04Vtn3QoTk7ae3Sk5yV6/Gj5ZDrGFOLgeiUbCITotADmqOFhu7YcX4oO0FaxrBFcXS2oiUXkBmpA6WbhnoLsXlJob0B+mJdodnRNFi++WOk6FWsp17i3QtTuGFq1CR+Uh1OOL5wEkx6fLEXADQHPS0NNDfU0tTciUMNtf+HbxYc9k4Lkq6PjsaLNDV34vTuDXkIdIyhRTIRM5CQQMNh6cMcHxnim59vY0sySLgcMhoaqtOBYjCEqY6xrCvcLgE9dhU0Baejl7rKdu+KKk8SxJPShxDfrCu858pl+SNIzIyhramGHt0ly4cPwsaEbNL1kZj0oPQ1Ut9kZEp2bMgqH/umQ8Zm7cOZMoUpaUaU3joa2yLJSQvdzfyKOsbUMgStnx5btO+1+oKtxaMOCVNCBsbzldR0GZFtOlKK0kJaUdtn6wopAp1OonbnX6gwRKBU7OFszV6eUb7LJ4atwHPRtO0Jdp6swzXJbC1EUvow4ot1xRXxviy/LqaAvIJU0nOSAAXL6d3UyT3QowMkjMlFZKe4L0F9VCJRbd24tFj0E3If9FWHhrO9CpvcQX1FFS5Nw9mvJ6PAPRdmiE7E0NmPQuQEXWyedPQBKs72cmobBuJDtaidVL97ht5YKzW9EmgOei0xFC2aQUR/B46Y9Al+dedHnzQfp62lk2qLHsUlERkbjTm7hIIoPZqrk8bGdqLzUr36JGIidYxqXYFK5YkjSDqwGaagSTpW37kFUOhOacWxSE9bw0jrCiOZm77Bg/O28vTeyWVrIZKSwD+GlOWPkjScDUeo7ElBba6nR+umrd1OYvFspqRGozQfp74znoIkE5qzi05nDJkl15I1yheMmmzDoY8kcWhI6aerw4opKZXoQM87+aGjrdOOU4old/pczJKGveUcnX0yCXEGNJcN2RB1+XvyYGnxpAMnfdY+Opp05C5cTKzOffMe1OJyoMbkM3XWNGJ0oPWfo9KYQoSk0N/lIibNdPnTXlj2SQcNDRYii66nMF4PrgbKS5uJyBqoNqc3YVB73XM1g4U5wkNHz3vH6He6U6Um96MooGnS4LatF47TaklEA9TuExyozGfJqla2NfjfTM1ez8nD1SQuWEFuCJcSi6Qk8I9xlOVX5D46G6sx95vRpCji4zRaL5bhlGRUfQKx0RJOZeCtuWYgPiNr2Iq2gFhXBEGHWbLjMnTRWNGMakglKz+biOaL1PcYkFQDSVnRI5JSELV40tHrHLCu6Mcpa2AaqSWGrJlFAyu5FPqbO4lOK0An92BREki/7DEpXPskmsTUSNrsTrT4CFRHP7qYBOSmahr0ejQZItKnDHHMDR8dl1lXGA1IyIPbqi4bqhLH31/9E7KxgHl3rSOj+08BaGT42FqIpBQgNGcDtWW1GKcuIivOEJCYOz6easS+Mbp1hbcbe1+W32Q0YYpJI2NKEjo0FJuZSGM8kQaQu89Q2ZzK9BmpVzi+r9YVwdbRT/ORTuy511BcEInScYqqujiKC6aSdMWDBVGLRx0AGmp/A3VVR+nSjdQyBMVCuzWRtAI9SIlkj/o5Yrj2CaCY6Hn/HU43ReLs0ZG9aDkZV6xXFT46RrOu0Okkega2daoGJEnHmtvvH3z1qAzaKnmyrpg8thYiKQUI1VLG2SNHiYwoITMuYdhrDl9j/lQjHg+jWld4jfdl+UfG9JEJRA7sxRCfTezFJhxaKqO/PRnduiJw+KoDMKWSkepekmtIyMTY0I1MnIf5imBqGaOtY8bdKD312BILhzxNTKY+UeitPoNWfAOzE4xojnrOVdSQOLvA/X1VGOu43LoCkHTkzVoIaLRUHKJeJzF6OczLrSsUD7FhhJGthVgSHiD0qevZ8Mmvs7Ik4bL37r7Gxl2NOMQMluVPNo8rBqA5LdgMccN/qcrdNFefx+Kc2CXV49NhJjbOQXuXwx13WJBN0ZdPKodAy5jn/IpxF71NNhJSoi8fk5OiT1QUp4Q5YqDEqyEKk+pAngQ6bE4jkVewlNHsDTR1xaOXpICs5NT6TvPW1t9T3hVeC8rFk1JYM85qxKFgHGX5h8Ww03X+DO12DUknoalmUopKhvwC1HB1lFPblsrsgglY8uWzDj3RebOIqThOZYsRtAjSpyWNSEoTqMVjW72IAzjbaJUzyI+8fA5pcvSJkfipOXRXHqFSb0CTISZ/9pC6heGr4zLrCk1BVRWqy99H0SeTUlxIZ1WrL60YQfjaWoik5CNaXxkndh8jYsndzEg3ByQG7mrER98sJXntfRTEB2mwGCJQak9R3evJusJLxlGWfzgRJE6dR+IVd+yNdUUA8VkHoI8nY+YSMsJBy5htHSMOYMpi+pwQ6xirrWPokCKyyZ+TfYVo+Oq4zLpC0qPT6SmcuRBw1777AE/WFZPX1iLMmjN5kFt2sP/FvxEbs4bp6VnDOt3X2ERVI3ZbV4T6DI6F27oiLdTNEFqEjrBlwLpi03hjDNparAi1hFEQSclHjIVf5nPPfTmgMbQOqnY3MfOBu0gKrydqgUAg8BmHU8bb2TuRlMKJkFVdFggEguAgIbHrvTpuXlbo1d9PgjVdAoFAIJiMSEj8blsZC4vTefxjS7zaRjwpBRC5eSeHT/YTlTyLkvnTxvjGQyAQhCcyveX/oKKpGzVzPQtnJolf7z6gaQwmpJ9/YY3X230ok5JTVnG6Ar82X+7vRVe0kVk5ESArOFGx173N6TOdoNfQJy1g5tzCgQ/4PMWGomA9+Qf21y1lzc2zBr/j0ewXqDpykn69hGzXkzJ/PXmJRiRkekpfoMyaSoxBAnSYpyxlWm6cn982+KtlvDoANOTuM5wvr8au6dFFFVF07TT8K2wRHB0etciNlL2xB1d6mvuCU610tiUzd9Mq4nyeOxyrrcHpE8/9FV594rmtnvYrYZ62jmvSj3DsggOHS5mwYqaqqqKoenfZIQZsKzQG71cuWUHToKvHNkEt8g6nS0ZFpd/mNhC022VkVWVZSea4EhJ8CJNSbJSJbquDnj5HwPct21VcOOjpG7gqlEbOn9aTveJWonUqjot/53R1MtMyIjzHhqD1l1PRPoVEs0Jvv2PgOx6ZvtOHsBXeRmGiEZxVnDlympgVMzEh06cmkDprNWkRH9y9e/3V66eW8etwf9x37pyJnNnruCTFZXPgl29mUHSMoUU1EjVtBfF5mRgAtecIvbpcVLuDnmDo8Cbu69jy0F/h1SdjtNWb/dpkZKeT3j7HhD0pyYqKXga7wz3KVdXtD3XpftXX70TTNCovtk9Qi7zD0ucASaWl04osa7R325mSFjPuhAQfwqSk00nkuYuABRyX3UijFkd+5qUbg4NecwXxiTFMiVTpaXNQ0eEgZ14aek+xSzvU+mnc28DU6+fR9Z6N3Iy4wV+P1jYTnXojeZmxKG39nE/IID8jDpMk033RSmn1ISTJiWzOp2TRHBL8LerljxafdLjoOHCRhMRkLGd20xkxlWuWzibO3wrOQdExlpY48gc/mZHprGklZ/5qClP8uPzGamtQ+sRTLNz6ZIy2enH9ad2R1HXFkJcZN2FPSo2NRiLMOpLj3QW46lp7kHQS+Znue1anxUZNQw/3rZ3hz2ECzl92nkJWZRYWp/PstnJK8lP4w9c3+LSvD11SCiTOsz/m+de6SMjfwLq7VhIx8g/0Kcxcmc+xfS9Ro48hOtL4gTeQp9gAcsu7VEVdx4o4B0eHRXTElKwm+i+/5c3yeGwtembfd93AjUFPTMkGFkZkEmcGZ/0u9r5zgVVrCv27cfihxScdWh9tNbX0LriJ1UvicV3YxjtH07lhiZ+mhEHRMVafDMHVRHVbDtMW+3npjTV+gtEn3moMiz4Zo63D9huBXPkWlbX76HV9i3vX504a76FwQlXh2W3lTM1O8DkhwYcgKUmShNOlIMuBn0Ny9rZhWvV1blsQC6qCS1Gwlf+CrX+uRzKCPnczN39kKXPXXgO4aHrxU+w+eYDOI/oRMZXeY7+nqf3P/OmJBve2ORuYHitRdEMKmlyHajvJ7l88j9Ug4bAaSJuaS/KqT7MhS0fj8w/w5++WkleUgUnSEzv3QVYtUJFl0KXOIvn9M1iceeOs/qvQW/okr++qGaeWETGvdUSg9p7gnVeepNLWhk1V6G2JZNltsaiyij59BuaTdfS7Usb5FX5wdGiairPzIHv/9DrdI7WknGPb40+w4wcnOFGQgUltp6Yqn7u++xmSW0rpzlpOpKYgy4HSMcr4CVCfyI1/5rn//Gfez09D7RvZXyc59PYR0m9aNM7ajKEYW8d467l/ZX+3E5NplPOX0Ip8rURzY7973gZQZRVNUVAG/j0RqJqGqmkoqjrwH4DG4P1LVlQ0wBqE6Qd/6LO7OFHVyqwC35+QLnHVJ6XslBiqG7rpd/g1EzEqrlYHFq2TilonAErTRc7s1bHoU98gQa/Sf+zH/N/uDJYUx6J17+AfexO59gtfJ8s8ItZ3lurTTlqqjMx8eGDbw9/knaqlTHf9H5JmofngK/TN+zarluaj73+bXb89zIxpVipkmS5nFukl65i6cilRekCDqtoOdyNtVdS5otDVd9AqDTactvMNmPPnEnelJYKuM7z3Kkx7aJxaRsa81tGH2naQU+VW5j38OQpNCv1nXqP6VD1aRiSatZp2NY2auo7h7/fH0hIUHVZ6Wu3UlB7EvuL7LMwxQ98QLQ49kUvuZapsJH3xEiI6/pd6ZR6djY3UHutAX6x+0D+B0DHa+AlIn3TTuX0HjryPMHf9CqLsw/sLWYfN2kbVxQ73uAvjsYXtIhVlCjO/8A0yTaOcvzOppBU10dPdTWVtB3pAs/ZhsVioqDVcPqfkjU4fsPY7ccoSLZ3uIzpcMqqqUTEwXvr6XSiqypN/ey9gxwxIu21Ori1K5Zmvrvd7X1d9UoqJNHLT0oKg7NvmjOa8OoXVc93WxQ5zPMeimsifmsXceJnmmk52H9iFPSIFWZfINTmJlBSPEjMWsPGhWzn+w+fIvrRtvZFDrqXctrSL41UurFEGbLoIll47BcMFO8dmLCS9cQ9Wk57elgtY48/QffAkbVGzmJ4t0WV3z58pcgyr7lrJtME6eir9p7fzu+7p3L8o98qVI1xO+l5554P2eKtlZMxrHTra971N8qzl2A/8igtx17Fx80dp3rOL+ppIJCWOtR9ZRGHs0AZ7oSUoOuqosehIzIqkOTqW6+emIZ8fqsVMbAxkXHcTi3ON1G2txnrPY6xJO8sbFQu4aVkuscPucn7qGG38BKRPjNj7bZjmLGX9gikoIzQqNph+y20szo0eXNEWnmPLjNLXTlZaLCXTr3D+Hl5DRvefaOjMYOXcHIyA0nmRfi2dVXMzR7zO81KnD2zv7SUyQkdhtrseXqfFjt0ms3puDgCtXTbaump45YnbA3fQMOOqT0oTiRRTQN7MRnb8+yc4mFtIenwCCfmrWHP7XExoOK818vKvv8J7pmSShsUANFI/uXBIPAKTpCOm+EbWFMOatYt46bHP8d81mXRXGrnll79nTaYB0HAsmU9XXAkZ0dB38ic89dYKPvPYMqJG+wHnrZmXsZB1w9rjrZaRMS91qI3s/vtTWEoe5N4bM7Ad+Q5PvzqNzz/wEPOu1EZvtARJBwD25bz02Ga+t2tknwzBfpxD5+Zx/X0mJOMcNt0bDB2jjZ8A9AnAxjm89NhDfL/Ug8ZwH1sD58d5/Wsezp+3Znnj0CnwCZGUAogp51bu/uStA/+SaXnpQeqn5Q4sUZUw5d7OPV+7fZTYWHEb5//4P2if38bjs2NRW1/lmSf/yuxvbSFVL2HOnDVYoTq6ZBNFL+6kXV5G7mhOYF6beXlqj68xDzokkBKWsWaZ2wI9dvZ64radxqoWk3il+QqvtARDx1h98sHR7Wdfpnnux0k3emii3zqC1Cd67zSG/djSe3P+hjJ5DPGuRsSHykFCbnmDV96dz62Lky77sNBTbPS4TF+XjuS0SCRAH5dLgrMd28BcqDZkFlbpKqcxupiEITcNf828fNUyLh26FIpmdHDkRCcaoHScpTc5h6gRI9QfLYHTMXafuBvbQ+WuZmYvz75sNVewdAS0T7zQOCnG1jjP32iEqyHe1Yh4UgoktjJ2/fFFGnu7sUUs5MZvfoHCCC9iY8Zjmfnwnbz41Gd5KjIWuRcKtzxOjhFQWznxh59wpE1Db9AhO1NY+sgXiRu8mfto5uWrFl91YCJn89fI++W/8dS+OCQ5jRs+NZ/hjlM+aAmKjrG0uNEsBznQu5Z70kZeZgHWEbQ+GUvjZBlb3vSnJ8LXEO9qRNI0zavVjs/uKOP0+Q7uXut+aC2r6eC9sy2snBfWvqhBxVH2X/zqpU6SCm/mjvtXEx+Oz51qK/v/3xYqP/YKD8+NCYiNstAidFxVOoYY4rnmfJ1HN+UNf7KdQJ3b3ykfttDh3dNNdHT289CmmYB7ocObB2vY/+S9/hwmrBFJSSAQCMIEkZTE67uA4qzbxrZDfcSmz2HZ9SXj/KBQIBCEBx+svlPyNrFxQYqo8DCBiKQUQDR7L6a5d7BueuTA/yhYK3ex52gbOoOGPn0pq1ZMH5i89xQbikzngV/xQtVKHnlw7kA1gzG2VToo37WHRoyocgwz1qwhN8rfDOmvlvHqUHF2VlF29ATnqnqY8/CjFEeOv9UTo8OL/Z7bzd7SVtDJOKQpLLppDTl+9clYbQ1Cn2h2Wkt3Utooo3NZcSYsYvXqQPz4muixNda27tV36wr2s6Ms8B/dCzwjklIwcTVw9KiJ5Zs/RopexXrqJd69MIUbpkYheYoN2YVmOcl7HVMpGOpx4HFbFWv5DmpybuOmWbGobW/z2v46MtfnYRy3gMBp8UWHrUshc9ktJNleJmBFVYKhY6z9qlYaLppZcMcDZJhAbvkHr+6rJWNjvu99MlZbg9EnkonEmRvZsMCEhEzr3uc43lLEiky/RlYIxtYHXLE/BSFDvGAKJjozkVoXPXYVNAWno5e6ynaUsWKX0PqoOVRF1tIZw+u9edzWScd5mfw894SsPmEaad019Pq7ktUfLT7pMBA/dSYZMYbATiwHQ8eY+42n+MZVZAx8pamPiEGyO/2rpzZWW4PSJzqMkSZ3f6j9dHXqiIkIwC1kwseWF/0pCBniSckPRq6+u7xKeBrz1hVwYOdfqDDEEBtlZNB5wVNsAGfjAcpirmdjgoM2b/eLitOhJ+LSf+gjiJL6cKrg14txP7T4piNIBEPHOLRorhZO7jlH7nX3+OdBNNbxgtgnam85+1/bxlndDWyODcATRojG1gfbWjl3+lmeO2NDG231nWBCEUnJD9S+diI3fJsHlw7UvkPFfvrnPLm1Dp1Jw1C4hXu2XMfqO+cDTmr/8BBvHDuI5Yge47CYQveh31BX/yd+83iDe9v8m5kRBdduysQgncdW8zxPf7MP42X7Veje/yNKD/6CF87HojrjycuMJ13WAAkUO31aFMZx/6CVsRz+Gc+/fmGcWkbEvNbhomXb4xw+dYKfvtjF6p8+y6KA2F4FQ0cNoGA5/BOe3Tbafh1c3Poo20+8yzPvq9ik2dz0+U9TrK/m4GvvoFtxL0vTxvvKy5OOUcZPIPpE66HsF4/y97YzbC3twZFxN1seWk187ExW3j+DhedeY+eRPG5dnjbOm/hEjy2F7kNP0xafhB4XTX/dzBOHNnPfijiuvcXdn/HXfJx1i47x/D47/v5+E/iHSEqBxNlIxb4oHvrRL8k2KnTteZzXj13DlgXxqK2v8PyedO759U+ZGzUi1n2CA2d09J9L5I7/+DrZRoXONz7NU2eX49jzKpXOMg4eaWPuZx9n0/I87G8P3fY4u/9RirriV3xsbQryxd/w861NTLtopWRWLGp3JW3xBSwZWuHBXs/Jw9UkLlhB7pVmqR1l7Hg9mju+PU4tI2Le6yhl78lkVj7yz0Q//236vTzlY2oJhg61m4sXLvLeLiN3/c8vmWoasW3HPrYfzeDO7/6IhXHgqn6Snz39v1Rnmchb9wAzE7ooP9pF8YKswQvQLx2jjJ+A9ImlivMR9/Opb95OtslJ7dbPsbt6GR8pMgM6TLExqBccw15DhufYOsGBynyW3heD1vYq2y8u45oEC+2yDtul/qx14TL2onj5TtUrnQKfEEkpkOjMmLVG2voUsuNlHD21lG7/A8n1Kcg6MwWZUeBUIHJEzFjAvI+u4fgPnxvc1qkp2M1zmD+1i+NnzRg1C1a7CtrIbTNIMSWTuSAZCTBmXk+hvIuEmh3srjOhuqKYv2ZojS8V27k32XdmOvev8HAx6WKI0RrGr2VEzHsdBVz3hS+THW2nHAAnre/t5FSbHUvNRZRdr1AXkcLMldeROWgV4IWWYOio6gbJ3dcdVoWpiZdrufV7/0V2tLsJeoNM2/G3aC/YiPnIGzSr3TRYF1M0mJT81DHK+AlUnyy4fxPZJkC10Nyg4ix7g10VoMn92Mli0brsITeR8B1b8+5aR6axm9Mv7qfo7o9S/xcLN25ei1b5Nu9VdTM+vNQp8AmRlAJI8KqEr2HFvNd4+YUnef7kyG0tHD38GoZLlpr6GGL1LnLW3k2BeZRGhrSSsycdIzGRtmgTN3o64SGtEq7hrH2Nl3/7FU6YrqxFs59j3wvnWfOdn7OhICJIOkYbP4HrE6VjD3/78fc5YvgkX735I1zxzWOYjy1HxVbeSXyERzP6eNF9sRA7pOq70rCVC3tBVAkPLSIpBZDgVQn3FDNgjnbS49QgWgLFSo8Wj9mvytr+tFwIcaQAAAlbSURBVMefytvjJKRVwsfWolre5dUfPYv+vp9wa4GHQmshrRI+tg598ho2f381Nx35Lr9/eT6f2Vw4+o0jnMeWq5p9L9m44YvFGCkdo32iSngoEc+eQSKwVcI9xSLJWWDi1IkONMDVtJ/atIWkDC0MGjaVnL0/B1cifKqEe4676l9h64/eIO2TP+fW6W0cfOMMzgnQETydOiJSklEsVoYVQp8kY0u1lFPnbOfIs9/huaee5tSpF/jbS+9hVfEKUSV84hBPSoEkWFXCPW6rI37ll7jmt//Nc2WRqI4E1j9y7ZBXSGFUydmjDhu1r/6MvbVW2o+XIv/2PzgTk891H/snCqM/mEMKmyrhnuJ9B3jmC/9Jx8Kb0b/yA6rlRqq67mHeLSUD/RJGVcI9xeRGjr7wNKe7FDSnhT5KuOmT10zCsQW6lE18/D820nXot2x/t8n7tvqjU+AToiCrH4gq4UKL0HEV6BBVwsMKkZQEAoEgTBBJScwpCQQCgSCMEHNKAURYVwgEVwOerCuErUWwEUkpgISHdYVM295n2NuVTqJJAnRET13F4uJ4Px+LQ2EvoOJsO8X771Vh1fToY2ewZIW/yT4E1hWuOt7+43bsUzLciwTUHhoaUtn48AZSfb4CQ2BdAWh953h391F6dRJOm56cVbcyJ83k5zxLKMaWp7gn6wphaxFsRFIKJiGxrgAMqVyz9nZmBPJRLQT2Amr3Md45YWbRhrsImLNAKKwrdDHkLrmJ7GtyMEugtO/ljVNFJPpz9YXCugKZ9vffQVn4AOuzjGj9p3lzWxlFd80j2p+hFgrrCi+vP8HEI14wBZOQWFcAcgdn9r7BW2/+jV17Sml3+mWS4L8Wn3S4aC0tR2+q473tf2Xn7uN0usJUx1jb6hMpnO1OSCDTUVZD6sx0/34RhsS6QsIYraOnsx8VDVdPO47IKP8ru4fCusKb608QEsSTkh+Ep3WFnvg5t7AqMpOkCLDX7OD1Xee57eaigZuij0y0vYBqpelcDV3LNnHbyngc515lx6F0bl+Z6d87/BBbV+Bq5ExTHtes8PPSC4l1hZ6EOWuIffZJ/nwigb5GPYsfvd6/cRWsPhnX+YlAqdjD2Zq9PKN8l08MWxLuomnbE+wcdbm4p5jAV0RS8oPwsK6wUf7ERl7pq0CtMgF6EhY+ys3L3A/BEVPmkn3oFBaliLRx9XYo7AV+S5PtKNt+/hu6VYWuejM3PpCIHojKvYao9+uxqZnjnFMKgXWF4zh/+MRPOT/tPH+tNoLSRtWZQh752RdIazhKZ8H1JIz77hUC6woUOv/xOPvfOcbWMyYcvQYyi3JIvvlf2JIbiWopZceOI+TfvYyEkPfJWGPL0/lR6E5pxbFIT1vDSOsKI5mbvsGD87by9N7xxECu/R1PbZvFI59eSpR4J+g1IikFkpBYV5zkeFc+a7/yXTZmuO8MmsYH5VX6GuiIyGTukJtGuNoL7C9tpVM/lbs+/QTJRpnWt5+ltM7K7OIY1N4m+mOnYBrR3LC0rrC2Ihfcy8Nf/SSFZpBrn+E3cctIN9ioO2GhYGXiZb+ow9K6ovt9tr9xhsxP/IUHZ0ag9bzBT768m7Sb3Asb9JFJRMuVbgNJnZc6QjS2PJ6fAWuLJata2dbg/21g8BzMv5OVfV9jf+NC1meLW623iDMVSEJiXTGF4hKV/f/7A7p0/TijZjE9W6LLDjqdhCLHMOPGDcNWtIWnvUA2qfpWemNOsv2Xb+GKu46Nm28hfc9LbDsTiaTEMXd93ohq4uFrXXHTVx8l2wzgpPHtUnJW/hOG/rOU22ayKn5kSgpX64pc5lyXwaEWC8pMM3JbI/rCOVh3Pcc2kxnFBhnX3zZkBWG4jq0xzs+AtUVG958CcBMYeg6SSFufzs63L7Dm/mniZusl4jwFkNBYV2g4mlLIjishIxr6Tv6Ep95awWceWzb6K4NwtRdQG9n92VJa7/oGn30gA9uR7/D0q9P4/AMPMe9KbZwE1hXYyzl0bh7X32dCMs5h073B0DHa+AmQ5YM9gYrHNvO9nZl0Vxq55Ze/Z03mFW4b4Tq2xjw/bpRBWyVP1hXjtLXInY/2Qjn9W6YRJ17heYVISgEkNNYVEubMWWQM7CW6ZBNFL+6kXV5G7mieEGFsLyAlLGPNMrfxXezs9cRtO41VLSbRLxuO0FlXANjPvkzz3I+T7smfI2ytK2yc/+P/oH1+G4/PjkVtfZVnnvwrs7+1hVS9rzpCM7bGZ5tyuXWF4lVslHNgiMKodLkdbUVS8gqxJDxITJx1hXsO6RJKVzmN0cXDJtInhb2ALoWiGR0cOdGJBigdZ+lNzrnsY+LJYl3hbmwPlbuamb08+/I5pElhXSHT16UjOS3SPYcUl0uCsx3bELuHSTG2xhkPJHJ7Fa7swlE+ihdcCfGkFEhCYV2htnLiDz/hSJuG3qBDdqaw9JEvEjdkDmly2AuYyNn8NfJ++W88tS8OSU7jhk/NZ7h57iSxrhhAsxzkQO9a7rls2eMksa4glpkP38mLT32WpyJjkXuhcMvj5Aw+XkyWseVdfwUeF62HS0lb/RBm/3f2oUFUCfcDYV0htAgdV4EOj9YVvsZA632HrT84y7pvf4IsL+2VRZVwkZQEAoEgKCidxzjnKKE40/tHMpGUxOs7gUAgCAr6pHkUB3ifnd12zKaru25EOL5wEggEAsEIOnvs7DvZwCc2zQ51U4KKSEoCgUAQ5nT22HnprXN8afMC7r1xhv87DGNEUhIIBIIwRlG1D01CAj/mlEwGPY3tvVTUtPm6C4FAIBAMwdrvwKXoaOnoBaC3146sanz5ng9HQgI/ktK0KQncfl0RPf3OUGsQCASCqwKjQYfJoCfKbKTfJmO1y2xZO+NDk5DAz9V3swqSQ91+gUAguGqoqG4iKlJPdkoMW3ec5aOrp/Mvmxf4v+NJhJhTEggEgjDC2i+zdcdZPrJqGv9636JQN2fC8fpJyaCTqGmyUN9iCXWbBQKB4KqkubOfxnYrd90w/UOZkGAcFR26eu189al9mIxX94dbAoFAECpcssrM/GS+ePf8UDclZHidlAQCgUAgCDZiTkkgEAgEYYNISgKBQCAIG0RSEggEAkHYIJKSQCAQCMIGkZQEAoFAEDaIpCQQCASCsEEkJYFAIBCEDSIpCQQCgSBsEElJIBAIBGGDSEoCgUAgCBv+P2C1ScFE/z3eAAAAAElFTkSuQmCC\" alt=\"3D Array\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(range(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((10,), dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((10,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5,  6,  7,  8,  9, 10, 11, 12, 13])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(4, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.        ,   1.18367347,   1.36734694,   1.55102041,\n",
       "         1.73469388,   1.91836735,   2.10204082,   2.28571429,\n",
       "         2.46938776,   2.65306122,   2.83673469,   3.02040816,\n",
       "         3.20408163,   3.3877551 ,   3.57142857,   3.75510204,\n",
       "         3.93877551,   4.12244898,   4.30612245,   4.48979592,\n",
       "         4.67346939,   4.85714286,   5.04081633,   5.2244898 ,\n",
       "         5.40816327,   5.59183673,   5.7755102 ,   5.95918367,\n",
       "         6.14285714,   6.32653061,   6.51020408,   6.69387755,\n",
       "         6.87755102,   7.06122449,   7.24489796,   7.42857143,\n",
       "         7.6122449 ,   7.79591837,   7.97959184,   8.16326531,\n",
       "         8.34693878,   8.53061224,   8.71428571,   8.89795918,\n",
       "         9.08163265,   9.26530612,   9.44897959,   9.63265306,\n",
       "         9.81632653,  10.        ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.  ,   1.25,   1.5 ,   1.75,   2.  ,   2.25,   2.5 ,   2.75,\n",
       "         3.  ,   3.25,   3.5 ,   3.75,   4.  ,   4.25,   4.5 ,   4.75,\n",
       "         5.  ,   5.25,   5.5 ,   5.75,   6.  ,   6.25,   6.5 ,   6.75,\n",
       "         7.  ,   7.25,   7.5 ,   7.75,   8.  ,   8.25,   8.5 ,   8.75,\n",
       "         9.  ,   9.25,   9.5 ,   9.75,  10.  ])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 10, 37)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Space\n",
    "\n",
    "```python\n",
    "np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)\n",
    "```\n",
    "\n",
    "Log space creates `num` values between $base^{start}$ and $base^{stop}$. By default, the base is 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e+01,   1.00000000e+02,   1.00000000e+03,\n",
       "         1.00000000e+04,   1.00000000e+05])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(1, 5, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e-01,   1.00000000e+00,   1.00000000e+01,\n",
       "         1.00000000e+02,   1.00000000e+03,   1.00000000e+04,\n",
       "         1.00000000e+05])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-1, 5, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.5,   1. ,   2. ,   4. ,   8. ,  16. ,  32. ])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-1, 5, 7, base=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy array as an image\n",
    "\n",
    "## Import `matplotlib`\n",
    "First we will import a library for showing images and charts called `matplotlib` (The next tutorial in this series is about it). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an RGB image\n",
    "Now we will create a 3D array of size 50 x 50 x 3 which will represent an image os the size 50px by 50px with RGB (Red Green Blue) values for each pixel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f07d2cafc50>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N0EWvKDYj2BN5R57X/XQ55cY8dHuW3V8kyhJyEuPYdexCujqIu+NypcZkUbNj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\nvsBKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d2b46320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randim_image = np.random.rand(50,50,3)\n",
    "plt.imshow(randim_image, interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Red to 0 (remove the red color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f07d2b8e0b8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eUTzGXy56Y0wSY8wWY8xuY0yEMWZI7PVBxpjlxpjDxphlxpjAv3ocRVHiD3+5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\nolcUj6GLXlE8xv8D44UJ9bJR5jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d2cdd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randim_image[:,:,0] = 0\n",
    "plt.imshow(randim_image, interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Divide Blue by 3 (reduce blue to third)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f07d2bba208>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3LoDuAMZ47ysBGJuIDcM4DjjiCjfe+4PtQE4AkB3AVgDtARycOPYDMAGHG/iX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\n6A0jZvw/Xkr/GokFjGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d2ba1c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randim_image[:,:,2] /= 3\n",
    "plt.imshow(randim_image, interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set green in every other row to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f07d0a98240>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f0Tb2Fdbeh9JxvlO4Bg2mhOBYqoB8RfRon0/7sJYTaQA2kaimAVeeAWhGSbDL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\nVYfXrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f07d2bd93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "randim_image[::2,:,1] = 1\n",
    "\n",
    "plt.imshow(randim_image, interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There is more\n",
    "\n",
    "Numpy is a large library with many useful out of the box functions like:\n",
    "\n",
    "- [Statistics](https://docs.scipy.org/doc/numpy/reference/routines.statistics.html#order-statistics)\n",
    "- [Linear algebra](https://docs.scipy.org/doc/numpy/reference/routines.dual.html#linear-algebra)\n",
    "- [Fast Fourier Transform (FFT) algorithm](https://docs.scipy.org/doc/numpy/reference/routines.fft.html)\n",
    "- [Financial](https://docs.scipy.org/doc/numpy/reference/routines.financial.html)\n",
    "- [Business Days](https://docs.scipy.org/doc/numpy/reference/routines.datetime.html#business-day-functions)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
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}


================================================
FILE: notebooks/jupyter/BibTex/Compile Article.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "ipython nbconvert mynotebook.ipynb --to latex --template citations.tplx\n",
    "latex mynotebook.tex\n",
    "bibtex mynotebook.aux\n",
    "pdflatex mynotebook.tex\n",
    "pdflatex mynotebook.tex\n",
    "pdflatex mynotebook.tex"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: notebooks/jupyter/BibTex/citations.tplx
================================================
((*- extends 'article.tplx' -*))

((* block author *))
\author{Your Name}
((* endblock author *))

((* block title *))
\title{Title of your paper}
((* endblock title *))

((* block bibliography *))
\bibliographystyle{plain}
\bibliography{ref}
((* endblock bibliography *))


================================================
FILE: notebooks/jupyter/BibTex/mynotebook.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#My Paper\n",
    "\n",
    "Depndincies\n",
    "```bash\n",
    "sudo apt-get install texlive-latex-extra\n",
    "sudo apt-get install texlive-bibtex-extra\n",
    "```\n",
    "\n",
    "Machine learning is used to in finger print images classification<cite data-cite=\"riesen2008iam\"></cite>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
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}


================================================
FILE: notebooks/jupyter/BibTex/ref.bib
================================================
@incollection{riesen2008iam,
  title={IAM graph database repository for graph based pattern recognition and machine learning},
  author={Riesen, Kaspar and Bunke, Horst},
  booktitle={Structural, Syntactic, and Statistical Pattern Recognition},
  pages={287--297},
  year={2008},
  publisher={Springer}
}


================================================
FILE: notebooks/live/.ipynb_checkpoints/1. painting-checkpoint.ipynb
================================================
{
 "cells": [],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: notebooks/live/1. painting.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Network Painting an Image\n",
    "\n",
    "Let's do this one more time. This time we will get it to work and from scratch! I'm mean really from scratch.\n",
    "\n",
    "To understand what we are about to do, let's go over some basic concepts of neural networks.\n",
    "\n",
    "## What is a neural network?\n",
    "\n",
    "This is the most basic neural network that you can use:\n",
    "\n",
    "![](images/simple_net.png)\n",
    "\n",
    "So what is this exactly? It is a representation of a mathematical function. We can write in mathematical notation as:\n",
    "\n",
    "$ y_1 = W_1 x_1 + b_1 $\n",
    "\n",
    "- $x_i$ is input to the network, that is your input data.\n",
    "- $W_i$ is called a weight. A higher weight will give the input more prevalence in calculating the output.\n",
    "- $B_i$ is called the bias. It is a values that is optionally added to your function.\n",
    "\n",
    "So let's visualize this function:\n",
    "\n",
    "We will use `mathplotlib` for that, so let's impport it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "tf.set_random_seed(2)\n",
    "tf.logging.set_verbosity(tf.logging.WARN)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1 = np.arange(-5,5)\n",
    "W_1 = 2\n",
    "b_1 = 5\n",
    "\n",
    "y_1 = W_1 * x_1 + b_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_1, y_1)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation Functions\n",
    "\n",
    "One more thing you should know about neural network which is activation functions. The network we just made has a single neuron which produced an output of what ever the input was. This is called a linear neuron.\n",
    "\n",
    "There are other types of neurons that perform applies some function to their input. Here is a short list of some really commonly used activation functions for neurons:\n",
    "\n",
    "### Rectifier\n",
    "\n",
    "It is a very common activation function commonly refered to as (ReLU) which is sort for \"Rectifier Linear Unit\". The function is:\n",
    "\n",
    "$$y = max(0, x)$$\n",
    "\n",
    "It is represented in a neural network as:\n",
    "\n",
    "![](images/relu.png)\n",
    "\n",
    "Let's visualize that and see how it looks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc99058dcc0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-5, 5)\n",
    "y = np.maximum(0, x)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sigmoid\n",
    "\n",
    "Sigmoid is another commonly used activation function. The output applies this mathematical function to its input:\n",
    "\n",
    "$$y = \\frac{e^x}{e^x + 1}$$\n",
    "\n",
    "It is represented in neural networks as:\n",
    "\n",
    "![](images/sigmoid.png)\n",
    "\n",
    "Let's visualize that and see how it looks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc990552400>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHKRJREFUeJzt3Xl4lfW57vHvQ0ggQAhTIEASggVBkNEIiO3ezoK1sFutgraCWumEta16ane7tbXndNd6Ouip29ZaplqhoLSl3Vh2tbRVkEIYRIFEIgYICiRhCgmQ6Tl/JMEFAgmwVt413J/r4nINL1nPtZSbn7/1rvc2d0dEROJLm6AHEBGR8FO4i4jEIYW7iEgcUriLiMQhhbuISBxSuIuIxCGFu4hIHFK4i4jEIYW7iEgcahvUC/fo0cNzc3ODenkRkZi0du3aMnfPaO64wMI9NzeX/Pz8oF5eRCQmmdn2lhynbRkRkTjUbLib2Swz22tmb53meTOzJ82syMw2mtno8I8pIiJnoyUr9znAhDM8PxEY2PhrBvD0+Y8lIiLno9lwd/d/APvOcMhkYJ43WAV0MbPe4RpQRETOXjj23PsCO0PulzQ+JiIiAWnVD1TNbIaZ5ZtZfmlpaWu+tIhIQglHuO8CskPuZzU+9iHu/oy757l7XkZGs6dpiojIOQpHuC8B7mg8a2YccNDd3w/DzxURiQv19U5xWSV/fms3T7y8lbd2HYz4azb7JSYzmw9cAfQwsxLgESAZwN1/DiwFbgCKgCrgzkgNKyIS7coPH6NwdwUFuyso2H2Iwt0VvL3nMEdq6gAwg26dUri4b3pE52g23N19ajPPO/DlsE0kIhIDjtbUsXXPYQp2H6Jgd8XxQC87fOz4Md07pjAoM40pY7IZnJnG4MzODOzViQ4pkb84QGCXHxARiQX19c6OfVUnrMQLd1dQXF5JvTcc065tGy7slcYVgzKOh/igzDQy0toFNrfCXUSkUdOWypbdFRSeZkulX7cODMpM48YRfbgoM41BmWn0696RpDYW8PQnUriLSMJp2lLZErISP9OWykWNK/HW2lIJh9iYUkTkPOyvrObFdSWs3b4/JrZUwkHhLiJxq2D3IeasKOZ363dxrLae3O4NWyqfGNGHwVG8pRIOCncRiSt19c7LW/YwZ0Uxr28rp31yGz41ui/TxucyOLNz0OO1GoW7iMSFg1U1/DZ/B/Ne307J/iP07ZLKQxMHc2teNl07pgQ9XqtTuItITNu6p4LZK4v53bpdHKmpY2z/bnz74xdxzUW9aJuUuH1ECncRiTl19c7ygr3MWVnMa0VlpLRtw7+N7MP08f0Z0idxtl7OROEuIjHj4JEaFuXvZN7r29mxr4re6e158PpBTB2TQ7cE3Ho5E4W7iES9or2HmbuymBfXlVBVXceluV35xoTBXDe0F8kJvPVyJgp3EYlK9fXO397ey+wVxby6tYyUpDZMGtmH6eNzI37RrXigcBeRqFJxtIYX1pYwd2UxxeVV9Orcjgeuu5ApY3Lo0Sm2v1jUmhTuIhIVtpUeZt7r21mUv5PK6jpG53Th69cNYuLFmdp6OQcKdxEJTH2984+tpcxZWczfCktJTjI+MbwP08bnMiK7S9DjxTSFu4i0usPHalm8roQ5K4vZVlpJRlo7vnbNhUwdm03PtPZBjxcXFO4i0mq2l1cyd2XD1kvFsVpGZKXz01tHcsOw3qS01dZLOCncRSSi3J3XisqYs6KYvxbuJcmMjw/vzfTxuYzK6Rr0eHFL4S4iEXOwqobPP5fPqm376N4xhXuvHMDt4/rRq7O2XiJN4S4iEfH+wSNMm7Wad8sq+d7kodxyaTbt2iYFPVbCULiLSNht3VPBHbNWU3G0lrl3jmH8gB5Bj5RwFO4iElZrivfxubn5pLRtw28/P46hffRt0iAo3EUkbJZt2s1X5q+nb5dU5t41huxuHYIeKWEp3EUkLJ5btZ2H//AWw7O6MGv6pbpKY8AU7iJyXtydn7y8lSdf2cpVg3vys9tG0SFF0RI0/RsQkXNWW1fPt3//FgvW7OSWvCy+/8lhCd1+FE0U7iJyTo5U1zHz+XW8UrCXe68awNevvRAzC3osaaRwF5Gztr+ymrvmrmHDzgN8b/JQPntZbtAjyUkU7iJyVnbuq2La7NWU7D/C07ePZsLFvYMeSU5B4S4iLbb5vUNMn72aozV1PHf3WMb07xb0SHIaCncRaZGV75Tx+Xlr6diuLYu+MJ5BmWlBjyRn0KKPtc1sgpkVmlmRmT10iudzzGy5ma03s41mdkP4RxWRoPxp43tMn7WGzPT2LP6Sgj0WNBvuZpYEPAVMBIYAU81syEmHfRtY6O6jgCnAf4V7UBEJxuwV73Lv/PWMyE7nhS+Mp0+X1KBHkhZoybbMGKDI3bcBmNkCYDKwOeQYBzo33k4H3gvnkCLS+tydx/5cyM///g7XD+3FE1NG0T5ZV3WMFS0J977AzpD7JcDYk475DvA/ZnYv0BG4JizTiUggaurq+caLG1m8bhefGZfDdyddTFIbncMeS8L1VbKpwBx3zwJuAH5tZh/62WY2w8zyzSy/tLQ0TC8tIuFUeayWu+fms3jdLh647kK+N1nBHotaEu67gOyQ+1mNj4W6G1gI4O6vA+2BD13A2d2fcfc8d8/LyMg4t4lFJGLKDh9j6i9XsaKojMduGsbMqwbqW6cxqiXhvgYYaGb9zSyFhg9Ml5x0zA7gagAzu4iGcNfSXCSGbC+v5KanV/L2ngqe+ewl3HppTtAjyXlods/d3WvNbCawDEgCZrn7JjN7FMh39yXA/cAvzexrNHy4Ot3dPZKDi0j4vFlykDvnrKau3nn+nnGMVnF1zGvRl5jcfSmw9KTHHg65vRm4PLyjiUhreHVrKV/49Vq6dEhh3t1j+EhGp6BHkjDQN1RFEtjv1pfw4KKNDOyVxpw7L6VX5/ZBjyRhonAXSUDuzi9f3cb3lxZw2QXd+cUdl9C5fXLQY0kYKdxFEkx9vfN/lm7hV6+9y43De/OjW0bQrq2+nBRvFO4iCeRYbR0PLNrIH994jzsvz+U/Pj6ENjqHPS4p3EUSRMXRGj7/67WsfKecb04czIx/uUDnsMcxhbtIAth76CjTZq9h654KfnzLCD41OivokSTCFO4ice6d0sNMm7WafZXVzJp+Kf9yob4dnggU7iJxbP2O/dw1Zw1tzFgwYxzDs7oEPZK0EoW7SJxaWVTG3XPz6dm5HXPvHENuj45BjyStSOEuEof2HjrKzPnrye6WyvP3jKNHp3ZBjyStLFyX/BWRKFFf73x94RtUVdfyX7ePVrAnKIW7SJx59rVtvFZUxiOfGMqAnuo6TVQKd5E48mbJQR5fVsiEoZlMuTS7+d8gcUvhLhInKo/V8pUF6+nRqR0/uGmYvqCU4PSBqkiceGTJJraXVzL/nnF06ZAS9DgSMK3cReLAkjfe44W1Jcy8cgBjL+ge9DgSBRTuIjFu574qvrX4TS7p15WvXD0w6HEkSijcRWJYbV099y1YDwY/vXUkbZP0R1oaaM9dJIY98cpW1u04wJNTR5HdrUPQ40gU0V/zIjFq1bZyfra8iJsvyWLSiD5BjyNRRuEuEoMOVFXztd9uILd7R747aWjQ40gU0raMSIxxdx568U3KDh9j8Rcvp2M7/TGWD9PKXSTGzF+9kz9v2s2D1w9iWFZ60ONIlFK4i8SQrXsqePRPm/jYwB587qMXBD2ORDGFu0iMOFpTx73z19MxpS0/umWEiq3ljLRZJxIjfvBSAQW7K5g9/VJ6prUPehyJclq5i8SAvxbsYc7KYqaPz+XKwT2DHkdigMJdJMrtPXSUBxdt5KLenXlo4uCgx5EYoXAXiWL19c79i96gsrqW/zd1JO2Tk4IeSWKEwl0kij372jZe3VrGwzeqVUnOjsJdJEqFtipNHaNWJTk7CneRKKRWJTlfLQp3M5tgZoVmVmRmD53mmFvMbLOZbTKz58M7pkhieWTJJorLK/nprSPVqiTnpNnz3M0sCXgKuBYoAdaY2RJ33xxyzEDgm8Dl7r7fzHSulsg5ampVuvcqtSrJuWvJyn0MUOTu29y9GlgATD7pmHuAp9x9P4C77w3vmCKJoalVaXROF+5Tq5Kch5aEe19gZ8j9ksbHQl0IXGhmK8xslZlNCNeAIonieKsS8MSUUWpVkvMSrssPtAUGAlcAWcA/zGyYux8IPcjMZgAzAHJycsL00iLx4Um1KkkYtWRpsAsIPQ8rq/GxUCXAEnevcfd3gbdpCPsTuPsz7p7n7nkZGRnnOrNI3PmnWpUkzFoS7muAgWbW38xSgCnAkpOO+T0Nq3bMrAcN2zTbwjinSNw6UFXNV3+7gX5qVZIwajbc3b0WmAksA7YAC919k5k9amaTGg9bBpSb2WZgOfCgu5dHamiReBHaqvTklFFqVZKwadF/Se6+FFh60mMPh9x24OuNv0SkhZpalb45cbBalSSs9HG8SEBCW5Xu+ZhalSS8FO4iAThaU8dXFmygQ0pbfvRptSpJ+GmDTyQAj/25gC3vH2LW9Dx6dlarkoSfVu4irWx5wV5mr2hoVbpqcK+gx5E4pXAXaUV7Dx3lgUVvqFVJIk7hLtJKQluVnpyiViWJLIW7SCtpalX6jxuHMLCXWpUkshTuIq0gtFXptjG6rpJEnsJdJMLUqiRB0KmQIhH2ncZWpfn3jFOrkrQardxFIuiPb7zHorUlzLxyAOPUqiStSOEuEiE791Xx72pVkoAo3EUiQK1KEjTtuYtEQFOr0hNTRqpVSQKh5YRImDW1Kt00OovJI0+uGxZpHQp3kTBqalXK6daB705Wq5IER9syImES2qr04hfH00mtShIgrdxFwmTBmoZWpQeuG8TwrC5BjyMJTuEuEgZFeyv47h/VqiTRQ+Eucp6O1tRx73y1Kkl00aagyHlSq5JEI63cRc6DWpUkWincRc5RU6vS4Mw0tSpJ1NG2jMg5CG1VWjB1nFqVJOpo5S5yDn712rtqVZKopnAXOUtvlhzkh8sK1KokUU3hLnIW1KoksUJ77iJnQa1KEiu0chdpoaZWpS9foVYliX4Kd5EWaGpVGpXThfuuUauSRD+Fu0gzQluVnpwyimS1KkkM0J67SDPUqiSxqEVLEDObYGaFZlZkZg+d4bibzMzNLC98I4oER61KEquaDXczSwKeAiYCQ4CpZjbkFMelAfcB/wz3kCJBOFhVw9fUqiQxqiUr9zFAkbtvc/dqYAEw+RTHfQ94DDgaxvlEAuHuPLR4I6WHj/Hk1FFqVZKY05Jw7wvsDLlf0vjYcWY2Gsh29/8+0w8ysxlmlm9m+aWlpWc9rEhrWbBmJy+9pVYliV3n/bG/mbUBfgzc39yx7v6Mu+e5e15GRsb5vrRIRDS1Kn10gFqVJHa1JNx3Adkh97MaH2uSBlwM/M3MioFxwBJ9qCqxKLRV6ce3qFVJYldLwn0NMNDM+ptZCjAFWNL0pLsfdPce7p7r7rnAKmCSu+dHZGKRCGpqVXr85uFqVZKY1my4u3stMBNYBmwBFrr7JjN71MwmRXpAkdYS2qp09UVqVZLY1qJTANx9KbD0pMcePs2xV5z/WCKtS61KEm90fpckPLUqSTzSRTIk4alVSeKRwl0SWlOr0nVDeqlVSeKKwl0SVlOrUveO7XjspuFqVZK4oj13SVhNrUrPf24cXTuqVUnii1bukpBCW5Uu+4halST+KNwl4ahVSRKBwl0SilqVJFFoz10SypN/LVKrkiQELVskYax+dx8/++tWtSpJQlC4S0I4WFXDVxesV6uSJAxty0jca2pV2ltxjBe/OF6tSpIQtHKXuHe8Ven6QYzIVquSJAaFu8S10FalGWpVkgSicJe4pVYlSWTafJS41dSq9KtpeWpVkoSjlbvEJbUqSaJTuEvc2VuhViURbctIXKmvd+5f+AaHj9WyYIZalSRxaeUucUWtSiINFO4SN/6yec/xVqXbx6pVSRKbwl3iwvzVO/j8r/MZ0rszP7xZrUoi2nOXmObuPPHKVn768lauGJTBU7eNpqMuLyCicJfYVVtXz3/8YRPzV+/g5kuy+M9PDdP12UUaKdwlJjV8+3Q9f9m8hy9f+REeuG6QtmJEQijcJeYcqKrm7rn5rNuxn+9OGsq08blBjyQSdRTuElN2HTjCtFmr2VFexVO3jeaGYb2DHkkkKincJWYU7D7EtFmrqaquY97dYxh3QfegRxKJWgp3iQmrtpVzz7x8OqQksegLlzE4s3PQI4lENYW7RL2X3nyf+367geyuqcy7eyx9u6QGPZJI1FO4S1Sbu7KY7/xxE6NzuvLsHXl07ZgS9EgiMaFFJwWb2QQzKzSzIjN76BTPf93MNpvZRjN7xcz6hX9USSTuzuPLCnhkySauHtyL33xurIJd5Cw0G+5mlgQ8BUwEhgBTzWzISYetB/LcfTjwAvDDcA8qiaOmrp4HX9jIU8vfYeqYHH7+mdG6uqPIWWrJyn0MUOTu29y9GlgATA49wN2Xu3tV491VQFZ4x5REUVVdyz3z8nlhbQlfvWYg3//kxbTVt05FzlpL9tz7AjtD7pcAY89w/N3AS+czlCSm8sPHuGtuPm+WHOD7nxzGbbqyo8g5C+sHqmb2GSAP+NfTPD8DmAGQk6M/uPKBHeVVTJu9mvcOHOEXn83j2iGqxhM5Hy35/91dQHbI/azGx05gZtcA3wImufuxU/0gd3/G3fPcPS8jI+Nc5pU49Naug3zq6ZXsq6zm+XvGKthFwqAl4b4GGGhm/c0sBZgCLAk9wMxGAb+gIdj3hn9MiVevbS1jyjOrSEkyXvziZVzSr1vQI4nEhWbD3d1rgZnAMmALsNDdN5nZo2Y2qfGwx4FOwCIz22BmS07z40SO+8OGXdw5ZzVZXVNZ/KXLGdBTtXgi4dKiPXd3XwosPemxh0NuXxPmuSTOPfvqNv73f29hbP9uPHNHHumpyUGPJBJX9A1VaVX19c5/vrSFX776LjcMy+THt4zUOewiEaBwl1ZTXVvPgy+8wR82vMcdl/XjkU8MJamNCjZEIkHhLq2i4mgNX3xuHa8VlfHg9YP40hUfUXOSSAQp3CXi9lYc5c7ZayjYXcHjNw/n03nZzf8mETkvCneJqHfLKrlj1j8pq6jm2Wl5XDmoZ9AjiSQEhbtEzIadB7hrzhoA5s8Yx8jsLgFPJJI4FO4SEcsL9/Kl59bRIy2FeXeNpX+PjkGPJJJQFO4Sdi+sLeEbL25kcGYas++8lJ5p7YMeSSThKNwlbNydp//+Dj/8cyGXD+jOzz9zCWnt9eUkkSAo3CUs6uqdR/+4ibmvb2fyyD48fvMIUtrqOuwiQVG4y3kr3F3B48sKeXnLHu75WH++OfEi2ujLSSKBUrjLOamrd17Zsoc5K4tZ+U457dq24dsfv4jPfeyCoEcTERTucpYOVtWwMH8nc18vpmT/Efqkt+ehiYO5NS9bBdYiUUThLi2ydU8Fc1YWs3jdLo7U1DGmfze+dcNFXDuklzpORaKQwl1Oq77eWV64lzkri3l1axkpbdvwbyP7MG18LkP7pAc9noicgcJdPuTQ0RoW5Zcw7/VitpdXkdm5PQ9eP4ipY3Lopq0XkZigcJfj3ik9zNyVxbywtoSq6jry+nXlwesHcf3QTJK19SISUxTuCa6+3vn71lLmrCjm72+XkpLUhk+M6MP08bkMy9LWi0isUrgnqIqjNby4toS5r2/n3bJKeqa14/5rL2Tq2Bx6dGoX9Hgicp4U7gnm3bLK41svh4/VMiqnC09MGcnEi3vrG6UicUThngDcnVe3ljFnZTHLC/fSto1x4/CGs150GV6R+KRwj2OVx2pZvK6EOSuLeae0kh6d2nHf1QO5bWyOrtQoEucU7nFoR3kVc18vZmH+TiqO1jI8K52f3DqCG4b1pl3bpKDHE5FWoHCPE+7OiqJy5qws5pWCPSSZMXFYb+68PJdR2V1URi2SYBTuMa7yWC2/37CLOSuK2br3MN07pjDzygHcPrYfmenaehFJVAr3GFFX7+zYV0XB+4co2F1B4e4KCvdUUFxeiTsM7dOZ//vpEdw4vDftk7X1IpLoFO5RqOzwMQrer6Bg96HjIf72ngqO1tQDYAb9u3dkcGYak0f24aMDenBJv67aehGR4xTuATpSXcfbexpW4QW7Kyjc0xDmZYerjx/To1MKgzM7c/vYfgzKTGNwZhoDe6aRmqLVuYicnsK9FdTVO9vLKz8I8ZO2VADaJ7dhUK80rhrck0GZnRmcmcagzDR9W1REzonCPcxKK441hvjpt1RyQ7ZUGkK8MzndOpCkajoRCROF+zmoqavn4JEadu0/oi0VEYlKCRvudfVOxdEaDh6p4UBVwz8PHqnhwJEaDjXdrqr+4PGqDx6vrK474We1T27DhdpSEZEo0qJwN7MJwBNAEvCsu//gpOfbAfOAS4By4FZ3Lw7vqB/m7hw+Vns8nA81hvPBkF+hoXzgSGNYV9VQcaz2+H73qbRPbkN6ajJdUlNIT00mq2sHuvRNJj214VeXDsn0TGunLRURiUrNhruZJQFPAdcCJcAaM1vi7ptDDrsb2O/uA8xsCvAYcGskBl64ZidP//0dDlRVc+hoLXX1p0/o5CQjPTWF9NS2pKcm0zOtPQN7ppGemkzn1GS6pJ4Y1k23O6cm61xxEYlpLVm5jwGK3H0bgJktACYDoeE+GfhO4+0XgJ+ZmbmfaW18brp2TOHivumkp7Y9vqpOT00mvcOHgzo1OUnnfotIQmpJuPcFdobcLwHGnu4Yd681s4NAd6As9CAzmwHMAMjJyTmnga8d0otrh/Q6p98rIpIoWrWdwd2fcfc8d8/LyMhozZcWEUkoLQn3XUB2yP2sxsdOeYyZtQXSafhgVUREAtCScF8DDDSz/maWAkwBlpx0zBJgWuPtm4G/RmK/XUREWqbZPffGPfSZwDIaToWc5e6bzOxRIN/dlwC/An5tZkXAPhr+AhARkYC06Dx3d18KLD3psYdDbh8FPh3e0URE5Fyp7l5EJA4p3EVE4pDCXUQkDllQJ7WYWSmwPZAXD58enPRFrQSn9+MDei9OpPfjROfzfvRz92a/KBRYuMcDM8t397yg54gWej8+oPfiRHo/TtQa74e2ZURE4pDCXUQkDincz88zQQ8QZfR+fEDvxYn0fpwo4u+H9txFROKQVu4iInFI4R4mZna/mbmZ9Qh6lqCY2eNmVmBmG83sd2bWJeiZgmBmE8ys0MyKzOyhoOcJkpllm9lyM9tsZpvM7L6gZwqamSWZ2Xoz+1MkX0fhHgZmlg1cB+wIepaA/QW42N2HA28D3wx4nlYXUks5ERgCTDWzIcFOFaha4H53HwKMA76c4O8HwH3Alki/iMI9PH4C/C8goT/AcPf/cffaxruraLj2f6I5Xkvp7tVAUy1lQnL39919XePtChpCrW+wUwXHzLKAjwPPRvq1FO7nycwmA7vc/Y2gZ4kydwEvBT1EAE5VS5mwYRbKzHKBUcA/g50kUD+lYSFYH+kXatElfxOdmb0MZJ7iqW8B/07DlkxCONN74e5/aDzmWzT87/hvWnM2iV5m1gl4Efiqux8Kep4gmNmNwF53X2tmV0T69RTuLeDu15zqcTMbBvQH3jAzaNiGWGdmY9x9dyuO2GpO9140MbPpwI3A1QnaxtWSWsqEYmbJNAT7b9x9cdDzBOhyYJKZ3QC0Bzqb2XPu/plIvJjOcw8jMysG8tw9IS+QZGYTgB8D/+rupUHPE4TGDuG3gatpCPU1wG3uvinQwQJiDaueucA+d/9q0PNEi8aV+wPufmOkXkN77hJOPwPSgL+Y2QYz+3nQA7W2xg+Um2optwALEzXYG10OfBa4qvG/iQ2NK1eJMK3cRUTikFbuIiJxSOEuIhKHFO4iInFI4S4iEocU7iIicUjhLiIShxTuIiJxSOEuIhKH/j/NPhJDPgMFfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(-5,5)\n",
    "y = np.exp(x)/(np.exp(x)+1)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are so many more, but let's get to the fun stuff. Let's have another go at painting `Tensy`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we want to build a a network that looks like this:\n",
    "\n",
    "![](images/painter.png)\n",
    "\n",
    "The input of this network will be a position of a pixel in the image (x,y) and the output will be the color in (RGB).\n",
    "\n",
    "So Let's get building!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc990505e10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = plt.imread(\"images/bird.png\")\n",
    "img = img[:,:,:3]\n",
    "\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = []\n",
    "train_y = []\n",
    "\n",
    "for h in range(img.shape[0]):\n",
    "    for w in range(img.shape[1]):\n",
    "        train_x += [[h,w]]\n",
    "        train_y += [img[h,w]]\n",
    "\n",
    "train_x = np.array(train_x)\n",
    "train_y = np.array(train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.variable_scope(\"placeholders\"):\n",
    "    x = tf.placeholder(tf.float32, shape=(None, 2), name=\"X\")\n",
    "    y = tf.placeholder(tf.float32, shape=(None, 3), name=\"Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_size = 250\n",
    "with tf.variable_scope(\"dense_1\"):\n",
    "    w_1 = tf.get_variable(\"W_1\", shape=(2,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_1 = tf.get_variable(\"b_1\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "    \n",
    "with tf.variable_scope(\"dense_2\"):\n",
    "    w_2 = tf.get_variable(\"W_2\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_2 = tf.get_variable(\"b_2\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "    \n",
    "with tf.variable_scope(\"dense_3\"):\n",
    "    w_3 = tf.get_variable(\"W_3\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_3 = tf.get_variable(\"b_3\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "    \n",
    "with tf.variable_scope(\"dense_4\"):\n",
    "    w_4 = tf.get_variable(\"W_4\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_4 = tf.get_variable(\"b_4\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "    \n",
    "with tf.variable_scope(\"dense_5\"):\n",
    "    w_5 = tf.get_variable(\"W_5\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_5 = tf.get_variable(\"b_5\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "    \n",
    "with tf.variable_scope(\"dense_6\"):\n",
    "    w_6 = tf.get_variable(\"W_6\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_6 = tf.get_variable(\"b_6\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "\n",
    "with tf.variable_scope(\"dense_7\"):\n",
    "    w_7 = tf.get_variable(\"W_7\", shape=(hidden_size,hidden_size), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_7 = tf.get_variable(\"b_7\", shape=(hidden_size,), initializer=tf.truncated_normal_initializer)\n",
    "\n",
    "with tf.variable_scope(\"dense_8\"):\n",
    "    w_8 = tf.get_variable(\"W_8\", shape=(hidden_size,3), initializer=tf.random_normal_initializer(mean=0.0, stddev=0.01))\n",
    "    b_8 = tf.get_variable(\"b_8\", shape=(3,), initializer=tf.truncated_normal_initializer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_1 = tf.nn.relu(tf.matmul(x, w_1) + b_1)\n",
    "l_2 = tf.nn.relu(tf.matmul(l_1, w_2) + b_2)\n",
    "l_3 = tf.nn.relu(tf.matmul(l_2, w_3) + b_3)\n",
    "l_4 = tf.nn.relu(tf.matmul(l_3, w_4) + b_4)\n",
    "l_5 = tf.nn.relu(tf.matmul(l_4, w_5) + b_5)\n",
    "l_6 = tf.nn.relu(tf.matmul(l_5, w_6) + b_6)\n",
    "l_7 = tf.nn.relu(tf.matmul(l_6, w_7) + b_7)\n",
    "out = tf.matmul(l_7, w_8) + b_8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = tf.losses.mean_squared_error(y, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = tf.placeholder(tf.float32, shape=[])\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.global_variables_initializer().run(session=sess)\n",
    "seq = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 0: 0.30167004466056824\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 10: 28974.66015625\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 20: 2736.043701171875\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 30: 1560899.625\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 40: 400.8169860839844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 50: 1.6070836782455444\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 60: 1.8041610717773438\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 70: 0.3010651171207428\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 80: 0.0904325321316719\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 90: 0.07690518349409103\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 100: 0.05362841859459877\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 200: 0.009619979187846184\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 300: 0.008745246566832066\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 400: 0.00811453815549612\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 500: 0.007663724943995476\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 600: 0.007481598760932684\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 700: 0.00793477613478899\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 800: 0.007531074807047844\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 900: 0.007441612426191568\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1000: 0.007371342740952969\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1100: 0.007531122304499149\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1200: 0.007371849380433559\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1300: 0.007416746579110622\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vc4Cbi/eGYRhzQbDSFJEXAK+neK65qj6nqj8EdgC7i2q7gYvCh9dk6aWkybrMxxpMIdf9r4vkjo/Gwhwy6VNM1ArrkhqrqckibMmQO1mAdbIcLb3KNm201WsLBUznqfs662JpbgMeB/67iHxDRP5KRNYCm1T1QFHnUWCTv+iBXaBKUmWy83Dz/ZRlzAx5rsqyPLYhv7h15TRVKoA2F7oKOS7LWZkFZMi93P2qMTT16SCngyteRReluRo4F7hSVV8N/JgZV1xVa1ebiOwUkT0isufQocMdhmEYhtEfXZTmPmCfqt5avL+OiRJ9TEQ2AxS/D1Y1VtVdqrpdVbevW3dK+UqHIYXSlvCJ+U2V0sUPT/q4yY6U8Ml+H+YYEz51tCR8KmUFJI+qSJ7waSBCwqeOYKWpqo8Cj4jIy4ui84H7gBuBS4uyS4EbHCViscsQeWHjdG+ZInaZo8Jc5v8ONIam2CWeN9toUHJeyqzP2KXL+ogdu/Rfk133ab4buEZETgIeBH6biSL+nIhcBjwMvLNjH4ZhGNnQSWmq6h3A9opL53eRm54mSyKVFZSH1ereKtJ4R+GKQxaJx6ppSnp2vKWNVznDnx33dsXD1uXITgR1pW9lmZdMt5aR3PGsFeWUaexyKHzuewl+2ezSdW9lFtDPYGfHffvzrbuSBVKadgwyVR/LyF5hli3LzO5MlN0xyKbyOvkObZzWh4vC7E9RlrEbdhiGYXgw55Zmn+54flZrr1bmaG62kcFNX1LGLkvdeLVxLveV71Mvr9hlHXOqNOfhvpfhcvuNXUaSlZRMkj1OyrKuYlN5cc1ZmXVwn5PdmchhK5E3adx3c88NwzA8mENLs++z43nItITPLDkkfIr+x57w8d6u5HrdsU7ShI//Gp4TpWmxy1SyVzAKlzyD2KXzVqIO23+8XOVY2fF5il2GrWFzzw3DMDwYuaVpCZ9UspcxCusSsk74gOfZ8Tp8Ej4OsuoY8nRP0qRP9zU8YqU5doWZOnYZ3scyso9dwnF3fKg7ZJWGseJyjOx47rFLlzoplKVtbjcMw8iekVmadnY8pfwllk1zzlZmBu64sysOwYmaeT47vtR/XhnyJkakNMd8djxcrm0lqiPDDHlt7LdDvDHLs+Mu9cYfu6zD3HPDMAwPRmBpLmbCx6/lomTIy9ZlZkkfrw3rIQmfDrJmSWZdOtYZScKnjkyVZts/RD6KLYVci13OkkHssjyMZZdSxi47yKpjsLPjvv351g2pH0aGSnPsxyDD5Frscpby2DKLXRbFixG7HGIrkaNcr3rxsJimYRiGB5lYmk0bk/OxBlPJs9jlLBlkxqe0xi47bP+J8tyejLcSZXp2vCudLE0R+X0RuVdE7hGRa0XkFBHZJiK3isheEfls8aTK0B66DK9G3vQnttywVu0tI4x5xfPGc1WY07Fl8AjdqmlK/gjdhja+CnNFHz6fe9etRPSQ7BluDQcrTRE5C/j3wHZV/XlgFXAx8FHgY6r6M8APgMtiDNQwDCMHusY0VwOnishqYA1wAHgjcF1xfTdwkZ/IvKzBZnn+4/RrFcsdTzWnsShnxwc+3bPCumRiNTlZmW1zXJUhb7Muq/pt6SfIJW+T67B+lubJx6J1rZ9SL/jJDVaaqrof+E/A95goy6eA24AfquqRoto+4Cw3iXlMiLvMsJap+1jGKDLkGbjjdTcKXqEA2lzoKuS4rNQ321j2neO69h0UvZNLntK9TrWGw+R2cc83ADuAbcBPA2uBCzza7xSRPSKy59Chw6HDMAzD6JUu7vmbgO+q6uOq+jzwBeB1wPrCXQfYAuyvaqyqu1R1u6puX7fu1A7DqCKVKxous5eED8y4k7lbmZm4404JHx8XunTNOeHjIKuK2qSSCx0tzNrQRWifVf2n+j8Ol9tFaX4PeK2IrBERAc4H7gO+AryjqHMpcEOHPgLIx8V3bxnJFR+FOz4lgwz5smKpUQA+rnLpE3dWZiEKucA5Puopd9DYpUP/QbSMwWM5dolp3sok4XM7cHchaxfwR8D7RGQv8ELgqtA+DMMwcqPT5nZV/RDwoZniB4Hzusj1J69AsVuriGMejYVpZ8edZdUx1Nnxpf5TJnAG+D8OWJKZnAgKJS9l6dYy0pizV5RTprHLoYjxZEgHxdc5M952zaePiH3CSM+Ou/xdYZLt7LlhGIYHI7Y0UyR8UreMlPCJJSsZZetyzPe9dCjv/GRIX+vSoU3MOqNzx0Pm04+RKc283PHeYpejue8lDOuOx3qEbtM1X3fcQVYVQz6GAkZ6o+C4scs6RqQ0B/hW6twilsLMXVFCFsmeykQPuFt4DsmS1Pe9BItdxu4/8pK0mKZhGIYHI7A087Aw/VotSoa8bF3OeexyprvOsmZJtpXIsc7oYpctchMux0yVZl6xS7eWMRVlZJlJmIetRE3XFiB2GfSlvDixyzrMPTcMw/AgQ0szHyvTthJVkUHCpzyMZcU+yZ0WKy3lI3SnBN/3smOdpAkf37oRZXZaku5jzkhpWuwyXzKOXUI8dzzKM3ta+g+OXbbVta1E0WXXYO65YRiGBxlZmjGxhE885iHh42CheVuAAfs8B9t/6dOXh9xO9SPIHMDKhLlSmikVZfc+lhhF7LKsKHOMXVZdSB27rCvPOHYJ87eVCHqLXdZh7rlhGIYHc2Jppk74hPexjOwTPjCsO95TwmemK+c2zuW+8n3qWcInumxPRq40U7vki+KOQxZbiZxjlxDmKle54yGxy5Z2Q90oOGitjcAlz0hhgrnnhmEYXozU0rT9l3EYev/llJoHnVXSoTz1HYqCXHJL+FSSmXVZZmRK0zLk8ZiHrURN10O2Elnssnv9CDIHyY67t2t1z0XkahE5KCL3lMrOEJGbROSB4veGolxE5BMisldE7hKRc4PGXz2S4FZuey/datYyNdY0gqykTMeW2SN0Af9H6NbNs9QYz3XKtenz8o1dulqOHRRm7eOGu/RZVTfmGnaQ2dnhCflC8P87XWKanwIumCm7ArhZVc8Bbi7eA1wInFP87ASu9BqNYRhG5rQqTVX9KvDkTPEOYHfxejdwUan80zrhFmC9iGwOH17YN4F7q0jfpkuxy9ytSxgmfqksszCr4peVFmaTZVhXXpch98m2N6yg0p/iJstlHK7XCXTHfS3M2DhYl8FLMkRHdPs7Q7Pnm1T1QPH6UWBT8fos4JFSvX1FmSfhf5Rbq4jKchTJniETPVq/rlcoy1BXWSr++UJkObiPlfXbPv+2cTiux2Tu+LR+bHznM0R+H22W03nLkaoG/UeKyE4R2SMiew4dOtx1GIZhGL0QqjQfm7rdxe+DRfl+4OxSvS1F2QpUdZeqblfV7evWnVK6Em5h9uKOjybhA1kmfJbNX5nAhE+tO+4qKzRB4ePyhlyb9h2a9HGtl2INp0z4dHHH4/ydoUrzRuDS4vWlwA2l8t8ssuivBZ4qufEtpIxdTmt3JPvYJSwf21D3vmyIXVZe6BC7XHG3KF9ZNTTGLl3d8Tbl3UBwdtylTcrYZY3caLHLVG3cZbfu0xSRa4E3ABtFZB/wIeAjwOdE5DLgYeCdRfUvAm8F9gLPAL/tPBLDMIwR0Ko0VfWSmkvnV9RV4PKug3KhX+syorxkTMeW2a3cwOPseNu1Kle8g6w67Ox4AGNN+PjLHtWJIPc/L9LCyD4zPmXI2CXUxi4BN1e8qbx0LctjkC71HGOX3qRTDFHk9n6yp5/5sBt2GIZheDAaS7NXK3N0Z8cze9hZ0rPjDm2cy+vk92Rdgp0dD5HfqU33+cheaVrscpaMY5fgqTDHGrsMHctsvykVoMUuvep5jN3cc8MwDA+ytDQt4TNL2brMLOFTFI8q4ePVh0+dFAmfzK3LKZ32X6ZqkyaMkJ3StNjlLBlkxqe0xi6rKrlcq3PHW9p49c1wp3qW+retRM6yO7dxHfeItxz1FruEEViWU4ZUmLFilzEty8B+Bt1K5NOXh9ygupHGMFZlCVFyFxbTNAzD8CAbS7OdRXTHB76d24riGK5yqXy0sUuHOvMWu8zaumypG3lnzAiU5qK45JlsJYIRxC5b+h/qGORS/xa7dJbduU1/ynKKueeGYRgeZGppLop1OSWDDHlWCZ/A5IudHY/ff+8POutYv9MacCNDpWmxy37wdcerKjaVN8UuHdo4lfvKT1DHjkH6y+/UJpU77l7f3HPDMAwPMrI0F8UlzyTh09kdb7pWl/BJvf8yVgImRcLHQ6533UhjGKuFCT3di3RCRkqzA9kryinzHrssrme7laitnsUuk8ju3KamfrJdEs2Ye24YhuHBuC3N0SR8YLibbaRO+MyUd7IyQxMUMfZXxrYyc0j4tMgdqzselPCJN7/jVJqjUJYwH+540zXfrUQOsqqY6xsFm7L0qjuwwgQH91xErhaRgyJyT6nsT0XkmyJyl4hcLyLrS9c+ICJ7ReRbIvKWqKNdMtaEvBXmdHxDJntqnttT+WjYOquvaZ6lxnj2kdXWR518V8uxg8IMft64z9hir+GQ+Ywou1ObhrorHv8baw3M1nXDJab5KeCCmbKbgJ9X1VcB3wY+ACAirwQuBn6uaPNfRGSV82gMwzAyp1VpqupXgSdnyr6sqkeKt7cAW4rXO4DPqOpPVPW7TJ5/fl6UkWZvYZbHNkT8svR1PDtNSxZTubDJCmhyoYsfZwsw0B2vtCxcYpcuFqaDlemMrwU2kDveaUmmjl3W1E22Bhz7ryFG9vx3gC8Vr88CHild21eUhbHsw85VWcJxV3zguxJVff6Vrriv21pSlM4L2bd8VnbTeKpwUZRtiiXEHfchxRpO5Y6X5adq4zru2F9I3cIjnZSmiHwQOAJcE9B2p4jsEZE9hw4d7jIMwzCM3gjOnovIbwFvA85X1en3wn7g7FK1LUXZClR1F7AL4CUvPnP59+EosuNT6xIGtTCnQ6kork68VNFipXllr31c9BJzed/LkPqRZPZ6ow2fNj5WcQqrv/vnEWRpisgFwPuBt6vqM6VLNwIXi8jJIrINOAf4Jy/ho4ldDpwZr4pdQil2Ob0Q6EIvZY892vgqzODYZYtcVznJtxL1rDCjxC5D5sT18/Jxx31kxo5zNtNqaYrItcAbgI0isg/4EJNs+cnATSICcIuq/q6q3isinwPuY+K2X66qR6OM1DAMIwNalaaqXlJRfFVD/Q8DH/YaxQprI1eGtDDBb7M6hLnKddZlSxvffgY7O+7bn2/dkPoR5FrCp0M9vLyOjE4E5aosp+MaODteHspSsW+M0kHhpL7ZRtDpnkjxzdFlxh3kjj52mWqeHesF3IDFbthhGIbhQUaWZo5k4I5Ph7HikmtmvOlanXXp0Ma53Fe+T71U1kRKizSC3Kxd8Za6ubjjEGRlginNGsou+RD4KMu6ik3lxbXOj6FwuJ7snoexM+MufYbW9SUnhRlhTnq572U6d3wWc88NwzA8MEtziRwSPkX/yRI+pXJL+Hj02aV+BLlZu+Pzm/Cpw5QmkEXs0nkrUYftP6nve+ndR9exzPadWgGmUJgps+MO8ju1yT122RSzD2fBlWbGsUvwUJixYpcOsupIdgQutmXpIderXuS+Fyp26VO3f8tyFotpGoZheLCglubUHR/Cwiz1mSw73lPssnb6YsQmU1iYOWTH5zB2CYndcY+6CS3MKQumNDNwx51dcQiOOc7zMcil/i3Z4yy7c5vcY5dF3Z7+rc09NwzD8GCBLM0MM+Sx73u5TGaorBRbiVzqzWPCx0Hu6M+OR5QbUq8Hd3yWOVeaZUWZWfzSa+9lSOyyg6xZkv+TtCkWi106y47SZih33LP+AAoTzD03DMPwYk4tzQwSPuVhLLuUMuHTQVYdg50d9+3Pt25I/Qhys7Yyh7QwPWQOGWVjLpVmhrHLongxYpdDbCVylOtVL0H/nR5DkarNELskAuoN5IpXYe65YRiGB3NiaY4p4dNhz6T3ZvKR7b+0s+P+8ju1GcP+S7KyMmH0SjPj2CUMdHY8sJ9Bz4779udb32KXXvVyUJiZKcoyre65iFwtIgdF5J6Ka38gIioiG4v3IiKfEJG9InKXiJybYtCGYRhD4RLT/BRwwWyhiJwNvBn4Xqn4QibPOj8H2Alc2X2IVZQ6+7dAAAAHcklEQVQtzIGPRK5wx2HpmeHLqHOVfa3MujZSc62uvESQldkmt+1vozRPPlaKa32fuj6EzGdE2cFtWuote2Z6yDzHqkfWViY4KE1V/SrwZMWljwHvZ/kS2QF8WifcAqwXkc1RRgocn/gh3fHipyp2uUIBNC2+BmUzlZX6ZhsrHp3s888Xen3af4hi8KmfAp/5jCi7cxuHcS/Vi/mF5PklUGls9IV7v0ExTRHZAexX1TtFlnV2FvBI6f2+ouxASD8zvZLlViKIF7ssuokmq4pBj0H69OUhN6hupDFkHbusqbvgxyCX49+/t9IUkTXAHzNxzYMRkZ1MXHjO2LCuiyjDMIzeCNmn+TJgG3CniDwEbAFuF5EXAfuBs0t1txRlK1DVXaq6XVW3r1t7Sk1X5W+BgV3yOne8k5VZcl8qXTufOGho7DKGizVk7LJcPyaOMcAUslvbdZC/IjOeICY5x1YmBFiaqno38FNL3U4U53ZVfUJEbgTeJSKfAV4DPKWqAa759I/JcCuRHYP0k2P3vXSX3bmN67hTfCYe9QY9Btl9zbhsOboW+BrwchHZJyKXNVT/IvAgsBf4b8DvdR6hYRhGRrRamqp6Scv1raXXClzebUiW8PGWVcdQp3vs7Hh4H0F1G+rb2fGCeP1nciJo6NhlrPteOpR3fjKkr6J0aBOzzujc8ZD5jCi/U5vY7niiz2OOFCbYDTsMwzC8yMTSzDDhA5432/C1MFMnfNr66DqW2f5TWo2W8PGql8PZ8TlI+NSRidIcAp/7XvqUzVxPfd9LsNhl7P6zPNXTUj+X2CUM7I6n79vcc8MwDA8WzNLsMeEz011nWbMktSzmMeHTInes7rglfEr00/8CKc0YW4mari1A7NLOjvvJjtJmKIXpWW+w+GX/itrcc8MwDA8WwNKsyZB7JXyayotrKZ87PmWwJ0MOaE0FkzLh4yg/uM1QYRnPugvijq/odXKIZ1hE5HHgx8ATQ4+lxEZsPG3kNiYbTzO5jQfyGtNLVPXMtkpZKE0AEdmjqtuHHscUG087uY3JxtNMbuOBPMfUhsU0DcMwPDClaRiG4UFOSnPX0AOYwcbTTm5jsvE0k9t4IM8xNZJNTNMwDGMM5GRpGoZhZM/gSlNELhCRb4nIXhG5YqAxnC0iXxGR+0TkXhF5T1H+JyKyX0TuKH7e2uOYHhKRu4t+9xRlZ4jITSLyQPF7Q09jeXlpDu4QkadF5L19z4+IXC0iB0XknlJZ5ZzIhE8U6+ouETm3p/H8qYh8s+jzehFZX5RvFZFnS3P1yZ7GU/sZicgHivn5loi8pafxfLY0lodE5I6iPPn8RENVB/sBVgHfAV4KnATcCbxygHFsBs4tXp8GfBt4JfAnwB8ONDcPARtnyv4jcEXx+grgowN9Zo8CL+l7foDXA+cC97TNCfBW4EtMdkC/Fri1p/G8GVhdvP5oaTxby/V6nJ/Kz6hY33cCJzN5UOJ3gFWpxzNz/T8D/6Gv+Yn1M7SleR6wV1UfVNXngM8AO/oehKoeUNXbi9c/Au5n8rz23NgB7C5e7wYuGmAM5wPfUdWH++5YVb8KPDlTXDcnO4BP64RbgPUisjn1eFT1y6p6pHh7C5MnsvZCzfzUsQP4jKr+RFW/y+S5Xuf1NR4REeCdwLUx++yDoZXmWcAjpff7GFhZichW4NXArUXRuwpX6+q+3OECBb4sIrcVz4gH2KTHn+75KLCpx/FMuZjlC32o+ZlSNyc5rK3fYWLtTtkmIt8Qkf8jIr/S4ziqPqOh5+dXgMdU9YFS2VDz48XQSjMrRGQd8LfAe1X1aeBKJs95/0XgABN3oi9+WVXPBS4ELheR15cv6sSn6XXrg4icBLwd+HxRNOT8rGCIOalDRD4IHAGuKYoOAC9W1VcD7wP+RkRO72EoWX1GJS5h+ZfvUPPjzdBKcz9wdun9lqKsd0TkRCYK8xpV/QKAqj6mqkdV9RiTRxJHdV+aUNX9xe+DwPVF349NXczi98G+xlNwIXC7qj5WjG2w+SlRNyeDrS0R+S3gbcBvFIqcwg3+fvH6NiYxxH+ReiwNn9GQ87Ma+DfAZ0vjHGR+QhhaaX4dOEdEthVWzMXAjX0PooivXAXcr6p/Viovx8D+NXDPbNtE41krIqdNXzNJLtzDZG4uLapdCtzQx3hKLLMOhpqfGerm5EbgN4ss+muBp0pufDJE5ALg/cDbVfWZUvmZIrKqeP1S4BzgwR7GU/cZ3QhcLCIni8i2Yjz/lHo8BW8Cvqmq+0rjHGR+ghg6E8Uky/ltJt8sHxxoDL/MxK27C7ij+Hkr8NfA3UX5jcDmnsbzUiaZzTuBe6fzArwQuBl4APjfwBk9ztFa4PvAC0plvc4PE4V9AHieSQzusro5YZI1/8tiXd0NbO9pPHuZxAqn6+iTRd1fLz7LO4DbgV/raTy1nxHwwWJ+vgVc2Md4ivJPAb87Uzf5/MT6sRNBhmEYHgztnhuGYYwKU5qGYRgemNI0DMPwwJSmYRiGB6Y0DcMwPDClaRiG4YEpTcMwDA9MaRqGYXjw/wGPBUdxEkFdXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1400: 0.00753899896517396\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1500: 0.007307794876396656\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-a0ed6d32edb7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     _ = sess.run(\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0;34m[\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m     )\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    903\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 905\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    906\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1140\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1141\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1142\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1319\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1321\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1325\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1310\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1311\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1312\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1314\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1418\u001b[0m         return tf_session.TF_Run(\n\u001b[1;32m   1419\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1420\u001b[0;31m             status, run_metadata)\n\u001b[0m\u001b[1;32m   1421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1422\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "feed_dict = {x : train_x, y : train_y, learning_rate: 0.1}\n",
    "for step in range(30000):\n",
    "    if (step < 100 and step % 10 == 0) or step % 100 == 0:\n",
    "        l, predictions = sess.run(\n",
    "            [loss, out], feed_dict = feed_dict\n",
    "        )\n",
    "        print('loss at step {0}: {1}'.format(step, l))\n",
    "        painting = predictions.reshape((img.shape[0], img.shape[1], 3))\n",
    "        painting[painting>1] = 1\n",
    "        painting[painting<0] = 0\n",
    "        plt.imshow(painting)\n",
    "        fname = \"image_seq/%04d.png\" % seq\n",
    "        plt.imsave(fname, painting)\n",
    "        plt.show()\n",
    "        seq += 1\n",
    "        #plt.hist(predictions)\n",
    "        #plt.show()\n",
    "        \n",
    "        \n",
    "    _ = sess.run(\n",
    "        [optimizer], feed_dict = feed_dict\n",
    "    )\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So While is is training, let's go over some other concepts that we skipped.\n",
    "\n",
    "## Loss Function\n",
    "\n",
    "Loss function is the function that measures how far are you from a correct prediction. To make your life easy, there are two main functions that you will have to deal with. \n",
    "\n",
    "- For classification, use cross entropy\n",
    "- For regression, use mean squared\n",
    "\n",
    "## Optimizer\n",
    "\n",
    "There are so many optimization functions that updates your weight values and biases. The most famous one (although not really used in production much) is Gradient Descent. The function that we are using here is called Adam Optimizer.\n",
    "\n",
    "## Calculation of Multiple Inputs\n",
    "\n",
    "The way you calculate multiple inputs is this:\n",
    "\n",
    "![](images/painter.png)\n",
    "\n",
    "$ Node_{1,1} = (w_{(1,1)} x_1 + b_{(1,1)}) + (w_{(2,1)} x_2 + b_{(1,1)})$\n",
    "\n",
    "Then you can apply the activation function to that.\n",
    "\n",
    "## Initialization of wieght values\n",
    "\n",
    "We initialized weight values with a standard deviation of 0.01 and the reason for that is we know that the values for output should be between 0,1 (which is the stadard format for GRB in here). Initializing the network's weights with values very close to 0 but to quite 0 will allow the output to be small enough that it will be around the desired output that we want.\n",
    "\n",
    "## Why ReLU?\n",
    "\n",
    "One more thing that we skipped through is why did we choose rectifier activation. The reason is first, we don't want negative output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 0: 0.0022454524878412485\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 500: 0.002034383360296488\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1000: 0.0019454010762274265\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1500: 0.0018444970482960343\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feed_dict = {x : train_x, y : train_y, learning_rate: 0.0001}\n",
    "for step in range(2000):\n",
    "    _ = sess.run(\n",
    "        [optimizer], feed_dict = feed_dict\n",
    "    )\n",
    "\n",
    "    if step % 500 == 0:\n",
    "        l, predictions = sess.run(\n",
    "            [loss, out], feed_dict = feed_dict\n",
    "        )\n",
    "        print('loss at step {0}: {1}'.format(step, l))\n",
    "        plt.imshow(predictions.reshape((img.shape[0], img.shape[1], 3)))\n",
    "        fname = \"image_seq/%04d.png\" % seq\n",
    "        plt.imsave(fname, predictions.reshape((img.shape[0], img.shape[1], 3)))\n",
    "        plt.show()\n",
    "        seq += 1\n",
    "        #plt.hist(predictions)\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 0: 0.0012141595361754298\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 500: 0.001206237473525107\n"
     ]
    },
    {
     "data": {
      "image/png": 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SI8tLOLI8ivdbkCk0z4Prn4flQhCU1lDKa/kVpk6UGE8rTErPLCpM3ESclBWmbszKqkalIsOolQ6MpNYaSrcwC8S2H1k/+RUMyHiGEfNJCqIoomiF2r3XnY0NvPTSK/j0j34aAPADH/so83LcP+6wg+DLQ0V7tj4Q0QqA/wfAf22MucuPGSv0tYqLRPQrRPQtIvrWNRfh1lNPPd1/2hNSIKIcliH8C2PMv3K7rxDRGWPMJSI6A+Bq27XGmN8C8FsA8Mwzz3T5r9x38h27fv0Gvv2NbwGw+RB8kZX1zc2GhYGjg6aFIfUVyNwyUkiBIavr6P31s0yicDUapRCQbjXLpEAmZSj5PihyrDh0sLo0wqpDB0eWlxLlIEcmFh0wP4VQJj4qHcGVjmZWGeg1/lorhhoigqjZ/klZJqhhXJYBXVhEYdHFtKpRuoxUVV2jdteXSqH2BW+MwVRpVB4dGAPdYrLgGaiNMVDaiS8l4fV33sFLrtDOkz/wFITsqnz9waO9WB8IwD8F8Iox5n9lh/4AwC8B+A3391/vqYf3ijrYkp/8N67fxKWrNk+CMSZkZm5emJoX25lCIQkrRY41J++vjAbBeQgESNfO88gU8jxH4UxoRZ6hyLJ02zGIpdEQKyPrrUdE0L6UPJBYG5KeN2pGJsVgFqglyc+YYRBuIq9qHcy2nnFUPpszYwrj6ZS1S8YsKlS1vVdVK9RKYVza3+DqeNII3eZWCd43x6ygcGd9A6+8YrND/8zP/SzWHn3EXdLbJPeCFH4awN8G8AIRfcft+x9gmcHvEdHnALwL4LN76+L9I4OYlv3ateuY+oKsxqCs7SrH4/39dlKVKdyNMMzskWPDAY6vLuMEYwoDpxwcFDmGTj8ghGATPzKFPMuQ53mIEhSsavSwKOKkN1FZZ7RO8yg2mQBjBIsWleWp4ANJCeNcIrRSgSlwBqEdgyjc9nBQQDmkU6vIPMqqamUQk2mJSVkFncTyVoG3b1nJtTY8b0M6wblbdllVeN0lgn31lVfxE2u26rUssn21KT6I5sm9WB/+Et3v+vO7vW9PPfV0f6n3aGwhvvD5Wo63b9/G1MUx2MKtEZYDETE0Kzd5yD7KJE6uWGSwujTA2pHVYBkYFFnwOBwURbAkDIo8tIu8SJCBFCJkErL6BtvOpAwa9wQNME+/gCB4yXl/HrfvdYYqzTvCKMugnSVB61nUoBNxwrVVFD+KLMPIIagjSyroF5RSmFYVxhP7e5wqyzBOr129gSqYN6NSxDC7rzEGtTZY37T5Hy9fvBRS5VlHJo+AaO9L/AMIFXqmgDTKMOxw5L0T796+ExOt6vSKLj2CIMKKg/wnj6zgiIPIp46t4vjqclQU5jmKIooJudMjZFKGyd4MRkptXdGLUGZZyLNgNBMTCAlTABMhjOGei9xPIJyc0K60wiYPij7tmEJgEkolTEFr345p3fk5tdIo8gxLg8JtK8Y8JV67ZK1Zm8rmnQAAMmk6OKXjvddv30bl0soXo8EDNYEPgvqAqJ566imhHik0qJk3wSOFK5cvMzg+3+LARYYTTmQ4ujzCmTVbSv3k0VUUeY5BERGBX+mFEDGzEZCs5ukTIwlByfXB809ENGBLzPuU8nAmxrZ7x9ebMUJ27DddMKtBGXzOA+vwpDuQgtKxHZBCrUKgmawVakHBGQmw4wsAy3cHWHUix2QyTQLafKYrA4JWOigqr16/jk3nobp87AjAFZUB/u8NPjxINo2eKWxDd9dtNuaLly+Fj7iqefbllClIQRg5+H9yZQlHlq3IcPLoKk4dOwIAWBoOIKVIJnJsU5IkhYsNTaaUBDSFvAkiphpr8hEjwn1McrxrkrP9ZnEm0bmfkdQiTPjmZIkWEwPhA52EhtB+jAyE1tG3QoiQZGVlNMAxxyBuT8vEC7IhGOG2+21fefMtXLlwEQCwdvo0pBNF0nDNB9Q9cRfUiw899dRTQj1SCNSuUHv7zXcAWM9FH+8wmU4S/wTuuZgLgUeOrAAAlkcFHnHo4PSJo8H/wKMEHtcQ4w06EASJGWVj23kkKNY3YNmJeEy0CbCYOyl1jEXiijC7P9yvZf+s0tJ3yz5fkPNbECIoR7XWwZFLCwPhRB6hBYxwbWPFJXKWDSEMhDu2OhoGhe7wzjpKL4qwXpEfGtehO5ubuHTRhut87Ec+BYkc7cSVywuihgcQXHzgmcLshIi0ubmFiw5W1lWFqQv0SZ2S0u1MUBAZ1lZXcOq4ZQqDIkfhnZKIGhOeQnkzrlNImIVIz7HncZGBtb13pEByr/DOblKapgIlHJ8ZoaQ5n0HMOeYnvlKWeTlxRmgD4xiEECLoPoQm6BCEpaHZ+cQYgdBRp7IyGmJ5aHNIrBQF1ust93CeCzv9/cqywtUr1lu1mpbIR0NsTwtqCR5Ak2QvPvTUU08JfeCRAjALFPz2eDzGhsvWU9c1pq7M22ygE8KOQsiwUp08diTkNhgOihTuE81s+zYPUebiQ0iu6pyVvA+DlA10ITy64Ggiog7jYx22jXEw7bud+GGa+/jgxTu4BnP4EgQyxN6HoQBjoLlC0VtMtGgoHfn4MTFDEFbdSn9sZQk3xtb/QDX8FID4G25ubuLKtesAgHIywRJWG2ewV9ztiv8AmR96puCo7dufTKa4fOECAGsSa4oM4S/jCkUmg3fiaFhgNLRMoWl2FC4nAmAnLxclEkbAJjhnHNL9BwBCSginfSchQK4tpGwVRQB0MgUzO9vDCM0yiMgI2i+JZk+tdCjYYvsiQMLpBIyAcPoBQ5rFbkSLQ6JfMGKGEXDLhE9BNxrkMWFNqy3E7lHa4PZdy/zLyXjWarNf9IAwhl586KmnnhLqkQIAvtRxxeN4PMG7Fy1S2NjaTNEBIwLLjZBlYQUfDQbIXRJVQan/gRAiVRy2iQyJhSLmU8izDFmWQzp/CCFkyBwkmo5QYlZ8iO8ZoX10TWhaDNq1sJ2RlI17ed8OIhPSS5GxKMFnS7KIyFkijAgIQGuLCABAGA3j0QBZJWNAB6STFHIDH00qJTLvs8HMD4R00SaYEOOyfucOTjr/CZF1T49dL/oHhUL2kT7wTMF/IIHYxnQ8xvrGRjiPX9NkEP7jyzOZpBgn5mAjGkwgZRKz58mGU1KWuaCnLEeeZ5EpSBnMkMlzpOxgCvat06zNs1xxHlNIhmzGIzKaFymMBcEE/YYJIgQAa0lILBFRFDDMJKkdgyCt06I1RMGxiBDFslxKJj4kHUy3DVA6y9LG+kbQfYiDnh2HlEF84JlCG/nf6s6t26jr9opHKZMg5GEVj1GKSmmIQdQbNJWB7R6NTFeQKB1ZJGQmbeCTbGEKjbZnCm1vkHyHSS51NpG43iEZILaHZ3VlsvuMSdVnShJk0QLTMbQqHbUOJkkineSUBDVyTNLsM/NMYOAYJ5V18y3CNhGwtWkT75bTmFXrntIhYhC9TqGnnnpKqEcKLeThcDWdtsLnpighiVDIdv4aAXuEy968yFFAq8jAdA1SRqQgZOasDEyP0IYUBK/L1OiUAUxILkkB5oMo1S8w+M3DsIGIDgixqKsVtqMXJffU5MjAcNOpYW2digVpxumoN+C1MEHcSSu+Zi5lRAow4fqmPxEB0C7wrS4rqKqthA17v/T1939hv8+ooWcKAABK8hL6Aq3VdBqDi2auiCQEQrp1gHkPNtySE5E++ZApuaZZyQmwVZVlojeIykVq6BGCm3NnjwEQmyRca2YQQodMQyxITZr2HuHeLPAosBUiGM/gjGFMwDKIINmz+/J083Z/y8QHpTUpwIrtUfwt8yzDsPCJbwkqFqlOhoMAlM4HpapKKJ5/09DM+feU7gOD6MWHnnrqKaEeKTTJ2IShAJAXWbAQGMJMklZPGfGipk09N1ov4tB4IeIrY1C0McsCEzlmlsGWLhgO/1kG42RhYmiAn29RA2ASmaGj22H84qrvjQUR/VPybs1U9P6ciPepRbkY7+VBi3Ukc+ZJEqicXdL2JfaZANROZJhsjaHVHPGhhe6ZT9I9Qg37UQxGEtHzRPT/uu0PE9HXiegNIvpdIjr0CfWb4+ud/QSJ8PV2RcURbOUmbUysRtT46OMz7MRIIHILGbRNMwpix6yjRDxAnZfwi937JB31fYv95McpuRmlN+fdmvNiccI3/ms2w6Qn1gXPOlgfmRWCn2/c/wQR8kwizyQKIdhvYhr9in4kd2/fhlbKWiD09ubY+0YGXR/Knmk/xIf/CsArbPsfAfjHxpgfBHALwOf24Rk99dTTPaI9MQUiOgfgPwbwf7htAvBXAfy+O+XzAP6zvTzjnlGyAlsWLISIu41pXaRDkqOkoIo9U2t+Dc2gh8U7FVVqydLMzkqWarbqtkKLmYWekuv5qstPbnpFttKiK1eCejhU4C+WooPQL78djkXU4FGez1g9zHP3n2z9/fzTBdn/tjY3oeoaqq4XqH8Rl+oDWrQXo31GDXvVKfxvAP57IISVrQG4bYzxQtl52ErUh46S8SMEfYFBVKRLIdLJ16FTANJqyT7N2LSqcYSZzZhQjJTFtLebfeQb1JxA/F3aL2oRO9wfw3z8yLAHNweGX2yS7Z1/jw0TI7G9TKfATTZNB6VUJ+H7YcIPSKCQ2GapKCAn1sIQU7RFJu6tPNPpJFgfjDFh1Zx5v3umSNgh7YPeYddIgYj+BoCrxpjndnl9X2C2p54OIe21bNx/SkR/HcAQwBEAvwngGBFlDi2cA3Ch7eJ7XWB24QdQ9FMQIoXLbeu6cM4+sYaBRll7pFCFlW7viwp13yRRhHac1KoFZNBnAXSw6BjuBjVwsajLeSkVndqdl2Cio7WUItSGWB4WyDfsOZWaDaP28Q5VVaF2Pgu7UTQeKgCxS9Swa6RgjPl1Y8w5Y8yTAH4RwJ8ZY/5zAH8O4Bfcab+EBQvMHoRMlohaZv5/XF5W2qYZF0Tsc23qB6IVwRiD2v+nbJ3CsqpQljW0NtC6Pa/xTt85mO66BGNq2egSpDsPmtCx2dyL4UAqSbeJFQGhRzg/l1q63LQ4NHUKQd/CrBS+sI0xVvxbGhZYGhZYWRpiKCWGUoJaRt3/5oDB5t11bN5dX7imZpPuq36hi3bQoYPwU/hVAL9DRP8TgOdhK1MvTPvBaZvf6My+OWSVTi6xh9bhyub08ROmdl9Aztxsa1cdeVpVFi0ArurTLj6VZKVkO7vMpNTRnjOqs73ig2ZazmnqE1oYA4CkmnWiiG1/auhnQ9lo987XKST1HdwBKWNk6dJggIFrU2nP5CPiy9upWmHsImNN8vu3sBKuV36IaF+YgjHmSwC+5NpvAfix/bhvTz31dO/pUHo07kWBOkdBvhgRsLpqU7TXdZ1A39iXCKw1DIyhELdvYxQs0qiVxsTF6a8sjRbrc1uHQrOhnyC+P66oiyKENkTQtejzA2bOiQkWYOig2U7Fk/YfqhkTEt6RGlYKJzb4e/vxh0GwKowGBUYuTV4mpkEHBHh06ERHVWOyteXaqjPZ+yJ0qPQLO6BDyRQ4Lcog9iIycOI6N1VViS7Ofzi6eWdCyIHAIWtRZKFYLFol2cX6s93OVh1i99WB2tB8KgrMFIibPbflpka3M4LY1u4/xiQ4g2h5g5kkMeylidKxlSyHhP/NvG4BAIoNQslvjsg86rrGxt27AIBycxPDlaWkL+2/4YM6/dupD4jqqaeeEjqcSKEDHnTpdfZX00vwcf+SaarnKfEJcXXKMoklX4xkNApig9eed/V323dI4iV4DEFLZ5grTxuZmUMd53WKFQ24z/IyWkTg07LrYN5N2yb8F7Y5omhBGjCmU9HozwW8GVmGoQhIYVDgmCv2O7y9jk2t0s/MnVdVFcqJTQs/2djA6qmT9riYYxKeQw+iLvJwMgX+TbcwiLkTaEE9QtPaxEVXf2xpaRSZgrF5EwCgNjEXgafRwKcVLwIjWF0ahRwInK0I2sEH1qKJj9szDRhtQC1ueHPYQyrfNzlGq1jRIlhsKzLMttsYgdYaOiRu1cFTVDesF1ynwH9LKdIcmZEpDHDM6YpGeQZZTlku13gDpRQq56ewwZK4hihUzNL+Lkr3n3rxoaeeekro0CCFLsCbAIV5WGwB5eKivih+QR6NRjC+qhE1FmZ2r0wIrDqAv++/AAAgAElEQVR0sDQaYMVVKFpZTi0OXE/WpZvazapjjIZWYQNkfG4F2X3HzgelWZ7bLmqGDiwmMrB2UDTG7YgOYlp4rXWjzdFERC5KR6WllGluC48U8pyCd+Mgs6nflUc37J2U1phOI1JQzjtV5N12iNR/pZ0eJFXkoWEKXdQmSVDLyG83mbrEhfZn2oPHjh3FivuQNje3WjmXIMIwyzAY2I9mZSnqETLZHN7IFZTWqbmtRXcyn8GZmHXYmJBj0RgTKjjbFGge9qYvPGM56FB0mFZuG60IgJ2kyeRvgfyJuNDQKWhWH0JpBeXeq67rEFxW1zWUS35S1wq1qlG5CauUCunobKedmCZ4rQ0RqnUNixxDKTHV1rGMfwxKxedPJ2OUzjyZjwZYZFp3WygeHMbQiw899dRTQocbKXSw3abucVGRoUUvN5eOnzyBTzz1FADg1p3vhtWM30KAcOLIarCBr46GWF3udlQCfJYmDaWdnV1rSFd5pDtfAcVqS0rBwEAY6d4xRQdBE28MyK3GNvtzCxxp7EgsDo3jfCwNg/ZcNOCoIWQwcm2/Ans04FEAX52b+2ufZZm364gSAGv58Q5LNhv2LFIgIowc6ls7egR3xhOs15Ub2ejnoJSGciJjOZlismldnlfWji8s2s23/Rx+OnRMYbtJ3qSFGcKCjICftnr0CM49cQ4AsPTiS1ifWu9EXnU6FwInjx/FkWXr5HJkZSkRG7yHPa9iFORj8tWPdPCwE6xojGEp3uq6SuC6NBkg4oQ3LWnhjUhzN/KU8oaB2ZQRxerQxugwiBz+a6VhtEqYgp/8JtEDpKKAn8i1qp044LdVEA0SplAr1K4vdV2jYvul5NWzAGHiO/j9GavGLYiQOUvQ0tDGQRRuPFTDwKOcgqaqSkyd+KCVBkk5Z0FZzPj4IJgoe/Ghp556SujQIYUZ8rq4Bf0PZi5fgCUTwG4elXZU1zh+/BgAa2HgPfD3XR4UWBoOAlJYXV7udrJiq26tFYyDwyVVseR8o8ak93PI8ywgECmlg8yxDoRvSxnrQySl6njxGLKihC+gKrMsIAKtDRMFVCsCMEqlYoLWjVU/FROAhihQ14misKoqJjLoUKqvZqihrlUYc+ney+sWuRsXsd+GiIJYISiW47Ml+whSeGeymHkJREF8UEphOh7bdlUHpLEo7RT1HhY6/ExhF7STMHhyF5ALXNJVGUxfdVniyOoRAMAwl8B49vosk1hZGuKI85bjfveJ0x/QgNJ1EBm0iuKD1jrKxKyobJFlyFy68iLLIVl1ac8kfLttv+DnZBmyPA+Vsu0Ek64vdRRTlEoYQe3CwGs3ifmE95C7W1egwzn+/YM4UdUoHcOoKmZxUCr8mJmUyaRMDTYc/yc/T9T9CM4gbBFaL2ZlRBg4k6Ot6C3Dj+a9G+vpBPlwsONJPs/UflhFiEPJFLoGchEFTqKEnGt2ZO2qwt0bNwAAd2/eAkyU7wuX46/I2leJTAhkMro2a+by2+ROVUNRFlfUbnu8v1uR5yG4Ks8kijwPHy+PzBSsLUVkEPZjd9fnOZaIQiWpZkLWqBy0ugO/zzCzoU0iYxlpWZXJ6homP2MKtVLB5l8r3UAKdUARVa3COCWMgMGBVmXsAjNsWFhFYy6lHbNQfUuE+hBFlkdElmWQjlnYMZnjXLLN89tQw2HVL/Q6hZ566imhQ4kUWmlBAY2fZkw7Wkh2GQNVTnHt4iUAwPXLl6DdCpblGWoHH0dF0crR/QrMLQaxLmVUhNQqroBVXaOuVVhdtWlHChxB1HV00PG6hjzj+gaPGnjJ+hi6nWUyIIUlLKGoKqtLwCxSCEPDdApaqyQPZRMReNQwLauIiDhSYO/r9wfLgqqjHqFWEb4zamahnNEjeEvKzJWRRg71nTh2BIMrVxPRqvDiQ56FcZF5hszVCCU6uPWzaWK/33SomcJBKWoStFfXGG9u4fZNKz5s3L4dYHKeZaG9trqCwbXrAICp0eEORMRyJqSel8aYOEEqzhSUm1RMUdflzuuZAlGiaxBCoaqdmFBWUWRgkDvPssA4sjzDsBiE/VWVI8v9B88SljSUaZrrPRo6hGSbTXhfdm9aVpiWlTtHM72DTpgCH4tMykQhG/wnml8CVyrscCatjoYYFQVG7nczXEzM88A8sywdo71O2UW+58PAIHrxoaeeekroUCOFGVrQPBmyfZuo6+tUOmY58jxH5RyTVF1DuxVdl1PkbuVcHg2dWRIAVODiWitbEt4XewVz+GGgVzPUUNXKQeiOwB8zKz4AMaGs99QL4kTDoy+ID7KKysVKhuvLskKZVyhcf4QQYYBkx0DV3MLgUEJQLmqNWs+iBqVi/32GawCoKoVax/MEWaWoHbT0l+XBV81VtLu+Z7g6uZHfGg0KjAZFSORaGiATPltWEcSHLM+DxSfxBt0D7cTb8X6hhj0xBSI6Blsy7hOw7/B3AbwG4HcBPAngHQCfNcbc2lMvd92/RkRfx8gOlkZ45LHHAADrt24G60O80CZMGeXuwy2rwGSEEFaL7+G3gatq7G3m3OwXmYpSOjAJzhRUQ3wIuQSNYR6JTaYQ24nLr5SQrpryaDgIkzKkoK/KcB5YP9tICIIqOfzXiRmSMwhuVUn3Rx2CjWx0N28GhLWJD4zDG7h8DD7XRWuPUzJMwVTkGZaHAxSOedbMDEzMKpPlGRMfRPeDaNFepJfsxXP3IJnEXsWH3wTwR8aYjwH4EdhCs78G4FljzEcAPOu2e+qppweEdo0UiOgogJ8F8HcAwBhTAiiJ6DMAfs6d9nnY1O+/uuvnuL+78Vng1AQKfJFCluPY2hoAoBgMsOW82EAIdQWXlkZYHdo8CbQ5DvfKZIoUQBQ8IgEw5SA1UEMM1qm1Cc47WrVbH7Q26WomKBUnWlDDoMghnW1ea43SKf2q3KKEqvQ+GHmCFESL5x4hPs8XTkkckxIl5KwCUWsdxJqRi72IKK5ZEyKSMS1tk4qGXc5qBIY6GGyUJHBsdQWFExOmVZWEsYd4CSEhvfVB7L/6baffMKeDFC32Ij58GMA1AP8nEf0IgOdgy9KfNsZccudcBnB6b11soR1gL65f6PIWMTLDkkvVdWxtDeN1m83Xu/MCwHBQ4MiSdWXObt6OLrfOESaKDJQoMDzkpyS23+UY9M4zUkBqN+GUTjz6uNkyYSoUowGtedI+T4jouZimQY99qura5iJ04kRR1zFwqsM8ye8hhUy8MLnnYs2iDAEL1QHrcBXzLUYRgG8DXkzwuxviA2vzQrLNe7EeM8YRDxgyOLqyHBzSRF0n0ZTxN2NBV3N1CvfXkLjfDGIv7C8D8FcA/BNjzKcBbKIhKpg0d3dCfYHZnno6nLQXpHAewHljzNfd9u/DMoUrRHTGGHOJiM4AuNp28U4LzO63zwI1NgonGqw9cgrXLpwHYFdUb30oigLHfeLPTKJyi2GeZxDCl5Z3Kw3rKA/dJcFXo7QUu4fmUggoD1WVCmnKKua8lAkBymKcRc58EzIZHZmyTCbBQTzPgNaaKR7LcE0TKSSoIShXCVkmsTWZVShKITB0eQuS1bkhIqSKQ8S2CXusiBBgg5lxEGvN6oR0PwJSJIYaDZZGw+DDIQSxFG4x74RgQWQ+m3b6De6fRWK/vu39QA27ZgrGmMtE9D4R/ZAx5jUAPw/gZfffLwH4DeygwOyOaUHzZDh99ttOtoWTvVeOHMHSyioA4M54HOR7Igriw0hKaNhJMMhz63ATOiRALk8C12MkDkKCGpMvrZOYO8htJ5/zyVcqmCoJhIwVncky5t3I2jx2wEcW+mfYvA32mXUdg5h43gfZSCfH30W4mA8AqIXCyMV+IEnG0tAbsNRuMzoFsHaTeWCWqfDxSxhMg1lwUSReTi6WxI0NCaZj4r9TZBBd5s/9or3oF7potwxir34K/yWAf0FEBYC3APwyrEjye0T0OQDvAvjsHp/RU0893UPaE1MwxnwHwDMth35+L/fdKe0L/PJOPsMhVo8eBQCs37wRogSFECEScrnIMZ5Y6D0cDjAoBowVU1Q6NlY2rmgUTB/JV6cizxLI7lc9xRR71qzPteTEXHNjjIOPpgTS+Aj/PG69CM/RKqQimlU4xm0hRHyO1kHMApAiAmYymEEQLTEiSbtLRAhQn49nHAu/Xxvjk1PBwMCBPgjYhLY+alKIceLlRswpLPiW3FNd4v7bFnYyPx4oj8ZWiOU4QtvQ7cg5xH1I+XCIpRWrO5B5jtrJzUJKjEaWKRxdGuGm84AcFAWEFEH7brj2miIsth9rquFPSqbLIrxQwiwQ2wHiowFzmfOMjcVwepA8C6JIkcV2nuVOXvaTKooSAJIci9FUx5iYA9OJTiOLzwn3SSwESGyHHPKH7UZ7kcIyfDz8YDD+HGE/WzkMGUymaZFZJL/NbAq7MNbh3Q6GS9BMlMd+CRSL9/eBYgqd1HxfE3cvMqRc9i8GAwyXbcKULC+gXPSfzPMQMTcqCgx5UhMSQcdgEJWGIB2zODETIhFBCokilBJoMgI/WdnHSPEcn8exTV/ht+Nz3MTNM+RZztpZcC22/Yr6jfjlm1QZE7vr+myPZRlXbvKiuu06hTalIy8Pl0z+lmuaisbUUSH2P5nGCYMgKF2hdN6ehJjXkXuK8mxV9xIqHIR+YSd36wOieuqpp4QeSKTAeXa7E0Q82MZ1OTKYuTTLsOSQQj4coBy7bL61gnSwfHU4xNBB8VqpYE3wz27TZAshwgrqIb434xFHBE3UwESEIG6QwKAoGtCeaclbUAeR/8eHVOdBnMhktu06SGD3avSH54jMMmkDxGARU5tVwYsVqWPR9iJD08LQZoZM+xz/5UhBg0BC+poxGOQFBq4WaJ4XIQhKsNR2bKjdM/eb2vu//8/Znh5IpsBp7o+0g1GNE1kgd2XfllZWMFlfB2CjIfPcyv3HWG6FSVU70cClVTeGyeqS6Q1kkLd9DYOm4tC220WJ5v48yzBwirIsyxLZuY0RcGYDd18P+XmUZIczo72mxTvSb0fTpwz5HgVT2vmxcY2GaICZCd/WbjKOVh0DN31yQyIbC2N82jzHJIscQ+enMhyNMFxy1amHo5Dc1otrXvc0d2XZR9p2ATwA6sWHnnrqKaEHHil42imsi2tJgwQF78bh0lJYKUxdIXMr8+rqcsitMCoKaGMCfNZGh5wEwggQRWWWX2HzPJ9FCsmKzkSGDqQgiIK5cVAUMcQ3UbSxt0u08n7FnF2FudKTEzWQAld0GpjEC5DneaDE7Il4xTaigb/vfPEBM9egeQ3rv1/ltTZQJloQBoMhhkNb1Wu0vISRS9c/Wl6KyW2FSL4tonu3codn4t4886FhCvtFhkSY/EvLKyHhhpYZhDMJ5nlM0DGdjFEqhSVnSiAdU4lLY+CMErasG5tUeZ4HM2Yy4ZtMoYtZECGXMW1Y8DmQMg1uEqlJDYgTLIXc6bFADUYAxInv763qOlpMBKtb0ahKFUSJFvi/K51CmzVj5nr/HoA3OyqoRHwYDodBfBgtLWPkxIfR8jLI/ZYk5+RTuId0cDqNSL340FNPPSX00CGFGYi1DWtt0xeFuP+VFWROuajKEtonRB0UITPw9WtXsbE1xoqv+wAdYgYkDEh7UUInz7Arva9pwFAA2n0T2sQKbn3gmD9RCLZAaX8Nz1DcuggyBKFNDEgSroqVCHA8ZmECwNBJQ5Rg7YXEh90qGlv9HKKSUDjRwf00GA6HGC1FkWHkrE/FaMSyZXWO0n2hgxQlHjqmsFcyDHKPVpaROVNVNRkHV+jR8jLWjlhX6FuXr2Ba1VDOh1aQgPABSQCEy5MAXc/I9CJM6nZHpHkMgvsY8fTnTraw79L0EuTXG2Chz8r3NYvikw+I4hPeF3rRWofKU8k5knkHClvCbaFJvYhJco5OIXiagoIJUlc1DIlgTRoOR4wpLAemIKQM4kOT5o1cF+vY70l8UKJELz701FNPCT2USKGNg86DWzMihHcNHgwwHFmtdDXeggfI+UBh1eVWyK5ew42rV3D65AkAwJCtmsPRENKLDXXsALcihP5xRMD2JT73zOg+sxpxP4MmQnBv6bsyN+EpNY6we/kSapnR0DrWdxCCkmxRma/hkDFFpxDBf8EXvvUu2IuKEul+3Y4otAll7W3ZPf/OgHIblQtDH3mLw2gU0MFoaTlYH4QUaKZh225VnidgzIf8u1/391uUeGiYwraDss3IJSZKFtfgg6MmW1vxB9caJx+1WeZG75/H1uYmavbBZe4DrbWOXoNASJgSutTGFBrtNvsgpf+wnem+VA5HnFRuh0kucc+cJ1a4W2d5AaMNRiwd29hV0lKqhnb6BWM0iLzzT6pTaFbBbk543+l5ooTf1qyitTYqyYzNmYJnXJXS0MYyAwAYjaIeYbS8jIHbnzCEe6ZO2N0U309Rohcfeuqpp4QeeKSwV87YZn3w9muZZTh55gwAoJxOsemhtNZYXrXZmY4sL+PmnTshKxKEsOGNACaTaUgOmmcSmYl2eq4p5M+nJgKYt58jirAr5kZI0ECjjSSCMfwzM54JADFxPcryPKzIo2HM5lzXsf6kUgpZZkK/eEYjbo3gIcpWzGlTQOoZEUHXriSdVmkxHR0jVv1vqYFQ5bpWCgbAkSNHbP8Th6XlEOMi5OKWh0WBxGIr+r3wRuimB5Ip7HqoFhhrA0SNOxMf1k6fRjmd2nO0QlZaq8Ta2hpuvvc+3nrtNQDAD3/846FYaiYFpi48tyhySPdga/Lkn1HTXMjEio79ybEWx6Rw17Y2Yxrx6W2DQoyRJFsArGemvY0OIsPG5laA6bKuoTLXzvNUbyJmxQnAQXYuMvjCOEqFdPtaq6TgbVeFrSg82b+VFx9qhXwwxPIxywhWjx7FaNnl31xeOpB07g8SPTBMYT94ZhdPaKIFv7qIPMfAfcjHT53EZMtGTN5UNXKXZ+GRxx7DOxcuYHNjAwBQGROuAQlMHCNZKnLr1Qj7sZNgwUJdcjy1KBQ79nejA78/NVHORxFxJOIzm4rb2IOcoQZbX8KOTSazMJFVXQdFpTebxrwFLBqRJaNRdQ1tZqtoaVdyLmUErM1679uVinkoNawSee3UKQDAcGUlZNsaDIcRIXRGh6V0cOqGxfUL+4kpPtgssaeeepqhQ48U7olU1aZYAGCEhMjtgeHSEtZOPwIA2Lh7N4gSK0ePYHVlFRNnnrt98xZOn7LVpmqlQuDUpKxCuXNjXEYmtyqTECDHn3kyLh7cxIngkna1eCumIoMJA9iKEtrOa7YTqwS7BaUJybwjkDEGY20RVV3XkJld6SUr3CuzzDpuBR0DtYoPM2KBjinl02Mmto0OSM8wgadWKogPBsDR48dx4hH7ew6XlgJSyAdFp9VhP77F+2tXWIz2WmD2vwHwX8D2+AXYbM5nAPwOgDXYqlF/29iScgvTgb8+Q2Vef2bYtifN3IBFluHI8eMAgOOnTmHqSssNhiOceuQRvHfRFsW6cfUKTjmmwD/8zckUS9486RVoDPLzHAxNoB47mjKBBOgn4kO7yMB9Fuz/2Xmd7ai0S3pk4oTjKDvPcxhtg4u2xmOWBl6G4DKZZZYxMHdoSmpSxAclIkNDXGhXLsZ+cZ0CFx/ywQDH1taw9ogVHwZLSxgtWzPkbMm8htl3Dx/n3r7r/fZG6KZdiw9EdBbAPwDwjDHmEwAkgF8E8I8A/GNjzA8CuAXgc/vR0Z566une0F7FhwzAiIgqAEsALgH4qwD+ljv+eQD/I4B/st2N7qXxZSc816MFIwiisKvGuaeexNSJD7VSOPPkhzB10LisFaYuzdpIyrA6ZUJg6qwSUlKCFIib3siwxK9g6KDRsYaHX9gd/vH7+fUNNNAlMnSKEvEZ3BJhwyoiovFWiZHRqLzXo5SQlS/xnkMqFZADL4pLQoKM8x2lKD4l4oLRrdsAoCn2TMPWtoT769uj4QjH19ZwwikaLXLx3paEuQpGhubvmT9T8vD2L3c/hYy9VIi6QET/C4D3AIwB/DGsuHDbGKcytqXlzu65l/tAiw5Wh3oBhgjKeeeJQuBDP/CUO2BghIR2oGtrYwO1mwjIWDVqITB2WvnhaGA9F1tceInB//QTaA8aiofbjjFfhEZ7cfGhpd0yTvy8kOU5L4J5UNV1GJdqOnET0TIPmUWLgxAiTPAkSzXzv9AzOgXmuRj5GAwBtY6+Cb4vw2VrbfCJaYSMQWxd1PaLPKy0F/HhOIDPAPgwgMcALAP4azu4vi8w21NPh5D2Ij78BwDeNsZcAwAi+lcAfhrAMSLKHFo4B+BC28VmhwVmd0Nzb9qAgcxZLyrQOi7VRKAV6w335Ed/EMfW1nDs5EkAwOXz53F3fSPewa10Va1gnDKtVBoDAsgrtWZEAd4DjibS92pPVsren4kMre2FxAem1OSogz2/mcg17EcUJcrpNMQniKqCnE5CqjuZZ5DuU/TBUuG+LW3jrA2+zmeXb4IxJgnUKlwA1LG1NQyZ56K1NrSZeebFlnR8XR1gYv8+8PnP3w8ssxem8B6AnyCiJVjx4ecBfAvAnwP4BVgLxC/hoArMNqhrILaV+8jLxLMXJPubz/OodmkFa8UAR518euzEGt56/XUAwK3r10NuAZAJH9mkqpHlMsI0Y1JRIrUrsP3x/Ll6hMTiEM+abW8nMnQ4OKU3htaazR/mXYmoa8jzArV3S5YSVVlCZDaIylsjAEAiS0SG0BaUMAtjTAy8im8CjZhmTTeYwtKyZeTHT5zAYGkpWDysLOe732BwaKd7r0+4d7Rr8cHYEvS/D+DbsOZIAbvy/yqA/5aI3oA1S/7TfehnTz31dI9orwVm/yGAf9jY/RaAH9vLfXfUh8b2bjg4McxlmvvDg9rXDEMEleWAtDD56OnTOO1qRdy6dQtgBWr96j6elhhlQwgX9y9llogCXn/O/21zRe5SPKbnLSo+cDFhfrt1m8dTtKAGIgpp7uq6gpQStavHWWXTxIfBh06nGaTFDIII6MBEdGAQUVytNEpn8SGZYbRig9hGq6tOdNnH9b4D1d8PteReLRGH3qOxjZKJu8d7BX7AkCR/UHgW037PxB0glgUthgOsHrUwNS8KGJdnAMx6YIxGqXTI+iGEjJNKGxjBdd1ssvk7tQY08WMdeoCk3RBHFmizYWkwSdPYNHG/a9mcks68q1VgDABQTyeoGFNAOE+36hS82dAzCYOYq0IzY+m0rjF1Fo9iuIRjay4RzvLy/AhIdl80zjJtG/dFlmjapvaP+tiHnnrqKaEHBilYpZWlnTDmRc5N7e3pAe/zbxC17EmRkUbDADhy0iodT546hSvvvw8AkFrD++8apVDXCkVuV0rFSr4TcUcmMFGB9a4Ru9DsU4IIWsWHduVlOKOr3ZCxTAMt+D/t61f8BYmE9VuQ0Rohpkzp6CwWWmuLFtAUJWgmVZ1/UlnXwfpQ1nVIwZblBY6dsEhhsLTcUC4C/CtofgP2T/NLYh9HBx2k6HCQ935gmMKijOCgkFwTLUaAPHumLyZz/PgxXDp/HgAgtIJxHnWCCFrVqMmLD6nDTjL5DJ9sc5hCMmEZI+gQGfZbfEj5xWxfNFIvSICgmDVClc6xKZuGM7QxgSlorWZ0CuDb7M4+h0VZq8Ag8uEQQ5exWeZypjeL0n56Du6G0uceTG8eGKbQRfdHnKNoQmw77OTVlaNHoy/C1MA4pSMJgqprTLV3exZJElN/VzJNBNA9kdNz2tAFGufsDCnY7ea9+HPYPtM4P+xOGUldOyYpmX6hnLISfCYmTGH5EyxKEAlSaCMNA+FqcIxGo5CuPwY9tfkhpLSb7+ugGMa9YkS9TqGnnnpK6IFCCvdNyQsswKbjif7U5eMnQsbgqipBDi5DW1m/dOKElDH9OWBTowPgjo62A40VvB3am2Qlb8Y7xEt2KD7Mu34B8SHtZjqY5XQK6cvXywy1tKZKQwhIwYoSLnOS0s5ZlFkmeH+CVEGhWtdoeSlmfjpIegjCIw41U7hXTGAvv2FwkWbzwIu6+aDAyVPW/fni+jp84QUDnyjE5QysqiA+yOEwwGResmxWjGjA9G0YQWvE4w6VizsVHxKBocEgOMMxxqAsrS5BygzKuR8bHgTFfDGMVkHXEG9O4Rr+5YTK3INBcKv2+gQTtjjtRo198Fxg/hP2lxP14kNPPfWU0KFDCvcCHeyWp85dF7jzk3OpE1LikUcfBQCcf+edoJwErBnSX1OzrEBVXYebzZSPjxuzSsAWh6Euk+WM0m8hi0NDfGgggu57z57TFvbtk71KmYUwZiGzkDo+UToaDa04WiAYaMwSBacmnmLNOMNFO1JYjB4CKaGTDg1TOMzMYKc3Dx8bAcsnbGq2Ii9QOflW1yWUVqHAbF0r1NIF7sg6iBJaiw7GMD+gKYX1LefEg+ySWfHBPanl/UzynPSS7Z/hj6VSh92ajLeC9aEYipiXUalUlOD5FFj69+aX5CMphZTBfdr2hxiT78iavTAdBhaxf6JMLz701FNPCR0apHDQdC/5uF/cjQGGrvLQI6cfwaUtm2ehno5tTgDnvEREqF1wlFIyJCglrYJW3t6vG/InasGOVb9L0cgOzRcZOuMdms8wzWZyTmfWKNgYBp8p2wZHOXSgWBUoJ0p48aFWNcrKih82fd6sjyoJimnuwtuxgK3k/MVwQ+favI/6x/uBPx56pnBggzr3i4jSqv8Oj6ydxCXn8uyzEQcvW02hUnUtFTInSojEQSfeN4gFfJIn7fmTvQu6x2u6RJHm8+YwnMZFMwyKbfN/YYDKmW6zsgyBS0Zrpl/QLleCYwq1CuNX6graV7cGIctigVse6DQb1AZ3TaSdiRSdzvL3mPYuyvTiQ0899ZTQQ40U7rfqh9Pa2ccwePlFAMCULBz2fgiaDFRIMFqjVnalk1JAaLdSsuIr85BCslZvgw6a+ID/mb1vO4JI+tPy3Na+JKJH+jJnlqAAACAASURBVC6ALR4L2LwLGXJ2jUcGdVLHwbZdvMO0hHboQJHEVuXL1lUh1iJ0py1cfhc0uzb7G88iqZ3Q7r/fvckvDyVTOBBmwH5521xg4INnE1AMRxgdt5aIjWtXofUk5gfQGtpbItjHnikNElGUmH1Si2VhRqafnazt+gPWanmldgepto++gylhvi6hzcpRV1Xw9NRGh/O11phOp0F8qLRK0q756tJGZlgvLVN45+13ceLRM/G51KFF2AczGLdqND6aB4J68aGnnnpK6KFBCvedCSfAYRZFEICjLiPTjSx3cQ1u5TMGxDTpde0iBmWsxgzJF7EWlLIDP4NW0aFNTGhe11R0JpfvTHxIH9lEJ3ajqqpQm4G7ORNse+pcoyulE6TgQ6crEtic2vYb77yDJ5/6MADgxKOPInfh7bO03x4z7Le652hhdw98KJjCPRvnDolhkaGv6zrE8y8fWcX6reshFzkBQXwgnWYgllKFk7yLDbUl99gGvjf1A6lOgt2gcQ677czDZsQB98ym7mL2usZNZ0SeeNwXkCFBMDq9l/dwLMsSZfAIVdhy3pF3JxU2nCixcf0GHnnxJQDA2pkzeOTcudm+O2pnC6bzCL/GNPaZ5tY9T8iw8wduyxSI6J8B+BsArrqakSCiEwB+F8CTAN4B8FljzC2y9rPfBPDXAWwB+DvGmG8v/gI7p/uOEOZSzN1YTiZhEt3YmoLbyDlS0EShPkItZSjQKoQI9yJD3ZXN5poBF5yU6Q06HrEYUoi7ttEndCASnxZeyixWm1LKnuKu0VqjcuhgazrF+tgiiAs372DT5WzYqhVee+sdAMDHz7+PtTNngrnyICmdkg+GjmERncI/x2zlp18D8Kwx5iMAnnXbAPAfAfiI++9XsEANyZ566ulw0bas0hjzF0T0ZGP3ZwD8nGt/HsCXYOs9fAbAbxu7LHyNiI4R0RljzKX96nCT5kmA94YRd7N8AoLDzWRzA+++/Q4A4ML753Ei92XnLflVkFsiFNOqSylZ9mLjast0vP12OoW5Mn0Taczun33MHH1CC1KJj9tefPCxC0Q6BDcppWFMmgrfezvWtcLtjS0AwNVbESkYmeF7r78FAPjoh1/Fo+cex6nHrQjBrUnJiM4M73wRIt5ruzPcvbbxd9pfMLG4rmS3+Ok0m+iXAZx27bMA3mfn+QKzB8YU5tHuB7VtAE3rGWabh1SutsHVy1fx3AtWpp2Ot7BSrEK7Y8NBkaRIC+67dY3MBVEp5tfQTCC7CHMAun0DtlMshtYcBtHlnbi757PnBjOkSsQHz2wB9zt7hqE1NidWfNgYT7HpzJbFUGDszJMvv/k2nj5/HifOPAYAEDkvLhsnfvtP6/fs3N+x/Qty97znuoZu2rNJ0szWOVuI+gKzPfV0OGm3SOGKFwuI6AyAq27/BQCPs/Pua4HZxWkRrfJ8SNCGHLTWeNuJDN/+zgu4eOsWAKC8cwc3r13Dx8/ZXAuDQRFWWm1iXUmlor+/YtmMjTE2eWno25zg33uhaOx8XlPBOE98iOe0iSVacaRQQzEnL37qtKqx5ZBCreO9qlpBZPb8V94+j5dffgWPPm4/1UfOnYtBbGiPf9jJB9p1zXxB8/AoIHfLFP4AtnjsbyAtIvsHAP4+Ef0OgB8HcOcg9Ql7o+3hX/PjCBYDamrL2+n2rdt44YWXAQCvvPtuKGF2dWMLdPc2PvG49bCzWd2Z+MDalbNEZFkGQSwNvDEu9Zjrjo+4nPnX0kxSlH3QKbR7RLYfT8WF2Wemj2kTH3QYi6qqUFY1KmeurKoqRJaub01wd2xrSGh2X6118IDcnFb4youv4OmnfxgAsPboGVAeMzwbNnDU+Ju+8c59GrqZTIuu4T5JFYuYJP9vWKXiSSI6D1s78jcA/B4RfQ7AuwA+607/Aqw58g1Yk+QvH0Cfe+qppwOkRawPf7Pj0M+3nGsA/L29dmp3tL1ycP65ix7tumJ2dXv+Oy/gle+/CQDYHE9QOU14bQzWN7fw2vsWRD394XMYupoExmho4xGAieKDUsFnIQoMTOmV6L/8WsyFisWSqPrtJpmO/fOuYVcu4Nsw04AxBpqlqfNIoXRIoXQFZMqqDqLExngSEIEGQoyD0gaCKXAvX7+D7znF76OPP4FTZ8/Cnx78QbBdwZj5SsfdSQL3X354QD0aF522u4d3847N+8l0rXDx4mUAwHPf/i7GzhW3rOtgXtMaMFLi7samPVZWGA4H7hkUNOnaWEclwOYYyENuAHLl5aKHo5/+KaiNiUTiUXekhTHEq2b3cZp1NkqPdsgSc8UH/sjw/lpHEaGuUfrKT1XtGENkClteZDAsl2P69MBgDAjjssJfvvAqAODpH/4YVo8fB2BTwYe8CwbdTmJ7pIU0VEysvJfUB0T11FNPCT0ASOGAWPU+PsHAoPYhuu++j+ef/x4AYH1rKygXa6WTzMSl0qjdKlBkEpvONXdQ5MidmGCVjtEpJ3FkAiK4NRQVjYYaL8RXmgbWCX/aEUNydUecQ/MJM8+cixrY9b4vRrPM1lVwXy4rLj5YpDB14sO0iiiMgLC8E0u/RgYxzR0RZFZgc8v6iXz129/BiVO2KPDjH/0opEcKfDmf85HQHKXjnhWFQZncDGI7ODqkTOHgGcEitOiPoJTG++cvAgC+9dx38Pob1nNuWsWPulYaU8c4KqVQKRVMZ7fXN3H0yAoAq3sYFjYycDgooqej0SFNmSA78YP1AQ2RIYgV6bskOoi58n2H2DBPbzBPnGg9PX1e8EhUKrxnVTGRoa4TBjF1egUAyKTApJztPzfbAhRqSBIRSBCmTvfw3dffxtMffRsAcOTECRx7xDIISYgWpzmixOzYLkY7YhjNKlgHSL340FNPPSV0iJDC/UEHe9UQa21w6+ZtvPZ9iw5e+/6bGE+s0quqbNowwCMFl3FY2xWoYiHSxkHjaaWgVPTxHw1t3D8RhZUyzzIXKengN1FQSBKlK1D7qPoju1M0WjTSds95u0zLsRYfhLoOCsSqqlEmIkMqPmSsOnVw+ebvYQz8updlMtaSFDaJq8zt2G5Oazz79ecAAB/60Iew4pSOVOQQFBFA8s4dn2uXKDFvoV/4G7xHaOEQMYWDoe0g2m6H2If2K6Xw7vsX8b3vvgAAGE+nmLqPWmsTUoPVOsrKyhhog6BTqJWCdB0dFjlurVurhNIqyMGj4QCjAcXzhWCMgGFbQwi5ARv6hRTgLiYKtAdUpTt2wyB4jEdVx8lfVZEpNEUGLkpkQiB3laQmZZXkWgivb0zYkDILCVuIhLMs2JHQlOHC1ZsAgG8+9zyOrNm0ecdPPwKfc9syYEqf0TK2kQ21vP8erY2EKDIeJHN46JnCQZBhZq/r127i6998DrfWbU2Hqo4rvUGMflTaBEZS1TUMCBM3EYy1wwEARoWEXrHJWG6tbwabu9ImMAitDZZGw5j+naEGIgqTgojQGv3XoEZoTvh3ZiKnJzVPaL+GeWd6vYFy1Z0CItIxr2LFdAUlM0Ny/YIQhDyTkMJNamNiebikEyYoG4UQoYaGZw6+b3meo3bXfPm7L+HjH/shAMDq8ePICp841vm0sjTxXRM81PNoOJ/bSd0+dDtWSB4gauh1Cj311FNCDz1S2E9eGv3wDaYTqx/4yte/hYuXroRVXGmdoAO/Umsd0cW0rGBgA3YAu6IIt+qR1lhxjkxKadzdtLkBKqUYUtCotcKwsOdlmYQkH1ZtGIKI7c7sxX67Zanqyohkx2JmcMIYaRMRTagFqTW0imOkNNOdMNRQ1916BN/HIsuRSRmQh9EmiA/cEmOMCeHmQsR8lyTSGp15lsXYic0p/vxr3wAAnDrzKNYeO+Out3qIGK6O8KQmCvNjY8hA8HMaUt6eP84DEiUeaqaw26HiprfwA7O7lVWNr3z9WwCAN958G2VVRddaExOD8Cg+K0r4LfvpVqEMmkb4tFxFZQBYGQ3CPTYnU9xxyUO01lDaYDK1svewyENR1kyKoIDLpAxFbPkksGJFhzvDjLWyXafAxQFtNPPW1KworE6YZYz41OkxxvAUS8LKRQYDg5FTDGZOdKhqz4h0Mln9exERYwoiVJ62HqGU/M5+zCiTePUtmxLkpRdfxqed+/lo9QiyIgeJcPc4KHNslV6UEDsQJXZMvIT2PiyDvfjQU089JfRQIoXd8MqgEEMHUjAmQNxvPvddfPVr3wQA3L27gVorKLZSKpYPQbOF1t9VZhIVESpX1WhSVom7u7dEGABHlob2vsZg7DI13VrfhNIaWdC+lyhcXEQmJTIZzXB+BcyzLCaBdStl8v6Iz5/ZiaZY4BSFfKVPRIO46qs21OC8O9Pz2q/xaGQ0KFC4982EsIlsW8LNBeK7WeUiQwoMNVmkgDC2g6B81Kjc/me/8RyWV5YBAB968kMYLS9j4DJyyzwPKITns2iChvAMF58i2EAHoMBBB3a51hNr7FGceCiYwl6GwH5QESZbQ4Cb4MYEObiuarzvAp2+8tVv4PaddQBwWvOoL7BytGuDM5j4tRTFACqfQDloPC2r8DtKKcK9QIRBbn+ilWFM2bY1KXHj7gZGTjOeSQnpGEEuZTDVZVIGxpFLGRiHlHaCiDB5YkVm4bz9fFuzycq9Dm2Sk7it28SEOZOd6xHaRAsAyDOJwr1/kWWJiMR9EzhTIP8jwjIC0RAZbFu4bTvMdV1j4MQEQRJC2GfevLWOP/3K1wEAz9xZx9nHHsXJUycBAMMOBkHADMP1XeJmTcHkt30VJewLxofugnrxoaeeekrogUUK+4EO7H1Mgg6U0qHuwq3bd3Hjpk2hdv36Tbzx1tsAgCtXb6B0/vlcvABSfwLDgliI4goiMwmR5TDVGAAwLquw0soig1FuBWQQ88jSKKzmRlsov75lrxckAlIQRBE1ZBEd8PYgzx064EghQu7QpmgVSRSALjhLqdlgr0UQQFQ0ziKKIs9C7EeRZwEpFXmGzK3GmZSYllWCzrii0dXYgRAyKhcFBZ8Fi5IEC5iKtTbyLItVuQYjXLpinZq+XH4Xj1+4iLNnbI7is2fO4NHTjwAAhivLGIwsasiKHEIydIJIhikeAe5PkooSwfkKs3Qv4iUeKKawV2TFxQIuT6paYeyiFK9ev4GLl6yYcPXqNVy7bj+Kmzdu4s5dKzJMyjKJ2ScgTCRtdHSdYcyGRNQ/GyLITEI5ODyp6/BRCjGEcJPFmJivkQAcXRkBsBMct6OYsjGeoBq7orQygj9BFHIwWKZgn1fkmf34W5kCBfNmk1l4xuWZAq9kxV2zO/UIDWuD7/8gzzAaFKHtGcEgz0Of7WSN5r2m+BA8R2tl3Zjh9Qh+gkadgtepcB2DZ14c+g8HBWpYseL27Q1sbozx9uUrAIBzFy7h3KOWQTx25jTOPGrzbS6vrgSxIh8MYp8dg4g6hjjBBTNVbqdTSIwM8zzSPO0iIUQvPvTUU08JPTBIYS8WBduOykRtDOrKrgy376zj8pVrAR1cvHQZly7Z1WB9fSOs4Epplhsh9T8QInLjBDAyts8VeKQIQmYgZ3cf13VY6WAi6lAMFsOYACtXl0bWsnD7rjukQwGUOxtbjf5YyrMsKCCLTKLIc6aZj4hAcqVjA0F4+K5cvUvfZ55Zea744JACjO2DRwfDPMOgiOhgwJSLeUAKMvTXKmZ57ISyCAHAeFo2fBNSiwPgRAkRt7llCYhZs8uqRuFEmRoGm+Nx8Jv4/uYE716ySczPXriIx05z1GDFiuPHj2O4YkPis6JwCAXhmcQ0jYKLEkxG4N89d6+3ClVm8djHFFGHmikszgjSqL02hyNjEOBqVVZ44+33AABvvPkWLl68jCtX7A+8NZ6EDywxr7FswAYA12RzxyQSItSF5JR+lAJFkcMYa25crzXqyjMfFSwGyihoRIgcmZzB0nCAx9ZsNF8m44QxxuDm3Y2Wa8DMkzKZcIJEiCPgegRBgrWjriKTIg32qmvULVYGrXQw1YIQRIFBkVtG4MyAg6JLZIiWFMkSpoydSbRiOg7fLmsVTMJSiJhzoml9AEUrj9YN5y6E8fNfYVEUkCsZJi4CdjqeonQLy5tbE7x72dYuOXPhIs46XcOZR0/j3FnrEXnqkVMYLi2FWAr7PL9IRVmCDMD9o5r6BZOIrdFixr+tvdJuC8z+zwD+EwAlgDcB/LIx5rY79usAPgdAAfgHxpgv7qRDizECriiE46gtvgWM1da1wi23sr7+xlt4+eXXAADvvvc+pmWVMAK/6iXmRc6lhQg/nP1pU1+1YJ5SmjErSs1WosDEhVKPVY27Tml44ugKBu7DEcT8HLgMrQ2EQDDXnVk7znQHWVAo3rwbkU6lVEh0ujWZQgoR/RmY52PKFJhHIFFgHN4Eyku1RaTAgpOIMMjiqj9soAH/nsM8C++SZxKFY16S6UcM4mrudRo83NozqI2qwtTrKoajoF9Aoli0vhbRtB9RHDTgbgsCUCZMNQumy1oyBlFVyB2DeGfrEs5fuQ4AOH3xEs6ct2VPnjh3Fk8+cQ6nnElzsLQEGXJumnTVD+gymiqtNTP1XPRLj9VVuG+TubbvlkHstsDsnwD4hDHmUwBeB/DrAEBEHwfwiwCedtf870SsYGJPPfV06GlXBWaNMX/MNr8G4Bdc+zMAfscYMwXwNhG9AeDHAHx12+ds21WGDhrORq5P4a9f3ZXSmLiUZ5euXMWrr30fAPDqa2/gxg1XrcnFLQSNeQMdxPsi4EqR9NfDOQY/uUyrVTgvmBRFKgcqA9zYtEjhsaoOK7AgROsDt1u59xTBREc4dWwVgNMXBN1Bhk2f8IWtplprTMsKU+88VdUsRgLg8RJtbY8a/ErOEZUQFMWErF1XMMhzDIsoPhRZ6nDFXhKcPOqpXbr3EG6tmPhgTMiBMC0rjJzFhmS0PmitAS7mEYH8+khR9KtqQErXhxLQmU6coYbDYRjbrYn9/SQRctev98dXcNHlaXjnwiW8d+ESnnzCFrX90ONnE9QQLCYgaKZTSFQFxkQTsW58gQwdE5sozfqji9B+6BT+LoDfde3/v71rC7UkO8vfv6p27XNOX6a7M5M4SSYzyZhEA0ISRH3w8iBIDGoUXxTBBIUgJmBQkWhe8pIHFRVEURTFC1FRVJwHBS+IvphEMyaZXNTEODPpnr7MTPe57VtVrfX7sNb6179q1z5nn54z52ylPug+tev6r1VVf/33/zXwTCIiNpi9T6xmBDpjUefmT6Yz7O15NeHuvV3cu7cLAHj62et4+ulnAADT2UKJnjbLbHSq7kEX2bRGukTv46WdjEnit28Q6ze2rb+5FMOOUeLFUKJ8UddompT9GKVaHZ0Xz2tMrDkI2Xbt8iVcuuBfhIs723ghRF7G0GQ9Z9FoNpnPpZiJjm4sTK4+aLVC6+iGSGwvs/kCW/HlrxIj2BqNlhiEhC2XRZo2Xma38U/GBForNDetxWFQjVoQYGJFptSNemRMppc7bUfoMAi9H3dCuzMjphhnDbbGiUEchns5KguU4Tm7Natx58VdPBsM2o/duIlHH/GvxqOPvAYPhcIu451tYbaMZFOgTuTtUpvA7IORVsWI3JPYIV+SVYKIPgSgBfDR+zh2aDA7YMAG4r4lBSJ6D7wB8ts5mbhPocHsaulA6hS0yWi2fzjB3t6+SARaOrh7bxd7u3t+v4OJlElr2lbF7btMTUg23c54sVrF0etJ/dBf98IUaMl/NRwzChiMgvhcGMbEeqPjvG6klmNZbIlxjzJJwYR/yqAk3g+WiLqHH7yKa5e9WnEwnaWvXlCx4pjrpsE0BG8xGGX40nqJJHwNO56IbvBP3DabLzALX0owJ5Wh0uqD93zI1445n9tM6Ar33Dm0NqkPbZu6Qi3aFov4bBAlA5sxoiKNd4wY5hhB0ozOB7gUfEYGcU9LhChAGOPnSydbiWpljHyJy6IAV97V2rQtFqEZUJQarj/rJYVbz9/Ds8/5DmFfUVLDgw9ek0ConQs7Um6e4nwodbavwxYjCQ2sXKDrRTqFMay9pwIRvQPAzwD4Nmaeqk1PAPgjIvplAK8G8EYAn1jnnNwz2C4jmIfCJjdv35EuTPd2d3Fvd08YwcH+AeYhm7BtrdgKmjb51VvrxIXmQ5FlyteDnvlIKPVviqI4KVHci64lqvDwtHOLRdh/92CKi9teFN3Z4ixmYDmhx6RrxBeWGY7jMSwqhneBJpdm5rFhSDzBvK5TPYYsoSiNiUKKXxYNEcfvUj2I6WwuRWy3RiPxMJCQwOl3D8vVJgUffh69HS5EVAaVpW3RRkqMEfXBlBW48NecTGa++5M+f3zOXFLeyQCi1KtB5x+OnBF6BhFtEkmVK4tCjlnUDRbUoAz0VK3Fs+F5fu7OXWEQj772NXjFdb985YEH8LrXvhoAcO3qFRCzuJ41I81NL+kJJMrpXRf322D2ZwGMAfxduNjHmPnHmPlzRPSnAD4Pr1a8j5lt/5kHDBiwibjfBrO/c8T+HwHwkZMQwUgSASvjUNsq78GtO7h+wzdcuX7jOYlAnE59lJldEXAkqc8q+Mg6p75L9xMJlssD2iAEIGtlnn1NTPqy60Ki7WKBUfB/3zmc4JXBk9C0FuNxoahcTasuu2aUJ0Y8ISoC0NtFdVwHoyyTmC+Gqkw66PxP6rdaBjs4UZkMdkLUom1biV9IRCwtLv2KP3V0ZIymnAd1cGYZVr70BUys0DQewwapyQJimJRS7woSg+JcGjhlspDMX1xhlOSmjcAi0YFE6mLH3ktRey9FXZao2uB9aVv8T1Dfrt9+Aa972Ac/Xbt2Df/z7HUAwKte+RAeee2rcXnHG5EvXbggqmU38lH/SrQc79+L2IyIRoaq0Wcxi4zg5m185YYXpa7feA63Qvjx/sGh3ODIBHT9wizgSEqjOSRWcD+MYDXtUDElUTQW0DJTKELIbmQYTAQEsfLedC56cN00InIbStl/8XpKGcgj8pT4mCLgCByqtyx1esp+F2J7kPF0zpuGpZlEPHfay7sxIfRLgFiwDeh5yllsgnhMVCh1E1SHWXRRAqIyFNUYReXVLyqrbJKi29KU5Upxmh0nlyDltPgu1MvKexbkpdREUjHLsRZEPHXTttI8tx2PMR6liNAvz70Z7taLu7j6gP9A3LzzPJ65fgNXHngAAPCKq1fwdW/6agDAuKpk3vStzO7RWXkfBgwY8P8PGyEpMDP2QwOU527exvUgHdy48Rxu3/buyr2DAzSqF2MKRfbx9VG0c8o4mQUfAXgpEoL/MCxLGj5YRO3XFbnDNh/wo8KKi0JoJmOkMQzgi7QCwMWdLVxQCTA6UWYViAzyZjDaldM9QdIT8plJxiwxwOlr9B0jp1b5BtamcPDMW+ENxxKom4m/+eDy3JOUHj1vGsxC3YnWGBQjr34V1RaKUOWaTZGGyABHVcI6X30qTVo2Dh0Ilw1R7ecTsoIRHEkSMJzUN+2hQJAmRsG4TMZIMNZ8PhdJuaocqvgs33OwC7/P4eEMu3sHqCovLY9HFZ4Jxva3vuXNeCxUne4+G0TLz+xx2AimMF8s8PFPPAkAuHnrNm7fCYxg7yC1EGvz/P1oK3CMLHss815kVzlFlSE7e66tdRlEJkprq7TST4uyhAsitStK7IY8iCsXd6Tc+RaSWOc618n8AJQ8KUwsXaRAKdKNOVYy7psTnb3Xsbx0GETfS6UjNYkIzqblzIVJpDJQbedmpR9iE1Lehsa2mDUtFlE1KyuYoDKU4y1hEBakxsyKKViURZEVw8nmr4f5pjGnjcn6z5LQVpSFMAs2DF6yNfjjfWBamo+oUh1OamyHSMlK2zcYIMtoCv/BmBcGk1Df43D3ADcefxQA8Lav/RoJHsvu8An0h0F9GDBgQIaNkBQm0yk+/dTnAAB7e/tYxJDVTDpQxkRlQIwlz7LU6TyIoPM34nQkB58Xn06XcWdOtBClrD9jTEiR9r+r0Qh1GOd4ewd3Zz71+eGmRV2n1nJLRnHuH1sSc0m2ZUYyycrjnsOXDZbpvN29+o1Y8WdRpAAlR7lVfqnyUdZCL54pL4gbRey6sdi3DFuGwrXKuFhUY1AxknGJJwaq4nOY/3g/dFFa0vNHqjdEUJdSWjWJjMhIckPTtJJ9mmW2sgmh0TI0kRyrqhJJwziL+WIh57JKonQupdXrdPlnn35GYkOaWY3HH38MAPCqa1dkKs0JnveNYAqL+QLPP+8TRxqbCmboKsE+dRmynBVQUeda3/Fy/56I7jVWuSRJ7a1z3k1RYL5YoCy9mFtVIyzCg2CpwCSM7XA2x6VQ4r1uGmyV+e3SLEGnkqeXD5n4GYkkZv+SQBPawyAon52V7IK6s5ie/KJQqoRiCqZHnQB8tKKTF0F5MrDMfPT18sCuLr3IdLkilIeXwLDSpA+OtfrN74wzV5OY0zwrZUKC5IqCEdO7IoPIE5TSMxhfdnIp96S1FoeHoUdp02BrXGFrnBriRHdnYQi3bt0MF3LYig1sqi1ce+Cy0LUuNoIpOMdSW6BVXFvXMPBhuX5/z5n7pIEu1pmIVbr1MUfFD0j4r88lybIiPDhhfWEMCtX2rCxS+XILBxsO2p8tcK1OmYzb3kXdydLs/kjwEY+KUJ2cz9xxRfYzCNKftk60RP6S6m3LBJmikP2j1CD5SB0GkdkalilU9PSDe/ibtmYy+2vGpLitcSX1FBylsvb5HHPgTEnjjkY8z2+1GMVCf/x4+UhGJ8zDkO5DkQ41RDDRDW0IrSTRtZizQ9P496QajYSRVKNSKkRNphPcDAzi2tUruHzZM4XLoX/FOhhsCgMGDMiwGZICc5brr9UEp9WEHqswVq86AdaUFrS8LlL5ei5JLdYb4/s9SoQfkUgKzjJGwaU2aRtJ4lrUjXgiRtU4u06Unfxix5KuTemySHlgUzeYKZMa4mK/ghDp7xEaMlGJwCBJ9SbvQVHSgbaXiCjftqm6L37RoQAAFKtJREFUkAoE0g4CmYG+B4A7Fbo6ulEU06ezOS6ESEG9C7vUo5IBwDHIRBsNkuuVOb8f6hzRDsLs8x6MqtsgHaZUopWGKQqMOqn3WTUqNWiRqpyVhK7d3bs4uOqDnWI6/TrYCKYAcL8REf0v+yqx8MgdT8euuIqCzLgoiz0Pb0RRFLB1yvpLdRcI1bYX9ab7d3EQMg4vN61EcVZVBVJCXtemkA07RhoiGcbCFuQxDP1j0+iOpc/dtcw6elQJ8gZIR6mseiqZb0U1dCBxQ86bNstkzUnVo+7OQBojd8YonbDYYDrzbuDtre3s2KjKccgq5UxvVEbILB4m7pLG75xDww1KDlYGVUwmYwhKz2ROtpbRaARdNKUoUq8PZogq5N27QUWBw2Lu8xVn00Osi0F9GDBgQIaNkBSYU3kp/9HqM1cdIREsn7F/1ZHSwsm8EasMjdkZNMna0gjAqJ6R1jpxTzpbwMZ8fhAOQxr4ommkxHzVWpRVt/Sl+lIqN5whzfe1oTGpBAwG6yjI3vPqkYmjVVav8j7ka9XxxkitgMbV6p6TLDfWYR5LtzctJguvSsV1qx6IVVJTPqJ0cwwZSaVfLBYYBws/kKo1gV2mzhqkBrVZJzA1E9zRK5lZXOwMoFReIqOPl2nS1/MShUROFql5blGkeho6D4hdClgrT/D53wimAKhHul/iPN2LHMsc1tc1ujaFJU9EdtpcJ5bELfL1FQAv0tr4iBQlJuEFmM5rXJLyYy1GVZV02u511LLQkumsJIxBCBIu12W+fXPRuW7XjtKL/MYSVGYsqRJogMRs1NZhEca/aFrsh/Dv3UWNOZKNYUlb0HXRM7Gel/cPiGJ5o1S50Ui9Huqlk5+JK2TX4WycihErRqI9K2VRgtVLu1RqDX6uDFKCWDUaZSX3xXulShM6ZRM5SZbkoD4MGDAgw8ZJCssM7WUQG05FleCeJazMfVhGqtzjODW1JUpfk9HWNmYhunFa15IHUrcWVWtRjBRPP2aalq3buZiquyXlgzj6vOn8x2zQXpHwMzZt0XbORWtRhy/dorWoQ7WlWWNxGAyzBw5oTQkKEY0xbfpIcpmzO+YvrWhSnqFYLcqYncyYF45UNMflPA+iT+OiKEIqKSaTFhB7XaRqTdk9C9eQ9PPSpkY5ZSkxC4VJ8S+xQG9cXhcbwRTu77VfLQqe+KIr39yce2RSasem0HtEV0IXmv0euqlpKy5ZK5Fq2+MtNIEpTOpW6ixsNb7EeRky7rSPTofz6uV4nV4o9YF0aG82Nl6a8j435DreB88QbCrFzywMonUstpPaOixssinMYjVnIlA5knDmohzBFLGxSurWxRkjWCalb5uuezGZTnEpBP14Bk4pqcrZ3HahfvTVToy2guiS1PYGZx0Se0jRjX48iVn7xL9Ud8SW0d0JjGLItwqEc+yyjOF1MagPAwYMyLARksLpomsoW++Qo6UFrNxhXUOjj5JV1npK5cFGZYmWk/oQQ2ktA00Qjadti1lIFNsJLdNGMZhpVEGJCitHYahf6vHSjjpexy/ovZS6gXxLv9VxiZigLrkW1jEspzZwi9B2rXFO1IdarZ81FjObvDJlMUIRvo5sCliRvlxSZZQElP0fVYeOShB3kb6cjjEJaeyXLl4I+StKREw6Q++5tCdBit1GNaXzPdaqSAp/L5RxmjLVpLUtrBvJMXlgXAyTLv7vqg9r4YgxnYrV4VgzQuIcwY7ce/0joxvVEcyQclxACkoZVyPJrW+oxGjsg2n26hmmwT15ubXerhBE62LE4t7rHdMSkZQF1mQgSBw/Z56Io2f5+GrBBOtSiXvHSU2YN63UW2xahzY8wLV1mAbGOW0a1IEEBx/wZWNHQkcpJcGxtHD2nb5jwZicGs0k0LEvQL1gMSjocDLFzvaWSrYigHtyJDpz0U1OyzNF8+v5k2WWj9QZnMxS3ZDUKTu/dgzKKopCqnk7t7zvKhyrPhDR7xLRHSL6bM+2nyIiJqIHw28iol8loi8R0WeI6O1rUzJgwICNwDqSwu8B+DUAf6BXEtEjAL4DwLNq9XfC93p4I4BvBPAb4e8p41Rkg9WnPk6V4B7BQkvfnd3T9mRo9OJfEO3qGuOQ79C2VrwM5YUCF3dCyHNT44Wpt4pfvdxirLouj5zL+kPkVlB9/SSK+mXRB/JhimudlEFMn5ewSrVYnrog1jubgrVCG/mYy1G3bZIa2hZNmJfaOsxia7umxTzWDGCAUMMEiaYoR4ALUoNhmDivxsgyqTZvfSpmn9GRKJXQq+sazIztrbFs01KAbueWngn1i/JtpFQJnaOiY818+APL+rIsk6TgOPOEaJC6zyJpnKAD9X01mA34FfiGMH+l1r0LwB+EjlEfI6IrRPQwM99cm6L86kf8ehlxrCqhdw03Tb87PYfrQh4ML87Fsuqz2UxuHoGlE1LTWsCEmgvbO9gNcez7sznG4wpVrDvRWhGTaY1yGkTdB2SFHYCAPAstX17lhZBISWIp/uHYSdRqa13GCLTtoG4smjaWXXM4DAFLe/MF5uKtIDi7QDC+oxwDJjSCJWekxLxxLltOtQxSr0YZT599AellKrhAHdL7gVgKP5xHp3h3VIecQejtiikr5pExC6QXPnofpAFvkRgGdxO/5LSq3+dpMoU+ENG7ANxg5k93dMnXAPiK+h0bzB7PFF7yG8/Zn7i4xnt99ClXqOrdCDF5QdTqxrbidrSqYEw0dMX6gwyIdOALjvgbOF8sULehXLkp0QRt78buAaqqkg5TY2tROH8rLTlpXBvPl36gf30Gyl6M9HElZYAMy30mCW117NTDiN26IkPQTCFKDYu2xSLq8bM5DgJTWFiHJjIVB8AArfPbSgbK4J0tikKaxRolKXipIdgajPOSwzpPB6XzMiDtCom8gdgP0+Q6e0+Rm2RD0C9/ugT1vMg6G5QRGRancUbmphK18jIZlF1/XZyYKRDRDoCfg1cd7htE9F4A7wWAnYuXXsqpBgwYcIq4H0nhcQCvBxClhNcCeJKIvgH32WD22kNfdXI5YU1vxAk0gRNcm5fOG9foxqet1Z2rrJIMWNYB3rIs3YuMwTiU05rN5ziYeJeYgUMbyn3XsChe3IWL+joIV4N4uEUGLljfi0wT0OIqcot3B/lXZckwAqnxqHMM1C7xeGuTpGBZByXZJelAbAp1g91Q7v/uwQTzmBKsUuodU6h14C9pFwuEau+oqio12VmlSrCXIEhZ9nuH2/ldFoXMTF03Ms6iMFKazc9Bn6SApELgKEktPVOxliUQpAHHYJNUBtEICBLUpFUJv6DtFuvhxEyBmZ8C8EoZANHTAL6emV8goicAvJ+I/gTewLh3//aE00XPo32yA1ccxGC0bZvy2RVTsK0V+0CmPsDnAeatxvytaK1D6ZLKMYkht+xQB1Ha1jPYuk62AWPkAb+4w7gUCoYYZ1HEW5xpEbTeQ6IeqtxWQliyMajTZR26whPatFY6NDWBIWj1YRKare4dTnHvwEdx7s1mmAW1yjnVISvMYKTHMcMGfZ+ZpZu36RodxUjnMnWCjVM2mSNeVpWlCIJ0B98aj7OCKXr/7rIuqps2K0OjEvm1/kAhacyJCqRUBudS3xPnwKzU1G4NijWwjkvyjwH8C4A3E9F1IvrRI3b/awBfBvAlAL8N4MfXpmTAgAEbgfttMKu3P6aWGcD7XjpZ6yMLQjnBMSdWJ9QlvCU9WNWtQ93UqbelbVXPRCuGRscu9x1R7AHoEb9AzKmUubUWsxCwVBJLoo5rW+yBcePuPQDeEj5WnYfi1+jSzg7K8NUwKNYzNKpAHu7ss/xLuSizU6QgpUbSoNuOYTEZGg9nc+wdepVh73CCeyGKcFo3aFQUY7Kw9yUp+R9106RiqWWZpDEVKWiClOBM+M3aCKkCnvSgw7XEiq88Bou6xlZ0VeaTvKQu9EkP2RR2bgspCcJ7I6LhNrmh2TBq6T9aq8LHqXtq16tyFDY/orHPgXzsfuuf9ijmEF2HgE9U0nnq0dVmnUXTNOI9yMQ3lyckZRmDykpMhNSdWFmSy6KQqEdHJDfeMGNat3D2QPYbB5G5GpXysBARimAhL8vRkmicFQSRlUoPPWJeutvl4etU3ZauTkveBov9YC/ZO5wIU7g7mWASC8vYVsKXu/F4+e3OazzGe6arKetQYGaGIwcTXYorGYRZOQmF0W3nIN3Rd7a3UgdrHMUEOjEMPUx6aY1yWejkKOdSBey6bjANJfx8s+XlLljHYUiIGjBgQIbNlxT6cIpRTNxZ8iK/bmufGpxG74H3JCRJgV1HNYjQEra+VviRuLcRo2GhglLIVJjNvTRAoxGiudn7/B3m4Utxa3dXJI1xVaUvlSGUhQ94GpUjFEU0OiovBLqLXVVgaaKCJb1fCXNQIiuzqE+NdVKxe940XjqYRJVhir2pp3OyqDFvg3S0FArR+dKKKG9SEdZOP4W+vhWxq5eI4nAwziT6aVm6IEPQZcEIyehIyuMyX9TSsCUXEulIqSHbT/kf9GJnBpIUq9LdF3WNWShCa63FzraPc7kqTWGOx0YyhZPoP8vHnvwI26aQYd+8Nr7wqjGNblvnklg6GpVe91YiY2pUo6zyWZsx787T44xeiu3RlqgfxphUL5CR6ie0LVxby8s/r1vcurcLwJfmev1XeeeQU5cfj8cS7BShH8beedNOBco3dHon5YlgEs6s6iRYK+Xq9w8n2D+cJjvCdCqdtmdNk+ohIC/TJiy0MIApfFdpBEZgks5OSn1KTVx77ASdEaVxUFoXX0ZxwaZtySVZAqEiAjFJgFNs0JLNU49dZxWDCNxXfqxSMBynwDBmFpf2uBpLderiBG/6oD4MGDAgw0ZKCn1YLQEsyZhrnS36e5umQdO0aILImqsPVvUYTFVsjMkTTQiQasAr0VElvONfKS9K0igkfNapRKlWPhpcGDjjE6kAb2WO8Qw3g8QA+GKnkeaqGon//uKFi4qo7lJOc1e9ihtoedXSmXRl4dYlGg8mM+xNJtiPXobFIgtS0tJBlx7Ae1hMWYo6ZYzJjKu9iUZque+0/UqTmoA+dUlZimMNA8cWjmIRWItq1H3F+o24ff0/NV1La5LNEUAKwCqLQoyOpWondxJsNlM4wvNwNB9YsTWsbtpWrPqRKdSKKUjkIXPn7i0/VIYI3Ek2ScqDLmrOufcBOSOIaK3FdhWLZyT1gQgoot7rCjgCXDiwWSzggpV+fzrFKDygXo9PkZKxZNe4GmeW9bxNTEeLzwwhnT163rGuSiLMt20xCVbxw9kMh/OFRDS2zqV6jV0KlK8u1gYoigIm/ANWMwV06hf0j7DfHNQ/xJwxxPs3n80kua0sy9RdnEk+MEXRKclPvdOXX5M6bz60OSgxiMIU2NrytoMLFy6I23Q2n/fWXDgOg/owYMCADBsoKZxeLIKGNibWTaMkBS81dDscA1gpJWixTgezeBo52083eo7wQUHan59kitZaGWdZFKBgHGTHcByMSc7BGhJJAc6hbaIqAexOfJiwZWUodSxZfdvjcVaVh0zWpuWIOVCSDedr5XtutJU9qQKtdZiH+IN53aC1VlQu63KVQZa1zc3oRiiFb5sWxlOoMG+jvq7L4dz932ZW41kSTld9zpGkorIsJU7FOifGzW7I89Z4LOpUdp78pNl6mVeRemhp11E1Ei/DzvY2toPUcHg4wUEw5gLXVg+kS89JghpeLhDR8wAmAF44b1oUHsRAz3HYNJoGeo7Go8z80HE7bQRTAAAi+jdm/vrzpiNioOd4bBpNAz2ng8GmMGDAgAwDUxgwYECGTWIKv3XeBHQw0HM8No2mgZ5TwMbYFAYMGLAZ2CRJYcCAARuAc2cKRPQOIvrP0EDmg+dEwyNE9I9E9Hki+hwR/URY/2EiukFEnwr/3nmGND1NRE+F6/5bWHeNiP6OiL4Y/l49I1rerObgU0S0T0QfOOv5oZ7GRKvmhDxe1sZEK+j5RSL6j3DNvySiK2H9Y0Q0U3P1m6dNz6khFvU4j38ACgD/DeANACoAnwbwlnOg42EAbw/LlwD8F4C3APgwgJ8+p7l5GsCDnXW/AOCDYfmDAH7+nO7ZLQCPnvX8APhWAG8H8Nnj5gTAOwH8DXy0zzcB+PgZ0fMdAMqw/POKnsf0fpv877wlhW8A8CVm/jIz1wD+BL6hzJmCmW8y85Nh+QDAF+D7VWwa3gXg98Py7wP43nOg4dsB/DczP3PWF2bmfwZwt7N61ZxIYyJm/hiAK0T08MtNDzP/LXPoFgx8DL6i+f8pnDdTWNU85txARI8BeBuAj4dV7w+i4O+elbgewAD+log+Sb5HBgC8ilN17FsAXnWG9ET8AIA/Vr/Pa34iVs3JJjxbPwIvrUS8noj+nYj+iYi+5YxpWRvnzRQ2CkR0EcCfA/gAM+/D98J8HMBb4btc/dIZkvPNzPx2+P6c7yOib9Ub2cukZ+o6IqIKwPcA+LOw6jznZwnnMSerQEQfAtAC+GhYdRPA65j5bQB+EsAfEdH65ZDOEOfNFNZuHvNyg4hG8Azho8z8FwDAzLeZ2bIvhfTb8OrOmYCZb4S/dwD8Zbj27SgCh793zoqegO8E8CQz3w60ndv8KKyak3N7tojoPQC+C8APBUYFZl4w84th+ZPwtrQ3nQU9J8V5M4V/BfBGInp9+Ar9AIAnzpoI8ulnvwPgC8z8y2q91kG/D8Bnu8e+TPRcIKJLcRneePVZ+Ll5d9jt3cib+54FfhBKdTiv+elg1Zw8AeCHgxfim3BGjYmI6B3wjZe/h5mnav1DRFSE5TfAd2b/8stNz33hvC2d8Fbi/4LnnB86Jxq+GV7s/AyAT4V/7wTwhwCeCuufAPDwGdHzBnhPzKcBfC7OC4BXAPgHAF8E8PcArp3hHF0A8CKAB9S6M50feIZ0E0ADbyP40VVzAu91+PXwXD0F38XsLOj5ErwtIz5Hvxn2/f5wLz8F4EkA330ez/o6/4aIxgEDBmQ4b/VhwIABG4aBKQwYMCDDwBQGDBiQYWAKAwYMyDAwhQEDBmQYmMKAAQMyDExhwIABGQamMGDAgAz/C8O9Korw2istAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1000: 0.001196705037727952\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 1500: 0.001184494118206203\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feed_dict = {x : train_x, y : train_y, learning_rate: 0.00001}\n",
    "for step in range(2000):\n",
    "    _ = sess.run(\n",
    "        [optimizer], feed_dict = feed_dict\n",
    "    )\n",
    "\n",
    "    if step % 500 == 0:\n",
    "        l, predictions = sess.run(\n",
    "            [loss, out], feed_dict = feed_dict\n",
    "        )\n",
    "        print('loss at step {0}: {1}'.format(step, l))\n",
    "        plt.imshow(predictions.reshape((img.shape[0], img.shape[1], 3)))\n",
    "        fname = \"image_seq/%04d.png\" % seq\n",
    "        plt.imsave(fname, predictions.reshape((img.shape[0], img.shape[1], 3)))\n",
    "        plt.show()\n",
    "        seq += 1\n",
    "        #plt.hist(predictions)\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 0: 0.0011687794467434287\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 10: 0.001168742193840444\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 20: 0.0011687043588608503\n"
     ]
    },
    {
     "data": {
      "image/png": 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fXVkKbXc710dqTfhORgBeJAZBLBCCoFykImADkjwjmJYlpjM7Nttliakbp+msTMQNzjAqpVF6K41SwdSqtIlmV8S2H1U/NsZYxmDbJlhirJUicAsod93a+ia+99IF/OjFHwUAvP/DH2Rejo/iND5Y2rf1gYhWAPzfAP4rY8waP2YMX2tb1/0iEX2TiL5548aN/XZjoIEGOiDaF1IgohyWIfwrY8y/druvEdFZY8xVIjoL4HrXtcaYXwfw6wDwqU99qk/PdP+opwd+940bN/HsN74JAJhtz0IhE48SoqIw6snJdK87fl3O3MFCSoyc220uJKRrF5mM9R6FQOb2SymQSYncrW6jIseKQwRHlic4urzs2ksJfG6KC3w7KhqjAhKIuJwrGj155yHFitEoFSMWldIhWe2sqlIEMasSkWPqRJGyqgNqKCsVrq+UZuKGxkxpVAydBUsERwds9LWJFoqyqnHhtddw4aULAID3vP99ESk8qpj/AGk/1gcC8E8BvGSM+V/Yod8G8PMAftX9/a199fABkgHC5L918xbecpWbtNFhv5/4icjQ55HoqBCE5TzHKSfvL40LSBFBm2cERSaDF16RZ0FEyLPMbedhe+Q06MuTCVaXltwzKVRr4iKKEClA5IDOmLYnZh91FZBXWkWxQsdxUlqhZvs585jOSmx7BjErsT317Rm2nfgxK6ugk6hrhbrW2HLHrm9PId0IK+YtaXgUFRAYZK0J99Y38L0XXwIA/KWf+kmccpWomkFk70TaD1L4KQB/G8DzRPQdt++/h2UGv0FEnwPwOoDP7q+LD5ZqJwdfv3ErxPODuSzHz6eNDppMwaOBo+MRjq8u4cSKZQqrS2OMXLLSUZFj5BSFmZSBERR5htwxgSLLkGcZMscwBBHITfTJaMRcfuOEthWkEfdzhZ6JuZMMs8n2swPTuymlDNxiHoPgTGGU51hWNoGKUvGaStUBQWxPy1APY7ssLZNwOomljQIX766Fvnh3auMcqi0xs6dWqCrChddsqbpXv/8qjp+2FbGzIm+5k++HDvBW9432Y334Kvrf9Wf2et+BBhrowdLgteEorqLpOuiLot67exelz42gTdjvrQ0+nVfT9OiR+kRKnFzxsv4IJ1aXcXTFmwuzTqQwznMUbn+R58icWCGEgJQCQnB9g2tLmZSf9ySEaDglRc9L04Ma5kCFXuKXZMjCM5RSiQ7C5llUYTu0VdQjFCrD2KGjlcmYnWOtFdOpRQ6nj5WYjO15F966GcQHbaKHk2EQThuC0gZrrgDPW5evQNdO91Lwt9jj+v4owgNGA1NAx7dv4l8vPqytrUf32ZbeIN6BEAN3hCAsO4h/6sgyVpdGtn10FSePrASdwKjImZiQo3DXSBkVjYLJxl49mIgnjvvILE5EwSa+7SbTGyBlCvGUdDTm6RSS87q22M48ywKT8gwiiBNKMQahkslfM2bh99dKo1AZJo55riqV6Fdevmp12xvKJO6phuVc0ACUS9Cwdu8uKicaFpNRvxvlQ0TzfpX9dnkIiBpooIESGpDCDuRXqhvXroeVrbl6NmMXvMgwlhInVq0l4OjKBGdckZLHjh1BUeTB+SiTMpoERYxRsNRezZuujkKI5Hq+1IXEqUSpuMBRhGHP4Tfu8S7s2r8oogAA5HkqTvQiBYYOaoXaVYsStULNnKwA4OiyNckurW1g1Ylc29vbUH7dY+8IEMqqDs+/dv0GNtesonL52FH+VjgoqHDQoIMHiHU9az80MIUdaG3NujNfuXI5ZGPWLI+iFxf8tiTCxNm8T64u4+iy1aqfOrKKMyfsB7c0GkFIEcyQlExqAjk/Afs3CijNH5uYidFfT8wSAcOCjhhTCJOa6Rg49U7wplWC6yiS83ba7/rsJnzTzTswL22gnSwmhIbQfoxMa9uP5ep4hGOOQdydzbDNAjZ5oBQRcMcxghdf/QGuuRybJx9/HNKJJXbmcTlxp/hVd/+HVORYlAbxYaCBBkpoQApAA1qmu9943RaBXd/cQOWLoG5voc9TPhOEU0dWAACrkxFOHzsCAHj85DFMHKy1lgORxDLEtGcixhiQ6BQrgiKRHRMcdTjlpDExNRrvqAkmFrbyd6GGxBLT3N/nq9CPGpLnawTkJYQISkhhIgIwwkD4LM1aQAh3vib3zuwajxSWJjiyZNHZ5J7ETFuRQzWK3vJ3Xt/cwltXLVL4kPo4ZJdblgEWqjBL9083yfWhB0nveKbQNaB+33Q6xcXX3wBgoyG9TiF1a07hby5E+ChPHV0JTGFU5DYaDy6giYsMDPKTaOc98Odwj0TZ0CMk4gP5e6XRj+H9HFOIYoLp5AnpRO7Yn/wxrcONO4ebaKVBZMI7C2MgvL6G4mTXhosIGtow8cHomA9Cx2tWJiMsj62VZ7kocK/yTmZpzgY+ceu6xrW3rLdqVZbIJ2N0kkGoitUrSDzEFotFaRAfBhpooITe8UgB6IdfW1vb2LhnlVFVVWNzy+ZMmMdJcymwMrEr1eljRzAZWZFhXMT4Bi86iMZ2aHtFI4kUQbDzpZTBeUkIES0SxBWVHHWk4od3dbbvb7pX9x1RQxsd9KEGAxMyPvtgLP/+Rkf4rxlqECRgvMhghC2+i6h0DOKD0BA6jt+qW+mPry7j1rYtULulTdAAku+XW/XXNzbw1nUbqVtOp1g6egT7omjkuC80zxKxFxqYgqOu+pBbW9u45SpFa6WS3ziIDyYVHwopg/PRZDTCxEFZr0cArIVBUNP6wHQKScRiW6fgRQcfTSkSBiEj85Cymym4F+52bGq6IXUxCHdkjkjBnwMAWiuYINYYkGFRmkJAeJ2C1jBMrAgigzZRp2AsQxBMJ8H1C54pj4ssJqxpa0EC1XWNO2s2VVs1nbKPoV9BYMOudpj191GUOEj9wiA+DDTQQAkNSAFAj44dW1tbuPimtT5sTbfn3sGnRiuyLMQhTFhIdKIodGiAesWH9n4iCi7PmYuQlD5KMkEHohM18PwJQIoIuChhx6AbHfSihjlOTtrlWSAjQA6uG6cM9duCBAztZInQEAw1SKGh/TtrzXJDEEbe5VkKZB6dQDdWU5OoHr1lae3ePZxUT9j7ZgcwPR6AKDGvG4vQO54ptOQxtjGbzrDugmas+BBlUmqMsi+6kmWSBSJF+E8dkz2xLHRYEqQQwZIgBIVQ6SzLkedZ+Gi5+EBN8aGLKbhqKtyjMU74NDiqj+Y5OPkjWqloCSETdR1GwJBJrSy9lgjvRZrqFLSm5HqeWs6LYrmUgVmHF+0gggn5GzfXN6Bd6LZ4G82O3fCkt9FrHxz5T+fu7TshIKrPnEWwadFz/yEyplArhbFTNArR+Ih7TIre3Ag4RsKZQkAKEiLLYrCUlBCSMQi+X3RJiARDzExpYrYBq3/r8NnocO3uzbvgE9AIEc5JTKWeQXjFnyCQaTNIbTRI+2t0K3dkkmMyHgztPJMYOcZJZel73amY297aBADMplPoOVWu+Mq/KxDwCJkqB53CQAMNlNCAFBrE176qjLkYOTV99QUR8s4VmTs5tb0Wo2jQEBmCHiJaJWSCADKnk+BIobvdR8SWOsM89bjTHrcepPoId5TleOSogSMiQ2004NuGIwdmiaBQc5MHPREzu7qjHeiA97PIJMbOYay5SBPS+BXldAp1Vdq0/YdBfXDzIaOBKQAAKJGvtZNd67JkSUzdQXc+J5tuPZYg65di2Ufd+JC5vB+VjhFKJ7kVHOOI+goWZcmYwlyfW2anJxCzwnEGYcKkttd4BmGvSv0V2kYx/l6GKRa9KBEUj2zCExrj4vtCYG1Kt5lxkNhvmWdZqIGREcELBTwwzN+7dDkiq5IzhcUw/54kg4dYnBjEh4EGGiihASl0kDdPZVnGVmPiFU0TSjXcbNXjq2wj/p8a24sQRxPW2zFVXNp+imTVTfrV2OQLPVFEQZ2+Ox2oIegpu4FC0mcuSiSIqdk1aham6bAwzFE0EkVDY5HFxLe5EKHwbVPJSATU7jefbm9DO6TQpZA8UHpIxYmDKAYjiejbRPQ7bvs9RPQMEb1KRF8komL/3TxcasJ97VKF8bTr3grAr/GWBwGCNsZaHSgVIbxLMQPHjSQqbTLo+HBZLxM5m8vajb6l56D1P/n/yN2DfP98k5Jn+P8anUA6gnOIdZfY/eIkd3en+E5xPzvH951vs7bLUw8isowhk8iFiOPS6K5A9CNZu3MHWilopWz6va4fo4MWPG3nGxwqF1qMDkJ8+C8BvMS2/xGAf2yMeT+AOwA+dwDPGGigge4T7YspENF5AP8RgP/dbROAvwrgN90pnwfwn+7nGfeFGiuHd/Dz8QZEFNKCNckrBRNE4JYk41YrXo9xtyJD2keGAVrq9Lia975Ysj9FDwEFeIQQVmeGGjqfseeXSZELew9i/8dzGqiFjUeKdOLvB2MCUhhnGch5MdpLU4WoRwpbm1sRKcxLMectMIextD9gxLBfncL/CuC/A7Dqtk8CuGuM8erbS7CVqB86ao25/05NPCpFW2Tgf5P7sQ/IFzOZVhVWacldtPuJ1LJctMSGjv408XH7YMe2YU0mSZPf9r3pcVbayxec9DPllolOwQteTkSwxzlj8AzN9YR5ZxogZHxeGRe4PbUZm32hWjA9itcLzWbTYJ4ES7u36BseqFHhAekc9owUiOhvALhujHl2j9cPBWYHGughpP2WjftPiOivAxgDOALg1wAcI6LMoYXzAC53XXy/C8zu9IBkPQw+9txCYKLzDbvAr6vK+zawsuplWc9VKu72pRNlI+913zPmoQQGCPgGEUJ9hATHNgNETH/MyG7JSwm2J01xi9h+fz4TL9i27UYM6ZZCYmlkw6iXxqPgij5TOhkNYi9TliUq57OwqwzVjHbl/vxAb9pNe0YKxphfMcacN8Y8BeDnAPyhMeY/A/BHAH7WnfbzWLDA7GGIUYlC18z/n8uwSisorVpxA1z8FYhaa8CgNtr+r7StnFzVmFU1tDbBGWpvlIoKYVfzrDAxKO1oS2gHn4Gd+pQwKInyvamJT7cNWD1K7OW3bL8n12kk1gqKzKNpfQiFbmAgpcDSuMDSuMDyeISRlBhJ2Tmvwm9OwNb6GrbW18Li8FDRfbBSHIafwi8B+AIR/Y8Avg1bmXphOgiZrGsFW3QMCQgRh4Ypm5xKy7Yp3k8ZDWiBXMZjvrpyWVWYBZ8H2duHuX1jcnMy0Ym6x2ne4FGzsdOopHCAh073aRfSikyNKlSMgcwl5o+RKiXjfs8Y4nn+5jH5iS2nZxn70qgILs+iLNFUG3t3dq01tl1kLGcKCbBakA5Uv9D3AE8H+KADYQrGmD8G8Meu/RqATx/EfQcaaKD7Tw+lR+N+GGCH6Ls7ImDFFX6t6zqkIbcK+vb6qAwgyATTpBQyWC1qrUMp9ZXJpP2gThzf10ZoxwV/B51CrxXCvQUTBZI4hgRd+ZW+cS3fasrejUpUfl+Sv8EwwaTnR7LgoEOn4K0VbJsXu/Jp6oyJv8vSuMAkZ3EQJg10889RdY2Zt1KoGjm8793eluL7pgo4QNTwUDIFTou+635EBk58GtZ1Hb5YAkK+PwWDprHKezxmUiBzFaKKPAuJUXb6ofr62pAY+u/FzXV9zMC0Gj196Vcuht3NW8S53yhP55Ok6KSd5ojcudJ1V3AUV0jyZ3qmbIwJeqGlcUz9nq8LzFTKFKT7/eq6xsY9m69xtrmJycpS6FZLhLiPyr9d0T77NQREDTTQQAk93EihoanpQw0Hq4gl+OCgXMpwd0IMAIKJq6nf5VenTMqQwXllMsGqq2tITGm2q/52WhE4bJhveZz/MA7fu9FBv9jQfcwYHUQuo3VQ1hltWu3OY2goJ9kzm8FRPJTanyWIQDLW0vSh70ujEY46sXDp7j1sqjpZUH1w2awsUTrxYba5Aa1P2XO8Ipm/8S5W4kNXOvY91NMuHv5wMwWg98V6v/VF9Aim3eTiuf/Yx5NxgFJ8SrZ/YIr1HcYFVl2FqNXlCaS3ZMAGT9lnUO+P1O57+2MM2x3yhC+euhO15HhuGeiRxdrSR1NH0LY49Ld1j2hhkkK+3pyrjWbX23f27+ldmgGrT+DvFpjCWOCoEwUmeQYxBbwAwV9XaxWYwtbaGozvH3pPAAAgAElEQVRiqeUa47rbhemBMAb+8AVpEB8GGmighB4apLCT5dwAaQblHpg8jyF2abnn6OwwHo3CqpWq71KenwnC6pIVE5Yno2BpWF1a6nla6pG3EKVufwkZY0IGYhgkqeN5j9s0b7TnWRxS0cEYHez8VmRwfdF9aED3ogitdUh8a9vef8DEZ/jr4R+jwjVSyES044V1lpxYN8oz5IIwC6n2IjxUSmPmPBrX7t4NWZhEPn+qLOr58SjQQ8MUOqmh7k14gknP2enH4B8xhX/6HmsPHjt+DKtjKwpsb253/vKCCIWUGI9c4M3SBCuOQUgpE50Al4mV0oCTUzMhEsi/84dFjhGo8HJkvBwtQtsYls15B13D7i0OpqE7YPDfT2rTozdo6hQaTKF276XqGrWyk7KuVdqua1RsO7wny/jC070Lst6NADDOc4xlhlKXYWz8qymlAiOYbU9RulKBxWTELE79tJOT08NqsOA0iA8DDTRQQg83UphDgePugBJClGz4Zz4Rolb55KkT+Pj73wsA+JO732k4vDi4SgInj6xi4lahlck4WBz6+2SgtA5yiq0t6bIOUyqkJNd5pZtSDh1E5wBiUNzXZSRjAMNL3Ps1oMPRmCkP54kN/hncsqC5cpChBp+XALArsK8WpZWGUnVEBEqFfBVKqWS/b9d1PL+qa+tY5romkwS3IvgckEgL7kxccNTJY0dwb2sb63XZHAVo/9sAKGdTTJ3L88rJ461z+5b9R12UeOiYQh/86tu/E0NILHo7PLfZXj16FOff9S4AwOS7L2CjnIVz/PTKSODk8aNBp3BkeQlSxmFNrBvuxspoV+jEOUNpgghViUTKGNyXbyeBY0Rau6rTvqRaWp1aB2YjQmm1JI8jCdszdl7kCSbRD3R5J9pUZVyPoIJOwzIFFdqKT+qaTWpVJ4wgnMfbNWMKyooM9noFKYjlu5DRsgNK0+IzppA7U+XKZGL1Cp5JNsy7/vlVWaLc3nLvrEFZf8r83VLzu32YxIlBfBhooIESeuiQQov2i8UWFBn8/YmJCKQUjh8/BgAh2s6f71fzcZ5heTIKNvAjzkHG35LZG0IyaK0NlIqraFUTpmFFE4lCMmNFZfMsFo6VUgQfCMmKytr6EBERcASRFFwRIhSoRZYFRKC1biEC3w7IwLU5UvDZppSyIci+nYgCtVcM1kEEsO+fIgJVM3TAUEMQ2aQAkQSrbtHpwUVEYSwkiVA3g8iKbMEdmpilhij0XyuF6bYtLKyqGrmUu3JY2knpyGmPfkaHQg8lU9gvHwh6hIUZgnG1BgFVloBjDHVZYmVlBQAwySXudhSenowKrCxNcGR52T2TWRnYOxggyNe1qhNorBSbYCyIhwQ5r0ogZ3EURZYnk1+wIKyEQQgB4SeFiMVkZCaR5UXCfPw1WumGfiBOED+JVVWhqhn812pHUYAzi6pSqBxjAKw4UVWxHa+PeossE4FBJlmlw4/INinuDkyZMQEi2w6p+YUIxXwKVqPTGBMcmarpFLkzae6G9vItP2gG8VAyhU6iaIacO8ALjn6iQyhLrN++DQBYu30nTAQpJQpW1pwH8IRrSSDPMqx45SL37gPYNWArY42qZhOGrbpa65DFSeuYISjPM+SOKeSZRJ7nATlIIdnkT5mCTJCGYypFjgmJUEmq6TOhmU7AqMgUDJvgVV0Fe35Zlg1FoWbtbtRQ1SowhdqNhx8br3vIZOx/5/TgEZTpLn5SaHmv0zzL7DhRrL41crkc8zxH5nRCMssCmvK1IPZKu0ENnA5O97D40wedwkADDZTQo4MUFqUmZu9aYPiG1lBlieuXrwAAbly9ClXbbElFnkPNrMVhaVQkvvZ+bcoz6UKlyT0yQl7fBSDK0bZtV0nF0EFECqax396hrGtWY9JCaR+inWdZgNZW12B5PRc5MhmRArCEIq/DKhhSpDeIowPuXVg7cYGLCb4W47SsQoWtmulNUlFCJ8gh0SnUKuhvUp0M0A6IYrSArDhyXomnjh3BxStZEL9GWRYrSTF0ILMcma8RKh4EkG/T7kWL3eOTh5op7NY8yY8D/QEoyX6lMN3axr3btwAAG3fvBJm6Ygq4E6uryK7ZrNOzcBersMrZedy70hjTEBmiYs1OqjZTUMydl++3cn+UiStRQ1a8+GwUHwIjqCSKLH7sY59PoMpRVVUnU2hWqtZM0djUG6STPIoM/j1nZYVZWcVzNGcQOtEd+HbOzX6mexJYXQFP5IqdyUT/k6XxCEujAhMZlbNep5DneRy/PItMgfYPqvcqQvTRYekeBvFhoIEGSuihRgotWlSVy8/bydk8yyAzGVNw1TW0g7+mKpG51dlnAwaATRWVTko5v/uwkmiE6H620imlUZZOfPArbY9yMaKG2AZidiebep558Yk6WhaEgHQrtZQSpfRZoPKAJsqqQlmVyAM0ZkVpe5Zd64jk0YBO0YFuKhTZed47kCWxraoaSsUYB8FMh6G6E5zfJVfa8g417JDc9NtFPCR8aVRgMiow8kpEojB+eZYF5zOZ5ZBZHvrYvvXu1+fD8nbsRtR7e8q+mAIRHYMtGfc0bL/+LoCXAXwRwFMALgL4rDHmzp6f4f7u5fV4boT+n48wXlrCmXPnAQDrt28nk9rL8StLE0x8pFxZhTsKIgu5/aQyiIEzhMS86C0Exrn4honEdAeaTSTLLHj0I3umoABprZu0t5gIJkownwUgQPkiL1GWOYrcbgspE3m9Z5gS/wOfEp1vA14UYEyNtf1+Lz6YXttxm6lybtEMzmoygnTLe2TGvXkmsTwZo3CiSgX2O/nfE0CWc53C4qB6kW/2oEWJlNg4sectSvsVH34NwO8bYz4E4BOwhWZ/GcBXjDEfAPAVtz3QQAM9IrRnpEBERwH8ZQB/BwCMMSWAkog+A+Cn3Wmfh039/kv76WT74Qv6LHBqaB2TmIQ8x7GTJwEAxXiCbRfjQABqB3mXl5ZwZGLDqC9vbAVpIcskSEgYrhlnK51feX0BU9sWFvp7RaPSwU6vmuKDP6fh1CRINJCDQw119IIcjazTD2DrJ3qkMCqqIEIAFjJTx0rZXF38imyMbikauW+CThBERD1ZQDCjRjbnZhAWI9PRNKYhWnRb8wlg940ZqQQJnDiygpFDCkrpMLYgiuMqo9KW5O7Xz90oxPdP8++ym2fsR3x4D4AbAP4PIvoEgGdhy9KfMcZcdee8BeDMPp6xb0rNiN1kZIaVI7ZG7rGTJzBdX3PXRDff8ajAEZc0JRe3w71GhZPV+UfloSrFdGBdZc544I6fPLUyQXuvGqJE6oEowoecZRK5f98sC2IKIY0S9F2s6hplVaKsnBdfXkd4PAdnBgYnhI0mTMSByNT8fiJipj6ZTNA0yQpz+GqlfmcT36TtaOXp7m/iPEZchDA4urISLDNTXaU6lSCWiSSI7HAh/17ocHqzH/EhA/BjAP6JMeaTADbREBVMM/Mmo6HA7EADPZy0H6RwCcAlY8wzbvs3YZnCNSI6a4y5SkRnAVzvuni3BWb36rOQPpRd1LhJ7mLtT585g5uXbU3celojd5ro0WiEEw5NLGUZSqf/y7IMgmUTbjoCEVt10lWbWit/OM8Xk1Gpcs7vz6RdtYILs5QB5uaZDPESFq77ZzDrBVmX7dKJE1VeMnfiKCZwpWOzwGsmZXBm4j4XUoqQzbpVNs7/DH4/szIkhWKYWMHPCUVkTYou0HoOE0sCVAQMy+e3NBlHN3Fe34PagWNhAPdAu1c6znNsvj84Zc9MwRjzFhG9SUQfNMa8DOBnAHzP/f/zAH4Vuygwu2tio70IY6DGSXyoCQA53/el1VWsHD0KALgz3Q4fG1HMwzjJJOrKTgJr6pPM4iBA4GZEJjJ4UUI0mUKciIIoeB4KikFAite1dJMyRlBG78a0zSIruQ5CpOJLrXQI1iICSDmxJuv+PMjpRzLm/LM88Vmr2URmk9Vvu0YK7RNRIGUEPJ+DZu8vwERDzlSQMoj0+fFXl1l07BJUJZYQLiZxh7HGKHSOzV6pn3ncf4Flv34K/wWAf0VEBYDXAPwCrEjyG0T0OQCvA/jsPp8x0EAD3UfaF1MwxnwHwKc6Dv3Mfu67K3IIYCFNLiV/0rZzUikmE6wcOQIAuHfzRuJm7MuOLRcFNmproRiPCoyKgsdNJo5Mnrj1IZRQ5y43btUuijyJ+IviQ+rybKUUt6ILEUOsZZaghhAZmWdB+99UVApBAbJrpUG+JnNjdQxiBSy09kihYIjEloPnq7ZrNRFDCx1wH4T2St8WRaI4k1hfWIxCH1KxyExgXNioSbG1nYh/iXKxoYBd5DvrklIfPiVlPz1SHo29Pwgb8b0Ovpc9i/EIy6s2h4LIMmgXECWlxNhldj66NMHdWQyaymQWtO9aIMTpE6t3bidyTDfOrQFSCpbEJZXdib10HSA+dQQIxbbXgxRZhtxr//MMhWN8udODhFTwjcnPk6wkgUAUG4SYmCTPsyTvA7Ffqmlx8O+SQv6mKMGsEh0Zn5uTnXc/0YOkB1iWaUJdVTGdHTjD6NbvdHpK7lKCWEjMXeCcw6ZHiinMpYaOAXFzV1SMxhi7hCl5UaB0fgqyKMKquzQqUMhoTiRBARQYhhSIGFIQlJgnhRBhwhZNRsCUg9zPoaWDoPZ5XQpBoMEgsgx5nodoSjE32IdaLX5/f78kQxTPUdkwI/p9iblxHiJg1zfPSStGdfR8DoOoqwpTF9nJ0QE4UhBRDwNafMLO4xWLKh53OucwaQiIGmiggRJ6JJHCXI7dgAk7cV3TuASZxMhVeCrGY1QuR5+uY6jxyniMiWuXdZ3I54aFoZAQgNMDCCGRZ8x6kMno5INUFOA1J7v2CyFQ5HmCDsCvYUgD7ByPVGzWpgy5EyeklFwy6CZKD3oRxr4nRZOojM5TgtIs0SlqQLryowMdzEEQ88SJKP/3IAUhQFUsMFtkOUaFCyt3qe7se8W0+M3YkF4PTPRbvndLD0qUeCSZArCgiNAzqk1GkGwToRhbprC0soqtNevdCK1CcMyxlRUU8iYAoFI6gZwwJuoOGFOSQgQTmE9XznUEqRISrM3FAi9K2Ik98ooynpSViQ/oEUW83sKnHSMhOoSE5lCm/Qo3dVdw8yTXVfBAoqZYwBWNnSJDc+I3xY1eJWRH/5n/iHbv7IW7UZEHfdFkMsHEiY/jySSaZZsmyf3O+Bb19Hvu0cOhQXwYaKCBEnpkkQKn/SgWm9cRCRRu1RgvLYWVQqkauVuZV1eWQ56FpfEISpuYAVibYH0QEBCu4ItFEBG+13Udwo05CmgiBdGBFIiiCAEAo6JIE5x2wGcwuI8AgdvOQ9QUE8L+uNIKsuISFzm4F6BgCszoMNUWJTrhfyJmoPOcgDR2QBQJxmf9NwaotQlIIS8KjB06nCwvB6QwWV6G8A5OjdDp+w3t7+fz3hZMgdOig9cr9wlC7rL+LrGPQlcSkM6LcVSECLvp5iYqrZE7j0hDOiY8YYyAoBmUJ+R5NGM2mYJgk79LfPCMI+oEsjgRGXxPbe7Umni8qGwXfE8N7dzTjxJznX8PO3ypSY+7eXO5PpnI6GEKO1gluq5vn+f7j2AV8nkwfSrN5fEEYxcB22QKPjqSpNhVzYfDoINY/BahQXwYaKCBEnrbIYUW7VJTYxDzCUxWVpA5kUGXZcgTkBcjjAs7dNevX8PaxiZWTp2w55mY/FQCoV6k4JWnGit90/oQxIc5YkVU8YWbhvdNFIFdCjiyYlL/wsfs/wzKc6TDLS4zlkMBRCxtXAMpMPt/HyJYRCzYUXzo9HMAjLckQNmkea7/4/EYExcWz5HCaLLEnJeARUrR+3P76KBW+MMUJ96WTGE3fKBliWCQe7y8hNyZqiq5DaHtcE2Wl3HqiA2aurt1A9rEYB0hiOVLJEjtwJiqE5m+zQiYyIB5jIC9YdMa4HeGATDM0Tq+KTHHobnE5HCZZSFoCqpuiQnekqKVsmXoYBmPYIyAtz1jsN1c0PrQFH8WYB6+zwYIhXd1bc2RPkfleDzGkmMES8vLoS0yGcSHRSfg3iWM3dsZDssyMYgPAw00UEJvS6TQRfPgVsuBycHMfDTC2DkyldvbYaWB1lhdtbkVshu3cOOtt3DKFaIdZ1nIljQqxsic2EAiKvrIP68XHbT3t14G6bGokIwvagEBFx8ilO65pe+Yu0F6pvfT8GXoybmAC0Ex9oOlk8syFl/R4b+Q+DAkMQ7eZ3wnRaOvv8kUpZrVk9DRQYnl2LbFZ7TBeOR9E5YS5aJvCxnRzU6r8YPUQR60KPG2ZAqdA7SbkQs5+iSW3OSfbm0hJHbXGqcffxwAMHnzEjY31lG5e2dahcmktA5FRgRRqFHpGUKq3Kfkbzgv2aCezVS30GVVsNtRW9/khEFeZqJF60N3O7Iih9Yak1FMslK6vJaKV6c2GkSx4EwiSiQVsRtWCZ1Ofn+vFoPw48yK0mqTprDzb2+AWK9SKShtghlyaSllCqOJ1S8kZlQ+PLuhBsbfGfLfb2NnmwbxYaCBBkrobYMU9uO0xHmzXYFd1uEsw+mzZwEA5XSKjbACayw7BHFkeRm3vCs03OLlIiins1koOJJlMjgyGea/0EXUhQDYZkukaKCI6LuTooQWgkgGrT2CyR5CTEgLIC/yAN+XxuOg0KuVQs6yPGcs3oNnNOLiA0cNaPocdKAGrVWKDhpVuzUTKzRb4SvWRwPgiMubYUWGldCWPjdEksF5DwJCQ/97mADgIJWOjyxT2NPLLzhyhmncJysWVp58/HFULtQWWkO6PAsnT53E7dffxA8uXAAAfPijHwkfXyYEpk7uzkdFgGU8H2LX+6RiQ/tjpI79iQWFMQOusU/3pbkO5nSAnZKKFZkzqWqtseScfza3tlE4Ry6laijlqi0VReqg1TBRxrRnPFYiigyqrqFclmvPBAIjMA2m4MUXQiI+hIK2SkFmOU4ePQ4AWDlyJLE+RO/FPZogF7hsPo/Y/RR/x+sU9joAfUNtWsccU8hzjN2HfPzUKUy3tgAAt+sa+dgyiMeeeAIXL13Gxvo6AKDUBuOQN0Fg6pjHJM+RMfdiHoG3kAWcOlSOnDF0KRQNOpiBfV7TByG9BmwU2GixVZf7SeR5HibiZGxCrQwpJZQrsadYQVtv6iTBfRhcLklBAampugoIpIUGetCB0hFpaKIwsjWrraGNzbB18rHHAKR5OYvJOCKErh+l7yM6FE3jYvBiofmwi0kz6BQGGmighB4ZpHCo+tiWJt6SFhLCjdB4eQmnHrd1bTbu3Qva9tUjR3FkZRWbDtqu3b2L8Unr3VgrFbIbbZclRi7HI5yFglzK8UXqFHaJDF1OPmx3p8gQdhtWV4m3mViRVl4isAzp9ojvP4A8L8JzpqxYby19sdsI/2WWoZlKPWSlEjKu9Np0igVtpGASUYKLDMEMqVQQ6zSAEydO4JRDCsVkglWHFPIib6Sga0KAPUrvZu7mHNqHtmCPk2a/BWb/awD/uXv887DZnM8C+AKAk7BVo/62sSXldk2HxggYKgvNjodpEgFLiQxYPWZ9EY4/9lioUl1MZjh15jFsX7FFsW6+9RZOOaZg7fH2R92elVh2gVZ5w00XxkQ5llLu1KWPbOYj6DoWXol7AcaTUh+G3jZ/QpNFAD5CyLBu53keqmptz6ZBf1JLCVm74LI6s4yhI0GqTW0Xn6Q7qnEHpmA69AjGBJGhyRR8abu8KHDi1CmcPOOYwniCybIzQ3boe8II9IkI98VJoV+UOGgd5p7FByI6B+AfAPiUMeZpWFf/nwPwjwD8Y2PM+wHcAfC5g+joQAMNdH9ov+JDBmBCRBWAJQBXAfxVAH/LHf88gP8BwD/ZzU3vu+sGxQW1yfS1U3oZQRCFPXr+vU8FS4TSGk889R5szdy2MZi5FWmSyaDcymS0RBR51kIK3vRGAuDhzhzypyAfqXKRKRHjSd2owbdT5BBkhu5289YE5iMI+EIrBAoxBcYY1E5kEKKKFa2yHNJZAOwxZp4UEsp7frLYD21MS7GYWB/Yu/l4DwNiNTpVsD6srB7F8ZOncOL0aQBWnJG5z0JF3fDMvV3/1mJ037/tPdB+KkRdJqL/GcAbALYBfBlWXLhrjPHOf5cAnFvofnvtyIK06P0b6oW4nwjKeeeJQuDJ970nnK9Jhg9xa3MzwGeijFWjFtiaOktEJpGxCUuNSQk2KRI20HTz7Xo/g5TZhP0dTIDrDhZps+thEN+N9cDqGuyeLM+hXX09VdfBKlHNZhBZFpiCzLIkslKr4JAR7kWI7+UZQiJa8GjIMBQmiAy1UoHZTFZWsXrsaHDbFlLOFRt66b77Nu9SUNijXLEf8eE4gM8AeA+AJwAsA/hru7h+KDA70EAPIe1HfPj3AfzQGHMDAIjoXwP4KQDHiChzaOE8gMtdF5tdFpjdC829KVPqEpgSKS6OvQuBJgItW2+4p97/Phw7fhzHTp0CAFy/chnrG5v2VsxzsVIqpHArawVBsQiL0QYQPghIgJiiL0kt1kTyXeHGfSJGBwLYUWRotBM00BBNujw0bYYpuxqXsxnq2vkiVCXELAt+CzLPIBGTyMZAsegFmaZTMy1Fo39rjThMxnAvRh3S7B0/dRKj5eXw/MTa0IgjCWN5AKjgMNFwr41iD8aL/TCFNwD8BBEtwYoPPwPgmwD+CMDPwlogfh6HVWC2QX3vvLNTkP14Oj2L5wyk/0hoaRknigJHnHx69PhxXPz+9wEAt2/dDA5LMCZaIqoKo1ER3Xk5J0rMixEMtwKbmmZI1kjFhzmTfR/iA2c4zf6lRVh8FakCqrbig5IS9WyGyld9loxBmCzORcYImunujQHTKbDM0KDw22hEL0ZlNCZj65p+/ORJjCZLSaGX8Lv3fjA0d/Ohp130d8/ig7El6H8TwLdgzZECduX/JQD/DRG9CmuW/Kd7fcZAAw10/2m/BWb/IYB/2Nj9GoBP7+e+u+pDY3svDJwa6CDcMw0o6LxWE8FkOeBqKBx7/HE8tmFdnm/fuQNyEFdIGRRjM61RZhqFX6iy6KRPPUih39kIKZRvooc5CGDf4gPS+7XGisI/NgzZ2DWoriur3PN1OjOGFLIsvENShJehBp9ENogMxgSlp0Hsc61MEB8MCJMVF/S0shKCnnYi/lqp3m53WrzDFB0Omh4Zj0ZOBgugvTnUvMY0DhI7EBkEoY8xGMZVivEIK65AbTEaQ0+33EkmWCWMMZgpFQvRah2TebTMi10TtMkA0n4lMQ7Juxye+MCPeUsMGf+PvY1w8Q1KWe9G5bar2SyaJ7MsxlhojcgT0sI4zVgQz3BtEJQ9UtZVYAr5aIxjJ08CAEYsS7O7e/oOfJWgzjMa193PKe+Z3+E9c4h9GGiggRJ6JJHCouhgL+e1UANHzzzKsfdmhNVT1n32+PFjuHHVIgWptY15gE1uqiTBuBBjrVRYBY0xwc2XhE5W+pbScQfXZJNA/nao9K7Eh57nxUOJ/BBP97sp7hdCQtUVlPMNUFWFiokS3mdAa8N8PhiEc05NabYqe++yruGrUJRVDeWePypGOOEsRGOXubkzRLz1FintFhsc9Hp+PzDJI8kUuuiwlcFcgmy2ecvAoHC5BU6cOI7rV21MhFF1/MCNzTJcK5fMJc+CJr3pxegpmYhzYhdsk4kPiYTB72s6r2/pIVr35Odyjsl6mux2GxrRYuP2BWuEkKil9Qitsiym1Wcil9Y6ZQJcx4D096+cF2WlTQijyEcjFC7fpswyd223bJDqERb5svboJbRLul9CyiPLFO6bRajhzwBYHQKZ5JNPL3Hy6vLqKsiXkzMmIAXrtVfDOfhBCkKeRZ2Cj55siuzt5Cndq3tr8iPVD1hm0aEjaLXnIIWmcrHjmUiapvU+wQVa1hCOQdTlLJgKLWhy91UKxnknekVj4htBHROcosvzeDJBPhq553UkYz2ED+p+ahoOkgadwkADDZTQI4MU7hcyOAgg6K9fPnkqVB4q6xJmy66GZGxdydIFVQkCpIgmsr78jfNW9050kMoCLevFzqHTycMbz+vpVwNBdAVUNTXnVTkLwVKqqiFcDgZjDLxWxQZEOfOiVlGEgLNMdDhyaa2RuUAnWxc0D8f7v6e9fmk92oYDggv3E3U81EzhUXAa62YituejyRgnT9g8gFc31uNk0gaaCLWDzIIoJGMZjUbQvtQcc/lt6wy6cyy2xQTeXkRkaDhKJ1IJP9ItPqS7076wRyTbSqkQdSqlhKhlOC8GOrE0a0pb5ay7vm0tjgbrzPk/FKMiREJ6rYZpnL0bWnjxeBBWy33SID4MNNBACT10SOFRQAed1GHXFFKGojGX33wTCEVmCUqrsPLVqkblg4WEQJ4zWOxv2WVd6IqdaKED1qkmfN9BfGCv0osgOp+TNE2zCdaR0J/ZzGaykiyMmYSIiVtNtCQYo0OVKkvExjYlf471qIx9SS1IB/vVHSQo6L/X4Vk8Hhqm8KCYwWEMa/jYCFg6blOz5XmOyrlCq6p0H6tnCgp1SMaiIETtridWtqxr4nfrC7pk/1R0aMr6O7hQI5na8U+Hb0J6WjeDaF8bn7O9vRWTsRRFYApKx3Tv2rmC+4pbWqtgyeBfkqH4HCFEMG82egubwK3vC9ytkHF/zJOHSYP4MNBAAyX00CCF+0mHy8eJ+RkQlo7YcN1Tp0/j2tYGAKCebdscAH4VI0Lt6iPUSoYYASKVWCLmVnxKNYL+lM52vB7JNjub3Tc9bnd3KBkTENHuS9LqQAl+j9EqiBJCyui8xGpUapjEsUkpFZS2NY+DNogOY6zgTJP6cEJrX0NEPGwd4s73PZwevGOYwoH/cM3fo+cB3hHn5JkzuHnF5psptS2C6m+htAaxkmaZ9LBYJJ6OzYmf6gsW0A+0ZPpkCrN78cc0mEFyQR/DaV3RYhB9TAkAKufVlZVldC1n1aR9TkZvmajrKPyedxMAACAASURBVH6VSkE7MY0Qq1iluRe9VqHd5blCQvuyHnoETQ6MBvFhoIEGSuhtjRQOlE8z/ZFtLroa2PNWT51GFpKDUhIEJQRFhVpdh0AhEgLkfRZINBYgvqJ3t9OVerFVun+/aYCB9Dn8Bl0OS7196eiPLxpT1xWkW/VNIi7UUMYkBWO9+FXOSmjn/KSERFm5e1UVlEMgzV9sT0ru1vfQvp+Zd9IcetD44m3JFB70oAZiH8JkZQVHHrPmyY2bN6DLGYT7fLQ2IGqnIhdCQrv9kGmFJksevvdM+JboMIcxtESDfv1AUwxJ5/hizKfV747n11WVRo+y953NZqg60rfXqkblGIHJMqzPLCN4/eIbOPG4rSB+FPsgLmcstC40gmcO5eM82BsP4sNAAw2U0NsOKTwIlEBAkg26awkhACtHbEYmIW29ROOWfh4WXKs6ZD3OpITS0ZEphh437s8hO1/RgQZaaMP20OStjkE8CPGhtx9N5ab7t6pjpWrNqkkDNpvzzOVgqJVCxeo7zJyYUE4JG6VFDa/88Id4z/veCwA48fhZFK6En39aECEOzWHGreaPgA7ybcEU7tv47uMHrasKk6VlAMDSkaNYv3s7QmMgpCsnTQwKq1A5SQsN8rUbqSPJS0c+hH45vhnG3DHh2aHWo8L5TfGkccEcBtXqc08/vX7BjlF6vbfMzMoy6BcqpbHtqnXd3ppio7L7N2/cxneffx4AcPLsWZw+x2sUpWaFhfgCv4QayXjYfdIe703HsBgdHLfZkSkQ0T8D8DcAXHc1I0FEJwB8EcBTAC4C+Kwx5g7Zr/zXAPx1AFsA/o4x5lv77uUc6hqCDo/jB0LEfvnZ9nb4iO9OS1jX3DgpTMgxaBKfBR8opUkE8yYZ2JwOXQ/t0yk0tvtW5+aA9TOI+ebG9uk76BO6mJJB8D8gIQNT0A2XZq1NUChOywr3NrcBAJdv3cWG279dG1x47SIA4GOXL+Pk2bPBc3IxShlHi3rmZDdmdHsWvub+0iI6hX+OduWnXwbwFWPMBwB8xW0DwH8I4APu/1/ELmtIDjTQQA+edkQKxpg/IaKnGrs/A+CnXfvzAP4Ytt7DZwD8C2OXga8T0TEiOmuMuXpQHW7Sg4qZ4FmYWrCYkXe4mW5u4oc/eA0A8OYbb+B4ZiBc2jFCXDm5fkEpDSW9WKEg4D0dYVPBJ7UcGfU4L8XDUTbnf2xzAWQQb946t5VleAHnpxQopLoKn6VZgGB4RSgTnb98xSjA5mW86yp0Xb9zD5u1G78sx/Ov/BAA8KEXv4fT587jsfNPuDvEdPHJWO70cXVctMPn0Lj5YYgS+7/ZXnUKZ9hEfwvAGdc+B+BNdp4vMHtoTOFhp8rJtzeuXcfXv/1dAEC5voHlE0dB7tiIK73YB17XdfBtEEJEl2djlY4xJVyszuxuEpsd+/x2v3KxC/7POd7DIPp1Gs3ns4taegi7W2sVxAelVGC2gJ0GfsyU1thwhXw3tqfYdArI8URg6kSJ57//A3zso5dw8qw1EWdJDYh5YkLPsR0kC9/H9lTdWZTYG+1PgN63SdIkyQIXp6HA7EADPZy0V6RwzYsFRHQWwHW3/zKAd7HzHmiB2b1TWxPdsXZ2XmHYkqC1xus/fB0A8M1vfxc31m3lqHJ9A3dv3cTTT1r42kQKHt4rraBceLDUIlgiCCYoG/22X4jTVG79CrzkrXpX+p43b56fHJvvsOTfMXlQs48dz9c6IiiPFJSq0aRpWWJz2yKFSuuQg6GsFYSLj3j59St4/oUX8di58wCA0+fPxQRX7F58xWxjr35o0JEsK1zRPWoPl1Vir0zht2GLx/4q0iKyvw3g7xPRFwD8OIB7h6lPODiiOVtx325/q3t37uE7z1kz2CtvXsLMfZR3tqeYXr+OTzxlzWK2YCr77LxYoE1IV57JDOQThriSaV39bOcGaE88oN8k2dYJtBqMJ+wwIgcQWRkiI1lSlbKqUFUVytJaJqq6DsfWt6ZY37LWB82ep5QOvgxb0xJ/+t0X8fTTHwEAnDj7OIicvgbEgyy51bFD3KF0E+mu3X00bVGCmkO2L1pc+7aISfL/glUqniKiS7C1I38VwG8Q0ecAvA7gs+7034U1R74Ka5L8hd10e6CBBnrwtIj14W/2HPqZjnMNgL+3307dX9oZJbSOzmHhXOn17e88h5d/YDXeG9tTlE4TXiqNtbLES29cAQB84n3vxshZIgwiUtDGBIWa0gpCe0cmV3jVIwrus2AQPWmaiGEntBD+7FZk6EAY4VjX/nnOUzy3AguX1jqgprKsMKuq6LlY1iF0en1ritrX7ATgx6BWGtI7ONUKN+6s49vftSjuzPnzOO1ECesf5q400Yt0L9T2dF0EzB9agMTC9LbwaLS0G43rQRsy4w9Z1wpvXbkGAPjmt76DbZeluKzrmJOxVhBFgXvr1nQ2nZUovHkyqRBlglNTXdchDbyAjZiMUz+Fsl37iR1p51VoCAddzALJvMXcMe6xyS3msGQSkcEzhbKuQ56FsqpRllVwWJpVFTa2puE+Kr5WMguDxYIIs1rjz59/CQDwox97GkdO+JJy4yB+GdrpS+lgvn1yXWOuz5/6hyI/LExDQNRAAw2U0COMFObx8INBAosyaQOD2q1ar198E89+y/ojrG9vh9VMaQMVbOkuF4CzJuSZxMa2XenGRY4iZzkE3Opa1TVyn0XIZWEKQoMBC5ZCYwHzogAlWCJd7PmS3eWk1B4N487daWS6B9G00Al33tIs9iOIDFWNkiGFWRWRgt3fVqgSUTIw3hEKRBBZjnVXnOer33gWx07aBLvnf+RHmAWnObDd1FL7svHfn/tBT1DFIdMjxBT2NtF3+6PseuwNoGqNy5eskeWZb3wLP3A+9tNZiar2H7jGzIkSNv5fB9FibWMTx48eAQBsbk+hlBUlRkUeHHZIx5gIEsJ+K8QnfHvyt4WKbhGrK9dC53jMYwI7MIi5mZ11rP6ktA6ZmcuqZkyBMYG6dnoFb5kRmPpxYr0mikCYDIUan744beW68Nz3L+LpD14EAKweP4Fjj50GAEhQiGSFcePa67vUpcdpUIcIkY5E1yVelLl/osQgPgw00EAJPeRI4UFFNixO2mjcvXMXF155FQDw6msXsblt7eS10ixlmMbUZQGqnSOOCpmDNIxbEbdnJduvMCqsyDAuipBRSEoJQSJRKBoHc20m6e5x86t1Kx8Da3ZVmUb77DnIoH3PvmOaxTLoYBmoU0TgRIaqSvfPqgqFy7VQax1FAyaXGJiAFqTMkOW5O8dWrJZue2NW48tf+wYA4Mknn8Sqq9VBuYRgooTh2l17m9abCY7IWDOAhD3LE3TfFI8PIVM4WEZwWGMY/fA1Xr90Bd/97gsAgM3pdviQlTYovfigdciToLSBMgYl89Dz+QfHRY57G1v2mrFmegiNiSulXqgMJBPzQ0Jh8veqz/tFCQ7td2ICbQ/JrufEZnDKcp7xwcqgFEouMoTJz/UGnClUKGSG3MWFVGz8kv6zNPh5lqWZnWGCNccYwls37gAAvvHst3HkhGUKxx8/ExygrOjQsOZ0vLcGIDpNwg3akxPj/dExPERM4eFHBZ6MiUzh5s3beOYvnsXdDVfTQWko5esTROWW0ho+C0CtNJQ22HQ6BZ5VaHU8inkXNreCGa1WKjAIrTVWV5ZtMlekMq0xaXBU1+cZz9/BjNvSLfR9kA39RAjUYtmSmNJUGwPN3kcpnSoU6w50wNqZFMizuIobHdO9E0cKxkA4PYIQIhSblXkBw5iUlBlqNxRf/c6L+OiHPwgAWD1xAlnh0YW9OzE9Th95k6ggExLjeKXj/ufy4ZsrB53CQAMNlNBDhBQOng6WkUYvPKUNSqcf+Nozz+Ly1WuoVVz1VDCvxdVY6ZgboKwqmy3IXcNNV6qqsDIZA7CIwpsqq1pFBOJWRi9OZJkM1Y/s6ul18OlK1fVO/mAUnRcIaAr7GUwPokG8xhgT9AaqgQwUc0yy7ahH4aZHjhSEyzxV5BnyTAZxTGsdnLyIm16NieMi4hj5bFa+z3mWxdiJ7RJ/8GdfBwCcOnMGp3zaNkFubLz4YIC+fBaOtImiBCE9t6lf2L2q4fBEibclU9jvGPHqxF25D6uqxteeeRYAcOHl76OsqvCB2nwI9sSqVqEvBlHkkFKiBAUlJDd1aa1RuI/2yNI4TKSt6SzI3coFB81cQNCoyEPehUzKkB9AChEnANphUonfQlBUAm2xwbUCLNcNRsAjO+MEV1qxdrpfK91gEiq0/VhypgAAI6cTyDOJTMaaDjwHRWSJcAV6Yz4KzxSIKFHUCiHCBBeZxCsXbWDvCy98Dz82mQAAllaPQBYZG7OGUqCHNXidhIBJIlgTUWLPcsXhiBKD+DDQQAMl9LZBCvtjlFE0MCZq1blTjzEIK92z33oOX3UQc31zK1ECKq1ZgtGoADOMmQspIaRMUABgC9EKANo5KRWZxNGlceilzxNwZ30TSusQRDUtS+ROicaRQi5F2J9nMmjbAbtaUgJ/u1WSIaGsMSEdmkdDfnWvGyt9UIj27PfIQCndcSyOZa1iEd7JqAienrmUdnENP1ps8/fi6MCiAY8UbBarULzWmDBO2mhUDjX9wde/gSOuQPCT734S4+UVjJaW7DgXWXQsYs5jaT6LOKrK2N9WsMO7ERl2jJU4QFHibcEU9jQOPJMyk6O5Vtpqyd3HWitcuvIWAOBP/+zrWHN5AMuqBoiCyGAnC59I/oHxayiKEcx4DJTW9Lg9K8MzRSYTmXzstN9eZwHYiMvbaxtYGjudgpSQTsueS4ncM4UsQ+b3ZxmKIFZICEFBe29htmcQjFkQE6USvwrrkRlMrKo5wfsZAd/fpVNQzHQ7yjOMnC/BKI/9z6VEpVRndmfhf0S0mQKx9+UFZ+u6RuGeIyAgpN1/Z20TX/rq1wAAn763jnedO4vjzh16vLyMsUvZL4s86GQIbcbgyfbXMawGczD7lgQOTpQYxIeBBhoooUcWKeyHGTa15YZbFpQOsflra+u4ecs6tVy/eRPff9VmY75x83aI5dfGg0evaOP2eOZIREwDLSUoy2FKu73lrBGAVQ5qhhT8SndkeZJozY1ByDAkiEINA0EUzsszGbz+8lwmq64gSiwWHikIighCCBHatY7l3mtl2zXz1uxe9XsUjU5ECBYH5tFY5DkmLj3dOM8DUhoVGXLhlakCZV2lik6f9ZkIXr3LLQ6pWEFB2ei/Ae8nUeR5HIvxMq5et7////fst/HkpUs4f9bWozx/7iweczESk+VljJYtashyhhooRQ1c2cz9SolZn3g6vybt4FUSz9qnKPFIMYWD0htok05cVStUTpP/1o1buHzZBjddu3Yd12/eBADcunkbG5sR7msmfhBM+BASPQJ7JjWqRgspodwHu11V4aOUYgyjouxuGBReXbaa8DyTybF7m1uolTVdZlIy+EyJTsEziFGeW+cfzgg4g2CThzOImGVaoeJFXWuVigYJI+gXETwjHOVZEIVGRYZx7l2788DICtZf7cy7XGTzIkRd18ziwJkCFx9Esu3fwR4jZsnJYHLbr3v3NvHS5hQ/vGpzZZy/dBnnXTboc088jnOuPVlZwdjrHUZFYgkhIDiwGWPi2EbrZmLImBdjtqOOIZy0+1kziA8DDTRQQg89UtgbOuDWA4Q2h+V+ZdjY2MLVt67h0hWLDi5fvoorrr2xuRXOU1qHMGjufwC3snJox4Bh6IuHrP75QkhotyJtVyr4+wNRCaWt1jNc48WC5aUxskzi5t01e57WoQDK2uZWyx4OWAThFXWFU+DJoITrRwd8fyhhp40TIWyfq4UUjdxN22CUZ1geOzGhyAM6GBVMZHBOSr7//l3Kqk6QktYm/DbbZRVcm6WU0cGIKARHkfvN/G9oi8swMc9dU9V1CEirAGxubwXfiO9vXsLFqzaJ+RNvXsb5x23pk3NPPI4nnrCo4dixYxiv2KLCXqxI64cGGAkRm/FbovQbtqIqox6FJjrPWXwmPdRMYbHXSPP99TkcGcTciVVZ47WLtmbNK6/+AJcvX8W1a/YH3p7OElmZFxnxFgADgGuyeT+FECFHYMIoKGrCNYCsyJFpCzM3me+/Uir46OtaRbOb1jAe7iPDZDTC4yePA7ATJkJrjdtrG62x8OcB0fnHm/jkPP1CaAtI186ktDkTmfgQLRENRsDMhqPcPn8yLiwjKOzzU91BjhEzr4bnizTjNYH9nnXMuzCrqvB7SCFDIhsSlP5mzOEJ3hoBoK4jUzdah/EbjQrIlVVMp1ZMm01nKBwjem37Ct68ZmuXnLl0GecYg3jyvPWIPHX6FMZLy5B5O2u0NlF+IBMXhZYoMc/blL1bNy3AQBzttcDs/wTgPwZQAvgBgF8wxtx1x34FwOcAKAD/wBjzpYV7gwUZQcOEGNV5TXQQ96taYc1NlguvvIrnX7T5+d544xLKsgoTvk7kYMNs2QgfOF9BAx9mpkc/wezqGVcgr9giAQhZYFZav4OZUaFSclnF1ammFN0EnwGhIYQM+oLHThwL7YLpDm6tbYTJwifO5rYJQUVANFEClhEEhVyPfiGTEpkU0XNTqQRR8cnr0cm4yFgYeJ6gA84gMpmFvlgmFNFVUEzWNao6RlZWtQpIYVMpTF1E0ngsYBwjJPabGWOzYAXZmZhzNEWlIygLCmUDa9YdOddyKbPAIFBVyFx16ze2r+HK9VsAgNcuXcb5Ny4BAN79rvN4z1NP4vRpmwuymEyCGZkXIrbZouDGP0UNQoikMlZC/juhlPnthfZaYPb/BfC0MebjAF4B8CuuEx8B8HMAPuqu+d/IJ9QfaKCBHgnaU4FZY8yX2ebXAfysa38GwBeMMTMAPySiVwF8GsDXdnzOzh1haIB5ILp/uUeiZg4zPgPw5SvX8NKFVwAAL114Gbfu3ANg4xia6CBotRseicQ9TpgHIO87AcxhhhDC/E3cbxxE1EF21bjpcig8dvwIjLFejII5RbXioQ0iWpECJ49az7s8k4nFwQdUcYjv4ya2XVAXUZWgg+42kv08rkKb6MUJE9HBKG9YEhriAtcdcPEl6l746MZ09x71hHDrug51IkttYBwiK6sa48JabEjI8PsprVnyFLu6Cr8+Uop0vMhVuuAuyUycHjXUqsa2Qw2SCJlDLeX0Jq7ftCbN1y5dxuuXruA977YF1J5697tw+pRHDeMgGkrE74LFXHkvu1AlzCLYDiRgmMMZRQvHQvoH/84Ln9lPfxfAF137HCyT8OQLzO6JmlWT4ndnQqCJ8VmMHLScTme4e88q4G7fuYvbt92P8sPXcfH/b+/aYi3JyvL3r6p9Oae7p3umewYGYRgGRxR9kJEgD4gPJASIgsYXjIkQSYgREokag/LCCw9oxMRoJBKIYLhEo8R50EQ0Rh90UMCB4aJch56+zdBzTp/LvlTVWuv3Yd3+Vbv2Ofv05ZwNqS/pPrXrutaqVf/67/93LwIAZvNasNUmc012fgbLGrRsn2inYPmyorBAZIsDB6iUH35V4Lr/eOtGxySwuaJPBPQoBVLJN8ERHNeGc3edwelN9yHcdWoD34vKSJk63W2HaMT92TwGVymVdAeFeGYhFI2KKGPHFVE0Cc6rOrKf8sOX5sUgLpTCtyKMTZj87SG3NvmPOHFBo2kSkZj496mFQtH5iTgUhcxUFSJYve6nTSCEmJECvQiWOSP4QRwsVIHR0BMIrbHv/UeGgxKl9kSl0tja2sWTl12tj4cuXcaDD3gC8cDzceHCeQDAaGMDIZUkI7kckF8Us2hUoTDLFiZBSGykCasrGm/JJElE7wGgAXz8Jq7tC8z26LGGuGlOgYjeCqeAfA2nJf3WC8wuFRMSd2B0Umzt7e3jxs4utrZvAAC2trbj9rNb29i54cSE3f1JNCc1WifForHemSk8R/RxSd/bNJfluXJ1Uio6KBHZtIJZDVUQyhDgo8aYGK90rGvMfUamUxvjXNEnVyml4koFlRRVllO+gOecvwd3+4CeveksKiptK/S5qhtMfbAVgzNTZeIOcg6C2rETkWVvMPNcT1VXiVMYDKKicejzP0TOBzlXKAc4jKssP994RWPg9maNxjz0jQgUAr+Uiu98OCZIFR1RyoolV1FydsO4HcPdlQJzCn/O4iqkmFUUsbCP1hpzz4ENyhJlU+DqZbcAXn92B09eclzDiy5dwYMPuApV9917Afd768Xm5mZmiSBAmKiFep0X52ToYxjBo+gcb4ooENHrAPwugJ9l5qk49CiATxDRBwA8D8DDAP5rlXvmsflunxW6At2YqB+4cvUZXL7iBnRr+wa2t3ewve3EhL29vZgg1RgTzYONlm66Nu63B3BV2ce+0F6/4QNQZHkx6acQ5W5FMeWXZQtQiYGfPHau0fhr9mdVTLKyMRqldGKUqk6r4JEngn1CcxQzbNgv2N3hcJj5PEiiEMYKcCXZSpHCrEu/4Gz+gJxwJKwEWodybjWqeYVwZiH7QmkQ5f+UNv1eju0zwgTaeK9KAJhqDRNlZxVTuavBEPAi2nQ2x8jnRghIliORj6FFFGI7rAVzkdpJSphL81wNQayzzFCR8NaoQClYzRhc9vP06evbePKSWzsffOD5uM/rGs6dPYsXPN9VJr9wz92ATToNcNsUv7iqub6Ecb29JsmuArO/B2AE4DN+4B5j5l9n5q8Q0V8D+CqcWPEOZl5iQ+nRo8c64mYLzH74gPPfB+B9R2kEA5nGP3IH2sRy41euXsNFT02feuoyrvgw5tl8jqbRuY+9SJYq03yl/UvLoa7Y4KQWlgxDPExp1ZOhu0oUJylUgcKH6E4rhcJzDd/bn+Lu086pyRiDYVxdxf3j3xbb6zZQREtM4hRcclhReLWlxA0OU8PhoJN9lynjgEVOQay7kVUtVMrnYHR6R2GVy9cuyS+kXdJ5LA/d1pj5uTG3DBMZBYXCxysUozEMJ4exMBeUX8llLEqWaTq8JylWwI1hMgyYTiWw81xNXENQpoKd2DP1olXZaGgf+DU0FpefcjEVz2zdwAvud+LDvRfO4ynvXXvfvffi/ufehzNjx0WePXM6cQ3I/XPkAEZm55itD7cOTiXVtDYx/+Hlq9dw8SlHCC5euoxrPhhlfzKJmucwWWSm47DNPpW6O48PFBWWNs3/JaCbkHgZQ54nOblMBhVsuSqEZYIUioGbINene3ihKJw68HqH9vNZCJIMCILD2WQPco0qUsq1dCPxIUSoqHsA8rlEGVlIByk7kaH8eVa4fytxitEavECYO0aXkeVjCHqgRhvUjcE0WBwAkBcTBqMxiqH7cKgcxtsSUk6KQcsqJKNcAZGfISOOBCKbUbIQ3KSsTVaaQkVLkAy6GgzKLG+F1gYTb6UwIwtrvcOaZXxn7hyerl3fwj3nzgIALl97Bt+5eAn33OO8WM+dOY2fePjFAIDxaCTmQk7I0js6JutDjx49fvCwFpwCMzCZOKp56co1POW5g0uXr+Caj0nY25tkvgXSEceICkEy8Cm3KrQp5c25gC60HdwSHfInhOcr4ezjXHkV4FckUirmAJgDUWNd1Q1OeaWjQlqZ/IM7QaRiX93qFsYlrYapLqFk/wWvE6JzpE++fIb/L+McpFIOIQw5cQdZPgMQtBQnRIeyt8IQYmGez2HWNJgF7lIVKD13oIYbKEcbvp9FSi3ADOv7Za2JK3tsW4f1SVobkvo/HQsvVwMohPgROIWi4EyUKIrkQEWkYkDZbDaP/RwOh7DWi1xmgmbu5sLu5j62tndw5ZrjlgdliSd9/dKX/fiP4sHn3Y9uBK7l+0x8mFcV/uOxzwFwuoMQnLS7tx9NSlpom7WIygtEoCsXosMytilj+FdE9zXyCW0CkUS6pJUufcq1SDCKAsbrY1VRYs9r7O+qN6JP/1h8YO3nZs8nMXtJmEXkNjOYRMSdIBBZ/+Q5UY8gz0gfTHa11yOQyQlBMmHmRMJo3Um8WThcySzPVaMxbTQq70FfDoZQI0cUyuEYhXckMizpm3BEMi4AyoiFJbOs+OdnRDgfmeBUGu8d2lZkczERiKBrkOKENOnGorp1jQ1vJRkKpzpiAgyg57W/n8K+d0zb3d7BpYcfAgD81Et/LAaeOZouW7waevGhR48eGdaCU5hMpnj8i08AAHZ3E3fQGA2thfUgsJIs3Jxb4aSrcQntcw7nFhbPSpyJVL5n57EoUE4QyqgC1pio5B6NRmjmM789xnVfoPZ800T349PMi6uU8LiSXENaHZKdGtwSJcS4ZS6wOduRFHUdXEIXR5pF/BVFbIvN/ByC30O6t9Fh1U4iBcsuipVeG4OZtckfoxyg8DUhisEwWRdaVoVwL1JuZQ4WF2NEiLoYP4V0feC/Yh/a886f12gdLQ7MLMQKp4DMcjp4xfOQBtCBa7I2Rl82TeOUiHDWJwajViGyNSktZxcvxmxdzazCwz/suIb77jm3zCxxINaCKFRVhetbzgtR6gu0Fok5OAUqZZMlTMLDpATggG//cMJw0FHGotjgrhGsMAuioBSqqsKGTzJSFgUqTwjUYIi9mUuYMq+bGLrbNBqjwk9i/1Rxd8iOkrBEcMboC9JBJCaM1A2IsWzJ0bm+Pu1oax0yUSKELhPFQJ8gOkiWPeaC1MkMScKS0na+OfB9ZIQk9ljoWvJnFoNBZtLOH9T9JKeHSPNRIoh8RSHS6TGj4G7LBAgY+LgQMsnUaazFnq9R2ugGo6rCyJsxpUlbEeHq09d8W2yMw9gYbeCCt14cxQi/FkTBWsbc5xPIC6kmjsBy/gmshPaJXRdS++BqXENcdfx/XXoFKao7pWOQL5XPeehPLCl+PIYZxl+0N69wtk7mybG3Ued+AV6P0kH8yK11ogHhI+SMU8iHhZB/7Au3zfodfnV/shwJIykVvy+LRaIgt4PnqjE295NYWf3TwTsKZRPDqUoJ8AAAFXxJREFUmXCD4no8GiJE+JOlmNY/v2OrxpZsc4trCGe5auL+SAGvRwpm6ZxrCL4thczVIXQddd04t2nPRYxGw+h5WhQqeovuT/Zx9WmngDx//m6cO+uIwtm7Uv2Qw9DrFHr06JFhPTgF5phFJxRPDduB/Y1igvtxCLdwdDnqZtE2Sa7iyOQoe5FpqUP1JgMbted7Qnyoah3HaDAc5joFIT5IpyYoWW6VxQLudQ0sVtEl6FqcpUVl1Wvc+V4eBsGKlV/ex1gb37nUHWV3Tm6F/k/OGeQiA7Jj8Q8lh6/pbI5TmxsLp8ncjTEASYgzLJQiJDlZMeQhX6Vli7IsBXdGKdhJcB3OKuS2y6LIvCbBHDkCay00AudJML6qGKyB8qbu3Z1t7OxsAQBOn1pmslzEWhAFgGF8Ci2bsbWr8Iu8ApG4Tci0ebk4syCFAJlJMgehLAvUPhrSWhtfttEWm77y0HR3O/ksiDTww0AUwkSSs5/TRyIDlSCIqmuvVDy2c0gsjmanKJF5BR4MqYCEUiiEjsEyxw/MCRxJAyJd003UNSxvp6eWabvTVO1+BJ8BxQqTqYvrO7W5mQcaBe9Yb2pUQbKgdG8nmYkR6CCy1jKaRseSfmAV+XTpMyF7RcyZm7yxNqWqK1LUKpBS1DvdCMfr68qZt6eTyUKblqEXH3r06JFhLTgF6YW4yPLdwn2xitrw5q7KFI3AgidjuBXLnWIFKVQRlVAug7PP0GMMgnptzsCud2Q6Wzeo/WowMiZmBQ4PYsEFZEYGJdfVpGjMtKDgbu1o2uG7sEwT3/rd+YvzPRQUoTl3AKKMa9B+zGaNxp533Nmvm8U5cuikyRWN8rcsdDObzzEejfPrAIDJiRPyiAyiEn1bUNzCjzlSaQEUnDg9kR4uhaUHTskHcfngKiVyWkTrg+A0jKjWBbZQ5K4PiXFXwVoQBSBN2OVhMquSiiPKEitSjvy09CtqPVpiA9q3dUqFbH8IvDGWkklKFdHl2RRFTDNWNTrWhqgbjc3BMO9mJASMTrsCyU866BTCoZQ+nak9fssHJ7MMLD2jRQz8Fsu8juIsK7Yby5EQzuoGOz4RzI2qwYQppm+XIlMmPSxYWFicL8Dpw9Jao1aO+LiisymvJpDMpe4y+czsCb6XBOknIs9wwVnufUpdgwQJszEjeF56vYwoKlyIBDzS29dYm8rpHeGj6MWHHj16ZFgbTmEldC2Nd0TDGFU9KzUmXx3SjqWKRq95T+nZOPq+A4jiQznawLRyCrB506AKqcW0wdAYlJ67AMnQX9EusSKqBUtBS0SIPCsvkX+WI/dZkGJG++K0P6sF6dyJ4jmNX41rY1H5VW/WGOyHRLPM0MoV6QUAKtrTWLJtXdxB+LEoGimlMPcimyIVxTrpEBWuzPZJTqHLqSnMBU5KVB39ITSKMhXmiXxN67UAyFIEhLD6oihQlinYzopzrHD+WxVrQxQ6WeFVzj3g9CP5uhxR+dDWKYSN7FZiIjpOUBIbii9MUSpAQgB8qghsbpxC7YnCftXgnMgx2WgdiUL2ecvZmmloOtySZeODJp0ob2f8jg9+KdQeiMUzoiOTsS7hTch1YdlGkcswo/YfS6UNKi8+TBuNacjmDAUqB1CeKKhyCFUMRPsXe99F4LrIukyYMplOccaXfXP7kvORMSZ9/Jlj1CGTV4qW4UM3FsFPFWVKK59dAG8hEnlHytLnhyhLDPxYlGWBIni+impdXQ5Zy9CLDz169MiwNpzCnZADjsQAdEoMh99hVUUj8+JKHVLNDYclWKeIhtJT+sYyGq9YmmgdQ8frxnEKA/97MBhkloFuriu51XZCqrxTEoLF40gcT9YdwSkssz7ELEreQS33QXDbjWFoE7YtZo3I2Oz3GwCDchg5BaYCJq66Rvg4td8d5/93SaCM6EhmrcXE2/fPnDkNRQo2FmKQDFmegVyWoCPkkyG6RmfvS1iPrI3+G4Uoquui52RQmIYxvuZo5s9QRJEn1Px0Y7w6p7BGRGEZDtcdLJqBbvFxC4TBPYWzU1bQKcQd4fz0gRAh+viDUlr10XAYJ462jMHYedptVzNMfXzIxniMujGxwGlZljGzs5vsSSbNEcxei92WplWKcq+8QbuXLT17h0lWnmE5JcIxzFkBnsbYWOGpMSbqFBprMfWxH9Nao/E3tESoGw0FkWsxJE6wHJ2yZMGarIGckwdpvZBQKiVC2Z9McWpzI3lhegvK4shQRgDSR035MTFmLjFOaFv68Jk55uAIAWzS9G2EY1V6TLJEFGUpCNzqX8ih4gMRfYSIniGiL3cc+20iYiK64H8TEf0JEX2TiL5ERI+s3JIePXqsBVbhFP4SwJ8C+JjcSUQvAPBaABfF7tfD1Xp4GMBPA/hz//eO4ihKytv1wAVpo0vRKNvTUvY3ruY5AFfWfMOnXdNax9BxNR7jtHd5ntQVrvl6k2fPnHIraoiFsMO0OgoRYangI7MzZY3MLQkkUrjl3lmc/wa6uQ8iZKXqWG5zFIfmjcbccwTzRqPpUDROtMYshNSzASyh8G0oykEq4KESp6BkzgWxgoYXuEzMClCkoPxKW9cNmBkbMVI1hV5L13xnWRL+KEJccMOejuXZnvx2tkwnrqFQCqos4m9ZBrDdl2hJIRWtGiF3wyq4qQKzHn8MVxDm78W+NwH4mK8Y9RgRnSOi+5n56sotuumPupud71QVrHqrpfoF+Sz/MR6iU7DWVYYK0FpHkWE6nQnTF6PyMRGj4RDwk3KwsYkblcu5sDudYzgcxrh9bVLOwQNffSbX0uKBVt8gRQmZ7pxp0ckp67O/hnOTWNAbWLZojEElCsTWwkkrOCxV2mDfmwf3qjqaJw0TjK5Q+raVQ0CFFHCWoxefVQrKym1PLFS7/+J9tuZfeEdcqKgDAly4teVUmCUGRHWJDEAkCNHw6mzSaVucJ4lKcpByf0PK/BCLEbYj7c7eSQq0CmLEKrjZClFvAnCZmb/YUub8EICnxO9QYHZ1ovB9gC71Y0ZHgtLLWmgfvWaEp1lYAWIdAkUxn8QGJTl4XteY1S6pBlGB2mvTLm7voijL6AU5aHSUPYkoVSZ2O2LL0gRtfxQSrY9FKhsWRoAXLxH9l7kz5ba2FrVP0w7kHEGtDeaeQOzN5tjxyWdmTYPaD59hp1zU7GsoMDDwzywKBeuX24xTYAYHYsEqFsntxuLKFJW/TajUjWgGZFFdXHIAiwRCKj9zhayMGJUZquJZ3hcicg5FgVJyKsIM3cU13NHErUS0CeD34USHmwYRvR3A2wFg8/SZW7lVjx49biNuhlN4MYAXAQhcwvMBfIGIXoGbLDB7/t7nHlFoaAuEK11xVP+k5QoCIWu3dQramKixlvoBYwysEYnUOMXaK6XiClQURSzEOp/PsRtXOovaBwTpGlB0I66Iz1VFzEtISoH8slUUaZDyhYJWXDkSNyDc8NOPzluQyCGQcwqGRTEXLcSHRse8nLO6xvauS0G2tTeJJknDHM2O2gBMJnFkXEVHqNFwBOXjmy2pmFvAWlHFyXMQQZygLO/EcjhuwZ1X103GmgcuJNcjtDiDJTqFtignRYzMdNm2TIiqVFFfw+I9tawSq+LIRIGZnwBwn3jYkwBezszXiehRAO8kok/BKRh3jqRPuBUch4Kx42EM//EHQmA0TNxOBEIWSAXcSw16BGdb9uY6rcHefbWqKky8LKjYYj718rWuYZsmEgKlCnAsdUYYD8Nktcg8gKWucMVJwiJjCOVzF9JlNxyyItCpnTCnESKC0yOk3xMvPt3Ym2DLE4Wd6QyzkJqNk1mNXWuS664xMD5vAFuOVZ+VsrA2pT+LHqRe1xBFC5uKxR7kyyHLwBGAylcyG49TElVJK53eIA1XVpXqAD2CFPmkHoiBZAZlEVkKldYra8U5aaYe5fNYxST5SQD/CeAlRHSJiN52wOn/AODbAL4J4EMAfuMIbenRo8ca4GYLzMrjD4ptBvCOW2/WUbEaHbxdlgjLHEUBY41PqulWDa1NzjUEkSFL/ZOURYBbGEK6cbY2EzlmPlFnSYSJL05agLEzm+PKtsuAPR4OMB6F1TFfjYIyEoL1DcqsQ0eNgZT+vT1qQtEoL7FprxQfapNzBuEfAOzP5tjZc9zBjb0JtnwWpEldow4FW1rBvxz/c+JYCBEO2awAN6YqVoVK9R65JT5YZaFCQlViMYaCdQ97KDgGpVW8qupoqgR1ezRK86P8295uD7PMnEUkVn5OAWVKWdSN63fTNMLik9LJHYVVWHOPRj7g1/J9txPSTTRVKzItolBHnwFrUwbfnBCgy6IZdwdNtrUmyodFUSTxA6liEVuLSWVh7R4A58469izzoCyziRfs1KcDcRBPPZQ4UhIM2uMsTY/Sns6h3347eN01osJXbRxB2Ju4j39nf4Ib+86deGsyiaLEvGmS+zJS7kaWPDmCxSX1ORDiNoEMYHI5FWN1cKb4zqyyUFHkyAvRStmgUAqEUPyXYmTlxsZYuIHTwsffRQwyPYQ8DgEKdwxN4ZgqztrkD1I3TVxIrLVZOrlV0QdE9ejRI8NacwqSRRR7DmQPVqGHB1kijKg7YYzJahmG6kXyHGtMzqY5j5+F+zonIL+ChjRcWeYht10OykjdCcCuX0ELpaC81tBa5/wTFErXtrcx8ErL8WgYNfFKqSiiDAYDbEqtI+W8AndsyVHqMsIgW0QTdySVW7F8vEniwryusbM/wY7v2439CXY817A/rzD3ykXLKbdE1hbvY8AqWVyUqKHQpSzM+Z3wK3AKwrrPgA08mW2x+62gsBSXkJo3r6pY1Sl7flssaPswLFM0LvQhbwOQ502o6xpTL35VVRWVrnedWb3uw1oThZVxCzIEw33kqQCJyaoFBQcja022HV7IcDgAmfY9O+QEoa2PiblEUoYQ7z4ejSLxUUphPGriDVIJNkDX80iYJvMaV7e2AQCDosALn3OvO0+YsAZliaFPHT+UkxntzSXkUkpCC3JvvjOTe2NUn8XMiwU7+1MvMnjxYTLFnmd5p02T8izIeyGNqyoK5+kZLDNKRUKgMlFCFFZpuX+L73ihn3GzRdwXBC5/uCzKeMySiXqN8EGmp7aQuTmnfd0EYuHieEunX0h6hKCfGo/HOLW5ufTxy9CLDz169MjwA8Ap3BybIBUzTdPEpKiykK21NtuWdu6QNDNotI1Ip9a96sqwVydKcJuTQPBfSGJCYEWtcFYxiqCIoSuvUGJg17OMV7a2IwdR1U3UUI+GAwz8ylUUztmpy1dhqT6KuniBdFC69oabWE45FLS10XoSRIedYGWoqujabNkK7qDFPvtnKKVcrIPgFCJHIJSDmcsw6IBVV/Sp7VuQut9xRVJUpmK1BKKgXNU++evye4jOLTwzi5Vo8zYk75ja7NrhfmyMxy1uZTX8ABCFo8C9xKbRUVxIRMGbFEWMAlu7dCIlbzYFAmL9x07TAgBwSkcWD3R8gVprjEcuMtJaA/ZEwRgDEzL2KpJzF001g/UU58b+fiQe2hhR7FTFCeq8/lQMkslatXTmSq0DtXcllj1TNKRiLnWjse/jGPanM+zPqxgEpa2NIoOzXiw2iCh5JAaiFoKgXDRjl8hAkCy6bNdBS8kROG0AwLyapziUsoT1bbE2WULKsjzyjbvFBznOoW+IRWVPbZ6KfZ3Nq47cnIejFx969OiRYU05hVv1PpBa5uA+bFCLsu5pu0HdNBn7n7iyLu0vMoofbdnUiIOp/SLtf7ow2rsXqbizcjhOpSwHkeo3TRM5gEK5lTG487DRMIHTsYwdn0JMZjcy1saw29FohKIsoZRYxZBW6gwd2sXOcQjaf5VWZykeaWMw927BldZorIH2ClVtbVbWUopZmVgQ3LqLwmcwTm7e8vkZp9ByHkrdyjk16Ve26uwLdxyPRtj31pOqUPE9ubFPlqCyLA+Qz8JNu+dcEIW6Fv6yGGDDZ+ja3BjH6uQ7u7u4sbsLADh//vSKvQLoKE4NdwpE9D0AEwDXT7otAhfQt+cwrFub+vYcjBcy872HnbQWRAEAiOhzzPzyk25HQN+ew7Fuberbc3vQ6xR69OiRoScKPXr0yLBOROEvTroBLfTtORzr1qa+PbcBa6NT6NGjx3pgnTiFHj16rAFOnCgQ0euI6P98AZl3n1AbXkBE/0pEXyWirxDRb/r97yWiy0T0uP/3hmNs05NE9IR/7uf8vnuI6DNE9A3/9+5jastLxBg8TkS7RPSu4x4f6ihMtGxMyOGOFiZa0p4/JKL/9c/8NBGd8/sfJKKZGKsP3u723DaEZJAn8Q9AAeBbAB4CMATwRQAvPYF23A/gEb99BsDXAbwUwHsB/M4Jjc2TAC609v0BgHf77XcDeP8JvbNrAF543OMD4NUAHgHw5cPGBMAbAPwjnP/PKwF89pja81oApd9+v2jPg/K8df530pzCKwB8k5m/zcw1gE/BFZQ5VjDzVWb+gt/eA/A1uHoV64Y3Afio3/4ogF84gTa8BsC3mPm7x/1gZv53AFut3cvGJBYmYubHAJwjovvvdHuY+Z+YObjHPgaX0fz7CidNFJYVjzkxENGDAF4G4LN+1zs9K/iR42LXPRjAPxHR58nVyACA53DKjn0NwHOOsT0BbwbwSfH7pMYnYNmYrMPc+jU4biXgRUT0P0T0b0T0M8fclpVx0kRhrUBEpwH8LYB3MfMuXC3MFwP4SbgqV390jM15FTM/Alef8x1E9Gp5kB1PeqymIyIaAngjgL/xu05yfBZwEmOyDET0HgAawMf9rqsAHmDmlwH4LQCfIKK7Tqp9B+GkicLKxWPuNIhoAEcQPs7MfwcAzPw0Mxt2ec8+BCfuHAuY+bL/+wyAT/tnPx1YYP/3meNqj8frAXyBmZ/2bTux8RFYNiYnNreI6K0Afg7Ar3hCBWaumPlZv/15OF3ajxxHe46KkyYK/w3gYSJ6kV+F3gzg0eNuBLkQug8D+Bozf0DslzLoLwL4cvvaO9SeU0R0JmzDKa++DDc2b/GnvQV5cd/jwC9DiA4nNT4tLBuTRwH8qrdCvBLHVJiIiF4HV3j5jcw8FfvvJaLCbz8EV5n923e6PTeFk9Z0wmmJvw5HOd9zQm14FRzb+SUAj/t/bwDwVwCe8PsfBXD/MbXnIThLzBcBfCWMC4DzAP4FwDcA/DOAe45xjE4BeBbAWbHvWMcHjiBdBdDA6QjetmxM4KwOf+bn1RNwVcyOoz3fhNNlhHn0QX/uL/l3+TiALwD4+ZOY66v86z0ae/TokeGkxYcePXqsGXqi0KNHjww9UejRo0eGnij06NEjQ08UevTokaEnCj169MjQE4UePXpk6IlCjx49Mvw/KipS53075EcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 30: 0.0011686686193570495\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 40: 0.0011686340440064669\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 50: 0.0011685993522405624\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 60: 0.0011685638455674052\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 70: 0.0011685279896482825\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 80: 0.0011684943456202745\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 90: 0.00116845837328583\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 100: 0.0011684229830279946\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 110: 0.0011683866614475846\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 120: 0.001168351387605071\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 130: 0.0011683152988553047\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 140: 0.001168278744444251\n"
     ]
    },
    {
     "data": {
      "image/png": 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QXYoAzBNB8LRACIK0MxUBMyHJKYJJUWBi341RFmZ5Mi0wYXTDKAxzfiklCuelkdK7WqXSwe2KsOzeqns3WhvFYJa198SYxNReW0Da89Y3tvD66+fw+fM/AgD42Kc+waIcH8RufLCyb+8DES0B+L8B/Fda63W+T+so40b9vF8ioueI6Lnr16/vtxm99NLLAcm+kAIRZTAK4V9rrf+N3XyViE5rrS8T0WkA19rO1Vr/BoDfAIAvfOELuwqDPxTpaIHbfP36DTz/7HMAgMl46guZOJQQDIXMYq/bxx13TGp35kmCgQ27zZIEiY0tyNMk1HsUAqk9JkkE0iTxwTiDPMOSRQQriyMcWVy0ywsRfK7TBb7uK1AzoyMQcDk3NDqRrOiMCzuWMsxYlFL5ZLXTssRkahFEUWAyLSPKMbFUpCgrjxqKUvrzS6kY3VCYSoWSoTPvieDogL19pYOHoigrnHvnHZx7/RwA4MmPfTQghQcV8x+g7Mf7QAD+GYDXtdb/C9v1OwB+AcCv2b+/va8W3kPRgO/8N2/cxBWbJEVr5be7jh9Rhg5XnZNcEBazDCcs318Y5l4RAPCKIE8TW/gEyLPUU4QsTe165tcH1oK+OBpheWHB3pN8tSZOUYSIASIHdFo3IzG7pK2AvFQy0AqlWLUqiYpt58pjMi0wdgpiWmA8cctTjC39mBalt0lUlURVKWzbfdfGEyT2DUsWLan5LCrAK8hKEe5sbOK1V18HAPzkT/8UTthKVObwD7ZW2A9S+GkAfwfAy0T0Xbvtv4dRBr9JRF8G8C6AL+2vifdWKsuDr12/6efzg4Ush8/HutfqkYps2aGBI8MBji0v4PiSUQrLC0MMbLLSQZ5hYA2FaZJ4RVBXAlmaIrUKQxCBbEcfDQYs5Dd0aKWUH+i16fnM3aq9e1Azn2y3OtCdq0mSeG0xS0FwpTDIMixKk0BFynBOKSuPIMaTwtfDGBeFURLWiLmwmeP82rpviwun1jag2kj4NaSSKEvCuXdMqbq333obx06aithpnqHllD3LAV7qrsl+vA/fQvez/uxer9tLL73cW+mjNqyEUTQeB11R1DtrayhcbgSl/XbnbXDpvOquR4fUR0mC1SXH9Qc4vryII0vOXZi2IoVhliG32/MsQ2pphRACSSIgBLc32OUkicrPOxFC1IKSQuSl7kANM6BCp/BTUqT+HlLKyAbBi+JKviyDHSGXKYYWHS2NhuwYhaKqMJkY5HDyaIHR0Bx37soNTx+UDhFOmnSINNQEqTTWbQGeKxcvQVlPCHL+FA/S+H5w0isFtHz7Ovx19GF9fQNaBaNbbDcIVyCwiTuCsGgh/omVRSwvDMzykWWsrix5OjDIM0YTMuT2nCQJhkaeYs2ZByN6YrVPkoaOKLT2bTZxwsxugFgp+Eev2RBm2RSi49rW2MYsTb2ScgrC0wkpmYKQUeevmLJw2yupkMsUI6s8l6WMqNUbl41te1PqKDxVW62gyLgtpU3QsHFnDaWlhvlo0B1GOa/cBZ0y61fZ7237CVG99NJLJD1S2EHcSHX96jU/stVHz/rcBU4Zji8bT8CRpRFOHTNFSh45uoI8z3zwUZokwSUowhwFI83RvB7qKISIzudDnU+cyicK6Zg+8Elg0ZN1RBe2bZ8XUQAAsiymE51IgaGDSqKy1aJEJVGxICsAOLJoXLIL65tYtpRrPB5DunGPPSNAKMrK3//KtevYWjeGysWjR/hT4aCG+7vp6dyvf79XCjvI+roJZ7506aLPxqxYHkVHF9x6QoSR9XmvLi/iyKKxqp9YWcYpW7loYTCASIR3Q1LUqQlk4wTM30BQ6j82MRejO5+YJwKaTTpiSsF3amZj4NLZweteiZbz69eNlUxYFEJA2A5fD/P2yktpKMvFhFAQyr0j3Vh373J5OMBRqyDWplOM2YRNPlGKCLhtFcGrb38fV22OzdVHH0ViaQmnXLCuynk69kHmZ+gSwv47f5f09KGXXnqJpEcKQH14iza/964pAruxtYnSFUEdb6MrUj4VhBMrSwCA5dEAJ4+uAAAeXT2KkYW1xnMgorkMIQuSCHMMSLTSCm9IZPsERx3WOKl1SI3GG6q9i4WN/G2oQTcwANveFavQhQ5qxkwFj7yEEN4IKXRAAFpoCJelWQkIYY9XZJ+ZneOQwsIIKwsGnY3uJJgqQzlkregtf+aN7W1cuWyQwiflZ5G0hWVpYK4Ks/Tg+yw+8EqhDYK5bZPJBOfffQ+AmQ3pbApxWHMMfzMh/Ed54siSVwqDPAvBRoKQcMrAID+JZt4DdwyPSExqdoSIPpC7Vjz70T+fVQqBJuhWnRB35Jbt0R/d2F27sr+IkgpE2j+z0BrC2WsodHalOUVQUJrRB61CDggVzlkaDbA4NF6exTzHndIFmcU5G3jHrcoKV6+YaNWyKJCNhmgVbVyb7npdx9wtrcCdJAcpPX3opZdeIvnAIwWgW9Nub4+xeccYo8qywta2yZkwS5NmicDSyIxUJ4+uYDQwlGGY50gSNrITxSN9ZDR0o7uIEQQ7PkkSH7wkhGDzormhkqOOmH64UGfz/Lp9dN8RNTTRQRdq0NA+47ObjOWeX6sA/xVDDYIEtKMMWkCo2Ojo6YNQECq8v2U70h9bXsTNsSlQu620twCSa5cd9Tc2N3HlmpmpW0wmWDiygn3JXYhT4HLQRsdeKVhpqw+5vT3GzWsGViopo9/Y0wcd04c8SXzw0WgwwMhCWWdHAAx9EFTzPrR4EuqKw3VqRx28kokURBKUR5K0KwX7wHHVqhbXJ7oUhN0zg1Lw+wCAUhLa0xoN0myWphAQzqagFDSjFZ4yKB1sCtooBMFsEty+4JTyME9DwpqmFcRLVVW4vW5StZWTCfsYug0EZtrVDr3+AaUSPX3opZdeIumRAoAOGzu2t7dx/n3jfdiejGdewaVGy9PUz0MYsSnRkaHQehWokz40txORD3lO7QzJxBsuOToQraiB508AYkTAqYR5B+3ooBM1zAhyUjbPAmkBsnBdW2OoWxckoCmM+rrVE6EgGGpIhIJyz6wUyw1BGLiQ50QgdegEqjaa6sj06DxL63fuYFU+Zq6bHkD3uAdUYlYz5pEPvFJo8DG2Mp1MsWEnzRj6EDhp0xVpPr40TdhEpAD/qaWzR56FFk9CIoT3JAhB3nuRphmyLPUfLacPVKcPbUrB5CmLIhpDh48nR3XJrAAnt0dJGTwhpIOtQwto0rGXhVMGZlNwlEHXbApKUXQ+Ty3nbDImYU0zOrQuBO3zN25tbELZqdviIeodu9FJD9FjH5y4T2ft1m0/IarhzmKBboJMbkUAyJhSqKTE0BoaeSyCizpsUwTO3Qg4WwNTCh4pJBBpGiZLJQlEwhQE3y7aGCJBE3NTavhR09jfWmI2WkK7O/MuuAQ0QvhjIlepUxDO8CcIpJsKkrskFalG7sgox2TY6ZezNMHAKk6aFq7VrYa58fYWAGA6mUDNqHLFR/5dgYC7aF/Yr/Q2hV566SWSHinUhI99ZRFyMXKp0wdBhKx1RGZeipaoxUANapTB2yGCezKJEEBqbRIcKbQvdwmxoU6zSD0etMe9B7E9wu5lOR45auCISFMTDbhlzZED80SQr7nJrf/E3K52bws64O3M0wRDO+mMavCAEM9fkdamUJWFSdt/GPKAoIVeKQAAKOLXynLXqihYEtNuMenWQwmybhbLPurah8z5fjA6Bigd5VawiiPYK9gsS6YUZsbcMj89gVWFihSE9p3anOMUhDkrjldoOsX4c2lmWHRUwhseWYcn1N6LawuBLVO8zpyDxH7LLE19DYyUCI4U8Ilh7tqFzRFZFlwpzNeLd9XXu8Iq7yPp6UMvvfQSSY8UWsS5p9I0ZaMxhTziNRUfW7jZqMdH2dr8f6qtzyMcTZhox9hwadopolE3aldtlQ/0btSOEAAHGi2owdsp24FC1GZOJSLEVG8a1QvTtHgYZhgaiYKjMU9D4ttMCF/4tm5kJAIq+5tPxmOoKkzpPshIwYbcp6jhIIrBJET0IhH9rl1/koieIaK3iehrRJTvv5mHK3W4r2yqMJ523XkB3EGOJggCBAhKa+N1oJhCuJBiBo5rSVSaotHy4bJWhtM9D2k8D9XXCI3/XWcistcg1z63Gh0M91+tEcC8XzRrLrHrhU5ur07hmcJ2dgyhuc6WbZ56EJFRDGmCTIjwXmrNFQhxJOu3b0PJCkpWJpVd24/RInMetvMFDlULzScHQR/+SwCvs/V/DOCfaK0/BuA2gC8fwD166aWXuyT7UgpEdBbAfwTgf7frBOCvAfgte8hXAPyn+7nHXZHayOFGdzffgIh8WrC6OKNghAjskKTtaMXrMe6WMsRtZBigfh0Ko3nng0XbKVr1KIB4Q4ktBjSxE9KZ82Fi5MKeI0YwCG3iqIW9jxjpeKAAaO2RwihNrcchlIoJtySPFLa3tqGkhJJydoo554E5jKH9HiOG/doU/lcA/x2AZbu+CmBNa+3MtxdgKlHfd9J45+47ZTuSmpuxjQL767EPyFVCmpQllmnBnrT7jtTwXDRoQ0t76vi4ubNlXbNFxqTJrbvWWG/BQXywUTtjbRnZFBzx8nTGUimubJkqdJmqXYtdxufFYY50YjI2S+2eNxhP3MSp6XTi3ZNgaffmfeQD9TreI5vDnpECEf1NANe01s/v8fy+wGwvvdyHst+ycf8JEf0NAEMAKwB+HcBRIkotWjgL4GLbyXe7wOxON4jGQx9jzz0EOgTfsBPcuCpdbINUKF2B1KKaCbV3+9CRsZG3uuses1ACAwR8hQi+PkKEY+sTRDTfpHf/MLwpxDFDnW4xNOAft9v7oNmU7kQkWBiYadQLw4EPRZ9KFb0NPjGqKAqUNmZhVxmqmewq/PmeXrRd9owUtNa/qrU+q7V+AsDPA/gjrfV/BuCPAfycPewXMGeB2cOgUZFBV8/+n3NYqSSkko3AJU5/BYLVGtCotDL/S2UqJ5cVpmUFpbQPhtqbxFTBb6of5TsGxQ1tkHbwHthiTwH8S4mM73VLfLyuwepRYi+/ZfM5nSJoeCsoKI+698EXuoFGkggsDHMsDHMsDgcYJAkGSdLar/xvTsD2xjq2N9ZDIZ37Se6Cl+Iw4hR+GcBXieh/BPAiTGXqueUgOFl9UGtsmyEE+BmHWqpQNwFhpCYK15NaAVogY6ObsykUZYmpj3lIOtsws20ekdRsCkTt72nWy6P6wk5vJUYAfOp06P71Uxh+qJWm4wpkprB4jNgo6d5xHNFo/ZD+/m67Kadnxr2FQe5DnkVRoG42duHsSimMN83kKK4UImA1pxyofaHrBk4O8EYHohS01n8C4E/s8jsAfuwgrttLL73cfbkvIxr3owBbqO/uhIAlW/i1qiqfhtwY6Jvjo9Q2GzG50SnxXotKKV9KfWk02vnG/m8Xvo8sCbURddY13SI/jr8cnlqNj+js3zb45dbq3LtWicpti/I36J29GQYcNG0K3lvBUBQvduXS1Gn2uywMc4wyNg9CxxPd3H1kVWFqE+pIWSGDi73b21B810wBB4ga7kulwGXeZ90PZeDCu2FVVf6LJcC7rSQ06s4qF/GYJgKprRCVZ6lPjNJsfNz5u9pa7wft10KA1nVFEumB+d7IjolbG/vCutaIKIPWLkmKipbjHJE7V7pumxzFDZL8nk4pu1gTwBgaXer3bENgKmOlkNjfr6oqbN4x+RqnW1sYLS34ZjUoxF00/u1K9tmufkJUL730Esn9jRRqlpou1HCwhliCmxyUJYm/OiFMAIIOo6nb5EanNEl8Buel0QjLtq4hMaPZrtrb6kXgsGG253H2zTh8b0cHbVmuW/f5aylPubRS3linlW4st+5DzTjJblqfHMWnUrujBBEoCbU0nQdpYTDAEUsLF9buYEtW0YDqJpdNiwKFDXKabm1CqRPmmKRG3YBd1Yw8dKNj102d7OLm97dSADofrPNbn8eOoJuLnJ67j304Gnooxbtk8wemUN9hmGPZVohaXhwFKAszecrcgzp/pGbbmx+jX2/hE6546k7SmWKx8QK5x6GuOOo2gqbHoXtZdVALHRXyde5cpRU73zyze04X0gwYewJ/Nq8UhgJHLBUYZSnEBHAEIrIRKelcfx0SAAAgAElEQVSVwvb6OrRkqeVq73W3A9M9UQz85nNKTx966aWXSO4bpLCT51wD0byELpg8SyG2jY4zbHYYDgZ+1IrNd7HOTwVhecHQhMXRwHsalhcWancII3s95fqOEof9RaK19hmIoRGljo+Oa1y0BTKxlW6PQ0wdtFbez28og22L6kIDqhNFKKV84luz7OIHQpZnfz7cbaQ/JxFJRO14YZ0FS+sGWYpMEKY+1V6Ah0oqTG1E4/rams/CJLLZXWXeyI8HQe4bpdAqNXNvpBN0fMxOPwb/iBveucZtzc6jx45ieWiowHhr3PrLCyLkSYLhwLi7lhZGWLIKIkmSyCbAreVSKsDy1FSICPLv/GGRVQTSPxxpx6OFX9aaZXPewdbQ6XHgm+s2hRZFEHVw3WE3qNsUakqhss8lqwqVNJ2yqmS8XFUo2bp/Tpbxhad7F2SiGwFgmGUYJikKVfh34x5NSukVwXQ8QWFLBeajAfM4dUvDQ1GT+9VhwaWnD7300ksk9zdSmCFe4+6AEhxC2AkdOCEEq/LqieP47Mc+AgD407UXzZTbWgsSElhdWcbIjkJLo6H3OMxqk1TK8xRTW9JlHY5JSnSeM7pJadGBG8bDstba12UkrQHNS9y7MaAl0JgZD2fRBncP7llQ3DjIUIPLSwCYEdhVi1JSQcoqIAIpfb4KKWW03S1XVTi+rCoTWGablkQJboWPOSARF9xxk6NWj67gzvYYG1VRfwtQWpvfBkAxnWBiiwEtrR5rHNs17D/oVOK+Uwpd8Ktr+04KIfLo7XDf+vLykSM4+6EPAQBG33sFm8XUH+O6V0oCq8eOeJvCyuICkiS81si7YS8steHKZCslS0UQviqRiBWD/fJNJ7CKSClbddqVVIurUyuvbIQvrRblcSRhWsaOCzpBR/aBtuhEJSW04nYE6W0aRilIvyx5p65Cp+5SChVfrphSkIYymPNlpAiAJHh2QHFafKYU3DyIpdHI2BWckqy5d939y6JAMd62z6xAaXfK/N1K/bu9n+hETx966aWXSO47pNCQ/WKxOSmDuz6xmHiSEseOHQUAP8q4491oPsxSLI4G3ge+YgNk3CWZv8Eng1ZKQ8owipYVYeJHNBEF6aSsqGyWhsKxSSKQ2NmcCSsqa+pDBETAEURUcEUIX6AWaRqs70o1EIFb9sjALnOkIC1SkNJMQXbLERWonGGw8hTAPH+MCGTF0AFDDZ6yJQJECVh1i9YILiLy7yIh4etmEBnK5rJwa2Kog8i3X0mJydjOgygrZEmyq4ClnYyOXPYYZ3Qocl8qhf3qAW9HmFshaJB1Q8miAKxiqIoCS0tLAIBRlmCtpfD0aDDA0sIIK4uL9p48TVh4Bg14fl3JKoLGUrIOxibxkCAbVQlkbB5FnmZR5xdsElakIISAcJ1ChGIySZogzfJI+bhzlFQ1+0DoIK4Ty7K0FMC2X8U2gTYqwJVFWUqUVjEAhk6UZVgO5we7RZoKryCjrNL+R2SrFDb7qEdBoUYnmWWfbk8IX8wnz0KNTq21D2QqJxNk1qW5G9nLt3yvFcR9qRRahYIbcuYLnvPtRzaEosDGrdsAgPVbt3xHSJIEOStrzifw+HOJkKUplpxxkUf3AewcsJGxQlmxDsNGXaWUz+KkVMgQlGUpMqsUsjRBlmUeOSQiYZ0/VgpJhDSsUskzjEj4SlL1mAnFbAJaBqWgWQcvq9L784uiqBkKFVtuMRpKiaqSXilU9n24d+NsD2kS2t/aPZjypXhTq7io0zxNzXuiUH1rYHM5ZmmG1NqEkjT1aErts5TcblADl4OzPcx/996m0EsvvUTy4CCFeaWO2dsGGL6iFGRR4NpFk0ry+uXLkJXJlpRnGaTNh7AwyKNYewdLszSxU6UtP0WAvK4JQODRZtmMkpKhg4AUdG27uUJRVazGpIHSbop2lqYeWhtbg9H1nHKkSUAKwALyrPKjoE+RXhOODnh0YWXpAqcJrhbjpCh9ha2K2U1iKqEi5BDZFCrp7TexTQZoTohC2DsHVxzYqMTVoyvIL6Wefg3SNCBChg6SNEPqaoTOUVP0bsjuqcXu8cl9rRR2657k+4HuCSjRdikx2R7jzq2bAIDNtdueU5fMALe6vIz0qsk6PfVXMQarjB3Hoyu11jXKEAxrplM1lYJk4bx8OzFjGBGhFBWSkhefDfTBK4IyCZQhTTF0+QTKDGVZtiqFeqVqxQyNdbtB3MkDZXDPOS1KTIsyHKO4glCR7cAtp9ztp9s7gbMV7JhjhosO8ScLwwEWBjlGSTDOOoWRZVl4f1kalALtH1TvlUJ0yc4KYm936+lDL730Esl9jRQaMq8plx+3U7B5miJJE0xdoZCqgrLwV5cFUpa5Z2BHli0ZjE5S2rh7P5Io+Nn9bKSTNsszYCCysca3GxcDagjLQMjuZFLPsyg+UQXPghBI7EidJImnFWWWeTRRlAWKMkPmoTErStsx7JpAJIcGVIwOVN2gyI5z0YEsiW1ZVpAyzHEQzHVoDLVuMazwn9I01P8DvtSRzjaaEr4wyDEa5Bg4IyKRf39ZmvrgsyTNkKSZb2Pz0runFIcV7diFqPci+1IKRHQUpmTcUzDt+nsA3gDwNQBPADgP4Eta69t7vof9u5eXyHMjdL8gwnBhAafOnAUAbNy6FXVqx+OXFkYYuZlyRemvKIgM5HadSiNMnCFE7kUfgYgq8sFLZjtQrCMZZcFnP7J7CvKQ1oRJO4+JYFQi0AoAHsrnWYmiKJFnZl0kScTXO15TFH/gUqLzdcBRAabU2LLb7uiD7vQdN5UqdKwg+AdRVwQU/XURmeGYLE2wNBoit1SlBPud3O8JIM24TWF+UD3PN3vQVCJI057l7jev7Jc+/DqAr2utPwngczCFZn8FwNNa648DeNqu99JLLw+I7BkpENERAH8VwN8FAK11AaAgoi8C+Bl72FdgUr//8n4a2bz5nDELXGpWx2hOQpbh6OoqACAfjjC2cxwIQGUh7+LCAlZGZhr1xc1tzxbSNAGJBJpbxtlI50ZeV8DULAuDGpyhUSrvp5d1+uCOqQU1CRI15GBRQxWiIAcDE/TjzndIYZCXKMoSRWk8BlmaxoFMPkgoFl+FSauWSUzB0KgiBBFQT+oRzKCWzbk+CYuJblnUukYtur35PEu1AySCBI6tLGNgkYKUKhT+IQrvNQlGW0p2P37uxiC+f5l9ld3cYz/04UkA1wH8H0T0OQDPw5SlP6W1vmyPuQLg1D7usW+J3YjtopMUSyumRu7R1eOYbKzbc0KY73CQY2XBhDJn4pa/1iB3XJ19VA6qUkgHFlefdjYBs54kie88ldTeei9rVCKOQBReSaRpgsw9b5r6QCZCPEvQvYuyqrxdAQDyrJoLHjuYLoQwswk9HQjeA04TiAh5FgKueCBXnGSFBXw1Ur+zjs+VQOTlaW9vFDxGnEJoHFlaRG47/EQHOmg8MSFJDZ9EdniQf69yOK3ZD31IAfwogH+qtf48gC3UqIKuZ95k0heY7aWX+1P2gxQuALigtX7Grv8WjFK4SkSntdaXieg0gGttJ++2wOxeYxbim7KTahfJ7Fz7k6dO4YYNZKomFTJriR4MBjhu0cRCmqKw9r80TSGIYGdBI6kF0hAbdeJRmxojvz/OFZORsXHObU8TM2r5EOYk8TA3SxM/XyJlCIaIeS/IhGwXlk6UWcHCieMgIf6SQlyAoSgumInHXCSJ8NmsG2Xj3M/gtjMvQ1QohtEKfgw8fYmvjcZ9GC3xUBHQLJ/fwmgYwsR5fQ9qThzzL3APsnujY9eRdw+n7FkpaK2vENH7RPQJrfUbAH4WwGv2/18A8GvYRYHZXQt72/O8LqodFH/uANnY94XlZSwdOQIAuD0Z+4+NKORhHKUJqtJ0gjzLbIdyPUaAwN2IjDI4KiHqSoFBczuXwiyHSUBSyqgtKXM3pmmIboyX2cxKboMQMX2ppPKTtYgAkpbWpOHziN6XtY+kLPhncWQnETFcH3VcIO7ELev+mBaXpGLXIiIIMGrIlQpiBRHfPzxFkiaePggqI0+ItwMxBd30yuzV4dcuOyuPu0dc9hun8F8A+NdElAN4B8AvwlCS3ySiLwN4F8CX9nmPXnrp5S7KvpSC1vq7AL7Qsutn93PdXYlFAHNZcin6Ey/bIJV8NMLSygoA4M6N61GYsSs7tpjn2KyMh2I4yDHIcz5vMgpkcsK9D76EOg+5saN2nmfRjD9uzONtMZHJdkQXIkyxTtIINfgw5yz11v+6oVII8pBdSQVyNZlro6OfhgwDrR1SyBkiMeXg+ahtl+qIoYEOeAxCc6RvUpFAZyLvC5uj0IVUDDITGOZm1qTYHjNvVJyhKtCH6M+ux+37z0jZLQ9URGPnD8Le+F5fvuOe+XCAxWWTQ0GkKdTUdP4kSTC0mZ2PLIywNg2TptIk9dZ3JeCTdxCrd0482MgqCPdRJ4lgSVxi7k7soSsP8allglBYdnaQPE2R+Zj+FHkaJv0I9vGLWufnSVYi2ExhgRDSp2dZGuV9IPZLcZdg3W4wi1q4bW0Zn+udPWpiZM+JdrAs04SqLL2XhlOZaI4Jpw87zqzbWeaiuXMcc9jyQCmFmVKzMSCs7krywRBDmzAly3MUNk4hyXM/6i4McuRJcCeSIA8KNEMKRDWkwNyTQgjfYfO6ImDGQR7n0LBBUPM4QtMgCNQURJoiyzI/m1LMmuzDO1ltk7snn6WZpqlXSgBzNXKXoo9L2BkRzLIVxBWj2prerSCqsvQVwTk6AFeWIthhQAfTYec1PO50zGFKPyGql156ieSBRAozNXYNJuykdXXtFKQJBrbCUz4corQ5+lQVphovDYcY2eWiqiJ+rkM8oxlxrB1AiARZyrwHaRKCfBBTAV5zsm27EAJ5lkXoAPwchjQQnW9H9iwzaMHSCe6O7ITEFO90FMY8JwWXaBKCpwTFWaJj1IB45I/ckPMhiC46Efh/B1IQAlSGArN5mmGQ22nlaebfB0+LX3fTdkZgotvzvVu5V1TigVQKwJwUoeOt1hVBtE6EfGiUwsLSMrbXTXQjlPSTY44uLSFPbgAASqkiyGlCJ22nYEopEcK7wFy6cm4jiI2QYMuh44djTMceOEMZT8rK6AM6qIizW7i0YyQE7z4drzJul7+oPYO7J7mtgkdKRobCuu1Az6EU6nSj0wjZ0n4KdEvZZ3bkbpBn3l40Go0wsvRxOBoFt2zdJbnfHt+QjnbP3Hs40tOHXnrpJZIHFilw2Y9hsX4ekUBuR43hwoIfKaSskNmReXlpMcqzIJUOGYCV9t4HAQHhQh21Doa5LENVVX66sRmF25GCiBBAGPUdhQCAQZ5HBWja4DMY3A+RgU2LP7H97FLRSCvI0CXmjIiiAAUzYIaAqSaVaIX/Ec1A6zEeaeyAKCKMz9qvNVAp7ZFClucYWHQ4Wlz0SGG0uAjhApxqc0PuNrS/m/d7KJQCl3lfXifvE4TMZv1dYB+FKhMgsVGMg9zPsJtsbaFUCpmNiNSkQsITpggIikF5QpYFN2ZdKXCaECkLxIoj2ATS0BEZfI997tToeLyobBt8r4Uxskg/iqL93HOY1xeHbPMwb87ro46MDqUwK06h4/zmca798LTO5cF0qTQXhyOM7AzYulJwsyMpEbuq+XAYchCD3zzS04deeuklkocOKTRkl5YajZBPYLS0hNRSBlUUPk9Alg8wtMjg2rWrWN/cwtKJ4+Y4HZKfJoCvFyl45anaSF/3Pnj60EErQrBRTA/cpsgQ2GaAI0OTugc+5v9nUJ4jFe5xmbIcCiBiaeNqSIEFBXUhgnlowY70oc1oCUA7TwKk8RLZ9g+HQ4zstHiOFAajBRa8ZGNQ5pBZRx3UCH+YdOKhVAq70QMNTwSD3MPFBWTWVVUmYwhlXtdocREnbCj02vZ1KK2h7McnBLF8iYREWTAmq4jTNxUBowxoURCcw9dibuuzGcML0CzQOjwpscChmcJ4eJKmftIUZNWgCc6ToqQ0ZehgFE9rdKBTEDt15B0UxzzKw7VZA77wrqoqKMDnqBwOh1iwimBhcdEvizTZdXKVvTOM3fsZDssz0dOHXnrpJZKHEim0ySy41QhgsjAzGwwwtIFMxXjsRxooheVlk1shvX4T169cwQlbiHaYpj5b0iAfIrW0gUQw9JG7Xyc6aG5vPAzifcGgGR7UAAJOHwKU7rika5i9QHyki9NwZejJhoALQa01LNKUhQy3xC9EMQzRHAcXMz6noZEvK1ZPQoUAJZZj2xSfURrDgYtNWIiMi25ZJAHd7DQa30sb5EFTiYdSKbS+oN28OZ+jL8GC7fyT7W34xO5K4eSjjwIARu9fwNbmBkp77VSFvAdSKV+4VBD5GpVOIcTGfYr++uOiFepYjW0LbV4Fsx6s9XVN6PkyoxaND91uSPMMSimMBiHJSmHzWkpenVorEIWCMxGViCpi17wSKu787loNJeDeMytKq3Scws49vQZCvUopIZXG0AWpLcRKYTAy9oXIjcpfz26khvF3hvx329nZlJ4+9NJLL5E8NEhhP0FLXDebEdhmHU5TnDx9GgBQTCbY9COwwqJFECuLi7jpQqFhBi+yMygn06kvOJKmiQ9k0ix+oU2oDQGw1QalqKGIELsTo4QGgoheWvMNRlsIISEtgCzPPMxfGA69Qa+SEhkrJ5ey+R48oxGnDxw1oCvmQAVkoJSM0UGtardLE6cRMlpoGITg2qgBrFhjsaEMS345cbkhIiPjHghCzf57mADgII2OD6xS2NPDz/nmNLO4j5YMrFx99FFfRBVKIbF5FlZPrOLWu+/j++fOAQA+9ZlP+48vFQITy7uzQe5hWTQBqaU5MW1ofozUsj3yoDBlwC328bY410HUiCZvsYfEtCK1LlWlFBZs8M/W9thHWkpZQUpbbSnP4wCtmosypD3jcyUCZZBVBWmzXDsl4BWBrikFFZQCpw++oK2USNIMq0eOAQCWVlYi70OIXtyjC3KO02briN138Q+8TWGvL6DrVevGPqsUsgxD+yEfO3ECk+1tAMCtqkI2NArikccew/kLF7G5sQEAKJTG0OdNEJhY5THKMqQsvDiagTene7D58XF00GJQ1GhRBuYp6zEI8Tlgb4G1jfFqZtZElmW+I46GGpWt2p2kCaQtsSdZQVvn6ozzFthckiwZTVWVHoE00EAHOpAqoAtF5G0lFautobTJsLX6yCMA4ryc+WgYEEJb5+76iA7F0jgfvJirP+yi0/Q2hV566SWSBwYpHKo9tmGJN6JEAmHf0HBxASceNXVtNu/c8db25SNHsLK0jC0LbdfX1jBcNdGNlZQ+u9G4KDCwOR6hJKA1yKYcn68QC5pUoi2wBwwctFAGv1mzukp8mdGKuPISgWVIN3tc+wFkWe7vM2HFeqvEFbsN8D9JU9RTqfusVCIUjVFKt9ICjxS0ah6nY4+Dd0NK6WmdAnD8+HGcsEghH42wbJFClmeRN6RJ3/bI3vXM1RmyD2vBHjvNfgvM/tcA/nN7+5dhsjmfBvBVAKswVaP+jjYl5XYth6YIGCrziy03UyQ8lhIpsHzUxCIce+QRX6U6H05x4tQjGF8yRbFuXLmCE1YpGH+8+VHH0wKLdqJVVvOzQ+vAYynWTm32yHo+grZ9/pF4FGA4KI5h6Fzmd6irCMDNENKs2VmW+apak+nE20+qJEFS2cllVWoUQ0uCVJPaLtxJtVTjbqUP3g2qPWWoKwVX2i7Lcxw/cQKrp6xSGI4wWrRuyJq9h0vnhKi7EqTQTSUO2oa5Z/pARGcA/EMAX9BaPwUT6v/zAP4xgH+itf4YgNsAvnwQDe2ll17ujuyXPqQARkRUAlgAcBnAXwPwt+3+rwD4HwD8091c9K6HblAYUOtKX1mjlxYEkZu9Zz/yBErriZBK4bEnnsS2TQIqtcbUjkijNGn1RORZ2kAKLmCHBMCnO3PIH4N8xMZFZkQMB7WjBrccIwfPGdqX65em2EDqCq0QyM8pUFqjspRBiDJUtEozJNYDYPYx96RIIF3kJ5v7obRuGBYj7wN7tuCGJFajU3rvw9LyERxbPYHjJ08CMHQmyVwWKmqHZ/bputfmk7v+be9B9lMh6iIR/c8A3gMwBvBNGLqwprV2wX8XAJyZ63p7bcicMu/1a+aFsJ0I0kbniVzg8Y8+6Y9XlPgPcXtry8NnojR0FhLYnlhPRJogZR2Wap0SrFNEaqAe5tv2fBqxsvHbW5QAtx3Ms8zOh0aotM1aYGwNwStR2Pp6sqp8Be9yOoVIU68UkjSNZlYq6QMy/LUIPEW7ow+MWvDZkOydVKzsnlM2o6VlLB894sO2RZLMpA2dctdjm3dJFPbIK/ZDH44B+CKAJwE8BmARwF/fxfl9gdleerkPZT/04d8H8AOt9XUAIKJ/A+CnARwlotSihbMALradrHdZYHYvMvOizKhLYEakMDh2DgSKCLRoouGe+NhHcfTYMRw9cQIAcO3SRWxsbplLscjFUkqfwq2oJASFvAhaaUC4SUACxAx9ranF3LO1TTfuohgtCGBHylBbjtBAjZq0RWiaDFNmNC6mU1SVjUUoC4hp6uMWkixFgpBEFizIKeSGgN+uLZUI3ofgcVAIv5/WmkUxKp9m79iJVQwWF/39I29DbR6Jf5cHgAoOEw13+ij24LzYj1J4D8BPENECDH34WQDPAfhjAD8H44H4BRxWgdmadD3zjr+ltSe0RhbPeJHuI6GFRRzPc6xYfnrk2DGcf+stAMCtmzd8wBK0Dp6IssRgkIcQXq6JIvdiAMONiU11NyRbiOnDjM6+D/rAFU69fXERFldFKoe0QU0ySVBNpyhd1eeEKQidhr7I8jnU091rDWZTYJmhQf63UQhRjFJrjIbGw3BsdRWD0UJU6MX/7p0fDM1cve9lF+3dM33QpgT9bwF4AcYdKWBG/l8G8N8Q0dswbsl/ttd79NJLL3df9ltg9h8B+Ee1ze8A+LH9XHdXbait70WBUw0d+GvGEwpaz1VE0GkG2GzKRx99FI9smpDnW7dvgyzEFUniDWNTpVCkCrkbqFLyN6UOpBCP9LUYhWiKcZ0+uOVDoA+Ir9d4V+T/MdOQdQhfFkkC4ep0pgwppKl/hqgIL0MNLomspwxae6OnRmhzJQN90DDp9WD/uklPOwl/rNhutzsr3mFSh4OWByaikYvGHGhvhtTP0bWdxHYEBUHoUgyaaZV8OMCSLVCbD4dQ4217kPZeCa01plKGQrRKhWQeDfdiu1LwxzafIJ7jED3L4dEHvs95Yki7f8xlhJ3fIKWJbpR2vZxOg3syTcMcC6UQdEJcGKc+F8QpXE2GQgBAUZVeKWSDIY6urgIABixLs716/Ax8lKDWI2rn3c0u75Tf4d2zn/vQSy+9RPJAIoV50cFejmugBo6e+SzHzosRlk+Y8NljR4/iukUKiVJmzgNMclOZELTNCM1LvmutfZgvCRWN9A2j4w6hyTqC/M2p0ruiDx33C7si/hAOd5spbBcigaxKSBsbIMsSJaMSLmZAKc1iPhiEs0FNcbYqc+2yqnyGrKKsIO39B/kAx62HaGgzN7dOEW88RSzt2KCbShz0eH43MMkDqRTa5LCNwfxnry/zJQ2N3OYWOH78GK5dNnMitKzCB65NluFK2mQuWeot6fUoRidRR9zB+t+YIh0uwK6rWzv5TDtEuEG8LfpS+Tlsp0Lw2Nht3hshElSJiQgt0zSk1WeUSykVKwFuYwDrrASUpVELURWowQC5zbeZpKk9t50bxHaE+8fNcLdIygOrFA7rp2ro/Fo8A2BsCKSjTz6+huWri8vLIFdOTmuPFEzUXgUb4IdEELI02BSoRSn4a0TL7aN7o/Mj7uBGWbTYCBrLM5BCdL32eyJa1I3n8SHQSQVhFURVTL2r0IAme10poW10ojM0RrERLXESmsLcquFohGwwsPdrScZ6CB/U3bQ0HKT0NoVeeuklkgcGKdw/IG5ncSPE4uoJX3moqArobTMakjZ1JV16N0FAIoKLzOV4rA81s0b3+JiWUZuN+rNtErXlHe7XaFcNQbRNqKpbzsti6idLybKCsDkYtNZwVhUzIcq6F5UMFALWM9ESyKWUQmpdnaYuaOb3d39Pe/3SDtcTcTdRx32tFB4ERdBuYjItH4yGWD1u8gBe3twInUlpKCKftkwQ+WQsg8EAypWaYyG/bR2/bjg0S3WawJfnoQy1QOmIlfA97fQh3hy3hd0iWpdS+lmnSZJAVIk/Lkx0YmnWpDLGWXt+01scHNapnf2YD3I/E9JZNXTt6N3I7qIUHizp6UMvvfQSyX2HFB4EdNAqLX5NkSS+aMzF998HfJFZgmRFYypZoXSThYRAljFY7C7Z5l1omzvRQAesUXX4vgN9YI/SiSBa7xMt6voiWEN8e6ZTk8kqYdOYSYiQuFVrbzTUWtkqVba2JYi921icV8dEVIa2xB6kg/3qDhJBdF/r8LDKfaMU7pUyOIzX6j82AhaOmdRsWZahtKHQsizsx+qUgkRlMw2niYQQlT2fWNmyto7ftBfMtjWgeXzLOXVbQrhTrTO3xCbEh7UriOa54T7j8XZIxpLnXilIFdK9KxsKHtyV0nsy+JekCT55jRDCH19rLUwCt64vcLck48EnFj196KWXXiK5b5DC3ZTD1ePkszRrTVhYMZWkTjxyEle3TZ6Fajo2OQDsyCeIUNn6CJVM/BwBIhn54mdWfIotgu6Q1uVwPqJ1djS7brzfbG4xMkYgotmWaKkFJbgtWklPJUSSBDTAalQq6CiwSUrpjbZVNA/aUA0AUcGZunThhMa2GkU87JkP9wpvfGCUwoG/4PoX0XEDF4iz+sgp3Lho8s0UyhRBdZeQSoFYSbM0cQVQxIxIx7q9YA77QIPTR12YXYsdVVcG0QldCqdxRkNBdCklAChtVFdaFCG0nFWTdjkZnWeiqgL9KqSEsjSNdKiUHXxc/W0AACAASURBVOdedFaFZpNnkoTmaR1ytyZKHc59evrQSy+9RPJQI4UD1Z/MfmQW59XS5rjlEyeR+uSgFE2CEkTBoFZVfqIQCQFyMQskagMDH9Hbl+ORer5Runu7roGB+D78Am0BS51taWmPKxpTVSUSO+rriC5UkFpHBWMd/SqmBZQNfpIiQeHmQZQlpEUg9V9sT0buxvfQvJ6eddAMuddmyodSKdzrl+qFfQijpSWsPGLck5s3rkMVUwj7+SitfTIWnopciASKrMU8iSs0GXHwvaPDN6jDDMXQoAbd9oE6DYn7+HzKp9HulvtXZRnPHmXPO51OUbakb69k5SdE6TTFxtQognfPv4fjj5oK4kewD+E8Y65xoTZ55lA+zoO9cE8feumll0geOqRwL1ACAVE26LYhhAAsWU+ESEy9RG2Hfj4tuJKVz3qcJgmkCoFMYepx7focsvMRHaihhSZs94t8qeUlHgR96GxHh/eirEKlasWqSQMmm/PU5mCopETJ6jtMLU0oJoTNwqCGN3/wAzz50Y8AAI4/ehq5LeHnniWkfWs++8GIHc3vlg1yH/JQKIW79n738YNWZeknRy2sHMHG2q0AjQGfrpwUMSgsfeUkJRTI1W6kliQvLfkQunl8fRpzS4dnuxq38sfX6UnthBkKqtHmjnY6+4J5R/H5zjMzLQpvXyilwthW67q1PcFmabZvXb+F7738MgBg9fRpnDzTXaNoLr3APRFUS8bDrhO3eG82hvnk4LTNjkqBiP45gL8J4JqtGQkiOg7gawCeAHAewJe01rfJfOW/DuBvANgG8He11i/su5UzpO0VtEQc3xMh9stPJ2P/Ea9NCpjQ3NAptM8xqKOYBTdRSpHw7k3SZMu2tUiXTaG23jU6119Yt4KY7W5sHr6DPaFNKWn4+AMSiVcKqhbSrJT2BsVJUeLO1hgAcPHmGjbt9nGlce6d8wCAH754EaunT/vIyfmEa4EW6eiT7ZjRbpn7nLsr89gU/gWalZ9+BcDTWuuPA3jargPAfwjg4/b/X8Iua0j20ksv9152RApa6z8loidqm78I4Gfs8lcA/AlMvYcvAviX2gwD3yGio0R0Wmt9+aAaXJd7NWeCZ2FqwGImLuBmsrmFH3z/HQDA+++/h2OJhrBpxwhh5OT2BSkVZOJohYSAi3TUII1aLUcmHcFLYXfg5vyPWZwDGYSLN45tZBmeI/gpBgqxrcJlaRYgH51ookFD8JerGAWYvIxrtkLXtdt3sFVZVJFmePnNHwAAPvnqazh55iweOfuYvUJIFx+9y50+rpaTdvgcahc/DCqx/4vt1aZwinX0KwBO2eUzAN5nx7kCs4emFO53KS2/vX7tGr7z4vcAAMXGJhaPHwHZfQNu9GIfeFVVPrZBCBFCnh198J2f4mxkHbMcI9lh1mN0bsdKN30IC/Pdn53UsEOYzUpJTx+klF7ZAqYb8IrUm7aQ78Z4gm1rgByOBCaWSrz81vfx1GcuYPW0cRGnUQ2IWTShY98OzMK1sdlVd6YSe5P9Eeh9uyR1lCxwfukLzPbSy/0pe0UKVx0tIKLTAK7Z7RcBfIgdd08LzO5dmrC8ZexsOQPGbehHN4V3f/AuAOC5F76H6xumclSxvom1Gzfw1OMGvtaRgoP3UklImzMgUcJ7Igh1+hC8CXGh124DXvRUnSN9x5PXj4/2zQ5Ycs8Y3ajexpb7KxUQlEMKUlaoy7gosDW2rkqlfA6GopIQdn7EG+9ewiuvvIpT1gNx8uzZkOCKXYuPmE3s1Q0NWpJl+TPa39r95ZXYq1L4HZjisb+GuIjs7wD4B0T0VQA/DuDOYdoTDlaoZSneu9vf6s7tO/juS8YN9ub7FzC1H+Xt8QSTa9fwuSfMR2kKprLPztECpVFal1yapCCXMMSWTGtrZzM3QLPjAd0uyaZNoLHAdMIOb+QAZlZyWhDsBiXKskRRGM9EWVV+38b2BBvbxvug2P2kVD6WYXtS4M++9yqe+synAQDHT58GkbXXILh1+Dtu/v61X6BNV+zqo2lSCaq/sn3J/Na3eVyS/xeMUfEEEV2AqR35awB+k4i+DOBdAF+yh/8+jDvybRiX5C/uptm99NLLvZd5vA9/q2PXz7YcqwH8/f026u4KzVjrOHaGCudGrxe/+xLe+L6xeG+OJyisJbyQCutFgdffuwQA+NxHP4yB9URoBKSgtPYGNakkhHKBTLbwqkMUvK6iRoikqSOGndCC/7NbytCCMPy+tu2zgqfCPq3ZdGmlPGoqihLTsgyRi0Xlp05vbE9QuZqdANw7qKRC4gKcKonrtzfwokVxp86excmzZ805xM7UIYp0L9KMdJ0HzB/aBIm55aGIaDSyG+500I7M8ENWlcSVS1cBAM+98F2MbZbioqpCTsZKQuQ57mwY19lkWiB37skob4L2QU1VVfk08AJmxmTo+jGUbdtObE8zr0KNHLQpC0T9FjPfc4dPbr6AJR1RBqcUiqryeRaKskJRlD5gaVqW2Nye+OvI8FjRZ+E9FkSYVgp/8fLrAIAf+eGnsLLqSsoNPf3qDBDjzwB+EzRYhZdaX5/d9Q+FP8wt/YSoXnrpJZIHGCl06fCDQwHzKmkNjcqOWu+efx/Pv2DiETbGYz+aSaUhnfVc2VwA1puQpQk2x2akG+YZ8ozlELCja1lVyGwxEyKKDYoabLIUagOYowIUYYl4sI+GbLQHMDXNbDtH6XSNdLqBTnjwlmJzPzxlKCsUDClMy4AUzPYWgypR9GJcIBSIINIMG9sGxX3r2edxdNUk2D37Qz/EPDj1F9suDbMve//7Cz/omFRxyPIAKYW9dfbd/ii7fvcakJXCxQvGyfLMsy/g+zbGfjItUFbuA1eYWiph5v8rTy3WN7dw7MgKAGBrPIGUhkoM8swH7JAKcyJICPOtEO/wzc7fJBXtQS1tuRZa38csJbCDgpilaLQK1Z+kUj4zc1FWTCkwJVBV1q7gPDMCE/ee2HUFBSBMmnyNT1ectrQd/qW3zuOpT5wHACwfO46jj5wEACQgP5MV2r7XztilNjtOTVooBDD7mwtU5u5RiZ4+9NJLL5Hc50jhXs1smF+UVli7vYZzb74NAHj7nfPYGhs/eSUVSxmmMLFZgCobiCN95iAFbUfE8bRg2yUGuaEMwzz3GYWSJIEgEY1NIXipy9IVRutGPga2OLNWQ3SxrmGrDRG071NsLoPynoEqRgSWMpRlvH1alshtroVKqUANtI6ehSxaSJLUJ3EFTMXqxK5vTit889vPAgAef/xxLNtaHZQlEIxKaG7dNZdpPJngiIwtepCwZz5Bd83weB8qhYNVBIf1DkMcvsJ7Fy7he997BQCwNRn7D1kqjcLRB6V8ngSpNKTWKFiEnss/OMwz3NncNucMFbNDKIxsKfVcpqYIrdcKdaIQXGrtr7ObSnBFsJMSaEZItt0nLPqgLBsZ770MUqLglMF3fm434EqhRJ6kyOy8kJK9P35fzdLgZ2mKNGWZnRGK+mpNuHL9NgDg2edfxMpxoxSOPXrKB0AZ6hDeW2S7YaIAiFaXcE32FMR4d2wM95FSuP9RgROtg1K4ceMWvvOXz2NtcxOAGfWldPUJgnFLSgX32VZSQSqNLWtT4FmFlocDf87a1pZ3o1VSegWhlMLy0qLnzJzTah1Pjmr7PMPxO0ycadgWZn2Q7v7hPF4UFsxoqrSGYs8jpYoNilULOmDLaSKQsVFcq5DunZgFU2sNYe0IQghfgTrJssiImyQpSvsqvvXdV/GZT30CALB8/DjS3KELc3VidpwucS5RQdonxnFGx/335cN3V/Y2hV566SWS+wgpHLwcrCINkXZSaRTWPvDtZ57HxctXUckw6kk/oSmMxlKH3ABFWZpsQfYc7rqSZYmlkaEJlZTeVVlWMiAQOzI6OpGmia9+ZEZPZ4OPR6q2Z3I7A3WeY0KT385gum5BClp7u4GsIQPJApPMcrCjcNcjRwrCwv48M9ShYtGOjj4Qd71qHd6LCO8o0IZALfzciXGBP/zz7wAATpw6hRMubZsg+24cfdBAVz4LK0oHKkGIj63bF3Zvajg8KvFQKoX9viNenThKmWYvXJYVvv3M8wCAc2+8haIs/QeqWeevKunbohEoR5KkKEDeCMldXUop5PbjXVkY+o60PZl63i3t5KCpnRA0yDOfdyFNEp8fIGGl0qLOgqa7UlvlQdxqCf4u4/BjRIqAz+wMHVwqyZbj7UqqmpKQftm9S64UAGBgbQJZmiBNQk0H/s6DSoQt0MvyUbh3QRQZaoUQvoOLNMGb583E3ldeeQ0/OhoBABaWV5DkabsC6PJVIkzKEtDRDNaISuyZVxwOlejpQy+99BLJQ4MU9qco+SgYrOo8qEdr+JHuuRdewrcsxNzY2o6MgFIplmA0GMCYpwwiERBJEqEAwKZ/B6BskFKeJjiyMPKt3LIZhW5vbEEq5SdRTYoCmTWicaSQJcJvz9LUJ4EFzGhJEfxtN0n6hLJa+3RobmR2o3tVG+m9QbRju0MGUqqWfRKRq9a+v9Eg95GeWZJYN2yAz26ZP5dgSMlkrnJIwWSx8sVrtfbvSWnlg5r+8DvPYsWm5X/8w49juLiEgc3InTLUwIPH4nwW4a1KbX5bwXbvhjLsOFfiAKnEQ6EU9vQeIlrAqh6zD8xYye3HWklcuHQFAPCtP/8O1m0ewKKsACIPX01n4R3J3TB8DXk+gB4OgcK4HsfTwt9TpEnEyYe59cXLkIhlczzBrfVNLAytTSFJkFgre5YkyJxSSFOkbnuaIve0IoEQ5K33BmY7BcGUBTEqFSkBE5HpXayy3sG7FQHf3mpTYNsHWYqBjSUYZKH9WZKglLI1u7NAaHNTKYTn5QVnq6pCbu8jICASs/32nS18/VvfBgD8+J0NfOjMaRyz4dDDxUUMFxbN+8wzb5MhNBWDE9Neq7BqykHvmwkcHJXo6UMvvfQSyQOLFPajDOvWcs09C1L5ufnr6xu4cdMEtVy7cQNvvWWyMV+/ccvP5VfagcfgcQhIgwUSEbNAJwkozaALs75tvRGAMQ4qhhTcSLeyOPLw3yEQl2FIEPkaBoLIH5eliY/6y7IkGnUFUeSxcEghXhYeTVQqlHuvpFmuWLRm26jfaWi0FKEdHWQY2fR0wyzD0MYJDPIUmXDGVIGiLGNDp8v6TATl3jnzOMS0gryx0X0DLk4iz7Lw/KNFXLlmfv//7/kX8fiFCzh72tSjPHvmNB6xcyRGi4sYLBrUkGYMNVCMGrixmceVEvM+8XR+dZnPHrl/KvFAKYWDshsoHXdcWUmU1pJ/5fpNXLxoJjdduXoN16/fAADcvHkLm1sB7itGPwjafwiRHYHdk2pVo0WSQNoPdlyW/qNMxBBaBu7OofDyorEvZGkS7Vvf2kYpjesyTQL9EIKYTSEoiEGWIUuTmiJgCoJ1HsH4ecgyLVHyoq6VjKlBpAjalIWhHk4RDrIUi5YKDfIUw8yFdmdekeWsvc4lyymboxBVVXmPQxIpBU4fRLTunsHsI+bJSaEz0647d7bw+tYEP7hscmWcvXARZ2026DOPPYozdnm0tIShszsM8sgTQoAPYNNah3cbvJuRI6MtknyHcLP4qD1SiZ4+9NJLL5Hc90hhb+iAew/glzksdyPD5uY2Ll+5iguXDDq4ePEyLtnlza1tf5xUyk+DLln8AezIyjU4A4a+LQ6yuvsLkUDZEWlcSh/vDwQjlDJWT3+OowWLC0OkaYIba+vmOKV8AZT1re2GPxwwCMIZ6nJrwEu8Ea4bHfDtvoSd0pZCmDaXswyNnEr4MG1t0YGlCXnm0cEgZ5QhS73RNLUeB8AYdzlSUkr732ZclD60OUmSEGBExLwP5JPfuvcXvBcB8pdV5SeklQC2xts+NuKtrQs4f9kkMX/s/Ys4+6gpfXLmsUdx5jGDGo4cPYrh0pJpv6UVcf1QDyMhwmL4lij+hg1VZdJh0GQHsIvN35Pua6Uw32PE+f7qAUc8XN/B37Ko8M55U7Pmzbe/j4sXL+PqVfMDjyfTiCsrxSFvyP3HLdm8nUIInyMwUhQkPIdVAkjzDKk2MHOLxf5LKX2MvqpkcLspBe06MQRGgwEeXT0GwHSYAK0Vbq1vNt6FOw4IwT/OxZfMaV9I7HKaJCZnIqMPwRNRcy8yt+EgM/cfDXOjCKxnJbYdZBgw96q/v4gzXhPY71mFvAvTsvS/RyISn8iGBMW/mQ4TssDsDVUVlLpWyr+/wSBHsrSMycTQtOlkitwqonfGl/D+VVO75NSFizjDFMTjZ01E5ImTJzFcWECSNbNGKx34A+kwKDSoxKxoU/Zs7bKTAgmy1wKz/xOA/xhAAeD7AH5Ra71m9/0qgC8DkAD+odb6G3O3BnMqgpoLMZjz6uggbJeVxLrtLOfefBsvv2ry87333gUURek7fBXxYM182fAfOB9BvZmAuR5dBzOjZBiB3HZoIElyTAsTdzDV0ldKLsowOlUUoxvXFi1MpJ6zFzxy/Khfzpnt4Ob6pu8svONsjbWZVFRzUQJGEXiDXId9IU0SpInwnL6UMkJUvPM6dDLMUzYNPIvQAVcQRhG4cOQQn6i1DobJqkJZhZmVZSU9UtiSEhM7I2k4FNBWERL7zbQ2WbA8d2bP7N6V2Z56g7KGcesObGh5kqReQaAskdrq1u+Nr+LStZsAgHcuXMTZ9y4AAD78obN48onHcfKkyQWZj0bejcwLEROR7wSihhqEEFFlrEjcd0Kx8tuL7LXA7P8L4Cmt9WcBvAngV20jPg3g5wF8xp7zv5FLqN9LL708ELKnArNa62+y1e8A+Dm7/EUAX9VaTwH8gIjeBvBjAL694312bghDAywC0f7LIxIVC5hxGYAvXrqK18+9CQB4/dwbuHn7DgAzj6GODrxVuxaRSDzihEUA8rYTAiIQguAQqtZhu4OIynNXhRs2h8Ijx1ag9dCcz4KiGvOhNQJaSQRWj5jIuzxNIo+Dm1DFIb6bNzG2k7qIyggdtC8j2p4IEWwMOkRxQgd0MMhqnoQaXeC2A05fgu2Fv92Q7t6hHj/duqp8nchCaWhL04qywjA3HhsSif/9pFIseYoZXUPqthjpOMpVWLrhR3cijxoqWWFsUUNChNSilmJyA9duGJfmOxcu4t0Ll/Dkh00BtSc//CGcOOFQwxDa5XhA+C7YnCsXZeffk/nGW5CAZgFnFDwcO9sfghyETeHvAfiaXT4DoyScuAKze5J61aTw3Wk/0US7LEYWWk4mU6zdMQa4W7fXcOuW/VF+8C7Ov/seAGA8KRisllFosokz6GpQ1zbWTgb5oqKwgIfoDgEKYV+/SHDDdt6irHwS2NjQxyY3CQESITbBKBzThiMry1i0odEr/3971xarSVaVv7Wr/ss53U33TM+FQRiawRFFH2QkyAPiAwkBoqDxBWMiRBJihESixqC88MIDGjExGokEIhgu0ShxHjQBjdEHHRTGGWYA5TrM9G2G4Zw+l/9SVXvv5cO+rV1//ef850z3Ob+kVtJ96q/aVbVr16611/rW7cwGvhfBSJk63W2HaMT92TwGVymVsAMZUFUIoFERZeK4IoomwXlVR/FTfvjSvBjUhVL4VoQPlvzkbw+5tcl/xKkLGk2TmMTEv08tAEUrLHJFkWeqcszWYz8C3WNQpmakQC+CZYYyEpxNUZejoWcQWmPf+48MByVK7ZlKpbG1tYsnrrhaH9+9fAWX7nUM4tK9L8QdFy8CAEabGyiSlhldDsgvilk0qgDMsoVJMBIbecLqQONzMkkS0fsAaACfPMa5fYHZnnpaQzq2pEBEb4cDIF/HaUl/7gVml6oJSTowOgFbe3v7uLGzi63tGwCAra3tuP39rW3s3HBqwu7+JJqTGq2FhYG9M1O4j3jGJc/e5rks28rVSanooERk0wpmNagglCHAR40xMR50rGvMfUamMxvjHOiTJkSVrBkOrg4qD+KKfvfF23GbD+jZm86i159lm62CVd1g6ouyMjgzVYbtglSUdAovqeSxE15krxvMvNRT1VWSFAaDCDQOff6HFNaNTotRPIa8/HwAGoO0N2s05uHZiEDeeQlKxXc+HBMkREeUsmIRcfL3AWVIfgx3VwrMKfw5i6uQalZRxMI+WmvMvQQ2KEuUTYFrV9wC+Oz3d/DEZSc1vOTyVVy611WouuvOO3CPt15sbm5mlohsnFjA67w4J8Mzhgc7CuZ4LKZARG8A8LsAfpaZp+LQgwA+RUQfAvACAPcD+M9VrpnH5rt9VjAI3ZiID1y99gyuXHUDurV9A9vbO9jedmrC3t5eTJBqjInmwUZLN10b99sDpKrsY1/or9/wXmOyvJj0U4h6tyLoaIKyKKnEwE8eO9do/Dn7swpnNxymsDEapXRilPIBqOCRJ4J9QncUM2zYL3TQ4XCY+TxIphDGCnAfdRTrVTe+4Gz+gJxwJKwEWodybjUqH9kJIOrjRbiWZAQBfU+bfi/H/iVMwVkbgkl0qjVM1J1VTOWuBkPAq2jT2RyjjRRxmnrv+hzUnzZTiP2wFsxF6qc040oGRxSTuNiCoSLjrVGBUrCaMbji5+nTz27jictu7bx07wtxl8caLpw/jxe90FUmv+P22wBrU6Qrt03xi6uaU4vCuN5ck2RXgdnfAzAC8Hk/cA8x868z81eI6K8BfBVOrXgXMy+xofTUU0/rSMctMPvRA9p/AMAHjtIJBjLEP3BArU0sN3712nU8+ZTjpk9dvoKrPox5Np+jaXTuYx+SpbbSfKX9S8uhrtjhBAtLgSEeprTqZauJKE5SqAKFD9GdzhUKLzV8b3+K2846pyZjDIZRUhDXj3/TTeOqRoRCqFwJrRb5EMIzxMfh6DA1HA46xffMOQ6LkoJYd6OoWqiUz8Ho9I7gpZR87ZLyQtolncdk6nttDGZ+bswtw0RBQaHw8QrFaAzD/vmR8mEoLwnJWJSYQ8MyEN6TeKkBnBSGgU4Q2ElXSWoIUhfYqa1Tr1qVjYbxgV8DY3HlKRdT8czWDbzoHqc+3HnHRTzlvWvvuvNO3PP8u3HOx4icP3c2y4/RDY6n/p609eG5E6eSalqbmP/wimAET16+gus+GGV/MonIc5gsMtNx2GafSt214wNVhaVd838J6GYkXseQ7aQkl+mgIrOwKoRlQikUAzdBnp3u4cWicOrA4w7t+7NQJN0EVfFInr0j6Lop5Vq6UFInEhVZunQ5lyhjC+kgZQ0ZyrezUc1wiHbYNlqDFxhzx+gysnwMwcGs0QZ1ozENFgcA5NWEwWiMYujULyqH8bIExPMHLauQjHIFRH6GjDkSiGymT4ZRUtYmK02hoiVIBl0NBmWWt0JrE60U45GFtQ5vMZbxnbmb89ef3cLtF84DAK5cfwbfefIybr/debFeOHcWP3H/S/35IzEXckaW3tEJWR966qmnHzxaC0mBGZhMHNe8fPU6nvLSweUrV3H9uotJ2NufZL4F0hHHiApBMvAptyq0OeXxXEAX+g5uqQ75HcL9lXD2KQvvrBPALaUQQqzmQESsq7rBGQ86KqSVyd+4k4hUfFa3uoVxSathqksoxX8h6wgwK5MGxHOBWpKDBOUQwpCTdCAzOikQtFQnxANlb4UTAGpMns9h1jSYBelSFSi9dKCGGyhHG/45i5RagBnWr+DWmuRy7vvelYJAWhsS/J+OhZerARQCxA2SQlFwpkoURXKgIlIxoGw2m8fnHA6HGEjnu7mbC7ub+9ja3sHV605aHpQlvuPrlz7w4z+KSy+4B92UgNFVaS2Ywryq8O8PfRGAww6u++Ckvb39aFLSAm3WIiovMIGuXIiOlolNmcC/InWfI+/QZhBJpUvORqVPuRYZRlHAeDxWFSX2PGL/vHoj+vSPxQfW1r+z+5OYvSTMInKbudXR9jOlcxZxBNkifTDZ2R5HIJMzAmnClElPjNadzJuFw1XIwQAAVaMxbTQq70FfDoZQI8cUyuEYhXckMowYfSirRRntnKWMWFgyy4q/f8aE85FxjkRhmzn2rcjmYmIQAWuQ6oQcj1hUt66x4a0kQ+FUR0yAAfS89tdT2PeOaXvbO7h8/30AgJ96+Y/FwDPH0zNdciXq1Yeeeuopo7WQFCaTKR559DEAwO5ukg4ao6G1sB4EUZKFm3MrnHQ1KaHd5nBpYbFVkkwk+J61Y1GgnCDAqALWmAhyj0YjNPOZ3x7jWV+g9mLTRPfjswKt5wAbCY+rRWnBd4rSSsNSlRDjRm2pQZocAlDXISV0SaQy4k8VReyLzfwcQixFurbRYdVOKgXLRxR+FdoYzKxN/hjlAIWvCVEMhsm6wAlElf4vNHArc7C4GCNC1MX4ySSwQf6Kz9Ced75do3W0ODCzUCscAJnldPDA85AG0EFqsjZGXzZN40BEuPFiMOqYVSqBlrMnn4zZuppZhft/2EkNd91+YZlZ4kBaC6ZQVRWe3XJeiBIv0Fok5uAUqJRNljAJD9MSgAO+/cMZw0FHGYtqgztHiMIsmIJSqKoKGz7JSFkUqDwjUIMh9mYuYcq8bmLobtNojAo/if1dxdUhVQppicgVAKFwCOehzNgoU3gJ55dWq4wRtlEHOVbhAyWiGOjTVh9C0hPA5TMIKoMcv7bzzYHvI2Mk8YkF1pLfsxgMMpN2fqPuOzkcIlfjAgWVryhEOj1mFNxtmQABAx8XQsbEfhlrsedrlDa6waiqMPJmTGnSVkS49rQz0VvYGIexMdrAHd56cRQj/FowBWsZc59PIC+kmiQCy/knsBK1G3adSO2Dq0kNcdXx/3XhClJVd6Bj0C+Vz3noG5YE8h+PYYb1J+3NK5yvk3lyPPamNvlF+r5kC0K8p0rPRYjcish7NAYvxDbnXOFjz7uw7JONMo0L4vK7LRaZgtwOnqvaWGFnX/p9dlCH7CjApsA4A3A9Hg0RIvzJUkzrn1+R86eUfeaFEQQQqon7IwU8jhTM0rnUm17M+QAAFUxJREFUEKS4QubqEFhHXTfObdpLEaPRMKbvLwoVvUUnk31ce9oBkBcv3oYL5x1TOP+88SFjlqjHFHrqqaeM1kNSYI5ZdEKm3rCdVrOkqx6kLcQGJ0Rtk+QqjkyOsxcZSh385Q1sRM/3hPpQ1TqO0WA4zDGFTH0Qg6NkBUkWC7hXC7qiwFrUtThLi8qq57j2Xh8mgjUSU0hnGGvjO7fcdjijvH2HxQJoqwzIjsU/lNSs6Wwew81lM5m7MQYgRSmKwAIUIaGKsRAVQ75KyxZlWQoFjlKwk5A6mJJEUhZF5jUJ5igRWGuhA3ZDBOOrisEaKG/q3t3Zxs7OFgDg7JllJstFWgumADCMT6Elg6DajrDLzj2cSdwkkt+g4ARdqgOAzCSZE6EsC9Q+GlIm7zDaYtNXHprubiefBZEGfhiYQphIcvZz+khkoBIEU3XfhPRhaOeQWBzN9nOQuH/X8cXzBVOHQlEkjMEyxw/MKRx+P5C5pi/o+7Kf8t3Ij7/TVO1+BJ8BxQqTqYvrO7O5mQcaBe9Yb2pUoQuUru00M8quLZ8c8MllGx1L+oFVlNOlz4R8FGLO3OSNtSlStUh5L4CUot5hIxzPr33Kv+lkglWpVx966qmnjNZCUpBeiIsi33O4LlaBDY93VgY0io3sbLloiJUFcOhxAKFcBme3glTGRKBxzsCud1Y5Xzeo/WowMiZmBQ43YiEFZEYGsZqEFZhaHo2uYQc6mnb4R1iGxLd+d/7ifA8FIDSXDkAkvEIJ2o/ZrNHY82OxXzf5HFlJUsyBRvlbFrqZzecYj8b5eQDA5NQJeUSof/LZuvoSvEbDio6Ck6Qn0sOlsHR3nSAdKR9cJXNaROuDkDRktS6whfIuWCEx7iq0FkwBSBN2eZjMqqziiLrEipwjb5Z+RdSjpTagfVkHKmT7Q+CNsRRNUkoV0eXZFAUmOqgPOvpv1I3G5mC48GG4vwypgEUi+UkHTCEcSunTmdrjt3xwJENcji5w3tb/YpHXUS4ELv+1a91YjoxwVjfY8YlgblQNJkzJHVs8a6Y9cHskEldoj134sLTWqJVjPq7obCotByRzqbhahu9kqmQY5/jMqYULznLvU2INkto5J5znpcdlRFHhQskM4nmpvlhO7wgfRa8+9NRTTxmtjaSwEnUtjbcEYUzcfZXO5KtD2rEUaPQ292CnZsvR9x1IfgrlaAPTygFg86ZJkoI2GBqD0ksXIBn6K/olVkS1YCloqQhRZuUl+s9yyn0WpJrRPjntt1Y6oyX1gUFo/GpcG4vKr3qzxmA/JJplhlauSC8AUNGexlJs65IOwo9F1UgphbmPPVGkolonHaLCmZn6ICWFJU5N5BDJeExHfwiNokyFeaJc03otALIUASGsvigKlGUKtrOijRXOf6vS2jCFTlF4lbYHND+Sr8sRwYejYgpOEpTMhkSFolSAhAA03hKzuXEGtWcK+1WDCyLHZKN1ZArZ582ic5lg3uGWLDsfkHSivJ8rMghqD8Rii+jIpK1LeBNyXVib8hRYZjT+Y6m0QeXVh2mjMQ3ZnKFA5QDKMwVVDqGKgej/4tN39b+LrcuEKZPpFOd82Te3LzkfGWPSx585Rh0yeTvyGzhx3+MAZUor70+IfWSnQwBw1oay9PkhyhKD4OZdFiiC56vJ84ysSr360FNPPWW0NpLCrdADjiQAdGoMh19hVaCReXGlDqnmhsMSrFNEQ+k5fWMZjQeWJlrH0PG6cZLCwP8eDAaZZaBb6kputZ0kIe+UhEA2SNKQsM8vnN/eL1a63H1dpM1jRpCkG8NRUmqMxawRGZv9fgNgUA6jpMBUwATx2pjkYLQgGnH+f5cGykiJV63FxNv3z507C0UKNhZikAJZnoE8vgvpJ9LqE4n1mCGsR9ZG/41CFNV10XMplqIxGgPja44Kf4ZCFVHlCTU/3bisLimsEVNYRodjB8vMQMe+3SGcxDVZAVOIO0L79IEQIfr4g1Ja9dFwGCeOtozB2HnabVczTH18yMZ4jLoxscBpWZYxs7Ob7EknzSmYvRafKfaM4GL30RaF20/ZwtkpP9o+w3JKhOOYQPJcbYzFPAR+GRMxhcZaTH3sx7TWaPwFLRHqRkNB5FoMiRMsR6csWbAm6yDn7MH9XpxBSqVEKPuTKc5sbmRJY7Kcl2kkMgaQPmrKj4kxc4lxQt84wypkQBmYsyQ+RjhWpdskS0RRloLBrf6FHKo+ENHHiOgZInq849hvExET0R3+NxHRnxDRN4noy0T0wMo96amnntaCVpEU/hLAnwL4hNxJRC8C8HoAT4rdb4Sr9XA/gJ8G8Of+7y2lo4CUx7+6yI0gAUTkGwuCRpdawh5Y9PuqeYUNn3ZNi0I1ajzGWe/yPKkrXPf1Js+fO+NW1BALYYdpdRQqwlKBR2ZnanVUWhJIpHDL88xx/hvolj6IYn4EaxmGpfrAUR2aNxpzLxHMG90JNE60xiyE1LMBLKHwNdGKwSAV8FBJUlAy54JYQYOosEzNCqRIQfmVtq4bMDM2YqRqCr2WrvnOsiT8UeK2lwzEsTzbk9/Olukk9SmloMoiSgXW2hTNmZrFe4X+B6tGyN2wCh2rwKynP4YrCPP3Yt9bAHzCV4x6iIguENE9zHxt5R4dm7rF+U6oYNVLLcUX5L38x3QIpmCtqwwVSGsdVYbpdCZMX4zKx0SMhkPAT8rBxiZuVC7nwu50juFwGOP2tUk5Bw989Zleu8wUkc2upErIvAohYcuC3iTNkXAOSrJYr6hQ1RiDShSIDcFeVaOjw1KlDfa9eXCvqqN50jDB6Aql71vJgAop4CxDkWc+SkFZuR0+sPbzi/fZWlTCO+JCRQwIcOHWNqa3EwFRXSoDEBkCxUOUGIR4H9RiKslT1f0NKfNZ5hfhJf0XgVZBjViFjlsh6i0ArjDzoy0w54cAPCV+hwKzqzOFm4833nTqgh0yPhJAL2uhffSa9DQLgFGsQ6Ao5pPYoKQHz+sas9ol1SAqUHs07cntXRRlGb0gB42O+R+JKOILDtOI01BM0C6mgNgueyoJNiyMAC+egjQxZe5Mua2tRa0Nap+mvxYSgdt2Y7Y3m2PHJ5+ZNQ1qP3yGHbio2ddQYGDg71kUCtYvt5mkwAwOzIJVLJLbTYuTMIK/TajUjWgGZMUQ1mWRPKXNICT4mTuNy4hRmaEqtuK8qpfzTRCSijBDL2Ac2X0PpyMzBSLaBPD7cKrDsYmI3gngnQCwefbcc7lUTz31dBPpOJLCSwG8BECQEl4I4GEiehWOWWD24p3PP6J80FYIVzrjqP5JywECoWu3MQVtTESsJT5gjIE1IpEap1h7pVRcgYqiiIVY5/M5dqNaYFH7gCBdA4puxBXx+apIKLVSIL9sFUUapHyhoBVXjiQNCDf89KPzEiRyCOS5MQKm0GiTSQQyrmNW19jadSnItvYm0SQpzZbaehQ+SGRcRUeo0XAE5eObLamYW8BaUcXJSxBBnaAs78RyctKCa1fXTTynKIooheQ4QksyWIIptAdSqhhZv1qWCVkIOMTRWIkvtKwSq9KRmQIzPwbgLnGzJwC8kpmfJaIHAbybiD4DBzDunAyegBNWO3L8QmudGIHRMHE7MQhZIBVwLzXgCEoVKEtvrtMa7N1Xq6rC1OuCxBbzqdevdQ3bNJERKFWAY6kzwngY9EiLzANYYoUrTpJYVQqcMxYCpMtuOCQTk2QJc9hGDKTWxuMI6ffEq0839iaRKexMZ5h5Zml4MZI2uu4aA1P5qtmWY9VnpSysTenPgs1eeawhqhY2VdQ+yJdDloEjICbVHY9TElXJKx1ukIYrq0q1FEdIWANa5k0GkhmURWQpVDJDWyvapJl6lM9jFZPkpwH8B4CXEdFlInrHAc3/AcC3AXwTwEcA/MYR+tJTTz2tAR23wKw8fklsM4B3PfduHZVW44M3yxJhmaMqYKzxSTV9slFtcqkhqAxZ6p8EFgFuYQjpxlkUPTHGYOoTdZZEmPjipAUYO7M5rm67DNjj4QDjUVgd89UogJEQom8Asw4dNQaIlo2akN/zU+JeqT7UxmSSQfgHAPuzOXb2nHRwY2+CLZ8FaVLXqEPBFqg8I1L8z6ljIUQ4ZLMC3JiqWBUq1XvklvpglYUKCVWJxRgKS0rYQ8ExKK3iVVVHUyWo26NRmh/l3/Z2e5hl5iwisfKzhfUmWaUs6pj1uxFBUElqO4qosOYejXzAr+X7bibJCkXJZdS0mEIdfQasTRl8c0aATqtf2B2QbGtN1A+LokjqB1LFIrYWk8rC2j0Azp117EXmQVmKiZfs1GcHgwXN9VDmSEkxaI8zif+DNSW0k9vB664RFb4Cg9ibuI9/Z3+CG/vOnXhrfxJViXnTJPdlpNyNLD4695wkyrGlyk9tBhmIyZkQY3VwpvjOrLJQUeWQhWg505MKpUAIxX8JM2863dwYCzdwWvj4u5hBhkMsU+soXNH3hpH6LOZm3TSY+YXEWpulk1uV+oConnrqKaO1lhSkiCj2HCgerMIPD7JEGFF3whiTBZQE7zzZxhqTi2nO42fhuiTg8pikNHM6cdvloIzcnQDs+hW0UArKo4bWOuef0O769jYGHrQcj4YRiVcq1QMYDAbYlKgj5bICd2zJUeoywuTAeJKOJLgVy8cbG9WFeV1jZ3+CHf9sN/Yn2JmEEPEqxkFYRswtkfXF+xhwAGEpWRbcary41uXyTvgVJAVh3WdXUMV1oCXut543qH9ImCHmVRWrOmX3b6sFbR+GJV6QiyTeGXdICnWNqVe/qqrC0Fuynndu9boPa80UVqbnoEMw3EceTILSSmCsjQ5G1ppsO7yQ4XAAMu1rdugJAq0P2nxkCpTUlHE5isxHKYXxqIkXSCXYAF3Po4lvMq9xbWsbADAoCrz47jtdO2HCGpQlhj51/FBOZrQ3l7BLqQkt6L35zqT3CoclYzDzasHO/tSrDF59mEyx50XeaVOnPAvyWkjjqorCeXqqYH1RkRGoTJUQhVVa7t/iO154zrjZYu4LH6k/XBZlPGbJRFwjWEHSXVuUuTmnfV0MYvEKiUk578aEIwR8ajwe44x3kz8KkNarDz311FNGPwCSwvHEBAnMNE2Dpkm+BbJEudyWdu4glseEmTrFNXTzds6yFHvMHO2mzn8hqQlBFLXCWcUogiKGrjygxMCuFxmvbm1H1aaqm+gfPxoOMPArV1EUWfFXSUvxKOqSBdJB6dobLmI55VDQ1kbrSVAddoKVoaowD0Aty2IurbWRAtquXKyDkBSiRCDAwcxlOLP/H0BChJDSRNeZSf0gUayWQBTAVe2Tvy6/hni4hXtmsRJt2YbkFdPLcf1wPzbG45a0shr9ADCFo5B7iU2jo7qQmEKoX5jUBLZ26URKgSYKBMAcZFoAAE7pyOKBji9Qa43xyIl81hqwZwrGGJiQcVgRpIrbVLMYnHNjfz8yD22MKHaq4gR1Xn8qBslkvVo6cyXqQO1dSWTPFO9UzKVuNPZ9HMP+dIb9eRWDoLS1UWVgtEZQiP/RI7EoHGMLEYCUOx+pDAfoQPX54KXkCJI2AGBezVMcSlnC+r5YmywhZVke+cJt9UH2TjIJIsSismc2z8Rnnc2rjtych1OvPvTUU08Zramk8Fy9DyTKHNyHjXDw0JmzR900mfgfqcVlaWFD2LKpEQdT/2NmMynuRV/YRS7urBxOUinLQeT6TZNCrQvlVsaQr4eNhgmSjmXs+BRixqbaCsbaGHY7Go1QlCWUEqsY0kqdUQe62DkOoYKySquzVI+0MZhXvoaFTy2nPaCqrRXZkpGpWXGlUyq6dRdeUpBu4vL+maTQch5Kj5VLatKvbNXZF644Ho2w760nVaHie3JjnyxBZVkeoJ+Fi3bPuaAKdS38ZTHAhs/QtbkxjtXJd3Z3cWPX+bJcvHh2xacC6ChODbeKiOh7ACYAnj3tvgi6A31/DqN161Pfn4Ppxcx852GN1oIpAAARfZGZX3na/QjU9+dwWrc+9f25OdRjCj311FNGPVPoqaeeMlonpvAXp92BFvX9OZzWrU99f24CrQ2m0FNPPa0HrZOk0FNPPa0BnTpTIKI3ENH/+gIy7z2lPryIiP6FiL5KRF8hot/0+99PRFeI6BH/700n2KcniOgxf98v+n23E9Hniegb/u9tJ9SXl4kxeISIdonoPSc9PtRRmGjZmJCjW1qYaEl//pCI/sff87NEdMHvv0REMzFWH77Z/blpFNJGn8Y/AAWAbwG4D8AQwKMAXn4K/bgHwAN++xyArwN4OYD3A/idUxqbJwDc0dr3BwDe67ffC+CDp/TOrgN48UmPD4DXAngAwOOHjQmANwH4Rzj/n1cD+MIJ9ef1AEq//UHRn0uy3Tr/O21J4VUAvsnM32bmGsBn4ArKnCgx8zVmfthv7wH4Gly9inWjtwD4uN/+OIBfOIU+vA7At5j5uyd9Y2b+NwBbrd3LxiQWJmLmhwBcIKJ7bnV/mPlzzBzcYx+Cy2j+/4pOmyksKx5zakRElwC8AsAX/K53e1HwYyclrntiAJ8joi+Rq5EBAHdzyo59HcDdJ9ifQG8F8Gnx+7TGJ9CyMVmHufVrcNJKoJcQ0X8T0b8S0c+ccF9WptNmCmtFRHQWwN8CeA8z78LVwnwpgJ+Eq3L1RyfYndcw8wNw9TnfRUSvlQfZyaQnajoioiGANwP4G7/rNMdngU5jTJYREb0PgAbwSb/rGoB7mfkVAH4LwKeI6Hmn1b+D6LSZwsrFY241EdEAjiF8kpn/DgCY+WlmNuwS6X8ETt05EWLmK/7vMwA+6+/9dBCB/d9nTqo/nt4I4GFmftr37dTGR9CyMTm1uUVEbwfwcwB+xTMqMHPFzN/321+Cw9J+5CT6c1Q6babwXwDuJ6KX+FXorQAePOlOkAuh+yiArzHzh8R+qYP+IoDH2+feov6cIaJzYRsOvHocbmze5pu9DXlx35OgX4ZQHU5rfFq0bEweBPCr3grxapxQYSIiegNc4eU3M/NU7L+TiAq/fR9cZfZv3+r+HItOG+mEQ4m/Dsc533dKfXgNnNj5ZQCP+H9vAvBXAB7z+x8EcM8J9ec+OEvMowC+EsYFwEUA/wzgGwD+CcDtJzhGZwB8H8B5se9ExweOIV0D0MBhBO9YNiZwVoc/8/PqMbgqZifRn2/CYRlhHn3Yt/0l/y4fAfAwgJ8/jbm+yr/eo7GnnnrK6LTVh5566mnNqGcKPfXUU0Y9U+ipp54y6plCTz31lFHPFHrqqaeMeqbQU089ZdQzhZ566imjnin01FNPGf0fbOBKCnYS0QcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 150: 0.0011682428885251284\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 160: 0.001168206101283431\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 170: 0.0011681694304570556\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 180: 0.0011681318283081055\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss at step 190: 0.0011680949246510863\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feed_dict = {x : train_x, y : train_y, learning_rate: 0.00001}\n",
    "for step in range(200):\n",
    "    _ = sess.run(\n",
    "        [optimizer], feed_dict = feed_dict\n",
    "    )\n",
    "\n",
    "    if step % 10 == 0:\n",
    "        l, predictions = sess.run(\n",
    "            [loss, out], feed_dict = feed_dict\n",
    "        )\n",
    "        print('loss at step {0}: {1}'.format(step, l))\n",
    "        plt.imshow(predictions.reshape((img.shape[0], img.shape[1], 3)))\n",
    "        fname = \"image_seq/%04d.png\" % seq\n",
    "        plt.imsave(fname, predictions.reshape((img.shape[0], img.shape[1], 3)))\n",
    "        plt.show()\n",
    "        seq += 1\n",
    "        #plt.hist(predictions)\n",
    "        #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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   "name": "python",
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================================================
FILE: notebooks/parallel_computing/1. Load Balancing.ipynb
================================================
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The IPython architecture consists of four components:\n",
      "\n",
      "- The IPython engine\n",
      "- The IPython hub\n",
      "- The IPython schedulers\n",
      "- The controller client\n",
      "\n",
      "![](http://ipython.org/ipython-doc/stable/_images/wideView.png)\n",
      "\n",
      "Dependencies:\n",
      "\n",
      "- ZeroMQ - http://zeromq.org/\n",
      "![](http://zeromq.wdfiles.com/local--files/admin:css/logo.gif)\n",
      "\n",
      "To install on Ubuntu:\n",
      "\n",
      "```bash\n",
      "sudo apt-get install libzmq-dev\n",
      "sudo easy_install pyzmq\n",
      "```\n",
      "- pyzmq\n",
      "\n",
      "To install on Ubuntu:\n",
      "\n",
      "```bash\n",
      "sudo pip install pyzmq\n",
      "```"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Parallel and Distributed Computing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Parallel computing**: Multiple processors or cores to distribute the work on a sinlge machine.\n",
      "\n",
      "**Distributed computing**: Multiple processors or cores located on multiple machines to distribute the work on a several machines.\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Load Balancing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This will be our first approach to parellel/distributed computing."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Start the engines"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I'll start the engines on a profile named \"nbserver\"."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Import `Client`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Create a `Client` and a `load_balanced_view`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cluster = Client(profile=\"nbserver\")\n",
      "lb_view = cluster.load_balanced_view()\n",
      "\n",
      "print \"Profile: %s\" % cluster.profile\n",
      "print \"Engines: %s\" % len(lb_view)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Profile: nbserver\n",
        "Engines: 12\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "With no Load Balancing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    result = 1.0\n",
      "    for counter in range(100000):\n",
      "        result = (result * x * 0.5)\n",
      "        if result % 5 == 0:\n",
      "            result -=4\n",
      "    return result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit -r 1 -n 1\n",
      "result = []\n",
      "for i in range(1000):\n",
      "    result.append(f(i))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 23.8 s per loop\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "With Load Balancing"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using load balanced view can help simplify the process of distributing code. There are two methods to implement this."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "`view.map(f, *sequences, block=self.block, chunksize=1, ordered=True)`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    result = 1.0\n",
      "    for counter in range(100000):\n",
      "        result = (result * x * 0.5)\n",
      "        if result % 5 == 0:\n",
      "            result -=4\n",
      "    return result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%timeit -r 1 -n 1\n",
      "result = lb_view.map(f, range(1000), block=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 1: 5.13 s per loop\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "`@view.parallel()` function decorator"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using `parallel` function decorator is by far the simplist way to implement parellel computing in IPython. It doesn't allow for much control but it is fast and works for most of the cases.\n",
      "\n",
      "```\n",
      "load_balanced_view.parallel(self, dist='b', block=None, **flags)\n",
      "\n",
      "Decorator for making a ParallelFunction\n",
      "```"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@lb_view.parallel(block=True)\n",
      "def f(x):\n",
      "    result = 1.0\n",
      "    for counter in range(100000):\n",
      "        result = (result * x * 0.5)\n",
      "        if result % 5 == 0:\n",
      "            result -=4\n",
      "    return result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = f.map(range(1000))\n",
      "print \"Results Count: %s\" % len(result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Results Count: 1000\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Asynchronous Processing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@lb_view.parallel(block=False)\n",
      "def f(x):\n",
      "    result = 1.0\n",
      "    for counter in range(100000):\n",
      "        result = (result * x * 0.5)\n",
      "        if result % 5 == 0:\n",
      "            result -=4\n",
      "    return result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result = f.map(range(1000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Retreving the results from AsyncMapResult "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "result"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<AsyncMapResult: finished>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Results Count: %s\" % len(result.result)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Results Count: 1000\n"
       ]
      }
     ],
     "prompt_number": 33
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/quick_tips/2. Pickle Data and Model.ipynb
================================================
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Saving your data and model is very handy to continue your work on in-memory objects over long time and multiple machines.\n",
      "\n",
      "**Video Tutorial**:\n",
      "\n",
      "http://youtu.be/SQDPVSVNq1k"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import Library"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "joblib library is available in external model inside scikit-learn library. This allows to store in memory objects as pickle files."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "from sklearn.neighbors import KNeighborsClassifier\n",
      "from sklearn.externals import joblib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Loading data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\"\n",
      "columns = [\"Sex\", \"Length\", \"Diameter\", \"Height\", \"Whole weight\", \"Shucked weight\",\n",
      "           \"Viscera weight\", \"Shell weight\", \"Rings\"]\n",
      "csv_data = pd.read_csv(url, names = columns)\n",
      "csv_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Sex</th>\n",
        "      <th>Length</th>\n",
        "      <th>Diameter</th>\n",
        "      <th>Height</th>\n",
        "      <th>Whole weight</th>\n",
        "      <th>Shucked weight</th>\n",
        "      <th>Viscera weight</th>\n",
        "      <th>Shell weight</th>\n",
        "      <th>Rings</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.455</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.5140</td>\n",
        "      <td> 0.2245</td>\n",
        "      <td> 0.1010</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.265</td>\n",
        "      <td> 0.090</td>\n",
        "      <td> 0.2255</td>\n",
        "      <td> 0.0995</td>\n",
        "      <td> 0.0485</td>\n",
        "      <td> 0.070</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.530</td>\n",
        "      <td> 0.420</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.6770</td>\n",
        "      <td> 0.2565</td>\n",
        "      <td> 0.1415</td>\n",
        "      <td> 0.210</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.5160</td>\n",
        "      <td> 0.2155</td>\n",
        "      <td> 0.1140</td>\n",
        "      <td> 0.155</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> I</td>\n",
        "      <td> 0.330</td>\n",
        "      <td> 0.255</td>\n",
        "      <td> 0.080</td>\n",
        "      <td> 0.2050</td>\n",
        "      <td> 0.0895</td>\n",
        "      <td> 0.0395</td>\n",
        "      <td> 0.055</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> I</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.300</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.3515</td>\n",
        "      <td> 0.1410</td>\n",
        "      <td> 0.0775</td>\n",
        "      <td> 0.120</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.530</td>\n",
        "      <td> 0.415</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.7775</td>\n",
        "      <td> 0.2370</td>\n",
        "      <td> 0.1415</td>\n",
        "      <td> 0.330</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.545</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.7680</td>\n",
        "      <td> 0.2940</td>\n",
        "      <td> 0.1495</td>\n",
        "      <td> 0.260</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.475</td>\n",
        "      <td> 0.370</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.5095</td>\n",
        "      <td> 0.2165</td>\n",
        "      <td> 0.1125</td>\n",
        "      <td> 0.165</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.8945</td>\n",
        "      <td> 0.3145</td>\n",
        "      <td> 0.1510</td>\n",
        "      <td> 0.320</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.525</td>\n",
        "      <td> 0.380</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.6065</td>\n",
        "      <td> 0.1940</td>\n",
        "      <td> 0.1475</td>\n",
        "      <td> 0.210</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.430</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.110</td>\n",
        "      <td> 0.4060</td>\n",
        "      <td> 0.1675</td>\n",
        "      <td> 0.0810</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.490</td>\n",
        "      <td> 0.380</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.5415</td>\n",
        "      <td> 0.2175</td>\n",
        "      <td> 0.0950</td>\n",
        "      <td> 0.190</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.535</td>\n",
        "      <td> 0.405</td>\n",
        "      <td> 0.145</td>\n",
        "      <td> 0.6845</td>\n",
        "      <td> 0.2725</td>\n",
        "      <td> 0.1710</td>\n",
        "      <td> 0.205</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.4755</td>\n",
        "      <td> 0.1675</td>\n",
        "      <td> 0.0805</td>\n",
        "      <td> 0.185</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.500</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.130</td>\n",
        "      <td> 0.6645</td>\n",
        "      <td> 0.2580</td>\n",
        "      <td> 0.1330</td>\n",
        "      <td> 0.240</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.280</td>\n",
        "      <td> 0.085</td>\n",
        "      <td> 0.2905</td>\n",
        "      <td> 0.0950</td>\n",
        "      <td> 0.0395</td>\n",
        "      <td> 0.115</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.340</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.4510</td>\n",
        "      <td> 0.1880</td>\n",
        "      <td> 0.0870</td>\n",
        "      <td> 0.130</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.295</td>\n",
        "      <td> 0.080</td>\n",
        "      <td> 0.2555</td>\n",
        "      <td> 0.0970</td>\n",
        "      <td> 0.0430</td>\n",
        "      <td> 0.100</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.320</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.3810</td>\n",
        "      <td> 0.1705</td>\n",
        "      <td> 0.0750</td>\n",
        "      <td> 0.115</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.280</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.2455</td>\n",
        "      <td> 0.0955</td>\n",
        "      <td> 0.0620</td>\n",
        "      <td> 0.075</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.380</td>\n",
        "      <td> 0.275</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.2255</td>\n",
        "      <td> 0.0800</td>\n",
        "      <td> 0.0490</td>\n",
        "      <td> 0.085</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.565</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.155</td>\n",
        "      <td> 0.9395</td>\n",
        "      <td> 0.4275</td>\n",
        "      <td> 0.2140</td>\n",
        "      <td> 0.270</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.415</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.7635</td>\n",
        "      <td> 0.3180</td>\n",
        "      <td> 0.2100</td>\n",
        "      <td> 0.200</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.615</td>\n",
        "      <td> 0.480</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 1.1615</td>\n",
        "      <td> 0.5130</td>\n",
        "      <td> 0.3010</td>\n",
        "      <td> 0.305</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.560</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.9285</td>\n",
        "      <td> 0.3825</td>\n",
        "      <td> 0.1880</td>\n",
        "      <td> 0.300</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.580</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.185</td>\n",
        "      <td> 0.9955</td>\n",
        "      <td> 0.3945</td>\n",
        "      <td> 0.2720</td>\n",
        "      <td> 0.285</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.590</td>\n",
        "      <td> 0.445</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.9310</td>\n",
        "      <td> 0.3560</td>\n",
        "      <td> 0.2340</td>\n",
        "      <td> 0.280</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.605</td>\n",
        "      <td> 0.475</td>\n",
        "      <td> 0.180</td>\n",
        "      <td> 0.9365</td>\n",
        "      <td> 0.3940</td>\n",
        "      <td> 0.2190</td>\n",
        "      <td> 0.295</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.575</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.8635</td>\n",
        "      <td> 0.3930</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.200</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.580</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 0.9975</td>\n",
        "      <td> 0.3935</td>\n",
        "      <td> 0.2420</td>\n",
        "      <td> 0.330</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.680</td>\n",
        "      <td> 0.560</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 1.6390</td>\n",
        "      <td> 0.6055</td>\n",
        "      <td> 0.2805</td>\n",
        "      <td> 0.460</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.665</td>\n",
        "      <td> 0.525</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 1.3380</td>\n",
        "      <td> 0.5515</td>\n",
        "      <td> 0.3575</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.680</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.175</td>\n",
        "      <td> 1.7980</td>\n",
        "      <td> 0.8150</td>\n",
        "      <td> 0.3925</td>\n",
        "      <td> 0.455</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.705</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.200</td>\n",
        "      <td> 1.7095</td>\n",
        "      <td> 0.6330</td>\n",
        "      <td> 0.4115</td>\n",
        "      <td> 0.490</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.465</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.105</td>\n",
        "      <td> 0.4795</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.1240</td>\n",
        "      <td> 0.125</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.540</td>\n",
        "      <td> 0.475</td>\n",
        "      <td> 0.155</td>\n",
        "      <td> 1.2170</td>\n",
        "      <td> 0.5305</td>\n",
        "      <td> 0.3075</td>\n",
        "      <td> 0.340</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.105</td>\n",
        "      <td> 0.5225</td>\n",
        "      <td> 0.2370</td>\n",
        "      <td> 0.1165</td>\n",
        "      <td> 0.145</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.575</td>\n",
        "      <td> 0.445</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.8830</td>\n",
        "      <td> 0.3810</td>\n",
        "      <td> 0.2035</td>\n",
        "      <td> 0.260</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.290</td>\n",
        "      <td> 0.090</td>\n",
        "      <td> 0.3275</td>\n",
        "      <td> 0.1340</td>\n",
        "      <td> 0.0860</td>\n",
        "      <td> 0.090</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.335</td>\n",
        "      <td> 0.105</td>\n",
        "      <td> 0.4250</td>\n",
        "      <td> 0.1865</td>\n",
        "      <td> 0.0910</td>\n",
        "      <td> 0.115</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.8515</td>\n",
        "      <td> 0.3620</td>\n",
        "      <td> 0.1960</td>\n",
        "      <td> 0.270</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.240</td>\n",
        "      <td> 0.175</td>\n",
        "      <td> 0.045</td>\n",
        "      <td> 0.0700</td>\n",
        "      <td> 0.0315</td>\n",
        "      <td> 0.0235</td>\n",
        "      <td> 0.020</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.205</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.055</td>\n",
        "      <td> 0.0420</td>\n",
        "      <td> 0.0255</td>\n",
        "      <td> 0.0150</td>\n",
        "      <td> 0.012</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.210</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.050</td>\n",
        "      <td> 0.0420</td>\n",
        "      <td> 0.0175</td>\n",
        "      <td> 0.0125</td>\n",
        "      <td> 0.015</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.390</td>\n",
        "      <td> 0.295</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.2030</td>\n",
        "      <td> 0.0875</td>\n",
        "      <td> 0.0450</td>\n",
        "      <td> 0.075</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.370</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.5795</td>\n",
        "      <td> 0.2930</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.140</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.460</td>\n",
        "      <td> 0.375</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.4605</td>\n",
        "      <td> 0.1775</td>\n",
        "      <td> 0.1100</td>\n",
        "      <td> 0.150</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.325</td>\n",
        "      <td> 0.245</td>\n",
        "      <td> 0.070</td>\n",
        "      <td> 0.1610</td>\n",
        "      <td> 0.0755</td>\n",
        "      <td> 0.0255</td>\n",
        "      <td> 0.045</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.525</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.160</td>\n",
        "      <td> 0.8355</td>\n",
        "      <td> 0.3545</td>\n",
        "      <td> 0.2135</td>\n",
        "      <td> 0.245</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.520</td>\n",
        "      <td> 0.410</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.5950</td>\n",
        "      <td> 0.2385</td>\n",
        "      <td> 0.1110</td>\n",
        "      <td> 0.190</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.320</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.3030</td>\n",
        "      <td> 0.1335</td>\n",
        "      <td> 0.0600</td>\n",
        "      <td> 0.100</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.485</td>\n",
        "      <td> 0.360</td>\n",
        "      <td> 0.130</td>\n",
        "      <td> 0.5415</td>\n",
        "      <td> 0.2595</td>\n",
        "      <td> 0.0960</td>\n",
        "      <td> 0.160</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.360</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.4775</td>\n",
        "      <td> 0.2105</td>\n",
        "      <td> 0.1055</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.405</td>\n",
        "      <td> 0.310</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.3850</td>\n",
        "      <td> 0.1730</td>\n",
        "      <td> 0.0915</td>\n",
        "      <td> 0.110</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.500</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.6615</td>\n",
        "      <td> 0.2565</td>\n",
        "      <td> 0.1755</td>\n",
        "      <td> 0.220</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.445</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.4425</td>\n",
        "      <td> 0.1920</td>\n",
        "      <td> 0.0955</td>\n",
        "      <td> 0.135</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.385</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.5895</td>\n",
        "      <td> 0.2765</td>\n",
        "      <td> 0.1200</td>\n",
        "      <td> 0.170</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.245</td>\n",
        "      <td> 0.190</td>\n",
        "      <td> 0.060</td>\n",
        "      <td> 0.0860</td>\n",
        "      <td> 0.0420</td>\n",
        "      <td> 0.0140</td>\n",
        "      <td> 0.025</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.505</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.5830</td>\n",
        "      <td> 0.2460</td>\n",
        "      <td> 0.1300</td>\n",
        "      <td> 0.175</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4177 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
        "0    M   0.455     0.365   0.095        0.5140          0.2245   \n",
        "1    M   0.350     0.265   0.090        0.2255          0.0995   \n",
        "2    F   0.530     0.420   0.135        0.6770          0.2565   \n",
        "3    M   0.440     0.365   0.125        0.5160          0.2155   \n",
        "4    I   0.330     0.255   0.080        0.2050          0.0895   \n",
        "5    I   0.425     0.300   0.095        0.3515          0.1410   \n",
        "6    F   0.530     0.415   0.150        0.7775          0.2370   \n",
        "7    F   0.545     0.425   0.125        0.7680          0.2940   \n",
        "8    M   0.475     0.370   0.125        0.5095          0.2165   \n",
        "9    F   0.550     0.440   0.150        0.8945          0.3145   \n",
        "10   F   0.525     0.380   0.140        0.6065          0.1940   \n",
        "11   M   0.430     0.350   0.110        0.4060          0.1675   \n",
        "12   M   0.490     0.380   0.135        0.5415          0.2175   \n",
        "13   F   0.535     0.405   0.145        0.6845          0.2725   \n",
        "14   F   0.470     0.355   0.100        0.4755          0.1675   \n",
        "15   M   0.500     0.400   0.130        0.6645          0.2580   \n",
        "16   I   0.355     0.280   0.085        0.2905          0.0950   \n",
        "17   F   0.440     0.340   0.100        0.4510          0.1880   \n",
        "18   M   0.365     0.295   0.080        0.2555          0.0970   \n",
        "19   M   0.450     0.320   0.100        0.3810          0.1705   \n",
        "20   M   0.355     0.280   0.095        0.2455          0.0955   \n",
        "21   I   0.380     0.275   0.100        0.2255          0.0800   \n",
        "22   F   0.565     0.440   0.155        0.9395          0.4275   \n",
        "23   F   0.550     0.415   0.135        0.7635          0.3180   \n",
        "24   F   0.615     0.480   0.165        1.1615          0.5130   \n",
        "25   F   0.560     0.440   0.140        0.9285          0.3825   \n",
        "26   F   0.580     0.450   0.185        0.9955          0.3945   \n",
        "27   M   0.590     0.445   0.140        0.9310          0.3560   \n",
        "28   M   0.605     0.475   0.180        0.9365          0.3940   \n",
        "29   M   0.575     0.425   0.140        0.8635          0.3930   \n",
        "30   M   0.580     0.470   0.165        0.9975          0.3935   \n",
        "31   F   0.680     0.560   0.165        1.6390          0.6055   \n",
        "32   M   0.665     0.525   0.165        1.3380          0.5515   \n",
        "33   F   0.680     0.550   0.175        1.7980          0.8150   \n",
        "34   F   0.705     0.550   0.200        1.7095          0.6330   \n",
        "35   M   0.465     0.355   0.105        0.4795          0.2270   \n",
        "36   F   0.540     0.475   0.155        1.2170          0.5305   \n",
        "37   F   0.450     0.355   0.105        0.5225          0.2370   \n",
        "38   F   0.575     0.445   0.135        0.8830          0.3810   \n",
        "39   M   0.355     0.290   0.090        0.3275          0.1340   \n",
        "40   F   0.450     0.335   0.105        0.4250          0.1865   \n",
        "41   F   0.550     0.425   0.135        0.8515          0.3620   \n",
        "42   I   0.240     0.175   0.045        0.0700          0.0315   \n",
        "43   I   0.205     0.150   0.055        0.0420          0.0255   \n",
        "44   I   0.210     0.150   0.050        0.0420          0.0175   \n",
        "45   I   0.390     0.295   0.095        0.2030          0.0875   \n",
        "46   M   0.470     0.370   0.120        0.5795          0.2930   \n",
        "47   F   0.460     0.375   0.120        0.4605          0.1775   \n",
        "48   I   0.325     0.245   0.070        0.1610          0.0755   \n",
        "49   F   0.525     0.425   0.160        0.8355          0.3545   \n",
        "50   I   0.520     0.410   0.120        0.5950          0.2385   \n",
        "51   M   0.400     0.320   0.095        0.3030          0.1335   \n",
        "52   M   0.485     0.360   0.130        0.5415          0.2595   \n",
        "53   F   0.470     0.360   0.120        0.4775          0.2105   \n",
        "54   M   0.405     0.310   0.100        0.3850          0.1730   \n",
        "55   F   0.500     0.400   0.140        0.6615          0.2565   \n",
        "56   M   0.445     0.350   0.120        0.4425          0.1920   \n",
        "57   M   0.470     0.385   0.135        0.5895          0.2765   \n",
        "58   I   0.245     0.190   0.060        0.0860          0.0420   \n",
        "59   F   0.505     0.400   0.125        0.5830          0.2460   \n",
        "   ...     ...       ...     ...           ...             ...   \n",
        "\n",
        "    Viscera weight  Shell weight  Rings  \n",
        "0           0.1010         0.150     15  \n",
        "1           0.0485         0.070      7  \n",
        "2           0.1415         0.210      9  \n",
        "3           0.1140         0.155     10  \n",
        "4           0.0395         0.055      7  \n",
        "5           0.0775         0.120      8  \n",
        "6           0.1415         0.330     20  \n",
        "7           0.1495         0.260     16  \n",
        "8           0.1125         0.165      9  \n",
        "9           0.1510         0.320     19  \n",
        "10          0.1475         0.210     14  \n",
        "11          0.0810         0.135     10  \n",
        "12          0.0950         0.190     11  \n",
        "13          0.1710         0.205     10  \n",
        "14          0.0805         0.185     10  \n",
        "15          0.1330         0.240     12  \n",
        "16          0.0395         0.115      7  \n",
        "17          0.0870         0.130     10  \n",
        "18          0.0430         0.100      7  \n",
        "19          0.0750         0.115      9  \n",
        "20          0.0620         0.075     11  \n",
        "21          0.0490         0.085     10  \n",
        "22          0.2140         0.270     12  \n",
        "23          0.2100         0.200      9  \n",
        "24          0.3010         0.305     10  \n",
        "25          0.1880         0.300     11  \n",
        "26          0.2720         0.285     11  \n",
        "27          0.2340         0.280     12  \n",
        "28          0.2190         0.295     15  \n",
        "29          0.2270         0.200     11  \n",
        "30          0.2420         0.330     10  \n",
        "31          0.2805         0.460     15  \n",
        "32          0.3575         0.350     18  \n",
        "33          0.3925         0.455     19  \n",
        "34          0.4115         0.490     13  \n",
        "35          0.1240         0.125      8  \n",
        "36          0.3075         0.340     16  \n",
        "37          0.1165         0.145      8  \n",
        "38          0.2035         0.260     11  \n",
        "39          0.0860         0.090      9  \n",
        "40          0.0910         0.115      9  \n",
        "41          0.1960         0.270     14  \n",
        "42          0.0235         0.020      5  \n",
        "43          0.0150         0.012      5  \n",
        "44          0.0125         0.015      4  \n",
        "45          0.0450         0.075      7  \n",
        "46          0.2270         0.140      9  \n",
        "47          0.1100         0.150      7  \n",
        "48          0.0255         0.045      6  \n",
        "49          0.2135         0.245      9  \n",
        "50          0.1110         0.190      8  \n",
        "51          0.0600         0.100      7  \n",
        "52          0.0960         0.160     10  \n",
        "53          0.1055         0.150     10  \n",
        "54          0.0915         0.110      7  \n",
        "55          0.1755         0.220      8  \n",
        "56          0.0955         0.135      8  \n",
        "57          0.1200         0.170      8  \n",
        "58          0.0140         0.025      4  \n",
        "59          0.1300         0.175      7  \n",
        "               ...           ...    ...  \n",
        "\n",
        "[4177 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Training Model"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Preparing data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "features = csv_data.drop(\"Sex\", 1)\n",
      "\n",
      "sex_dict = {\"M\":0, \"I\":1, \"F\":2}\n",
      "results = csv_data[\"Sex\"].map(sex_dict)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Training a model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trained_model = KNeighborsClassifier(n_neighbors=5, weights='uniform')\n",
      "trained_model.fit(features, results)\n",
      "trained_model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           n_neighbors=5, p=2, weights='uniform')"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Saving Data and Model on Disk"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "joblib.dump(csv_data, \"data.pkl\")\n",
      "joblib.dump(trained_model, \"model.pkl\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2. Pickle Data and Model.ipynb  model.pkl         model.pkl_06.npy\r\n",
        "Folder Management.ipynb         model.pkl_01.npy  model.pkl_07.npy\r\n",
        "data.pkl                        model.pkl_02.npy  model.pkl_08.npy\r\n",
        "data.pkl_01.npy                 model.pkl_03.npy  model.pkl_09.npy\r\n",
        "data.pkl_02.npy                 model.pkl_04.npy\r\n",
        "data.pkl_03.npy                 model.pkl_05.npy\r\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading Data and Model"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loaded_data = joblib.load(\"data.pkl\")\n",
      "loaded_model = joblib.load(\"model.pkl\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loaded_data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Sex</th>\n",
        "      <th>Length</th>\n",
        "      <th>Diameter</th>\n",
        "      <th>Height</th>\n",
        "      <th>Whole weight</th>\n",
        "      <th>Shucked weight</th>\n",
        "      <th>Viscera weight</th>\n",
        "      <th>Shell weight</th>\n",
        "      <th>Rings</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.455</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.5140</td>\n",
        "      <td> 0.2245</td>\n",
        "      <td> 0.1010</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.265</td>\n",
        "      <td> 0.090</td>\n",
        "      <td> 0.2255</td>\n",
        "      <td> 0.0995</td>\n",
        "      <td> 0.0485</td>\n",
        "      <td> 0.070</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.530</td>\n",
        "      <td> 0.420</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.6770</td>\n",
        "      <td> 0.2565</td>\n",
        "      <td> 0.1415</td>\n",
        "      <td> 0.210</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.5160</td>\n",
        "      <td> 0.2155</td>\n",
        "      <td> 0.1140</td>\n",
        "      <td> 0.155</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> I</td>\n",
        "      <td> 0.330</td>\n",
        "      <td> 0.255</td>\n",
        "      <td> 0.080</td>\n",
        "      <td> 0.2050</td>\n",
        "      <td> 0.0895</td>\n",
        "      <td> 0.0395</td>\n",
        "      <td> 0.055</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> I</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.300</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.3515</td>\n",
        "      <td> 0.1410</td>\n",
        "      <td> 0.0775</td>\n",
        "      <td> 0.120</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.530</td>\n",
        "      <td> 0.415</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.7775</td>\n",
        "      <td> 0.2370</td>\n",
        "      <td> 0.1415</td>\n",
        "      <td> 0.330</td>\n",
        "      <td> 20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.545</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.7680</td>\n",
        "      <td> 0.2940</td>\n",
        "      <td> 0.1495</td>\n",
        "      <td> 0.260</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> M</td>\n",
        "      <td> 0.475</td>\n",
        "      <td> 0.370</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.5095</td>\n",
        "      <td> 0.2165</td>\n",
        "      <td> 0.1125</td>\n",
        "      <td> 0.165</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> F</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.8945</td>\n",
        "      <td> 0.3145</td>\n",
        "      <td> 0.1510</td>\n",
        "      <td> 0.320</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.525</td>\n",
        "      <td> 0.380</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.6065</td>\n",
        "      <td> 0.1940</td>\n",
        "      <td> 0.1475</td>\n",
        "      <td> 0.210</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.430</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.110</td>\n",
        "      <td> 0.4060</td>\n",
        "      <td> 0.1675</td>\n",
        "      <td> 0.0810</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.490</td>\n",
        "      <td> 0.380</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.5415</td>\n",
        "      <td> 0.2175</td>\n",
        "      <td> 0.0950</td>\n",
        "      <td> 0.190</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.535</td>\n",
        "      <td> 0.405</td>\n",
        "      <td> 0.145</td>\n",
        "      <td> 0.6845</td>\n",
        "      <td> 0.2725</td>\n",
        "      <td> 0.1710</td>\n",
        "      <td> 0.205</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.4755</td>\n",
        "      <td> 0.1675</td>\n",
        "      <td> 0.0805</td>\n",
        "      <td> 0.185</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.500</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.130</td>\n",
        "      <td> 0.6645</td>\n",
        "      <td> 0.2580</td>\n",
        "      <td> 0.1330</td>\n",
        "      <td> 0.240</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.280</td>\n",
        "      <td> 0.085</td>\n",
        "      <td> 0.2905</td>\n",
        "      <td> 0.0950</td>\n",
        "      <td> 0.0395</td>\n",
        "      <td> 0.115</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.340</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.4510</td>\n",
        "      <td> 0.1880</td>\n",
        "      <td> 0.0870</td>\n",
        "      <td> 0.130</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.365</td>\n",
        "      <td> 0.295</td>\n",
        "      <td> 0.080</td>\n",
        "      <td> 0.2555</td>\n",
        "      <td> 0.0970</td>\n",
        "      <td> 0.0430</td>\n",
        "      <td> 0.100</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.320</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.3810</td>\n",
        "      <td> 0.1705</td>\n",
        "      <td> 0.0750</td>\n",
        "      <td> 0.115</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.280</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.2455</td>\n",
        "      <td> 0.0955</td>\n",
        "      <td> 0.0620</td>\n",
        "      <td> 0.075</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.380</td>\n",
        "      <td> 0.275</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.2255</td>\n",
        "      <td> 0.0800</td>\n",
        "      <td> 0.0490</td>\n",
        "      <td> 0.085</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.565</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.155</td>\n",
        "      <td> 0.9395</td>\n",
        "      <td> 0.4275</td>\n",
        "      <td> 0.2140</td>\n",
        "      <td> 0.270</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.415</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.7635</td>\n",
        "      <td> 0.3180</td>\n",
        "      <td> 0.2100</td>\n",
        "      <td> 0.200</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.615</td>\n",
        "      <td> 0.480</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 1.1615</td>\n",
        "      <td> 0.5130</td>\n",
        "      <td> 0.3010</td>\n",
        "      <td> 0.305</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.560</td>\n",
        "      <td> 0.440</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.9285</td>\n",
        "      <td> 0.3825</td>\n",
        "      <td> 0.1880</td>\n",
        "      <td> 0.300</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.580</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.185</td>\n",
        "      <td> 0.9955</td>\n",
        "      <td> 0.3945</td>\n",
        "      <td> 0.2720</td>\n",
        "      <td> 0.285</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.590</td>\n",
        "      <td> 0.445</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.9310</td>\n",
        "      <td> 0.3560</td>\n",
        "      <td> 0.2340</td>\n",
        "      <td> 0.280</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.605</td>\n",
        "      <td> 0.475</td>\n",
        "      <td> 0.180</td>\n",
        "      <td> 0.9365</td>\n",
        "      <td> 0.3940</td>\n",
        "      <td> 0.2190</td>\n",
        "      <td> 0.295</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.575</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.8635</td>\n",
        "      <td> 0.3930</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.200</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.580</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 0.9975</td>\n",
        "      <td> 0.3935</td>\n",
        "      <td> 0.2420</td>\n",
        "      <td> 0.330</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.680</td>\n",
        "      <td> 0.560</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 1.6390</td>\n",
        "      <td> 0.6055</td>\n",
        "      <td> 0.2805</td>\n",
        "      <td> 0.460</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.665</td>\n",
        "      <td> 0.525</td>\n",
        "      <td> 0.165</td>\n",
        "      <td> 1.3380</td>\n",
        "      <td> 0.5515</td>\n",
        "      <td> 0.3575</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.680</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.175</td>\n",
        "      <td> 1.7980</td>\n",
        "      <td> 0.8150</td>\n",
        "      <td> 0.3925</td>\n",
        "      <td> 0.455</td>\n",
        "      <td> 19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.705</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.200</td>\n",
        "      <td> 1.7095</td>\n",
        "      <td> 0.6330</td>\n",
        "      <td> 0.4115</td>\n",
        "      <td> 0.490</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.465</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.105</td>\n",
        "      <td> 0.4795</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.1240</td>\n",
        "      <td> 0.125</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.540</td>\n",
        "      <td> 0.475</td>\n",
        "      <td> 0.155</td>\n",
        "      <td> 1.2170</td>\n",
        "      <td> 0.5305</td>\n",
        "      <td> 0.3075</td>\n",
        "      <td> 0.340</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.105</td>\n",
        "      <td> 0.5225</td>\n",
        "      <td> 0.2370</td>\n",
        "      <td> 0.1165</td>\n",
        "      <td> 0.145</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.575</td>\n",
        "      <td> 0.445</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.8830</td>\n",
        "      <td> 0.3810</td>\n",
        "      <td> 0.2035</td>\n",
        "      <td> 0.260</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.355</td>\n",
        "      <td> 0.290</td>\n",
        "      <td> 0.090</td>\n",
        "      <td> 0.3275</td>\n",
        "      <td> 0.1340</td>\n",
        "      <td> 0.0860</td>\n",
        "      <td> 0.090</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.450</td>\n",
        "      <td> 0.335</td>\n",
        "      <td> 0.105</td>\n",
        "      <td> 0.4250</td>\n",
        "      <td> 0.1865</td>\n",
        "      <td> 0.0910</td>\n",
        "      <td> 0.115</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.550</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.8515</td>\n",
        "      <td> 0.3620</td>\n",
        "      <td> 0.1960</td>\n",
        "      <td> 0.270</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.240</td>\n",
        "      <td> 0.175</td>\n",
        "      <td> 0.045</td>\n",
        "      <td> 0.0700</td>\n",
        "      <td> 0.0315</td>\n",
        "      <td> 0.0235</td>\n",
        "      <td> 0.020</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.205</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.055</td>\n",
        "      <td> 0.0420</td>\n",
        "      <td> 0.0255</td>\n",
        "      <td> 0.0150</td>\n",
        "      <td> 0.012</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.210</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 0.050</td>\n",
        "      <td> 0.0420</td>\n",
        "      <td> 0.0175</td>\n",
        "      <td> 0.0125</td>\n",
        "      <td> 0.015</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.390</td>\n",
        "      <td> 0.295</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.2030</td>\n",
        "      <td> 0.0875</td>\n",
        "      <td> 0.0450</td>\n",
        "      <td> 0.075</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.370</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.5795</td>\n",
        "      <td> 0.2930</td>\n",
        "      <td> 0.2270</td>\n",
        "      <td> 0.140</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.460</td>\n",
        "      <td> 0.375</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.4605</td>\n",
        "      <td> 0.1775</td>\n",
        "      <td> 0.1100</td>\n",
        "      <td> 0.150</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.325</td>\n",
        "      <td> 0.245</td>\n",
        "      <td> 0.070</td>\n",
        "      <td> 0.1610</td>\n",
        "      <td> 0.0755</td>\n",
        "      <td> 0.0255</td>\n",
        "      <td> 0.045</td>\n",
        "      <td>  6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.525</td>\n",
        "      <td> 0.425</td>\n",
        "      <td> 0.160</td>\n",
        "      <td> 0.8355</td>\n",
        "      <td> 0.3545</td>\n",
        "      <td> 0.2135</td>\n",
        "      <td> 0.245</td>\n",
        "      <td>  9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.520</td>\n",
        "      <td> 0.410</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.5950</td>\n",
        "      <td> 0.2385</td>\n",
        "      <td> 0.1110</td>\n",
        "      <td> 0.190</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.320</td>\n",
        "      <td> 0.095</td>\n",
        "      <td> 0.3030</td>\n",
        "      <td> 0.1335</td>\n",
        "      <td> 0.0600</td>\n",
        "      <td> 0.100</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.485</td>\n",
        "      <td> 0.360</td>\n",
        "      <td> 0.130</td>\n",
        "      <td> 0.5415</td>\n",
        "      <td> 0.2595</td>\n",
        "      <td> 0.0960</td>\n",
        "      <td> 0.160</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.360</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.4775</td>\n",
        "      <td> 0.2105</td>\n",
        "      <td> 0.1055</td>\n",
        "      <td> 0.150</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.405</td>\n",
        "      <td> 0.310</td>\n",
        "      <td> 0.100</td>\n",
        "      <td> 0.3850</td>\n",
        "      <td> 0.1730</td>\n",
        "      <td> 0.0915</td>\n",
        "      <td> 0.110</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.500</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.140</td>\n",
        "      <td> 0.6615</td>\n",
        "      <td> 0.2565</td>\n",
        "      <td> 0.1755</td>\n",
        "      <td> 0.220</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.445</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.120</td>\n",
        "      <td> 0.4425</td>\n",
        "      <td> 0.1920</td>\n",
        "      <td> 0.0955</td>\n",
        "      <td> 0.135</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td> M</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 0.385</td>\n",
        "      <td> 0.135</td>\n",
        "      <td> 0.5895</td>\n",
        "      <td> 0.2765</td>\n",
        "      <td> 0.1200</td>\n",
        "      <td> 0.170</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td> I</td>\n",
        "      <td> 0.245</td>\n",
        "      <td> 0.190</td>\n",
        "      <td> 0.060</td>\n",
        "      <td> 0.0860</td>\n",
        "      <td> 0.0420</td>\n",
        "      <td> 0.0140</td>\n",
        "      <td> 0.025</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td> F</td>\n",
        "      <td> 0.505</td>\n",
        "      <td> 0.400</td>\n",
        "      <td> 0.125</td>\n",
        "      <td> 0.5830</td>\n",
        "      <td> 0.2460</td>\n",
        "      <td> 0.1300</td>\n",
        "      <td> 0.175</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4177 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "   Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
        "0    M   0.455     0.365   0.095        0.5140          0.2245   \n",
        "1    M   0.350     0.265   0.090        0.2255          0.0995   \n",
        "2    F   0.530     0.420   0.135        0.6770          0.2565   \n",
        "3    M   0.440     0.365   0.125        0.5160          0.2155   \n",
        "4    I   0.330     0.255   0.080        0.2050          0.0895   \n",
        "5    I   0.425     0.300   0.095        0.3515          0.1410   \n",
        "6    F   0.530     0.415   0.150        0.7775          0.2370   \n",
        "7    F   0.545     0.425   0.125        0.7680          0.2940   \n",
        "8    M   0.475     0.370   0.125        0.5095          0.2165   \n",
        "9    F   0.550     0.440   0.150        0.8945          0.3145   \n",
        "10   F   0.525     0.380   0.140        0.6065          0.1940   \n",
        "11   M   0.430     0.350   0.110        0.4060          0.1675   \n",
        "12   M   0.490     0.380   0.135        0.5415          0.2175   \n",
        "13   F   0.535     0.405   0.145        0.6845          0.2725   \n",
        "14   F   0.470     0.355   0.100        0.4755          0.1675   \n",
        "15   M   0.500     0.400   0.130        0.6645          0.2580   \n",
        "16   I   0.355     0.280   0.085        0.2905          0.0950   \n",
        "17   F   0.440     0.340   0.100        0.4510          0.1880   \n",
        "18   M   0.365     0.295   0.080        0.2555          0.0970   \n",
        "19   M   0.450     0.320   0.100        0.3810          0.1705   \n",
        "20   M   0.355     0.280   0.095        0.2455          0.0955   \n",
        "21   I   0.380     0.275   0.100        0.2255          0.0800   \n",
        "22   F   0.565     0.440   0.155        0.9395          0.4275   \n",
        "23   F   0.550     0.415   0.135        0.7635          0.3180   \n",
        "24   F   0.615     0.480   0.165        1.1615          0.5130   \n",
        "25   F   0.560     0.440   0.140        0.9285          0.3825   \n",
        "26   F   0.580     0.450   0.185        0.9955          0.3945   \n",
        "27   M   0.590     0.445   0.140        0.9310          0.3560   \n",
        "28   M   0.605     0.475   0.180        0.9365          0.3940   \n",
        "29   M   0.575     0.425   0.140        0.8635          0.3930   \n",
        "30   M   0.580     0.470   0.165        0.9975          0.3935   \n",
        "31   F   0.680     0.560   0.165        1.6390          0.6055   \n",
        "32   M   0.665     0.525   0.165        1.3380          0.5515   \n",
        "33   F   0.680     0.550   0.175        1.7980          0.8150   \n",
        "34   F   0.705     0.550   0.200        1.7095          0.6330   \n",
        "35   M   0.465     0.355   0.105        0.4795          0.2270   \n",
        "36   F   0.540     0.475   0.155        1.2170          0.5305   \n",
        "37   F   0.450     0.355   0.105        0.5225          0.2370   \n",
        "38   F   0.575     0.445   0.135        0.8830          0.3810   \n",
        "39   M   0.355     0.290   0.090        0.3275          0.1340   \n",
        "40   F   0.450     0.335   0.105        0.4250          0.1865   \n",
        "41   F   0.550     0.425   0.135        0.8515          0.3620   \n",
        "42   I   0.240     0.175   0.045        0.0700          0.0315   \n",
        "43   I   0.205     0.150   0.055        0.0420          0.0255   \n",
        "44   I   0.210     0.150   0.050        0.0420          0.0175   \n",
        "45   I   0.390     0.295   0.095        0.2030          0.0875   \n",
        "46   M   0.470     0.370   0.120        0.5795          0.2930   \n",
        "47   F   0.460     0.375   0.120        0.4605          0.1775   \n",
        "48   I   0.325     0.245   0.070        0.1610          0.0755   \n",
        "49   F   0.525     0.425   0.160        0.8355          0.3545   \n",
        "50   I   0.520     0.410   0.120        0.5950          0.2385   \n",
        "51   M   0.400     0.320   0.095        0.3030          0.1335   \n",
        "52   M   0.485     0.360   0.130        0.5415          0.2595   \n",
        "53   F   0.470     0.360   0.120        0.4775          0.2105   \n",
        "54   M   0.405     0.310   0.100        0.3850          0.1730   \n",
        "55   F   0.500     0.400   0.140        0.6615          0.2565   \n",
        "56   M   0.445     0.350   0.120        0.4425          0.1920   \n",
        "57   M   0.470     0.385   0.135        0.5895          0.2765   \n",
        "58   I   0.245     0.190   0.060        0.0860          0.0420   \n",
        "59   F   0.505     0.400   0.125        0.5830          0.2460   \n",
        "   ...     ...       ...     ...           ...             ...   \n",
        "\n",
        "    Viscera weight  Shell weight  Rings  \n",
        "0           0.1010         0.150     15  \n",
        "1           0.0485         0.070      7  \n",
        "2           0.1415         0.210      9  \n",
        "3           0.1140         0.155     10  \n",
        "4           0.0395         0.055      7  \n",
        "5           0.0775         0.120      8  \n",
        "6           0.1415         0.330     20  \n",
        "7           0.1495         0.260     16  \n",
        "8           0.1125         0.165      9  \n",
        "9           0.1510         0.320     19  \n",
        "10          0.1475         0.210     14  \n",
        "11          0.0810         0.135     10  \n",
        "12          0.0950         0.190     11  \n",
        "13          0.1710         0.205     10  \n",
        "14          0.0805         0.185     10  \n",
        "15          0.1330         0.240     12  \n",
        "16          0.0395         0.115      7  \n",
        "17          0.0870         0.130     10  \n",
        "18          0.0430         0.100      7  \n",
        "19          0.0750         0.115      9  \n",
        "20          0.0620         0.075     11  \n",
        "21          0.0490         0.085     10  \n",
        "22          0.2140         0.270     12  \n",
        "23          0.2100         0.200      9  \n",
        "24          0.3010         0.305     10  \n",
        "25          0.1880         0.300     11  \n",
        "26          0.2720         0.285     11  \n",
        "27          0.2340         0.280     12  \n",
        "28          0.2190         0.295     15  \n",
        "29          0.2270         0.200     11  \n",
        "30          0.2420         0.330     10  \n",
        "31          0.2805         0.460     15  \n",
        "32          0.3575         0.350     18  \n",
        "33          0.3925         0.455     19  \n",
        "34          0.4115         0.490     13  \n",
        "35          0.1240         0.125      8  \n",
        "36          0.3075         0.340     16  \n",
        "37          0.1165         0.145      8  \n",
        "38          0.2035         0.260     11  \n",
        "39          0.0860         0.090      9  \n",
        "40          0.0910         0.115      9  \n",
        "41          0.1960         0.270     14  \n",
        "42          0.0235         0.020      5  \n",
        "43          0.0150         0.012      5  \n",
        "44          0.0125         0.015      4  \n",
        "45          0.0450         0.075      7  \n",
        "46          0.2270         0.140      9  \n",
        "47          0.1100         0.150      7  \n",
        "48          0.0255         0.045      6  \n",
        "49          0.2135         0.245      9  \n",
        "50          0.1110         0.190      8  \n",
        "51          0.0600         0.100      7  \n",
        "52          0.0960         0.160     10  \n",
        "53          0.1055         0.150     10  \n",
        "54          0.0915         0.110      7  \n",
        "55          0.1755         0.220      8  \n",
        "56          0.0955         0.135      8  \n",
        "57          0.1200         0.170      8  \n",
        "58          0.0140         0.025      4  \n",
        "59          0.1300         0.175      7  \n",
        "               ...           ...    ...  \n",
        "\n",
        "[4177 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "loaded_model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
        "           n_neighbors=5, p=2, weights='uniform')"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/quick_tips/3. Sentiment Analysis.ipynb
================================================
{
 "metadata": {
  "name": "",
  "signature": "sha256:4d925c45189783ac33d4472859cf2e8441fb82698b89e81a3850fa5bea529982"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Tutorial Brief"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using textblob library to perform basic sentement analysis.\n",
      "\n",
      "Text Blob Library Links:\n",
      "\n",
      "- Docs: http://textblob.readthedocs.org/en/dev/\n",
      "- PyPI: https://pypi.python.org/pypi/textblob"
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Import The Library"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from textblob import TextBlob"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Making a `TextBlob` Object"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sent = TextBlob(\"This tutorial is the best thing in the history of mankind\")\n",
      "sent"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "TextBlob(\"This tutorial is the best thing in the history of mankind\")"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Measuring sentiments"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sent.sentiment"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "Sentiment(polarity=1.0, subjectivity=0.3)"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sent.polarity"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sent.subjectivity"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "0.3"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}


================================================
FILE: notebooks/quick_tips/4. XPath.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XPath\n",
    "\n",
    "XPath is short for XML Path Language which is a query language for selecting nodes in an XML document. This is very useful in webscraping because all HTML documents are a form of XML documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from lxml import html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html>\n",
       "  <body>\n",
       "    <h1>Favorite Python Librarires</h1>\n",
       "    <ul>\n",
       "      <li>Numpy</li>\n",
       "      <li>Pandas</li>\n",
       "      <li>requests</li>\n",
       "    </ul>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<html>\n",
    "  <body>\n",
    "    <h1>Favorite Python Librarires</h1>\n",
    "    <ul>\n",
    "      <li>Numpy</li>\n",
    "      <li>Pandas</li>\n",
    "      <li>requests</li>\n",
    "    </ul>\n",
    "  </body>\n",
    "</html>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load HTML Code\n",
    "Now I'll read the code from cell number 2 and store it in `html_code`. Finally we will parse that into a lxml node object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<html>\n",
      "  <body>\n",
      "    <h1>Favorite Python Librarires</h1>\n",
      "    <ul>\n",
      "      <li>Numpy</li>\n",
      "      <li>Pandas</li>\n",
      "      <li>requests</li>\n",
      "    </ul>\n",
      "</html>\n"
     ]
    }
   ],
   "source": [
    "html_code = In[2]\n",
    "html_code = html_code[42:-2].replace(\"\\\\n\",\"\\n\")\n",
    "print(html_code)\n",
    "\n",
    "doc = html.fromstring(html_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using xpath to find nodes in a document\n",
    "\n",
    "There many methods for fidning a node that you are interested in from a XML or HTML document. The first way is to write the whole path separated by forward slashes `/`\n",
    "\n",
    "## Reading `<h1>` tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Element h1 at 0x7f447cafa458>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title = doc.xpath(\"/html/body/h1\")[0]\n",
    "title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To read the text inside that tag you can use the text variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Favorite Python Librarires'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title.text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way is read the text is to use the `text()` function in xpath."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Favorite Python Librarires'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title = doc.xpath(\"/html/body/h1/text()\")[0]\n",
    "title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with multiple items\n",
    "\n",
    "xpath always returns a list. If there are no matches, it will return an empty list. If there is one match it will return a list with one item."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Element li at 0x7f447cafa9a8>,\n",
       " <Element li at 0x7f447cafaae8>,\n",
       " <Element li at 0x7f447cafab38>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_list = doc.xpath(\"/html/body/ul/li\")\n",
    "item_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `text()` function with multiple items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Numpy', 'Pandas', 'requests']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = html.fromstring(html_code)\n",
    "item_list = doc.xpath(\"/html/body/ul/li/text()\")\n",
    "item_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tag selector without full path\n",
    "\n",
    "you can select any node in your document that matches a node selector without using the full path with a double forward slash `//`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Numpy', 'Pandas', 'requests']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = html.fromstring(html_code)\n",
    "item_list = doc.xpath(\"//li/text()\")\n",
    "item_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting one result\n",
    "\n",
    "You can select one result from a list using `[index]` after your tag selector. Make sure you use it on the tag selector and not a function selector.\n",
    "\n",
    "**Notice**: This is `index` starts from 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Numpy']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = html.fromstring(html_code)\n",
    "item_list = doc.xpath(\"/html/body/ul/li[1]/text()\")\n",
    "item_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<html>\n",
       "  <body>\n",
       "    <h1 class=\"text-muted\">Favorite Python Librarires</h1>\n",
       "    <ul class=\"nav nav-pills nav-stacked\">\n",
       "      <li role=\"presentation\"><a href=\"http://www.numpy.org/\">Numpy</a></li>\n",
       "      <li role=\"presentation\"><a href=\"http://pandas.pydata.org/\">Pandas</a></li>\n",
       "      <li role=\"presentation\"><a href=\"http://python-requests.org/\">requests</a></li>\n",
       "    </ul>\n",
       "    <h1 class=\"text-success\">Favorite JS Librarires</h1>\n",
       "    <ul class=\"nav nav-tabs\">\n",
       "      <li role=\"presentation\"><a href=\"http://getbootstrap.com/\">Bootstrap</a></li>\n",
       "      <li role=\"presentation\"><a href=\"https://jquery.com/\">jQuery</a></li>\n",
       "      <li role=\"presentation\"><a href=\"http://d3js.org/\">d3.js</a></li>\n",
       "    </ul>\n",
       "</html>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<html>\n",
    "  <body>\n",
    "    <h1 class=\"text-muted\">Favorite Python Librarires</h1>\n",
    "    <ul class=\"nav nav-pills nav-stacked\">\n",
    "      <li role=\"presentation\"><a href=\"http://www.numpy.org/\">Numpy</a></li>\n",
    "      <li role=\"presentation\"><a href=\"http://pandas.pydata.org/\">Pandas</a></li>\n",
    "      <li role=\"presentation\"><a href=\"http://python-requests.org/\">requests</a></li>\n",
    "    </ul>\n",
    "    <h1 class=\"text-success\">Favorite JS Librarires</h1>\n",
    "    <ul class=\"nav nav-tabs\">\n",
    "      <li role=\"presentation\"><a href=\"http://getbootstrap.com/\">Bootstrap</a></li>\n",
    "      <li role=\"presentation\"><a href=\"https://jquery.com/\">jQuery</a></li>\n",
    "      <li role=\"presentation\"><a href=\"http://d3js.org/\">d3.js</a></li>\n",
    "    </ul>\n",
    "</html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<html>\n",
      "  <body>\n",
      "    <h1 class=\"text-muted\">Favorite Python Librarires</h1>\n",
      "    <ul class=\"nav nav-pills nav-stacked\">\n",
      "      <li role=\"presentation\"><a href=\"http://www.numpy.org/\">Numpy</a></li>\n",
      "      <li role=\"presentation\"><a href=\"http://pandas.pydata.org/\">Pandas</a></li>\n",
      "      <li role=\"presentation\"><a href=\"http://python-requests.org/\">requests</a></li>\n",
      "    </ul>\n",
      "    <h1 class=\"text-success\">Favorite JS Librarires</h1>\n",
      "    <ul class=\"nav nav-tabs\">\n",
      "      <li role=\"presentation\"><a href=\"http://getbootstrap.com/\">Bootstrap</a></li>\n",
      "      <li role=\"presentation\"><a href=\"https://jquery.com/\">jQuery</a></li>\n",
      "      <li role=\"presentation\"><a href=\"http://d3js.org/\">d3.js</a></li>\n",
      "    </ul>\n",
      "</html>\n"
     ]
    }
   ],
   "source": [
    "html_code = In[11]\n",
    "html_code = html_code[42:-2].replace(\"\\\\n\",\"\\n\")\n",
    "print(html_code)\n",
    "\n",
    "doc = html.fromstring(html_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attributes selector\n",
    "\n",
    "In this example we have two `<h1>` tags with different css classes. We can select tags based on css classes as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Favorite Python Librarires'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title = doc.xpath(\"/html/body/h1[@class='text-muted']/text()\")[0]\n",
    "title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `contains()` function\n",
    "\n",
    "I want to select all items in the first list. I could use the full class for selection or I could just use one of the classed only used in the first list with the `contains()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Numpy', 'Pandas', 'requests']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_list = doc.xpath(\"/html/body/ul[contains(@class,'nav-stacked')]/li/a/text()\")\n",
    "item_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returning attributes\n",
    "\n",
    "What if we want to read the `href` attribute of the `<a>` tag to get the link. This is how you do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['http://www.numpy.org/',\n",
       " 'http://pandas.pydata.org/',\n",
       " 'http://python-requests.org/']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_list = doc.xpath(\"/html/body/ul[contains(@class,'nav-stacked')]/li/a/@href\")\n",
    "item_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real world example\n",
    "\n",
    "Read the list of languages with 1M+ articles on http://www.wikipedia.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "response = requests.get(\"http://www.wikipedia.org\")\n",
    "doc = html.fromstring(response.content, parser=html.HTMLParser(encoding=\"utf-8\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Deutsch',\n",
       " 'English',\n",
       " 'Español',\n",
       " 'Français',\n",
       " 'Italiano',\n",
       " 'Nederlands',\n",
       " 'Polski',\n",
       " 'Русский',\n",
       " 'Sinugboanong Binisaya',\n",
       " 'Svenska',\n",
       " 'Tiếng Việt',\n",
       " 'Winaray']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lang_list = doc.xpath(\"//div[@class='langlist langlist-large hlist'][1]/ul/li/a/text()\")\n",
    "lang_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: notebooks/quick_tips/5. The largest Prime.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The largest prime (so far)\n",
    "\n",
    "A new record for the largest prime has been found lately. Let explore this number and know more about it and how to deal with it using some Python, Numpy and finally using we will take a loog at Cython and GMP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "from datetime import datetime\n",
    "\n",
    "%load_ext Cython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This number of was found in 2016 using the Great Internet Mersenne Prime Search (GIMPS). This prime is one of the Mersenne Prime whice are of the defined by the following formula:\n",
    "\n",
    "$$2^p-1$$\n",
    "\n",
    "Where $p$ is a prime number. This is called a psudo-prime meaning not all Mersenne Prime are primes.\n",
    "\n",
    "$2^2-1=3$\n",
    "\n",
    "$2^3-1=7$\n",
    "\n",
    "$2^5-1=31$\n",
    "\n",
    "$2^7-1=127$   ...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**3-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**5-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**7-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The smallest none-prime (composite) number is $2^{11}-1=2047$ which is a composite number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2047"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**11-1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest prime number is that was found lately is:\n",
    "$$2^{74,207,281}-1$$\n",
    "\n",
    "The reason why I didn't put the actual value is that the number if very large (over 22 million digits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = 74207281\n",
    "the_number = (2 ** p) - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lucas–Lehmer test\n",
    "\n",
    "A very easy way to test a Mersenne Prime is the Lucas-Lehmer test which uses the Lucas series.\n",
    "\n",
    "$4, 14, 37634 ... $\n",
    "\n",
    "The series starts with 4 then uses the last value in this formula:\n",
    "\n",
    "$S_i=S_{i-1}^2-2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "S = 4\n",
    "print(S)\n",
    "print(len(str(S)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "2 digits\n"
     ]
    }
   ],
   "source": [
    "S = S ** 2 - 2\n",
    "print(S)\n",
    "print(len(str(S)), \"digits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "194\n",
      "3 digits\n"
     ]
    }
   ],
   "source": [
    "S = S ** 2 - 2\n",
    "print(S)\n",
    "print(len(str(S)), \"digits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37634\n",
      "5 digits\n"
     ]
    }
   ],
   "source": [
    "S = S ** 2 - 2\n",
    "print(S)\n",
    "print(len(str(S)), \"digits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1416317954\n",
      "10 digits\n"
     ]
    }
   ],
   "source": [
    "S = S ** 2 - 2\n",
    "print(S)\n",
    "print(len(str(S)), \"digits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2005956546822746114\n",
      "19 digits\n"
     ]
    }
   ],
   "source": [
    "S = S ** 2 - 2\n",
    "print(S)\n",
    "print(len(str(S)), \"digits\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22338618 digits\n"
     ]
    }
   ],
   "source": [
    "# The largest prime (so far)\n",
    "the_number = 2 ** 74207281 - 1\n",
    "print(int(math.log10(the_number))+1, \"digits\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test is if the Mersenne Prime number of $n^2-1$ is a divided by $S_{n-2}$ you should get a remaining of 0. The problem is this series goes very large very fast. So there is another way to do this test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0:00:00.000423 \n",
      "1 0:00:00.000017 \n",
      "2 0:00:00.000012 \n",
      "3 0:00:00.000014 \n",
      "4 0:00:00.000014 \n",
      "5 0:00:00.000013 \n",
      "6 0:00:00.000014 \n",
      "7 0:00:00.000014 \n",
      "8 0:00:00.000015 \n",
      "9 0:00:00.000014 \n",
      "10 0:00:00.000036 \n",
      "11 0:00:00.000051 \n",
      "12 0:00:00.000127 \n",
      "13 0:00:00.000358 \n",
      "14 0:00:00.001040 \n",
      "15 0:00:00.002735 \n",
      "16 0:00:00.004968 \n",
      "17 0:00:00.014782 \n",
      "18 0:00:00.044420 \n",
      "19 0:00:00.132890 \n",
      "20 0:00:00.398497 \n",
      "21 0:00:01.198042 \n",
      "22 0:00:03.592832 \n",
      "23 0:00:10.796848 \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-4c550569e183>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mtime_stamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mS\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mS\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mthe_number\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mtime_stamp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "p = 74207281\n",
    "the_number = (2 ** p) - 1\n",
    "\n",
    "S = 4\n",
    "time_stamp = datetime.now()\n",
    "for i in range(p-2):\n",
    "    S = (S ** 2 - 2) % the_number\n",
    "    if i % 1 == 0:\n",
    "        print(i, datetime.now() - time_stamp,\"\")\n",
    "        time_stamp = datetime.now()\n",
    "if S == 0:\n",
    "    print(\"PRIME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "PRIME\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.4/dist-packages/IPython/utils/path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module\n",
      "  warn(\"get_ipython_cache_dir has moved to the IPython.paths module\")\n"
     ]
    }
   ],
   "source": [
    "%%cython\n",
    "cdef unsigned long p = 61\n",
    "cdef unsigned long P = (2 ** p) - 1\n",
    "\n",
    "S = 4\n",
    "for i in range(p-2):\n",
    "    S = S ** 2 - 2\n",
    "    S = S % P\n",
    "    if i % 10 == 0:\n",
    "        print(i)\n",
    "if S == 0:\n",
    "    print(\"PRIME\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1000\n",
      "2000\n",
      "3000\n",
      "4000\n",
      "5000\n",
      "6000\n",
      "7000\n",
      "8000\n",
      "9000\n",
      "10000\n",
      "11000\n",
      "12000\n",
      "13000\n",
      "14000\n",
      "15000\n",
      "16000\n",
      "17000"
     ]
    }
   ],
   "source": [
    "%%cython --link-args=-lgmp\n",
    "\n",
    "cdef extern from \"gmp.h\":\n",
    "    ctypedef struct mpz_t:\n",
    "        pass\n",
    "    \n",
    "    ctypedef unsigned long mp_bitcnt_t\n",
    "    \n",
    "    cdef void mpz_init(mpz_t)\n",
    "    cdef void mpz_init_set_ui(mpz_t, unsigned int)\n",
    "    \n",
    "    cdef void mpz_add(mpz_t, mpz_t, mpz_t)\n",
    "    cdef void mpz_add_ui(mpz_t, const mpz_t, unsigned long int)\n",
    "    \n",
    "    cdef void mpz_sub (mpz_t, const mpz_t, const mpz_t)\n",
    "    cdef void mpz_sub_ui (mpz_t, const mpz_t, unsigned long int)\n",
    "    cdef void mpz_ui_sub (mpz_t, unsigned long int, const mpz_t)\n",
    "    \n",
    "    cdef void mpz_mul (mpz_t, const mpz_t, const mpz_t)\n",
    "    cdef void mpz_mul_si (mpz_t, const mpz_t, long int)\n",
    "    cdef void mpz_mul_ui (mpz_t, const mpz_t, unsigned long int)\n",
    "    \n",
    "    cdef void mpz_mul_2exp (mpz_t, const mpz_t, mp_bitcnt_t)\n",
    "    \n",
    "    cdef void mpz_mod (mpz_t, const mpz_t, const mpz_t)\n",
    "    \n",
    "    cdef unsigned long int mpz_get_ui(const mpz_t)\n",
    "\n",
    "#cdef unsigned long p = 61\n",
    "cdef mp_bitcnt_t p = 74207281\n",
    "cdef mpz_t t # = 1\n",
    "cdef mpz_t a # = 1\n",
    "cdef mpz_t P # = (2 ** p) - 1\n",
    "cdef mpz_t S # = 4\n",
    "\n",
    "mpz_init(t)\n",
    "mpz_init_set_ui(t, 1)\n",
    "\n",
    "mpz_init(a)\n",
    "mpz_init_set_ui(a, 2)\n",
    "\n",
    "mpz_init(P)\n",
    "mpz_mul_2exp(P,t,p)\n",
    "mpz_sub_ui(P,P,1)\n",
    "\n",
    "mpz_init(S)\n",
    "mpz_init_set_ui(S, 4)\n",
    "\n",
    "for i in range(p-2):\n",
    "    #S = S ** 2 - 2\n",
    "    mpz_mul(S,S,S)\n",
    "    mpz_sub_ui(S,S,2)\n",
    "    \n",
    "    #S = S % P\n",
    "    mpz_mod(S,S,P)\n",
    "    \n",
    "    if i % 1000 == 0:\n",
    "        print(i)\n",
    "if mpz_get_ui(S) == 0:\n",
    "    print(\"PRIME\")\n",
    "else:\n",
    "    print(\"COMPOSITE\")\n",
    "\n",
    "#print(mpz_get_ui(P))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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    "version": 3
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   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}


================================================
FILE: notebooks/tf/.ipynb_checkpoints/1. Machine Learning Basics-checkpoint.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td style=\"text-align:left;\"><div style=\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\">1. Machine Learning Basics</div><img src=\"images/roshan.png\" style=\"width:30%; display: inline; text-align: left; float:right;\"></td>\n",
    "        <td></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why TensorFlow?\n",
    "\n",
    "TensorFlow is Google's framework for developing Machine Learning models and is has developed into the most popular machine learning library. Since 2011, Google Brain Team has been developing TensorFlow and was used internally until is was released as an open-source software in Nov, 2015. It comes packed with the wide range of tools from dataset management to a good selection of high-level algorithms and even ready made models. That's a lot to pack in one solution!\n",
    "\n",
    "The team that is working on developing TensorFlow are some of the most influencal researchers in machine learning. It also has a huge amount of contributors and commits for such a relativly new project on Github. You quickly realize that TensorFlow is getting more attention from people in the field than any other existing machine learning solution and will be the technology starndard for machine learning development.\n",
    "\n",
    "__OS Support__:\n",
    "\n",
    "- Linux\n",
    "- Windows\n",
    "- macOS\n",
    "- Android\n",
    "- iOS\n",
    "\n",
    "__Programming Languages__:\n",
    "\n",
    "- Python\n",
    "- C/C++\n",
    "- Java\n",
    "- Golang\n",
    "\n",
    "__Hardware__:\n",
    "\n",
    "- CPU\n",
    "- GPU\n",
    "- TPU\n",
    "- Distributed Systems\n",
    "\n",
    "There is also TensorFlow.js version which runs in browser on client-side with no need for a back-end. There is also TensorFlow Lite which converts machine learning models to a format that can run on mobile devices running iOS and Android.\n",
    "\n",
    "With in two years of its release, TensorFlow has been downloaded over 11 million times from almost every country on earth.\n",
    "\n",
    "![](images/tf_map.png)\n",
    "\n",
    "From Ragat Monga [@rajatmonga](https://twitter.com/rajatmonga) Slides in [TensorFlow Dev Summit 2018]() (2018-03-30)\n",
    "\n",
    "# What is Machine Learning?\n",
    "\n",
    "Before we dive any deeper in TensorFlow, I will go over some basic concepts of machine learning and deep learning.\n",
    "\n",
    "This tutorial is for developers who can develope in python but not well versed in machine learning or familiar with some other machine learning libraries but new to TensorFlow. It is also be useful for non-developers who are good at a specific knowledge domain like mathematics, statistics, medical field or, social studies and eager to experiment with machine learning in their fields.\n",
    "\n",
    "Machine Learning (ML) is a branch of Artificial Intelligence (AI). It uses data instead of code to produce logic. The goal is to perform tasks that we usually equate human level intelligence and in many cases exceeds human level of intelligence like what happened with Alpha Go and many other examples. You actually use machine learning all the time without realizing when you use Google, YouTube, Amazon and so many other services that reley on machine learning to give you a better experience.\n",
    "\n",
    "Machine learning is built on algorithms that use data to train models to perform a specific task.\n",
    "\n",
    "Meet `Tensy`, a robot eager to learn and solve human problems. [@TensyRobot](https://twitter.com/TensyRobot)\n",
    "\n",
    "![Algorithm](images/algorithm.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Styles\n",
    "\n",
    "Models can learn in three styles depending the the training algorithms:\n",
    "\n",
    "## Supervised Learning (SL)\n",
    "\n",
    "Supervised learning is teaching a model to give an output based on some input. The the algorithm teachs the model by giving the it plenty of examples and their outputs. There are two types of problems that we can solve with supervised learning which are:\n",
    "\n",
    "### Classification\n",
    "\n",
    "Classification is used to teach a model to output a category or a class based on a data point. Like learning if a photo has a cat or a dog.\n",
    "\n",
    "I want `Tensy` to learn how to tell cats from dog. We can give `Tensy` photos of cats and until he learns to tell a cat from a dog. I can later test him on a new photos he has never seen before and see how accurate he is.\n",
    "\n",
    "![CatsDogs!](images/CatsDogs.png)\n",
    "\n",
    "### Regression\n",
    "\n",
    "Regression is used to teach a model to output a continuous numerical value based on a data point. Like telling the age of a person based on their photo.\n",
    "\n",
    "This time we are teaching `Tensy` to tell the age of a person based on their photo. To do that we will give him a large amount of photos and tell him the age of each person. We can later test him by asking the age of people in new photos that he didn't see before.\n",
    "\n",
    "![CatsDogs!](images/age.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervoised Learning (UL)\n",
    "\n",
    "Unsupervised Learning teaches a model to produce an output based on a data point without teaching the model the output. Like giving a model photos taken outdoor and photos taken indoor and ask then model to split them to two piles. There are many problems that can be solved using UL but the most common ones are:\n",
    "\n",
    "### Clustering\n",
    "\n",
    "Clustering is grouping similar data points into groups or clusters. different algorithms can find similarities in different ways and can end up clustering your data points into different groups. Some of these algorithms need you to tell them how many clusters do you want them to split your data into and some will try and figure that out of their own.\n",
    "\n",
    "This time we gave `Tensy` a group if photos and we asked him to split them into two groups. He is finding that all indoor photos are similar and all out door photos are similar.\n",
    "\n",
    "![CatsDogs!](images/outdoorindoor.png)\n",
    "\n",
    "Other UL algorithms can solve some interesting problems using like:\n",
    "\n",
    "- Anomaly Detection\n",
    "- Generative adversarial networks (GANs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reinforcement Learning (RL)\n",
    "\n",
    "Reinforcement Learning  uses an algorithm to teach a model to produce a desired output without explicitly explaining how to perform it. This works by gaving the model an input and rewarding good outcome and punishing bad outcome. This might be similar to how you would teach a child or even train an animal to perform the desired actions.\n",
    "\n",
    "Let's teach `Tensy` to play some Atari games. The input in the screen feed from the game. After each frame from the input he will be able to choose one action (right, left, up, down and press action button). That action might lead to increased score if it is a good action or to no increase score if it is a neutral action or to loosing a life and lower score for a bad action.\n",
    "\n",
    "![CatsDogs!](images/atari.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning is More That!\n",
    "\n",
    "These major categories are not inclusive of every algorithms that you might use where some evolutionary algorithms, or recommendation systems do not fall under any of these categories. Some more complicated algorithms use multiple stages in a pipeline some with supervised and unsupervised so your end-to-end algorithm might also not fall under any specific category or sometimes will be called \"Semi-Supervised\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Math Behind It\n",
    "\n",
    "This section will explain some mathematical concepts at the end of each tutorial. These tutorials are going to be code intensive and that will help you develop your coding skills and get your model working. But what if it doesn't work?\n",
    "\n",
    "The math behind these algorithms use Linear Algebra, Tensor Calculus and Statistics. This section in each tutorial will explain the math behind what we discussed and will assume very little to no math background and will explain things from the ground up. This could help you fix your model or even improve your accuracy.\n",
    "\n",
    "Finally, It will serve as a bidge between machine learning concepts and centuries of mathimatical reasearch. Mathematics is the language of science and learning this language will open the door for you to explore some of the latest academic research papers in the machine learning due to the fact that they mostly look like this:\n",
    "\n",
    "![CatsDogs!](images/svm.png)\n",
    "A small section of \"[A Training Algorithm for Optimal Margin Classiers](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.3818&rep=rep1&type=pdf)\" By Boser, Guyon & Vapnik published 1992. AKA the original SVM paper\n",
    " \n",
    " So to kick of this part I'll start with a few simple concepts:\n",
    " \n",
    " ## The real numbers $\\mathbb{R}$\n",
    " \n",
    " The real numbers include positive, nagative numbers and zero. They can be of arbitrary precesion meaning they can have any number of decimal points or even numbers with infinite precision. The set real is denoted in mathimatics as $\\mathbb{R}$. Examples of real numbers include:\n",
    "\n",
    "- 5.0\n",
    "- -2.5\n",
    "- $\\frac{1}{3} = 0.3333\\overline3$\n",
    "- $\\pi = 3.14159 ...$ (Pi) this is small letter Pi\n",
    "- $e = 2.71828 ...$ (Euler's Number)\n",
    "\n",
    "## Sum $\\sum$\n",
    "\n",
    "To represent a finite sum of numbers in math, you can use the following:\n",
    "\n",
    "$$ 1 + 2 + 3 + 4 + 5 $$\n",
    "\n",
    "For longer sums like getting the sum of all numbers from 1 to 1000 can be harder to write in that way. We use sum represented by capital letter Sigma $\\Sigma$. To rewite the previous equation from 1 to 1000 using sum, you can do this:\n",
    "\n",
    "$$\\sum_{n=1}^{1000} n$$\n",
    "\n",
    "This can facilitate writing more complex functions like this:\n",
    "\n",
    "$$f(x) = x + 2x + 3x + 4x + 5x = \\sum_{n=1}^{5}nx$$\n",
    "\n",
    "With this notation we can even represent an infinite sum of numbers:\n",
    "\n",
    "$$\\frac{1}{1} + \\frac{1}{2} + \\frac{1}{4} + \\frac{1}{8} + \\frac{1}{16} ... = \\sum_{n=0}^{\\infty} \\frac{1}{2^n} = 2$$\n",
    "\n",
    "## Product $\\Pi$\n",
    "\n",
    "To represent a finite product of numbers in math, you can use the following:\n",
    "\n",
    "$$ 1 \\times 2 \\times 3 \\times 4 \\times 5 $$\n",
    "\n",
    "For longer products like getting the products of all numbers from 1 to 1000 can be harder to write in that way. We use products represented by capital letter Pi $\\Pi$. To rewrite the previous product from 1 to 5, we can write it this way:\n",
    "\n",
    "$$\\prod_{n=1}^{5} n$$\n",
    "\n",
    "> NOTE: It should also be noted that the previous product can also be written as $5!$ which is called 5 factorial.\n",
    "\n",
    "This can facilitate writing more complex products like:\n",
    "\n",
    "$$f(x) = x \\times 2x \\times 3x \\times 4x \\times 5x = \\prod_{n=1}^{5}nx$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\n",
    "\n",
    "Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>"
   ]
  }
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================================================
FILE: notebooks/tf/.ipynb_checkpoints/2. Tensors-checkpoint.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td style=\"text-align:left;\"><div style=\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\">2. Tensors</div><img src=\"images/roshan.png\" style=\"width:30%; display: inline; text-align: left; float:right;\"></td>\n",
    "        <td></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we go into tensors and programming in TensorFlow, let's take a look at how does it work.\n",
    "\n",
    "# TensorFlow Basics\n",
    "\n",
    "TensorFlow has a few concepts that you should be familiar with. TensorFlow executes your code inside a execution engine that you communicate with using an API. In TensorFlow your data is called a `Tensor` that you can apply operations (`OP`) to. Your code is converted into a `Graph` that is executed in an execution engine called a `Session`. So your python code is just a representation of your graph that can be executed in a session.\n",
    "\n",
    "![](images/graph.png)\n",
    "\n",
    "You can see how your data (or tensors) flow from one operation to the next, hens the name TensorFlow.\n",
    "\n",
    "Since version 1.5 and as of version 1.8 there are two methods to execute code in TensorFlow:\n",
    "\n",
    "- Graph Execution\n",
    "- Eager Execution\n",
    "\n",
    "The main difference is in graph execution is a type of `declarative programming` and eager execution is a type of `imperative programming`. In plain English, the difference is graph execution defines your code as a graph and executes in a session. Your objects in python are not the actual objects inside the session, they are only a reference to them. Eager execution executes your code as you run it giving you a better control of your program while it is running so you are not stuck with a predefined graph.\n",
    "\n",
    "## So why do we even bother with graph execution?\n",
    "\n",
    "Performance is a big issue in machine learning and a small difference in execution time can save you in a long project weeks of your time and 1000s of hours of GPU time. Support is another issue for now where some features of TensorFlow do not work in Eager Execution like high level estimators.\n",
    "\n",
    "We will focus in the following tutorials only on graph execution and we will cover eager execution later in this series.\n",
    "\n",
    "# Tensors\n",
    "\n",
    "Tensors can be though of a scalar variable or an array of any dimension. Tensors are the main object to store pass data, sore variables and constants and all operations that take in data take it in tensor format and all operations that output data uses the tensor format for that too.\n",
    "\n",
    "## Tensor Shape and Rank\n",
    "\n",
    "Tensors have a rank and a shape so for scalar values, we use rank-0 tensors of a shape `()` which is an empty shape\n",
    "\n",
    "Assuming we need a variable or a constant number to use in our software, we can represent it as a tensor of rank-0.\n",
    "\n",
    "![](images/rank-0.png)\n",
    "\n",
    "A rank-1 tensor can be though of as an __vector__ or a one dimensional array. To create a rank-1 tensor with shape `(3)` this will create a tensor that can hold three values in a single dimensional array.\n",
    "\n",
    "![](images/rank-1.png)\n",
    "\n",
    "\n",
    "A rank-2 is a __matrix__ or a two dimensional array. This can be used to hold two dimensional data like a black and white image. The shape of the tensor can match the shape of the image so to hold a 256x256 pixel image in a tensor, you can create a rank-2 tensor of shape `(256,256)`.\n",
    "\n",
    "![](images/rank-2.png)\n",
    "\n",
    "\n",
    "A rank-3 tensor is a three dimensional array. To can be used to hold three dimensional data like a color image represented in (RGB). To create a tensor to hold an color image of size 256x256, you can create a rank-3 tensor of shape `(256,256,3)`.\n",
    "\n",
    "![](images/rank-3.png)\n",
    "\n",
    "\n",
    "TensorFlow allows tensors in higher dimensions but you will very rarely see tensors of a rank exceeding 5 of shape `(batch size, width, height, RGB, frames)` for representing a batch of video clips.\n",
    "\n",
    "## Importing Tensor Flow\n",
    "\n",
    "Let's import TensorFlow and start working with some tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version: 3.5.2\n",
      "TensorFlow Version: 1.8.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import sys\n",
    "\n",
    "print(\"Python Version:\",sys.version.split(\" \")[0])\n",
    "print(\"TensorFlow Version:\",tf.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Execution\n",
    "\n",
    "TensorFlow executes your code inside a C++ program and returns the results through the TensorFlow API. Since we are using Python, we will be using TensorFlow Python API which is the most documented and most used API of TensorFlow.\n",
    "\n",
    "Sice we are using graph execution, there are two ways to create a session:\n",
    "\n",
    "- Session\n",
    "- Interactive Session\n",
    "\n",
    "Sessions and interactive sessions, use your code to build a \"Graph\" which is a representation of your code inside TensorFlow's execution engine. The main difference between them is interactive session makes itself the default session so since we are using only one session for our code, we will use that.\n",
    "\n",
    "For now let's start an interactive session and start flowing some tensors!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating new Tensors\n",
    "\n",
    "Since we have an interactive session, let's create a tensor. There are two common ways to create a tensor `tf.zeros()` and `tf.ones()`. Each one of them takes a python tuple or an array as the shape of the tensor.\n",
    "\n",
    "Let's start be creating a rank-0 tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = tf.zeros(())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a tensor and assigned it to a local variable named `a`. When we check the value of `a` this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'zeros:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice there is no value. You need to call `eval()` method of the tensor to get the actual value. This method takes an optional parameter where you can pass your session. Since we are using interactive session, we have a default one so we don't need to pass a session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should know that `eval()` method returns a numpy.float32 (or what ever the type of the tensor is) if the rank of the tensor is 0 and numpy.ndarray if the tensor of rank 1 or higher. Numpy is a multi-dimensional array library for python that runs the operations in a C program and interfaces back with python to ensure fast array operations.\n",
    "\n",
    "We can also check the rank and shape of the tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the rank would be the number of dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape.ndims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the name inside the TensorFlow execution engine is not `a`. It is `zeros:0` which is an auto generated name for the variable. The auto generated name is the name of the operation that generated the tensor and then an the index of the of the tensor in the output of the operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'zeros:0'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you created another variable using the name operation, it will be named `zeros_1:0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'zeros_1:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros(())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a second tensor of shape (3) which is going to be a rank-1 tensor. This time we will name it `b` and store it in a local variable named `b`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'b:0' shape=(3,) dtype=float32>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = tf.zeros((3), name=\"b\")\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the name of the variable now is `b:0` which is the name that we gave it and an auto incrementing index. We can also get the value in the same way using `eval()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get the value of a tensor by executing the tensor using your interactive session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also fill the tensor with any other value you want other than 0 and 1 using `fill()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5],\n",
       "       [5, 5]], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.fill((2,2), 5).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the data type of this tensor is `int32` and not `float32` because you initialized the tensor with an integer `5` and not `5.0`.\n",
    "\n",
    "## Tensor Shape\n",
    "\n",
    "For multi dimensional tensors, the shape is passed as a tuple or an array. The way this array is arranged is from the outer most dimension to the inner dimensions. So for a tensor that should represents 10 items and each item has three numbers, the shape would be `(10,3)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((10,3)).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For higher dimensions the same rules applies. Let say we have 2 items and each item has 3 parts and each on of these parts consists of 4 numbers the shape would be `(2,3,4)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((2,3,4)).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note__: This is the opposite of the how matrix shape notation is written in mathematics. A matrix of shape $A_{(3,10)}$ can be represented in TensorFlow as `(10,3)`. The reason for that is in mathematics the shape is $(Columns,Rows)$ and TensorFlow uses `(Outer,Inner)` which translates in 2-D tensor as `(Rows,Columns)`.\n",
    "\n",
    "## Generating Tensors with Random Values\n",
    "\n",
    "In many cases, you want to generate a new tensor but we want to start with random values stored in the tensor. The way we do that is using one the random generator of TensorFlow.\n",
    "\n",
    "### Normal Distribution\n",
    "\n",
    "`tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'random_normal:0' shape=(1000,) dtype=float32>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = tf.random_normal((1000,))\n",
    "arr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns random values using normal distribution which is also known as Gaussian distribution or informally called a \"Bell Curve\". To better understand it, let's first look at a graph showing this distribution. Normal distribution of of mean $\\mu$ and standard deviation $\\sigma$ is denoted $N(\\mu, \\sigma)$ (More about that in \"The Math Behind It\").\n",
    "\n",
    "To do that we will import Matplotlib which is a very common charting library for Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(arr1.eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the bill shape of the curve where you get more values values around your mean a few an values as you move away from the mean. You can also change the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kh/cDX0nySQan0X5jwvX0cReDgPwb4K38/w/MLXtJ1gMvB24GVlfVw92mR4DVEyprKPuMZUVbYCynA5cc7Hr62HcsST4OvB14AvjtcR57Wc/oF3EaK2Q2v4DfBz5QVUcCHwC2TbiePk4H3p3kFgZ/ov5owvUsWZLnAl8C3r/PTIsa/G29Yi5NW2gsK83+xpLko8Be4MJJ1Xag5htLVX20+92/EHjPOI+/IoM+ySrgd1hhr+jz2ARc3i3/NYO7fq5IVXVPVb2+qn6VwQvwv066pqVI8gwGv4AXVtVT/xaPJlnTbV8D7JlUfQdiP2NZkfY3liTvAN4EvK17EV72lvDvciFjPtW5IoMeeB1wT1XtmnQhPX0HeHW3/FpgxZ6GSvLC7vEngD8EPj/ZihbXvc+zDdhRVZ+as+lKBi/CdI9XHOzaDtQCY1lx9jeWJCcAZwFvrqofTqq+A7HAWDbM6bYRuGesdSznF8UkFwGvYXC3t0eBc6pqW5ILgJuqatmHyVPmGwtwL/AZBu+V/Cfw7qq6ZVI1LtV+xvJc4Myuy+XA2ct9xpXkN4F/Au4AnuyaP8LgHOqlwFHAg8ApVTXvRQHLxQJjeRbwZ8AU8DhwW1W9YSJFLtECY/ksg/H8W9d2U1W96+BXuHQLjOUMBldDPcng/9i7qmr3vDsZRR3L/HdRktTTSj11I0laIoNekhpn0EtS4wx6SWqcQS9JjTPoJalxBr0kNc6gl6TG/S/i2w9akWdgGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = tf.random_normal((1000,), mean=20.0)\n",
    "\n",
    "plt.hist(arr1.eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also control how much of concentrated your random numbers will be around the mean by controlling the standard deviation. Higher standard deviation means less values around the mean and wider distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = tf.random_normal((1000,), stddev=2, name=\"arr1\")\n",
    "arr2 = tf.random_normal((1000,), stddev=1, name=\"arr2\")\n",
    "\n",
    "plt.hist([arr1.eval(), arr2.eval()], bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One more note on normal distribution, if you created a large tensor with millions or tens of millions of random values, some of these values will fall really far from the normal. With some machine learning algorithms this might create instability. You can avoid that by using `truncated_normal()` function instead of `random_normal()`. This will re-sample any values that falls more than 2 standard deviations from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(tf.truncated_normal((1000,)).eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform Distribution\n",
    "\n",
    "The other common distribution will the uniform one. This will generate values with equal probability of falling anywhere between two numbers.\n",
    "\n",
    "`tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = tf.random_uniform((1000,))\n",
    "plt.hist(arr1.eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Tensors with Sequence Values\n",
    "\n",
    "You can generate tensors with sequence values using the following function:\n",
    "\n",
    "`tf.range(start, limit=None, delta=1, dtype=None, name='range')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.range(5).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.range(0, 5).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the output of `range()` function will never reach the limit parameter.\n",
    "\n",
    "You can also control the delta which is the spacing between the tensor elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4], dtype=int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.range(0, 5, 2).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping Tensors\n",
    "\n",
    "You can reshape tensors using this function:\n",
    "\n",
    "`tf.reshape(tensor, shape, name=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5]], dtype=int32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.range(6)\n",
    "tf.reshape(a, (3,2)).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensor Arithmetics\n",
    "\n",
    "You can use standard python arithmetics on tensors and get results in a new tensor.\n",
    "\n",
    "## Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11., 11.],\n",
       "       [11., 11.]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.ones((2,2))\n",
    "b = tf.fill((2,2), 10.0) # Notice we used 10.0 and not 10 to ensure the data type will be float32\n",
    "\n",
    "c = a + b\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Element-wise Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22., 22.],\n",
       "       [22., 22.]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = c * 2.0\n",
    "d.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25., 25.],\n",
       "       [25., 25.]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d + 3).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Operations\n",
    "\n",
    "TensorFlow support a variety of matrix operations.\n",
    "\n",
    "### Identity Matrix\n",
    "\n",
    "An identity matrix is a 2 dimensional matrix where all the values are zeros exception diagonally where it has values of 1. This is an example of an identity matrix of shape (3,3).\n",
    "\n",
    "$$\\begin{bmatrix}\n",
    "{1} & {0} & {0} \\\\\n",
    "{0} & {1} & {0} \\\\\n",
    "{0} & {0} & {1} \\\\\n",
    "\\end{bmatrix}$$\n",
    "\n",
    "To do that in TensorFlow use the `tf.eye()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = tf.eye(3,3)\n",
    "i.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpose\n",
    "\n",
    "Transpose is another operation that is commonly used in matrix calculations. Transpose converts rows to columns. Assume you have a matrix $\\mathbf{A}$, a transpose operation over this matrix produces $\\mathbf{A^T}$ pronounced _transpose of $\\mathbf{A}$_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]], dtype=int32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.range(1,9)\n",
    "i = tf.reshape(a, (2,4))\n",
    "i.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 5],\n",
       "       [2, 6],\n",
       "       [3, 7],\n",
       "       [4, 8]], dtype=int32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "it = tf.matrix_transpose(i)\n",
    "it.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix Multiplication\n",
    "\n",
    "One of the most common operations for matrices in deep learning in matrix multiplication. Matrix multiplication is not an element-wise operation. The exact math will be discussed in the last section of this tutorial \"The Math Behind It\". But for now to give you the basics you should know the following:\n",
    "\n",
    "Assume we have two matrices $\\mathbf{A}$ and $\\mathbf{B}$. The shape of $\\mathbf{A}$ is (m,n) and the shape of $\\mathbf{B}$ is (o,p) we can write these two matrices with their shape as $\\mathbf{A}_{(m,n)}$ and $\\mathbf{B}_{(o,p)}$. Multiplying these two matrices produces a matrix of the shape (m,p) __IF__ $n=o$ like this:\n",
    "\n",
    "$\\mathbf{A}_{(m,n)} \\mathbf{B}_{(o,p)}=\\mathbf{C}_{(m,p)} \\leftarrow n=o$\n",
    "\n",
    "Notice the inner shape of these two matrices is the same and the output matrix has the shape of the outer shape of it these two matrices. If the inner shape of the matrices does not match the product doesn’t exist.\n",
    "\n",
    "We can use `tf.matmul()` function to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c has the shape of: (2, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.ones((2,3))\n",
    "b = tf.ones((3,4))\n",
    "\n",
    "c = tf.matmul(a ,b)\n",
    "\n",
    "print(\"c has the shape of:\", c.shape)\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Math Behind It\n",
    "\n",
    "## Standard Deviation $\\sigma$ or $s$\n",
    "\n",
    "Standard deviation is the measure of how elements in a set vary from the mean. So a sample with most data points close to the mean has low standard deviation and it gets higher as the data points start moving away from the mean. In statistics, standard deviation is denoted as the small letter sigma $\\sigma$.\n",
    "\n",
    "The formula to calculate standard deviation for a whole population is:\n",
    "\n",
    "$$\\sigma={\\sqrt {\\frac {\\sum_{i=1}^N(x_{i}-{\\overline {x}})^{2}}{N}}}$$\n",
    "\n",
    "Let's break it down and see how to calculate standard deviation.\n",
    "\n",
    "Assume we have exam grades of 10 students and the grades are so follow:\n",
    "\n",
    "| ID    | Grade  |\n",
    "| ----- |:------:|\n",
    "| 1     |  88    |\n",
    "| 2     |  94    |\n",
    "| 3     |  71    |\n",
    "| 4     |  97    |\n",
    "| 5     |  84    |\n",
    "| 6     |  82    |\n",
    "| 7     |  80    |\n",
    "| 8     |  98    |\n",
    "| 9     |  91    |\n",
    "| 10    |  93    |\n",
    "\n",
    "First thing we need to do is calculate the mean. The mean is denoted as $\\overline {x}$ (_pronouned \"x bar\"_). To calculate the mean (AKA average) get the sum all numbers and divide it by their count. It is also commonly denoted as a small letter mu $\\mu$. Assume you have $N$ values, this will be the formula to calculate the mean:\n",
    "\n",
    "$$\\overline {x} = \\frac{x_1 + x_2 + ... + x_N}{N} = \\frac{\\sum_{i=1}^N x_i}{N}$$\n",
    "\n",
    "So let's calculate that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = [88, 94, 71, 97, 84, 82, 80, 98, 91, 93]\n",
    "\n",
    "total = sum(g)\n",
    "count = len(g)\n",
    "\n",
    "mean = total/count\n",
    "mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know the mean, we can go back to the original equation and calculate the standard deviation. For that we will do it with loop comprehension (One of the greatest features of python).\n",
    "\n",
    "So looking at the equation one more:\n",
    "\n",
    "$$\\sigma={\\sqrt {\\frac {\\sum_{i=1}^N(x_{i}-{\\overline {x}})^{2}}{N}}}$$\n",
    "\n",
    "First we need to get each element in our grades $x_i$ and subtract it from the mean $\\overline {x}$ then square it and take the sum of that.\n",
    "\n",
    "```python\n",
    "a = [(x-mean)**2 for x in g]\n",
    "b = sum(a)\n",
    "```\n",
    "\n",
    "Divide that by the number of elements $N$ then take the square root\n",
    "\n",
    "```python\n",
    "variance = b / count\n",
    "σ = sqrt(variance)\n",
    "```\n",
    "\n",
    "We can write the whole thing in one like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.096912991998865"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "\n",
    "σ = sqrt(sum([(x-mean)**2 for x in g]) / count)\n",
    "σ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that standard deviation is a build in function in NumPy, TensorFlow and many other languages and libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.096912991998865"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.std(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = tf.constant(g, dtype=tf.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.096912991998865"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_t, var_t = tf.nn.moments(t, axes=0)\n",
    "sqrt(var_t.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance $\\sigma^2$, $s^2$ or $Var(X)$\n",
    "\n",
    "It is just the square of the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.56"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance = sum([(x-mean)**2 for x in g]) / count\n",
    "variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Multiplication\n",
    "\n",
    "Arguably, the most common matrix operation you will perform in deep learning is multiplying matrices. Understanding this operation is a good start to understanding the math behind neural networks. This operation is also known as \"dot product\".\n",
    "\n",
    "Assume we have two matrices $\\mathbf{A}_{(2,3)}$ and $\\mathbf{B}_{(3,2)}$. The dot product of these two matrices $\\mathbf{A}_{(2,3)} . \\mathbf{B}_{(3,2)}$ is calculated as follows:\n",
    "\n",
    "$\\mathbf{A}_{(2,3)} = \\begin{bmatrix}\n",
    "{1} & {0} \\\\\n",
    "{3} & {2} \\\\\n",
    "{1} & {4} \\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "$\\mathbf{B}_{(3,2)} = \\begin{bmatrix}\n",
    "{2} & {1} & {2} \\\\\n",
    "{1} & {2} & {3} \\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "$\\mathbf{C}_{(2,2)} = \\mathbf{A}_{(2,3)} . \\mathbf{B}_{(3,2)}$\n",
    "\n",
    "$\\mathbf{C}_{(2,2)} = \\begin{bmatrix}\n",
    "{2\\times1 + 3\\times1 + 1\\times2} & {0\\times2 + 2\\times1 + 4\\times2} \\\\\n",
    "{1\\times1 + 3\\times2 + 1\\times3} & {0\\times1 + 2\\times2 + 4\\times3} \\\\\n",
    "\\end{bmatrix} = \\begin{bmatrix}\n",
    "{2 + 3 + 2} & {0 + 2 + 8} \\\\\n",
    "{1 + 6 + 3} & {0 + 4 + 12} \\\\\n",
    "\\end{bmatrix}= \\begin{bmatrix}\n",
    "{7} & {10} \\\\\n",
    "{10} & {16} \\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "This is an animation to shows how that is done step by step.\n",
    "\n",
    "![](images/Webp.net-gifmaker.gif)\n",
    "\n",
    "Now let's confirm it with TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[1,0],\n",
    "     [3,2],\n",
    "     [1,4],\n",
    "    ]\n",
    "\n",
    "b = [[2,1,2],\n",
    "     [1,2,3],\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember from before, that mathematical shape of a matrix is opposite of TensorFlow shape of a tensor. So instead of rewriting our arrays, we will just use transpose you make rows into columns and columns into rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 10],\n",
       "       [10, 16]], dtype=int32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.constant(a)\n",
    "b = tf.constant(b)\n",
    "\n",
    "c = tf.matmul(tf.matrix_transpose(a), tf.matrix_transpose(b))\n",
    "\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily there is also a easier way to to that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 10],\n",
       "       [10, 16]], dtype=int32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = tf.matmul(a,b, transpose_a=True, transpose_b=True)\n",
    "\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\n",
    "\n",
    "Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>"
   ]
  }
 ],
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================================================
FILE: notebooks/tf/1. Machine Learning Basics.ipynb
================================================
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td style=\"text-align:left;\"><div style=\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\">1. Machine Learning Basics</div><img src=\"images/roshan.png\" style=\"width:30%; display: inline; text-align: left; float:right;\"></td>\n",
    "        <td></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why TensorFlow?\n",
    "\n",
    "TensorFlow is Google's framework for developing Machine Learning models and is has emerged as the most popular machine learning library. Since 2011, Google Brain Team has been developing TensorFlow and used it internally. It was released as an open-source software in Nov, 2015. It comes packed with a wide range of tools from dataset management to a good selection of high-level algorithms and even ready made models and a web based dashboard. That's a lot to pack in one solution!\n",
    "\n",
    "The team that is working on developing TensorFlow are some of the most influencal researchers in machine learning. There is also a huge amount of contributors and commits for a relativly new project on Github. You quickly realize that TensorFlow is getting more attention from people in the field than any other existing machine learning solution and will be the technology starndard for machine learning development.\n",
    "\n",
    "__OS Support__:\n",
    "\n",
    "- Linux\n",
    "- Windows\n",
    "- macOS\n",
    "- Android\n",
    "- iOS\n",
    "\n",
    "__Programming Languages__:\n",
    "\n",
    "- Python\n",
    "- C/C++\n",
    "- Java\n",
    "- Golang\n",
    "\n",
    "__Hardware__:\n",
    "\n",
    "- CPU\n",
    "- GPU\n",
    "- TPU\n",
    "- Distributed Systems\n",
    "\n",
    "There is also TensorFlow.js version which runs in browser on client-side with no need for a back-end. There is also TensorFlow Lite which converts machine learning models to a format that can run on mobile devices running iOS and Android.\n",
    "\n",
    "With in two years of its release, TensorFlow has been downloaded over 11 million times from almost every country on earth.\n",
    "\n",
    "![](images/tf_map.png)\n",
    "\n",
    "From Ragat Monga [@rajatmonga](https://twitter.com/rajatmonga) Slides in [TensorFlow Dev Summit 2018]() (2018-03-30)\n",
    "\n",
    "# What is Machine Learning?\n",
    "\n",
    "Before we dive any deeper in TensorFlow, I will go over some basic concepts of machine learning.\n",
    "\n",
    "This tutorial is for developers who can develope in python but not well versed in machine learning or familiar with some other machine learning libraries but new to TensorFlow. It is also useful for non-developers working in other fields like statistics, medical field, marketing or social studies and eager to experiment with machine learning in their fields.\n",
    "\n",
    "Machine Learning (ML) is a branch of Artificial Intelligence (AI). It uses data instead of code to produce logic. The goal is to create software that can perform tasks that we usually equate with human level intelligence. In some cases it could exceed human level of intelligence like what happened with Alpha Go and some other examples. You actually use machine learning all the time without realizing when you use Google, YouTube, Amazon and so many other services that reley on machine learning to give you a better experience.\n",
    "\n",
    "Machine learning is built on algorithms that use data to train models to perform a specific task.\n",
    "\n",
    "Meet `Tensy`, a robot eager to learn and solve human problems. [@TensyRobot](https://twitter.com/TensyRobot)\n",
    "\n",
    "![Algorithm](images/algorithm.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Learning Styles\n",
    "\n",
    "Models can learn in three styles depending the the training algorithms:\n",
    "\n",
    "## Supervised Learning (SL)\n",
    "\n",
    "Supervised learning is teaching a model to give an output based on some input. The the algorithm teachs the model by giving the it plenty of examples and their outputs. There are two types of problems that we can solve with supervised learning which are:\n",
    "\n",
    "### Classification\n",
    "\n",
    "Classification is used to teach a model to output a category or a class based on a data point. Like learning if a photo has a cat or a dog.\n",
    "\n",
    "I want `Tensy` to learn how to tell cats from dog. We can give `Tensy` photos of cats and dogs until he learns to tell a cat from a dog. I can later test him on new photos he has never seen before and see how accurate he is.\n",
    "\n",
    "![CatsDogs!](images/CatsDogs.png)\n",
    "\n",
    "### Regression\n",
    "\n",
    "Regression is used to teach a model to output a continuous numerical value based on a data point. Like telling the age of a person based on their photo.\n",
    "\n",
    "This time we are teaching `Tensy` to tell the age of a person based on their photo. To do that we will give him a large amount of photos and tell him the age of each person. We can later test him by asking the age of people in new photos that he didn't see before.\n",
    "\n",
    "![CatsDogs!](images/age.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervoised Learning (UL)\n",
    "\n",
    "Unsupervised Learning teaches a model to produce an output based on a data point without teaching the model the output. Like giving a model photos taken outdoor and photos taken indoor and ask then model to split them to two piles. There are many problems that can be solved using UL but the most common ones are:\n",
    "\n",
    "### Clustering\n",
    "\n",
    "Clustering is grouping similar data points into groups or clusters. different algorithms can find similarities in different ways and can end up clustering your data points into different groups. Some of these algorithms need you to tell them how many clusters do you want them to split your data into and some will try and figure that out of their own.\n",
    "\n",
    "This time we gave `Tensy` a group if photos and we asked him to split them into two groups. He is finding that all indoor photos are similar and all outdoor photos are similar.\n",
    "\n",
    "![CatsDogs!](images/outdoorindoor.png)\n",
    "\n",
    "Other UL algorithms can solve some interesting problems using like:\n",
    "\n",
    "- Anomaly Detection\n",
    "- Generative adversarial networks (GANs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reinforcement Learning (RL)\n",
    "\n",
    "Reinforcement Learning  uses an algorithm to teach a model to produce a desired output without explicitly explaining how to perform it. This works by giving the model an input and rewarding good outcome and punishing bad outcome. This is similar to we humans and animals learn.\n",
    "\n",
    "Let's teach `Tensy` to play some Atari games. The input is the screen feed from the game. After each frame from the input he will be able to choose one action (right, left, up, down and press action button). That action might lead to increased score if it is a good action or to no increase score if it is a neutral action. It could also lead to to loosing a life and lower score for a bad action.\n",
    "\n",
    "![CatsDogs!](images/atari.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning is More That!\n",
    "\n",
    "These major categories are not inclusive of every algorithm that you might use. Some evolutionary algorithms, or recommendation systems do not fall under any of these categories. Some more complicated algorithms use multiple stages in a pipeline some with supervised and unsupervised so your end-to-end algorithm might also not fall under any specific category or sometimes will be called \"Semi-Supervised\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Math Behind It\n",
    "\n",
    "This section will explain some mathematical concepts at the end of each tutorial. These tutorials are going to be code intensive and that will help you develop your coding skills and get your model working. But what if it doesn't work?\n",
    "\n",
    "The math behind these algorithms use Linear Algebra, Tensor Calculus and Statistics. This section in each tutorial will explain the math behind what we discussed and will assume very little to no math background and will explain things from the ground up. This could help you fix your model or even improve your accuracy.\n",
    "\n",
    "Finally, It will serve as a bidge between machine learning concepts and centuries of mathimatical reasearch. Mathematics is the language of science and learning this language will open the door for you to explore some of the latest academic research papers in the machine learning due to the fact that they mostly look like this:\n",
    "\n",
    "![SVM Paper!](images/svm.png)\n",
    "\n",
    "A small section of \"[A Training Algorithm for Optimal Margin Classiers](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.21.3818&rep=rep1&type=pdf)\" published 1992. AKA the original SVM paper\n",
    " \n",
    "To kick of this section, We'll start with a few simple concepts:\n",
    " \n",
    "## The real numbers $\\mathbb{R}$\n",
    " \n",
    "The real numbers include positive, nagative numbers and zero. They can be of arbitrary precesion meaning they can have any number of decimal points or even numbers with infinite precision. The set real is denoted in mathimatics as $\\mathbb{R}$. Examples of real numbers include:\n",
    "\n",
    "- 5.0\n",
    "- -2.5\n",
    "- $\\frac{1}{3} = 0.3333\\overline3$\n",
    "- $\\pi = 3.14159 ...$ (Pi) this is small letter Pi\n",
    "- $e = 2.71828 ...$ (Euler's Number)\n",
    "\n",
    "## Sum $\\sum$\n",
    "\n",
    "To represent a finite sum of numbers in math, you can use the following:\n",
    "\n",
    "$$ 1 + 2 + 3 + 4 + 5 $$\n",
    "\n",
    "For longer sums like getting the sum of all numbers from 1 to 1000 can be harder to write in that way. We use sum denoted in mathematics as capital letter Sigma $\\Sigma$. To rewite the previous equation from 1 to 1000 using sum, you can do this:\n",
    "\n",
    "$$\\sum_{n=1}^{1000} n$$\n",
    "\n",
    "This can facilitate writing more complex functions like this:\n",
    "\n",
    "$$f(x) = x + 2x + 3x + 4x + 5x = \\sum_{n=1}^{5}nx$$\n",
    "\n",
    "With this notation we can even represent an infinite sum of numbers:\n",
    "\n",
    "$$\\frac{1}{1} + \\frac{1}{2} + \\frac{1}{4} + \\frac{1}{8} + \\frac{1}{16} ... = \\sum_{n=0}^{\\infty} \\frac{1}{2^n} = 2$$\n",
    "\n",
    "## Product $\\Pi$\n",
    "\n",
    "To represent a finite product of numbers in math, you can use the following:\n",
    "\n",
    "$$ 1 \\times 2 \\times 3 \\times 4 \\times 5 $$\n",
    "\n",
    "For longer products like getting the products of all numbers from 1 to 1000 can be harder to write in that way. We use product denoted in mathematics as capital letter Pi $\\Pi$. To rewrite the previous product from 1 to 1000, we can write it this way:\n",
    "\n",
    "$$\\prod_{n=1}^{1000} n$$\n",
    "\n",
    "> NOTE: It should also be noted that the previous product can also be written as $1000!$ which is called 1000 factorial.\n",
    "\n",
    "This can facilitate writing more complex products like:\n",
    "\n",
    "$$f(x) = x \\times 2x \\times 3x \\times 4x \\times 5x = \\prod_{n=1}^{5}nx$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\n",
    "\n",
    "Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>"
   ]
  }
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================================================
FILE: notebooks/tf/2. Tensors.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td style=\"text-align:left;\"><div style=\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\">2. Tensors</div><img src=\"images/roshan.png\" style=\"width:30%; display: inline; text-align: left; float:right;\"></td>\n",
    "        <td></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we go into tensors and programming in TensorFlow, let's take a look at how does it work.\n",
    "\n",
    "# TensorFlow Basics\n",
    "\n",
    "TensorFlow has a few concepts that you should be familiar with. TensorFlow executes your code inside an execution engine that you communicate with using an API. In TensorFlow your data is called a `Tensor` that you can apply operations (`OPs`) to. Your code is converted into a `Graph` that is executed in an execution engine called a `Session`. So your python code is just a representation of your graph that can be executed in a session.\n",
    "\n",
    "![](images/graph.png)\n",
    "\n",
    "You can see how your data (or tensors) flow from one operation to the next, hence the name TensorFlow.\n",
    "\n",
    "Since version 1.5 and as of version 1.8 there are two methods to execute code in TensorFlow:\n",
    "\n",
    "- Graph Execution\n",
    "- Eager Execution\n",
    "\n",
    "The main difference is graph execution is a type of `declarative programming` and eager execution is a type of `imperative programming`. In plain English, the difference is graph execution defines your code as a graph and executes in a session. Your objects in python are not the actual objects inside the session, they are only a reference to them. Eager execution executes your code as you run it giving you a better control of your program while it is running so you are not stuck with a predefined graph.\n",
    "\n",
    "## So why do we even bother with graph execution?\n",
    "\n",
    "Performance is a big issue in machine learning and a small difference in execution time can save you in a long project weeks of your time and 1000s of hours of GPU time. Support is another issue for now where some features of TensorFlow do not work in Eager Execution like high level estimators.\n",
    "\n",
    "We will focus in the following tutorials only on graph execution and we will cover eager execution later in this series.\n",
    "\n",
    "# Tensors\n",
    "\n",
    "Tensors represent your data. It a scalar variable or an array of any dimension. Tensors are the main object to store and pass data between operations, the input and output of all operations are always tensors.\n",
    "\n",
    "## Tensor Shape and Rank\n",
    "\n",
    "Tensors have a rank and a shape so for scalar values, we use rank-0 tensors of a shape `()` which is an empty shape\n",
    "\n",
    "Assuming we need a variable or a constant number to use in our software, we can represent it as a tensor of rank-0.\n",
    "\n",
    "![](images/rank-0.png)\n",
    "\n",
    "A rank-1 tensor can be though of as an __vector__ or a one dimensional array. creating a rank-1 tensor with shape `(3)` will create a tensor that can hold three values in a one-dimensional array.\n",
    "\n",
    "![](images/rank-1.png)\n",
    "\n",
    "\n",
    "A rank-2 is a __matrix__ or a two dimensional array. This can be used to hold two dimensional data like a black and white image. The shape of the tensor can match the shape of the image so to hold a 256x256 pixel image in a tensor, you can create a rank-2 tensor of shape `(256,256)`.\n",
    "\n",
    "![](images/rank-2.png)\n",
    "\n",
    "\n",
    "A rank-3 tensor is a three dimensional array. It can be used to hold three dimensional data like a color image represented in (RGB). To create a tensor to hold an color image of size 256x256, you can create a rank-3 tensor of shape `(256,256,3)`.\n",
    "\n",
    "![](images/rank-3.png)\n",
    "\n",
    "\n",
    "TensorFlow allows tensors in higher dimensions but you will very rarely see tensors of a rank exceeding 5 `(batch size, width, height, RGB, frames)` for representing a batch of video clips.\n",
    "\n",
    "## Importing Tensor Flow\n",
    "\n",
    "Let's import TensorFlow and start working with some tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version: 3.5.2\n",
      "TensorFlow Version: 1.8.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import sys\n",
    "\n",
    "print(\"Python Version:\",sys.version.split(\" \")[0])\n",
    "print(\"TensorFlow Version:\",tf.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Execution\n",
    "\n",
    "TensorFlow executes your code inside a C++ program and returns the results through the TensorFlow API. Since we are using Python, we will be using TensorFlow Python API which is the most documented and most used API of TensorFlow.\n",
    "\n",
    "Since we are using graph execution, there are two ways to create a session:\n",
    "\n",
    "- Session\n",
    "- Interactive Session\n",
    "\n",
    "Sessions and interactive sessions, use your code to build a \"Graph\" which is a representation of your code inside TensorFlow's execution engine. The main difference between them is, an interactive session makes itself the default session. Since we are using only one session for our code, we will use that.\n",
    "\n",
    "For now let's start an interactive session and start flowing some tensors!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating new Tensors\n",
    "\n",
    "Since we have an interactive session, let's create a tensor. There are two common ways to create a tensor `tf.zeros()` and `tf.ones()`. Each one of them takes a python tuple or an array as the shape of the tensor.\n",
    "\n",
    "Let's start be creating a rank-0 tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = tf.zeros(())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a tensor and assigned it to a local variable named `a`. When we check the value of `a` this is what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'zeros:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice there is no value. You need to call `eval()` method of the tensor to get the actual value. This method takes an optional parameter where you can pass your session. Since we are using interactive session, we don't have ato pass anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should know that `eval()` method returns a numpy.float32 (or what ever the type of the tensor is) if the rank of the tensor is 0 and numpy.ndarray if the tensor has rank 1 or higher.\n",
    "\n",
    " > __Numpy__: is a multi-dimensional array library for python that runs the operations in a C program and interfaces back with python to ensure fast array operations.\n",
    "\n",
    "We can also check the rank and shape of the tensor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the rank would be the number of dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape.ndims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the name inside the TensorFlow execution engine is not `a`. It is `zeros:0` which is an auto generated name for the variable. The auto generated name is the name of the operation that generated the tensor and then an the index of the of the tensor in the output of the operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'zeros:0'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you created another variable using the same operation, it will be named `zeros_1:0`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'zeros_1:0' shape=() dtype=float32>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros(())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create a second tensor of shape (3) which is going to be a rank-1 tensor. This time we will name it `b` and store it in a local variable named `b`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'b:0' shape=(3,) dtype=float32>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = tf.zeros((3), name=\"b\")\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the name of the variable now is `b:0` which is the name that we gave it and the index of `0` of the return of the operation. We can also get the value in the same way using `eval()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get the value of a tensor by executing the tensor using your session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also fill the tensor with any other value you want other than 0 and 1 using `fill()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5],\n",
       "       [5, 5]], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.fill((2,2), 5).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the data type of this tensor is `int32` and not `float32` because you initialized the tensor with an integer `5` and not `5.0`.\n",
    "\n",
    "## Tensor Shape\n",
    "\n",
    "For multi dimensional tensors, the shape is passed as a tuple or an array. The way this array is arranged is from the outer most dimension to the inner dimensions. So for a tensor that should represents 10 items and each item has three numbers, the shape would be `(10,3)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((10,3)).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note__: This is the opposite of the how matrix shape notation is written in mathematics. A matrix of shape $A_{(3,10)}$ can be represented in TensorFlow as `(10,3)`. The reason for that is in mathematics the shape is $(Columns,Rows)$ and TensorFlow uses `(Outer,Inner)` which translates in 2-D tensor as `(Rows,Columns)`.\n",
    "\n",
    "For higher dimensions the same rules applies. Let say we have 2 items and each item has 3 parts and each part consists of 4 numbers the shape would be `(2,3,4)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.],\n",
       "        [0., 0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.zeros((2,3,4)).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Tensors with Random Values\n",
    "\n",
    "In many cases, you want to generate a new tensor but we want to start with random values stored in the tensor. The way we do that is using one the random generators of TensorFlow.\n",
    "\n",
    "### Normal Distribution\n",
    "\n",
    "`tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'random_normal:0' shape=(1000,) dtype=float32>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = tf.random_normal((1000,))\n",
    "arr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function returns random values using normal distribution which is also known as Gaussian distribution or informally called a \"Bell Curve\". To better understand it, let's first look at a graph showing this distribution. In Mathematics, a normal distribution of mean $\\mu$ and standard deviation $\\sigma$ is denoted $N(\\mu, \\sigma)$ (More about that in \"The Math Behind It\").\n",
    "\n",
    "To do that we will import Matplotlib which is a very common charting library for Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(arr1.eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the bill shape of the curve where you get more values around your mean a fewer values as you move away from the mean.\n",
    "\n",
    "You can also change the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = tf.random_normal((1000,), mean=20.0)\n",
    "\n",
    "plt.hist(arr1.eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also control how concentrated your random numbers will be around the mean by controlling the standard deviation. Higher standard deviation means less values around the mean and wider distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = tf.random_normal((1000,), stddev=2, name=\"arr1\")\n",
    "arr2 = tf.random_normal((1000,), stddev=1, name=\"arr2\")\n",
    "\n",
    "plt.hist([arr1.eval(), arr2.eval()], bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One more note on normal distribution, if you created a large tensor with millions or tens of millions of random values, some of these values will fall really far from the mean. With some machine learning algorithms this might create instability. You can avoid that by using `truncated_normal()` function instead of `random_normal()`. This will re-sample any values that falls more than 2 standard deviations from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(tf.truncated_normal((1000,)).eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uniform Distribution\n",
    "\n",
    "The other common distribution is the uniform one. This will generate values with equal probability of falling anywhere between two numbers.\n",
    "\n",
    "`tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1 = tf.random_uniform((1000,))\n",
    "plt.hist(arr1.eval(), bins=15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Tensors with Sequence Values\n",
    "\n",
    "You can generate tensors with sequence values using the following function:\n",
    "\n",
    "`tf.range(start, limit=None, delta=1, dtype=None, name='range')`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.range(5).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which is equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.range(0, 5).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the output of `range()` function will never reach the limit parameter.\n",
    "\n",
    "You can also control the delta which is the spacing between the tensor's elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4], dtype=int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.range(0, 5, 2).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping Tensors\n",
    "\n",
    "You can reshape tensors using this function:\n",
    "\n",
    "`tf.reshape(tensor, shape, name=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5]], dtype=int32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.range(6)\n",
    "tf.reshape(a, (3,2)).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensor Arithmetics\n",
    "\n",
    "You can use standard python arithmetics on tensors and get results in a new tensor.\n",
    "\n",
    "## Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11., 11.],\n",
       "       [11., 11.]], dtype=float32)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.ones((2,2))\n",
    "b = tf.fill((2,2), 10.0) # Notice we used 10.0 and not 10 to ensure the data type will be float32\n",
    "\n",
    "c = a + b\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Element-wise Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22., 22.],\n",
       "       [22., 22.]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = c * 2.0\n",
    "d.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[25., 25.],\n",
       "       [25., 25.]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d + 3).eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Operations\n",
    "\n",
    "TensorFlow support a variety of matrix operations.\n",
    "\n",
    "### Identity Matrix\n",
    "\n",
    "An identity matrix is a matrix where all the values are zeros exception diagonally where it has values of 1. This is an example of an identity matrix of shape (3,3).\n",
    "\n",
    "$$\\begin{bmatrix}\n",
    "{1} & {0} & {0} \\\\\n",
    "{0} & {1} & {0} \\\\\n",
    "{0} & {0} & {1} \\\\\n",
    "\\end{bmatrix}$$\n",
    "\n",
    "To do that in TensorFlow use the `tf.eye()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = tf.eye(3,3)\n",
    "i.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpose\n",
    "\n",
    "Transpose is another operation that is commonly used in matrix calculations. Transpose converts rows to columns. Assume you have a matrix $\\mathbf{A}$, a transpose operation over this matrix produces $\\mathbf{A^T}$ pronounced _$\\mathbf{A}$ transpose_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]], dtype=int32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.range(1,9)\n",
    "i = tf.reshape(a, (2,4))\n",
    "i.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 5],\n",
       "       [2, 6],\n",
       "       [3, 7],\n",
       "       [4, 8]], dtype=int32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "it = tf.matrix_transpose(i)\n",
    "it.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix Multiplication\n",
    "\n",
    "One of the most common operations for matrices in deep learning in matrix multiplication. Matrix multiplication is not an element-wise operation. The exact math will be discussed in the last section of this tutorial \"The Math Behind It\". But for now to give you the basics you should know the following:\n",
    "\n",
    "Assume we have two matrices $\\mathbf{A}$ and $\\mathbf{B}$. The shape of $\\mathbf{A}$ is (m,n) and the shape of $\\mathbf{B}$ is (o,p) we can write these two matrices with their shape as $\\mathbf{A}_{(m,n)}$ and $\\mathbf{B}_{(o,p)}$. Multiplying these two matrices produces a matrix of the shape (m,p) __IF__ $n=o$ like this:\n",
    "\n",
    "$\\mathbf{A}_{(m,n)} . \\mathbf{B}_{(o,p)}=\\mathbf{C}_{(m,p)} \\leftarrow n=o$\n",
    "\n",
    "Notice the inner shape of these two matrices is the same and the output matrix has the shape of the outer shape of these two matrices. If the inner shape of the matrices does not match the product doesn’t exist.\n",
    "\n",
    "We can use `tf.matmul()` function to do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c has the shape of: (2, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.ones((2,3))\n",
    "b = tf.ones((3,4))\n",
    "\n",
    "c = tf.matmul(a ,b)\n",
    "\n",
    "print(\"c has the shape of:\", c.shape)\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Math Behind It\n",
    "\n",
    "## Standard Deviation $\\sigma$ or $s$\n",
    "\n",
    "Standard deviation is the measure of how elements in a set vary from the mean. So a sample with most data points close to the mean has low standard deviation and it gets higher as the data points start moving away from the mean. In statistics, standard deviation is denoted as the small letter sigma $\\sigma$ or $s$.\n",
    "\n",
    "The formula to calculate standard deviation for a whole population is:\n",
    "\n",
    "$$\\sigma={\\sqrt {\\frac {\\sum_{i=1}^N(x_{i}-{\\overline {x}})^{2}}{N}}}$$\n",
    "\n",
    "Let's break it down and see how to calculate standard deviation.\n",
    "\n",
    "Assume we have exam grades of 10 students and the grades are so follow:\n",
    "\n",
    "| ID    | Grade  |\n",
    "| ----- |:------:|\n",
    "| 1     |  88    |\n",
    "| 2     |  94    |\n",
    "| 3     |  71    |\n",
    "| 4     |  97    |\n",
    "| 5     |  84    |\n",
    "| 6     |  82    |\n",
    "| 7     |  80    |\n",
    "| 8     |  98    |\n",
    "| 9     |  91    |\n",
    "| 10    |  93    |\n",
    "\n",
    "First thing we need to do is calculate the mean. The mean is denoted as $\\overline {x}$ (_pronouned \"x bar\"_). To calculate the mean (or average) get the sum all numbers and divide it by their count. It is also commonly denoted as a small letter mu $\\mu$. Assume you have $N$ values, this will be the formula to calculate the mean:\n",
    "\n",
    "$$\\overline {x} = \\frac{x_1 + x_2 + ... + x_N}{N} = \\frac{\\sum_{i=1}^N x_i}{N}$$\n",
    "\n",
    "So let's calculate that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.8"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = [88, 94, 71, 97, 84, 82, 80, 98, 91, 93]\n",
    "\n",
    "total = sum(g)\n",
    "count = len(g)\n",
    "\n",
    "mean = total/count\n",
    "mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know the mean, we can go back to the original equation and calculate the standard deviation.\n",
    "\n",
    "So looking at the equation one more:\n",
    "\n",
    "$$\\sigma={\\sqrt {\\frac {\\sum_{i=1}^N(x_{i}-{\\overline {x}})^{2}}{N}}}$$\n",
    "\n",
    "First we need to get each element in our grades $x_i$ and subtract it from the mean $\\overline {x}$ then square it and take the sum of that.\n",
    "\n",
    "```python\n",
    "a = [(x-mean)**2 for x in g]\n",
    "b = sum(a)\n",
    "```\n",
    "\n",
    "Divide that by the number of elements $N$ then take the square root\n",
    "\n",
    "```python\n",
    "variance = b / count\n",
    "σ = sqrt(variance)\n",
    "```\n",
    "\n",
    "We can write the whole thing in one like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.096912991998865"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "\n",
    "σ = sqrt(sum([(x-mean)**2 for x in g]) / count)\n",
    "σ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that standard deviation is a build in function in NumPy, TensorFlow and many other languages and libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.096912991998865"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.std(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = tf.constant(g, dtype=tf.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.096912991998865"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_t, var_t = tf.nn.moments(t, axes=0)\n",
    "sqrt(var_t.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance $\\sigma^2$, $s^2$ or $Var(X)$\n",
    "\n",
    "It is just the square of the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.56"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variance = sum([(x-mean)**2 for x in g]) / count\n",
    "variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix Multiplication\n",
    "\n",
    "Arguably, the most common matrix operation you will perform in deep learning is multiplying matrices. Understanding this operation is a good start to understanding the math behind neural networks. This operation is also known as \"dot product\".\n",
    "\n",
    "Assume we have two matrices $\\mathbf{A}_{(2,3)}$ and $\\mathbf{B}_{(3,2)}$. The dot product of these two matrices $\\mathbf{A}_{(2,3)} . \\mathbf{B}_{(3,2)}$ is calculated as follows:\n",
    "\n",
    "$\\mathbf{A}_{(2,3)} = \\begin{bmatrix}\n",
    "{1} & {0} \\\\\n",
    "{3} & {2} \\\\\n",
    "{1} & {4} \\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "$\\mathbf{B}_{(3,2)} = \\begin{bmatrix}\n",
    "{2} & {1} & {2} \\\\\n",
    "{1} & {2} & {3} \\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "$\\mathbf{C}_{(2,2)} = \\mathbf{A}_{(2,3)} . \\mathbf{B}_{(3,2)}$\n",
    "\n",
    "$\\mathbf{C}_{(2,2)} = \\begin{bmatrix}\n",
    "{2\\times1 + 3\\times1 + 1\\times2} & {0\\times2 + 2\\times1 + 4\\times2} \\\\\n",
    "{1\\times1 + 3\\times2 + 1\\times3} & {0\\times1 + 2\\times2 + 4\\times3} \\\\\n",
    "\\end{bmatrix} = \\begin{bmatrix}\n",
    "{2 + 3 + 2} & {0 + 2 + 8} \\\\\n",
    "{1 + 6 + 3} & {0 + 4 + 12} \\\\\n",
    "\\end{bmatrix}= \\begin{bmatrix}\n",
    "{7} & {10} \\\\\n",
    "{10} & {16} \\\\\n",
    "\\end{bmatrix}$\n",
    "\n",
    "This is an animation to shows how that is done step by step.\n",
    "\n",
    "![](images/Webp.net-gifmaker.gif)\n",
    "\n",
    "Now let's confirm it with TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [[1,0],\n",
    "     [3,2],\n",
    "     [1,4],\n",
    "    ]\n",
    "\n",
    "b = [[2,1,2],\n",
    "     [1,2,3],\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember from before, that mathematical shape of a matrix is opposite of TensorFlow shape of a tensor. So instead of rewriting our arrays, we will just use transpose you make rows into columns and columns into rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 10],\n",
       "       [10, 16]], dtype=int32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = tf.constant(a)\n",
    "b = tf.constant(b)\n",
    "\n",
    "c = tf.matmul(tf.matrix_transpose(a), tf.matrix_transpose(b))\n",
    "\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily there is also a easier way to to that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 10],\n",
       "       [10, 16]], dtype=int32)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = tf.matmul(a,b, transpose_a=True, transpose_b=True)\n",
    "\n",
    "c.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\n",
    "\n",
    "Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>"
   ]
  }
 ],
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================================================
FILE: notebooks/tf/3. Variables.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "        <td style=\"text-align:left;\"><div style=\"font-family: monospace; font-size: 2em; display: inline-block; width:60%\">3. Variables</div><img src=\"images/roshan.png\" style=\"width:30%; display: inline; text-align: left; float:right;\"></td>\n",
    "        <td></td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far all the tensors that we created were all constants where you can do operations that generate new tensors but you can never change the value of any tensor after creating it. To start doing \"stateful\" programming which keeps and updates values (or state) or tesnors you need to wrap your tensors in an instance of `ft.Varbiable()`.\n",
    "\n",
    "As usaul before we start, let's import TensorFlow and start an interactive session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version: 3.5.2\n",
      "TensorFlow Version: 1.7.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import sys\n",
    "\n",
    "print(\"Python Version:\",sys.version.split(\" \")[0])\n",
    "print(\"TensorFlow Version:\",tf.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Variables\n",
    "\n",
    "Let start a new interactive session and create some variables. To create a variables, your this function:\n",
    "\n",
    "`tf.Variable(initial_value=None, trainable=True, collections=None, validate_shape=True, caching_device=None, name=None, variable_def=None, dtype=None, expected_shape=None, import_scope=None, constraint=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'a:0' shape=(2, 2) dtype=float32_ref>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "\n",
    "a = tf.Variable(tf.ones((2,2)), name=\"a\")\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first parameter that we passed to when creating an instanse of `tf.Variable()` is `initial_value`. This can accept a tensor that has values or a tensor initilizer method. We will discuss some of these later in this tutorial.\n",
    "\n",
    "You can also use `ft.get_variable()` function to create a variable.\n",
    "\n",
    "`tf.get_variable(name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True, use_resource=None, custom_getter=None, constraint=None)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'b:0' shape=(2, 2) dtype=float32_ref>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = tf.get_variable(\"b\", [2,2])\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates a variable named `b` with shape `(2,2)`.\n",
    "\n",
    "To initlize the value of your variable, you could use one of the many inilization method available in TensorFlow.\n",
    "\n",
    "`tf.zeros_initializer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'c:0' shape=(2, 2) dtype=float32_ref>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = tf.get_variable(\"c\", [2,2], dtype=tf.float32, initializer=tf.zeros_initializer)\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to `tf.zeros_initializer` there is `tf.ones_initializer` which initializes your tensor with ones.\n",
    "\n",
    "There are also `tf.random_normal_initializer` and `random_uniform_initializer` that inialize your variables with a normal or uniform distribution. For truncated normal distribution, you can use `tf.truncated_normal_initializer` which will limit your normal distribution to 2 standard diviations from the mean.\n",
    "\n",
    "You can also initialize your variables with a constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'd:0' shape=(3,) dtype=int32_ref>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = tf.get_variable(\"d\", initializer=tf.constant([1,2,3]))\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize Variables\n",
    "\n",
    "Before you can use any of your variables, you should first run an operation that initializes them. To initialize all the variabes that that created already, you can use `tf.global_variables_initializer` to create the operation this you have to run that operation using your session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you initlized your variables, you can start executing this and getting their values. To do that, you can just call `eval()` method of the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually Initializing Variables\n",
    "\n",
    "Sometimes specially in an interactive environment, you would want to initialize some extra variables after you initialized all your variables using `tf.global_variables_initializer`. To do that, you can run one variable initializer which is an operation that can be retrieved for a single variable from the `initializer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = tf.get_variable(\"e\", initializer=tf.constant([2,2,2]))\n",
    "sess.run(e.initializer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you try to redecalre your variable with the same name, you will get an error message because TensorFlow does't know if you want to resue the same variable or you want a new one.\n",
    "\n",
    "To avoid that clarify that you mean to reuse the same variable and you just to reinitize the variable using "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Trying to share variable e/e, but specified dtype float32 and found dtype int32_ref.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-6f62305b28d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreuse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAUTO_REUSE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_or_scope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"e\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"e\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m   1295\u001b[0m       \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m       \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_getter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1297\u001b[0;31m       constraint=constraint)\n\u001b[0m\u001b[1;32m   1298\u001b[0m get_variable_or_local_docstring = (\n\u001b[1;32m   1299\u001b[0m     \"\"\"%s\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m   1091\u001b[0m           \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m           \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_getter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m           constraint=constraint)\n\u001b[0m\u001b[1;32m   1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m   def _get_partitioned_variable(self,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m    437\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m           \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m           constraint=constraint)\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m   def _get_partitioned_variable(\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_true_getter\u001b[0;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint)\u001b[0m\n\u001b[1;32m    406\u001b[0m           \u001b[0mtrainable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m           use_resource=use_resource, constraint=constraint)\n\u001b[0m\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcustom_getter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_get_single_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint)\u001b[0m\n\u001b[1;32m    756\u001b[0m         raise ValueError(\"Trying to share variable %s, but specified dtype %s\"\n\u001b[1;32m    757\u001b[0m                          \" and found dtype %s.\" % (name, dtype_str,\n\u001b[0;32m--> 758\u001b[0;31m                                                    found_type_str))\n\u001b[0m\u001b[1;32m    759\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mfound_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Trying to share variable e/e, but specified dtype float32 and found dtype int32_ref."
     ]
    }
   ],
   "source": [
    "with tf.variable_scope(reuse=tf.AUTO_REUSE, name_or_scope=\"e\"):\n",
    "    e = tf.get_variable(\"e\", initializer=tf.constant([2,2,2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'e/e:0' shape=(3,) dtype=int32_ref>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assigning Value to Variables\n",
    "\n",
    "So far variables are not much different that any constant tensor. Variables get interesting once you can change their values. To do that you can use `tf.assign()` function or the `assign()` method of a variable. These are operation and should be run using a your session for them to perform their assignment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4], dtype=int32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(d.assign([2,3,4]))\n",
    "d.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6], dtype=int32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.assign_add(d, [2,2,2]))\n",
    "d.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(tf.assign_sub(d, [3,3,3]))\n",
    "d.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variable Scrope\n",
    "\n",
    "You can group variables in a few way in TensorFlow and one these methods is Variable Scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'a:0' shape=(3,) dtype=float32_ref>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tf.name_scope(\"dense1\"):\n",
    "    a = tf.get_variable(\"a\", (3,))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'dense1_2/b:0' shape=(2, 2) dtype=float32_ref>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tf.name_scope(\"dense1\"):\n",
    "    b = tf.Variable(tf.ones((2,2)), name=\"b\")\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense1_3/add:0' shape=(2, 2) dtype=float32>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tf.name_scope(\"dense1\"):\n",
    "    c = tf.Variable(tf.ones((2,2)), name=\"c\")\n",
    "    w = c+1\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Variable dense1/d already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n\n  File \"<ipython-input-7-53e30bd6b3ba>\", line 2, in <module>\n    d = tf.get_variable(\"d\", (3,))\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-53e30bd6b3ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariable_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"dense1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m   1295\u001b[0m       \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1296\u001b[0m       \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_getter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1297\u001b[0;31m       constraint=constraint)\n\u001b[0m\u001b[1;32m   1298\u001b[0m get_variable_or_local_docstring = (\n\u001b[1;32m   1299\u001b[0m     \"\"\"%s\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m   1091\u001b[0m           \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1092\u001b[0m           \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_getter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_getter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1093\u001b[0;31m           constraint=constraint)\n\u001b[0m\u001b[1;32m   1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m   def _get_partitioned_variable(self,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36mget_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint)\u001b[0m\n\u001b[1;32m    437\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartitioner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartitioner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m           \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_resource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_resource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m           constraint=constraint)\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m   def _get_partitioned_variable(\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_true_getter\u001b[0;34m(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint)\u001b[0m\n\u001b[1;32m    406\u001b[0m           \u001b[0mtrainable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcollections\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m           \u001b[0mcaching_device\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcaching_device\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidate_shape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m           use_resource=use_resource, constraint=constraint)\n\u001b[0m\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcustom_getter\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/variable_scope.py\u001b[0m in \u001b[0;36m_get_single_variable\u001b[0;34m(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint)\u001b[0m\n\u001b[1;32m    745\u001b[0m                          \u001b[0;34m\"reuse=tf.AUTO_REUSE in VarScope? \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m                          \"Originally defined at:\\n\\n%s\" % (\n\u001b[0;32m--> 747\u001b[0;31m                              name, \"\".join(traceback.format_list(tb))))\n\u001b[0m\u001b[1;32m    748\u001b[0m       \u001b[0mfound_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_compatible_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfound_var\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Variable dense1/d already exists, disallowed. Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope? Originally defined at:\n\n  File \"<ipython-input-7-53e30bd6b3ba>\", line 2, in <module>\n    d = tf.get_variable(\"d\", (3,))\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2963, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py\", line 2903, in run_ast_nodes\n    if self.run_code(code, result):\n"
     ]
    }
   ],
   "source": [
    "with tf.variable_scope(\"dense1\"):\n",
    "    d = tf.get_variable(\"d\", (3,))\n",
    "    e = d + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense1_10/add:0' shape=(3,) dtype=float32>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with tf.variable_scope(\"dense1\", ):\n",
    "    f = tf.get_variable(\"f\", (3,))\n",
    "    g = tf.get_variable(\"g\", (3,))\n",
    "    h = f + g\n",
    "h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Match Behind It\n",
    "\n",
    "Since we are talking about variables we cannot escape the fact that we will use them in the next tutorial for creating a predective model. This means we will have to get a head start start with some basic concepts about calculus. Calculus is branch of math that studies change. It can study the change of a variable as it relates to another variable. So basically it studies the realtionship between two or more variables. There are two main studies in calculus:\n",
    "\n",
    "- Diffrentiation\n",
    "- Integration\n",
    "\n",
    "## Diffrentiation\n",
    "\n",
    "We will focus for now on diffrentiation because the we will use that to train neural network with an algorithm called \"Back Probagation\". Diffrentiation is the study of the rate of change or the slope.\n",
    "\n",
    "We will use numpy and matplotlib for illustration so let's import them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slope\n",
    "\n",
    "Slope is the mesure of change of a variable as another variables changes. In here we will see the change in $y$ as $x$ changes. The mathematical way of saying that is:\n",
    "\n",
    "$$\\frac{dy}{dx} = \\frac{\\Delta y}{\\Delta x}$$\n",
    "\n",
    "$\\Delta$ is the capital letter delta and it means the change of. So the change of $y$ as $x$ changes.\n",
    "\n",
    "- Slope is __positive__ if numbers are __increasing__.\n",
    "- Slope is __nagative__ if numbers are __decreasing__.\n",
    "- Slope is __zero__ if numbers are __not changing__.\n",
    "\n",
    "Let's look at three lines to show how slope works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UFm7+wetYIiIiP6GGV8Rv+8HjdB02m09X7uJPtzbiha5XEBejEsmPa+pXYOqgROKLxNBj5FwmLth67geJiIgEifbmIsDCzT+QlDKLbQfSebNva+7tUBvf2YYkv+pWjGdaciKtapflt5OW8vcPV5GVrV9mExER76nhlYg3aeE2eoycS3yRGKYOSuSa+volowtVpngcY/q25u52NRn5zUbue2s+R05keB1LREQinBpeiVhZ2Y6/f7iKxycuoVXtskxLTqRuxXivY4W82Ogo/pLUhL91bsK36/bRZWgam/cf8zqWiIhEMDW8EpGOnMjgvrfmM/KbjfRpV5MxfVtTpnic17HCSq+2NXm7X2v2HT1J0pBU0jbs8zqSiIhEKDW8EnE27z/GbUPT+GbdPp7t3IS/JjUhNlqlEAjt65RnenIi5eOL0Gf0PN6du9nrSCIiEoG0l5eIMnvDfpKGpLLnyEnG3tua3m1reh0p7NW8pARTBrXnqnrleXrqcp6ZvpzMrGyvY4mISARRwysRY9zcLfQePZfy8UWYnpxI+7rlvY4UMUoVjeX1u1vR/+rLeGv2Zu55cz6H0jWZTUREgkMNr4S9zKxsnpm+nKemLuOqeuWZMqg9tcqX8DpWxImOMp66+XJe6taUed//QOehqazfc9TrWCIiEgHU8EpYO5SewT1vzuet2Zu5/6ravH53K0oVjfU6VkS7PaEG4+5vw5ETGXQZmspXa/Z4HUlERMKcGl4JW+v3HKXz0FTmfr+fF7s15elbGhEdpR+TKAwSapVjWnIi1csW594x8xk963uc049UiIhIYKjhlbD09dq9dBmayuHjGYy/vy13JNTwOpLkUr1scSYNaMcvG1Xi2Q9W8uSUZZzK1GQ2EREpeGp4Jaw453hj1vf0fXMe1coUY/rgRBJqlfM6lpxBiSIxDOvZkgevr8uE+Vvp9fpc9h896XUsEREJM2p4JWycyszmySnL+OsHK7nh8kpMHtie6mWLex1LziEqynjsxga81r05S7YdJGlIKqt3HfY6loiIhBE1vBIW9h89Sa/Rc5kwfyuDr6vL8F4tKVEkxutYch6Smlfj/QfacSozm65D05i5crfXkUREJEyo4ZWQt3rXYZKGpLJk60Fe696cx3/VgChNTgtJzWqUYcbgDtSpGE//sQsY+tV6TWYTEZGLpoZXQtrMlbvpOjSNU5nZvP9AO5KaV/M6klykyqWL8v4D7bi1aVVe/HgNv3lvMScysryOJSIiIUzf+UpIcs4x/OuNvPjJaq6oVpqRvROoXLqo17GkgBSNjeZf3ZvToFI8L3+6lk370xnZuyUVS+n/sYiInD8d4ZWQcyIji0ffX8L/fbyaW66ownv926nZDUNmxuDr6zG8V0vW7DpC0pBUlm8/5HUsEREJQWp4JaTsOXKC7iPnMPW77Tz2y/r8u0cLisVFex1LAqhjk8pMGtgOA7oNT+O/S3d6HUlEREKMGl4JGcu3HyIpJZU1u44wvNeVPPiLephpclokaFy1NNMHd6Bx1dIkj1vEP2auJTtbk9lERCR/1PBKSPjv0p10G56GAZMGtqNjkypeR5Igq1CyCOPub0O3ltV57fN1DB6/iPRTmV7HEhGREKBJa1KoZWc7/vXFOv752Tpa1izL8F4tqVCyiNexxCNFYqJ5qVtTGlQqyd8/WsXm/emM6pNA1TLFvI4mIiKFmI7wSqF1/FQWD47/jn9+to6uV1Zn3P1t1OwKZsb9V1/GG3e3YvP+dDqlpLJoywGvY4mISCGmhlcKpZ2HjnP7iDQ+XL6Tp25uyMu3N6VIjCanyY+ua1iRqYPaUzwumu4j5zBl0TavI4mISCGVr4bXzMqY2SQzW21mq8ysXaCDSeRatOUAnVJS2bQvndF3J9D/6jqanJYPZvaSv0aXmtlUMyvjdaZAq1epJNOTE7ny0jI8+v4SXvhoNVmazCZBZGY1zOxLM1tpZivM7OE81jEz+5eZrffX55U5lmWZ2WL/ZUZw04tEjvwe4X0N+Ng51xBoBqwKXCSJZFMWbaP7yDkUi41myqD2XN+wkteRQslMoIlzrimwFnjS4zxBUbZEHGP7teGuNpcy/OsNPDB2AUdPajKbBE0m8JhzrhHQFkg2s0a51rkJqOe/9AeG5Vh23DnX3H/pFJTEIhHonA2vmZUGrgZGAzjnTjnnDgY6mESW7GzHCx+t5tH3l9CiRhmmJSdSv1JJr2OFFOfcp865053eHKC6l3mCKTY6iuc6N+GvSY35cs1eug5NY+sP6V7HkgjgnNvpnFvkv34E3wGh3L9xngS87XzmAGXMTKeaEQmi/BzhrQ3sBd40s+/M7HUzK5F7JTPrb2YLzGzB3r17CzyohK+jJzPpP3YBw7/eQI/WlzK2XxvKlYjzOlaouxf46EwLw7FezYw+7WrxVt/W7Dx0nE4ps5i7cb/XsSSCmFktoAUwN9eiasDWHLe38WNTXNRfi3PMrPMZtht29SoSbPlpeGOAK4FhzrkWwDHg97lXcs6NdM4lOOcSKlSoUMAxJVxt/SGdrkPT+HLNXv6a1Ji/d2lCXIzmUp6JmX1mZsvzuCTlWOdpfF+zvnum7YRzvXaoV57pgztQtkQcPV+fy4R5W7yOJBHAzOKBycAjzrnD5/HQms65BOAu4J9mVif3CuFcryLBkp/z8G4DtjnnTn9inUQeDa/I+Zq7cT8D311EZlY2b/VtTYd65b2OVOg5524423Izuwe4FfiFcy5iZ2/VLl+CqYMSeXD8d/x+yjLW7D7C0zdfTky0PkxJwTOzWHzN7rvOuSl5rLIdqJHjdnX/fTjnTv93o5l9he8I8YaABhaJQOf86++c2wVsNbMG/rt+AawMaCoJe+/N30Kv0XMpUzyWacmJanYLgJl1BH4HdHLORfwA1tLFYnnj7gTuTazNm6mb6DtmPoeOZ3gdS8KM+U4hMxpY5Zx79QyrzQD6+M/W0BY45JzbaWZlzayIfzvlgUS0fxUJiPz+0tqDwLtmFgdsBPoGLpKEs8ysbJ77cBVvpm7iqnrlSbnrSkoXi/U6VrhIAYoAM/2ncZvjnBvgbSRvxURH8adfN6J+pXj+OH05XYam8nqfBC6rEO91NAkfiUBvYJmZLfbf9xRwKYBzbjjwIXAzsB5I58d96OXACDPLxncA6gXnnBpekQDIV8PrnFsMJAQ4i4S5Q8czGDxuEd+u20ffxFr6irmAOefqep2hsOre+lJqly/BwHcX0XlIKkN6XslV9TQWUi6ec24WcNYThfuHFyXncX8acEWAoolIDuo2JCg27j1Kl6GpzNm4nxduu4Jnft1Yza4EVZvLLmF6ciJVShfjnjfn81baJiJ4mLOISERRxyEBN2vdPjoPSeVgegbv9GtD99aXeh1JIlSNcsWZPKg91zWoyDMzVvD0tOVkZGV7HUtERAJMDa8EjHOOt2dv4u4351GldDGmJyfS5rJLvI4lES6+SAwje7dk4LV1GDd3C71Hz+XAsVNexxIRkQBSwysBkZGVzR+mLedP01dwXYMKTB7UnhrlinsdSwSAqCjjiY4N+cedzVi05SBJQ1JZu/uI17FERCRA1PBKgTtw7BS9R8/l3blbGHhtHUb0TiC+SH5PCCISPF1aVGdC/7akn8ritqFpfLF6t9eRREQkANTwSoFat/sISUNSWbTlIP+4sxlPdGxIdNRZJzCLeOrKS8syY3AitcoXp99bCxj5zQZNZhMRCTNqeKXAfLF6N12GppF+KosJ/dvSpUV1ryOJ5EvVMsWY+EB7bm5Shb9/uJrHJy7lZGaW17FERKSA6HtmuWjOOUZ9u5HnP1pNoyqlGNUngaplinkdS+S8FIuLJuWuFtT/vCT/+Gwtm/YfY3ivllQoWcTraCIicpF0hFcuysnMLB6fuJS/f7iam5pUZuKAdmp2JWSZGQ/fUI+hPa9kxY5DJKXMYsWOQ17HEhGRi6SGVy7Y3iMnuWvUXCYv2sYjN9QjpceVFI/TlwYS+m6+ogqTBrTHAd2Gzebj5Tu9jiQiIhdBDa9ckJU7DtN5SCordhxiyF1X8sgN9YnS5DQJI02qlWZ6ciINKpdkwDuL+Pfn6zSZTUQkRKnhlfP28fJddB2WRrZzTBrQnluaVvE6kkhAVCxVlAn923Jbi2q8MnMtD01YzIkMTWYTEQk1+v5Z8s05R8oX63ll5lqa1yjDyN4tqViqqNexRAKqaGw0r9zRjPqVS/J/H69m8/5jjOydQOXSeu+LiIQKHeGVfDmRkcVDExbzysy1dGlRjQn926rZlYhhZgy4pg6jeiewYc9ROqXMYsnWg17HEhGRfFLDK+e069AJ7hgxmw+W7uCJjg159Y5mFI2N9jqWSNDd0KgSUwYlEhcTxR0jZjN98XavI4mISD6o4ZWzWrL1IJ1SZrFhz1FG9k5g4LV1MNPkNIlcDSqXZHpyIs1qlOHhCYt55dM1ZGdrMpuISGGmhlfOaPri7dwxYjZxMVFMHtSeXzaq5HUkkULhkvgivNOvDd1b1eDfX6xn4LsLOXYy0+tYIiJyBmp45Weysx2vfLqGhycsplmNMkxPTqRh5VJexxIpVOJionj+tit45teNmLlyN12HpbHtQLrXsUREJA9qeOUnjp3MZOC7C/n3F+vp3qoG7/RrwyXx+mlVkbyYGX0TazOmb2u2HzxOUkoqCzb94HUsERHJRQ2v/M+2A+l0HZbGzJW7+dOtjXj+tiuIi9FbRORcrq5fgWnJiZQqFkuPUXOYuGCr15FERCQHdTMCwIJNP5CUksr2g8d5s29r7u1QW5PTRM5DnQrxTBuUSJval/DbSUt57r8rydJkNhGRQkENrzBxwVZ6jJpDyaIxTB2UyDX1K3gdSSQklS4ey5i+rbinfS1Gffs99701n8MnMryOJSIS8dTwRrCsbMdz/13JbyctpU3tS5iWnEjdivFexxIJaTHRUfy5U2Oe69KEb9ft47ahaWzef8zrWCIiEU0Nb4Q6fCKD+96az6hvv+ee9rUY07cVZYrHeR1LJGz0bFOTsf3asO/oSZKGpJK2YZ/XkUREIpYa3gi0ef8xbhuaxrfr9vFclyb8uVNjYqL1VhApaO3qXMKM5A5UiC9Cn9HzeGfOZq8jiYhEJHU5ESZtwz6ShqSy7+hJxvZrQ882Nb2OJBLWLr2kOFMGtefq+hX4w7Tl/Gn6cjKysr2OJSISUdTwRpB35mymz+h5VIgvwvTkRNrVucTrSCIRoWTRWEb1SeCBqy/j7dmbuefNeRxMP+V1LBGRiKGGNwJkZGXzp+nL+cO05VxdvwJTBrWn5iUlvI4lElGio4wnb76cl29vxvzvD9B5SCrr9xz1OpaISETId8NrZtFm9p2ZfRDIQFKwDqaf4p435/H27M08cPVljOqTQMmisV7HkgAys8fMzJlZea+zyM91a1md8f3bcvRkJl2GpvLVmj1eR5KLYGY1zOxLM1tpZivM7OE81jEz+5eZrTezpWZ2ZY5ld5vZOv/l7uCmF4kc53OE92FgVaCCSMFbv+conYekMv/7A7x8ezOevPlyoqP0YxLhzMxqADcCW7zOImfWsmZZpg/uQI2yxbl3zHxGz/oe5/QjFSEqE3jMOdcIaAskm1mjXOvcBNTzX/oDwwDMrBzwDNAGaA08Y2ZlgxVcJJLE5GclM6sO3AI8Bzwa0ERSIL5eu5fB4xZRJCaK8f3b0LJmOa8jSXD8A/gdMN3rIHJ21coUY9LAdjz63hKe/WAla3cd4dnOTfRz3iHGObcT2Om/fsTMVgHVgJU5VksC3na+TzVzzKyMmVUBrgVmOud+ADCzmUBHYPyF5vnLf1awcsfhC324SKHSqGopnvl14wLZVn7/sv4T3070jFOLzay/mS0wswV79+4tkHBy/pxzjJ71PX3fnEf1ssWZPriDmt0IYWZJwHbn3JJ8rKt6LQSKx8UwtOeVPHR9Xd5bsJVer89l/9GTXseSC2RmtYAWwNxci6oBW3Pc3ua/70z3596u6lXkIp3zCK+Z3Qrscc4tNLNrz7Sec24kMBIgISFB38154FRmNn+ctpz3FmzlV40r8eodzSlRJF8H8SVEmNlnQOU8Fj0NPIVvOMM5qV4Lj6go49EbG1CvUkken7iETimpjL4ngYbvV4vsAAAYmklEQVSVS3kdTc6DmcUDk4FHnHMFeoj1fOq1oI6GiYSb/BzhTQQ6mdkmYAJwvZm9E9BUct72Hz1Jr9fn8t6CrTx0fV2G9WypZjcMOeducM41yX0BNgK1gSX+Wq0OLDKzvJpjKYR+3awqEwe0IzM7m65D05i5crfXkSSfzCwWX7P7rnNuSh6rbAdq5Lhd3X/fme4XkQJ2zobXOfekc666c64W0B34wjnXK+DJJN9W7zpMp5RUlmw7yL96tODRGxsQpclpEcU5t8w5V9E5V8tfq9uAK51zuzyOJuehafUyzBjcgboV4+k/dgFDv1qvyWyFnJkZMBpY5Zx79QyrzQD6+M/W0BY45B/7+wlwo5mV9U9Wu9F/n4gUMB0CDHEzV+7mkQnfEV80hokD2tG0ehmvI4nIRahUqijvPdCOJyYv5cWP17B21xFe6NqUorHRXkeTvCUCvYFlZrbYf99TwKUAzrnhwIfAzcB6IB3o61/2g5k9C8z3P+6vpyewiUjBOq+G1zn3FfBVQJLIeXHOMezrDbz0yRqaVivNyD4JVCpV1OtYUkj4j/JKiCoaG80/72xO/UoleemTNWzan87I3i2pqBovdJxzs4CzfqXmPztD8hmWvQG8EYBoIpKDzn8Tgk5kZPGb9xbz4sdr+HXTqrz3QDs1uyJhxsxIvq4uI3q3ZO3uI3RKSWXZtkNexxIRCUlqeEPMnsMn6D5yDtMW7+C3v2rAa92b66tOkTD2q8aVmTSgPdFRxu0j0vjv0p1eRxIRCTlqeEPIsm2H6JSSytrdRxjeqyXJ19XFN19CRMJZo6qlmD44kSZVS5M8bhH/mLmW7GxNZhMRyS81vCHiv0t3cvuINKKjjEkD2tOxic42JRJJyscX4d3723B7y+q89vk6Bo9fRPqpTK9jiYiEBJ2loZDLzna89vk6Xvt8HQk1yzK8d0vKxxfxOpaIeKBITDQvdmtKg8ol+fuHq9i8P51RfRKoWqaY19FERAo1HeEtxNJPZTJ4/CJe+3wdt7eszrv3t1GzKxLhzIz7rrqM0fe0Ysv+dDqlpLJoywGvY4mIFGpqeAupHQePc/vw2Xy8fBd/uOVyXuzWlCIxmpwmIj7XNajI1OT2lCgSTfeRc5iyaJvXkURECi01vIXQoi0H6JSSypb96Yy+uxX3XXWZJqeJyM/UrViSaYMSSahZlkffX8ILH60mS5PZRER+Rg1vITNl0Ta6j5xDiSLRTBnUnusaVvQ6kogUYmVLxPHWva3p1fZShn+9gQfGLuDoSU1mExHJSQ1vIZGV7Xjho9U8+v4SWl5almmDEqlXqaTXsUQkBMRGR/G3zlfwbFJjvlyzl65D09j6Q7rXsURECg01vIXA0ZOZPDB2AcO/3kDPNpfydr/WlC0R53UsEQkxvdvV4u17W7Pr8Ak6pcxi7sb9XkcSESkU1PB6bOsP6XQdmsaXa/bybFJjnutyBbHR+t8iIhcmsW55piUnUrZEHD1fn8v4eVu8jiQi4jl1Vh6au3E/nVJmsfPQcd7q25re7Wp5HUlEwkDt8iWYOiiR9nXL8+SUZfx5xgoys7K9jiUi4hk1vB6ZMG8LPV+fS9kScUwf3IEO9cp7HUlEwkjpYrG8cXcC/TrUZkzaJvqOmc+h9AyvY4mIeEINb5BlZmXzl/+s4PdTltG+bnmmDkqkdvkSXscSkTAUEx3FH29txItdmzJn4366DE1l496jXscSEQk6NbxBdCg9g75j5vNm6ib6dajNG3cnULpYrNexRCTM3dGqBuPub8vB4xl0HpLKt+v2eh1JRCSo1PAGyca9R+kyNJU5G/fzf12v4I+3NiJGk9NEJEha1SrH9OREqpYpxj1vzuettE04px+pEJHIoI4rCL5dt5fOQ1I5eDyDd+9ry52tLvU6kohEoBrlijNpYHuua1CRZ2as4KmpyzmVqclsIhL+1PAGkHOOt9I2cc+b86lSuhjTkxNpXbuc17FEJILFF4lhZO+WDLq2DuPnbaH36Ln8cOyU17FERAJKDW+AZGRl8/S05TwzYwXXNajI5EHtqVGuuNexRESIijJ+17Eh/7yzOd9tPUjSkFms3X3E61giIgGjhjcADhw7Re/Rcxk3dwsDr63DyN4tiS8S43UsEZGf6NyiGu/1b8uJjGxuG5rG56t2ex1JRCQg1PAWsLW7j5A0JJVFWw7yjzub8UTHhkRFmdexRETy1OLSsswYnEit8sW57+0FjPh6gyaziUjYUcNbgD5ftZvbhqZxPCOL9/q3pUuL6l5HEhE5pyqlizHxgfbcfEUVnv9oNY9NXMKJjCyvY4mIFBh9z14AnHOM/GYjL3y8msZVSzGqTwJVShfzOpaISL4Vi4smpUcLGlQqyasz17Jp3zGG925JxZJFvY4mInLRdIT3Ip3MzOLxiUt5/qPV3NykChMfaK9mV0RCkpnx0C/qMbTnlazceZjOKaks337I61giIhdNDe9F2HvkJD1GzmHyom385ob6pNzVgmJx0V7HEhG5KDdfUYVJA9rjgNuHz+ajZTu9jiQiclHU8F6gFTsOkZQyi5U7DzO055U8fEM9zDQ5TUTCQ5NqpZk+OJGGVUoy8N1F/OvzdZrMJiIhSw3vBfh4+U66DZuNAyYN8E30EBEJNxVLFmX8/W25rUU1Xp25lgfHf8fxU5rMJiKh55wNr5nVMLMvzWylma0ws4eDEawwcs7xr8/XMeCdRTSoXJLpyYk0qVba61gi/2NmD5rZan+tvuh1Hgl9RWOjeeWOZvz+pob8d9lO7hgxm12HTngdq9AwszfMbI+ZLT/D8rJmNtXMlprZPDNrkmPZJjNbZmaLzWxB8FKLRJ78HOHNBB5zzjUC2gLJZtYosLEKn+Onsnhw/He8OnMtXVpUY0L/tlQspdnLUniY2XVAEtDMOdcYeNnjSBImzIwB19RhVO8ENu49SqeUWSzeetDrWIXFGKDjWZY/BSx2zjUF+gCv5Vp+nXOuuXMuIUD5RIR8NLzOuZ3OuUX+60eAVUC1QAcrTHYdOsGdI2fz32U7eaJjQ169oxlFYzU5TQqdgcALzrmTAM65PR7nkTBzQ6NKTBmUSFxMFHeMmM30xdu9juQ559w3wA9nWaUR8IV/3dVALTOrFIxsIvKj8xrDa2a1gBbA3DyW9TezBWa2YO/evQWTrhBYvPUgnVJmsWHPUUb2TmDgtXU0OU0Kq/rAVWY218y+NrNWZ1oxXOtVAq9B5ZLMGNyB5jXK8PCExbz0yWqyszWZ7SyWALcBmFlroCZw+leJHPCpmS00s/5n2oDqVeTi5bvhNbN4YDLwiHPucO7lzrmRzrkE51xChQoVCjKjZ6Yv3s6dI2YTFxPFlEGJ/LKRPpSLt8zsMzNbnsclCd8PyZTDN/Tot8D7doZPZ+FYrxI85UrE8U6/NnRvVYMhX27ggXcWcuxkptexCqsXgDJmthh4EPgOOD3zr4Nz7krgJnzDBa/OawOqV5GLl69fWjOzWHzN7rvOuSmBjeS97GzHqzPXkvLlelrXLsewnldySXwRr2OJ4Jy74UzLzGwgMMX5zh01z8yygfKADglJgYuLieL5266gQeWSPPvBSroOS+P1uxOoXra419EKFf8Bor4A/g+g3wMb/cu2+/+7x8ymAq2BbzyKKhLW8nOWBgNGA6ucc68GPpK3jp3MZMA7C0n5cj3dW9XgnX5t1OxKqJgGXAdgZvWBOGCfp4kkrJkZfRNrM6Zva7YfPE5SSirzN51tOGvkMbMyZhbnv3kf8I1z7rCZlTCzkv51SgA3Anme6UFELl5+hjQkAr2B6/2nTllsZjcHOJcnth1Ip+uwND5btZs/3dqI52+7grgYnapYQsYbwGX+0yNNAO52+qUACYKr61dgWnIipYrFcteoObw/f6vXkYLGzMYDs4EGZrbNzPqZ2QAzG+Bf5XJguZmtwTd04fSpPSsBs8xsCTAP+K9z7uNg5xeJFOcc0uCcmwWE/Syt+Zt+YMDYhZzKyubNvq25pr7GSUlocc6dAnp5nUMiU50K8UwblEjyuEX8bvJS1u4+wpM3X050VHjvPpxzPc6xfDa+CaW5798INAtULhH5KR2+BN5fsJW7Rs2hVLFYpiUnqtkVEbkApYvHMqZvK+5pX4vXZ33PvWPmc/hEhtexREQiu+HNynb87YOV/G7SUtrUvoRpgxKpUyHe61giIiErJjqKP3dqzN+7XEHq+n10GZLKpn3HvI4lIhEuYhvewycy6PfWfF6f9T33tK/FmL6tKF081utYIiJh4a42lzK2Xxv2HztF0pBU0tZr/qSIeCciG95N+45x29A0Zq3bx3NdmvDnTo2JiY7IfwoRkYBpV+cSZiR3oGLJIvR+Yx5j52z2OpKIRKiI6/LS1u+j89BU9h09ydh+bejZpqbXkUREwtallxRnyqD2XFO/An+ctpw/TltORla217FEJMJEVMM7ds5mer8xjwrxRZiR3IF2dS7xOpKISNgrWTSWUX0SeODqyxg7ZzN3vzGPg+mnvI4lIhEkIhrejKzs/x1ZuKZ+BaYMas+ll+jXgEREgiU6ynjy5st55fZmLNh0gKQhqazfc8TrWCISIcK+4T2Yfoq7/WPHHrj6Mkb1SaBkUU1OExHxQteW1Rnfvy3HTmbSZUgaX67Z43UkEYkAYd3wrt9zhM5DUlmw6QAv394sIk6CLiJS2LWsWZbpgztQo1xx+o2Zz+vfbkQ/CigigRS2De9Xa/bQZUgaR09mMr5/G7q1rO51JBER8atWphiTBrbjxkaV+dt/V/HE5KWczMzyOpaIhKmwa3idc4z2/8JPjXLFmT64Ay1rlvM6loiI5FI8LoahPa/koevr8v6CbfR6fS77jp70OpaIhKGwanhPZWbz+8nLePaDldzYqDKTBrajWpliXscSEZEziIoyHr2xAf/u0YKl2w6RlJLKqp2HvY4lImEmbBrefUdP0vP1Oby3YCsPXV+XoT2vpHhcjNexREQkH37drCoTB7QjMzubrsPS+HTFLq8jiUgYCYuGd9XOwySlpLJ02yH+3aMFj97YgChNThMRCSlNq5dhxuAO1KsYT/+xCxny5XpNZhORAhHyDe+nK3bRdVgamdnZTBzQjl83q+p1JBERuUCVShXlvQfakdS8Ki99soZH3lvMiQxNZhORixOy3/k75xj61QZe/nQNTauVZmSfBCqVKup1LBERuUhFY6P5553NqV+pJC99soZN+9MZ1bslFfU3XkQuUEge4T2RkcVv3lvMS5+soVOzqrz3QDs1uyIiYcTMSL6uLiN6t2Td7iN0Skll2bZDXscSkRAVcg3vnsMnuHPkHKYt3sFvf9WAf97ZnKKx0V7HEhGRAPhV48pMHtie6Cjj9hFpfLB0h9eRRCQEhVTDu2zbITqlpLJu9xFG9G5J8nV1MdPkNBGRcHZ5lVJMH5zIFdVKM3jcd7w6cy3Z2ZrMJiL5FzIN7wdLd3D7iDSio4zJA9vzq8aVvY4kIiJBUj6+CO/c14bbW1bnX5+vI3ncItJPZXodS0RCRKFveLOzHa/OXMvgcd9xRbXSTB+cyOVVSnkdS0REgqxITDQvdmvKH265nE9W7KLbsNlsP3jc61giEgIKdcObfiqT5HGL+Nfn67i9ZXXeua8N5eOLeB1LREQ8Ymbcd9VljL6nFVt/SCcpJZWFmw94HUtECrlC2/DuOHicbsNm88mKXfzhlst5sVtTisRocpqIiMB1DSoyNbk9JYpE02PkHCYv3OZ1JBEpxAplw7tw8wE6paSy9Yd0Rt/TivuuukyT00RE5CfqVizJtEGJJNQqy2MTl/D8R6vI0mQ2EclDoWt4pyzaRo+RcyhRJJqpye25rkFFryOJiEghVbZEHG/d25rebWsy4uuN9H97AUdOZHgdS0QKmULT8GZlO174aDWPvr+EhFplmZ6cSN2KJb2OJSIihVxsdBTPdm7Cs0mN+WrtXroOS2PL/nSvY4lIIVIoGt4jJzLo//YChn+9gd5ta/LWva0pUzzO61giIhJCererxdh7W7P78EmShsxizsb9XkcSkULC84Z3y/50ug5L46u1e3k2qTHPdm5CbLTnsUREJAS1r1ue6cmJlCsRR6/X5zJu7havI4lIIZCvztLMOprZGjNbb2a/L6gnn7NxP0lDZrH78EnG3tua3u1qFdSmRSKOmTU3szlmttjMFphZa68ziXihVvkSTE1OJLFueZ6auow/z1hBZlZ2QJ7LzN4wsz1mtvwMy8ua2VQzW2pm88ysSY5lAdm3isjPnbPhNbNoYAhwE9AI6GFmjS72icfP20Kv1+dSrkQc05MTaV+3/MVuUiTSvQj8xTnXHPiT/7ZIRCpVNJY37mnF/VfVZkzaJvqOmc+h9IBMZhsDdDzL8qeAxc65pkAf4DUI3L5VRPKWnyO8rYH1zrmNzrlTwAQg6UKfMDMrmz/PWMGTU5bRoV55piYnUqt8iQvdnIj8yAGnf4awNLDDwywinouOMp6+pREvdmvKnI376TI0lY17jxboczjnvgF+OMsqjYAv/OuuBmqZWSUKeN8qImeXn4a3GrA1x+1t/vt+wsz6+79GXbB3794zbixtw37GpG3i/qtqM/ruVpQqGnveoUUkT48AL5nZVuBl4MkzrZjfehUJB3ck1GDc/W05dDyD5z9aHeynXwLcBuAfZlQTqE4+963+x6leRS5STEFtyDk3EhgJkJCQcMYzf19dvwL/GdyBK6qXLqinFokYZvYZUDmPRU8DvwB+45ybbGZ3AKOBG/LaTn7rVSRctKpVjmnJiZQoUmC7vfx6AXjNzBYDy4DvgKzz2YDqVeTi5afytwM1ctyu7r/vgqnZFbkwzrk8G1gAM3sbeNh/cyLwelBCiYSIGuWKB/05nXOHgb4A5vvJ0O+BjUAxCnjfKiJnlp8hDfOBemZW28zigO7AjMDGEpELsAO4xn/9emCdh1lEBDCzMv59J8B9wDf+Jlj7VpEgOucRXudcppkNBj4BooE3nHMrAp5MRM7X/fi+Oo0BTgD9Pc4jEvbMbDxwLVDezLYBzwCxAM654cDlwFtm5oAVQD//Mu1bRYIoX4OZnHMfAh8GOIuIXATn3Cygpdc5RCKJc67HOZbPBuqfYZn2rSJBop80ExEREZGwpoZXRERERMKaOVfwZzgxs73A5nOsVh7YV+BPHjihlheUOVjyk7mmc65CMMKcrzCtVwi9zKGWF8I3s+o1+JQ58EItLxRgvQak4c0PM1vgnEvw5MkvQKjlBWUOllDMfL5C8TWGWuZQywvKXFiF4mtU5sALtbxQsJk1pEFEREREwpoaXhEREREJa142vCM9fO4LEWp5QZmDJRQzn69QfI2hljnU8oIyF1ah+BqVOfBCLS8UYGbPxvCKiIiIiASDhjSIiIiISFgLesNrZh3NbI2ZrTez3wf7+c+Xmb1hZnvMbLnXWfLLzGqY2ZdmttLMVpjZw15nOhszK2pm88xsiT/vX7zOlF9mFm1m35nZB15nCYRQq1cIvZoNtXqF0K3ZcK9XCL2aVb0GXqjWKxRszQa14TWzaGAIcBPQCOhhZo2CmeECjAE6eh3iPGUCjznnGgFtgeRC/u98ErjeOdcMaA50NLO2HmfKr4eBVV6HCIQQrVcIvZoNtXqF0K3ZsK1XCNmaHYPqNdBCtV6hAGs22Ed4WwPrnXMbnXOngAlAUpAznBfn3DfAD17nOB/OuZ3OuUX+60fwvVmqeZvqzJzPUf/NWP+l0A8uN7PqwC3A615nCZCQq1cIvZoNtXqF0KzZCKhXCMGaVb0GXijWKxR8zQa74a0GbM1xexuF/I0S6sysFtACmOttkrPzf22xGNgDzHTOFeq8fv8Efgdkex0kQFSvQRYq9QohWbPhXq+gmg0q1WvAFWjNatJaGDOzeGAy8Ihz7rDXec7GOZflnGsOVAdam1kTrzOdjZndCuxxzi30OouEh1CqVwitmlW9SkFTvQZWIGo22A3vdqBGjtvV/fdJATOzWHzF+K5zborXefLLOXcQ+JLCP6YrEehkZpvwfW14vZm9422kAqd6DZJQrVcImZqNhHoF1WxQqF6DosBrNtgN73ygnpnVNrM4oDswI8gZwp6ZGTAaWOWce9XrPOdiZhXMrIz/ejHgl8Bqb1OdnXPuSedcdedcLXzv4y+cc708jlXQVK9BEGr1CqFXsxFSr6CaDTjVa3AEomaD2vA65zKBwcAn+AZ6v++cWxHMDOfLzMYDs4EGZrbNzPp5nSkfEoHe+D4RLfZfbvY61FlUAb40s6X4/mDPdM6F7WmDQkUo1iuEZM2GWr2CarZQCsWaVb0GheoV/dKaiIiIiIQ5TVoTERERkbCmhldEREREwpoaXhEREREJa2p4RURERCSsqeEVERERkbCmhldEREREwpoaXhEREREJa2p4RURERCSs/T/+XBl4dF7cQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "fig = plt.figure(figsize=(12,3))\n",
    "axarr = fig.subplots(1,3)\n",
    "\n",
    "axarr[0].plot(x, x*2)\n",
    "axarr[0].set_title(\"Positive Slope\")\n",
    "axarr[1].plot(x, x*-2)\n",
    "axarr[1].set_title(\"Negative Slope\")\n",
    "axarr[2].plot(x, x*0 + 2)\n",
    "axarr[2].set_title(\"Zero Slope\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Diffrentiation\n",
    "\n",
    "Consider this situation where you are driving at a constant speed of 10 $km/h$.\n",
    "\n",
    "We can represent this in a mathematical way with a simple function that looks like this:\n",
    "\n",
    "$Distance = 10 \\times Time$\n",
    "\n",
    "Or to make it more abstract we can call $Distance$ $y$ because it will be represented on the Y axis and call $Time$ $x$ because it will be represented on the X axis. So our function for this relationship will look like this:\n",
    "\n",
    "$y = 10x$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "speed = 10\n",
    "time = np.arange(0,9)\n",
    "distance = speed * time\n",
    "\n",
    "plt.xlabel(\"Time $Hours$\")\n",
    "plt.ylabel(\"Distance $km$\")\n",
    "plt.grid()\n",
    "plt.plot(time, distance);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After 1 hour you have travelled 10 $km$ and after 4 hours would have travelled 40 $km$. This realtionship is called linear because it can be represented by a straight line. With linear relationships we can measure the slope using any two points $(x_1,y_1)$ and $(x_2,y_2)$. If we get these two points are 1 hour and 4 hours we get these two points $(1,10)$ and $(4,40)$. To measure the slope (represented by the letter $m$) now we use this function:\n",
    "\n",
    "$$m = \\frac{y_2 - y_1}{x_2 - x_1}$$\n",
    "\n",
    "If we substitute our points we can measure the slope.\n",
    "\n",
    "$$m = \\frac{40 - 10}{4 - 1} = \\frac{30}{3} = 10$$\n",
    "\n",
    "There is nothing interesting about this finding! We already knew the speed was 10 $km/h$. This is a linear function and most of calculus is more interested in non-linear functions. So let's see how does that work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Diffrentiation\n",
    "\n",
    "Consider a situation where you are not travelling at constant speed but perhapse your speed in increasing over time. So in the begenning you are starting at a low speed and over time your speed increases. We call this acceleration and it is measured in $m/s^2$ but for a unit that we can relate to I'll use $km/h^2$. Let's illustrate this and it would make more sense.\n",
    "\n",
    "We can represent this in a mathematical way with a simple function that looks like this:\n",
    "\n",
    "$Distance = Acceleration * Time^2$\n",
    "\n",
    "Again to abstract this function we can write it like this:\n",
    "\n",
    "$y = ax^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acceleration = 2\n",
    "time = np.arange(0,9)\n",
    "distance = acceleration * time**2\n",
    "\n",
    "plt.xlabel(\"Time $Hours$\")\n",
    "plt.ylabel(\"Distance $km$\")\n",
    "plt.grid()\n",
    "plt.plot(time, distance);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's look at the distance over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   2,   8,  18,  32,  50,  72,  98, 128])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice that we travelled 2 $km$ in the first hour and 8 $km$ after two hours making the distance we travelled for the second hour 6 $km$. This means our speed is changing over time. To measure the slope for this, we will need to specify at what time do we want the speed because it is changing over time.\n",
    "\n",
    "To get the slope we derive another function that measure the slope of this function. This function is called __derevative__ and has the notation $f'$ or $\\frac{dy}{dx}$\n",
    "\n",
    "There is no standard mthematical way to come up with the the derevative function. It depends of the function type. Let's look a polipomial function and see how to get the derevative of that function:\n",
    "\n",
    "$f(x) = ax^n$\n",
    "\n",
    "to detive a polinomial function we use this rule:\n",
    "\n",
    "$f'(x)=n \\times ax^{n-1}$\n",
    "\n",
    "to apply this to our function from before:\n",
    "\n",
    "$y = 2x^2$\n",
    "\n",
    "$y' = 2 \\times 2x^{2-1} = 4x$\n",
    "\n",
    "to to get the speed at any point in time, we can use the derevative function. So the speed after 3 hours is:\n",
    "\n",
    "$y' = 4 \\times 3 = 12$\n",
    "\n",
    "Now let's visualize both of these functions to see how does they ralate to each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acceleration = 2\n",
    "time = np.arange(0,9)\n",
    "distance = acceleration * time**2\n",
    "speed = 4 * time\n",
    "\n",
    "plt.xlabel(\"Time $Hours$\")\n",
    "plt.ylabel(\"Distance $km$\")\n",
    "plt.grid()\n",
    "l1 = plt.plot(time, distance, label=\"Distance\")\n",
    "plt.twinx()\n",
    "plt.ylabel(\"Speed $km/h$\")\n",
    "l2 = plt.plot(time, speed, \"r\", label=\"Speed\")\n",
    "plt.legend(l1+l2, (\"Distance\", \"Speed\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since you know the rule now, let's try to apply it to out first example\n",
    "\n",
    "$y = 10x = 10x^1$\n",
    "\n",
    "$y' = 1 \\times 10x^{1-1} = 10x^0 = 10 \\times 1 = 10$\n",
    "\n",
    "Anything to the power 0 is equal to 1. It might be weird, but that's just math!\n",
    "\n",
    "### Complex Ploynominal Functions\n",
    "\n",
    "The same rule applies for any polynominal function. Let's try some examples:\n",
    "\n",
    "$f(x) = 2x^2 + 4x + 10$\n",
    "\n",
    "$f'(x) = 2 \\times 2x^{2-1} + 1 \\times 4x{1-1} + 0 = 4x + 4$\n",
    "\n",
    "Notice that we can apply the rule to each part of the function seperatly. Any part of the functiona that's not muliplied by x will give you a derevative of 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trigonometry\n",
    "\n",
    "Derevatives of trigonometric functions can be derived to some level straight forward out of a lookup table. This is an incomlete table of functions and their derevatives:\n",
    "\n",
    "| Function $f(x)$ | Deravative  $f'(x)$|\n",
    "| --------------- | ------------------ |\n",
    "| $sin(x)$        | $cos(x)$           |\n",
    "| $cos(x)$        | $-sin(x)$          |\n",
    "| $tan(x)$        | $sec^2(x)$         |\n",
    "| $cot(x)$        | $-csc^2(x)$        |\n",
    "| $sec(x)$        | $sec(x)tan(x)$     |\n",
    "| $csc(x)$        | $-csc(x)cost(x)$   |\n",
    "\n",
    "You don't have to memorize this. This is available everywhere!\n",
    "\n",
    "Just for fun let's visualize one of them as see if it makes sense.\n",
    "\n",
    "$f(x) = sin(x)$\n",
    "\n",
    "$f'(x) = cos(x)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fe+1HvYPWHkrnxIVrPDK8o9fMH96IzaZ4dEQnjl+4xrpDVsJvGUaOUAYAqSJyQkSuAwuBKVWsPxv42CPK3I1S+qI/ffoHJVlyC4uZv/0UY7s3p2Okewvf1ZX7h7Qn2N+H/9tsjVK8iuPHYdEiePhhCA83Wk3VPP20vqF66aXvFokIr205TofIYMb1iDFQXPWM6xFDh8hgXt18zIr4cmCkQWkJnC33Os2x7EcopdoC7YHN5RYHKqV2KaV2KKUS3CfTTYwapUt8//Wv3xWOXPDNGXILS3h4mHeESFZF0yB/5g5qx5qD6RzLtEYpXsNf/6rzTR5/3Ggl1dO5sy4a+frrcFE3stqckkVy+lUevq2jx8sM1RYfm+KRYZ1Iyci1Ir4cGJzpVGPuBD4TkfKxtm1F5JxSqgOwWSl1UESO37ihUuoh4CGA6OhoEhMTnRKQl5fn9LaVETVhAnHPP8/BP/+Z9FsG8a8vC+gebuNS6j68pVp2VefdzSb42+D3n2zjwV4BnhXmRtzxWXsCv8uXueXtt8kcNYqjR47AkSO12t6I8w4aPpwB8+dz8te/5tQ99/CnHYWEByqaXU0lMfFHl7PLqes5N7ULkY0ULyzfg09moNe66Mrj1s9ZRAx5AAOB9eVePwU8Vcm6e4FBVezrPWBGdceMj48XZ9myZYvT21ZKcbFIu3YiQ4bIhztOSdsnVslXxy64/jh1oLrzfmbZQen0m9WSmVPgGUEewC2ftSf47W9FlBI5csSpzQ077/HjRaKiZFvSWWn7xCp5f9tJjx3aFee84JvTXnntVoYz5wzskhr8rhvp8toJdFZKtVdK+aNHIT+K1lJKxQLNgO3lljVTSgU4nkcAg4HDHlHtSnx94b//G776iq3vr6J3qyYM6ujlfu8buHdwe0rswvvbTxstpWGTm6snuKdO1RPeZuKXv4SsLFL+8gbhwf7c0b+10YpqxdS+LQkP9ufdr61KxIYZFBEpAR4F1gPJwKcikqSUek4pVT5q605gocNKltEN2KWU2g9sAV4UEfMZFID77uN646ZM2bSAn93mvVEtldEuIpiR3aL56JvTFBZblYgN4623dHsEb47sqowRI7jevSeDV37A3QNaE+jnY7SiWhHo58PdN7fh85QsTl28ZrQcQzE0D0VE1ohIFxHpKCIvOJY9IyIryq3zexF58obttolITxHp7fj7tqe1u4yQEFYPnsKYY9sZ5WfORMH7h7Tncn4xS/acM1pKw6S0FF59FYYO1eHoZkMpVo+eTZeLZ7gvr3bzPt7CnFva4mtTvNfAq3F7VwpqA+RIRi5/6jAS8fHF943XjZbjFDe3D6NHy8a8/dUJ7FaSl+dZu1aXWvnFL4xW4hR5RSX8IagnV5pF0vT1V42W4xRRjQOZ2KsFn+1OI7ew2Gg5hmEZFIN5f/spcpqGUzJ1Grz3Hlwz35BZKcX9Q9pz/MI1vjhWdcE/Czfw2mvQogUkmC96HuCzXWe5UqLIf+hh+PxzOGTOdtP3Dm5HXlEJn+5KM1qKYVgGxUByCrSbaErvFgQ89gvIyYGPzZm7OaFnC6JCA6wGXJ7m2DHdjfGnP9Utfk2G3S7M336avm2a0uJ/HgV/f/j3v42W5RS9WjUlvm0z5m871WDLsVgGxUAW7TpLQXEpcwe1033ne/XSd5smzLr197Vx502t+eLoBc5eslqkeox//UsbkoceMlqJU3xx9AInL17j3sHtITJSl7Z//33IyzNamlPcN7g9Zy7lsyWlYSY6WgbFIOx24YMdp+nftpkuAqkU/PznsG8ffPON0fKcYtaANijgk51nq13XwgVcuwbvvqt/hJt7ZxHF6vhwx2kiQwO+byL38MNw9WqV7R28mdHdo4kICWDhzjNGSzEEy6AYxNbUi5zOzucng9p9v/DuuyE0VN91mpCWTRsxvGsUn+w6S7HVJ8L9fPSRdpM+8ojRSpwiPaeALUeymNm/FX4+jp+iwYOhe3d44w1jxTmJn4+Nmf1bsTkli/ScmrU5rk9YBsUgPtl5hrBgf8Z0j/5+YUgIzJ0Ln3xSbTc7b+Wum9twIbeITYczq1/Zom688YburTNokNFKnGLRrjTsAnfeVK6BllJ6lLJ7N+zcaZy4OnDnTW2wC3y6s+FNzlsGxQAu5hWx8XAm0/q2JMD3hiSuhx/WFVjffdcYcXVkWNcoWjQJ5KNvGuaQ32Ps2QN798KDD+ofYZNRahc+2XmWoZ0jaB0W9MM358yBoCDTjlLahAcxtHMEn+w80+Am5y2DYgBL95yjuFSYdVMFJSbi4vSw/513TDk572NT3DmgDV+lXmzwWcNu5e23ISAA7rrLaCVOsfXYBc5dKfjh6KSMJk20+/fjj3X2vwmZPaAN53MK+fKoOT0NzmIZFA8jIizceYb4ts3oHB1a8Ur33acrxW7fXvH7Xs6sm1rjY1N83EAnJt1OQYGeP5k+HZo1M1qNUyz89izhwf6MiouueIWHHtLnadLJ+ZHdookI8WfBtw3rGrAMiofZdfoyxy9cq3h0UsYdd0BwsB6lmJDoxoGMiI1iyZ5zlFiT865nyRI9GX///UYrcYqs3EI2JWcyI75V5f3i4+OhRw/Tun79fW3c0b81m1OyyMgpNFqOx7AMiodZ+O1ZQgJ8mdirim50oaEwc6aenDdpPP6M+FZcyC1i67GLRkupf7z9NnToAMOGGa3EKT7bnUaJvRKXbxlKwb33wrffwmFz1n2986bWlNqFxXsazuS8ZVA8yNXCYlYfPM/kPi0I8q+mt9l992lj8tlnnhHnYoZ3jSIs2J/Pdjeci8kjHD8OW7bo74fNfJeviPDZ7jQGtAujQ3VtrufM0S0e3nvPI9pcTdvwYG5q14zFe9IaTItg830jTczqA+kUFtuZWZN+D4MH6xapJnV7+fvamNKnBRsPZ3Il/7rRcuoP77yjDcm8eUYrcYr9aTmcuHCN6fEVdvv+IVFRMGECfPABlJS4X5wbmN6vFScuXGPfWXMGF9QWy6B4kKV7ztExMpjerZpUv3LZkH/rVjh61P3i3MCM+FZcL7Wzcv95o6XUD+x2XZZkzBhoWYMfZC9k8e40AnxtjOtZhcu3PPfeCxkZul6ZCRnfK4YAX1uDcXtZBsVDnL2Uz7enLjGtX6uaN9H6yU/03ej8+e4V5ya6t2hCt5jGltvLVXzxBaSl6e+FCSkqKWXlgfOM6d6cxoE1LGQ5fryu8WVSt1fjQD/GdG/Oyv3pFJXU/wZ0hhoUpdRYpdQRpVSqUurJCt6fp5S6oJTa53g8UO69uUqpY47HXM8qrz1L9+rmUwl9a3Fn2bIljBqlQ0Tt5oyWmhHfiv1pORzNzDVaivn54AMdsDFlitFKnGJLShZX8ouZ1q8W14Cfn55LWbECLpozwGN6fCtyCor5PLn+F4w0zKAopXyA14BxQBwwWykVV8Gqn4hIH8fjLce2YcCzwM3AAOBZpZTXBuSLCEv2pHFLhzBaNm1Uu43nzIHTp+Hrr90jzs1M6dMCX5uyRil1JT9fB2jMmAGNavkd8hIW7zlHZGgAQzpF1G7DefOguBg+/dQtutzNkE4RRDcOYEkDcHsZOUIZAKSKyAkRuQ4sBGp66zUG2Cgil0TkMrARGOsmnXVm79krnMrOZ1q/VrXfOCFBl6H46CPXC/MAESEBDOsaxfJ95xpcGQqXsnIl5ObqGwwTkp1XxJaULKb2bYmvTy1/dnr10jkpJr0GfGyKhL4tSTxygYt5RUbLcStGGpSWQPk652mOZTcyXSl1QCn1mVKqLDyqptt6BUv2OCYiezhRYjwkRBuVTz+FInN+GRP6tiDzahHfnMw2Wop5+eADaNXKtLknK/efp8QutXN3lefuu2HbNt3q2ITM6NeKEruwYl/9DlCpJhnCcFYCH4tIkVLqp8B8YERtdqCUegh4CCA6OprExESnhOTl5Tm1bbFdWLo7n76RPuze4ZzbKqxXL3otWMChl1/m4pAhTu3DWZw97/L4lQqBPvDG2t1c7xHgGmFuxBXn7Er8Ll9m0Nq1nJ01ixNffum247jzvN/fXkDrUBsZKXvISKn99gHt2jEQOPHCC5xx4SjNk591m1AbH25NoUPJaY8crzLces4iYsgDGAisL/f6KeCpKtb3AXIcz2cD/y733r+B2dUdMz4+Xpxly5YtTm237lC6tH1ilWxOyXT62FJcLBIZKTJ9uvP7cBJnz/tG/vuTvdLj2XVSWFzikv25E1eds8v4xz9EQOTQIbcexl3nfepinrR9YpW8nphatx0NHSrSrZuI3e4aYeLZz/qNxFRp+8QqOXUxz2PHrAhnzhnYJTX4XTfS5bUT6KyUaq+U8gfuBFaUX0EpVT5YfTKQ7Hi+HhitlGrmmIwf7VjmdazYf56wYH+G1nYisjy+vjB7tvajm7T66pQ+LcktLCHxSMOqvuoSPvwQ+vbVjadMSJmbZ1LvFnXb0d13Q3Ky7mpqQsrOvz67vQwzKCJSAjyKNgTJwKcikqSUek4pNdmx2n8ppZKUUvuB/wLmOba9BDyPNko7geccy7yKa0UlfJ6cyfiezWs/EXkjc+boPimLF7tGnIcZ3DGciBB/lu87Z7QUc3H8uG40NXu20UqcQkRYsf88N7VrVvsIxxu54w4dRvzhh64R52FaNG3EgHZhLN9/vt6WYjE0D0VE1ohIFxHpKCIvOJY9IyIrHM+fEpHuItJbRIaLSEq5bd8RkU6Oh1eWJN2UnElhsZ3JvV0QL9C/vy7FsmBB3fdlAL4+Nib2asGm5CxyC4uNlmMePvlE/501y1gdTpKSkcuxrDwm13V0AhAWBuPG6T4ppeZMEpzcpwWpWXkkp9fPvCwrU96NrNx/npgmgfRv64IUGaXgzjshMVGXojAhk/u04HqJnXWHzKnfEBYu1HXd2lTQiMoErNh/Hh+bYnxNS61Ux913Q3q6rhpgQsb3jMHXplhRT8sRWQbFTVzJv84XRy8wsVcMNpuLWrTOmqUz5k1agbhv66a0CQuqtxeTy0lKgoMH9Y2ECRERVu4/z5BOEYSHuCi6b+JE3SuobORmMsKC/RnaOYKV+89jr4d5WZZBcRPrkzIoLpW6T0SWp3t3/TDpxaSUYlLvGLYdzya7nid4uYRPPtG13GbMMFqJU+w5c4W0ywWucXeVERQEkybpucRic7pOJ/dpwbkrBew5c9loKS7HMihuYsX+87QLD6JnyxpUFq4Ns2bBV1/pIoEmZELPFpTahXVJlturSkS0u2v4cGjuREKsF7Bi3zkCfG2M7l5Jm19nmTULsrN1XxgTMiquOYF+tno5UrcMihvIyi1k+/FsJvVuUfPKwjWlbHLWpHWNusWE0iEimNUH0o2W4t3s3QvHjpnW3VVqF9YcymB41yhCa1pZuKaMHauLZJp0pB4S4MvwrlGsPZRR78oRWQbFDaw9mIFdXBB3XxFduuicBJNeTEopJvaKYceJbC7kWm6vSlm4UOcfTZtmtBKn2HnqEhdyi5hQVatrZwkM1BWXly7VofQmZHzPGC7kFrHzlNdlO9QJy6C4gdUH0+kcFUKX6FD3HGDWLN1r26R1jSb0aoFdsNxelSGibxhGj9ahsiZkzcF0AnxtjIiNcs8BZs2Cy5dh0yb37N/NjIiNItDPxpqD9WukbhkUF5N1tZCdpy65LkyyImbO1H9N6vbqEh1Cp6gQVh+ofz5kl7BzJ5w58/3nbDJK7cLaQxmMiI0iOMBN5QJHj4YmTUx7DQQH+DIiNoo1B+uX28syKC5mXVIGIrhnqF9G+/YwYICp3V4TesbwzclLZF0tNFqO97Fokc4Inzy5+nW9kDJ3l1tvqvz9YepUWLbMtFW4x/eM4WJeEd+erD9uL8uguJg1B9Pp5E53Vxl33KEnbk+ccO9x3MSEXjGIwForyfGHiOg8o5EjoZnX9oyrkjUH0wn0c6O7q4xZsyAnB9Z7ZRm/aqmPbi/LoLiQC7n6bsOtd2ZlTJ+u/5q0tleX6FC6RIewynJ7/ZA9e+DUKX3DYELK3F3Du7rR3VXG7bdro2vSayDIX7u96lO0l2VQXMi6JB3dNb6nB/IG2reH+HjTZs2DHvLvOn2ZrFzL7fUdixbp6C6T9o33iLurjDK34PLlpo32mtCzRb1ye1mrjPQ2AAAgAElEQVQGxYWsOZBOh8hgurrb3VXGjBk62uvMGc8cz8WM66HdXuuTMo2W4h2UubtGjDB1dJdH3F1lzJih3V6bN3vmeC5meGwkjfx8WH2wfozULYPiIi7m6Ra3E3rGuD6ZsTLK3F5LlnjmeC6mS3QIHSKCWXeo/viQ68T+/bpcvUndXXa7sM5T7q4yRo3SSY4mHakH+fsyrGsk65My60VtL8uguIj137m7PDDUL6NzZ+jd27QXk1KKcT2bs+PEJS5dM6fLwqUsWgQ+PpCQYLQSp9hz5jJZuUWM7eHBUjEBAbq217JlUFLiueO6kLE9mnMht6he1PayDIqLWHcog3bhQcQ295C7q4wZM+Drr+GcORtXjesRQ6ld2Hi4gUd7lbm7hg+HiDp09zSQdYcy8PfxoLurjOnTdW0vk5a0HxEbhb+PrV5EPBpqUJRSY5VSR5RSqUqpJyt4/5dKqcNKqQNKqc+VUm3LvVeqlNrneKy4cVtPkpNfzPbj2Yzt4UF3VxlllWiXLvXscV1E9xaNadWsUb24mOpEUhIcPfq9G9NkiOjoriGdI1xfu6s6xo7VVYhNGu0VGujHkM4RrDuUYfpOjoYZFKWUD/AaMA6IA2YrpeJuWG0v0F9EegGfAS+Ve69ARPo4HoZmgG1KzqTELp4d6pcRG6tL2pvZ7dWjOV+nXiSnwJzlyF3CkiW6iZpJ3V1J569y7koBY7sbcA0EBcH48fp/aNJOjmN7NOfclQIOnssxWkqdMHKEMgBIFZETInIdWAj8IFZSRLaISL7j5Q6glYc11oh1SRnENAmkdysXl6qvKdOmwdatcOGCMcevI2N7xFBcKmxOacDRXkuW6M6MJi1Vv/ZQOj42xcg4F5eqrynTp0NmJmzbZszx68iobtH42JTpR+oeCsWokJbA2XKv04Cbq1j/fmBtudeBSqldQAnwoogsq2gjpdRDwEMA0dHRJCYmOiU2Ly+vwm0LS4TElHyGtfblC4N8uCFt2tDfbiflpZfImDDBpfuu7LxdiV2EpgGKD7YcollOqluPVRM8cc7lCTx3jlv27yf15z8nzYPHvZG6nPeSb/Pp0lRxYKcxP+g+jRsz2M+P8//4B6m1GKV4+rOuithmiiXfnmBAQLpbXeduPWcRMeQBzADeKvf6HuCflaw7Bz1CCSi3rKXjbwfgFNCxumPGx8eLs2zZsqXC5av2n5e2T6yS7ccvOr3vOmO3i7RvLzJ+vMt3Xdl5u5pnlh2ULr9dI9eKij1yvKrw1Dl/x8svi4DIyZOePe4NOHveRzOuStsnVsn72066VE+tmThRpE0bfT3UEI9/1lXwwfZT0vaJVZKSftWtx3HmnIFdUoPfdSNdXueA1uVet3Is+wFKqZHAb4HJIvJdFTgROef4ewJIBPq6U2xlrEvKIDzYn5vaGZiIppQulLdpE1y9apyOOjCmR3OKSux8ccScbrs6sWQJ9OsH7doZrcQp1jncNKONmD8pz7RpOsl3zx5jdTjJ6O7RKKXdh2bFSIOyE+islGqvlPIH7gR+EK2llOoL/BttTLLKLW+mlApwPI8ABgOHPabcQWFxKZuTMxkVp/2fhjJtmi4/sWaNsTqcZEC7MJoF+bG+ofVIOX8etm83bSMt0AU+49s2I7pxoLFCJk0Cm820EY9RoYH0b9vsOwNtRgwzKCJSAjwKrAeSgU9FJEkp9ZxSqixq62UgBFh0Q3hwN2CXUmo/sAU9h+Jxg/J16kWuXS81JrrrRgYO1BO6Js2a9/WxcXu3aD5PyeJ6id1oOZ5jmWPqz6QG5eylfA6nX2WMq/vGO0NEBNx6q2kNCsCY7s1JycjlTHZ+9St7IYbmoYjIGhHpIiIdReQFx7JnRGSF4/lIEYmWG8KDRWSbiPQUkd6Ov28boX99UgahAb4M6ugFiWg2my4ouGYNFBQYrcYpxnRvTm5hCTtOZBstxXMsWaJDv7t1M1qJU5SNKMcY7e4qY9o0OHwYjhwxWolTjI7T/8cNJk30tTLlnaSk1M6m5CxGdIvC39dL/o3TpsG1a6Ztizq0cwRB/j4Nx+116RIkJur5L5OyISmT2OahtA0PNlqKpiyPx6SjlDaOahtmvQa85JfQfOw+fZlL165/d0fhFQwbBk2bmtbtFejnw7CukWw8XD8K5VXLqlU6Ec+k7q6LeUXsOn3J+Mn48rRuDTfdZFqDAnq0t+v0ZS7kmq8TpWVQnGR9Uib+vjZu6xpptJTv8feHiRNhxQrTFsob0705WblF7D17xWgp7mfpUmjZUve1MSGfJ2diF7xj/qQ8U6fqtg5paUYrcYox3ZsjoitwmA3LoDiBiLA+KYMhnSII8VSZ7poydap2pXz1ldFKnGJ4bBR+Psq0Q/4ak5+vW9cmJOiwbxOyPimTlk0bERfT2GgpP6TMhbiswlxnr6dbTCitwxqZ8hqwDIoTHE7XdYu87s4MYMwYCAw07ZC/caAfAztGsD7J/IXyqmTDBh08YdL5k7yiEr46dpEx3Zt7viBqdcTG6odJDYpSijFxzdmWmk1uobnq21kGxQnWJ2ViUzCymxcalOBg3XRo2TJdEt2EjOkezensfI5m5hktxX0sW6b7od96q9FKnOKLIxe4Xmr3zpsq0IY6MREum7PHyJgezbleaifRZIm+lkFxgg1JGfRvG0Z4SIDRUiomIUFnDO/bZ7QSpxgVpzOGN5hwyF8jSkpg5Uo93+Xn4VLvLmK9o0JEfyMrRFRFQoIOeFi92mglTtGvTTMiQvxN5/ayDEotOZOdT0pGLqO99c4Mvs8YNumQPyo0kL6tm7LhsPkmJWvE1q16nsukpeqvl9jZkpLF7d2ijK8QURn9+0OLFqa9BnxsilFx0SQeuUBRiXlK8lsGpZaUJRx5TSJXRURG6lLoJr2YQNeFOnguh/NXzJmkWSXLlul5rjFjjFbiFDtOZJNbVOLd10BZou/ataZN9B0d15y8ohK2HzdPoq9lUGpJWSJX67Ago6VUzdSpcOAAnDhhtBKnGO3oq7Gxvo1SRLRBGT1az3eZkA2HMwjy92FwJy+oEFEVCQk6ms6kib4DO4YT7O9jqpG6ZVBqQVkil1ffmZUxxdGrzKSjlA6RIXSKCjFtCYpK2btXz2+Z1N1ltwsbD2dyW5dIAv18jJZTNcOGQePGpr0GAv18uK1rJJtMlOhrGZRasDk5C7vg3fMnZXToAL16mTZ8GPQoZceJS+Tkmyt0skqWL9fumEmTjFbiFAfO5ZB5tcgc14C/P0yYoBN9TdoaeHScTvTdn2aORF/LoNSCDYe9NJGrMhISdEvUrKzq1/VCRndvTqld2HzEPEP+alm2DIYM0ZVxTciGpAx8bIoRXU1gUEBfAxcvmrY18PCuUfjalGncXpZBqSFFJcLWYxccIa1eGtlyIwkJYLfrmlEmpFfLJkQ3DmBDkjkupmo5eVLPa5nU3QX6purm9mE0CTJJuPO4cXqkYtKRepMgP27pEG6aEHrLoNSQQ9mlFJXYzTHUL6NPH2jTRrtZTIitXOhkYbE5XRY/oOxzKJvfMhnHL+SRmpX3XcCEKQgNhdtv1/97kyb6ju4ezfEL10jN8v5EX0MNilJqrFLqiFIqVSn1ZAXvByilPnG8/41Sql25955yLD+ilHJ7/OWezFKaNPJjgLcmclWEUvrHa8MGXdbehIyOa05BcSlfp140WkrdWbYMevbU81smpCzibpQZglLKk5Cgox0PHTJaiVOUVeQwQ8SjYQZFKeUDvAaMA+KA2UqpuBtWux+4LCKdgFeA/3VsG4duGdwdGAv8y7E/t1BSamffhRJuj43C18dkg7qEBCgs1EbFhNzSIZzQAF/zu70uXtQJjWZ2dyVl0KNlY1o2bWS0lNoxebK+uTLpSL1F00b0atXEFBGP1f46KqV+oZRq5oZjDwBSReSEiFwHFgI3+gKmAPMdzz8Dbld6AmMKsFBEikTkJJDq2J9b2HnqMteKTRLddSNDh+oeKSa9mPx9bQyLjWJTcialJgmdrJBVq/R8lkkNSlZuIXvPXmFUN5ONTkC3xr75ZtOGDwOM6hbNvrNXyLpaaLSUKqnJ7XY0sFMp9anDReWqGemWwNlyr9Mcyypcx9GDPgcIr+G2LmPD4Qz8bHBrFy/qfVJT/Px0zaiVK03bI2VUXDTZ166z94w5C/0B2qC3bg19+xqtxCk+T85CBMb0MOFNFWhDvns3nD1b/bpeyOjveqR4d8Rmtc08ROR3SqmngdHAvcA/lVKfAm+LyHF3C6wrSqmHgIcAoqOjSUxMrPU+Tp0pokeY8O02c/YYiejUiR6XLrHvn//kSp8+tdo2Ly/Pqf+ZK/EtFnwUvLVuF3mx/m4/nqvP2VZYyOC1a0kfP57UL75w2X5dTVXn/fHuQiIbKdKTd5ORYpIox3IExcQwADj6l79wvlzLAG/4ftcEESEqSLFw62FaFNSt+oVbz1lEavQAegN/B1KA14G9wEs13b6C/Q0E1pd7/RTw1A3rrAcGOp77AhcBdeO65der6hEfHy/OsnnzZqe3NZzcXJGAAJHHH6/1plu2bHG9Hie45+1v5LaXNovdbnf7sVx+zsuWiYDIxo2u3a+Lqey8cwuLpfNv1shzK5M8K8jVdO0qMnLkDxZ5y/e7Jjy/Mkk6/2aN5BYW12k/zpwzsEtq8LtekzmUx5RSu4GXgK+BniLyMBAPTK+DLdsJdFZKtVdK+aMn2VfcsM4KYK7j+Qxgs+PkVgB3OqLA2gOdgW/roKVaTJN7UhEhITBypKl7pIyKi+ZUdr4pQid/xPLleh7rttuMVuIUXx7VvU9MFS5cEQkJukfKFXNknd/I6O66R8oXXtwjpSZzKGHANBEZIyKLRKQYQETswERnDyx6TuRR9OgiGfhURJKUUs8ppSY7VnsbCFdKpQK/BJ50bJsEfAocBtYBj4hIPUhUcCMJCXDqlE6sMyGjHKGTZskY/o6SEl36Y8IE0/Y+2ZCUQbMgP+LbuiM2x4NMmaI/jzVrjFbiFPFtmxEW7O/V0V7VGhQReVZETlfyXnJdDi4ia0Ski4h0FJEXHMueEZEVjueFInKHiHQSkQEicqLcti84tusqImvroqNBMGmSDp00aaRL8yaB9G7d1DQZw9+xbRtkZ5s2uqu41M7mlCxu7xZtvpD5G7n5Zh3xZdKseR+bYmS3KDanZFFcajdaToWY/BtiUWOio2HQINMaFNDFIven5ZCR492hkz9g2TIICDBt75NvT17iamGJ+d1doItyTp4M69bp3CwTMiquObmFJXxz4pLRUirEMigNiYQE3Rb41CmjlTjFdz1Skk3i9irrfTJypC4BYkI2JGUQ6GdjaGcThsxXREIC5OXB5s1GK3GKoZ0jaOTn47VuL8ugNCTKakituDH2wRx0igqhfUSwedxeBw/qgpAmdXeJCBsOZzK0cySN/L2890lNGTFCB6mYdKQe6OfDrV0i2Hg4syzC1auwDEpDonNn6N7dtBeTUorRcdFsP55NToEJeqQsX67nrUza++TQuauk5xQyqj64u8oICIDx4/VnY+IeKek5hRw8l2O0lB9hGZSGRkICfPmlnig2IaO7N6fELiQe8e6MYUAb7oED9fyVCdlwOAOb+r44Yb0hIUH3CPrmG6OVOMWI2Ch8bMor69tZBqWhkZCg78xWrzZaiVP0bd2UiBAT9Eg5cwb27DGtuwtgQ1ImN7ULIyzY/dUJPMr48TqE26Qj9WbB/gxoF+aV8yiWQWloxMdDy5amvZi+75GS5d09Usr+vybtfXLq4jWOZOYyxmyl6mtCkyYwfLgOH/bCeYiaMKZ7NEcz8zh50bvaUlgGpaGhlL5rXrcO8vONVuMUo7tHc+16KduPe7HbbtkyiIuDLl2MVuIUZXe/9Wr+pDwJCZCaStDpClPsvJ6ynjTeFqBiGZSGSEICFBTAxo1GK3GKQR3DCfb33tBJsrP1PFW5IoRmY0NSJnExjWkdFmS0FPcwWRfjiPjKnAVfWzZtRI+Wjb2ucoRlUBoit90GzZqZNmM4wNeHYbFRbDzspT1SVq7U81QmNSgXcovYfeZy/XR3ldGyJQwYQMTXXxutxGlGxzVnz5nLZOV6T5KmZVAaIvWgR8rouGgu5nlpj5SlS3Xvk379jFbiFJuSMxExaUO52jB1Ko1TUkzbI2VMWY+Uw94T8WgZlIbK1Klw6ZJ2zZiQ4bFR+Pko1nuZD5lr13S75YQEPV9lQjYkZdA6rBGxzc2Z3V9jykaQJg1Q6RIdQtvwIK9y/VoGpaEyZgw0amRat1fjQD8GdYxgXVKGd2UMr1+v60SZ1N2VV1TC16nZjIlrbu6WDTWha1eutW1r2mugLNH369SLXC30jkRfy6A0VIKCtFExcY+UsT2ac/ZSAcnpuUZL+Z6lSyEsDIYONVqJU2xJydK9T+rz/Ek5Lg4daupE37E9mlNcKmxJ8Q63l2VQGjIJCZCWBrt2Ga3EKUZ2i0YpvMftVVwMq1bpUiu+1XbX9krWJWUQEeJv/t4nNeTCkCE6gGLlSqOlOEXf1s2IDA3wmmvAMigNmUmTwMfHtEP+yNAAbmob5jUXE198obsBmtTddb1USEzJYlRcc3xs9dzd5SCvSxdo0waWLDFailPYbNrtlXjkglck+hpiUJRSYUqpjUqpY46/P7odUkr1UUptV0olKaUOKKVmlXvvPaXUSaXUPsejj2fPoJ4QFqZDiE1qUEBHIqVk5HI62wsyhhcv1q7EUaOMVuIUh7NLuXa9lLE9Goa7C9CBE1On6kCKPBO2l0a7vfKvl7L12EWjpRg2QnkS+FxEOgOfO17fSD7wExHpDowF/q6Ualru/f8nIn0cj33ul1xPmToVUlIguU7NNw2jLFfC8FFKaak2zOPHa6NiQnZnlhIa6MvADuFGS/EsU6dCUZGuHmFCbukQTuNAX9YdMn6kbpRBmQLMdzyfD/yogp6IHBWRY47n54EsoJ50+fEiytwzJh3ytw4LokfLxsZfTNu3Q2YmTJtmrA4nKSm1syerhJHdovH3bWCe8CFDICLCtNeAn4+Nkd2i2ZScaXhrYKO+OdEiku54ngFUmUGllBoA+APHyy1+weEKe0UpFeAmnfWfli3hllu0u8akjIlrzp4zV8i6amDG8JIl4O8PEyYYp6EOfHvyEteKqd/Z8ZXh46OLeK5erUcqJmRMj+bkFBQb3hrYbaEoSqlNQEXfzt+WfyEiopSqNG5VKRUDfADMFZEy8/sU2hD5A28CTwDPVbL9Q8BDANHR0SQmJtbuRBzk5eU5va2307pPHzq+8QY7FiygsEWLH7xnhvMOK9Bfi1eXbeX2Nn513l+tz1mEWxYs4Fq/fhzcs6fOxzeCDw4X4WcTVGYyiRdTjJbjMco+67DOnel19SoH/vY3Lg0caLSs2lMq+NvgnY27KTlX9f21W69pEfH4AzgCxDiexwBHKlmvMbAHmFHFvoYBq2py3Pj4eHGWLVu2OL2t13PihAiIvPzyj94yw3nb7XYZ8ZctMvvN7S7ZX63Pedcu/f975x2XHN/TlJbaZcALG2Xa39YaLcXjfPdZFxWJNGkicu+9huqpCz99f5fc9MeNUlpqr3I9Z65pYJfU4DfWKJfXCmCu4/lcYPmNKyil/IGlwPsi8tkN78U4/ir0/Msht6qt77RvD337mtaHrJRiXI8Yvjl5iew8A1wWixdrt4mjgq3Z2Hv2CplXi4iPNmfujEvw99dh9MuW6XwiEzKuZ3OycovYe9a4+nZGGZQXgVFKqWPASMdrlFL9lVJvOdaZCdwKzKsgPPgjpdRB4CAQAfzRs/LrIdOn64nlc+eMVuIU43o2p9QubPR0OW8RbVCGDYNwc0ZHrT2Yjp+Pok+kj9FSjGXGDLh8GbzcxVsZI2Kj8PexseagcQEqhhgUEckWkdtFpLOIjBSRS47lu0TkAcfzD0XET74PDf4uPFhERohITxHpISJzRMScAeTeRFl0kklzUuJiGtM2PIg1no72OnwYjh7VBtmEiAhrD2UwtHMkQX4NI5mxUkaPhuBg0waohAb6cWuXCNYeTMduUFuHBhYfaFEp3brph0kvJqUUY3s0Z1vqRXLyPeiyWLz4+y6YJuRAWg7nrhQwvmeM0VKMp1EjHaW3dKnOKzIh43rEcD6nkP1pVww5vmVQLL5n+nRdKC/LOwrN1ZbxPWIosQsbkz3o9lq0SOcxxJjzB3nNwXR8bYpR3ep575OaMn26/v6btJPjyG7R+Pko1hqUl2UZFIvvmTED7HbT9ofo1aoJLZs2Yu3B9OpXdgXJyXDoENxxh2eO52JEhDWH0hncKYImQXUPt64XjB8PgYGmHak3CfJjcKcI1hxMN6Stg2VQLL6nVy/o0gU+/dRoJU5R5vbaeuwiuZ7oD7FokXZ3mXT+JOn8Vc5eKmCC5e76npAQGDtWGxS7sVnnzjK+Rwxplws4dO6qx49tGRSL71FK321v2WJat9e4Hs25Xmrn82QP6F+0CAYPhhuSQc3C6oPp+NgUo+Isd9cPuOMOOH8etm0zWolTjO4eja9NseaQh0bq5bAMisUPmTlT35mZNCelX5tmNG8cyKoDbr6YUlJM7+5aezCdQR3DaRbsb7Qc72LSJO32MulIvWmQPwM7hrPWALeXZVAsfkjPntC1q2kvJptNMb5nDF8evUBOgRvdXosW6b8mdXclp+dyKjvfiu6qiNBQPZeyaJFpo73G94zhVHY+Sec96/ayDIrFD1FKj1K++EJXzzUhE3vHcL3UziZ3JjmWubtatnTfMdzIqgPn8XE0Z7KogJkzISPDtNFeY7o3x9em3D9SvwHLoFj8mDvuMLXbq2/rprRs2ohVB8675wApKXDwoKndXSsPnGdwpwjCQ6xC3RUycaLOSzHpSD0s2J/BnSJYdeC8R91elkGx+DE9ekBsrGkvJqUUE3rFsPXYRa7kX3f9AUzu7jqQlsPZSwVM7GW5uyolOFgblc8+g5ISo9U4xaTeLUi7XMC+s55LcrQMisWPKef28s/ONlqNU0zspZMcXd7JUQQ+/hiGDoVWrVy7bw+xcv95/HxUw+x9UhtmztTRjl9+abQSpxjdPRp/H5tH3V6WQbGomJkzQYTIL74wWolT9GzZhDZhQa6/mA4c0AmNs2e7dr8ewm4XVh1I57YuUTRpZCUzVsn48XqkYtKReuNAP27rGsnqA56r7WUZFIuK6d4devUi6vPPjVbiFEopJvaKYdvxbNeWtP/4Y12qfsYM1+3Tg+w6fZmMq4VM6m25u6olKEiHEC9ebNqS9hN7xZBxtZBdpz1T0t4yKBaVM3s2TQ4fhhMnjFbiFBN7taDULq6rayQCCxfCqFEQGemafXqYVQfOE+ine5Bb1IDZs+HiRdi40WglTjGyWzSBfjZW7ndTgMoNWAbFonLuvFP/XbjQWB1O0i0mlI6RwazY56KLaft2OH3atO6uklI7aw6mc3tsNMEBDbiZVm0YOxaaNYMFC4xW4hTBAb7cHhvN2kPplJS6v5SMZVAsKqddO3J69ICPPtJ35yZDKUVCn5Z8e+oSaZfz677Djz/WGdQmLVW/48QlLuZdt6K7aoO/vw4PX7YMrl0zWo1TTOodw+X8YpLTc91+LEMMilIqTCm1USl1zPG3WSXrlZbr1rii3PL2SqlvlFKpSqlPHO2CLdxA5u236yZSBw8aLcUppvTRiYcr6jrkLynRk7MTJkDjxi5Q5nmW7TtHaIAvw2OjjJZiLu66SxuTFSuqX9cLGR4bxbe/uZ2erZq4/VhGjVCeBD4Xkc7A547XFVFQrltj+Ybd/wu8IiKdgMvA/e6V23C5MGyYnoQ26ZC/TXgQ8W2bsWzvuboleJUVzDSpu6vgeilrD6YzrmdzAv0aeKvf2lIWIv7RR0YrcYoAXx+PJbAaZVCmAPMdz+cDNfYhKKUUMAL4zJntLWpHcdOmehJ64ULTlvNO6NOCo5l5dRvyL1jwfY0nE7IxOZNr10tJ6GvOUjGGYrPpG4n16/UEvUWlGGVQokWkLEEgA6gs5CRQKbVLKbVDKVVmNMKBKyJSlr6aBlhXiTu56y49GW3Sct4TerXA16ZYvu+cczu4dk1nTN9xhy7HYUKW7kkjpkkgt7QPN1qKObn7bu32LKuSYFEhbgv1UEptAipKxf1t+RciIkqpynwRbUXknFKqA7BZKXUQyKmljoeAhwCio6NJTEyszebfkZeX5/S2ZiYvL4+t4eEMCgwk88UXOfqrXxktySm6h9tY9O1Jbm6UgU2pKte98bOO2riRuLw89vbsSY4JvwNXi4QvjuYztp0fX35ZeaJqQ/yO1/icRbipbVtK/vUv9nbr5nZd7sStn7OIePwBHAFiHM9jgCM12OY9YAaggIuAr2P5QGB9TY4bHx8vzrJlyxantzUz3533nDkijRuL5OcbqsdZlu87J22fWCXbUi9Wu+6PPuvRo0XathUpLXWLNnfzzlcnpO0Tq+RIxtUq12uI3/FanfMLL4iAyPHjbtPjCZz5nIFdUoPfWKNcXiuAuY7nc4HlN66glGqmlApwPI8ABgOHHSe3BW1cKt3ewsXMmwdXr8Jyc/6rR3WLJtjfhyV70mq34blzsGkT/OQn2pduQpbtPUdcTGO6RIcaLcXc3HOPrnP3/vtGK/FajLpCXgRGKaWOASMdr1FK9VdKveVYpxuwSym1H21AXhSRw473ngB+qZRKRc+pvO1R9Q2R4cOhdWuYP7/6db2QRv4+TOgVw+qD6VwrqkX12AULdDDCPfe4T5wbOX4hj/1pOUzrZ00z1pnWrWHkSH0NmDRAxd0YYlBEJFtEbheRziIyUkQuOZbvEpEHHM+3iUhPEent+Pt2ue1PiMgAEekkIneIiAuLNVlUiM2mf1Q3bND9tk3IHf1bk3+9lDUHa1gwUkT/eAwcCJ07u1ecm1iyJw2bgsm9zdn33uuYNw9OndIN6Cx+hDnH8BbG8JOf6Dszk8bj9yjlgrAAABuESURBVG/bjPYRwSzaXUO31969kJSkz9uElJTa+Wx3Grd1iSSqcaDRcuoHCQk6sfW994xW4pVYBsWi5nTtqu/W5883bSmWGfGt+PbkJU5n16CMxvz5uvTGzJnuF+cGth67SObVImbd1NpoKfWHoCBd4+6zzyDX/aVMzIZlUCxqx9y5+q59zx6jlTjFtH4tsSn4rLpRSmEhfPihviMNC/OMOBfzyc6zhAf7MyLWqizsUubNg/x8bVQsfoBlUCxqx6xZukDi2+aMg4hp0oghnSNZvDuN0qqaDi1ZApcuwYMPek6cC7mYV8Sm5Eym9WuJv691mbuUW27Ro3XL7fUjrG+aRe1o2lS7gD780LTVV++Ib8X5nEK2Ha+ijMZ//gPt28OIEZ4T5kKW7jlHiV2Y2d9yd7kcpfQo5csv4dgxo9V4FZZBsag9Dz2k/ccm7ZMyKi6aJo38+GTn2Qrfb5SWBomJ8MADpsw9ERE+2XWWvm2a0tnKPXEPc+eCry+8+abRSrwK810tFsYzaBDExZn2Ygr082F6v1asT8rgQu6PI85jVq/WFZbvvdcAdXVnz5krpGblMcsanbiPmBg9v/buu3q+zQKwDIqFMygFP/0pfPst7NtntBqnuPuWNhSXCp/uumGUcv06zdevh4kT9Y+GCVn47Rka+fkw0co9cS8//SlkZ+v5NgvAMigWznLPPXpy3qSjlI6RIQzsEM6Cb878cHJ+5Ur8L1827WT85WvXWbH/PFP7tSTEavPrXkaMgE6d4I03jFbiNVgGxcI5mjUz/eT8nFvacu5KAV8czfp+4ZtvUhgZqXuJm5BFu89SVGLnJwPbGi2l/mOz6VHK1q06lN7CMigWdaBscv7jj41W4hSju0cTGRrAhzvO6AUpKbBhA+mTJuk5FJNRahc+2HGaAe3CiG1uzjbFpmPePJ38+u9/G63EK7AMioXzDBoEPXvCq6+aMnPez8fGnTe1ZsuRLM5eyod//hP8/Tk/caLR0pzii6NZnL1UwE8GWaMTjxERoRuvvf++aUfqrsQyKBbOoxQ8/jgcOKDDbE3I7AFtUMDizYd0otrs2RQ3a2a0LKd4f/tpokIDGNO9or52Fm7j4YchJwc++MBoJYZjGRSLunHXXfou7e9/N1qJU7Ro2oiR3aIpeesdfYf5i18YLckpTmdf44ujF5g9oA1+PtZl7VEGDYKbboJXXmnwZe2tb55F3QgM1HdoK1dCaqrRapziwcFtueOb5WT16g/x8UbLcYoPtp/GRynuurmN0VIaHkrBL38JR4/C2rVGqzEUy6BY1J2HH9ZZw6++arQSp+iftJ22VzL4V8/xVdf38lJyCor5+NszTOgVQ7RVpt4Ypk/XDbj+9jejlRiKIYHqSqkw4BOgHXAKmCkil29YZzjwSrlFscCdIrJMKfUecBuQ43hvnog4lWFXXFxMWloahdVkuzZp0oTk5GRnDuH1BAYG0qpVK/z8/JzbQUyMLun9zjvw3HPQpIlrBboZ9Y9/UBAdw4cx/bjlcCZm+0n+cMdprl0v5ae3djRaSsPFz0+7S3/9a53s26eP0YoMwajMpyeBz0XkRaXUk47XT5RfQUS2AH3gOwOUCmwot8r/E5E6149OS0sjNDSUdu3aoZSqdL3c3FxCQ+tfXSQRITs7m7S0NNq3b+/8jh57TE9Kvv22Hv6bhW+/hc2b8f/zizRXoby19QSPdjNaVM0pLC7l3a9PcWuXSOJaWKHChvLgg/CHP+i5FJO2yq4rRrm8pgBl//H5QEI1688A1opIvquFFBYWEh4eXqUxqc8opQgPD692hFYt8fEwbBj89a/mqm30pz9Bs2b4PPJz7hvcnl2nL5N6pdRoVTVmyZ5zXMwr4me3djBaikXTpnD//Tovy6RtsuuKEgPyB5RSV0SkqeO5Ai6Xva5k/c3A30RkleP1e8BAoAj4HHiysr7ySqmHgIcAoqOj4xfeUCG3SZMmdOrUqVrNpaWl+Jgw2a2mpKamkpOT86PleXl5hISE1GgfTXfvps+vfsXRxx/n/JQprpbocoJPnuSm++7j1Ny5nJo3j4IS4ZeJ+XRtIjx+U83O2UjsIjy1tYAgX8UzAwPrfFNUm8+6vuDqcw5MT+fmOXNImzaN44884rL9uhJnznn48OG7RaR/tSuKiFsewCbgUAWPKcCVG9a9XMV+YoALgN8NyxQQgB7hPFMTTfHx8XIjhw8f/tGyirh69WqN1jMrlf0ftmzZUvOd2O0iAweKtGkjUlTkGmHu5O67RYKDRS5e/G7Ry+tSpO0TqyQ5PcdAYTVjzYHz0vaJVbJq/3mX7K9Wn3U9wS3nfO+9IoGBIunprt+3C3DmnIFdUoPfWLe5vERkpIj0qOCxHMhUSsUAOP5mVbGrmcBSESkut++yT6oIeBcY4K7zMIIHHniAw4cPV7ve3//+d95///0q17nzzjs55qkmQErB00/DmTO6xpc3c/y4dk08/DCEh3+3+IGh7WnkC3/f6N2Nk+x24Z9bUmkbHsTYHlYio1fx299CcTG89JLRSjyOUXMoK4C5judzgeVVrDsb+EGxqHLGSKHnXw65QaNhvPXWW8TFxVW5TklJCe+88w533XVXles9/PDDvOTJL/bYsdC/v56bKCnx3HFry0sv6VDnGwIImgb5M7qtH+uSMkg6/2MXoLew9lAGSeev8tjtnfGxNcz5P6+lY0eYMwdefx0yMoxW41GMivJ6EfhUKXU/cBo9CkEp1R/4mYg84HjdDmgNfHHD9h8ppSLRbq99wM9cIeoPK5M4fP5qhe85O4cS16Ixz07qXun7165dY+bMmaSlpVFaWsrTTz/N66+/zl/+8hf69+9PSEgIjz32GKtWraJRo0YsX76c6OhoNm/eTL9+/fD19aWkpISBAwfy8ssvM2zYMJ566ilsNhsvvPACQ4cOZd68eZSUlODr64GPWyn43e9086GFC/WF5W2kpuoQ5wcfrLDnyeh2fvz/9u48vqrqWuD4b5EEE6YgYagMNgiBMgk0TKJAAWdTsZVnn5XBSAvVMliRByqCTEWGokWsFiEoNdUHloK1yCTSWKQMEiOgEAHhAUGmMAQEAsl6f+wLBRqSkNx7T5K7vp/P/ST33HPuWfuTYd1z9t5rr9yn/H7F18zsU/Bt42A7n5PLtOXbiKtZiR6t6ngdjsnLyJHuKn3KFDdQJUR4coWiqkdUtbuqxvlujWX6tm+4kEx8z3epah1Vzb3i+G6q2sJ3C62Xqp4Mdhv8ZcmSJdSuXZu0tDQ2b97M3VeUTT916hQdOnQgLS2Nzp0788YbbwCwevVq4n2zusPDw3nzzTd5/PHHWbFiBUuWLGH06NEAlCtXjoYNG5KWlha8Rv34x9CyJYweDWfzHCvhreeecxVin38+z5crRgi/uO0mln15gM37St5Vyl9T97Hj0CmG3tnIrk5KqoYN4ZFHQu4qxVbguUR+VxKBmofSokULhg4dyvDhw0lISKBTp06XvV6+fHkSfNVv4+PjWb58OQD79++nSZN/T5ho1qwZvXv3JiEhgTVr1lC+fPmLr9WsWZOMjIyLCSjgypVzt5TuugtefbVkzUtZtw7mzXPJJJ8VGRNviyVp9TdMW55O0qNtgxhg/s6ez+HlFV/Tok60FYEs6UaOhD//GV54IWQW4bLSKx5r1KgRGzdupEWLFowcOZKxY8de9npERMTF4aBhYWGc9/VLREVF/cfckU2bNlG1alUOHrx8jMOZM2eIiooKYCvycOedrj9l3Di3TGpJoOpmMteoAcOG5btrlcgIHv9RA1ZuPUhK+qEgBViwd9ftYd+x0wy7q3HIzp0qNeLi4Ikn4I03XEXuEGAJxWMZGRlUqFCBXr16MWzYMDZu3Fio45o0acL2S4oxLliwgMzMTFJSUhg0aBDHjh27+Fp6ejrNmzf3e+wFmjoVTpxwSaUkWLwY/vEPdyuuEFebibfGEhtTgTF/28K5HO+ryB49lc3LK9JpX78aneKqex2OKYzRo92ExyefLJVrBl0rSyge27RpE+3ataNVq1aMGTOGkSNHFuq4e+65h5SUFAAOHz7MiBEjmDVrFo0aNWLgwIEMGTIEgAMHDhAVFcX3vufB7ZFmzeAXv3C3vYI1dPlqzp1zVydxcW6lyUK4LjyM5xOasuPQKd76dFdg4yuEyUu3ceLMeV64v5ldnZQW1aq5+nYffwyL8hvMWkYUZrJKWXmUtYmNDzzwgKanp+e7z7Rp03TWrFkFvpdfJjbm5dtvVStVUk1IcBMfvTJhgiqoLlpU4K6Xtjk3N1f7zF6rzUct0UNZZwIYYP427s7U2BEf6Ni/bQnYOWxiY4CcO6fatKlqgwaqZ7z7HbqgVE5sNIH34osvsn///nz3qVq1Kn379s13n4CqVcsVzPvgAzeM2Avp6e5TYs+ecP/913SoiDDqx005fS6HyUu2BijA/OXkKiMXbqZm5et48vY4T2IwxRAe7gpG7thR5ic7WkIpxRo3bkznzp3z3ScxMTE480/yM2QIdOjgynsfOBDcc+fmultcUVFFXq+lQY1K9LutPvM27PWkg/7tf+1mS8YJnk9oSuXIIi4xYLx1553w8MPug01qavDPH6SVJC2hmMALC3MTCbOyYODA4J47Kcl1xE+ZAsXoR/rNHY2Iq1mJp+encfRUth8DzN/OQyeZtGQrneKqc1+Lqw9zNqXAjBluuew+fYI7P2vdOjcvLD094KeyhGKCo0kTNx7/vffcIxh27oSnn3Zl9fv1K9ZbRUaE8dLPWnH0u2xGLtx8oUhpQJ09n8Ogd1IpH16OSQ/ebB3xpV21ajBrFmze7P4WguHoUXjoITfasnrgRwZaQjHBM2yYWzelf//Arz//3Xfw05+6UjCzZ7uvxdS8TjS/uaMRf9+0n4Wf7/NDkPl78cOtbMk4wZSeLaldNcjziExg3Hef+3AzeTL885+BPZcqJCa6tVnmzXMJLcAsoZjgCQ93v9gi0KOHuwUWCKrwq1+5yWTJyXCT/xafGtC5AW1jr+f5hVvY+m3edd/8YfmXB5izehePdozljqa1AnYe44Fp0yA21q1Dv2tX4M7z0ktuqPLkydC+feDOcwlLKGVAamoq/Qq4pTNjxgySkpKCFFE+brrJJZVt26B378B0Fr72mluOePRouPdev751WDlh+sOtqXRdOI8mrWf/8dN+fX+ALzNOMHTe5zSrXYVn7v2B39/feKxKFTfq8exZSEiAPBa2K7aUFBg+HH7yEzcoJkgsoZQBv/3tbxk8eHC++zz22GO8UsRRTn7Xvbv7lLZokat35E8LF7o/oHvvvWrxx+K6ITqKOYltOXn2PIlz1nPizLmCDyqkbw6fok/SWipeF87rveK5LrzsrhIa0po0gb/8xX2w+tnP/LvUw9q1LlE1aOAGpQSx782KQ17qySfh88/zfCkqJ8eNVrpWrVrByy/nu8vcuXOZOnUqIsLNN9/MuHHjeOyxxzh8+DA1atRgzpw53HjjjcyfP58xY8YQFhZGdHQ0KSkpZGVl8cUXX9CyZUsAhgwZQkxMDKNGjWLp0qVMmDCBVatWUaFCBWJjY1m3bh3t2pWA9cgGDXKdkxMnuj+mSZOK/4s/fz78/OeunyY52RWpDJAmN1ThtV4/JHHOeh5/+zPe6NOGCuWL9+eUcew0vWatJVfhT/3aU69aBT9Fa0qk7t3hD39wfYp9+8KcOa4KdnF89pkrylqzJnz0kSv7EkSWUDy2ZcsWxo8fz6effkr16tXJzMykb9++Fx9JSUkMHjyYhQsXMnbsWJYuXUqdOnUu1urasGHDZXW6Jk6cSNu2benUqRODBw9m8eLFlPP9Y23Tpg2ffPJJyUgoIu7WVESEG9KbmekqshZ1zkxyshuOecstrmZXlSr+jTcPneJqMLnnzTw9P42er61hVt82Re48Tz+QRf+5Gzhx+hzv9O9Aw5qhtbZ7yPrlL13x1GeecZ3nCxbA9dcX7b3WrXMFWatWhZUroY4Ha+UUZjq9vx/AfwFbgFygTT773Q1sA7YDIy7ZXh9Y69v+v0D5wpy3JJZemT59uj777LOXbYuJidHs7GxVVc3OztaYmBhVVR0wYIDefvvtOnPmTD3sWwc9OTlZBwwYcNnxq1ev1rCwMJ0+ffpl22fOnKlPPfVUnnEErPRKQXJzVUeNcmVREhKufR3uU6dUn3pKVUS1SxfVrKxih3StbV659YA2H7VE48ct1427M6/5fPM37NHGIxdr/LjlumHXkWs+3l+s9IqH3n5bNSJCtUkT1R07ru3YnBzVSZNUw8NVb7yxwOPLYumVzcBPgZSr7SAiYcCrwD1AU+BhEbmwLu4k4CVVbQgcBYo3yaCUeP311xk/fjx79uwhPj6eI0eOXLWMfUxMDBkZGZdt96SMfUFEXGmWV16BZcugcWM3ASwnp+BjV692txSnTYMBA9yVSaXgf7Lv2rgmC57oSIXyYTz0xzW88P4WDmadKfC4PZnfMXReGk/PT6NVvaosHnIb8d8P/NBOUwI98ggsXequUpo1g2efdXNHCrJjh5uFP3y4WyU1NdWvoxqvlVcrNn6lqtsK2K0dsF1Vd6pqNvAu0MO3jnw34MLsuLdw68qXSt26dWP+/Pkc8a0ZkpmZSceOHXnXV/cqOTn54qJbO3bsoH379owdO5YaNWqwZ8+e/yhjv3v3bn73u9+RmprKhx9+yNq1ay++5lkZ+8IYOBA2bYJ27Vz/SsuWruz9xo2Xl/0+eNBVL77tNvfIzoYVK9ztswre9TnE1arMol/fyoM/rMuf/rWbLpNXMXHxV6zefpjj37lOe1XlyMmzfLr9ME8kf0aXKR+z8PN9DOzakLf7tadm5UjP4jclQNeubqj7gw+6vsW4OPdha9UqOO0bTagKx465Dv277nIrQ65Z4yZMBmmuSX5Kch9KHWDPJc/3Au2BGOCYqp6/ZHupXVi7WbNmPPfcc3Tp0oWwsDBat27NK6+8QmJiIlOmTLnYKQ8wbNgwvv76a1SV7t2707JlS0SE48ePk5WVRaVKlejXrx9Tp06ldu3azJ49m0cffZT169cTGRnJ6tWreSFYM3SLolEjd5Uyb5676hg9GkaN+vfaJWfPugQC7lPchAku+QRgJc2iuL5ieV588GYGdGnAyyvSmfnJTv6YshOA71WJ5Pjpc5w+5668oqMi6N+5AX07fp8bokvYVaPxzo03urXohwxxVx1jxrgkEhHhZrofPuyWYgCoV8+93q+fN/0leRANUAkJEVkB5FU86TlVXeTbZxXwtKpuyOP4nsDd6ltjXkR64xLKC8C/fLe7EJF6wIeqmudHbxHpD/QHqFWrVvy7V1S8jY6OpmHDhgW2Jycnh7CijPIKghkzZlC5cuV8qwqnpaUxY8aMi2vSX2n79u0cz2M8/MmTJ6nkwW0kgIijR6m2di2V09PRsDA0IoLzFSty5JZbOBXAy3p/tflktrLrRA67jueScUqpHAExUeWoHiU0iwnjuvCSVUrFy5+1V0p6m8OzsojevJnoL74g4sQJsqtW5Vx0NN/FxpIZH1+kkadFaXPXrl0/U9U2Be5YmI6WQD2AVVylUx64BVh6yfNnfA8BDgPhee2X36Mkdsr7w+nTp3Xu3Ln57rNs2TL95ptvrvq6Z53yJVAotlk1NNttbS4cSninfGGsB+JEpL6IlAf+G3jf17iPgZ6+/foCIbAU2tVFRkbSu3fvfPe54447iI2NDU5AxpiQ5ElCEZGfiMhe3NXF30VkqW97bRFZDKCuj2QgsBT4Cpinqlt8bzEceEpEtuP6VGYXJx4NQuXYkizU22+M8Q9POuVV9a/AX/PYngHce8nzxcDiPPbbiRsFVmyRkZEcOXKEmJiYkCwPrqocOXKEyEgbYWSMKZ6SPMorKOrWrcvevXs5dCj/lfjOnDlTZv/pRkZGUrduXa/DMMaUciGfUCIiIqhfv36B+61atYrWrVsHISJjjCmdSnKnvDHGmFLEEooxxhi/sIRijDHGLwI2U74kEpFDwO4iHl4dN6Ey1IRiu0OxzRCa7bY2F873VbVGQTuFVEIpDhHZoIUpPVDGhGK7Q7HNEJrttjb7l93yMsYY4xeWUIwxxviFJZTCm+l1AB4JxXaHYpshNNttbfYj60MxxhjjF3aFYowxxi8soRSCiNwtIttEZLuIjPA6nkATkXoi8rGIfCkiW0RkiNcxBYuIhIlIqoh84HUswSIiVUXkPRHZKiJficgtXscUaCLyG9/v9mYReUdEymShPhFJEpGDIrL5km3VRGS5iHzt+3q9v85nCaUAIhIGvArcAzQFHhaRpt5GFXDngaGq2hToAPw6BNp8wRDccgmh5PfAElX9AdCSMt5+EakDDMYt7tccCMOtt1QWvQncfcW2EcBHqhoHfOR77heWUArWDtiuqjtVNRt4F+jhcUwBpar7VXWj7/ss3D+YkrFodQCJSF3gPmCW17EEi4hEA53xrSmkqtmqeszbqIIiHIgSkXCgApDhcTwBoaopQOYVm3sAb/m+fwt4wF/ns4RSsDrAnkue7yUE/rleICKxQGtgrbeRBMXLwP8AuV4HEkT1gUPAHN+tvlkiUtHroAJJVfcBU4H/A/YDx1V1mbdRBVUtVd3v+/5boJa/3tgSirkqEakE/AV4UlVPeB1PIIlIAnBQVT/zOpYgCwd+CLymqq2BU/jxFkhJ5Osz6IFLprWBiiLSy9uovOFbUt1vQ30toRRsH1Dvkud1fdvKNBGJwCWTZFVd4HU8QXArcL+I7MLd1uwmIm97G1JQ7AX2quqFK9D3cAmmLLsd+EZVD6nqOWAB0NHjmILpgIjcAOD7etBfb2wJpWDrgTgRqS8i5XGdd+97HFNAiVsLeTbwlapO8zqeYFDVZ1S1rqrG4n7GK1W1zH9qVdVvgT0i0ti3qTvwpYchBcP/AR1EpILvd707ZXwgwhXeB/r6vu8LLPLXG4f8io0FUdXzIjIQWIobDZKkqls8DivQbgV6A5tE5HPftmdVdbGHMZnAGQQk+z4w7QQSPY4noFR1rYi8B2zEjWhMpYzOmBeRd4AfAdVFZC8wGngRmCci/XDV1x/y2/lsprwxxhh/sFtexhhj/MISijHGGL+whGKMMcYvLKEYY4zxC0soxhhj/MISijHGGL+whGKMMcYvLKEY4yERaSsiX4hIpIhU9K3R0dzruIwpCpvYaIzHRGQ8EAlE4epqTfQ4JGOKxBKKMR7zlTxZD5wBOqpqjschGVMkdsvLGO/FAJWAyrgrFWNKJbtCMcZjIvI+rmR+feAGVR3ocUjGFIlVGzbGQyLSBzinqn8WkTDgUxHppqorvY7NmGtlVyjGGGP8wvpQjDHG+IUlFGOMMX5hCcUYY4xfWEIxxhjjF5ZQjDHG+IUlFGOMMX5hCcUYY4xfWEIxxhjjF/8PcmIduUmOiL4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0,10, 0.1)\n",
    "sin_x = np.sin(x)\n",
    "cos_x = np.cos(x)\n",
    "\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.grid()\n",
    "plt.plot(x, sin_x, label=\"sin(x)\")\n",
    "plt.plot(x, cos_x, \"r\", label=\"cos(x)\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next\n",
    "\n",
    "Did I get existed about calculus yet? well you will love the next part where we get into some more interesting derevativesa dn a more intuative understanding of why do we need that whole derevative in the first place."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>This work in <b>open sourced</b> and licensed under GNU General Public License v2.0<br />\n",
    "\n",
    "Copyright © 2018 Abdullah Alrasheed and other contributes<br /><br />Roshan Logo is not open sourced and is not covered by the GNU license</center>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}


================================================
FILE: notebooks/vlogs/4th dem debate.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing the Democratic Debate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from lxml import html\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from pprint import pprint\n",
    "import re\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = \"https://www.washingtonpost.com/news/the-fix/wp/2016/01/17/the-4th-democratic-debate-transcript-annotated-who-said-what-and-what-it-meant/\"\n",
    "response = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc = html.fromstring(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "para_list = doc.xpath(\"//article/p/text()\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "para_list = para_list[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"HOLT: We'll begin with 45 second opening statements from each candidate, \"\n",
      " 'starting with Secretary Clinton. ',\n",
      " 'CLINTON: Well, good evening. And I want to thank the Congressional Black '\n",
      " 'Caucus Institute and the people of Charleston for hosting us here on the '\n",
      " 'eve of Martin Luther King Day tomorrow. ']\n",
      "['HOLT: All right. Well thank you and thanks to all of you for being here tonight shedding light on some of the differences as Americans get ready to vote. ', \"I also want to thank the Congressional Black Caucus Institute and certainly my friend and colleague, Andrea Mitchell. This has been great. It's been a great spirited conversation and American people appreciate it. \"]\n"
     ]
    }
   ],
   "source": [
    "pprint(para_list[:2], compact=True)\n",
    "print(para_list[-2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOLT: We'll begin with 45 second opening state...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CLINTON: Well, good evening. And I want to tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SANDERS: Thank you. As we honor the extraordin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SANDERS: And then, to make a bad situation wor...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HOLT: Senator, thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>And Governor O'Malley, your opening statement,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HOLT: All right. And Governor, thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HOLT: All right, to our first question, now. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>President Obama came to office determined to s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Senator Sanders.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SANDERS: Well, that's what our campaign is abo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HOLT: Secretary Clinton, same question, my fir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>HOLT: Welcome back everybody. Finally, before ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>And, we'll start with Governor O'Malley.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>HOLT: Didn't see that coming, did you?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>MITCHELL: ...too long (ph).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>O'MALLEY: I believe there are many issues. I h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>O'MALLEY: There are so many issues that we hav...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>O'MALLEY: We haven't discussed the fact that i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>O'MALLEY: Thanks a lot.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>HOLT: Secretary Clinton?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>CLINTON: I want to be a president who takes ca...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>HOLT: Thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>Senator Sanders?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>SANDERS: Well, Secretary Clinton was right and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>HOLT: All right. Well thank you and thanks to ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>I also want to thank the Congressional Black C...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   raw\n",
       "0    HOLT: We'll begin with 45 second opening state...\n",
       "1    CLINTON: Well, good evening. And I want to tha...\n",
       "2    You know, I remember well when my youth minist...\n",
       "3    And that is our fight still. We have to get th...\n",
       "4    I understand that this is the hardest job in t...\n",
       "5                                           (APPLAUSE)\n",
       "6    HOLT: Thank you. Senator Sanders, your opening...\n",
       "7    SANDERS: Thank you. As we honor the extraordin...\n",
       "8    SANDERS: And then, to make a bad situation wor...\n",
       "9    This campaign is about a political revolution ...\n",
       "10                           HOLT: Senator, thank you.\n",
       "11                                          (APPLAUSE)\n",
       "12   And Governor O'Malley, your opening statement,...\n",
       "13   O'MALLEY: Thank you. My name is Martin O'Malle...\n",
       "14   And I want to thank the people of South Caroli...\n",
       "15   You taught us, in fact, in keeping with Dr. Ki...\n",
       "16   Eight years ago, you brought forward a new lea...\n",
       "17   But in order to make good on the promise of eq...\n",
       "18   We need new leadership. We need to come togeth...\n",
       "19   That's why I'm running for president. I need y...\n",
       "20                                         Thank you. \n",
       "21           HOLT: All right. And Governor, thank you.\n",
       "22                                          (APPLAUSE)\n",
       "23   HOLT: All right, to our first question, now. T...\n",
       "24   President Obama came to office determined to s...\n",
       "25                                   Senator Sanders. \n",
       "26   SANDERS: Well, that's what our campaign is abo...\n",
       "27   So, what my first days are about is bringing A...\n",
       "28                                          (APPLAUSE)\n",
       "29   HOLT: Secretary Clinton, same question, my fir...\n",
       "..                                                 ...\n",
       "592  HOLT: Welcome back everybody. Finally, before ...\n",
       "593           And, we'll start with Governor O'Malley.\n",
       "594                                         (LAUGHTER)\n",
       "595             HOLT: Didn't see that coming, did you?\n",
       "596  O'MALLEY: Yes, but we're going to have to get ...\n",
       "597                                         (LAUGHTER)\n",
       "598                        MITCHELL: ...too long (ph).\n",
       "599  O'MALLEY: I believe there are many issues. I h...\n",
       "600           HOLT: Sixty seconds, we'd appreciate it.\n",
       "601  O'MALLEY: There are so many issues that we hav...\n",
       "602                                         (APPLAUSE)\n",
       "603  O'MALLEY: We haven't discussed the fact that i...\n",
       "604  I guess the bottom line is this, look we are a...\n",
       "605  We're on the threshold of a new era of America...\n",
       "606                            HOLT: And that's time. \n",
       "607                           O'MALLEY: Thanks a lot. \n",
       "608                          HOLT: Secretary Clinton? \n",
       "609  CLINTON: Well Lester, I spent a lot of time la...\n",
       "610  He had request for help and he had basically s...\n",
       "611  So I sent my top campaign aide down there to t...\n",
       "612                            HOLT: And that's time. \n",
       "613  CLINTON: I want to be a president who takes ca...\n",
       "614                                         (APPLAUSE)\n",
       "615                                  HOLT: Thank you. \n",
       "616                                  Senator Sanders? \n",
       "617  SANDERS: Well, Secretary Clinton was right and...\n",
       "618  Now, we are a great nation -- and we've heard ...\n",
       "619  We've got to get rid of Super PACs, we've got ...\n",
       "620  HOLT: All right. Well thank you and thanks to ...\n",
       "621  I also want to thank the Congressional Black C...\n",
       "\n",
       "[622 rows x 1 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.DataFrame(para_list, columns=[\"raw\"])\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_name(x):\n",
    "    r = re.findall(r\"^([A-Z']*):\", x)\n",
    "    if r:\n",
    "        return r[0]\n",
    "    else:\n",
    "        return np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw</th>\n",
       "      <th>speaker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOLT: We'll begin with 45 second opening state...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CLINTON: Well, good evening. And I want to tha...</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "      <td>CLINTON</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "      <td>CLINTON</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\n",
       "      <td>HOLT</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SANDERS: Thank you. As we honor the extraordin...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SANDERS: And then, to make a bad situation wor...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HOLT: Senator, thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>And Governor O'Malley, your opening statement,...</td>\n",
       "      <td>HOLT</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\n",
       "      <td>O'MALLEY</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "      <td>O'MALLEY</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "      <td>O'MALLEY</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Thank you.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HOLT: All right. And Governor, thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>HOLT</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HOLT: All right, to our first question, now. T...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>President Obama came to office determined to s...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Senator Sanders.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SANDERS: Well, that's what our campaign is abo...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HOLT: Secretary Clinton, same question, my fir...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>HOLT: Welcome back everybody. Finally, before ...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>And, we'll start with Governor O'Malley.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>HOLT: Didn't see that coming, did you?</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>MITCHELL: ...too long (ph).</td>\n",
       "      <td>MITCHELL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>O'MALLEY: I believe there are many issues. I h...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>O'MALLEY: There are so many issues that we hav...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>O'MALLEY: We haven't discussed the fact that i...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>O'MALLEY: Thanks a lot.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>HOLT: Secretary Clinton?</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>CLINTON: I want to be a president who takes ca...</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>HOLT: Thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>Senator Sanders?</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>SANDERS: Well, Secretary Clinton was right and...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "      <td>SANDERS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>HOLT: All right. Well thank you and thanks to ...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>I also want to thank the Congressional Black C...</td>\n",
       "      <td>HOLT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   raw   speaker\n",
       "0    HOLT: We'll begin with 45 second opening state...      HOLT\n",
       "1    CLINTON: Well, good evening. And I want to tha...   CLINTON\n",
       "2    You know, I remember well when my youth minist...   CLINTON\n",
       "3    And that is our fight still. We have to get th...   CLINTON\n",
       "4    I understand that this is the hardest job in t...   CLINTON\n",
       "5                                           (APPLAUSE)   CLINTON\n",
       "6    HOLT: Thank you. Senator Sanders, your opening...      HOLT\n",
       "7    SANDERS: Thank you. As we honor the extraordin...   SANDERS\n",
       "8    SANDERS: And then, to make a bad situation wor...   SANDERS\n",
       "9    This campaign is about a political revolution ...   SANDERS\n",
       "10                           HOLT: Senator, thank you.      HOLT\n",
       "11                                          (APPLAUSE)      HOLT\n",
       "12   And Governor O'Malley, your opening statement,...      HOLT\n",
       "13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY\n",
       "14   And I want to thank the people of South Caroli...  O'MALLEY\n",
       "15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY\n",
       "16   Eight years ago, you brought forward a new lea...  O'MALLEY\n",
       "17   But in order to make good on the promise of eq...  O'MALLEY\n",
       "18   We need new leadership. We need to come togeth...  O'MALLEY\n",
       "19   That's why I'm running for president. I need y...  O'MALLEY\n",
       "20                                         Thank you.   O'MALLEY\n",
       "21           HOLT: All right. And Governor, thank you.      HOLT\n",
       "22                                          (APPLAUSE)      HOLT\n",
       "23   HOLT: All right, to our first question, now. T...      HOLT\n",
       "24   President Obama came to office determined to s...      HOLT\n",
       "25                                   Senator Sanders.       HOLT\n",
       "26   SANDERS: Well, that's what our campaign is abo...   SANDERS\n",
       "27   So, what my first days are about is bringing A...   SANDERS\n",
       "28                                          (APPLAUSE)   SANDERS\n",
       "29   HOLT: Secretary Clinton, same question, my fir...      HOLT\n",
       "..                                                 ...       ...\n",
       "592  HOLT: Welcome back everybody. Finally, before ...      HOLT\n",
       "593           And, we'll start with Governor O'Malley.      HOLT\n",
       "594                                         (LAUGHTER)      HOLT\n",
       "595             HOLT: Didn't see that coming, did you?      HOLT\n",
       "596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY\n",
       "597                                         (LAUGHTER)  O'MALLEY\n",
       "598                        MITCHELL: ...too long (ph).  MITCHELL\n",
       "599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY\n",
       "600           HOLT: Sixty seconds, we'd appreciate it.      HOLT\n",
       "601  O'MALLEY: There are so many issues that we hav...  O'MALLEY\n",
       "602                                         (APPLAUSE)  O'MALLEY\n",
       "603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY\n",
       "604  I guess the bottom line is this, look we are a...  O'MALLEY\n",
       "605  We're on the threshold of a new era of America...  O'MALLEY\n",
       "606                            HOLT: And that's time.       HOLT\n",
       "607                           O'MALLEY: Thanks a lot.   O'MALLEY\n",
       "608                          HOLT: Secretary Clinton?       HOLT\n",
       "609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON\n",
       "610  He had request for help and he had basically s...   CLINTON\n",
       "611  So I sent my top campaign aide down there to t...   CLINTON\n",
       "612                            HOLT: And that's time.       HOLT\n",
       "613  CLINTON: I want to be a president who takes ca...   CLINTON\n",
       "614                                         (APPLAUSE)   CLINTON\n",
       "615                                  HOLT: Thank you.       HOLT\n",
       "616                                  Senator Sanders?       HOLT\n",
       "617  SANDERS: Well, Secretary Clinton was right and...   SANDERS\n",
       "618  Now, we are a great nation -- and we've heard ...   SANDERS\n",
       "619  We've got to get rid of Super PACs, we've got ...   SANDERS\n",
       "620  HOLT: All right. Well thank you and thanks to ...      HOLT\n",
       "621  I also want to thank the Congressional Black C...      HOLT\n",
       "\n",
       "[622 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"speaker\"] = dataset.raw.apply(get_name).fillna(method='ffill')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SANDERS     168\n",
       "HOLT        152\n",
       "CLINTON     131\n",
       "O'MALLEY    113\n",
       "MITCHELL     43\n",
       "TODD          7\n",
       "BROWNLEE      4\n",
       "FRANTA        2\n",
       "MILLER        2\n",
       "Name: speaker, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.speaker.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw</th>\n",
       "      <th>speaker</th>\n",
       "      <th>speach</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOLT: We'll begin with 45 second opening state...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>We'll begin with 45 second opening statements ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CLINTON: Well, good evening. And I want to tha...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, good evening. And I want to thank the Co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Thank you. Senator Sanders, your opening state...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SANDERS: Thank you. As we honor the extraordin...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Thank you. As we honor the extraordinary life ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SANDERS: And then, to make a bad situation wor...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>And then, to make a bad situation worse, we ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HOLT: Senator, thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Senator, thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>And Governor O'Malley, your opening statement,...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And Governor O'Malley, your opening statement,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you. My name is Martin O'Malley, I was b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Thank you.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HOLT: All right. And Governor, thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>All right. And Governor, thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HOLT: All right, to our first question, now. T...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>All right, to our first question, now. The fir...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>President Obama came to office determined to s...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>President Obama came to office determined to s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Senator Sanders.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Senator Sanders.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SANDERS: Well, that's what our campaign is abo...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well, that's what our campaign is about. It is...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HOLT: Secretary Clinton, same question, my fir...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Secretary Clinton, same question, my first 100...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>HOLT: Welcome back everybody. Finally, before ...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Welcome back everybody. Finally, before we go ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>And, we'll start with Governor O'Malley.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And, we'll start with Governor O'Malley.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>(LAUGHTER)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>HOLT: Didn't see that coming, did you?</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Didn't see that coming, did you?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Yes, but we're going to have to get 20 minutes...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(LAUGHTER)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>MITCHELL: ...too long (ph).</td>\n",
       "      <td>MITCHELL</td>\n",
       "      <td>...too long (ph).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>O'MALLEY: I believe there are many issues. I h...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>I believe there are many issues. I have 60 sec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Sixty seconds, we'd appreciate it.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>O'MALLEY: There are so many issues that we hav...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>There are so many issues that we haven't been ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>O'MALLEY: We haven't discussed the fact that i...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We haven't discussed the fact that in our hemi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And that's time.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>O'MALLEY: Thanks a lot.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thanks a lot.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>HOLT: Secretary Clinton?</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Secretary Clinton?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well Lester, I spent a lot of time last week b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And that's time.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>CLINTON: I want to be a president who takes ca...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I want to be a president who takes care of the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>HOLT: Thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Thank you.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>Senator Sanders?</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Senator Sanders?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>SANDERS: Well, Secretary Clinton was right and...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well, Secretary Clinton was right and what I d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>HOLT: All right. Well thank you and thanks to ...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>All right. Well thank you and thanks to all of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>I also want to thank the Congressional Black C...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>I also want to thank the Congressional Black C...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   raw   speaker  \\\n",
       "0    HOLT: We'll begin with 45 second opening state...      HOLT   \n",
       "1    CLINTON: Well, good evening. And I want to tha...   CLINTON   \n",
       "2    You know, I remember well when my youth minist...   CLINTON   \n",
       "3    And that is our fight still. We have to get th...   CLINTON   \n",
       "4    I understand that this is the hardest job in t...   CLINTON   \n",
       "5                                           (APPLAUSE)   CLINTON   \n",
       "6    HOLT: Thank you. Senator Sanders, your opening...      HOLT   \n",
       "7    SANDERS: Thank you. As we honor the extraordin...   SANDERS   \n",
       "8    SANDERS: And then, to make a bad situation wor...   SANDERS   \n",
       "9    This campaign is about a political revolution ...   SANDERS   \n",
       "10                           HOLT: Senator, thank you.      HOLT   \n",
       "11                                          (APPLAUSE)      HOLT   \n",
       "12   And Governor O'Malley, your opening statement,...      HOLT   \n",
       "13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY   \n",
       "14   And I want to thank the people of South Caroli...  O'MALLEY   \n",
       "15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY   \n",
       "16   Eight years ago, you brought forward a new lea...  O'MALLEY   \n",
       "17   But in order to make good on the promise of eq...  O'MALLEY   \n",
       "18   We need new leadership. We need to come togeth...  O'MALLEY   \n",
       "19   That's why I'm running for president. I need y...  O'MALLEY   \n",
       "20                                         Thank you.   O'MALLEY   \n",
       "21           HOLT: All right. And Governor, thank you.      HOLT   \n",
       "22                                          (APPLAUSE)      HOLT   \n",
       "23   HOLT: All right, to our first question, now. T...      HOLT   \n",
       "24   President Obama came to office determined to s...      HOLT   \n",
       "25                                   Senator Sanders.       HOLT   \n",
       "26   SANDERS: Well, that's what our campaign is abo...   SANDERS   \n",
       "27   So, what my first days are about is bringing A...   SANDERS   \n",
       "28                                          (APPLAUSE)   SANDERS   \n",
       "29   HOLT: Secretary Clinton, same question, my fir...      HOLT   \n",
       "..                                                 ...       ...   \n",
       "592  HOLT: Welcome back everybody. Finally, before ...      HOLT   \n",
       "593           And, we'll start with Governor O'Malley.      HOLT   \n",
       "594                                         (LAUGHTER)      HOLT   \n",
       "595             HOLT: Didn't see that coming, did you?      HOLT   \n",
       "596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY   \n",
       "597                                         (LAUGHTER)  O'MALLEY   \n",
       "598                        MITCHELL: ...too long (ph).  MITCHELL   \n",
       "599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY   \n",
       "600           HOLT: Sixty seconds, we'd appreciate it.      HOLT   \n",
       "601  O'MALLEY: There are so many issues that we hav...  O'MALLEY   \n",
       "602                                         (APPLAUSE)  O'MALLEY   \n",
       "603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY   \n",
       "604  I guess the bottom line is this, look we are a...  O'MALLEY   \n",
       "605  We're on the threshold of a new era of America...  O'MALLEY   \n",
       "606                            HOLT: And that's time.       HOLT   \n",
       "607                           O'MALLEY: Thanks a lot.   O'MALLEY   \n",
       "608                          HOLT: Secretary Clinton?       HOLT   \n",
       "609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON   \n",
       "610  He had request for help and he had basically s...   CLINTON   \n",
       "611  So I sent my top campaign aide down there to t...   CLINTON   \n",
       "612                            HOLT: And that's time.       HOLT   \n",
       "613  CLINTON: I want to be a president who takes ca...   CLINTON   \n",
       "614                                         (APPLAUSE)   CLINTON   \n",
       "615                                  HOLT: Thank you.       HOLT   \n",
       "616                                  Senator Sanders?       HOLT   \n",
       "617  SANDERS: Well, Secretary Clinton was right and...   SANDERS   \n",
       "618  Now, we are a great nation -- and we've heard ...   SANDERS   \n",
       "619  We've got to get rid of Super PACs, we've got ...   SANDERS   \n",
       "620  HOLT: All right. Well thank you and thanks to ...      HOLT   \n",
       "621  I also want to thank the Congressional Black C...      HOLT   \n",
       "\n",
       "                                                speach  \n",
       "0    We'll begin with 45 second opening statements ...  \n",
       "1    Well, good evening. And I want to thank the Co...  \n",
       "2    You know, I remember well when my youth minist...  \n",
       "3    And that is our fight still. We have to get th...  \n",
       "4    I understand that this is the hardest job in t...  \n",
       "5                                           (APPLAUSE)  \n",
       "6    Thank you. Senator Sanders, your opening state...  \n",
       "7    Thank you. As we honor the extraordinary life ...  \n",
       "8    And then, to make a bad situation worse, we ha...  \n",
       "9    This campaign is about a political revolution ...  \n",
       "10                                 Senator, thank you.  \n",
       "11                                          (APPLAUSE)  \n",
       "12   And Governor O'Malley, your opening statement,...  \n",
       "13   Thank you. My name is Martin O'Malley, I was b...  \n",
       "14   And I want to thank the people of South Caroli...  \n",
       "15   You taught us, in fact, in keeping with Dr. Ki...  \n",
       "16   Eight years ago, you brought forward a new lea...  \n",
       "17   But in order to make good on the promise of eq...  \n",
       "18   We need new leadership. We need to come togeth...  \n",
       "19   That's why I'm running for president. I need y...  \n",
       "20                                         Thank you.   \n",
       "21                 All right. And Governor, thank you.  \n",
       "22                                          (APPLAUSE)  \n",
       "23   All right, to our first question, now. The fir...  \n",
       "24   President Obama came to office determined to s...  \n",
       "25                                   Senator Sanders.   \n",
       "26   Well, that's what our campaign is about. It is...  \n",
       "27   So, what my first days are about is bringing A...  \n",
       "28                                          (APPLAUSE)  \n",
       "29   Secretary Clinton, same question, my first 100...  \n",
       "..                                                 ...  \n",
       "592  Welcome back everybody. Finally, before we go ...  \n",
       "593           And, we'll start with Governor O'Malley.  \n",
       "594                                         (LAUGHTER)  \n",
       "595                   Didn't see that coming, did you?  \n",
       "596  Yes, but we're going to have to get 20 minutes...  \n",
       "597                                         (LAUGHTER)  \n",
       "598                                  ...too long (ph).  \n",
       "599  I believe there are many issues. I have 60 sec...  \n",
       "600                 Sixty seconds, we'd appreciate it.  \n",
       "601  There are so many issues that we haven't been ...  \n",
       "602                                         (APPLAUSE)  \n",
       "603  We haven't discussed the fact that in our hemi...  \n",
       "604  I guess the bottom line is this, look we are a...  \n",
       "605  We're on the threshold of a new era of America...  \n",
       "606                                  And that's time.   \n",
       "607                                     Thanks a lot.   \n",
       "608                                Secretary Clinton?   \n",
       "609  Well Lester, I spent a lot of time last week b...  \n",
       "610  He had request for help and he had basically s...  \n",
       "611  So I sent my top campaign aide down there to t...  \n",
       "612                                  And that's time.   \n",
       "613  I want to be a president who takes care of the...  \n",
       "614                                         (APPLAUSE)  \n",
       "615                                        Thank you.   \n",
       "616                                  Senator Sanders?   \n",
       "617  Well, Secretary Clinton was right and what I d...  \n",
       "618  Now, we are a great nation -- and we've heard ...  \n",
       "619  We've got to get rid of Super PACs, we've got ...  \n",
       "620  All right. Well thank you and thanks to all of...  \n",
       "621  I also want to thank the Congressional Black C...  \n",
       "\n",
       "[622 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_speach = lambda x: re.sub(\"^[A-Z']*:\\s\", \"\", x)\n",
    "dataset[\"speach\"] = dataset.raw.apply(get_speach)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "applause_ds = dataset[dataset.speach == \"(APPLAUSE)\"]\n",
    "len(applause_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SANDERS     12\n",
       "CLINTON     12\n",
       "O'MALLEY     7\n",
       "HOLT         3\n",
       "Name: speaker, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "applause_ds.speaker.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HOLT         3\n",
       "O'MALLEY     7\n",
       "SANDERS     12\n",
       "CLINTON     12\n",
       "Name: speaker, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1xn8ucHtVfbe7j1nda5OJ6ll04EzOQpLWHyMjI4yMjNy/vnjx4jXqP9WMDuClwMeTHMXg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feabc0afe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "applause_counts = applause_ds.speaker.value_counts().sort_values()\n",
    "\n",
    "bottom = [index for index, item in enumerate(applause_counts.index)]\n",
    "plt.barh(bottom, width=applause_counts, color=\"orange\", linewidth=0)\n",
    "\n",
    "y_labels = [\"%s %.1f%%\" % (item, 100.0*applause_counts[item]/len(applause_ds)) for index,item in enumerate(applause_counts.index)]\n",
    "plt.yticks(np.array(bottom)+0.4, y_labels)\n",
    "\n",
    "applause_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "word_count = lambda x: len(re.findall(\"[A-Z]{2,}(?![a-z])|[A-Z][a-z]+(?=[A-Z])|[\\'\\w\\-]+\",x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw</th>\n",
       "      <th>speaker</th>\n",
       "      <th>speach</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOLT: We'll begin with 45 second opening state...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>We'll begin with 45 second opening statements ...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CLINTON: Well, good evening. And I want to tha...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, good evening. And I want to thank the Co...</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HOLT: Thank you. Senator Sanders, your opening...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Thank you. Senator Sanders, your opening state...</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SANDERS: Thank you. As we honor the extraordin...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Thank you. As we honor the extraordinary life ...</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SANDERS: And then, to make a bad situation wor...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>And then, to make a bad situation worse, we ha...</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HOLT: Senator, thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Senator, thank you.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>And Governor O'Malley, your opening statement,...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And Governor O'Malley, your opening statement,...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you. My name is Martin O'Malley, I was b...</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Thank you.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HOLT: All right. And Governor, thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>All right. And Governor, thank you.</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HOLT: All right, to our first question, now. T...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>All right, to our first question, now. The fir...</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>President Obama came to office determined to s...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>President Obama came to office determined to s...</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Senator Sanders.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Senator Sanders.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SANDERS: Well, that's what our campaign is abo...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well, that's what our campaign is about. It is...</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HOLT: Secretary Clinton, same question, my fir...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Secretary Clinton, same question, my first 100...</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>HOLT: Welcome back everybody. Finally, before ...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Welcome back everybody. Finally, before we go ...</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>And, we'll start with Governor O'Malley.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And, we'll start with Governor O'Malley.</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>HOLT: Didn't see that coming, did you?</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Didn't see that coming, did you?</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Yes, but we're going to have to get 20 minutes...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>MITCHELL: ...too long (ph).</td>\n",
       "      <td>MITCHELL</td>\n",
       "      <td>...too long (ph).</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>O'MALLEY: I believe there are many issues. I h...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>I believe there are many issues. I have 60 sec...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>HOLT: Sixty seconds, we'd appreciate it.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Sixty seconds, we'd appreciate it.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>O'MALLEY: There are so many issues that we hav...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>There are so many issues that we haven't been ...</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>O'MALLEY: We haven't discussed the fact that i...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We haven't discussed the fact that in our hemi...</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And that's time.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>O'MALLEY: Thanks a lot.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thanks a lot.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>HOLT: Secretary Clinton?</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Secretary Clinton?</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well Lester, I spent a lot of time last week b...</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>HOLT: And that's time.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>And that's time.</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>CLINTON: I want to be a president who takes ca...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I want to be a president who takes care of the...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>HOLT: Thank you.</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Thank you.</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>Senator Sanders?</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>Senator Sanders?</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>SANDERS: Well, Secretary Clinton was right and...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well, Secretary Clinton was right and what I d...</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>HOLT: All right. Well thank you and thanks to ...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>All right. Well thank you and thanks to all of...</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>I also want to thank the Congressional Black C...</td>\n",
       "      <td>HOLT</td>\n",
       "      <td>I also want to thank the Congressional Black C...</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>622 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   raw   speaker  \\\n",
       "0    HOLT: We'll begin with 45 second opening state...      HOLT   \n",
       "1    CLINTON: Well, good evening. And I want to tha...   CLINTON   \n",
       "2    You know, I remember well when my youth minist...   CLINTON   \n",
       "3    And that is our fight still. We have to get th...   CLINTON   \n",
       "4    I understand that this is the hardest job in t...   CLINTON   \n",
       "5                                           (APPLAUSE)   CLINTON   \n",
       "6    HOLT: Thank you. Senator Sanders, your opening...      HOLT   \n",
       "7    SANDERS: Thank you. As we honor the extraordin...   SANDERS   \n",
       "8    SANDERS: And then, to make a bad situation wor...   SANDERS   \n",
       "9    This campaign is about a political revolution ...   SANDERS   \n",
       "10                           HOLT: Senator, thank you.      HOLT   \n",
       "11                                          (APPLAUSE)      HOLT   \n",
       "12   And Governor O'Malley, your opening statement,...      HOLT   \n",
       "13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY   \n",
       "14   And I want to thank the people of South Caroli...  O'MALLEY   \n",
       "15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY   \n",
       "16   Eight years ago, you brought forward a new lea...  O'MALLEY   \n",
       "17   But in order to make good on the promise of eq...  O'MALLEY   \n",
       "18   We need new leadership. We need to come togeth...  O'MALLEY   \n",
       "19   That's why I'm running for president. I need y...  O'MALLEY   \n",
       "20                                         Thank you.   O'MALLEY   \n",
       "21           HOLT: All right. And Governor, thank you.      HOLT   \n",
       "22                                          (APPLAUSE)      HOLT   \n",
       "23   HOLT: All right, to our first question, now. T...      HOLT   \n",
       "24   President Obama came to office determined to s...      HOLT   \n",
       "25                                   Senator Sanders.       HOLT   \n",
       "26   SANDERS: Well, that's what our campaign is abo...   SANDERS   \n",
       "27   So, what my first days are about is bringing A...   SANDERS   \n",
       "28                                          (APPLAUSE)   SANDERS   \n",
       "29   HOLT: Secretary Clinton, same question, my fir...      HOLT   \n",
       "..                                                 ...       ...   \n",
       "592  HOLT: Welcome back everybody. Finally, before ...      HOLT   \n",
       "593           And, we'll start with Governor O'Malley.      HOLT   \n",
       "594                                         (LAUGHTER)      HOLT   \n",
       "595             HOLT: Didn't see that coming, did you?      HOLT   \n",
       "596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY   \n",
       "597                                         (LAUGHTER)  O'MALLEY   \n",
       "598                        MITCHELL: ...too long (ph).  MITCHELL   \n",
       "599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY   \n",
       "600           HOLT: Sixty seconds, we'd appreciate it.      HOLT   \n",
       "601  O'MALLEY: There are so many issues that we hav...  O'MALLEY   \n",
       "602                                         (APPLAUSE)  O'MALLEY   \n",
       "603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY   \n",
       "604  I guess the bottom line is this, look we are a...  O'MALLEY   \n",
       "605  We're on the threshold of a new era of America...  O'MALLEY   \n",
       "606                            HOLT: And that's time.       HOLT   \n",
       "607                           O'MALLEY: Thanks a lot.   O'MALLEY   \n",
       "608                          HOLT: Secretary Clinton?       HOLT   \n",
       "609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON   \n",
       "610  He had request for help and he had basically s...   CLINTON   \n",
       "611  So I sent my top campaign aide down there to t...   CLINTON   \n",
       "612                            HOLT: And that's time.       HOLT   \n",
       "613  CLINTON: I want to be a president who takes ca...   CLINTON   \n",
       "614                                         (APPLAUSE)   CLINTON   \n",
       "615                                  HOLT: Thank you.       HOLT   \n",
       "616                                  Senator Sanders?       HOLT   \n",
       "617  SANDERS: Well, Secretary Clinton was right and...   SANDERS   \n",
       "618  Now, we are a great nation -- and we've heard ...   SANDERS   \n",
       "619  We've got to get rid of Super PACs, we've got ...   SANDERS   \n",
       "620  HOLT: All right. Well thank you and thanks to ...      HOLT   \n",
       "621  I also want to thank the Congressional Black C...      HOLT   \n",
       "\n",
       "                                                speach  word_count  \n",
       "0    We'll begin with 45 second opening statements ...          14  \n",
       "1    Well, good evening. And I want to thank the Co...          31  \n",
       "2    You know, I remember well when my youth minist...          67  \n",
       "3    And that is our fight still. We have to get th...          50  \n",
       "4    I understand that this is the hardest job in t...          42  \n",
       "5                                           (APPLAUSE)           1  \n",
       "6    Thank you. Senator Sanders, your opening state...           8  \n",
       "7    Thank you. As we honor the extraordinary life ...          88  \n",
       "8    And then, to make a bad situation worse, we ha...          28  \n",
       "9    This campaign is about a political revolution ...          18  \n",
       "10                                 Senator, thank you.           3  \n",
       "11                                          (APPLAUSE)           1  \n",
       "12   And Governor O'Malley, your opening statement,...           7  \n",
       "13   Thank you. My name is Martin O'Malley, I was b...          21  \n",
       "14   And I want to thank the people of South Caroli...          38  \n",
       "15   You taught us, in fact, in keeping with Dr. Ki...          37  \n",
       "16   Eight years ago, you brought forward a new lea...          34  \n",
       "17   But in order to make good on the promise of eq...          47  \n",
       "18   We need new leadership. We need to come togeth...          23  \n",
       "19   That's why I'm running for president. I need y...          26  \n",
       "20                                         Thank you.            2  \n",
       "21                 All right. And Governor, thank you.           6  \n",
       "22                                          (APPLAUSE)           1  \n",
       "23   All right, to our first question, now. The fir...          18  \n",
       "24   President Obama came to office determined to s...          52  \n",
       "25                                   Senator Sanders.            2  \n",
       "26   Well, that's what our campaign is about. It is...          68  \n",
       "27   So, what my first days are about is bringing A...          61  \n",
       "28                                          (APPLAUSE)           1  \n",
       "29   Secretary Clinton, same question, my first 100...          16  \n",
       "..                                                 ...         ...  \n",
       "592  Welcome back everybody. Finally, before we go ...          48  \n",
       "593           And, we'll start with Governor O'Malley.           6  \n",
       "594                                         (LAUGHTER)           1  \n",
       "595                   Didn't see that coming, did you?           6  \n",
       "596  Yes, but we're going to have to get 20 minutes...          14  \n",
       "597                                         (LAUGHTER)           1  \n",
       "598                                  ...too long (ph).           3  \n",
       "599  I believe there are many issues. I have 60 sec...          12  \n",
       "600                 Sixty seconds, we'd appreciate it.           5  \n",
       "601  There are so many issues that we haven't been ...          61  \n",
       "602                                         (APPLAUSE)           1  \n",
       "603  We haven't discussed the fact that in our hemi...          27  \n",
       "604  I guess the bottom line is this, look we are a...          80  \n",
       "605  We're on the threshold of a new era of America...          33  \n",
       "606                                  And that's time.            3  \n",
       "607                                     Thanks a lot.            3  \n",
       "608                                Secretary Clinton?            2  \n",
       "609  Well Lester, I spent a lot of time last week b...          72  \n",
       "610  He had request for help and he had basically s...          38  \n",
       "611  So I sent my top campaign aide down there to t...          59  \n",
       "612                                  And that's time.            3  \n",
       "613  I want to be a president who takes care of the...          25  \n",
       "614                                         (APPLAUSE)           1  \n",
       "615                                        Thank you.            2  \n",
       "616                                  Senator Sanders?            2  \n",
       "617  Well, Secretary Clinton was right and what I d...          32  \n",
       "618  Now, we are a great nation -- and we've heard ...          58  \n",
       "619  We've got to get rid of Super PACs, we've got ...          73  \n",
       "620  All right. Well thank you and thanks to all of...          28  \n",
       "621  I also want to thank the Congressional Black C...          33  \n",
       "\n",
       "[622 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"word_count\"] = dataset.speach.apply(word_count)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "words_ds = dataset[dataset.speaker.isin([\"CLINTON\",\"SANDERS\",\"O'MALLEY\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "speaker\n",
       "O'MALLEY    25.115044\n",
       "SANDERS     27.351190\n",
       "CLINTON     32.656489\n",
       "Name: word_count, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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afLp3LibXvw7w0STn0wXIx/T2fVZVXVFVt7V97NzW31xVE8+PLAG2GJ5eSdJdaVbPIAC0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\nJNowfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feab583bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "words_counts = words_ds.pivot_table(values=\"word_count\", index=\"speaker\", columns=None, aggfunc='mean',).sort_values()\n",
    "\n",
    "bottom = [index for index, item in enumerate(words_counts.index)]\n",
    "plt.barh(bottom, width=words_counts, color=\"orange\", linewidth=0)\n",
    "\n",
    "y_labels = [\"%s %.1f words/paragraph\" % (item, words_counts[item]) for index,item in enumerate(words_counts.index)]\n",
    "plt.yticks(np.array(bottom)+0.4, y_labels)\n",
    "\n",
    "words_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "speaker\n",
       "O'MALLEY    2838\n",
       "CLINTON     4278\n",
       "SANDERS     4595\n",
       "Name: word_count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HSU5j9c+SZ88S38XwUtQpw4vkfLuqDgBIsh3wsKr6wqhPy5L8EfDJ9ub5j4A/mnCei4FH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\nKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feab57e6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "words_counts = words_ds.pivot_table(values=\"word_count\", index=\"speaker\", columns=None, aggfunc='sum',).sort_values()\n",
    "\n",
    "bottom = [index for index, item in enumerate(words_counts.index)]\n",
    "plt.barh(bottom, width=words_counts, color=\"orange\", linewidth=0)\n",
    "\n",
    "y_labels = [\"%s %d (%.1f%%)\" % (item, words_counts[item], 100.0*words_counts[item]/np.sum(words_counts)) for index,item in enumerate(words_counts.index)]\n",
    "plt.yticks(np.array(bottom)+0.4, y_labels)\n",
    "\n",
    "words_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CLINTON': 0, \"O'MALLEY\": 2, 'SANDERS': 1}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speaker_dict = {value:index for index,value in enumerate(words_ds.speaker.unique())}\n",
    "speaker_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-c:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw</th>\n",
       "      <th>speaker</th>\n",
       "      <th>speach</th>\n",
       "      <th>word_count</th>\n",
       "      <th>speaker_no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CLINTON: Well, good evening. And I want to tha...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well, good evening. And I want to thank the Co...</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>You know, I remember well when my youth minist...</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And that is our fight still. We have to get th...</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I understand that this is the hardest job in t...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SANDERS: Thank you. As we honor the extraordin...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Thank you. As we honor the extraordinary life ...</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SANDERS: And then, to make a bad situation wor...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>And then, to make a bad situation worse, we ha...</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>This campaign is about a political revolution ...</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>O'MALLEY: Thank you. My name is Martin O'Malle...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you. My name is Martin O'Malley, I was b...</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>And I want to thank the people of South Caroli...</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>You taught us, in fact, in keeping with Dr. Ki...</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Eight years ago, you brought forward a new lea...</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>But in order to make good on the promise of eq...</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We need new leadership. We need to come togeth...</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>That's why I'm running for president. I need y...</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Thank you.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you.</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>SANDERS: Well, that's what our campaign is abo...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well, that's what our campaign is about. It is...</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>So, what my first days are about is bringing A...</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>CLINTON: I would work quickly to present to th...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I would work quickly to present to the Congres...</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>I would also...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I would also...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>I would also be presenting my plans to build o...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I would also be presenting my plans to build o...</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>And third, I would be working, in every way th...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>And third, I would be working, in every way th...</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>O'MALLEY: Thank you. First of all, I would lay...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thank you. First of all, I would lay out an ag...</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Secondly, I believe the greatest business oppo...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Secondly, I believe the greatest business oppo...</td>\n",
       "      <td>48</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>O'MALLEY: Finally -- I'm sorry, that was secon...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Finally -- I'm sorry, that was second, Lester.</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>O'MALLEY: And third and finally, we need a new...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>And third and finally, we need a new agenda fo...</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>We just have to do more of it, and we have to ...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>We just have to do more of it, and we have to ...</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>SANDERS: Great ideas, Governor O'Malley, Secre...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Great ideas, Governor O'Malley, Secretary Clin...</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>So here's a promise that I make -- and I menti...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>So here's a promise that I make -- and I menti...</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>Here's a promise. If elected president, Goldma...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Here's a promise. If elected president, Goldma...</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>SANDERS: I was asked a question. You know, one...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>I was asked a question. You know, one of the t...</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>I have avoided doing that. Trying to run an is...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>I have avoided doing that. Trying to run an is...</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>SANDERS: I was asked a question.</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>I was asked a question.</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>SANDERS: Well -- then if I don't answer it, th...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well -- then if I don't answer it, then there'...</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>And I mean this seriously. You know that. We'v...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>And I mean this seriously. You know that. We'v...</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>O'MALLEY: Yes, but we're going to have to get ...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Yes, but we're going to have to get 20 minutes...</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(LAUGHTER)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>O'MALLEY: I believe there are many issues. I h...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>I believe there are many issues. I have 60 sec...</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>O'MALLEY: There are so many issues that we hav...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>There are so many issues that we haven't been ...</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>O'MALLEY: We haven't discussed the fact that i...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We haven't discussed the fact that in our hemi...</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>I guess the bottom line is this, look we are a...</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>We're on the threshold of a new era of America...</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>O'MALLEY: Thanks a lot.</td>\n",
       "      <td>O'MALLEY</td>\n",
       "      <td>Thanks a lot.</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>CLINTON: Well Lester, I spent a lot of time la...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>Well Lester, I spent a lot of time last week b...</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>He had request for help and he had basically s...</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>So I sent my top campaign aide down there to t...</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>CLINTON: I want to be a president who takes ca...</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>I want to be a president who takes care of the...</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614</th>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>CLINTON</td>\n",
       "      <td>(APPLAUSE)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>SANDERS: Well, Secretary Clinton was right and...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Well, Secretary Clinton was right and what I d...</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>Now, we are a great nation -- and we've heard ...</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "      <td>SANDERS</td>\n",
       "      <td>We've got to get rid of Super PACs, we've got ...</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>412 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   raw   speaker  \\\n",
       "1    CLINTON: Well, good evening. And I want to tha...   CLINTON   \n",
       "2    You know, I remember well when my youth minist...   CLINTON   \n",
       "3    And that is our fight still. We have to get th...   CLINTON   \n",
       "4    I understand that this is the hardest job in t...   CLINTON   \n",
       "5                                           (APPLAUSE)   CLINTON   \n",
       "7    SANDERS: Thank you. As we honor the extraordin...   SANDERS   \n",
       "8    SANDERS: And then, to make a bad situation wor...   SANDERS   \n",
       "9    This campaign is about a political revolution ...   SANDERS   \n",
       "13   O'MALLEY: Thank you. My name is Martin O'Malle...  O'MALLEY   \n",
       "14   And I want to thank the people of South Caroli...  O'MALLEY   \n",
       "15   You taught us, in fact, in keeping with Dr. Ki...  O'MALLEY   \n",
       "16   Eight years ago, you brought forward a new lea...  O'MALLEY   \n",
       "17   But in order to make good on the promise of eq...  O'MALLEY   \n",
       "18   We need new leadership. We need to come togeth...  O'MALLEY   \n",
       "19   That's why I'm running for president. I need y...  O'MALLEY   \n",
       "20                                         Thank you.   O'MALLEY   \n",
       "26   SANDERS: Well, that's what our campaign is abo...   SANDERS   \n",
       "27   So, what my first days are about is bringing A...   SANDERS   \n",
       "28                                          (APPLAUSE)   SANDERS   \n",
       "30   CLINTON: I would work quickly to present to th...   CLINTON   \n",
       "31                                     I would also...   CLINTON   \n",
       "32                                          (APPLAUSE)   CLINTON   \n",
       "33   I would also be presenting my plans to build o...   CLINTON   \n",
       "34   And third, I would be working, in every way th...   CLINTON   \n",
       "35                                          (APPLAUSE)   CLINTON   \n",
       "37   O'MALLEY: Thank you. First of all, I would lay...  O'MALLEY   \n",
       "38   Secondly, I believe the greatest business oppo...  O'MALLEY   \n",
       "39                                          (APPLAUSE)  O'MALLEY   \n",
       "41   O'MALLEY: Finally -- I'm sorry, that was secon...  O'MALLEY   \n",
       "42   O'MALLEY: And third and finally, we need a new...  O'MALLEY   \n",
       "..                                                 ...       ...   \n",
       "573  We just have to do more of it, and we have to ...   CLINTON   \n",
       "576  SANDERS: Great ideas, Governor O'Malley, Secre...   SANDERS   \n",
       "577  So here's a promise that I make -- and I menti...   SANDERS   \n",
       "578  Here's a promise. If elected president, Goldma...   SANDERS   \n",
       "579                                        (APPLAUSE)    SANDERS   \n",
       "581  SANDERS: I was asked a question. You know, one...   SANDERS   \n",
       "582  I have avoided doing that. Trying to run an is...   SANDERS   \n",
       "583                                         (APPLAUSE)   SANDERS   \n",
       "584                  SANDERS: I was asked a question.    SANDERS   \n",
       "586  SANDERS: Well -- then if I don't answer it, th...   SANDERS   \n",
       "587                                        (LAUGHTER)    SANDERS   \n",
       "588  And I mean this seriously. You know that. We'v...   SANDERS   \n",
       "589                                        (APPLAUSE)    SANDERS   \n",
       "596  O'MALLEY: Yes, but we're going to have to get ...  O'MALLEY   \n",
       "597                                         (LAUGHTER)  O'MALLEY   \n",
       "599  O'MALLEY: I believe there are many issues. I h...  O'MALLEY   \n",
       "601  O'MALLEY: There are so many issues that we hav...  O'MALLEY   \n",
       "602                                         (APPLAUSE)  O'MALLEY   \n",
       "603  O'MALLEY: We haven't discussed the fact that i...  O'MALLEY   \n",
       "604  I guess the bottom line is this, look we are a...  O'MALLEY   \n",
       "605  We're on the threshold of a new era of America...  O'MALLEY   \n",
       "607                           O'MALLEY: Thanks a lot.   O'MALLEY   \n",
       "609  CLINTON: Well Lester, I spent a lot of time la...   CLINTON   \n",
       "610  He had request for help and he had basically s...   CLINTON   \n",
       "611  So I sent my top campaign aide down there to t...   CLINTON   \n",
       "613  CLINTON: I want to be a president who takes ca...   CLINTON   \n",
       "614                                         (APPLAUSE)   CLINTON   \n",
       "617  SANDERS: Well, Secretary Clinton was right and...   SANDERS   \n",
       "618  Now, we are a great nation -- and we've heard ...   SANDERS   \n",
       "619  We've got to get rid of Super PACs, we've got ...   SANDERS   \n",
       "\n",
       "                                                speach  word_count  speaker_no  \n",
       "1    Well, good evening. And I want to thank the Co...          31           0  \n",
       "2    You know, I remember well when my youth minist...          67           0  \n",
       "3    And that is our fight still. We have to get th...          50           0  \n",
       "4    I understand that this is the hardest job in t...          42           0  \n",
       "5                                           (APPLAUSE)           1           0  \n",
       "7    Thank you. As we honor the extraordinary life ...          88           1  \n",
       "8    And then, to make a bad situation worse, we ha...          28           1  \n",
       "9    This campaign is about a political revolution ...          18           1  \n",
       "13   Thank you. My name is Martin O'Malley, I was b...          21           2  \n",
       "14   And I want to thank the people of South Caroli...          38           2  \n",
       "15   You taught us, in fact, in keeping with Dr. Ki...          37           2  \n",
       "16   Eight years ago, you brought forward a new lea...          34           2  \n",
       "17   But in order to make good on the promise of eq...          47           2  \n",
       "18   We need new leadership. We need to come togeth...          23           2  \n",
       "19   That's why I'm running for president. I need y...          26           2  \n",
       "20                                         Thank you.            2           2  \n",
       "26   Well, that's what our campaign is about. It is...          68           1  \n",
       "27   So, what my first days are about is bringing A...          61           1  \n",
       "28                                          (APPLAUSE)           1           1  \n",
       "30   I would work quickly to present to the Congres...          35           0  \n",
       "31                                     I would also...           3           0  \n",
       "32                                          (APPLAUSE)           1           0  \n",
       "33   I would also be presenting my plans to build o...          67           0  \n",
       "34   And third, I would be working, in every way th...          78           0  \n",
       "35                                          (APPLAUSE)           1           0  \n",
       "37   Thank you. First of all, I would lay out an ag...          77           2  \n",
       "38   Secondly, I believe the greatest business oppo...          48           2  \n",
       "39                                          (APPLAUSE)           1           2  \n",
       "41      Finally -- I'm sorry, that was second, Lester.           8           2  \n",
       "42   And third and finally, we need a new agenda fo...          77           2  \n",
       "..                                                 ...         ...         ...  \n",
       "573  We just have to do more of it, and we have to ...          32           0  \n",
       "576  Great ideas, Governor O'Malley, Secretary Clin...          27           1  \n",
       "577  So here's a promise that I make -- and I menti...          43           1  \n",
       "578  Here's a promise. If elected president, Goldma...          24           1  \n",
       "579                                        (APPLAUSE)            1           1  \n",
       "581  I was asked a question. You know, one of the t...          58           1  \n",
       "582  I have avoided doing that. Trying to run an is...          11           1  \n",
       "583                                         (APPLAUSE)           1           1  \n",
       "584                           I was asked a question.            5           1  \n",
       "586  Well -- then if I don't answer it, then there'...          17           1  \n",
       "587                                        (LAUGHTER)            1           1  \n",
       "588  And I mean this seriously. You know that. We'v...          51           1  \n",
       "589                                        (APPLAUSE)            1           1  \n",
       "596  Yes, but we're going to have to get 20 minutes...          14           2  \n",
       "597                                         (LAUGHTER)           1           2  \n",
       "599  I believe there are many issues. I have 60 sec...          12           2  \n",
       "601  There are so many issues that we haven't been ...          61           2  \n",
       "602                                         (APPLAUSE)           1           2  \n",
       "603  We haven't discussed the fact that in our hemi...          27           2  \n",
       "604  I guess the bottom line is this, look we are a...          80           2  \n",
       "605  We're on the threshold of a new era of America...          33           2  \n",
       "607                                     Thanks a lot.            3           2  \n",
       "609  Well Lester, I spent a lot of time last week b...          72           0  \n",
       "610  He had request for help and he had basically s...          38           0  \n",
       "611  So I sent my top campaign aide down there to t...          59           0  \n",
       "613  I want to be a president who takes care of the...          25           0  \n",
       "614                                         (APPLAUSE)           1           0  \n",
       "617  Well, Secretary Clinton was right and what I d...          32           1  \n",
       "618  Now, we are a great nation -- and we've heard ...          58           1  \n",
       "619  We've got to get rid of Super PACs, we've got ...          73           1  \n",
       "\n",
       "[412 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_ds[\"speaker_no\"] = words_ds.speaker.map(speaker_dict)\n",
    "words_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>the</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>to</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>and</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>of</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>we</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>that</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>in</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>is</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>have</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>it</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>on</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>you</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>what</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>for</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>people</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>our</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>with</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>this</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>do</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>not</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>but</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>as</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>are</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>be</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>can</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>applause</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>need</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>was</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>so</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>all</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1938</th>\n",
       "      <td>leaders</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>boldly</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>lay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941</th>\n",
       "      <td>book</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942</th>\n",
       "      <td>border</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>larger</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>land</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1945</th>\n",
       "      <td>lake</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946</th>\n",
       "      <td>laid</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947</th>\n",
       "      <td>lady</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1948</th>\n",
       "      <td>legally</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>less</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1950</th>\n",
       "      <td>lost</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951</th>\n",
       "      <td>lesson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>loop</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>biosurveillance</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>living</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1955</th>\n",
       "      <td>blame</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1956</th>\n",
       "      <td>listened</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>listen</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>lindsey</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>limit</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</th>\n",
       "      <td>block</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>lifting</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>liable</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>liability</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>level</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>lethal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>bloodiest</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>zero</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1968 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 word  count\n",
       "0                 the    546\n",
       "1                  to    414\n",
       "2                 and    362\n",
       "3                  of    293\n",
       "4                  we    280\n",
       "5                that    274\n",
       "6                  in    223\n",
       "7                  is    157\n",
       "8                have    144\n",
       "9                  it    126\n",
       "10                 on    117\n",
       "11                you    104\n",
       "12               what    102\n",
       "13                for    102\n",
       "14             people     86\n",
       "15                our     85\n",
       "16               with     82\n",
       "17               this     78\n",
       "18                 do     71\n",
       "19                not     69\n",
       "20                but     62\n",
       "21                 as     61\n",
       "22                are     58\n",
       "23                 be     55\n",
       "24                can     51\n",
       "25           applause     49\n",
       "26               need     49\n",
       "27                was     48\n",
       "28                 so     48\n",
       "29                all     47\n",
       "...               ...    ...\n",
       "1938          leaders      1\n",
       "1939           boldly      1\n",
       "1940              lay      1\n",
       "1941             book      1\n",
       "1942           border      1\n",
       "1943           larger      1\n",
       "1944             land      1\n",
       "1945             lake      1\n",
       "1946             laid      1\n",
       "1947             lady      1\n",
       "1948          legally      1\n",
       "1949             less      1\n",
       "1950             lost      1\n",
       "1951           lesson      1\n",
       "1952             loop      1\n",
       "1953  biosurveillance      1\n",
       "1954           living      1\n",
       "1955            blame      1\n",
       "1956         listened      1\n",
       "1957           listen      1\n",
       "1958          lindsey      1\n",
       "1959            limit      1\n",
       "1960            block      1\n",
       "1961          lifting      1\n",
       "1962           liable      1\n",
       "1963        liability      1\n",
       "1964            level      1\n",
       "1965           lethal      1\n",
       "1966        bloodiest      1\n",
       "1967             zero      1\n",
       "\n",
       "[1968 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer()\n",
    "count_matrix = cv.fit_transform(words_ds.speach)\n",
    "count_matrix = count_matrix.toarray()\n",
    "\n",
    "word_count = pd.DataFrame(cv.get_feature_names(), columns=[\"word\"])\n",
    "word_count[\"count\"] = count_matrix.sum(axis=0)\n",
    "word_count = word_count.sort_values(by=\"count\", ascending=False).reset_index(drop=True)\n",
    "word_count[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vocab</th>\n",
       "      <th>Vocab_index</th>\n",
       "      <th>proba</th>\n",
       "      <th>anti_proba</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>major</td>\n",
       "      <td>1064</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.454097</td>\n",
       "      <td>-2.626476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>goldman</td>\n",
       "      <td>759</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.677241</td>\n",
       "      <td>-2.403332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>sachs</td>\n",
       "      <td>1507</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.677241</td>\n",
       "      <td>-2.403332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>countries</td>\n",
       "      <td>423</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.810772</td>\n",
       "      <td>-2.269801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>contributions</td>\n",
       "      <td>406</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.810772</td>\n",
       "      <td>-2.269801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>corrupt</td>\n",
       "      <td>418</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.810772</td>\n",
       "      <td>-2.269801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1627</th>\n",
       "      <td>spending</td>\n",
       "      <td>1627</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.964923</td>\n",
       "      <td>-2.115650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>companies</td>\n",
       "      <td>381</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.964923</td>\n",
       "      <td>-2.115650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1411</th>\n",
       "      <td>real</td>\n",
       "      <td>1411</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.964923</td>\n",
       "      <td>-2.115650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>vermont</td>\n",
       "      <td>1852</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-6.964923</td>\n",
       "      <td>-2.115650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>contributors</td>\n",
       "      <td>407</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>revolution</td>\n",
       "      <td>1480</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>man</td>\n",
       "      <td>1070</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>rhetoric</td>\n",
       "      <td>1481</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1776</th>\n",
       "      <td>transform</td>\n",
       "      <td>1776</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>super</td>\n",
       "      <td>1672</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>rid</td>\n",
       "      <td>1484</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1782</th>\n",
       "      <td>treasury</td>\n",
       "      <td>1782</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1576</th>\n",
       "      <td>she</td>\n",
       "      <td>1576</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.147245</td>\n",
       "      <td>-1.933329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>crumbling</td>\n",
       "      <td>447</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>latino</td>\n",
       "      <td>997</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>hampshire</td>\n",
       "      <td>795</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>terms</td>\n",
       "      <td>1712</td>\n",
       "      <td>-8.387426</td>\n",
       "      <td>-6.677241</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>supported</td>\n",
       "      <td>1675</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>private</td>\n",
       "      <td>1346</td>\n",
       "      <td>-8.387426</td>\n",
       "      <td>-6.677241</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>priority</td>\n",
       "      <td>1343</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>largest</td>\n",
       "      <td>995</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>area</td>\n",
       "      <td>151</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>anti</td>\n",
       "      <td>133</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>pacs</td>\n",
       "      <td>1238</td>\n",
       "      <td>-9.080573</td>\n",
       "      <td>-7.370388</td>\n",
       "      <td>-1.710185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>andrea</td>\n",
       "      <td>128</td>\n",
       "      <td>-6.307985</td>\n",
       "      <td>-7.658070</td>\n",
       "      <td>1.350086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>thank</td>\n",
       "      <td>1719</td>\n",
       "      <td>-6.682678</td>\n",
       "      <td>-8.063535</td>\n",
       "      <td>1.380857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>frank</td>\n",
       "      <td>718</td>\n",
       "      <td>-6.682678</td>\n",
       "      <td>-8.063535</td>\n",
       "      <td>1.380857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>defend</td>\n",
       "      <td>481</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>matter</td>\n",
       "      <td>1084</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>try</td>\n",
       "      <td>1799</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>haven</td>\n",
       "      <td>809</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>intelligence</td>\n",
       "      <td>918</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>age</td>\n",
       "      <td>86</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>equal</td>\n",
       "      <td>621</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>visiting</td>\n",
       "      <td>1874</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>around</td>\n",
       "      <td>156</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>door</td>\n",
       "      <td>562</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>forces</td>\n",
       "      <td>707</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>use</td>\n",
       "      <td>1842</td>\n",
       "      <td>-7.288814</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.467869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</th>\n",
       "      <td>year</td>\n",
       "      <td>1957</td>\n",
       "      <td>-6.515624</td>\n",
       "      <td>-8.063535</td>\n",
       "      <td>1.547911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>keep</td>\n",
       "      <td>972</td>\n",
       "      <td>-7.134663</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.622019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>build</td>\n",
       "      <td>273</td>\n",
       "      <td>-7.134663</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.622019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1634</th>\n",
       "      <td>stage</td>\n",
       "      <td>1634</td>\n",
       "      <td>-7.001132</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.755551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>actually</td>\n",
       "      <td>59</td>\n",
       "      <td>-6.307985</td>\n",
       "      <td>-8.063535</td>\n",
       "      <td>1.755551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>attacks</td>\n",
       "      <td>169</td>\n",
       "      <td>-7.001132</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.755551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>costs</td>\n",
       "      <td>420</td>\n",
       "      <td>-7.001132</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.755551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>had</td>\n",
       "      <td>791</td>\n",
       "      <td>-6.307985</td>\n",
       "      <td>-8.063535</td>\n",
       "      <td>1.755551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>come</td>\n",
       "      <td>365</td>\n",
       "      <td>-7.001132</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.755551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589</th>\n",
       "      <td>since</td>\n",
       "      <td>1589</td>\n",
       "      <td>-6.777988</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.978694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>dodd</td>\n",
       "      <td>553</td>\n",
       "      <td>-6.777988</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.978694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>again</td>\n",
       "      <td>84</td>\n",
       "      <td>-6.777988</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>1.978694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>plan</td>\n",
       "      <td>1281</td>\n",
       "      <td>-6.682678</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>2.074004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1021</th>\n",
       "      <td>lester</td>\n",
       "      <td>1021</td>\n",
       "      <td>-6.682678</td>\n",
       "      <td>-8.756682</td>\n",
       "      <td>2.074004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1968 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Vocab  Vocab_index     proba  anti_proba  difference\n",
       "1064          major         1064 -9.080573   -6.454097   -2.626476\n",
       "759         goldman          759 -9.080573   -6.677241   -2.403332\n",
       "1507          sachs         1507 -9.080573   -6.677241   -2.403332\n",
       "423       countries          423 -9.080573   -6.810772   -2.269801\n",
       "406   contributions          406 -9.080573   -6.810772   -2.269801\n",
       "418         corrupt          418 -9.080573   -6.810772   -2.269801\n",
       "1627       spending         1627 -9.080573   -6.964923   -2.115650\n",
       "381       companies          381 -9.080573   -6.964923   -2.115650\n",
       "1411           real         1411 -9.080573   -6.964923   -2.115650\n",
       "1852        vermont         1852 -9.080573   -6.964923   -2.115650\n",
       "407    contributors          407 -9.080573   -7.147245   -1.933329\n",
       "1480     revolution         1480 -9.080573   -7.147245   -1.933329\n",
       "1070            man         1070 -9.080573   -7.147245   -1.933329\n",
       "1481       rhetoric         1481 -9.080573   -7.147245   -1.933329\n",
       "1776      transform         1776 -9.080573   -7.147245   -1.933329\n",
       "1672          super         1672 -9.080573   -7.147245   -1.933329\n",
       "1484            rid         1484 -9.080573   -7.147245   -1.933329\n",
       "1782       treasury         1782 -9.080573   -7.147245   -1.933329\n",
       "1576            she         1576 -9.080573   -7.147245   -1.933329\n",
       "447       crumbling          447 -9.080573   -7.370388   -1.710185\n",
       "997          latino          997 -9.080573   -7.370388   -1.710185\n",
       "795       hampshire          795 -9.080573   -7.370388   -1.710185\n",
       "1712          terms         1712 -8.387426   -6.677241   -1.710185\n",
       "1675      supported         1675 -9.080573   -7.370388   -1.710185\n",
       "1346        private         1346 -8.387426   -6.677241   -1.710185\n",
       "1343       priority         1343 -9.080573   -7.370388   -1.710185\n",
       "995         largest          995 -9.080573   -7.370388   -1.710185\n",
       "151            area          151 -9.080573   -7.370388   -1.710185\n",
       "133            anti          133 -9.080573   -7.370388   -1.710185\n",
       "1238           pacs         1238 -9.080573   -7.370388   -1.710185\n",
       "...             ...          ...       ...         ...         ...\n",
       "128          andrea          128 -6.307985   -7.658070    1.350086\n",
       "1719          thank         1719 -6.682678   -8.063535    1.380857\n",
       "718           frank          718 -6.682678   -8.063535    1.380857\n",
       "23               30           23 -7.288814   -8.756682    1.467869\n",
       "481          defend          481 -7.288814   -8.756682    1.467869\n",
       "1084         matter         1084 -7.288814   -8.756682    1.467869\n",
       "1799            try         1799 -7.288814   -8.756682    1.467869\n",
       "809           haven          809 -7.288814   -8.756682    1.467869\n",
       "918    intelligence          918 -7.288814   -8.756682    1.467869\n",
       "86              age           86 -7.288814   -8.756682    1.467869\n",
       "621           equal          621 -7.288814   -8.756682    1.467869\n",
       "1874       visiting         1874 -7.288814   -8.756682    1.467869\n",
       "156          around          156 -7.288814   -8.756682    1.467869\n",
       "562            door          562 -7.288814   -8.756682    1.467869\n",
       "707          forces          707 -7.288814   -8.756682    1.467869\n",
       "1842            use         1842 -7.288814   -8.756682    1.467869\n",
       "1957           year         1957 -6.515624   -8.063535    1.547911\n",
       "972            keep          972 -7.134663   -8.756682    1.622019\n",
       "273           build          273 -7.134663   -8.756682    1.622019\n",
       "1634          stage         1634 -7.001132   -8.756682    1.755551\n",
       "59         actually           59 -6.307985   -8.063535    1.755551\n",
       "169         attacks          169 -7.001132   -8.756682    1.755551\n",
       "420           costs          420 -7.001132   -8.756682    1.755551\n",
       "791             had          791 -6.307985   -8.063535    1.755551\n",
       "365            come          365 -7.001132   -8.756682    1.755551\n",
       "1589          since         1589 -6.777988   -8.756682    1.978694\n",
       "553            dodd          553 -6.777988   -8.756682    1.978694\n",
       "84            again           84 -6.777988   -8.756682    1.978694\n",
       "1281           plan         1281 -6.682678   -8.756682    2.074004\n",
       "1021         lester         1021 -6.682678   -8.756682    2.074004\n",
       "\n",
       "[1968 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl = MultinomialNB()\n",
    "cl.fit(count_matrix, words_ds.speaker==\"SANDERS\")\n",
    "\n",
    "df_vocab = pd.DataFrame(list(cv.vocabulary_.keys()), columns=[\"Vocab\"])\n",
    "df_vocab[\"Vocab_index\"] = cv.vocabulary_.values()\n",
    "df_vocab = df_vocab.sort_values(\"Vocab_index\").reset_index(drop=True)\n",
    "df_vocab[\"proba\"] = cl.feature_log_prob_[0]\n",
    "df_vocab[\"anti_proba\"] = cl.feature_log_prob_[1]\n",
    "df_vocab[\"difference\"] = cl.feature_log_prob_[0] - cl.feature_log_prob_[1]\n",
    "df_vocab.sort_values(\"difference\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vocab</th>\n",
       "      <th>Vocab_index</th>\n",
       "      <th>proba</th>\n",
       "      <th>anti_proba</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>dodd</td>\n",
       "      <td>553</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-6.410175</td>\n",
       "      <td>-2.701008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1799</th>\n",
       "      <td>try</td>\n",
       "      <td>1799</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-6.921001</td>\n",
       "      <td>-2.190182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>frank</td>\n",
       "      <td>718</td>\n",
       "      <td>-8.418036</td>\n",
       "      <td>-6.314865</td>\n",
       "      <td>-2.103171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>white</td>\n",
       "      <td>1925</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.103322</td>\n",
       "      <td>-2.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>pleased</td>\n",
       "      <td>1287</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.103322</td>\n",
       "      <td>-2.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>someone</td>\n",
       "      <td>1608</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.103322</td>\n",
       "      <td>-2.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>spent</td>\n",
       "      <td>1628</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.103322</td>\n",
       "      <td>-2.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>especially</td>\n",
       "      <td>625</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.103322</td>\n",
       "      <td>-2.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>introduced</td>\n",
       "      <td>927</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.103322</td>\n",
       "      <td>-2.007861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>rights</td>\n",
       "      <td>1487</td>\n",
       "      <td>-8.418036</td>\n",
       "      <td>-6.515535</td>\n",
       "      <td>-1.902500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1516</th>\n",
       "      <td>sanctions</td>\n",
       "      <td>1516</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349</th>\n",
       "      <td>problem</td>\n",
       "      <td>1349</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>incomes</td>\n",
       "      <td>895</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>experience</td>\n",
       "      <td>650</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>comments</td>\n",
       "      <td>370</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1912</th>\n",
       "      <td>week</td>\n",
       "      <td>1912</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>fighters</td>\n",
       "      <td>679</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>approach</td>\n",
       "      <td>147</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>sector</td>\n",
       "      <td>1544</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563</th>\n",
       "      <td>serious</td>\n",
       "      <td>1563</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2011</td>\n",
       "      <td>16</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>-1.784717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>791</th>\n",
       "      <td>had</td>\n",
       "      <td>791</td>\n",
       "      <td>-7.724888</td>\n",
       "      <td>-6.073703</td>\n",
       "      <td>-1.651186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>costs</td>\n",
       "      <td>420</td>\n",
       "      <td>-8.418036</td>\n",
       "      <td>-6.766850</td>\n",
       "      <td>-1.651186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>voted</td>\n",
       "      <td>1877</td>\n",
       "      <td>-8.012571</td>\n",
       "      <td>-6.515535</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1639</th>\n",
       "      <td>standing</td>\n",
       "      <td>1639</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.614148</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>treaty</td>\n",
       "      <td>1786</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.614148</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>acted</td>\n",
       "      <td>55</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.614148</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>flint</td>\n",
       "      <td>699</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.614148</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>votes</td>\n",
       "      <td>1878</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.614148</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>lone</td>\n",
       "      <td>1041</td>\n",
       "      <td>-9.111183</td>\n",
       "      <td>-7.614148</td>\n",
       "      <td>-1.497035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>ph</td>\n",
       "      <td>1274</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>provide</td>\n",
       "      <td>1371</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852</th>\n",
       "      <td>vermont</td>\n",
       "      <td>1852</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>809</th>\n",
       "      <td>haven</td>\n",
       "      <td>809</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1935</th>\n",
       "      <td>without</td>\n",
       "      <td>1935</td>\n",
       "      <td>-7.319423</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.393337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>forward</td>\n",
       "      <td>711</td>\n",
       "      <td>-6.546233</td>\n",
       "      <td>-8.019613</td>\n",
       "      <td>1.473379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>seconds</td>\n",
       "      <td>1540</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>ever</td>\n",
       "      <td>632</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>front</td>\n",
       "      <td>729</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>budget</td>\n",
       "      <td>271</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1792</th>\n",
       "      <td>true</td>\n",
       "      <td>1792</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>corrupt</td>\n",
       "      <td>418</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>person</td>\n",
       "      <td>1271</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>percent</td>\n",
       "      <td>1267</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>contributions</td>\n",
       "      <td>406</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>done</td>\n",
       "      <td>561</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>issues</td>\n",
       "      <td>952</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>countries</td>\n",
       "      <td>423</td>\n",
       "      <td>-7.165273</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.547487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>second</td>\n",
       "      <td>1537</td>\n",
       "      <td>-7.031741</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.681019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1542</th>\n",
       "      <td>secretary</td>\n",
       "      <td>1542</td>\n",
       "      <td>-5.645447</td>\n",
       "      <td>-7.326466</td>\n",
       "      <td>1.681019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>sachs</td>\n",
       "      <td>1507</td>\n",
       "      <td>-7.031741</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.681019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>goldman</td>\n",
       "      <td>759</td>\n",
       "      <td>-7.031741</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.681019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>andrea</td>\n",
       "      <td>128</td>\n",
       "      <td>-6.277969</td>\n",
       "      <td>-8.019613</td>\n",
       "      <td>1.741643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>things</td>\n",
       "      <td>1731</td>\n",
       "      <td>-6.220811</td>\n",
       "      <td>-8.019613</td>\n",
       "      <td>1.798802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1712</th>\n",
       "      <td>terms</td>\n",
       "      <td>1712</td>\n",
       "      <td>-6.913958</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.798802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>major</td>\n",
       "      <td>1064</td>\n",
       "      <td>-6.808598</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>1.904162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>these</td>\n",
       "      <td>1728</td>\n",
       "      <td>-6.472125</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>2.240635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>clinton</td>\n",
       "      <td>350</td>\n",
       "      <td>-6.066660</td>\n",
       "      <td>-8.712760</td>\n",
       "      <td>2.646100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1968 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Vocab  Vocab_index     proba  anti_proba  difference\n",
       "553            dodd          553 -9.111183   -6.410175   -2.701008\n",
       "1799            try         1799 -9.111183   -6.921001   -2.190182\n",
       "718           frank          718 -8.418036   -6.314865   -2.103171\n",
       "1925          white         1925 -9.111183   -7.103322   -2.007861\n",
       "1287        pleased         1287 -9.111183   -7.103322   -2.007861\n",
       "1608        someone         1608 -9.111183   -7.103322   -2.007861\n",
       "1628          spent         1628 -9.111183   -7.103322   -2.007861\n",
       "625      especially          625 -9.111183   -7.103322   -2.007861\n",
       "927      introduced          927 -9.111183   -7.103322   -2.007861\n",
       "1487         rights         1487 -8.418036   -6.515535   -1.902500\n",
       "1516      sanctions         1516 -9.111183   -7.326466   -1.784717\n",
       "1349        problem         1349 -9.111183   -7.326466   -1.784717\n",
       "895         incomes          895 -9.111183   -7.326466   -1.784717\n",
       "650      experience          650 -9.111183   -7.326466   -1.784717\n",
       "370        comments          370 -9.111183   -7.326466   -1.784717\n",
       "1912           week         1912 -9.111183   -7.326466   -1.784717\n",
       "679        fighters          679 -9.111183   -7.326466   -1.784717\n",
       "147        approach          147 -9.111183   -7.326466   -1.784717\n",
       "1544         sector         1544 -9.111183   -7.326466   -1.784717\n",
       "1563        serious         1563 -9.111183   -7.326466   -1.784717\n",
       "16             2011           16 -9.111183   -7.326466   -1.784717\n",
       "791             had          791 -7.724888   -6.073703   -1.651186\n",
       "420           costs          420 -8.418036   -6.766850   -1.651186\n",
       "1877          voted         1877 -8.012571   -6.515535   -1.497035\n",
       "1639       standing         1639 -9.111183   -7.614148   -1.497035\n",
       "1786         treaty         1786 -9.111183   -7.614148   -1.497035\n",
       "55            acted           55 -9.111183   -7.614148   -1.497035\n",
       "699           flint          699 -9.111183   -7.614148   -1.497035\n",
       "1878          votes         1878 -9.111183   -7.614148   -1.497035\n",
       "1041           lone         1041 -9.111183   -7.614148   -1.497035\n",
       "...             ...          ...       ...         ...         ...\n",
       "1274             ph         1274 -7.319423   -8.712760    1.393337\n",
       "1371        provide         1371 -7.319423   -8.712760    1.393337\n",
       "1852        vermont         1852 -7.319423   -8.712760    1.393337\n",
       "7                15            7 -7.319423   -8.712760    1.393337\n",
       "809           haven          809 -7.319423   -8.712760    1.393337\n",
       "23               30           23 -7.319423   -8.712760    1.393337\n",
       "1935        without         1935 -7.319423   -8.712760    1.393337\n",
       "711         forward          711 -6.546233   -8.019613    1.473379\n",
       "1540        seconds         1540 -7.165273   -8.712760    1.547487\n",
       "632            ever          632 -7.165273   -8.712760    1.547487\n",
       "729           front          729 -7.165273   -8.712760    1.547487\n",
       "271          budget          271 -7.165273   -8.712760    1.547487\n",
       "1792           true         1792 -7.165273   -8.712760    1.547487\n",
       "418         corrupt          418 -7.165273   -8.712760    1.547487\n",
       "1271         person         1271 -7.165273   -8.712760    1.547487\n",
       "1267        percent         1267 -7.165273   -8.712760    1.547487\n",
       "406   contributions          406 -7.165273   -8.712760    1.547487\n",
       "561            done          561 -7.165273   -8.712760    1.547487\n",
       "952          issues          952 -7.165273   -8.712760    1.547487\n",
       "423       countries          423 -7.165273   -8.712760    1.547487\n",
       "1537         second         1537 -7.031741   -8.712760    1.681019\n",
       "1542      secretary         1542 -5.645447   -7.326466    1.681019\n",
       "1507          sachs         1507 -7.031741   -8.712760    1.681019\n",
       "759         goldman          759 -7.031741   -8.712760    1.681019\n",
       "128          andrea          128 -6.277969   -8.019613    1.741643\n",
       "1731         things         1731 -6.220811   -8.019613    1.798802\n",
       "1712          terms         1712 -6.913958   -8.712760    1.798802\n",
       "1064          major         1064 -6.808598   -8.712760    1.904162\n",
       "1728          these         1728 -6.472125   -8.712760    2.240635\n",
       "350         clinton          350 -6.066660   -8.712760    2.646100\n",
       "\n",
       "[1968 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl = MultinomialNB()\n",
    "cl.fit(count_matrix, words_ds.speaker==\"CLINTON\")\n",
    "\n",
    "df_vocab = pd.DataFrame(list(cv.vocabulary_.keys()), columns=[\"Vocab\"])\n",
    "df_vocab[\"Vocab_index\"] = cv.vocabulary_.values()\n",
    "df_vocab = df_vocab.sort_values(\"Vocab_index\").reset_index(drop=True)\n",
    "df_vocab[\"proba\"] = cl.feature_log_prob_[0]\n",
    "df_vocab[\"anti_proba\"] = cl.feature_log_prob_[1]\n",
    "df_vocab[\"difference\"] = cl.feature_log_prob_[0] - cl.feature_log_prob_[1]\n",
    "df_vocab.sort_values(\"difference\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vocab</th>\n",
       "      <th>Vocab_index</th>\n",
       "      <th>proba</th>\n",
       "      <th>anti_proba</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>knew</td>\n",
       "      <td>983</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>momentum</td>\n",
       "      <td>1129</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>mom</td>\n",
       "      <td>1127</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>mixed</td>\n",
       "      <td>1124</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>mission</td>\n",
       "      <td>1122</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1121</th>\n",
       "      <td>missing</td>\n",
       "      <td>1121</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>minutes</td>\n",
       "      <td>1120</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>minus</td>\n",
       "      <td>1119</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1131</th>\n",
       "      <td>months</td>\n",
       "      <td>1131</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>mindful</td>\n",
       "      <td>1116</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>militarize</td>\n",
       "      <td>1111</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>midst</td>\n",
       "      <td>1109</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>michigan</td>\n",
       "      <td>1107</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>message</td>\n",
       "      <td>1105</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1103</th>\n",
       "      <td>mention</td>\n",
       "      <td>1103</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>mentally</td>\n",
       "      <td>1102</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1099</th>\n",
       "      <td>memphis</td>\n",
       "      <td>1099</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1114</th>\n",
       "      <td>millionaires</td>\n",
       "      <td>1114</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1132</th>\n",
       "      <td>moon</td>\n",
       "      <td>1132</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1133</th>\n",
       "      <td>moral</td>\n",
       "      <td>1133</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140</th>\n",
       "      <td>moving</td>\n",
       "      <td>1140</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1173</th>\n",
       "      <td>nothing</td>\n",
       "      <td>1173</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>normalize</td>\n",
       "      <td>1171</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>normalization</td>\n",
       "      <td>1170</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>nonsense</td>\n",
       "      <td>1169</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>nightmare</td>\n",
       "      <td>1165</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1164</th>\n",
       "      <td>nickel</td>\n",
       "      <td>1164</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1160</th>\n",
       "      <td>neither</td>\n",
       "      <td>1160</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>neighbors</td>\n",
       "      <td>1159</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158</th>\n",
       "      <td>negotiating</td>\n",
       "      <td>1158</td>\n",
       "      <td>-8.792398</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>-1.207625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>all</td>\n",
       "      <td>103</td>\n",
       "      <td>-5.614344</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>1.970429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1602</th>\n",
       "      <td>so</td>\n",
       "      <td>1602</td>\n",
       "      <td>-5.593725</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>1.991048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1897</th>\n",
       "      <td>was</td>\n",
       "      <td>1897</td>\n",
       "      <td>-5.593725</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>1.991048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>applause</td>\n",
       "      <td>144</td>\n",
       "      <td>-5.573522</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.011251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>need</td>\n",
       "      <td>1154</td>\n",
       "      <td>-5.573522</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.011251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>can</td>\n",
       "      <td>293</td>\n",
       "      <td>-5.534301</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.050472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>be</td>\n",
       "      <td>202</td>\n",
       "      <td>-5.460193</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.124580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>are</td>\n",
       "      <td>150</td>\n",
       "      <td>-5.408007</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.176766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>as</td>\n",
       "      <td>158</td>\n",
       "      <td>-5.358411</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.226363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>but</td>\n",
       "      <td>284</td>\n",
       "      <td>-5.342410</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.242363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>not</td>\n",
       "      <td>1172</td>\n",
       "      <td>-5.237050</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.347723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>do</td>\n",
       "      <td>551</td>\n",
       "      <td>-5.208879</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.375894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1737</th>\n",
       "      <td>this</td>\n",
       "      <td>1737</td>\n",
       "      <td>-5.116097</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.468676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>with</td>\n",
       "      <td>1933</td>\n",
       "      <td>-5.066704</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.518069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1223</th>\n",
       "      <td>our</td>\n",
       "      <td>1223</td>\n",
       "      <td>-5.031198</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.553575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>people</td>\n",
       "      <td>1265</td>\n",
       "      <td>-5.019637</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.565136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>705</th>\n",
       "      <td>for</td>\n",
       "      <td>705</td>\n",
       "      <td>-4.850816</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.733957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>what</td>\n",
       "      <td>1917</td>\n",
       "      <td>-4.850816</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.733957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>you</td>\n",
       "      <td>1961</td>\n",
       "      <td>-4.831585</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.753189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>on</td>\n",
       "      <td>1198</td>\n",
       "      <td>-4.714860</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.869913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>953</th>\n",
       "      <td>it</td>\n",
       "      <td>953</td>\n",
       "      <td>-4.641358</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>2.943415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808</th>\n",
       "      <td>have</td>\n",
       "      <td>808</td>\n",
       "      <td>-4.508811</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.075962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>943</th>\n",
       "      <td>is</td>\n",
       "      <td>943</td>\n",
       "      <td>-4.422950</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.161823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>in</td>\n",
       "      <td>886</td>\n",
       "      <td>-4.073899</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.510874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1721</th>\n",
       "      <td>that</td>\n",
       "      <td>1721</td>\n",
       "      <td>-3.868774</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.715999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1903</th>\n",
       "      <td>we</td>\n",
       "      <td>1903</td>\n",
       "      <td>-3.847190</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.737583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>of</td>\n",
       "      <td>1186</td>\n",
       "      <td>-3.801965</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.782808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>and</td>\n",
       "      <td>127</td>\n",
       "      <td>-3.591142</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>3.993631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1751</th>\n",
       "      <td>to</td>\n",
       "      <td>1751</td>\n",
       "      <td>-3.457266</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>4.127507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1722</th>\n",
       "      <td>the</td>\n",
       "      <td>1722</td>\n",
       "      <td>-3.181096</td>\n",
       "      <td>-7.584773</td>\n",
       "      <td>4.403677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1968 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Vocab  Vocab_index     proba  anti_proba  difference\n",
       "983            knew          983 -8.792398   -7.584773   -1.207625\n",
       "1129       momentum         1129 -8.792398   -7.584773   -1.207625\n",
       "1127            mom         1127 -8.792398   -7.584773   -1.207625\n",
       "1124          mixed         1124 -8.792398   -7.584773   -1.207625\n",
       "1122        mission         1122 -8.792398   -7.584773   -1.207625\n",
       "1121        missing         1121 -8.792398   -7.584773   -1.207625\n",
       "1120        minutes         1120 -8.792398   -7.584773   -1.207625\n",
       "1119          minus         1119 -8.792398   -7.584773   -1.207625\n",
       "1131         months         1131 -8.792398   -7.584773   -1.207625\n",
       "1116        mindful         1116 -8.792398   -7.584773   -1.207625\n",
       "1111     militarize         1111 -8.792398   -7.584773   -1.207625\n",
       "1109          midst         1109 -8.792398   -7.584773   -1.207625\n",
       "1107       michigan         1107 -8.792398   -7.584773   -1.207625\n",
       "1105        message         1105 -8.792398   -7.584773   -1.207625\n",
       "1103        mention         1103 -8.792398   -7.584773   -1.207625\n",
       "1102       mentally         1102 -8.792398   -7.584773   -1.207625\n",
       "1099        memphis         1099 -8.792398   -7.584773   -1.207625\n",
       "1114   millionaires         1114 -8.792398   -7.584773   -1.207625\n",
       "1132           moon         1132 -8.792398   -7.584773   -1.207625\n",
       "1133          moral         1133 -8.792398   -7.584773   -1.207625\n",
       "1140         moving         1140 -8.792398   -7.584773   -1.207625\n",
       "1173        nothing         1173 -8.792398   -7.584773   -1.207625\n",
       "1171      normalize         1171 -8.792398   -7.584773   -1.207625\n",
       "1170  normalization         1170 -8.792398   -7.584773   -1.207625\n",
       "1169       nonsense         1169 -8.792398   -7.584773   -1.207625\n",
       "1165      nightmare         1165 -8.792398   -7.584773   -1.207625\n",
       "1164         nickel         1164 -8.792398   -7.584773   -1.207625\n",
       "1160        neither         1160 -8.792398   -7.584773   -1.207625\n",
       "1159      neighbors         1159 -8.792398   -7.584773   -1.207625\n",
       "1158    negotiating         1158 -8.792398   -7.584773   -1.207625\n",
       "...             ...          ...       ...         ...         ...\n",
       "103             all          103 -5.614344   -7.584773    1.970429\n",
       "1602             so         1602 -5.593725   -7.584773    1.991048\n",
       "1897            was         1897 -5.593725   -7.584773    1.991048\n",
       "144        applause          144 -5.573522   -7.584773    2.011251\n",
       "1154           need         1154 -5.573522   -7.584773    2.011251\n",
       "293             can          293 -5.534301   -7.584773    2.050472\n",
       "202              be          202 -5.460193   -7.584773    2.124580\n",
       "150             are          150 -5.408007   -7.584773    2.176766\n",
       "158              as          158 -5.358411   -7.584773    2.226363\n",
       "284             but          284 -5.342410   -7.584773    2.242363\n",
       "1172            not         1172 -5.237050   -7.584773    2.347723\n",
       "551              do          551 -5.208879   -7.584773    2.375894\n",
       "1737           this         1737 -5.116097   -7.584773    2.468676\n",
       "1933           with         1933 -5.066704   -7.584773    2.518069\n",
       "1223            our         1223 -5.031198   -7.584773    2.553575\n",
       "1265         people         1265 -5.019637   -7.584773    2.565136\n",
       "705             for          705 -4.850816   -7.584773    2.733957\n",
       "1917           what         1917 -4.850816   -7.584773    2.733957\n",
       "1961            you         1961 -4.831585   -7.584773    2.753189\n",
       "1198             on         1198 -4.714860   -7.584773    2.869913\n",
       "953              it          953 -4.641358   -7.584773    2.943415\n",
       "808            have          808 -4.508811   -7.584773    3.075962\n",
       "943              is          943 -4.422950   -7.584773    3.161823\n",
       "886              in          886 -4.073899   -7.584773    3.510874\n",
       "1721           that         1721 -3.868774   -7.584773    3.715999\n",
       "1903             we         1903 -3.847190   -7.584773    3.737583\n",
       "1186             of         1186 -3.801965   -7.584773    3.782808\n",
       "127             and          127 -3.591142   -7.584773    3.993631\n",
       "1751             to         1751 -3.457266   -7.584773    4.127507\n",
       "1722            the         1722 -3.181096   -7.584773    4.403677\n",
       "\n",
       "[1968 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl = MultinomialNB()\n",
    "cl.fit(count_matrix, words_ds.speaker==\"O'MALLY\")\n",
    "\n",
    "df_vocab = pd.DataFrame(list(cv.vocabulary_.keys()), columns=[\"Vocab\"])\n",
    "df_vocab[\"Vocab_index\"] = cv.vocabulary_.values()\n",
    "df_vocab = df_vocab.sort_values(\"Vocab_index\").reset_index(drop=True)\n",
    "df_vocab[\"proba\"] = cl.feature_log_prob_[0]\n",
    "df_vocab[\"anti_proba\"] = cl.feature_log_prob_[1]\n",
    "df_vocab[\"difference\"] = cl.feature_log_prob_[0] - cl.feature_log_prob_[1]\n",
    "df_vocab.sort_values(\"difference\", ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "pygments_lexer": "ipython3",
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================================================
FILE: notebooks/youtube.csv
================================================
Date,Region,Channel,Display name,Views,Unique users,Unique users (7 days),Unique users (30 days),Popularity,Comments,Favorites,Rating 1,Rating 2,Rating 3,Rating 4,Rating 5,Subscriptions,Unsubscriptions
2015-03-23,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-03-23,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,5,17,0,0,0,0,0,0,0,1,0,0
2015-03-23,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,15,0,0,0,0,0,0,0,0,0,0
2015-03-23,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,11,51,0,0,0,0,0,0,0,0,0,0
2015-03-23,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-23,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,18,0,0,0,0,0,0,0,0,0,0
2015-03-23,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-23,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-23,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,3,0,0,0,0,0,0,0,1,0,0
2015-03-23,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,25,67,0,0,0,0,0,0,0,0,0,0
2015-03-23,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,8,4,26,83,0,0,0,0,0,0,0,0,0,0
2015-03-23,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,20,0,0,0,0,0,0,0,0,0,0
2015-03-23,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,13,0,0,0,0,0,0,0,0,0,0
2015-03-23,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-23,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,28,0,0,0,0,0,0,0,0,0,0
2015-03-23,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-03-23,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,54,175,0,0,0,0,0,0,0,0,0,0
2015-03-23,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,5,27,0,0,0,0,0,0,0,0,0,0
2015-03-23,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-23,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-23,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-23,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,19,53,0,0,0,0,0,0,0,0,0,0
2015-03-23,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,11,0,0,0,0,0,0,0,0,0,0
2015-03-23,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,17,58,0,0,0,0,0,0,0,0,0,0
2015-03-23,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,32,159,0,0,0,0,0,0,0,0,0,0
2015-03-23,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-23,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,2,15,0,0,2,0,0,0,0,0,0,0
2015-03-23,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,19,0,0,0,0,0,0,0,0,0,0
2015-03-23,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-23,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-03-23,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,18,0,0,0,0,0,0,0,0,0,0
2015-03-23,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,33,0,0,0,0,0,0,0,0,0,0
2015-03-23,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,31,0,0,0,0,0,0,0,0,0,0
2015-03-23,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,14,10,71,221,0,0,0,0,0,0,0,0,0,0
2015-03-23,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-23,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,9,34,0,0,0,0,0,0,0,0,0,0
2015-03-23,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,45,0,0,0,0,0,0,0,0,0,0
2015-03-23,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-23,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,45,4,19,45,0,0,0,0,0,0,0,0,1,0
2015-03-23,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-23,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,7,0,0,0,0,0,0,0,0,0,0
2015-03-23,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-23,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-23,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-23,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-23,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,14,39,0,0,0,0,0,0,0,0,0,0
2015-03-23,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,22,0,0,0,0,0,0,0,0,0,0
2015-03-23,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-03-23,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,11,3,17,51,0,0,0,0,0,0,0,0,0,0
2015-03-23,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,23,0,0,0,0,0,0,0,0,0,0
2015-03-23,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-23,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-03-23,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-23,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,10,0,0,0,0,0,0,0,0,0,0
2015-03-23,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,4,0,0,0,0,0,0,0,0,1,0
2015-03-23,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,37,0,0,0,0,0,0,0,0,0,0
2015-03-23,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-23,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,19,0,0,0,0,0,0,0,0,0,0
2015-03-23,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-03-23,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-23,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,43,0,0,0,0,0,0,0,0,0,0
2015-03-23,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,6,17,0,0,0,0,0,0,0,0,0,0
2015-03-23,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,30,0,0,0,0,0,0,0,0,0,0
2015-03-23,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,24,0,0,0,0,0,0,0,0,0,0
2015-03-23,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-03-23,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-23,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-23,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-03-23,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,21,5,14,47,0,0,0,0,0,0,0,0,0,0
2015-03-23,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,31,0,0,0,0,0,0,0,0,0,0
2015-03-23,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0
2015-03-23,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,US,98x5I1LVPhtnUHDyujq7zg,Roshan,89,39,244,843,0,1,0,0,0,0,0,2,2,0
2015-03-23,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-23,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-23,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-23,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,27,0,0,0,0,0,0,0,0,0,0
2015-03-23,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,13,0,0,0,0,0,0,0,0,0,0
2015-03-24,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-03-24,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-03-24,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,15,0,0,0,0,0,0,0,0,0,0
2015-03-24,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,12,52,0,0,0,0,0,0,0,0,0,0
2015-03-24,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-24,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,3,4,19,0,0,0,0,0,0,0,0,0,0
2015-03-24,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-24,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-24,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,21,66,0,0,0,0,0,0,0,1,0,0
2015-03-24,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,25,84,0,0,0,0,0,0,0,0,0,0
2015-03-24,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,19,0,0,0,0,0,0,0,0,0,0
2015-03-24,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-03-24,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-24,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,29,0,0,0,0,0,0,0,0,0,0
2015-03-24,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,13,0,0,0,0,0,0,0,0,0,0
2015-03-24,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,33,13,59,174,0,0,0,0,0,0,0,0,0,0
2015-03-24,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,6,29,0,0,0,0,0,0,0,0,0,0
2015-03-24,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-24,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0
2015-03-24,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-24,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,22,6,20,54,0,0,0,0,0,0,0,0,0,0
2015-03-24,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0
2015-03-24,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,20,60,0,0,0,0,0,0,0,0,0,0
2015-03-24,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,31,157,0,0,0,0,0,0,0,0,0,0
2015-03-24,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-24,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-03-24,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,18,0,0,0,0,0,0,0,0,0,0
2015-03-24,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-24,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0
2015-03-24,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-24,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,11,2,11,31,0,0,0,0,0,0,0,0,0,0
2015-03-24,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,29,0,0,0,0,0,0,0,0,0,0
2015-03-24,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,15,9,71,224,0,0,0,0,0,0,0,0,1,0
2015-03-24,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-24,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-24,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,32,0,0,0,0,0,0,0,0,0,0
2015-03-24,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,12,4,12,49,0,0,0,0,0,0,0,0,0,0
2015-03-24,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-24,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,47,4,16,46,0,0,0,0,0,0,0,0,1,0
2015-03-24,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-24,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-24,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-24,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-24,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,38,0,0,0,0,0,0,0,0,0,0
2015-03-24,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,21,0,0,0,0,0,0,0,0,0,0
2015-03-24,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-24,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,12,4,20,51,0,0,0,0,0,0,0,0,0,0
2015-03-24,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,22,0,0,0,0,0,0,0,0,1,0
2015-03-24,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-24,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-24,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-03-24,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,10,0,0,0,0,0,0,0,0,0,0
2015-03-24,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-24,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,34,0,0,0,0,0,0,0,0,0,0
2015-03-24,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-24,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,19,0,0,0,0,0,0,0,0,0,0
2015-03-24,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-03-24,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-24,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,12,44,0,0,0,0,0,0,0,0,0,0
2015-03-24,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-24,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,7,33,0,0,0,0,0,0,0,0,0,0
2015-03-24,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,25,0,0,0,0,0,0,0,0,0,0
2015-03-24,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0
2015-03-24,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-24,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,4,9,0,0,0,0,0,0,0,0,0,0
2015-03-24,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-24,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,16,2,14,48,0,0,0,0,0,0,0,0,0,0
2015-03-24,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,14,32,0,0,0,0,0,0,0,0,0,0
2015-03-24,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-03-24,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,US,98x5I1LVPhtnUHDyujq7zg,Roshan,65,37,247,843,0,0,0,0,0,0,0,0,1,0
2015-03-24,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-24,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-03-24,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-24,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,10,27,0,0,0,0,0,0,0,0,0,0
2015-03-24,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,14,0,0,0,0,0,0,0,0,0,0
2015-03-25,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-03-25,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,12,5,9,21,0,0,0,0,0,0,0,0,0,0
2015-03-25,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,16,0,0,0,0,0,0,0,0,0,0
2015-03-25,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,15,54,0,0,0,0,0,0,0,0,0,0
2015-03-25,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,8,0,0,0,0,0,0,0,0,0,0
2015-03-25,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,19,0,0,0,0,0,0,0,0,0,0
2015-03-25,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,20,68,0,0,0,0,0,0,0,0,0,0
2015-03-25,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,25,83,0,0,0,0,0,0,0,0,1,0
2015-03-25,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,10,21,0,0,0,0,0,0,0,0,0,0
2015-03-25,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0
2015-03-25,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,27,0,0,0,0,0,0,0,0,0,0
2015-03-25,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0
2015-03-25,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,6,57,172,0,0,0,0,0,0,0,0,0,0
2015-03-25,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,29,0,0,0,0,0,0,0,0,0,0
2015-03-25,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-25,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-03-25,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,24,57,0,0,0,0,0,0,0,0,0,0
2015-03-25,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,2,11,0,0,0,0,0,0,0,0,0,0
2015-03-25,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,19,60,0,0,0,0,0,0,0,0,0,0
2015-03-25,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,28,15,38,163,0,0,0,0,0,0,0,0,2,0
2015-03-25,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-25,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,5,16,0,0,0,0,0,0,0,0,0,0
2015-03-25,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,20,0,0,0,0,0,0,0,0,1,0
2015-03-25,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0
2015-03-25,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,9,0,0,0,0,0,0,0,0,0,0
2015-03-25,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,18,0,0,0,0,0,0,0,0,0,0
2015-03-25,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,12,30,0,0,0,0,0,0,0,0,0,0
2015-03-25,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,8,29,0,0,0,0,0,0,0,0,0,0
2015-03-25,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,20,14,71,227,0,0,0,0,0,0,0,0,0,0
2015-03-25,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-25,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,32,0,0,0,0,0,0,0,0,0,0
2015-03-25,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,12,48,0,0,0,0,0,0,0,0,0,0
2015-03-25,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-25,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,15,48,0,0,0,0,0,0,0,0,1,0
2015-03-25,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-25,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-25,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,20,4,12,42,0,1,0,0,0,0,0,1,1,0
2015-03-25,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,21,0,0,0,0,0,0,0,0,0,0
2015-03-25,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-25,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,21,52,0,0,0,0,0,0,0,0,0,0
2015-03-25,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,22,0,0,0,0,0,0,0,0,0,0
2015-03-25,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-25,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0
2015-03-25,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,11,0,0,0,0,0,0,0,0,0,0
2015-03-25,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,10,0,0,0,0,0,0,0,0,0,0
2015-03-25,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-25,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,33,0,0,0,0,0,0,0,0,0,0
2015-03-25,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-25,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,19,0,0,0,0,0,0,0,0,0,0
2015-03-25,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-03-25,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-25,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,15,45,0,0,0,0,0,0,0,0,0,0
2015-03-25,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-25,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,10,36,0,0,0,0,0,0,0,0,1,0
2015-03-25,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,25,0,0,0,0,0,0,0,0,0,0
2015-03-25,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,14,0,0,0,0,0,0,0,0,0,0
2015-03-25,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-25,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,9,0,0,0,0,0,0,0,0,0,0
2015-03-25,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-25,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,15,48,0,0,0,0,0,0,0,0,0,0
2015-03-25,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,32,0,0,0,0,0,0,0,0,0,0
2015-03-25,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,13,0,0,0,0,0,0,0,0,0,0
2015-03-25,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,US,98x5I1LVPhtnUHDyujq7zg,Roshan,114,41,241,840,0,0,0,0,0,0,0,1,1,0
2015-03-25,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-25,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-25,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-25,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,13,30,0,0,0,0,0,0,0,0,0,0
2015-03-25,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,7,14,0,0,0,0,0,0,0,0,0,0
2015-03-26,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-26,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,11,23,0,0,0,0,0,0,0,0,0,0
2015-03-26,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0
2015-03-26,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,18,55,0,0,0,0,0,0,0,0,1,0
2015-03-26,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-26,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,19,0,0,0,0,0,0,0,0,0,0
2015-03-26,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-26,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-26,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,21,65,0,0,0,0,0,0,0,0,1,0
2015-03-26,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,11,5,27,84,0,0,0,0,0,0,0,0,0,0
2015-03-26,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,22,0,0,0,0,0,0,0,0,0,0
2015-03-26,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0
2015-03-26,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-26,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0
2015-03-26,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-03-26,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,26,12,59,176,0,0,0,0,0,0,0,0,0,0
2015-03-26,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,29,0,0,0,0,0,0,0,0,0,0
2015-03-26,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-26,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-26,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-26,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,21,57,0,0,0,0,0,0,0,0,0,0
2015-03-26,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,2,11,0,0,0,0,0,0,0,0,0,0
2015-03-26,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,20,62,0,0,0,0,0,0,0,0,0,0
2015-03-26,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,17,6,41,164,0,0,0,0,0,0,0,0,1,0
2015-03-26,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,6,0,0,0,0,0,0,0,0,0,0
2015-03-26,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,16,3,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-26,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0
2015-03-26,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0
2015-03-26,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-03-26,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,8,19,0,0,0,0,0,0,0,0,0,0
2015-03-26,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,29,0,0,0,0,0,0,0,0,0,0
2015-03-26,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,28,0,0,0,0,0,0,0,0,0,0
2015-03-26,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,22,13,69,230,0,0,0,0,0,0,0,0,0,0
2015-03-26,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-26,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-26,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,31,0,0,0,0,0,0,0,0,0,0
2015-03-26,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,11,5,14,51,0,0,0,0,0,0,0,0,0,0
2015-03-26,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-26,KG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,15,50,0,0,0,0,0,0,0,0,0,0
2015-03-26,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-26,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-26,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,MO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-26,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,10,2,11,42,0,0,0,0,0,0,0,0,0,0
2015-03-26,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,22,0,0,0,0,0,0,0,0,0,0
2015-03-26,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-26,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,20,51,0,0,0,0,0,0,0,0,0,0
2015-03-26,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,23,0,0,0,0,0,0,0,0,0,0
2015-03-26,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-26,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-03-26,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-03-26,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-26,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-03-26,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,8,31,0,0,0,0,0,0,0,0,0,0
2015-03-26,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-26,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,19,0,0,0,0,0,0,0,0,0,0
2015-03-26,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-03-26,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-26,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,44,0,0,0,0,0,0,0,0,0,0
2015-03-26,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,16,0,0,0,0,0,0,0,0,0,0
2015-03-26,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,9,35,0,0,0,0,0,0,0,0,0,0
2015-03-26,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,26,0,0,0,0,0,0,0,0,0,0
2015-03-26,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,15,0,0,0,0,0,0,0,0,0,0
2015-03-26,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-26,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,9,0,0,0,0,0,0,0,0,0,0
2015-03-26,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-26,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,13,47,0,0,0,0,0,0,0,0,1,0
2015-03-26,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,13,35,0,0,0,0,0,0,0,0,0,0
2015-03-26,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,12,0,0,0,0,0,0,0,0,0,0
2015-03-26,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,US,98x5I1LVPhtnUHDyujq7zg,Roshan,50,32,244,851,0,0,0,0,0,0,0,0,0,0
2015-03-26,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-26,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-26,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-03-26,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,31,0,0,0,0,0,0,0,0,0,0
2015-03-26,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,14,0,0,0,0,0,0,0,0,0,0
2015-03-27,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-27,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,23,0,0,0,0,0,0,0,0,0,0
2015-03-27,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,17,0,0,0,0,0,0,0,0,0,0
2015-03-27,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,20,52,0,0,0,0,0,0,0,0,0,0
2015-03-27,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-27,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-27,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-27,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,21,66,0,0,0,0,0,0,0,0,0,0
2015-03-27,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,9,4,24,85,0,0,0,0,0,0,0,0,0,0
2015-03-27,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,22,0,0,0,0,0,0,0,0,0,0
2015-03-27,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0
2015-03-27,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-27,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0
2015-03-27,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-03-27,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,9,58,180,0,0,0,0,0,0,0,0,1,0
2015-03-27,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,29,0,0,0,0,0,0,0,0,0,0
2015-03-27,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-03-27,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-27,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-27,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,21,53,0,0,0,0,0,0,0,0,0,0
2015-03-27,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-03-27,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,19,8,25,69,0,0,0,0,0,0,0,0,1,0
2015-03-27,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,38,164,0,0,0,0,0,0,0,0,0,0
2015-03-27,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-27,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-27,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,20,0,0,0,0,0,0,0,0,0,0
2015-03-27,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0
2015-03-27,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-27,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,19,0,0,0,0,0,0,0,0,0,0
2015-03-27,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,28,0,0,0,0,0,0,0,0,0,0
2015-03-27,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,26,0,0,0,0,0,0,0,0,0,0
2015-03-27,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,15,9,70,230,0,0,0,0,0,0,0,0,0,0
2015-03-27,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-27,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-27,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,7,32,0,0,0,0,0,0,0,0,0,0
2015-03-27,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,18,55,0,0,0,0,0,0,0,0,0,0
2015-03-27,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-27,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,50,0,0,0,0,0,0,0,0,0,0
2015-03-27,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-27,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-27,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-27,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-27,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,9,41,0,0,0,0,0,0,0,0,0,0
2015-03-27,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,22,0,0,0,0,0,0,0,0,0,0
2015-03-27,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-27,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,51,0,0,0,0,0,0,0,0,0,0
2015-03-27,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,23,0,0,0,0,0,0,0,0,0,0
2015-03-27,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-27,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-03-27,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,12,0,0,0,0,0,0,0,0,0,0
2015-03-27,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-27,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-27,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,31,0,0,0,0,0,0,0,0,0,0
2015-03-27,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-27,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,20,0,0,0,0,0,0,0,0,0,0
2015-03-27,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,12,0,0,0,0,0,0,0,0,0,0
2015-03-27,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-27,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,44,0,0,0,0,0,0,0,0,0,0
2015-03-27,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,17,0,0,0,0,0,0,0,0,0,0
2015-03-27,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,10,34,0,0,0,0,0,0,0,0,0,0
2015-03-27,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,27,0,0,0,0,0,0,0,0,0,0
2015-03-27,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,15,0,0,0,0,0,0,0,0,0,0
2015-03-27,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-03-27,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-27,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-27,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,12,46,0,0,0,0,0,0,0,0,0,0
2015-03-27,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,12,38,0,0,0,0,0,0,0,0,0,0
2015-03-27,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,12,0,0,0,0,0,0,0,0,0,0
2015-03-27,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,US,98x5I1LVPhtnUHDyujq7zg,Roshan,69,28,231,847,0,0,0,0,0,0,0,0,2,0
2015-03-27,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-27,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-27,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-03-27,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,32,0,0,0,0,0,0,0,0,0,0
2015-03-27,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,13,0,0,0,0,0,0,0,0,0,0
2015-03-28,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-28,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,11,24,0,0,0,0,0,0,0,0,0,0
2015-03-28,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0
2015-03-28,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,48,0,0,0,0,0,0,0,0,0,0
2015-03-28,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-28,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-28,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-28,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,20,66,0,0,0,0,0,0,0,0,0,0
2015-03-28,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,22,85,0,0,0,0,0,0,0,0,0,0
2015-03-28,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,22,0,0,0,0,0,0,0,0,0,0
2015-03-28,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,13,0,0,0,0,0,0,0,0,0,0
2015-03-28,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-28,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,23,0,0,0,0,0,0,0,0,0,0
2015-03-28,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,CY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0
2015-03-28,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-03-28,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,54,182,0,0,0,0,0,0,0,0,0,0
2015-03-28,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,29,0,0,0,0,0,0,0,0,0,0
2015-03-28,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-28,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-28,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-28,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,51,0,0,0,0,0,0,0,0,0,0
2015-03-28,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-03-28,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,29,7,29,74,0,0,0,0,0,0,0,0,1,0
2015-03-28,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,16,10,41,167,0,0,0,0,0,0,0,0,0,0
2015-03-28,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-28,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,17,0,0,0,0,0,0,0,0,0,0
2015-03-28,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,19,0,0,0,0,0,0,0,0,0,0
2015-03-28,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-28,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-28,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,20,0,0,0,0,0,0,0,0,0,0
2015-03-28,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,8,28,0,0,0,0,0,0,0,0,0,0
2015-03-28,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,26,0,0,0,0,0,0,0,0,0,0
2015-03-28,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,19,13,72,234,0,0,0,0,0,0,0,0,0,0
2015-03-28,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-28,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-28,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,8,34,0,0,0,0,0,0,0,0,0,0
2015-03-28,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,0,0,0,1,0,0,0,0,0,1,0
2015-03-28,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,17,56,0,0,0,0,0,0,0,0,0,0
2015-03-28,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-28,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,14,51,0,0,0,0,0,0,0,0,0,0
2015-03-28,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-28,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-28,LY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-28,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-28,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,9,40,0,0,0,0,0,0,0,0,0,0
2015-03-28,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,20,0,0,0,0,0,0,0,0,0,0
2015-03-28,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-28,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,16,51,0,0,0,0,0,0,0,0,0,0
2015-03-28,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,23,0,0,0,0,0,0,0,0,0,0
2015-03-28,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-28,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-28,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-03-28,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-28,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-28,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,31,0,0,0,0,0,0,0,0,0,0
2015-03-28,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-28,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,20,0,0,0,0,0,0,0,0,0,0
2015-03-28,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0
2015-03-28,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-28,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,42,0,0,0,0,0,0,0,0,0,0
2015-03-28,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,17,0,0,0,0,0,0,0,0,0,0
2015-03-28,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,13,4,13,37,0,0,0,0,0,0,0,0,0,0
2015-03-28,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,28,0,0,0,0,0,0,0,0,0,0
2015-03-28,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-03-28,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-03-28,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-03-28,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-28,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,13,47,0,0,0,0,0,0,0,0,0,0
2015-03-28,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,12,40,0,0,0,0,0,0,0,0,0,0
2015-03-28,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,13,0,0,0,0,0,0,0,0,0,0
2015-03-28,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,US,98x5I1LVPhtnUHDyujq7zg,Roshan,87,39,234,864,0,0,0,0,0,0,0,0,0,0
2015-03-28,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-28,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-28,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,12,0,0,0,0,0,0,0,0,0,0
2015-03-28,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,10,31,0,0,0,0,0,0,0,0,0,0
2015-03-28,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,12,0,0,0,0,0,0,0,0,0,0
2015-03-29,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,2,6,0,0,0,0,0,0,0,0,0,0
2015-03-29,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,13,27,0,0,0,0,0,0,0,0,0,0
2015-03-29,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0
2015-03-29,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,16,47,0,0,0,0,0,0,0,0,0,0
2015-03-29,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,7,0,0,0,0,0,0,0,0,0,0
2015-03-29,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-03-29,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-29,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,66,0,0,0,0,0,0,0,0,0,0
2015-03-29,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,20,86,0,0,0,0,0,0,0,0,0,0
2015-03-29,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,24,0,0,0,0,0,0,0,0,0,0
2015-03-29,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,12,0,0,0,0,0,0,0,0,0,0
2015-03-29,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-29,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,24,0,0,0,0,0,0,0,0,1,0
2015-03-29,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-03-29,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,29,9,55,190,0,0,0,0,0,0,0,0,0,0
2015-03-29,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,11,30,0,0,0,0,0,0,0,0,0,0
2015-03-29,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-03-29,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-29,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-29,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,15,54,0,0,0,0,0,0,0,0,0,0
2015-03-29,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-03-29,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,32,76,0,0,0,0,0,0,0,0,0,0
2015-03-29,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,21,8,41,169,0,0,0,0,0,0,0,0,1,0
2015-03-29,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-29,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,18,0,0,0,0,0,0,0,0,0,0
2015-03-29,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,20,0,0,0,0,0,0,0,0,1,0
2015-03-29,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-29,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-29,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0
2015-03-29,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,7,28,0,0,0,0,0,0,0,1,0,0
2015-03-29,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,6,28,0,0,0,0,0,0,0,0,0,0
2015-03-29,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,26,6,68,231,0,0,0,0,0,0,0,0,0,0
2015-03-29,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-29,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-03-29,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,12,1,8,34,0,0,1,0,0,0,0,0,0,0
2015-03-29,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,18,57,0,0,0,0,0,0,0,0,0,0
2015-03-29,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-29,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,30,6,19,55,0,0,0,0,0,0,0,0,0,0
2015-03-29,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-29,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,4,5,0,0,0,0,0,0,0,0,0,0
2015-03-29,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-03-29,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-29,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,7,40,0,0,0,0,0,0,0,0,0,0
2015-03-29,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,18,0,0,0,0,0,0,0,0,0,0
2015-03-29,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-29,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,16,53,0,0,0,0,0,0,0,0,0,0
2015-03-29,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,23,0,0,0,0,0,0,0,0,0,0
2015-03-29,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-29,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-29,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-03-29,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-29,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-29,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,7,32,0,0,0,0,0,0,0,0,0,0
2015-03-29,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-29,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,0,0
2015-03-29,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-03-29,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-29,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,13,44,0,0,0,0,0,0,0,0,0,0
2015-03-29,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0
2015-03-29,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,37,0,0,0,0,0,0,0,0,0,0
2015-03-29,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,26,0,0,0,0,0,0,0,0,0,0
2015-03-29,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,16,0,0,0,0,0,0,0,0,0,0
2015-03-29,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-29,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-29,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-03-29,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,13,48,0,0,0,0,0,0,0,0,0,0
2015-03-29,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,41,0,0,0,0,0,0,0,0,0,1
2015-03-29,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0
2015-03-29,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,US,98x5I1LVPhtnUHDyujq7zg,Roshan,77,35,233,875,0,0,0,0,0,0,0,1,3,0
2015-03-29,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-29,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-29,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-03-29,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,32,0,0,0,0,0,0,0,0,0,0
2015-03-29,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,12,0,0,0,0,0,0,0,0,0,0
2015-03-30,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,6,0,0,0,0,0,0,0,0,0,0
2015-03-30,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,0,12,26,0,0,0,0,0,0,0,0,1,0
2015-03-30,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,18,0,0,0,0,0,0,0,0,1,0
2015-03-30,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,18,49,0,0,0,0,0,0,0,0,0,0
2015-03-30,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-03-30,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,8,21,0,0,0,0,0,0,0,0,0,0
2015-03-30,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-30,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,18,70,0,0,0,0,0,0,0,0,0,0
2015-03-30,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,19,3,19,87,0,0,0,0,0,0,0,0,0,0
2015-03-30,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,24,0,0,0,0,0,0,0,0,0,0
2015-03-30,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,13,0,0,0,0,0,0,0,0,0,0
2015-03-30,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-30,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0
2015-03-30,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-03-30,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,15,10,60,195,0,0,0,0,0,0,0,0,0,0
2015-03-30,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,29,0,0,0,0,0,0,0,0,0,0
2015-03-30,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,11,0,0,0,0,0,0,0,0,0,0
2015-03-30,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-03-30,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-30,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,53,0,0,0,0,0,0,0,0,0,0
2015-03-30,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,13,0,0,0,0,0,0,0,0,0,0
2015-03-30,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,32,77,0,0,0,0,0,0,0,0,0,0
2015-03-30,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,21,14,50,177,0,0,0,0,0,0,0,0,1,0
2015-03-30,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-30,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,18,0,0,0,0,0,0,0,0,0,0
2015-03-30,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-03-30,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-03-30,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-30,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,21,0,0,0,0,0,0,0,0,0,0
2015-03-30,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,11,33,0,0,0,0,0,0,0,0,1,0
2015-03-30,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,28,0,0,0,0,0,0,0,0,0,0
2015-03-30,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,18,12,71,239,0,0,0,0,0,0,0,0,0,0
2015-03-30,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-30,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-30,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,9,0,7,33,0,0,0,0,0,0,0,0,0,0
2015-03-30,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,55,0,0,0,0,0,0,0,0,0,0
2015-03-30,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-30,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,56,0,0,0,0,0,0,0,0,0,0
2015-03-30,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-30,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,4,4,0,0,0,0,0,0,0,0,0,0
2015-03-30,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,5,0,0,0,0,0,0,0,0,0,0
2015-03-30,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-30,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-30,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,9,42,0,0,0,0,0,0,0,0,1,0
2015-03-30,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,16,0,0,0,0,0,0,0,0,0,0
2015-03-30,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-30,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,13,53,0,0,0,0,0,0,0,0,0,0
2015-03-30,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,13,2,6,24,0,0,0,0,0,0,0,0,0,0
2015-03-30,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-30,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-30,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-03-30,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-03-30,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-30,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,31,0,0,0,0,0,0,0,0,0,0
2015-03-30,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-30,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0
2015-03-30,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,14,0,0,0,0,0,0,0,0,0,0
2015-03-30,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-30,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,12,43,0,0,0,0,0,0,0,0,0,0
2015-03-30,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,11,1,5,17,0,0,0,0,0,0,0,0,0,0
2015-03-30,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,37,0,0,0,0,0,0,0,0,0,0
2015-03-30,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,27,0,0,0,0,0,0,0,0,0,0
2015-03-30,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0
2015-03-30,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-30,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-03-30,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-30,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,9,46,0,0,0,0,0,0,0,0,0,0
2015-03-30,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,15,43,0,0,1,0,0,0,0,0,0,0
2015-03-30,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0
2015-03-30,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,US,98x5I1LVPhtnUHDyujq7zg,Roshan,46,26,219,880,0,0,1,0,0,0,0,0,0,0
2015-03-30,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-30,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-30,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,13,0,0,0,0,0,0,0,0,0,0
2015-03-30,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,33,0,0,0,0,0,0,0,0,0,0
2015-03-30,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,5,14,0,0,0,0,0,0,0,0,0,0
2015-03-31,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-03-31,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,13,27,0,0,0,0,0,0,0,0,0,0
2015-03-31,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0
2015-03-31,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,17,47,0,0,0,0,0,0,0,0,0,0
2015-03-31,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-03-31,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,10,25,0,0,0,0,0,0,0,0,0,0
2015-03-31,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-31,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,20,72,0,0,0,0,0,0,0,0,0,0
2015-03-31,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,22,91,0,0,0,0,0,0,0,0,0,0
2015-03-31,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,6,24,0,0,0,0,0,0,0,0,0,0
2015-03-31,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-03-31,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-31,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,14,4,7,24,0,0,0,0,0,0,0,0,0,0
2015-03-31,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-03-31,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,13,7,55,198,0,0,0,0,0,0,0,0,0,0
2015-03-31,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0
2015-03-31,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0
2015-03-31,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-31,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-03-31,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,53,0,0,0,0,0,0,0,0,0,0
2015-03-31,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,6,14,0,0,0,0,0,0,0,0,0,0
2015-03-31,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,30,78,0,0,0,0,0,0,0,0,0,0
2015-03-31,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,16,7,52,175,0,0,0,0,0,0,0,0,2,0
2015-03-31,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-03-31,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,18,2,9,19,0,0,0,0,0,0,0,0,1,0
2015-03-31,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-03-31,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-03-31,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-31,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,23,0,0,0,0,0,0,0,0,0,0
2015-03-31,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,33,0,0,0,0,0,0,0,0,0,0
2015-03-31,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,27,0,0,0,0,0,0,0,0,0,0
2015-03-31,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,32,16,75,244,0,0,0,0,0,0,0,0,0,0
2015-03-31,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-31,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-03-31,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,10,35,0,0,0,0,0,0,0,0,0,0
2015-03-31,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,15,56,0,0,0,0,0,0,0,0,0,0
2015-03-31,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-03-31,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,17,58,0,0,0,0,0,0,0,0,1,0
2015-03-31,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0
2015-03-31,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-03-31,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,5,0,0,0,0,0,0,0,0,0,0
2015-03-31,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,7,0,0,0,0,0,0,0,0,0,0
2015-03-31,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-31,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,43,0,0,0,0,0,0,0,0,0,0
2015-03-31,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,4,17,0,0,0,0,0,0,0,0,0,0
2015-03-31,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-31,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,11,53,0,0,0,0,0,0,0,0,0,0
2015-03-31,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,7,26,0,0,0,0,0,0,0,0,0,0
2015-03-31,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-03-31,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-03-31,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,11,0,0,0,0,0,0,0,0,0,0
2015-03-31,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-03-31,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-03-31,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,31,0,0,0,0,0,0,0,0,0,0
2015-03-31,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,22,0,0,0,0,0,0,0,0,0,0
2015-03-31,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-03-31,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,3,6,0,0,0,0,0,0,0,0,0,0
2015-03-31,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,12,45,0,0,0,0,0,0,0,0,0,0
2015-03-31,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0
2015-03-31,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,10,36,0,0,0,0,0,0,0,0,0,0
2015-03-31,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,27,0,0,0,0,0,0,0,0,0,0
2015-03-31,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,16,0,0,0,0,0,0,0,0,0,0
2015-03-31,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-03-31,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0
2015-03-31,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-03-31,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,9,43,0,0,0,0,0,0,0,0,0,0
2015-03-31,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,46,0,0,0,0,0,0,0,0,0,0
2015-03-31,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-03-31,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,US,98x5I1LVPhtnUHDyujq7zg,Roshan,66,33,213,887,0,1,0,0,0,0,0,1,1,0
2015-03-31,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-03-31,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-03-31,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-03-31,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,31,0,0,0,0,0,0,0,0,0,0
2015-03-31,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,14,0,0,0,0,0,0,0,0,0,0
2015-04-01,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-01,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,8,26,0,0,0,0,0,0,0,0,0,0
2015-04-01,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-01,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,44,0,0,0,0,0,0,0,0,0,0
2015-04-01,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-01,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,26,0,0,0,0,0,0,0,0,0,0
2015-04-01,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-01,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-01,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,8,6,22,74,0,0,0,0,0,0,0,0,0,0
2015-04-01,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-01,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,4,25,91,0,0,0,0,0,0,0,0,0,0
2015-04-01,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,7,26,0,0,0,0,0,0,0,0,0,0
2015-04-01,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-01,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-01,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,8,27,0,0,0,0,0,0,0,0,0,0
2015-04-01,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-04-01,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,17,5,55,196,0,0,0,0,0,0,0,0,0,0
2015-04-01,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,26,0,0,0,0,0,0,0,0,0,0
2015-04-01,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0
2015-04-01,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-01,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-01,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-01,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,8,5,12,57,0,0,0,0,0,0,0,0,0,0
2015-04-01,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,14,0,0,0,0,0,0,0,0,0,0
2015-04-01,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,20,9,36,83,0,0,0,0,0,0,0,0,0,0
2015-04-01,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,23,9,46,181,0,0,0,0,0,0,0,0,1,0
2015-04-01,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,1,2,0,0,0,0,0,0,0,0,1,0
2015-04-01,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-01,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-04-01,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-01,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-04-01,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-01,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,23,0,0,0,0,0,0,0,0,0,0
2015-04-01,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,9,33,0,0,0,0,0,0,0,0,0,0
2015-04-01,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-01,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,25,7,71,245,0,0,0,0,0,0,0,0,0,0
2015-04-01,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-01,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-01,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,13,38,0,0,0,0,0,0,0,0,0,0
2015-04-01,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-01,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,14,3,17,57,0,0,0,0,0,0,0,0,0,0
2015-04-01,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-01,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,56,0,0,0,0,0,0,0,0,0,0
2015-04-01,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0
2015-04-01,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-01,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-01,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-04-01,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-01,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-01,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,45,0,0,0,0,0,0,0,0,0,0
2015-04-01,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-01,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-01,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,53,0,0,0,0,0,0,0,0,0,0
2015-04-01,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,25,0,0,0,0,0,0,0,0,0,0
2015-04-01,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-01,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-04-01,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-01,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-04-01,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-01,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,7,30,0,0,0,0,0,0,0,0,0,0
2015-04-01,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-01,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,20,0,0,0,0,0,0,0,0,0,0
2015-04-01,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-01,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,7,0,0,0,0,0,0,0,0,0,0
2015-04-01,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,45,0,0,0,0,0,0,0,0,0,0
2015-04-01,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-01,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,8,36,0,0,0,0,0,0,0,0,0,0
2015-04-01,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,25,0,0,0,0,0,0,0,0,0,0
2015-04-01,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-01,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-01,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-01,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-01,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,8,42,0,0,0,0,0,0,0,0,0,0
2015-04-01,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,16,46,0,0,0,0,0,0,0,0,0,0
2015-04-01,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-01,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,US,98x5I1LVPhtnUHDyujq7zg,Roshan,53,29,197,890,0,0,0,0,0,0,0,0,3,0
2015-04-01,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-01,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-01,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,12,0,0,0,0,0,0,0,0,0,0
2015-04-01,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,8,30,0,0,0,0,0,0,0,0,0,0
2015-04-01,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-02,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-02,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,26,0,0,0,0,0,0,0,0,0,0
2015-04-02,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-02,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,12,3,12,46,0,0,0,0,0,0,0,0,0,0
2015-04-02,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-02,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,11,27,0,0,0,0,0,0,0,0,0,0
2015-04-02,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-02,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,21,73,0,0,0,0,0,0,0,0,0,0
2015-04-02,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,23,92,0,0,0,0,0,0,0,0,0,0
2015-04-02,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,25,0,0,0,0,0,0,0,0,0,0
2015-04-02,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-02,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-02,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,9,28,0,0,0,0,0,0,0,0,0,0
2015-04-02,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-04-02,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,18,10,52,199,0,0,0,0,0,0,0,0,0,0
2015-04-02,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,25,0,0,0,0,0,0,0,0,0,0
2015-04-02,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-02,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-02,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,13,58,0,0,0,0,0,0,0,0,0,0
2015-04-02,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,14,0,0,0,0,0,0,0,0,0,0
2015-04-02,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,35,85,0,0,0,0,0,0,0,0,0,0
2015-04-02,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,14,4,44,176,0,0,0,0,0,0,0,0,1,0
2015-04-02,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-02,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-02,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-02,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-02,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-02,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,9,33,0,0,0,0,0,0,0,0,0,0
2015-04-02,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,26,0,0,0,0,0,0,0,0,0,0
2015-04-02,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,19,12,68,248,0,0,0,0,0,0,0,0,0,0
2015-04-02,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,15,40,0,0,0,0,0,0,0,0,0,0
2015-04-02,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,6,6,20,62,0,0,0,0,0,0,0,0,0,0
2015-04-02,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,14,56,0,0,0,0,0,0,0,0,0,0
2015-04-02,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,LA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0
2015-04-02,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-02,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-02,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-02,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,45,0,0,0,0,0,0,0,0,0,0
2015-04-02,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-02,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-02,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,10,53,0,0,0,0,0,0,0,0,0,0
2015-04-02,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,23,0,0,0,0,0,0,0,0,0,0
2015-04-02,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-02,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,8,0,0,0,0,0,0,0,0,0,0
2015-04-02,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,6,14,0,0,0,0,0,0,0,0,0,0
2015-04-02,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-04-02,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-02,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,29,0,0,0,0,0,0,0,0,0,0
2015-04-02,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,21,0,0,0,0,0,0,0,0,0,0
2015-04-02,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-02,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,1
2015-04-02,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,46,0,0,0,0,0,0,0,0,0,0
2015-04-02,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-02,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,36,0,0,0,0,0,0,0,0,0,0
2015-04-02,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,4,24,0,0,0,0,0,0,0,0,0,0
2015-04-02,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-02,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-02,SY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0
2015-04-02,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-02,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-02,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,43,0,0,0,0,0,0,0,0,0,0
2015-04-02,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,44,0,0,0,0,0,0,0,0,0,0
2015-04-02,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-02,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-02,US,98x5I1LVPhtnUHDyujq7zg,Roshan,46,29,193,885,0,0,0,0,0,0,0,1,2,1
2015-04-02,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-02,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-02,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,6,14,0,0,0,0,0,0,0,0,0,0
2015-04-02,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,31,0,0,0,0,0,0,0,0,0,0
2015-04-02,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-03,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,7,0,0,0,0,0,0,0,0,0,0
2015-04-03,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,7,27,0,0,0,0,0,0,0,0,0,0
2015-04-03,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,19,0,0,0,0,0,0,0,0,0,0
2015-04-03,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,6,6,15,50,0,0,0,0,0,0,0,0,0,0
2015-04-03,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-03,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,28,0,0,0,0,0,0,0,0,0,0
2015-04-03,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-03,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,20,8,27,79,0,0,0,0,0,0,0,0,0,0
2015-04-03,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-03,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,5,5,25,94,0,0,0,0,0,0,0,0,0,0
2015-04-03,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,26,0,0,0,0,0,0,0,0,0,0
2015-04-03,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,6,16,0,0,0,0,0,0,0,0,0,0
2015-04-03,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-03,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,30,0,0,0,0,0,0,0,0,0,0
2015-04-03,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-04-03,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,21,10,53,205,0,0,0,0,0,0,0,0,0,0
2015-04-03,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,24,0,0,0,0,0,0,0,0,0,0
2015-04-03,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-03,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-03,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,16,5,17,61,0,0,0,0,0,0,0,0,0,0
2015-04-03,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-03,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,17,9,37,91,0,0,0,0,0,0,0,0,0,0
2015-04-03,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,9,9,53,180,0,0,0,0,0,0,0,0,0,0
2015-04-03,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-03,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,19,0,0,0,0,0,0,0,0,0,0
2015-04-03,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,19,0,0,0,0,0,0,0,0,0,0
2015-04-03,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-03,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-03,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-03,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,10,33,0,0,0,0,0,0,0,0,0,0
2015-04-03,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,10,27,0,0,0,0,0,0,0,0,0,0
2015-04-03,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,36,10,70,247,0,0,0,0,0,0,0,0,0,0
2015-04-03,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,14,39,0,0,0,0,0,0,0,0,0,0
2015-04-03,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-03,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,20,66,0,0,0,0,0,0,0,0,0,0
2015-04-03,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,15,56,0,0,0,0,0,0,0,0,1,0
2015-04-03,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-04-03,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-03,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-03,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-03,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,9,45,0,0,0,0,0,0,0,0,0,0
2015-04-03,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-03,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-03,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,11,54,0,0,0,0,0,0,0,0,0,0
2015-04-03,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-03,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-03,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-04-03,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,6,15,0,0,0,0,0,0,0,0,0,0
2015-04-03,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-04-03,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-03,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,29,0,0,0,0,0,0,0,0,0,0
2015-04-03,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-03,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-03,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,22,0,0,0,0,0,0,0,0,0,0
2015-04-03,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-03,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-03,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,45,0,0,0,0,0,0,0,0,0,0
2015-04-03,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-03,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,38,0,0,0,0,0,0,0,0,0,0
2015-04-03,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,6,25,0,0,0,0,0,0,0,0,0,0
2015-04-03,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-03,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-03,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-03,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-03,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,10,45,0,0,0,0,0,0,0,0,0,0
2015-04-03,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,44,0,0,0,0,0,0,0,0,0,0
2015-04-03,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-03,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-03,US,98x5I1LVPhtnUHDyujq7zg,Roshan,87,54,221,907,0,0,0,0,0,0,0,0,3,0
2015-04-03,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-03,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-03,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,14,0,0,0,0,0,0,0,0,0,0
2015-04-03,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,32,0,0,0,0,0,0,0,0,0,0
2015-04-03,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,16,0,0,0,0,0,0,0,0,0,0
2015-04-04,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-04,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-04,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,19,0,0,0,0,0,0,0,0,0,0
2015-04-04,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,50,0,0,0,0,0,0,0,0,0,0
2015-04-04,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-04,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,28,0,0,0,0,0,0,0,0,0,0
2015-04-04,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-04,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,29,81,0,0,0,0,0,0,0,0,0,0
2015-04-04,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-04,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,14,7,29,100,0,0,0,0,0,0,0,0,0,0
2015-04-04,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,11,29,0,0,0,0,0,0,0,0,0,0
2015-04-04,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,16,0,0,0,0,0,0,0,0,0,0
2015-04-04,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,13,32,0,0,0,0,0,0,0,0,0,0
2015-04-04,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-04,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,13,0,0,0,0,0,0,0,0,0,0
2015-04-04,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,12,3,49,201,0,0,0,0,0,0,0,3,2,0
2015-04-04,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0
2015-04-04,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-04,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-04,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-04,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,19,62,0,0,0,0,0,0,0,0,0,0
2015-04-04,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-04,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,6,6,35,95,0,0,0,0,0,0,0,0,0,0
2015-04-04,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,52,181,0,0,0,0,0,0,0,0,0,0
2015-04-04,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-04,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,19,0,0,0,0,0,0,0,0,0,0
2015-04-04,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,5,19,0,0,0,0,0,0,0,0,0,0
2015-04-04,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-04,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-04,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,24,0,0,0,0,0,0,0,0,0,0
2015-04-04,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,33,0,0,0,0,0,0,0,0,0,0
2015-04-04,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,27,0,0,0,0,0,0,0,0,0,0
2015-04-04,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,33,14,72,258,0,0,0,0,0,0,0,0,0,0
2015-04-04,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,14,39,0,0,0,0,0,0,0,0,0,0
2015-04-04,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,18,65,0,0,0,0,0,0,0,0,0,0
2015-04-04,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,15,58,0,0,0,0,0,0,0,0,0,0
2015-04-04,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-04,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-04,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-04,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,46,0,0,0,0,0,0,0,0,0,0
2015-04-04,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-04,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-04,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,13,54,0,0,0,0,0,0,0,1,1,0
2015-04-04,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,7,24,0,0,0,0,0,0,0,0,0,0
2015-04-04,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-04,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0
2015-04-04,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,16,0,0,0,0,0,0,0,0,0,0
2015-04-04,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-04-04,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,7,30,0,0,0,0,0,0,0,0,0,0
2015-04-04,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-04,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-04,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-04,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-04,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,45,0,0,0,0,0,0,0,0,0,0
2015-04-04,RW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0
2015-04-04,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,7,38,0,0,0,0,0,0,0,0,0,0
2015-04-04,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,6,25,0,0,0,0,0,0,0,0,0,0
2015-04-04,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,14,0,0,0,0,0,0,0,0,0,0
2015-04-04,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-04,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-04,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-04,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,43,0,0,0,0,0,0,0,0,0,0
2015-04-04,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,42,0,0,0,0,0,0,0,0,0,0
2015-04-04,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-04,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-04,US,98x5I1LVPhtnUHDyujq7zg,Roshan,68,34,218,916,0,0,0,0,0,0,0,0,1,1
2015-04-04,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-04,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-04,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,20,2,6,16,0,0,0,0,0,0,0,0,0,0
2015-04-04,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,32,0,0,0,0,0,0,0,0,0,0
2015-04-04,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,8,18,0,0,0,0,0,0,0,0,0,0
2015-04-05,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-05,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,28,0,0,0,0,0,0,0,0,0,0
2015-04-05,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-05,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,16,51,0,0,0,0,0,0,0,0,0,0
2015-04-05,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,9,0,0,0,0,0,0,0,0,0,0
2015-04-05,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,28,0,0,0,0,0,0,0,0,0,0
2015-04-05,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-05,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,29,81,0,0,0,0,0,0,0,0,0,0
2015-04-05,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-05,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,29,98,0,0,0,0,0,0,0,0,0,0
2015-04-05,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,9,29,0,0,0,0,0,0,0,0,0,0
2015-04-05,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,17,0,0,0,0,0,0,0,0,0,0
2015-04-05,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,30,0,0,0,0,0,0,0,0,0,0
2015-04-05,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-05,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0
2015-04-05,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,6,47,204,0,0,0,0,0,0,0,0,0,0
2015-04-05,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,22,0,0,0,0,0,0,0,0,0,0
2015-04-05,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-05,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-05,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-05,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,17,61,0,0,0,0,0,0,0,0,0,0
2015-04-05,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,17,0,0,0,0,0,0,0,0,1,0
2015-04-05,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,31,95,0,0,0,0,0,0,0,0,0,0
2015-04-05,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,8,5,50,180,0,0,0,0,0,0,0,0,0,0
2015-04-05,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-05,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,19,0,0,0,0,0,0,0,0,0,0
2015-04-05,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,6,22,0,0,0,0,0,0,0,1,0,0
2015-04-05,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-05,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-05,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,24,0,0,0,0,0,0,0,0,0,0
2015-04-05,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,32,0,0,0,0,0,0,0,0,0,0
2015-04-05,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-05,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,21,12,76,264,0,0,0,0,0,0,0,1,2,0
2015-04-05,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,7,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,15,41,0,0,0,0,0,0,0,0,0,0
2015-04-05,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,21,67,0,0,0,0,0,0,0,0,0,0
2015-04-05,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,18,6,14,58,0,0,0,0,0,0,0,0,0,0
2015-04-05,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-05,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-05,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-05,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,45,0,0,0,0,0,0,0,0,0,0
2015-04-05,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-05,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0
2015-04-05,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,11,53,0,0,0,0,0,0,0,0,0,0
2015-04-05,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,22,0,0,0,0,0,0,0,0,0,0
2015-04-05,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-05,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-05,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,16,0,0,0,0,0,0,0,0,0,0
2015-04-05,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-04-05,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,30,0,0,0,0,0,0,0,0,0,0
2015-04-05,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-05,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,24,0,0,0,0,0,0,0,0,0,0
2015-04-05,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0
2015-04-05,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-05,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,45,0,0,0,0,0,0,0,0,0,0
2015-04-05,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,1,0
2015-04-05,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,8,38,0,0,0,0,0,0,0,0,0,0
2015-04-05,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-05,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-05,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-05,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-05,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-05,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,43,0,0,0,0,0,0,0,0,0,0
2015-04-05,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,41,0,0,0,0,0,0,0,0,0,0
2015-04-05,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-05,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-05,US,98x5I1LVPhtnUHDyujq7zg,Roshan,87,31,216,920,0,0,0,0,0,0,0,0,0,0
2015-04-05,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-05,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-05,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,16,0,0,0,0,0,0,0,0,0,0
2015-04-05,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,31,0,0,0,0,0,0,0,0,0,0
2015-04-05,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,18,0,0,0,0,0,0,0,0,0,0
2015-04-06,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-06,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,28,0,0,0,0,0,0,0,0,0,0
2015-04-06,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0
2015-04-06,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,15,52,0,0,0,0,0,0,0,0,0,0
2015-04-06,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,9,0,0,0,0,0,0,0,0,0,0
2015-04-06,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,27,0,0,0,0,0,0,0,0,0,0
2015-04-06,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-06,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,25,83,0,0,0,0,0,0,0,0,0,0
2015-04-06,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-06,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,21,5,31,102,0,0,0,0,0,0,0,0,0,0
2015-04-06,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,9,29,0,0,0,0,0,0,0,0,0,0
2015-04-06,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,17,0,0,0,0,0,0,0,0,0,0
2015-04-06,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,29,0,0,0,0,0,0,0,0,0,0
2015-04-06,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-06,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-06,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,9,45,203,0,0,0,0,0,0,0,0,0,0
2015-04-06,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,23,0,0,0,0,0,0,0,0,0,0
2015-04-06,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,12,0,0,0,0,0,0,0,0,0,0
2015-04-06,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-06,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,17,62,0,0,0,0,0,0,0,0,0,0
2015-04-06,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-06,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,3,30,98,0,0,0,0,0,0,0,0,0,0
2015-04-06,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,32,14,48,189,0,0,0,0,0,0,0,1,0,0
2015-04-06,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-06,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-06,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,23,0,0,0,0,0,0,0,0,0,0
2015-04-06,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-06,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-06,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-06,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,23,6,11,38,0,0,0,0,0,0,0,0,0,0
2015-04-06,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,28,0,0,0,0,0,0,0,0,0,0
2015-04-06,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,18,9,74,266,0,0,1,0,0,0,0,0,1,0
2015-04-06,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,16,42,0,0,0,0,0,0,0,0,0,0
2015-04-06,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,24,70,0,0,0,0,0,0,0,0,0,0
2015-04-06,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,57,0,0,0,0,0,0,0,0,0,0
2015-04-06,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-06,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-06,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-06,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,45,0,0,0,0,0,0,0,0,0,0
2015-04-06,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-06,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-06,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,1,12,54,0,0,0,0,0,0,0,0,0,0
2015-04-06,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,22,0,0,0,0,0,0,0,0,0,0
2015-04-06,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-06,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-06,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,16,0,0,0,0,0,0,0,0,0,0
2015-04-06,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-04-06,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,30,0,0,0,0,0,0,0,0,0,0
2015-04-06,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,24,0,0,0,0,0,0,0,0,0,0
2015-04-06,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0
2015-04-06,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,7,0,0,0,0,0,0,0,0,0,0
2015-04-06,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,11,6,13,46,0,0,0,0,0,0,0,0,0,0
2015-04-06,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,18,0,0,0,0,0,0,0,0,0,0
2015-04-06,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,35,0,0,0,0,0,0,0,0,0,0
2015-04-06,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-06,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-06,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-06,SV,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0
2015-04-06,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-06,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-06,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,44,0,0,0,0,0,0,0,0,0,0
2015-04-06,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,41,0,0,0,0,0,0,0,0,0,0
2015-04-06,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,14,0,0,0,0,0,0,0,1,1,0
2015-04-06,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-06,US,98x5I1LVPhtnUHDyujq7zg,Roshan,84,46,239,946,0,0,0,0,0,0,0,0,3,0
2015-04-06,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-06,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-06,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0
2015-04-06,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,4,32,0,0,0,0,0,0,0,0,0,0
2015-04-06,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-04-07,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-07,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,28,0,0,0,0,0,0,0,0,0,0
2015-04-07,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-07,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,14,52,0,0,0,0,0,0,0,0,0,0
2015-04-07,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,11,0,0,0,0,0,0,0,0,0,0
2015-04-07,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,27,0,0,0,0,0,0,0,0,0,0
2015-04-07,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-07,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,24,85,0,0,0,0,0,0,0,0,0,0
2015-04-07,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-07,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,30,107,0,0,0,0,0,0,0,0,1,0
2015-04-07,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,28,0,0,0,0,0,0,0,0,0,0
2015-04-07,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,6,17,0,0,0,0,0,0,0,0,0,0
2015-04-07,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,30,0,0,0,0,0,0,0,0,0,0
2015-04-07,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-07,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-07,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,26,6,44,207,0,0,0,0,0,0,0,0,1,0
2015-04-07,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,25,0,0,0,0,0,0,0,0,0,0
2015-04-07,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,13,0,0,0,0,0,0,0,0,1,0
2015-04-07,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-07,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-07,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-07,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,18,64,0,0,0,0,0,0,0,0,0,0
2015-04-07,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,1,0
2015-04-07,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,11,4,32,102,0,0,0,0,0,0,0,0,0,0
2015-04-07,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,21,13,53,192,0,0,0,0,0,0,0,0,1,0
2015-04-07,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-07,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-07,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0
2015-04-07,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,8,22,0,0,0,0,0,0,0,0,0,0
2015-04-07,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-07,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-07,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,24,0,0,0,0,0,0,0,0,0,0
2015-04-07,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,12,39,0,0,0,0,0,0,0,0,0,0
2015-04-07,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,7,28,0,0,0,0,0,0,0,0,0,0
2015-04-07,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,64,267,0,0,0,0,0,0,0,0,0,0
2015-04-07,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-07,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,40,0,0,0,0,0,0,0,0,0,0
2015-04-07,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,21,69,0,0,0,0,0,0,0,0,0,0
2015-04-07,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-07,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,13,58,0,0,0,0,0,0,0,0,0,0
2015-04-07,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-07,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-07,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-07,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-07,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-07,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,44,0,0,0,0,0,0,0,0,0,0
2015-04-07,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0
2015-04-07,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-07,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,54,0,0,0,0,0,0,0,0,0,0
2015-04-07,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,8,23,0,0,0,0,0,0,0,0,0,0
2015-04-07,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-07,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-07,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,17,0,0,0,0,0,0,0,0,0,0
2015-04-07,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-04-07,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,29,0,0,0,0,0,0,0,0,0,0
2015-04-07,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,6,26,0,0,0,0,0,0,0,0,0,0
2015-04-07,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0
2015-04-07,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-07,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,46,0,0,0,0,0,0,0,0,0,0
2015-04-07,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-07,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,33,0,0,0,0,0,0,0,0,0,0
2015-04-07,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,7,25,0,0,0,0,0,0,0,0,0,0
2015-04-07,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-07,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-07,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-07,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-07,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,8,44,0,0,0,0,0,0,0,0,0,0
2015-04-07,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,40,0,0,0,0,0,0,0,0,0,0
2015-04-07,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-07,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-07,US,98x5I1LVPhtnUHDyujq7zg,Roshan,73,44,250,966,0,0,0,0,0,0,0,0,1,0
2015-04-07,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-07,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-07,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,6,16,0,0,0,0,0,0,0,0,0,0
2015-04-07,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,7,34,0,0,0,0,0,0,0,0,0,0
2015-04-07,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,19,0,0,0,0,0,0,0,0,0,0
2015-04-08,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-08,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,28,0,0,0,0,0,0,0,0,0,0
2015-04-08,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,10,2,4,18,0,0,0,0,0,0,0,0,2,1
2015-04-08,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,15,51,0,0,0,0,0,0,0,0,0,0
2015-04-08,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0
2015-04-08,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,27,0,0,0,0,0,0,0,0,0,0
2015-04-08,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-08,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,19,84,0,0,0,0,0,0,0,0,0,0
2015-04-08,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,30,105,0,0,0,0,0,0,0,0,0,0
2015-04-08,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,29,0,0,0,0,0,0,0,0,0,0
2015-04-08,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,18,0,0,0,0,0,0,0,0,0,0
2015-04-08,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-08,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,31,0,0,0,0,0,0,0,0,0,0
2015-04-08,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-08,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-08,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,45,205,0,0,0,0,0,0,0,0,0,0
2015-04-08,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,25,0,0,0,0,0,0,0,0,0,0
2015-04-08,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-08,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-08,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-08,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,63,0,0,0,0,0,0,0,0,0,0
2015-04-08,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-08,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,26,98,0,0,0,0,0,0,0,0,0,0
2015-04-08,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,13,7,50,190,0,0,0,0,0,0,0,0,0,0
2015-04-08,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-08,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-08,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,21,0,0,0,0,0,0,0,0,0,0
2015-04-08,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-08,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-08,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,24,0,0,0,0,0,0,0,0,0,0
2015-04-08,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,11,40,0,0,0,0,0,0,0,0,0,0
2015-04-08,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,7,29,0,0,0,0,0,0,0,0,0,0
2015-04-08,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,17,5,63,267,0,0,0,0,0,0,0,0,0,0
2015-04-08,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-08,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,40,0,0,0,0,0,0,0,0,0,0
2015-04-08,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-08,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,21,68,0,0,0,0,0,0,0,0,0,0
2015-04-08,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-08,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,16,59,0,0,0,0,0,0,0,0,1,0
2015-04-08,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-08,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-08,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-08,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-08,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-08,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-08,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,7,46,0,0,0,0,0,0,0,0,0,0
2015-04-08,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-08,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-08,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,11,53,0,0,0,0,0,0,0,0,0,0
2015-04-08,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,23,0,0,0,0,0,0,0,0,0,0
2015-04-08,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-08,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,15,0,0,0,0,0,0,0,0,0,0
2015-04-08,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-08,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-08,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,3,28,0,0,0,0,0,0,0,0,0,0
2015-04-08,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-08,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-08,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-08,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-04-08,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,12,46,0,0,0,0,0,0,0,0,0,0
2015-04-08,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-08,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,6,34,0,0,0,0,0,0,0,0,0,0
2015-04-08,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,23,0,0,0,0,0,0,0,0,0,0
2015-04-08,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,14,0,0,0,0,0,0,0,0,0,0
2015-04-08,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-08,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-08,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-08,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,44,0,0,0,0,0,0,0,0,0,0
2015-04-08,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,4,42,0,0,0,0,0,0,0,0,0,0
2015-04-08,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-08,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-08,US,98x5I1LVPhtnUHDyujq7zg,Roshan,107,48,268,984,0,0,0,0,0,0,0,0,1,0
2015-04-08,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-08,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-08,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-08,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,35,0,0,0,0,0,0,0,0,0,0
2015-04-08,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,7,20,0,0,0,0,0,0,0,0,0,0
2015-04-09,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-09,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,27,0,0,0,0,0,0,0,0,0,0
2015-04-09,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-09,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,5,18,56,0,0,0,0,0,0,0,0,0,0
2015-04-09,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-09,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,12,0,0,0,0,0,0,0,0,0,0
2015-04-09,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,0,3,27,0,0,0,0,0,0,0,1,1,0
2015-04-09,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-09,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,BO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,17,80,0,0,0,0,0,0,0,0,0,0
2015-04-09,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,30,102,0,0,0,0,0,0,0,0,0,0
2015-04-09,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,10,32,0,0,0,0,0,0,0,0,0,0
2015-04-09,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,18,0,0,0,0,0,0,0,0,0,0
2015-04-09,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,21,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,32,0,0,0,0,0,0,0,0,0,0
2015-04-09,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-09,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-09,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,21,10,46,210,0,0,0,0,0,0,0,0,0,0
2015-04-09,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,7,23,0,0,0,0,0,0,0,0,1,0
2015-04-09,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-09,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,62,0,0,0,0,0,0,0,0,0,0
2015-04-09,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,4,16,0,0,0,0,0,0,0,0,0,0
2015-04-09,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,25,7,30,103,0,0,0,0,0,0,0,0,1,0
2015-04-09,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,13,6,49,186,0,0,0,0,0,0,0,0,0,0
2015-04-09,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-09,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-09,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,22,0,0,0,0,0,0,0,0,0,0
2015-04-09,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,11,0,0,0,0,0,0,0,0,0,0
2015-04-09,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,23,0,0,0,0,0,0,0,0,0,0
2015-04-09,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,12,40,0,0,0,0,0,0,0,0,0,0
2015-04-09,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-09,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,9,9,61,266,0,0,0,0,0,0,0,0,0,0
2015-04-09,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-09,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,40,0,0,0,0,0,0,0,0,0,0
2015-04-09,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,62,0,0,0,0,0,0,0,0,0,0
2015-04-09,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-09,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,11,6,20,61,0,0,0,0,0,0,0,0,0,0
2015-04-09,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-09,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-09,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-09,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-09,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,44,0,0,0,0,0,0,0,0,0,0
2015-04-09,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-09,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0
2015-04-09,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,16,8,17,59,0,0,0,0,0,0,0,0,0,0
2015-04-09,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,22,0,0,0,0,0,0,0,0,0,0
2015-04-09,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,8,0,0,0,0,0,0,0,0,0,0
2015-04-09,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-09,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-09,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,3,26,0,0,0,0,0,0,0,0,0,0
2015-04-09,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-09,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-09,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-09,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,45,0,0,0,0,0,0,0,0,0,0
2015-04-09,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-09,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,33,0,0,0,0,0,0,0,0,0,0
2015-04-09,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,23,0,0,0,0,0,0,0,0,1,0
2015-04-09,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,15,0,0,0,0,0,0,0,0,0,0
2015-04-09,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-09,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-09,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-09,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,44,0,0,0,0,0,0,0,0,0,0
2015-04-09,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,40,0,0,0,0,0,0,0,0,0,0
2015-04-09,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0
2015-04-09,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-09,US,98x5I1LVPhtnUHDyujq7zg,Roshan,56,34,274,981,0,0,0,0,0,0,0,0,3,0
2015-04-09,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-09,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-09,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-09,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,36,0,0,0,0,0,0,0,0,0,0
2015-04-09,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0
2015-04-10,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-04-10,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,26,0,0,0,0,0,0,0,0,0,0
2015-04-10,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,6,20,0,0,0,0,0,0,0,0,0,0
2015-04-10,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,55,0,0,0,0,0,0,0,0,0,0
2015-04-10,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-10,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,12,0,0,0,0,0,0,0,0,0,0
2015-04-10,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,2,24,0,0,0,0,0,0,0,0,0,0
2015-04-10,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-10,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,81,0,0,0,0,0,0,0,0,0,0
2015-04-10,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,3,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,28,101,0,0,0,0,0,0,0,0,0,0
2015-04-10,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,10,31,0,0,0,0,0,0,0,0,0,0
2015-04-10,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0
2015-04-10,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,30,0,0,0,0,0,0,0,0,0,0
2015-04-10,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-10,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,3,12,0,0,0,0,0,0,0,4,1,0
2015-04-10,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,9,3,41,204,0,0,0,0,0,0,0,0,0,0
2015-04-10,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,24,0,0,0,0,0,0,0,0,0,0
2015-04-10,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-10,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-10,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-10,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,59,0,0,0,0,0,0,0,0,0,0
2015-04-10,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,4,16,0,0,0,0,0,0,0,0,0,0
2015-04-10,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,12,5,26,103,0,0,0,0,0,0,0,0,0,0
2015-04-10,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,8,5,47,176,0,0,0,0,0,0,0,0,0,0
2015-04-10,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-10,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,16,0,0,0,0,0,0,0,0,0,0
2015-04-10,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,20,0,0,0,0,0,0,0,0,0,0
2015-04-10,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,10,0,0,0,0,0,0,0,0,0,0
2015-04-10,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,23,0,0,0,0,0,0,0,0,0,0
2015-04-10,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,12,39,0,0,0,0,0,0,0,0,0,0
2015-04-10,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,28,0,0,0,0,0,0,0,0,0,0
2015-04-10,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,58,265,0,0,0,0,0,0,0,0,0,0
2015-04-10,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,9,40,0,0,0,0,0,0,0,0,0,0
2015-04-10,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,11,58,0,0,0,0,0,0,0,0,0,0
2015-04-10,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-10,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,4,21,63,0,0,0,0,0,0,0,0,0,0
2015-04-10,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-10,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-10,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-10,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,44,0,0,0,0,0,0,0,0,0,0
2015-04-10,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-10,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-04-10,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,14,2,16,58,0,0,0,0,0,0,0,0,1,0
2015-04-10,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,23,0,0,0,0,0,0,0,0,0,0
2015-04-10,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-10,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-04-10,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0
2015-04-10,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-10,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,4,26,0,0,0,0,0,0,0,1,0,0
2015-04-10,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-10,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,21,0,0,0,0,0,0,0,0,0,0
2015-04-10,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-10,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-10,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,9,43,0,0,0,0,0,0,0,0,0,0
2015-04-10,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-10,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,32,0,0,0,0,0,0,0,0,0,0
2015-04-10,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,5,24,0,0,0,0,0,0,0,0,0,0
2015-04-10,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,13,0,0,0,0,0,0,0,0,0,0
2015-04-10,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-10,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-10,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-10,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,43,0,0,0,0,0,0,0,0,0,0
2015-04-10,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,39,0,0,0,0,0,0,0,0,0,0
2015-04-10,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-10,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-10,US,98x5I1LVPhtnUHDyujq7zg,Roshan,67,39,258,960,0,2,0,0,0,0,0,1,1,0
2015-04-10,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-10,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-10,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-10,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,17,4,11,38,0,1,0,0,0,0,0,0,2,0
2015-04-10,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,21,0,0,0,0,0,0,0,0,0,0
2015-04-11,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-11,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,26,0,0,0,0,0,0,0,0,0,0
2015-04-11,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,19,0,0,0,0,0,0,0,0,0,0
2015-04-11,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,11,54,0,0,0,0,0,0,0,0,0,0
2015-04-11,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,13,0,0,0,0,0,0,0,0,0,0
2015-04-11,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,24,0,0,0,0,0,0,0,0,0,0
2015-04-11,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-11,BH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,11,83,0,0,0,0,0,0,0,1,0,0
2015-04-11,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,4,0,0,0,0,0,0,0,0,0,0
2015-04-11,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,27,104,0,0,0,0,0,0,0,0,0,0
2015-04-11,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,8,30,0,0,0,0,0,0,0,0,0,0
2015-04-11,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-11,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-11,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,30,0,0,0,0,0,0,0,0,0,0
2015-04-11,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-11,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,13,0,0,0,0,0,0,0,1,0,0
2015-04-11,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,6,45,204,0,0,0,0,0,0,0,0,0,0
2015-04-11,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,10,26,0,0,0,0,0,0,0,0,0,0
2015-04-11,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-11,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-11,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-11,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-11,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,7,56,0,0,0,0,0,0,0,0,0,0
2015-04-11,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-11,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,15,7,25,106,0,0,0,0,0,0,0,0,0,0
2015-04-11,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,47,172,0,0,0,0,0,0,0,0,1,0
2015-04-11,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-11,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-11,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,16,0,0,0,0,0,0,0,0,0,0
2015-04-11,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,19,0,0,0,0,0,0,0,0,0,0
2015-04-11,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-11,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,10,0,0,0,0,0,0,0,0,0,0
2015-04-11,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,21,0,0,0,0,0,0,0,0,0,0
2015-04-11,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,14,40,0,0,0,0,0,0,0,0,0,0
2015-04-11,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,27,0,0,0,0,0,0,0,0,0,0
2015-04-11,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,15,10,55,266,0,0,0,0,0,0,0,0,0,0
2015-04-11,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-11,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-11,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,39,0,0,0,0,0,0,0,0,0,0
2015-04-11,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,11,57,0,0,0,0,0,0,0,1,0,0
2015-04-11,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-11,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,22,62,0,0,0,0,0,0,0,0,0,0
2015-04-11,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-11,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-11,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-11,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-11,MD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,43,0,0,0,0,0,0,0,0,0,0
2015-04-11,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-11,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-04-11,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,14,59,0,0,0,0,0,0,0,0,0,0
2015-04-11,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,22,0,0,0,0,0,0,0,0,0,0
2015-04-11,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-11,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-11,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0
2015-04-11,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-11,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,27,0,0,0,0,0,0,0,0,0,0
2015-04-11,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,22,0,0,0,0,0,0,0,0,0,0
2015-04-11,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,RE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,14,0,0,0,0,0,0,0,0,0,0
2015-04-11,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-11,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,39,0,0,0,0,0,0,0,0,0,0
2015-04-11,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-11,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,28,0,0,0,0,0,0,0,0,0,0
2015-04-11,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,4,24,0,0,0,0,0,0,0,0,0,0
2015-04-11,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-04-11,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-11,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-11,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-11,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,5,40,0,0,0,0,0,0,0,0,0,0
2015-04-11,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,38,0,0,0,0,0,0,0,0,0,0
2015-04-11,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-11,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-11,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-11,US,98x5I1LVPhtnUHDyujq7zg,Roshan,52,29,255,957,0,0,0,0,0,0,0,0,1,0
2015-04-11,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-11,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0
2015-04-11,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,11,37,0,0,0,0,0,0,0,0,0,0
2015-04-11,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,0,0
2015-04-12,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-12,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,26,0,0,0,0,0,0,0,0,0,0
2015-04-12,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,5,18,0,0,0,0,0,0,0,0,0,0
2015-04-12,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,10,54,0,0,0,0,0,0,0,0,0,0
2015-04-12,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,13,0,0,0,0,0,0,0,0,0,0
2015-04-12,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,4,7,27,0,0,0,0,0,0,0,0,0,0
2015-04-12,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,82,0,0,0,0,0,0,0,0,0,0
2015-04-12,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,4,0,0,0,0,0,0,0,0,0,0
2015-04-12,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,28,104,0,0,0,0,0,0,0,0,1,0
2015-04-12,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,8,29,0,0,0,0,0,0,0,0,0,0
2015-04-12,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,15,0,0,0,0,0,0,0,0,0,0
2015-04-12,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-12,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,29,0,0,0,0,0,0,0,0,0,0
2015-04-12,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-12,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,3,13,0,0,0,0,0,0,0,1,0,0
2015-04-12,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,8,46,207,0,0,0,0,0,0,0,0,0,0
2015-04-12,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,18,2,11,26,0,0,0,0,0,0,0,0,0,0
2015-04-12,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-12,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-12,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-12,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,8,58,0,0,0,0,0,0,0,0,0,0
2015-04-12,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,15,0,0,0,0,0,0,0,0,0,0
2015-04-12,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,15,7,30,110,0,0,0,0,0,0,0,0,0,0
2015-04-12,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,20,8,49,175,0,0,0,0,0,0,0,1,0,0
2015-04-12,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-12,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-12,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,15,0,0,0,0,0,0,0,0,0,0
2015-04-12,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,19,0,0,0,0,0,0,0,0,0,0
2015-04-12,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-12,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,9,0,0,0,0,0,0,0,0,0,0
2015-04-12,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,22,0,0,0,0,0,0,0,0,0,0
2015-04-12,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,13,39,0,0,0,0,0,0,0,0,0,0
2015-04-12,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,28,0,0,0,0,0,0,0,0,0,0
2015-04-12,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,12,6,50,265,0,0,0,0,0,0,0,0,0,0
2015-04-12,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-12,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-12,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,36,0,0,0,0,0,0,0,0,0,0
2015-04-12,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,10,58,0,0,0,0,0,0,0,0,0,0
2015-04-12,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-12,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,17,62,0,0,0,0,0,0,0,0,0,0
2015-04-12,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-12,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-12,LV,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0
2015-04-12,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,41,0,0,0,0,0,0,0,0,0,0
2015-04-12,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-12,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,9,0,0,0,0,0,0,0,0,0,0
2015-04-12,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,15,59,0,0,0,0,0,0,0,0,0,0
2015-04-12,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,22,0,0,0,0,0,0,0,0,0,0
2015-04-12,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-12,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-04-12,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-12,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,7,27,0,0,0,0,0,0,0,0,0,0
2015-04-12,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-12,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-12,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,5,18,0,0,0,0,0,0,0,0,0,0
2015-04-12,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,9,38,0,0,0,0,0,0,0,0,0,0
2015-04-12,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,13,2,5,19,0,0,0,0,0,0,0,0,0,0
2015-04-12,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,7,29,0,0,0,0,0,0,0,0,0,0
2015-04-12,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0
2015-04-12,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,12,0,0,0,0,0,0,0,0,0,0
2015-04-12,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-12,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,2,9,0,0,0,0,0,0,0,0,0,0
2015-04-12,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-12,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,6,42,0,0,0,0,0,0,0,0,0,0
2015-04-12,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,37,0,0,0,0,0,0,0,0,0,0
2015-04-12,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-12,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,13,0,0,0,0,0,0,0,0,0,0
2015-04-12,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,US,98x5I1LVPhtnUHDyujq7zg,Roshan,64,36,258,964,0,0,0,0,0,0,0,0,0,0
2015-04-12,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-12,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-12,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,12,37,0,0,0,0,0,0,0,0,0,0
2015-04-12,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,4,21,0,0,0,0,0,0,0,0,0,0
2015-04-13,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-13,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,26,0,0,0,0,0,0,0,0,0,0
2015-04-13,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,18,0,0,0,0,0,0,0,0,0,0
2015-04-13,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,10,56,0,0,0,0,0,0,0,0,1,0
2015-04-13,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,12,0,0,0,0,0,0,0,0,0,0
2015-04-13,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,8,27,0,0,0,0,0,0,0,0,0,0
2015-04-13,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-13,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,11,81,0,0,0,0,0,0,0,0,1,0
2015-04-13,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,4,0,0,0,0,0,0,0,0,0,0
2015-04-13,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,27,107,0,0,0,0,0,0,0,0,0,0
2015-04-13,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,19,6,14,35,0,0,0,0,0,0,0,0,0,0
2015-04-13,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-13,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,2,3,0,0,0,0,0,0,0,0,1,0
2015-04-13,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,28,0,0,0,0,0,0,0,0,0,0
2015-04-13,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-13,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-13,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,17,8,46,206,0,0,0,0,0,0,0,0,0,0
2015-04-13,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,27,0,0,0,0,0,0,0,0,0,0
2015-04-13,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-13,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-13,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-13,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-13,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,11,1,8,59,0,0,0,0,0,0,0,0,0,0
2015-04-13,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-13,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,32,110,0,0,0,0,0,0,0,0,0,0
2015-04-13,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,9,7,44,176,0,0,0,0,0,0,0,0,0,0
2015-04-13,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-13,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,15,0,0,0,0,0,0,0,0,0,0
2015-04-13,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,6,21,0,0,0,0,0,0,0,0,0,0
2015-04-13,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-13,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-04-13,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,22,0,0,0,0,0,0,0,0,0,0
2015-04-13,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,40,0,0,0,0,0,0,0,0,0,0
2015-04-13,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,28,0,0,0,0,0,0,0,0,0,0
2015-04-13,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,33,18,59,274,0,0,0,0,0,0,0,0,1,0
2015-04-13,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-13,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-13,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,36,0,0,0,0,0,0,0,0,0,0
2015-04-13,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,8,59,0,0,0,0,0,0,0,0,0,0
2015-04-13,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-13,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,18,64,0,0,0,0,0,0,0,0,0,0
2015-04-13,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,3,11,0,0,0,0,0,0,0,1,0,0
2015-04-13,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-13,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-13,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,5,0,0,0,0,0,0,0,0,0,0
2015-04-13,MK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-13,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,4,40,0,0,0,0,0,0,0,0,0,0
2015-04-13,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-13,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-13,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,58,0,0,0,0,0,0,0,0,0,0
2015-04-13,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0
2015-04-13,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-13,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,7,0,0,0,0,0,0,0,0,0,0
2015-04-13,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-13,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,4,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-13,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,7,27,0,0,0,0,0,0,0,0,0,0
2015-04-13,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-13,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-13,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,17,0,0,0,0,0,0,0,0,0,0
2015-04-13,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-13,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,22,4,8,40,0,0,0,0,0,0,0,0,0,0
2015-04-13,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,18,0,0,0,0,0,0,0,0,0,0
2015-04-13,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-13,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,23,0,0,0,0,0,0,0,0,0,0
2015-04-13,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,12,0,0,0,0,0,0,0,0,0,0
2015-04-13,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-13,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-13,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-13,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,7,43,0,0,0,0,0,0,0,0,0,0
2015-04-13,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,5,38,0,0,0,0,0,0,0,0,0,0
2015-04-13,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-13,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-13,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,US,98x5I1LVPhtnUHDyujq7zg,Roshan,46,31,248,971,0,1,0,0,0,0,0,0,1,0
2015-04-13,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-13,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-13,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,35,0,0,0,0,0,0,0,0,0,0
2015-04-13,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0
2015-04-14,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-14,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,11,3,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,26,0,0,0,0,0,0,0,0,0,0
2015-04-14,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,18,0,0,0,0,0,0,0,0,0,0
2015-04-14,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,57,0,0,0,0,0,0,0,0,0,0
2015-04-14,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0
2015-04-14,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,28,0,0,0,0,0,0,0,0,0,0
2015-04-14,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-14,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,13,84,0,0,0,0,0,0,0,2,0,0
2015-04-14,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,22,105,0,0,0,0,0,0,0,0,0,0
2015-04-14,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,11,6,19,41,0,0,0,0,0,0,0,0,0,0
2015-04-14,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,16,0,0,0,0,0,0,0,0,0,0
2015-04-14,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,29,0,0,0,0,0,0,0,0,0,0
2015-04-14,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-14,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,11,0,0,0,0,0,0,0,0,0,0
2015-04-14,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,10,7,47,206,0,0,0,0,0,0,0,0,0,0
2015-04-14,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,27,0,0,0,0,0,0,0,0,0,0
2015-04-14,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-14,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-14,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-14,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-14,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,11,2,7,60,0,0,0,0,0,0,0,0,1,0
2015-04-14,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,14,0,0,0,0,0,0,0,0,0,0
2015-04-14,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,5,34,114,0,0,0,0,0,0,0,0,0,0
2015-04-14,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,15,6,36,176,0,0,0,0,0,0,0,0,0,0
2015-04-14,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,15,0,0,0,0,0,0,0,0,0,0
2015-04-14,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,6,23,0,0,0,0,0,0,0,0,0,0
2015-04-14,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-14,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-14,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,22,0,0,0,0,0,0,0,0,0,0
2015-04-14,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,38,0,0,0,0,0,0,0,0,0,0
2015-04-14,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,26,0,0,0,0,0,0,0,0,0,0
2015-04-14,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,10,8,62,276,0,0,0,0,0,0,0,0,1,0
2015-04-14,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-14,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,36,0,0,0,0,0,0,0,0,0,0
2015-04-14,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,11,61,0,0,0,0,0,0,0,0,0,0
2015-04-14,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,18,64,0,0,0,0,0,0,0,0,0,0
2015-04-14,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,1,0
2015-04-14,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-14,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-14,MM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-14,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-14,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,35,0,0,0,0,0,0,0,0,0,0
2015-04-14,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-14,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-14,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,15,59,0,0,0,0,0,0,0,0,0,0
2015-04-14,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,22,0,0,0,0,0,0,0,0,0,0
2015-04-14,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-14,OM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,0,0,0,0,0,0,0,0,0,0,0,0
2015-04-14,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-14,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-14,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,26,0,0,0,0,0,0,0,0,0,0
2015-04-14,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-14,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,25,0,0,0,0,0,0,0,0,0,0
2015-04-14,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,16,0,0,0,0,0,0,0,0,0,0
2015-04-14,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-14,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,11,43,0,0,0,0,0,0,0,0,0,0
2015-04-14,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,18,0,0,0,0,0,0,0,0,0,0
2015-04-14,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,9,29,0,0,0,0,0,0,0,0,0,0
2015-04-14,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,5,21,0,0,0,0,0,0,0,0,0,0
2015-04-14,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-14,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-14,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,8,0,0,0,0,0,0,0,0,0,0
2015-04-14,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-14,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,9,42,0,0,0,0,0,0,0,0,0,0
2015-04-14,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,38,0,0,0,0,0,0,0,0,0,0
2015-04-14,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-14,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-14,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-14,US,98x5I1LVPhtnUHDyujq7zg,Roshan,101,49,249,981,0,0,0,0,0,0,0,1,0,0
2015-04-14,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-14,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-14,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,9,35,0,0,0,0,0,0,0,0,0,0
2015-04-14,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,23,0,0,0,0,0,0,0,0,0,0
2015-04-15,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-15,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,24,0,0,0,0,0,0,0,0,0,0
2015-04-15,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,17,0,0,0,0,0,0,0,0,0,0
2015-04-15,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,11,55,0,0,0,0,0,0,0,0,0,0
2015-04-15,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0
2015-04-15,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-15,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,14,83,0,0,0,0,0,0,0,0,0,0
2015-04-15,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-15,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,21,105,0,0,0,0,0,0,0,0,0,0
2015-04-15,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,19,42,0,0,0,0,0,0,0,0,0,0
2015-04-15,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,16,0,0,0,0,0,0,0,0,0,0
2015-04-15,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,28,0,0,0,0,0,0,0,0,0,0
2015-04-15,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-15,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,20,9,49,209,0,0,0,0,0,0,0,0,0,0
2015-04-15,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,28,0,0,0,0,0,0,0,0,0,0
2015-04-15,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-15,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-15,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,9,2,8,59,0,0,0,0,0,0,0,0,0,0
2015-04-15,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,5,14,0,0,0,0,0,0,0,0,0,0
2015-04-15,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,8,4,35,114,0,0,0,0,0,0,0,0,1,0
2015-04-15,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,18,8,39,173,0,0,0,0,0,0,0,0,0,0
2015-04-15,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-15,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,13,0,0,0,0,0,0,0,0,0,0
2015-04-15,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,7,24,0,0,0,0,0,0,0,0,0,0
2015-04-15,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,23,0,0,0,0,0,0,0,0,0,0
2015-04-15,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,9,35,0,0,0,0,0,0,0,0,0,0
2015-04-15,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,27,0,0,0,0,0,0,0,0,0,0
2015-04-15,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,14,10,67,280,0,0,0,0,0,0,0,0,0,0
2015-04-15,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-15,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-15,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,7,38,0,0,0,0,0,0,0,0,0,0
2015-04-15,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-15,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,10,61,0,0,0,0,0,0,0,0,0,0
2015-04-15,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-15,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,17,63,0,0,0,0,0,0,0,0,0,0
2015-04-15,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-15,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-15,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,5,0,0,0,0,0,0,0,0,0,0
2015-04-15,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,33,0,0,0,0,0,0,0,0,0,0
2015-04-15,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,13,0,0,0,0,0,0,0,0,0,0
2015-04-15,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-15,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,17,58,0,0,0,0,0,0,0,0,0,0
2015-04-15,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,4,19,0,0,0,0,0,0,0,0,0,0
2015-04-15,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-15,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,2,8,0,0,0,0,0,0,0,0,0,0
2015-04-15,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,9,27,0,0,0,0,0,0,0,0,1,0
2015-04-15,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-15,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,22,0,0,0,0,0,0,0,0,0,0
2015-04-15,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,13,0,0,0,0,0,0,0,0,0,0
2015-04-15,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-15,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,14,2,12,44,0,0,0,0,0,0,0,0,0,0
2015-04-15,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-15,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,0,8,29,0,0,0,0,0,0,0,0,0,0
2015-04-15,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,20,0,0,0,0,0,0,0,0,0,0
2015-04-15,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,12,0,0,0,0,0,0,0,0,0,0
2015-04-15,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-15,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-15,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-15,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,38,0,0,0,0,0,0,0,0,0,0
2015-04-15,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,6,38,0,0,0,0,0,0,0,0,0,0
2015-04-15,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-15,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-15,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-15,US,98x5I1LVPhtnUHDyujq7zg,Roshan,68,37,235,979,0,0,0,0,0,0,0,1,1,0
2015-04-15,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-15,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-15,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,35,0,0,0,0,0,0,0,0,0,0
2015-04-15,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,3,22,0,0,0,0,0,0,0,0,0,0
2015-04-16,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-16,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,23,0,0,0,0,0,0,0,0,0,0
2015-04-16,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,17,0,0,0,0,0,0,0,0,0,0
2015-04-16,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,55,0,0,0,0,0,0,0,0,0,0
2015-04-16,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-16,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,27,0,0,0,0,0,0,0,0,0,0
2015-04-16,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-16,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,7,4,18,82,0,0,0,0,0,0,0,0,0,0
2015-04-16,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-16,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,14,10,29,111,0,0,0,0,0,0,0,0,0,0
2015-04-16,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,42,0,0,0,0,0,0,0,0,0,0
2015-04-16,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,15,0,0,0,0,0,0,0,0,0,0
2015-04-16,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,28,0,0,0,0,0,0,0,0,0,0
2015-04-16,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-16,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,6,44,209,0,0,0,0,0,0,0,0,0,0
2015-04-16,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,27,0,0,0,0,0,0,0,0,0,0
2015-04-16,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,11,0,0,0,0,0,0,0,0,0,0
2015-04-16,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-16,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-16,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,10,58,0,0,0,0,0,0,0,0,0,0
2015-04-16,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,15,0,0,0,0,0,0,0,0,0,0
2015-04-16,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,17,5,33,115,0,0,0,0,0,0,0,0,0,0
2015-04-16,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,38,172,0,0,0,0,0,0,0,0,1,1
2015-04-16,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,14,0,0,0,0,0,0,0,0,0,0
2015-04-16,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,24,0,0,0,0,0,0,0,0,0,0
2015-04-16,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,8,0,0,0,0,0,0,0,0,0,0
2015-04-16,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-16,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-16,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,7,35,0,0,0,0,0,0,0,0,0,0
2015-04-16,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,4,27,0,0,0,0,0,0,0,0,0,0
2015-04-16,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,13,8,66,278,0,0,0,0,0,0,0,0,0,0
2015-04-16,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,6,0,0,0,0,0,0,0,0,1,0
2015-04-16,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-16,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,7,37,0,0,0,0,0,0,0,0,0,0
2015-04-16,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-16,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,59,0,0,0,0,0,0,0,0,0,0
2015-04-16,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-16,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,3,14,60,0,0,0,0,0,0,0,0,0,0
2015-04-16,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-16,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-16,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-16,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-16,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,32,0,0,0,0,0,0,0,0,0,0
2015-04-16,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-16,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,15,2,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-16,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,47,2,11,59,0,0,0,0,0,0,0,0,0,0
2015-04-16,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,4,18,0,0,0,0,0,0,0,0,0,0
2015-04-16,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,7,0,0,0,0,0,0,0,0,0,0
2015-04-16,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,15,4,7,20,0,0,0,0,0,0,0,0,1,0
2015-04-16,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,6,1,3,9,0,0,0,0,0,0,0,0,0,0
2015-04-16,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,12,30,0,0,0,0,0,0,0,0,0,0
2015-04-16,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-16,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,21,0,0,0,0,0,0,0,0,0,0
2015-04-16,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,12,0,0,0,0,0,0,0,0,0,0
2015-04-16,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-16,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,44,0,0,0,0,0,0,0,0,0,0
2015-04-16,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,4,16,0,0,0,0,0,0,0,0,0,0
2015-04-16,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,8,2,9,30,0,0,0,0,0,0,0,0,0,0
2015-04-16,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,0,0
2015-04-16,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0
2015-04-16,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-16,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,10,0,0,0,0,0,0,0,0,1,0
2015-04-16,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-16,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,37,0,0,0,0,0,0,0,0,0,0
2015-04-16,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,12,2,8,39,0,0,0,0,0,0,0,0,1,0
2015-04-16,TZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-16,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,US,98x5I1LVPhtnUHDyujq7zg,Roshan,73,45,241,985,0,0,0,0,0,0,0,2,2,0
2015-04-16,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-16,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-16,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-16,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,7,35,0,0,0,0,0,0,0,0,0,0
2015-04-16,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,22,0,0,0,0,0,0,0,0,0,0
2015-04-17,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-17,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,25,0,0,0,0,0,0,0,0,0,0
2015-04-17,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,4,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-17,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,9,55,0,0,0,0,0,0,0,0,0,0
2015-04-17,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,3,1,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-17,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,10,30,0,0,0,0,0,0,0,0,0,0
2015-04-17,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-17,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,16,77,0,0,0,0,0,0,0,0,0,0
2015-04-17,BW,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-17,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,30,111,0,0,0,0,0,0,0,0,0,0
2015-04-17,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,10,3,17,43,0,0,0,0,0,0,0,0,0,0
2015-04-17,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-17,CM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,3,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,28,0,0,0,0,0,0,0,0,0,0
2015-04-17,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-17,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,8,48,209,0,0,0,0,0,0,0,0,0,0
2015-04-17,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,28,0,0,0,0,0,0,0,0,0,0
2015-04-17,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,11,0,0,0,0,0,0,0,0,0,0
2015-04-17,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-17,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-17,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,10,59,0,0,0,0,0,0,0,0,0,0
2015-04-17,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-17,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,10,6,34,116,0,0,0,0,0,0,0,0,0,0
2015-04-17,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,38,168,0,0,0,0,0,0,0,0,0,0
2015-04-17,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,14,0,0,0,0,0,0,0,0,0,0
2015-04-17,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,6,24,0,0,0,0,0,0,0,0,0,0
2015-04-17,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,8,0,0,0,0,0,0,0,0,0,0
2015-04-17,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-17,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,23,0,0,0,0,0,0,0,0,0,0
2015-04-17,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,35,0,0,0,0,0,0,0,0,0,0
2015-04-17,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,27,0,0,0,0,0,0,0,0,0,0
2015-04-17,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,17,8,68,276,0,0,0,0,0,0,0,0,1,0
2015-04-17,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-17,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-17,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,11,3,7,38,0,0,0,0,0,0,0,0,1,0
2015-04-17,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-17,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,8,4,14,61,0,0,0,0,0,0,0,0,0,0
2015-04-17,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-17,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,4,15,60,0,0,0,0,0,0,0,0,0,0
2015-04-17,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,6,2,4,10,0,0,0,0,0,0,0,0,0,0
2015-04-17,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,7,0,0,0,0,0,0,0,0,0,0
2015-04-17,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-17,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-17,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,MT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,30,0,0,0,0,0,0,0,0,0,0
2015-04-17,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-17,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-17,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,7,5,15,63,0,0,0,0,0,0,0,0,0,0
2015-04-17,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0
2015-04-17,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-17,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,20,0,0,0,0,0,0,0,0,0,0
2015-04-17,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-17,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,6,5,15,33,0,0,0,0,0,0,0,0,1,0
2015-04-17,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-17,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,20,0,0,0,0,0,0,0,0,0,0
2015-04-17,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,11,0,0,0,0,0,0,0,0,0,0
2015-04-17,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-17,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,12,46,0,0,0,0,0,0,0,0,0,0
2015-04-17,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,17,0,0,0,0,0,0,0,0,0,0
2015-04-17,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,7,2,8,31,0,0,0,0,0,0,0,0,0,0
2015-04-17,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,21,0,0,0,0,0,0,0,0,1,0
2015-04-17,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,12,0,0,0,0,0,0,0,0,0,0
2015-04-17,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-17,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-17,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-17,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,37,0,0,0,0,0,0,0,0,0,0
2015-04-17,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,6,3,10,37,0,0,0,0,0,0,0,0,0,0
2015-04-17,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-17,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,US,98x5I1LVPhtnUHDyujq7zg,Roshan,83,43,245,980,0,0,0,0,0,0,0,0,2,0
2015-04-17,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-17,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-17,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-17,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,9,5,8,39,0,0,0,0,0,0,0,0,0,0
2015-04-17,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,21,0,0,0,0,0,0,0,0,0,0
2015-04-18,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-18,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-18,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,25,0,0,0,0,0,0,0,0,0,0
2015-04-18,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-18,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,11,57,0,0,0,0,0,0,0,0,0,0
2015-04-18,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-18,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-18,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,10,31,0,0,0,0,0,0,0,0,0,0
2015-04-18,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,6,0,0,0,0,0,0,0,0,0,0
2015-04-18,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,13,76,0,0,0,0,0,0,0,0,0,0
2015-04-18,BW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-18,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,7,3,27,112,0,0,0,0,0,0,0,0,0,0
2015-04-18,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,10,4,19,44,0,0,0,0,0,0,0,0,0,0
2015-04-18,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-18,CM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,28,0,0,0,0,0,0,0,0,0,0
2015-04-18,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-18,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,0,1,9,0,0,0,0,0,0,0,0,0,0
2015-04-18,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,16,7,49,206,0,0,0,0,0,0,0,0,0,0
2015-04-18,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,27,0,0,0,0,0,0,0,0,0,0
2015-04-18,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,10,0,0,0,0,0,0,0,0,0,0
2015-04-18,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-04-18,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-18,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-18,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,12,58,0,0,0,0,0,0,0,0,0,0
2015-04-18,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-18,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,5,3,29,116,0,0,0,0,0,0,0,0,0,0
2015-04-18,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,12,5,38,170,0,1,0,0,0,0,0,2,1,0
2015-04-18,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,4,0,0,0,0,0,0,0,0,0,0
2015-04-18,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-18,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,23,0,0,0,0,0,0,0,0,0,0
2015-04-18,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-18,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-18,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,6,23,0,0,0,0,0,0,0,0,0,0
2015-04-18,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,5,4,8,36,0,0,0,0,0,0,0,0,0,0
2015-04-18,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,5,28,0,0,0,0,0,0,0,0,0,0
2015-04-18,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,17,11,69,275,0,0,0,0,0,0,0,0,0,0
2015-04-18,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-18,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-18,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,8,38,0,0,0,0,0,0,0,0,0,0
2015-04-18,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-18,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,5,2,16,62,0,0,0,0,0,0,0,0,0,0
2015-04-18,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-18,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,59,0,0,0,0,0,0,0,0,0,0
2015-04-18,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,9,1,4,9,0,1,0,0,0,0,0,0,0,0
2015-04-18,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,7,0,0,0,0,0,0,0,0,0,0
2015-04-18,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-18,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-18,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,28,0,0,0,0,0,0,0,0,0,0
2015-04-18,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,11,0,0,0,0,0,0,0,0,0,0
2015-04-18,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-18,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,16,63,0,0,0,0,0,0,0,0,0,0
2015-04-18,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,17,0,0,0,0,0,0,0,0,0,0
2015-04-18,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-18,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-18,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,8,20,0,0,0,0,0,0,0,0,0,0
2015-04-18,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,2,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-18,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,35,0,0,0,0,0,0,0,0,0,0
2015-04-18,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-18,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,19,0,0,0,0,0,0,0,0,0,0
2015-04-18,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,11,0,0,0,0,0,0,0,0,0,0
2015-04-18,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-18,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,12,45,0,0,0,0,0,0,0,0,0,0
2015-04-18,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-18,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,31,0,0,0,0,0,0,0,0,0,0
2015-04-18,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,20,0,0,0,0,0,0,0,0,0,0
2015-04-18,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-18,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-18,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,11,0,0,0,0,0,0,0,0,0,0
2015-04-18,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-18,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,35,0,0,0,0,0,0,0,0,0,0
2015-04-18,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,11,36,0,0,0,0,0,0,0,0,0,0
2015-04-18,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,13,0,0,0,0,0,0,0,0,0,0
2015-04-18,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-18,US,98x5I1LVPhtnUHDyujq7zg,Roshan,65,36,252,979,0,0,0,0,0,0,0,0,2,0
2015-04-18,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-18,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-18,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,3,2,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-18,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,36,0,0,0,0,0,0,0,0,0,0
2015-04-18,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,20,0,0,0,0,0,0,0,0,0,0
2015-04-19,AE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-19,AO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,3,0,0,0,0,0,0,0,0,0,0
2015-04-19,AR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,24,0,0,0,0,0,0,0,0,0,0
2015-04-19,AT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,17,0,0,0,0,0,0,0,0,0,0
2015-04-19,AU,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,13,60,0,0,0,0,0,0,0,0,0,0
2015-04-19,BA,98x5I1LVPhtnUHDyujq7zg,Roshan,8,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,BD,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,14,0,0,0,0,0,0,0,0,0,0
2015-04-19,BE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,7,31,0,0,0,0,0,0,0,0,0,0
2015-04-19,BF,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,BG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,6,0,0,0,0,0,0,0,0,0,0
2015-04-19,BR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,14,76,0,0,0,0,0,0,0,0,0,0
2015-04-19,BW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,BY,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,CA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,26,108,0,0,0,0,0,0,0,0,0,0
2015-04-19,CH,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,20,44,0,0,0,0,0,0,0,0,0,0
2015-04-19,CL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,13,0,0,0,0,0,0,0,0,0,0
2015-04-19,CM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,CN,98x5I1LVPhtnUHDyujq7zg,Roshan,16,1,2,3,0,0,0,0,0,0,0,0,0,0
2015-04-19,CO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,28,0,0,0,0,0,0,0,0,0,0
2015-04-19,CR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-19,CZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,8,0,0,0,0,0,0,0,0,0,0
2015-04-19,DE,98x5I1LVPhtnUHDyujq7zg,Roshan,13,4,43,198,0,0,0,0,0,0,0,0,0,0
2015-04-19,DK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,5,27,0,0,0,0,0,0,0,0,0,0
2015-04-19,DO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,DZ,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,10,0,0,0,0,0,0,0,0,0,0
2015-04-19,EC,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,6,0,0,0,0,0,0,0,0,0,0
2015-04-19,EE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-19,EG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-19,ES,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,11,58,0,0,0,0,0,0,0,0,0,0
2015-04-19,FI,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-19,FR,98x5I1LVPhtnUHDyujq7zg,Roshan,14,6,29,119,0,0,0,0,0,0,0,0,0,0
2015-04-19,GB,98x5I1LVPhtnUHDyujq7zg,Roshan,12,7,36,168,0,0,0,0,0,0,0,0,0,0
2015-04-19,GE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,GH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,GR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,14,0,0,0,0,0,0,0,0,0,0
2015-04-19,HK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,6,22,0,0,0,0,0,0,0,0,0,0
2015-04-19,HR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,8,0,0,0,0,0,0,0,0,0,0
2015-04-19,HU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,5,0,0,0,0,0,0,0,0,0,0
2015-04-19,ID,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,22,0,0,0,0,0,0,0,0,0,0
2015-04-19,IE,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,9,35,0,0,0,0,0,0,0,0,0,0
2015-04-19,IL,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,29,0,0,0,0,0,0,0,0,0,0
2015-04-19,IN,98x5I1LVPhtnUHDyujq7zg,Roshan,9,4,65,271,0,0,0,0,0,0,0,0,0,0
2015-04-19,IQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-19,IR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,IS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,IT,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,8,38,0,0,0,0,0,0,0,0,0,0
2015-04-19,JM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,JO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-19,JP,98x5I1LVPhtnUHDyujq7zg,Roshan,8,3,16,65,0,0,0,0,0,0,0,0,0,0
2015-04-19,KE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,KH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,KR,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,15,58,0,0,0,0,0,0,0,0,0,0
2015-04-19,KW,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,KZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,LB,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,LK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,4,9,0,0,0,0,0,0,0,0,0,0
2015-04-19,LT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,7,0,0,0,0,0,0,0,0,0,0
2015-04-19,LU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,MA,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,8,0,0,0,0,0,0,0,0,0,0
2015-04-19,MN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,MQ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,MU,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,MX,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,25,0,0,0,0,0,0,0,0,0,0
2015-04-19,MY,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,4,12,0,0,0,0,0,0,0,0,0,0
2015-04-19,NG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-19,NL,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,17,61,0,0,0,0,0,0,0,0,0,0
2015-04-19,NO,98x5I1LVPhtnUHDyujq7zg,Roshan,10,1,3,18,0,0,0,0,0,0,0,0,0,0
2015-04-19,NP,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,NZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,2,5,0,0,0,0,0,0,0,0,0,0
2015-04-19,PE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,1,7,21,0,0,0,0,0,0,0,0,1,0
2015-04-19,PH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,7,0,0,0,0,0,0,0,0,0,0
2015-04-19,PK,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,PL,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,15,33,0,0,0,0,0,0,0,0,0,0
2015-04-19,PR,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,PS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,PT,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,19,0,0,0,0,0,0,0,0,0,0
2015-04-19,PY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,QA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,2,2,0,0,0,0,0,0,0,0,0,0
2015-04-19,RO,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,11,0,0,0,0,0,0,0,0,0,0
2015-04-19,RS,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,6,0,0,0,0,0,0,0,0,0,0
2015-04-19,RU,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,13,44,0,0,0,0,0,0,0,0,0,0
2015-04-19,SA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,2,4,17,0,0,0,0,0,0,0,0,0,0
2015-04-19,SD,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,SE,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,8,33,0,0,0,0,0,0,0,0,0,0
2015-04-19,SG,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,5,21,0,0,0,0,0,0,0,0,0,0
2015-04-19,SI,98x5I1LVPhtnUHDyujq7zg,Roshan,5,1,4,14,0,0,0,0,0,0,0,0,0,0
2015-04-19,SK,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,4,0,0,0,0,0,0,0,0,0,0
2015-04-19,TH,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,10,0,0,0,0,0,0,0,0,0,0
2015-04-19,TM,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,TN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,5,0,0,0,0,0,0,0,0,0,0
2015-04-19,TR,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,5,35,0,0,0,0,0,0,0,0,0,0
2015-04-19,TW,98x5I1LVPhtnUHDyujq7zg,Roshan,2,2,13,34,0,0,0,0,0,0,0,0,0,0
2015-04-19,UA,98x5I1LVPhtnUHDyujq7zg,Roshan,4,3,5,16,0,0,0,0,0,0,0,0,1,0
2015-04-19,UG,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,0,3,0,0,0,0,0,0,0,0,0,0
2015-04-19,US,98x5I1LVPhtnUHDyujq7zg,Roshan,102,40,255,977,0,0,0,0,0,0,0,1,5,0
2015-04-19,UY,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,1,0,0,0,0,0,0,0,0,0,0
2015-04-19,VE,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,1,3,0,0,0,0,0,0,0,0,0,0
2015-04-19,VN,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,4,15,0,0,0,0,0,0,0,0,0,0
2015-04-19,ZA,98x5I1LVPhtnUHDyujq7zg,Roshan,1,1,8,37,0,0,0,0,0,0,0,0,0,0
2015-04-19,ZZ,98x5I1LVPhtnUHDyujq7zg,Roshan,0,0,3,20,0,0,0,0,0,0,0,0,0,0