Repository: hirokidaichi/gilot
Branch: master
Commit: 0099c706ce24
Files: 20
Total size: 559.2 KB

Directory structure:
gitextract_cikj_3fb/

├── .cursorrules
├── .gitignore
├── LICENSE
├── README.md
├── pyproject.toml
├── pytest.ini
├── sample/
│   ├── Makefile
│   └── sample.ipynb
├── setup.cfg
├── src/
│   └── gilot/
│       ├── __init__.py
│       ├── app.py
│       ├── core.py
│       ├── filetracker.py
│       ├── hotgraph.py
│       ├── hotspot.py
│       └── plotter.py
└── tests/
    ├── __init__.py
    ├── conftest.py
    ├── test_app.py
    └── test_core.py

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

================================================
FILE: .cursorrules
================================================
# Gilot Project Development Rules

## プロジェクト概要
- Gitログの視覚的分析ツール
- **Python 3.12以上**が必要
- **uv** を使用した依存関係管理・仮想環境運用

## 開発環境セットアップ
```bash
uv venv create         # 仮想環境の作成（初回のみ）
uv venv exec bash      # 仮想環境に入る
uv pip install -r requirements.txt  # 依存関係のインストール
# または
uv pip install .[dev]  # 開発用依存も含めてインストール
```
- Python 3.12以上がインストールされていることを確認してください。

## コードスタイル
- flake8とautopep8に従うこと
- mypyによる型チェックを必須とする
- テストカバレッジの維持を重視（pytest-cov使用）

## プロジェクト構造
主要コンポーネント：
- core.py: コアロジック
- plotter.py: 可視化機能
- app.py: メインアプリケーション
- hotgraph.py: ホットスポット分析
- filetracker.py: ファイル追跡機能

## テスト
- すべての新機能にはテストを追加すること
- 仮想環境内で `pytest` でテストを実行
- カバレッジレポートの生成: `pytest --cov=gilot`

## 型ヒント
- すべての新しいコードには型ヒントを付けること
- `mypy`による静的型チェックを実施

## コミットルール
- 機能追加は`feature/`ブランチで開発
- コミットメッセージは具体的で明確に
- リファクタリングは独立したコミットとして管理

## CI/CD
- テスト、リント、型チェックをプッシュ前に実行
- カバレッジレポートの確認

## 注意点
- データ処理時のメモリ使用量に注意
- 大規模リポジトリ解析時のパフォーマンス考慮
- 可視化関連の依存関係（matplotlib, seaborn, python-louvain等）のバージョン互換性に注意

## ドキュメント
- コードには適切なドキュメンテーションを含めること
- README.mdは最新の状態を維持
- 重要な変更は必ずドキュメントに反映

## 補足
- **uv環境下では、仮想環境内で`pytest`や`mypy`等を直接実行してください。**
- 依存追加時は `uv pip install パッケージ名` を推奨します。 

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

# C extensions
*.so
# Avoid including the secret info
*.csv
study/
sample/repos/
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

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

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/


================================================
FILE: LICENSE
================================================
MIT License

Copyright (c) 2020 hirokidaichi

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


================================================
FILE: README.md
================================================
# gilot
![image](./sample/react.png)
![image](./sample/react.hotgraph.png)
"gilot" is a tool to analyze and visualize git logs.

One of the most reliable records of a software project's activity is the history of the version control system. This information is then used to create graphs to visualize the state of the software development team in a mechanical way.

``gilot plot``  creates four graphs.

- The first graph shows the bias in the amount of code changes for a given time slot as a Gini coefficient and a Lorentz curve. The closer the Gini coefficient is to 1, the more unequal it is, and the closer it is to 0, the more perfect equality it is an indicator of economics. It tends to go down when a project has stable agility, and the more volatile and planaristic the project, the closer it is to 1.

- The second graph shows a histogram of the bias in the amount of code changes in a given time slot.

- The third graph shows the change in the amount of code changes per time slot. It is displayed in green when the total amount of codes is increasing and in red when the total amount of codes is decreasing.

- The fourth graph shows the number of authors who committed per given time slot. The effective team size is estimated.
 
 ``gilot hotgraph`` visualizes the hidden connections between files in a repository and allows you to see the hidden connections in the and refactoring. Assuming that there is a structural connection between the files committed at the same time, the Analyze the network structure. The "shopping cart" and "products" are often used as examples of association analysis, but in this case, we will use The "shopping cart" is "1 commit" and "products" is "modified files".




## Installation

just:

    pip install gilot

or 

    pip install git+https://github.com/hirokidaichi/gilot

## Usage

### simple way (1 liner using pipe)
    gilot log REPO_DIR | gilot plot

### 2-phase way

    gilot log REPO_DIR > repo.csv
    gilot plot -i repo.csv -o graph.png

## Command 
``gilot`` has 5 commands, ``log`` , ``plot`` , ``info`` , ``hotgraph`` and ``hotspot`` 
+  ``log`` command generates a csv from the repository information

+  ``plot``  command generates a graph image (or matplotlib window) from that csv.

+ ``info``  command, like the plot command, takes a csv file as input and outputs only JSON format statistical information.

+ ``hotspot``  command displays the files that are likely to contain bugs in ranking. We judge that the most recently modified files are likely to contain bugs.( like ``bugspots``)

+ ``hotgraph`` command is able to visualize hidden file connections from a given CSV.

### gilot log (generate csv)
The simplest way to use the ``gilot log`` command is to specify the repository directory as follows. This means saving the output as a CSV file.

    gilot log REPO > REPO.csv
    gilot log REPO -o REPO.csv

The default period is six months, but you can specify the time.

    gilot log REPO --since 2020-01-20 -o REPO.csv
    gilot log REPO --month 18 -o REPO.csv

By specifying a period of time, such as when you want to see the stability of the service after the launch, you can eliminate the impact of commits during the initial release.

    gilot log REPO --branch develop -o REPO.csv

You can use the branch option to see what the development branch looks like, or to see the results for each branch. By default, ``origin/HEAD`` is specified. This is because we want to see how well we can develop in a trunk-based way.

## --full option 

Also, with the ``--full`` option, detailed information such as the committed file name and the number of lines will be output. It is used for verification including file names such as hotspot command and ignore-files/allow-files.

    gilot log REPO --full | gilot plot --ignore-files "*.lock" "package.json"

If you want to count so that a specific file is not included, use the --full option and --ignore-files together as follows.

    gilot log REPO --full | gilot hotspot


All options are here

    usage: gilot log [-h] [-b BRANCH] [-o OUTPUT] [--since SINCE] [--until UNTIL] [--month MONTH] [--full] repo

    positional arguments:
    repo                  REPO must be a root dir of git repository

    optional arguments:
    -h, --help            show this help message and exit
    -b BRANCH, --branch BRANCH
                            target branch name. default 'origin/HEAD'
    -o OUTPUT, --output OUTPUT
    --since SINCE         SINCE must be ISO format like 2020-01-01.
    --until UNTIL         UNTIL must be ISO format like 2020-06-01.
    --month MONTH         MONTH is how many months of log data to output. default is 6
    --full                If this flag is enabled, detailed data including the commuted file name will be output.


### gilot plot (generate graph)

The simplest way to use the ``gilot plot`` command is to take the CSV file output from the gilot log command as input and specify the name of the file you want to save as output, as shown below.


    gilot plot -i TARGET.csv -o TARGET_REPORT.png
    gilot plot --input TARGET.csv -o TARGET_REPORT.png

Also, since the input from the standard input is also interpreted as a CSV, it can be connected to a pipe as shown below.


    cat target.csv | gilot plot 
    gilot repo . | gilot plot

For example, if one team is working in a *multi-repository* services, you may want to know the activity of multiple repositories as a whole, instead of focusing on one repository. In this case, you can combine multiple inputs into a graph as follows.


    gilot log repo-a > repo-a.csv
    gilot log repo-b > repo-b.csv
    gilot plot -i repo*.csv

All options are here:

    usage: gilot plot [-h] [-i [INPUT [INPUT ...]]] [-t TIMESLOT] [-o OUTPUT] [-n NAME] [--allow-files [ALLOW_FILES [ALLOW_FILES ...]]] [--ignore-files [IGNORE_FILES [IGNORE_FILES ...]]]

    optional arguments:
    -h, --help            show this help message and exit
    -i [INPUT [INPUT ...]], --input [INPUT [INPUT ...]]
    -t TIMESLOT, --timeslot TIMESLOT
                            resample period like 2W or 7D or 1M
    -o OUTPUT, --output OUTPUT
                            OUTPUT FILE
    -n NAME, --name NAME  name
    --allow-files [ALLOW_FILES [ALLOW_FILES ...]]
                            Specify the files to allow. You can specify more than one like 'src/*' '*.rb'. Only data with the --full flag is valid.
    --ignore-files [IGNORE_FILES [IGNORE_FILES ...]]
                            Specifies files to ignore. You can specify more than one like 'dist/*' '*.gen.java'. Only data with the --full flag is valid.



### gilot info (dump statistical infomation)

``info``  command, like the plot command, takes a csv file as input and outputs only JSON format statistical information.


    # gilot info -i sample/react.csv
    {
        "gini": 0.42222013847205725,
        "output": {
            "lines": 242999,
            "added": 70765,
            "refactor": 0.7087848098140321
        },
        "since": "2019-12-03T10:53:08.000000000",
        "until": "2020-05-30T06:34:43.000000000",
        "timeslot": "2 Weeks",
        "insertions": {
            "mean": 11205.857142857143,
            "std": 10565.324647217372,
            "min": 781.0,
            "25%": 3788.75,
            "50%": 8544.0,
            "75%": 16761.25,
            "max": 39681.0
        },
        "deletions": {
            "mean": 6151.214285714285,
            "std": 4437.0289466743825,
            "min": 327.0,
            "25%": 3397.0,
            "50%": 5076.0,
            "75%": 9333.75,
            "max": 13477.0
        },
        "lines": {
            "mean": 17357.071428571428,
            "std": 14236.531424279776,
            "min": 1108.0,
            "25%": 7383.25,
            "50%": 12860.0,
            "75%": 26531.75,
            "max": 52914.0
        },
        "files": {
            "mean": 377.7857142857143,
            "std": 271.95196933718574,
            "min": 70.0,
            "25%": 155.75,
            "50%": 402.0,
            "75%": 450.0,
            "max": 1062.0
        },
        "authors": {
            "mean": 13.357142857142858,
            "std": 4.70036238958302,
            "min": 4.0,
            "25%": 10.0,
            "50%": 15.0,
            "75%": 16.0,
            "max": 21.0
        },
        "addedlines": {
            "mean": 5054.642857142857,
            "std": 7742.596112089604,
            "min": -1210.0,
            "25%": 266.5,
            "50%": 2062.5,
            "75%": 5770.75,
            "max": 26448.0
        }
    }
Integration with ``jq`` command makes it easy to get only the information you need.

### When only the Gini coefficient is required
    # gilot info -i sample/react.csv | jq .gini
    > 0.42222013847205725

### If you want to find the total number of lines in all commits in a period

    # gilot info -i sample/react.csv | jq .output.lines

All options are here :

    usage: gilot info [-h] [-i [INPUT [INPUT ...]]] [-t TIMESLOT] [--allow-files [ALLOW_FILES [ALLOW_FILES ...]]] [--ignore-files [IGNORE_FILES [IGNORE_FILES ...]]]

    optional arguments:
    -h, --help            show this help message and exit
    -i [INPUT [INPUT ...]], --input [INPUT [INPUT ...]]
    -t TIMESLOT, --timeslot TIMESLOT
                            resample period like 2W or 7D or 1M
    --allow-files [ALLOW_FILES [ALLOW_FILES ...]]
                            Specify the files to allow. You can specify more than one like 'src/*' '*.rb'. Only data with the --full flag is valid.
    --ignore-files [IGNORE_FILES [IGNORE_FILES ...]]
                            Specifies files to ignore. You can specify more than one like 'dist/*' '*.gen.java'. Only data with the --full flag is valid.
## gilot hotspot 
``hotspot``  command displays the files that are likely to contain bugs in ranking. We judge that the most recently modified files are likely to contain bugs.( like ``bugspots``)

    gilot hotspot -i react-full.csv --ignore-files "*/__tests__/*" "*.lock" -n 10

output 

    ------------------------------------------------------------
        gilot hotspot ( https://github.com/hirokidaichi/gilot )
    ------------------------------------------------------------

    hotspot  commits  authors file_name
        8.81       29        4 packages/react-reconciler/src/ReactFiberWorkLoop.new.js
        7.14       25        4 packages/react-reconciler/src/ReactFiberWorkLoop.old.js
        7.01       44        3 packages/react-dom/src/events/DOMModernPluginEventSystem.js
        5.50       44        8 packages/react-dom/src/client/ReactDOMHostConfig.js
        4.85       29        8 scripts/rollup/bundles.js
        4.47       17        5 packages/react-reconciler/src/ReactFiberBeginWork.new.js
        3.73       17        5 packages/react-reconciler/src/ReactFiberCommitWork.new.js
        3.32       13        4 packages/react-reconciler/src/ReactFiberHooks.new.js
        3.28       17        5 packages/react-reconciler/src/ReactFiberCompleteWork.new.js
        3.18       15        5 packages/react-reconciler/src/ReactFiberCommitWork.old.js

All options are here :

    usage: gilot hotspot [-h] [-i [INPUT [INPUT ...]]] [--csv] [-o OUTPUT] [--allow-files [ALLOW_FILES [ALLOW_FILES ...]]] [--ignore-files [IGNORE_FILES [IGNORE_FILES ...]]]

    optional arguments:
    -h, --help            show this help message and exit
    -i [INPUT [INPUT ...]], --input [INPUT [INPUT ...]]
    --csv                 dump csv
    -o OUTPUT, --output OUTPUT
    --allow-files [ALLOW_FILES [ALLOW_FILES ...]]
                            Specify the files to allow. You can specify more than one like 'src/*' '*.rb'. Only data with the --full flag is valid.
    --ignore-files [IGNORE_FILES [IGNORE_FILES ...]]
                            Specifies files to ignore. You can specify more than one like 'dist/*' '*.gen.java'. Only data with the --full flag is valid.

# gilot hotgraph
 ``gilot hotgraph`` visualizes the hidden connections between files in a repository and allows you to see the hidden connections in the and refactoring. Assuming that there is a structural connection between the files committed at the same time, the Analyze the network structure. The "shopping cart" and "products" are often used as examples of association analysis, but in this case, we will use The "shopping cart" is "1 commit" and "products" is "modified files".

    gilot hotgraph -i this.csv --allow-files "*.ts"


![image](./sample/TypeScript.hotgraph.png)


All options are here :

    usage: gilot hotgraph [-h] [-v] [-i [INPUT [INPUT ...]]] [-r RANK] [--stop-retry] [--csv] [-o OUTPUT] [--allow-files [ALLOW_FILES [ALLOW_FILES ...]]]
                        [--ignore-files [IGNORE_FILES [IGNORE_FILES ...]]]

    optional arguments:
    -h, --help            show this help message and exit
    -v, --verbose         increase log level
    -i [INPUT [INPUT ...]], --input [INPUT [INPUT ...]]
    -r RANK, --rank RANK
    --stop-retry
    --csv                 dump csv
    -o OUTPUT, --output OUTPUT
    --allow-files [ALLOW_FILES [ALLOW_FILES ...]]
                            Specify the files to allow. You can specify more than one like 'src/*' '*.rb'. Only data with the --full flag is valid.
    --ignore-files [IGNORE_FILES [IGNORE_FILES ...]]
                            Specifies files to ignore. You can specify more than one like 'dist/*' '*.gen.java'. Only data with the --full flag is valid.

# gilot author
TODO

![image](./sample/TypeScript.author.png)

## Example Output

### facebook/react
![image](./sample/react.png)
![image](./sample/react.hotgraph.png)
![image](./sample/react.author.png)

### tensorflow/tensorflow
![image](./sample/tensorflow.png)
![image](./sample/tensorflow.hotgraph.png)
![image](./sample/tensorflow.author.png)
### pytorch/pytorch
![image](./sample/pytorch.png)
![image](./sample/pytorch.hotgraph.png)
![image](./sample/pytorch.author.png)
### optuna/optuna
![image](./sample/optuna.png)
![image](./sample/optuna.hotgraph.png)
![image](./sample/optuna.author.png)

### microsoft/TypeScript
![image](./sample/TypeScript.png)
![image](./sample/TypeScript.hotgraph.png)
![image](./sample/TypeScript.author.png)

### microsoft/vscode
![image](./sample/vscode.png)
![image](./sample/vscode.hotgraph.png)
![image](./sample/vscode.author.png)




 


================================================
FILE: pyproject.toml
================================================
[project]
name = "gilot"
version = "0.2.7"
description = "a git log visual analyzer"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    "argparse>=1.4.0",
    "datetime>=5.5",
    "gitpython>=3.1.44",
    "matplotlib>=3.7.5,<3.8.0",
    "networkx>=3.1",
    "numpy>=1.26.0",
    "pandas>=2.0.3",
    "pyfpgrowth>=1.0",
    "python-louvain>=0.16",
    "scipy>=1.10.1",
    "seaborn>=0.12.0,<0.13.0",
    "timeout-decorator>=0.5.0",
]

[dependency-groups]
dev = [
    "autopep8>=2.3.1",
    "flake8>=7.1.1",
    "jupyter>=1.1.1",
    "mypy>=1.14.1",
    "pytest>=8.3.4",
    "pytest-cov>=5.0.0",
]

[project.scripts]
gilot = "gilot.app:main"

[tool.setuptools]
package-dir = {"" = "src"}
packages = ["gilot"]

[build-system]
requires = ["setuptools>=42", "wheel"]
build-backend = "setuptools.build_meta"


================================================
FILE: pytest.ini
================================================
[pytest]
testpaths = ./tests
python_files = test_*.py

================================================
FILE: sample/Makefile
================================================
REPOS := react optuna pytorch TypeScript tensorflow vscode

TARGET := $(foreach r,${REPOS},$(r).png)
HOTGRAPH := $(foreach r,${REPOS},$(r).hotgraph.png)
AUTHORS := $(foreach r,${REPOS},$(r).author.png)
CSVS := $(foreach r,${REPOS},$(r).csv)

.PHONY: all
all: png

png: $(TARGET) $(HOTGRAPH) $(AUTHORS)

csv: $(CSVS)

%.csv:
	gilot log ./repos/$*/ --full --since 2020-01-01 --month 6 > $@

clean-png:
	rm ./*.png

%.png: %.csv
	gilot plot -i $< -o $@ -n "$* GIT LOG REPORT"

react.hotgraph.png: react.csv
	gilot hotgraph -i $< --allow-files "*.js" --ignore-files "*__tests__*" --rank 80 --output $@

optuna.hotgraph.png :optuna.csv
	gilot hotgraph -i $< --allow-files "*.py" --output $@

pytorch.hotgraph.png :pytorch.csv
	gilot hotgraph -i $< --allow-files "*.py" "*.cpp" --rank 100 --output $@

TypeScript.hotgraph.png:TypeScript.csv
	gilot hotgraph -i $< --allow-files "*.ts" --rank 60 --output $@

tensorflow.hotgraph.png:tensorflow.csv
	gilot hotgraph -i $< --allow-files "tensorflow/python/*.py" --output $@

vscode.hotgraph.png:vscode.csv
	gilot hotgraph -i $< --allow-files "*.ts" --output $@


%.hotgraph.png : %.csv
	gilot hotgraph -i $< --output $@

%.author.png : %.csv
	gilot author -i $< --output $@ --name "$*"

================================================
FILE: sample/sample.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gilot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "fatal: destination path 'repos/react' already exists and is not an empty directory.\n"
    }
   ],
   "source": [
    "!git clone https://github.com/facebook/react.git repos/react"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x648 with 4 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df = gilot.from_dir(\"./repos/react\")\n",
    "df.to_csv(\"./react.csv\")\n",
    "gilot.plot(df,name=\"facebook/react GIT LOG REPORT\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "         insertions    deletions         lines       files\ncount    662.000000   662.000000    662.000000  662.000000\nmean     231.870091   126.676737    358.546828    7.845921\nstd     1079.842373   461.822278   1297.153861   16.415662\nmin        0.000000     0.000000      0.000000    0.000000\n25%        5.000000     2.000000     10.250000    1.000000\n50%       37.000000    10.000000     60.000000    3.000000\n75%      143.000000    51.000000    207.000000    7.000000\nmax    22052.000000  5588.000000  22052.000000  215.000000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>insertions</th>\n      <th>deletions</th>\n      <th>lines</th>\n      <th>files</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>662.000000</td>\n      <td>662.000000</td>\n      <td>662.000000</td>\n      <td>662.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>231.870091</td>\n      <td>126.676737</td>\n      <td>358.546828</td>\n      <td>7.845921</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1079.842373</td>\n      <td>461.822278</td>\n      <td>1297.153861</td>\n      <td>16.415662</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>5.000000</td>\n      <td>2.000000</td>\n      <td>10.250000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>37.000000</td>\n      <td>10.000000</td>\n      <td>60.000000</td>\n      <td>3.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>143.000000</td>\n      <td>51.000000</td>\n      <td>207.000000</td>\n      <td>7.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>22052.000000</td>\n      <td>5588.000000</td>\n      <td>22052.000000</td>\n      <td>215.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Dominic Gannaway      112\nBrian Vaughn          109\nDan Abramov            99\nSebastian Markbåge     99\nAndrew Clark           78\n                     ... \nFaelivrinx              1\nKunuk Nykjær            1\nLaura buns              1\nIan Obermiller          1\n伊撒尔                     1\nName: author, Length: 93, dtype: int64"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df.author.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "         insertions    deletions         lines       files\ncount    662.000000   662.000000    662.000000  662.000000\nmean     231.870091   126.676737    358.546828    7.845921\nstd     1079.842373   461.822278   1297.153861   16.415662\nmin        0.000000     0.000000      0.000000    0.000000\n25%        5.000000     2.000000     10.250000    1.000000\n50%       37.000000    10.000000     60.000000    3.000000\n75%      143.000000    51.000000    207.000000    7.000000\nmax    22052.000000  5588.000000  22052.000000  215.000000",
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     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df_csv = gilot.from_csv(\"./react.csv\")\n",
    "df_csv.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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eTlJSEqtWraJdu3a4urry3nvvMWjQIP136tuoVq1auu+VN4lvzZo1fPzxx9SuXRt3d3c+++wzjh8//tpl3blzh2PHjr1VPP924cIFzp8/n6NlCpEXDPM7ACEyolarGThwINeuXeOLL76gQYMGqFQqLly4wIIFCzhz5gw//vgjCoUi0zL27duHt7c3zZs3f6tYwsLC6NatG6ampowZM4bKlSsTFhbGsmXL6NKlCxs2bKBixYoAPHr0iJ49e2JnZ8eUKVOoWLEiT5484ccff6RTp04sXLiQJk2a6MsuUaIEv/76a4bntba2fuV+KpVK/7NSqczwS9HQMPM/c39/fwYNGkSjRo2YN28exYoV486dO8yZM4e//vqL9evXY2Zmlunx27dvx97ePt12c3PzNK+//fZbmjZtiqOjY4bluLq6cvLkSf3rYcOGUaJECSZOnKjfVrRo0UzjeJlWq2X8+PHExsam2X78+HHGjh3L5MmTadKkCdeuXWPKlCkkJyfzxRdfZFjW6NGjSUxMZM2aNSgUCr7++mu++OILtm7dqr8uZ2dn5s6dy9dff42fnx9jx44FYO3atXh6elKqVKlsxS2EEOLtxMfH06tXL54/f87w4cOpXbs2sbGxrFu3ju7du+Pn54eHh0e+xZeUlMTnn3/O/fv3GTFiBB4eHsTGxrJnzx4GDx7MkCFDGDp0aLbLGzJkCG3atKFp06Y5FmOPHj2YPn06devWzbEyhcgLktSKAmnNmjX4+/uzbds2KlWqpN9esmRJateuTatWrTh+/HiWH+SlSpVi2rRp1KtXL9sJUUamTZuGTqdjw4YN+mStVKlSLFmyhI4dOzJnzhxWrVoFwPjx47Gzs2PTpk0YGxsDKQmpm5sb48ePZ8KECezfvx8rKysgJRHNKCn8t+zul519UiUmJjJ27Fg8PT1ZsGCBfnupUqWoXLkyH374IRs3bmTAgAGZlmFjY/PKcxoYGGBlZcWUKVP09fRvxsbGacoxMjLCxMTkta4n1cqVKzEwMECpVKbZ/ssvv/DBBx/Qo0cPAEqXLs3du3fZtm1bhkltTEwMZ86cYenSpVSrVg2AQYMGMWDAAF68eIGVlRV37txh0qRJlClThubNm3P06FEAXrx4wYYNG9i+fftrxy+EEOLNLFiwgAcPHrBnzx6KFSum3z579mzCw8OZPn06e/bsyfKBeG5asmQJ165dY8eOHWkeeFapUoUyZcowZcoU3N3dqVevXrbK0+l0OR5jbpQpRF6Q7seiwNHpdGzcuJF27dqlSWhTlS5dmn379uHp6ZllOePGjSM5OZlvvvnmjWMJDQ3l8OHD9O7dO13ro5GREfPnz8fHxweAa9euceHCBQYPHqxPaF82ZswYnj9/zt69e984npx05MgRQkJCGDJkSLr3nJycWLt2LR07dnzr8ygUCqZPn86ff/75Vt22evbsSc+ePbPc5/r166xZs4bZs2ene2/w4MHpnoAbGBik6TL8MpVKhampKTt27CAmJobY2Fh27NhBmTJlsLS0BFIespw/fx6tVsv58+cpUaIEACtWrKB9+/ZvlJQLIYR4fUlJSWzbto1OnTqlSWhTTZ06lfnz5+sT2uDgYEaNGoWHhweurq4MGTIkzXCRFy9eMGbMGOrWrUujRo0yfEh56NAh2rZtS82aNWnZsiWrV69Gq9VmGF/qkKoOHTpk2IOnc+fOlC1blg0bNgApw3dSH6imenlbz549efToEYsXL8bLywsALy8v/Pz86N27N7Vq1aJ169b8/vvv+uMXLVpEixYt0pT58jYvLy80Gg2TJk165fetEAWNJLWiwAkKCuLp06dZdhEqU6bMK5+02traMmnSJLZv386JEyfeKJbr16+j1WqpXbt2hu9XrFiRsmXLAnDx4kUgpSttRooVK0bZsmW5cOHCG8WS065evYqpqWmGDw4A6tSpg62tbY6cq2HDhnTs2JHZs2cTEhLyRmUsWrSIRYsWZfp+UlIS48aNY+TIkRneMNSqVYsKFSroX8fExLBp0yYaN26cYXlGRkbMnj0bf39/3NzccHNz49y5c/qWYICRI0eydetWatSowfXr1xk4cCBPnjxh9+7d9OvX742uUwghxOsLDAwkKioq0+/rUqVKUaVKFSDl879r165ERkayevVq1q9fT3R0ND169NDPJzFixAhu3brFqlWrWLp0KRs2bEgzB0PqkJZevXqxd+9exo0bx7p161i6dGmG579//z6RkZGZ3iMA1K9fP9v3CIsWLaJEiRJ89tlnbNmyJc32hg0bsmPHDlq1asXw4cOzPUZ2y5YtKJVKJk+enOX3rRAFkXQ/FgVOWFgYkHZMKUDbtm3TPEVt06YNX3/9dZZltWvXjv379zN16lT27NmTrrX1VVJb8VJb5rKzb2rX4oxYWVnx/Plz/evAwMAMv+CqVavGxo0bs9zPwcGBgwcP6l9rNJoMywoICEjXFTc1XgsLiyyu6NVatmyZ4cOFv/76C1NT0zTbJk2axJ9//omvry9Llix57XNlVa8A8+fPp1ixYnTt2vWVZcXHxzNkyBASExMZM2ZMpvvdu3ePSpUqMWzYMAwMDPjhhx8YOnQomzZtwtzcnGrVqnHs2DEiIiL0rbKpT7g1Gg39+/fn9u3btG7dmnHjxuVblzchhHjXvc739c6dO4mKiuK7777Tf7f88MMPeHl5sWvXLjw8PDhz5gwbN27Uf6/OmTMHb29vfRnLly+na9eudOrUCUjpRRYbG8uUKVMYMmSI/uFnqsjISCD9vc3LrK2tiYiIyNb1WllZoVQqMTU1xcbGRr+9adOm+mFDw4YN019HdsbIppZjYWHxyu9cIQoaSWpFgZP6QZr6BZBq+fLlJCcnAzBhwgSSkpKyVZ6vry/e3t76yXxeR+qXz79jyUhq3DExMZmO4Y2OjqZkyZL6146Ojvz000/p9nt5AqjM9vt3oqpUKtmxY0e6sjJKaCHl2qKiotDpdG+cbK1atSrDLrZFihRJt83CwgJfX18GDx7M3r17sbOze6NzZuTMmTPs2LGDXbt2vXLfiIgIhgwZwp07d1izZo2+y/C/BQQE8MMPP3Ds2DF9V7YlS5bw/vvvs337dn3XrJfHO9+5c4e//vqLqVOnMnfuXEqVKsXChQvp3r07hw4dStftSwghRM5I/b5+8eLFK/e9ffs25cqVS5O42djY4OzszK1bt/TJXfXq1fXvV6hQIc3EidevX+fy5cv88ssv+m1arZaEhAQeP36crsfQy/cImYmKikqToL6Jf4/HrV279hv3VhOiMJHux6LAKV26NHZ2dumWnnFycqJMmTKUKVMGExOTbJdXvHhxJkyYwObNmzlz5sxrxVKjRg0MDQ31XYv/bffu3YwcOZLExET9U9DMuvmEh4dz//79NK2phoaG+mt6+V/x4sXTHJvRfi8nx6kyKiszLi4uxMfHc/PmzQzfnzNnziu7H5UsWTLDc2aWJHt5efHRRx8xffr0bD+Nzo6dO3cSHR1Ny5YtcXV1xdXVFY1Gw1dffZWmG3BQUBBdu3YlKCiIDRs2UKtWrUzLvHjxIvb29mnGZllaWlK2bFkePnyY4THfffcdAwcOpEiRIpw/fx5PT0+KFClCgwYN0v0+CyGEyDmlS5fG1taWS5cuZfh+6mz/ISEhmd5DaLVajIyM9N9h/540ycjIKM3PgwYNYseOHfp/u3bt4vfff89wTG+ZMmWws7PLsitwQEAALi4umb6f1RJ0qf694oFWq83ywbVarX5lmUIUBpLUigJHqVTSvXt3tm3bxt27d9O9n5SU9NoJ0SeffIKHh4d+UqfsKlq0KC1atGDt2rXplohJTExk5cqVvHjxApVKRcWKFWnYsCGLFy9Ot44uwPfff4+FhUWa7kv5qWHDhjg5ObFs2bJ07z18+JBNmzZl2sr7Nnx8fDAwMGD+/Pk5VubYsWPZv39/mpsLpVLJ8OHDmTlzJpDyUKFXr176yTpSx1Zlpnjx4oSHhxMeHq7fFh8fT1BQkH4c9csuXLjAzZs3+eSTT4CUCbJSb4jUarXMKCmEELnIwMCA9u3bs3XrVp49e5bmPZ1Oh5+fH/fv38fe3h5nZ2fu37+fplU3IiKC+/fv4+zsrP9+eHl8a1BQUJr9K1SowIMHD9I80L116xbff/99hvEplUp69uzJ5s2bM3wwumvXLm7fvk337t2BlKRZo9EQHx+v3+fBgwdpjskoWb1y5Uqa1xcvXtRPLmVkZJTuXubfscgwGVFYSVIrCqQBAwbQoEEDunbtyo8//sjt27cJDAxk9+7ddOzYkXv37r32GmozZsxIk6Bk18SJE9HpdHTv3p0jR44QGBjImTNn6NevH8+ePWPq1Kn6fWfNmkVsbCzdu3fn+PHjBAcHc+nSJcaMGcOePXuYO3dumu5OGo2G0NDQDP+9/EWWG4yNjZkxYwZHjx7VTyQRGBjIvn376Nu3LxUrVqRv375ZlhEREZFh7JnNKAwpXcSmTJmSZnx0drx48SLTbmW2trYZtlDb2trqn5j7+vry/Plz5s+fj4mJiT7W1DHcqdeTOknI+++/T/HixRk5ciRXrlzh5s2bjB07FpVKRbt27dLFMG/ePIYNG6Z/kl+rVi22b9/O7du3OXLkSJZP34UQQry9IUOGULJkSbp168aePXsIDAzkwoULDB8+nHPnzjFz5kwUCgVt27bFxsaG0aNHc/XqVa5evcro0aOxtLTE29ubsmXL0qxZM3x9fTl79izXr19nwoQJacbJpg6l8fPz48GDBxw7doypU6diYmKS4QoIAP369aN+/fp0796drVu3EhgYyJ07d1i0aBGTJ09m6NChuLu7Aym9qRQKBQsXLiQoKIh9+/alm4HZzMyMBw8epEnid+3axa+//sr9+/f5/vvvuXz5Mr1799aXGR4ezk8//URQUBA///xzuq7JZmZm3Llz543ul4TITzKmVhRIhoaGLF26lJ07d7Jt2zaWL19OXFwcTk5ONGrUiEWLFmXYWpaVkiVLMmbMGKZPn/5axxUvXpxff/2VFStWMGvWLEJCQrCxsaF+/frMnDmT0qVLp9n3t99+Y/Xq1cyaNYsnT55gZWVFgwYN2Lp1K87OzmnKfvz4MY0aNcrwvOPHj+fzzz9/rVhfV8OGDdm0aRMrVqxg5MiRREZG4ujoSJs2bejfv3+GY2Nf1r59+wy3N23alBUrVmR6XKtWrdi/f3+aia5eZdiwYQCsX78+28ekSkhI4I8//kCr1dK5c+c07ymVSq5duwZAp06dqF+/PrNnz8bMzIx169Yxd+5c+vfvj06no06dOmzcuDHdhGPHjh0jMjKStm3b6rcNHTqU0aNH06VLF9q2bUvLli1fO24hhBDZZ2ZmxoYNG1i5ciWLFy/myZMnWFhYULt2bX799VeqVq0KpMxbsXr1ambPnk2PHj1QKpU0aNCAjRs36ieamjdvHt988w1ffPEFBgYG9O/fP83D2CZNmjB37lz8/PxYuHAhNjY2tGvXjlGjRmUaX+q9zebNm/n555+ZMWMGxsbG1KhRg6VLl9KkSRP9vqVKlcLX15cVK1awYcMG6taty/jx45k8ebJ+nz59+jBjxgxOnjzJ6dOngZTv5d27dzNjxgwqVqzIypUr9S21Hh4eDBs2jJUrV/L999/TpEkThg8fnmZiyv79+7N06VJOnTqV4TwdQhRUCp30iRNCCCGEEKJQ8/LyolOnThmuPy/Eu066HwshhBBCCCGEKLQkqRVCCCGEEEIIUWhJ92MhhBBCCCGEEIWWtNQKIYQQQgghhCi0JKkVQgghhBBCCFFovTNL+jx/HotWKz2p35atrTnh4TH5HcY7Qeoy50hd5hypy5xhYKDA2tosv8PIV8+fx2JtbSa/T5mQv7UUUg+Zk7pJS+ojc1I3r/7efWeSWq1WJ0ltDpF6zDlSlzlH6jLnSF2KnJD6eyS/T5mTukkh9ZA5qZu0pD4yJ3WTNel+LIQQQgghhBCi0JKkVgghhBBCCCFEoSVJrRBCCCGEEEKIQitXx9TGxMTQpUsXli9fTsmSJTl16hTffPMNiYmJtGrVilGjRgFw/fp1fHx8iImJwc3NDV9fXwwN3z40jUbN8+ehqNVJb13Wf0VIiOJPPNkAACAASURBVAFarTa/w9AzNDTG2toepfKdGf4thBBCCCHeAXmVaxS0+/Pc9Kb3/rmWKVy6dAkfHx8ePHgAQEJCApMnT2b9+vU4OjoycOBAjh8/jqenJ+PGjWPGjBm4uLgwefJkNm/eTLdu3d46hufPQzExMcXMrDgKheKty/svMDQ0QK0uGH80Op2O2Ngonj8Pxc7OMb/DEUIIIYQQQi+vco2CdH+em97m3j/Xuh9v3ryZr776CgcHBwD++ecfypQpQ6lSpTA0NKRNmzYcOHCAx48fk5CQgIuLCwAdOnTgwIEDORKDWp2EmZmlJLSFlEKhwMzMUlrahRAFTnxoUH6HIIQQhZpOp+P8zVBi4pPzO5Q3JrlGznqbe/9ca6mdOXNmmtchISHY29vrXzs4OPDs2bN02+3t7Xn27Nlrn8/W1jzdtpAQA4yMlK9d1n+doWHBGmptYGCAvb1FfofxRgpr3AWR1GXOkbp8OwmBN4g/tACqLM/vUIQQotA6fimYdQduUqeSPUM71MzvcN6YJLQ5603rM88GKup06ddWUigUmW5/XeHhMenWb9Jqtf+JpvrMhIWFMnv2dObNW8jJkycICnpEly49sjymIHZv0Gq1hIZG53cYr83e3qJQxl0QSV3mHKnL16dTJ5F85zRoNSSW8uLRQycequvTO78DE0KIQiooNIZNh25jZmLI37dCufnouTxwzQF//x3AmjV+lCxZinbtOlKlSrX8DinP5FlSW6xYMcLCwvSvQ0JCcHBwSLc9NDRU32VZvB07O3vmzVsIwM2b1/M5GiGEKFy08VEkXz1M0rUjkBBNrEU1rgQ3R6NI5ERMSUlqhRDiDSQma1i+8ypFVIb49KzL7J//5pfDd3jPtVR+h/bOmDhxSn6HkOfyLKmtXbs29+/f5+HDh5QsWZI9e/bQsWNHSpQogUql4vz589StW5cdO3bQpEmTvAorz+h0OpYtW8SJE8cwNFTStm0HPDzeY+7cmURHR2FiUoSRI8dStWp1Zs6cholJEf755yIxMdEMHz6Ggwf3cefOLRo3bsqwYaPYt283p06dJCwslJCQZ3zySVeePXvG33+fw9KyKPPmLSQiIpxhwwby7bc/sHPnNgCKF3fE27ttPteGEEIUbEnXjpJ4eiNo1ESZu/DYsRUa2yrYm8G1oPD8Dk8IIQqtXw7fJjgsljGfumBnVYSOns6s3H2NY38HUrOMdX6H904YOnQAn302AID163/ExMSEBw/u4+xcga++momRkRH79+/ht982odXqqFy5CqNHT0CpVPLNN77cu3cXgPbtO9O2bfv8vJRsy7OkVqVSMXv2bIYNG0ZiYiKenp60bNkSgHnz5uHj40NsbCzVqlWjV69eOX7+vy4/4eQ/T3K8XIBGtRxpWDPrGbqOHj3M5cuXWLfuF9RqNUOG9GPbtt8YPHgonp5eXLlyGR+fCWzalJJ8hoWFsnbtJvbv38M33/iyadM2VCoV7dq1pm/f/gBcv36Vdet+ITo6mk6d2jB//iJGjBjDsGEDOXv2NBUqVAKgXLnyfPxxBwBJaIUQIgM6nQ7N42sYWNqTZOxAaGJpks2b8MTyQ4ztHFEZ/2/fh2HhmCgK1twDQghRGJy9/ozjF4Np5VGa6uVsAHCvVoxDAYGs23edGf3cURXS+XByM9fwdHXCo1rxNzr2ypV/2LhxC3Z29gwc2Ad//9M4OZVg9+4dLFu2BpVKxfLli9m0aT21a7sSFRXFjz/+TGTkCxYvXiBJbaojR47of27QoAG7du1Kt0+VKlXYsmVLboeSry5ePI+XVwuMjY0xNjZm6dJVdOz4EZ6eXgDUqFETS0tLHj16CICHx3sAFCtWnHLlnLG2TvnDt7S0JDo6CoCaNWtjZmaOmVnKJFl169YDUlpjo6NlzJwQQryKTpOM+q4/Sf8cRBsRSLTTB9ww7YlCUYmiFSti/q97K61Wy8PQcOoVNcqfgIUQopAKfRHP2gM3KO9kSfvG5fXbDRQKPvWqyOyNf3Pw7CPaNiyXj1G+e8qVc8bBoRgAZcqUIzo6igsXnhAUFMjAgX0BUKuTqVSpCu3bd+LRo4eMHj0UD4+GDB48LD9Dfy151lKb3xrWfHVram4yNExb1cHBj9NNkqXTgUajAcDI6H83TEplxk+sXt4no3MIIYTIXNI/B0i6tB9dfCSJRUry2L4f0RYeWFvqMMikIfZZZBSJajWVTOXzVgghskut0bJi11VAwcC21TFUpv2QrVTKivdqObL/zCOa1HbCylyVP4G+hdzMNd5mIldj4/91NUqdpFej0eLl1ZyRI8cBEBcXh0ajwcLCgvXrN3PunD+nT//FZ5/1YP36zVhYFPxJvKT/VB6pXbsOx48fQa1Wk5CQwNSpE1EoFBw/ntKSfeXKZSIiwilf3jlXzq9UKvUJsxBC/Fdpo0PR6XSo1RDz5BlRytLcKD6eW+VnQvkmWFgZZ5rQAjwIDUcBVLeXJRyEECK7tv95j3vBUfRpVQV7qyIZ7tPHuzpqjZbtJ+7lcXT/Pa6udTlx4hjPn0eg0+mYP/8bNm/+mZMnj/P111N4771GjBw5liJFihAS8vpLreYHedScRzw93+fGjWt89ll3tFodnTt3pU4dN779dharV6/AyMiYmTPnpmt9zSkuLnWYOXMaNjY2dOrUJVfOIYQQBZFOp0Pz5AZJ/xxE8+gikfV9uBtZmWSDPlhWUKBSgfGriwHgQVg4xQ0MsLbL+KZMCCFEWlfuh7P/zCM8XZyoVyXzFU4c7cxoVrckf5wLpFndkpQuVvBbBwurihUr0bdvf4YPH4ROp6Nixcr06NEHpVLJ0aOH6dnzE4yNjfH09MLZuUJ+h5stCl1GC8UWQhmtU/v06UOKFy+TTxEVTgVxndrC+v8o64HmHKnLnPNfqkudVo367tmU8bLhD9EYWfDUvBmhVi0wtbHgdUdsxCUmsfLICTxVWvq3dqJoI/fcCbyQCA+PwdbW/D/z+/S6/kt/a1mResjcf6FuImMS+WrNWSxMjfHp7ZblJFD29hY8CIxg4vLTlC5mwdguLigUBbtXTF7doxbE+/PclFG9GhgosLU1z/QYaakVQgjxTtHptCgUBug0GuL/+plkAwuC7PoSZdMQ86LGWL7hwJuHYSlL+bhavxPPgoUQIldpdTpW7blGQpKGcV2rZ2tWYzMTIz5uVI6fD93m0t1wXCrY5UGk4l0gSa0QQoh3gjYqhKTLv6N+epvYhtO4d78I6mLTUFraYWZugOVblv8gNIwiCgUVnQrfBCZCCJHXDvg/4uqD5/RqWZkS9pm3sP1bU9cSHP77Mb8dvUONcjbpJpUSIiPyWyKEEKLQ0ul0qJ/eJv73RcT+MoGka0cJSy7F9X+SUCrB0skBM/O3/6rT6nQ8DA2nnEKHma1ZDkQuhBDvrruPI9l2/B5uVRzwrO30WscaKg345H1nnoTHcfxicC5FKN410lIrhBCi0NI8vkr8vnloDc14at2GkKItMLUtinUOf7s9e5GylE91Cy0GhvI8WAghMhOXkMyKXVexsVTRp2XlNxoX61LBjiqlrdh58j4NqhfD1ETWBhdZk29mIYQQhYYuKS5lfdmrh3n+HK6G1eCeXT/+KbeAuAqdsCxW9LUngMqOB2FhKIC6xeRrUwghMqPT6fjpwE2eRycysG31N05GFQoFn3pVJDY+mT2nH+ZwlOJdJC21QgghCjxtdChJVw6RfOM4JCcQWbQ+N558QJEiOsydm7z1eNlXeRgaTjEDA2yLm+TymYQQovA6fimYgBshdGrqjHOJom9VVpniFrxXsziHAgJp6loCh0zWtxUCJKkVQghRwCVd2k/i2c3oMOC5RX2Ci7XCwLYs9iZ5MwtxXGISzyKjaGysRWUhk0QJIURGHofGsOnQbaqXtaale+kcKbNDE2fO3Qhhy7G7DGlXI0fKFO8m6UdViMya5UuXLh34448Dr3XctWtXWLp0YS5FlbVOndrw5EkwJ08eZ9Wq5QCsXr2CS5cu5Es8QoiCT6fVknzvHNqoUGJj4XFSBZ4Ubc0/pecTWmEwpiXKYpKHDaaP9Ev55N05hRCiMElM1rB851WKqAzp16Y6Bjm0vqy1hYpW7mUIuBHC7aAXOVLmu+zGjWvMnj09V8oODn7MN998nevneVPSUluI7N+/hyNHTmFk9HrjEx48uM/z5xG5FFX2NGrkSaNGngBcuHAeV9e6+RqPEKLg0SXFk3zrJEmXf0cXHcqLEm25ZdIZpbIyRStWxiKfHsM+CAuniEJBZSeZqEQIITLyy+HbPA6LZfSntSlqZpyjZbesX5rjFx/zy+E7fNmrbo4lzO+iKlWqMXFitVwp++nTJzx+HJTr53lTktTmkb//DmDNGj+USkNCQp5SrVp1JkyYgrGxMfv37+G33zah1eqoXLkKo0dPQKVS8dFHzalUqSoREeHY2tqh0+no378333+/mDNnTmV4zO+/H2DdutWAgqpVqzFkyAhWrVpOfHw8a9eupnfvz/UxaTQali79gQsXzqPRaGnd+iO6d++JTqdj8eLv+euvk9jZ2WFtbUODBg1xda3LsGED2bJlN5DS4grw+ecD2br1Vw4c2EdCQjwGBgb4+n5D2bLl9Ofat283Fy6cp04dN27evM6cOTOYNWse48aNYMuW3RgYGHDhwnk2bFjL/Pn506oshMg/iQHbSbryOyTFE2dWkSCHriQUrYOtuY78vH/RvbyUj13211kUQoj/inM3Qjh+MZhWHqWpUc42x8tXGSvp0MSZNfuuc/b6MzyqFc/xc7wrUvMNgGrVqnPp0kVevHjOyJHjaNCgIb//foCff16HgYEBTk5OTJkyHZVKxfr1P3H06B9oNFrc3T0YPHg4T58+YcyYYRQtaoWxsYoXLyIIDn7M/PlzeP/9ZqxZ48fixX48evSQuXNnEh0dhYlJEUaOHEvVqtWZOXMaZmbm3Lx5ndDQEPr27Y+3d1sCAs6ydOlCFAoFFhYWTJs2Cysrq7e+9v9UUhu3+5t02wzL18e4ejN06kTi93+X7n2jSo0wqtwYbUI0CX8sTv9+NS+MnN2zdf5r167y008bKVWqDFOmTGTbts3Ur9+A3bt3sGzZGlQqFcuXL2bTpvX06dOPFy9e0KNHb+rUcQOgUSM3fvrpZ+7du5vhMd7ebVm06DtWr16Pg0Mxpk+fwuXLl+jXbxAXLpxPk9AC7N69HYA1azaSlJTE6NFDqV69Os+ehXLz5g02bNhMVFQkffp0o0GDhpleV2xsDCdOHGfx4hWoVCasWrWc7dt/Y9So8en2bdXqI/bu3cVnnw3A2bkCTk4luHDhPHXr1mP//j20bv1RtupSCFH4aZ4Ho7R2IikJokMjiTOpxWO7lijsnClSBApCCvksMoqE5GRZykcIITIQ+iKen/bfoLyTJe0bl8+187xXsziHzgey9dhd6lS0x9hImWvnelP+T85z+sm5XCm7Ycn61HOo81rHJCerWbHiR06ePMHKlcto0KAhK1cuw8/vR6ytbfDzW8qjRw8IDw/n5s3rrFy5DoVCwfTpU/n99/3UquXCo0cP+e23RTg6OukT5jFjJvD33wH680yfPoUePfrg6enFlSuX8fGZwKZN2wAICXnG0qWruHfvLsOGDcTbuy1r165m3LhJVK1and9++4Vbt25Qv77HW9fRfyqpzW8uLq6ULl0WgJYtW7Nr13aMjIwICgpk4MC+AKjVyVSqVEV/TPXq6QfFX7gQkOExV678Q82atXFwKAbAlCkpfd337dudYTwBAWe5ffsW58+n/GLGx8dx584d7t69i6enF4aGhtjY2NKwYZMsr8vMzJxp02Zw6NDvBAY+wt//FBUrVs5WnXh7t+XgwX1Ur16T8+fPMXbspGwdJ4QonHQ6LeqHF0m+fBDNk5uEu33F/cgK6Iw/w7ICmBawHr4PQlPG09ZxkIRWCCFeptZoWbHrKqBjYNvqGCpz73PS4P+X+Pl20wX+CAjEu0HZXDvXu8LdvQEA5cs7Ex0dBUDDho0ZPPhzGjduiqenFxUrVubgwQVcu3aFzz/vCUBiYgLFihWnVi0XrK1tcHR0yvQccXFxBAUF4enpBUCNGjWxtLTk0aOUZZjq13dHoVBQvrwzUVGRADRq1ITJk8fRuLEnjRt7Uq/e2ye08B9Lak3bZJ4wKQxVWb5vYGKR5fvZoVT+76mSVqtDqVSi0Wjx8mrOyJHjgJRfDo1Go99PpUo/G0pmx1y8eD7Nfs+fP88yHo1Gy5Ahw/W/iC9evMDc3JTly5ei0/1vVlHD/1/0UaFQpNmuVqsxNDTk2bOnDBs2kI4dP8HD4z1sbGy5fftmturk/feb4+e3lKNHD9GgQUOMjXN2HIYQomDQqZNIvvlnynjZqGeojW0Jtu1GxAtHilrpUBa8h+4APAwNo7iBAfaylI8QQqSx/c973AuOYnC7GtjnwXI7VctY41LBjr2nH9KollOOj919W+6OdXF3zJ05YwwNDVCrta91TOo99cv37yNHjuXOnY85ffok06dP4bPPBqDVavjkk6506dIDgOjoaJRKJZGRL1Cpsp7xX6fTpskNUrahz2WMjVX6GFJ9+ml3GjZswqlTf7J06UKaNr2arjfpm5BHz3non38uEhoaglar5cCBvbi7v4era11OnDjG8+cR6HQ65s//hs2bf86ynMyOqVq1OteuXSE8PAyARYu+4+TJ4/+fPGvSlVO3rhu7du1ArVYTFxfHkCGfc/XqFerX9+DIkT9ISkoiJiaGM2f+AsDc3ILo6GieP39OUlIS/v6ngZQZ0EqWLMWnn3anWrUanDlzCq02/flSKZWG+nhMTEzw8HgPP7+ltGrV5o3qVQhRcOn+/7NAq9GScHYrcWozbjsM4arzPJKdW2Jpa1pgE9r4pCSeRkZRQalBZZn3S/ns3LkTb29vvL29mTNnDgDXr1+nY8eOfPjhh3z55Zeo1WoAgoOD6d69Oy1btmTw4MHExsYCEBUVxYABA2jVqhXdu3cnNDQUgKSkJMaNG0erVq1o3749d+/ezfPrE0IUXlfuh7P/zCM8XZyoV8Uhz87b+X1nktVadv55L8/O+a5Qq9V06dIeKysrevbsS8uW3ty6dZM6depx8OA+4uLiUKvVTJo0hmPHDqc7/uX791RmZuaUKFGS48ePAHDlymUiIsIpX9450zj69+9NXFwsn3zSjU8+6catWzdy5Pr+Uy21+c3Ozp4ZM74iNDSEevXcadOmHUqlkr59+zN8+CB0Oh0VK1amR48+WZZTsWKlDI9RqVSMGDGG0aOHodVqqFGjFq1bt+Hx4yDWrPFj2bJFDB48TF9Ou3adCAoKpG/fbmg0Glq3bkPdum6o1Vpu3rzBZ5/1wMLCAhublEH/5ubmdOvWk/79e+HgUIxq1aoDUK+eB9u3b6FHj84YGRlRrVoN7t3L/AbJ3b0B8+Z9g4+PLzVr1qZZsw+4fPlShl2thRCFkybsIUmXD6IJDyTS42vu3TdD5zgL46LWFDFVYJHfAWbDw7CUWeNdbV7v6XhOiI+PZ+bMmRw4cABLS0u6du3KqVOnmDVrFjNmzMDFxYXJkyezefNmunXrhq+vL926dcPb25slS5awdOlSxo0bx4IFC3Bzc8PPz48dO3Ywc+ZMFixYwPr16ylSpAj79+/n3LlzTJw4kd9++y3Pr1MIUfhExiSyavc1StiZ0aVZxTw9t6OtGe+7luDw30F41S1JSfuCMPtC4WBoaMjnnw9k5MghqFQmmJtb4OMzDXt7B+7cucWAAX3QajW4u79Hq1Yf8fTpkzTHly1blpiYaKZPn4K398f67VOnTufbb2exevUKjIyMmTlzbpYrtQwc+AUzZ/qiVCpRqVSMG5czQw8Vun+3GRdS4eExaLVpL+Xp04cUL14mnyJKK3Vw9eLFfvkdSpYy6t4wc+Y0XF3r0rp1zrekajQa/PyWYm1tre/28G8F6f/xddjbWxAaGp3fYbwTpC5zTm7WpU6nRRP4D0n/HEQTfB2tUkWohSeB1p0xt1JR2EYXHLx0hQdPnjHfxQCL4pZp3jMwUVG0UfYmCXwTMTExNG3alJ07d2Jra0vXrl2ZNGkSkydP5tChQwAEBASwcOFCVq9ejbu7O2fPnsXQ0JAnT57Qo0cPDh8+jJeXFxs3bsTR0RG1Wk39+vXx9/fns88+Y8SIEbi5pUxE2Lx5c9atW4eTU+Zjp/4tPDwGW1tz+dvMhHxupZB6yFxhrButTsf3v17kVlAkU3u7USIHk8rs1kdMfDITl5+mfAlLRn/ikmPnfxN5dY/6Jt2PC7OM6tXAQIGtbea/b9JSK/JVv349KVrUijlz0s88LYQoXNQPL5Dw+yLUxtYE23xKhPX7mFmbYlNAuxdnJXUpn7IKHaY2Znl+fnNzc0aMGEGrVq0wMTGhfv36GBkZYW9vr9/H3t6eZ8+e8fz5c8zNzfXzH6RuBwgJCdEfY2hoiLm5OREREWm2px7z9OnT10pqhRD/PQf9H3H1wXN6taycownt6zAvYkSbhmX59cgdLt8Lp2b5nF9GSBQ+ktTmkTp13PRL8xQ2X345LdfK/vHHrMcPCyEKLm1cJMnXjoCJJTGOzbkf6oqy2BBirN2wsDTEMh/Xl31bIVHRxP//Uj5K47zPym/cuMHWrVs5evQoFhYWjB07lr/++ivdfv+ewO/l7ZkxMMh4Oo3Mtmcm9Ym5vX1h6EyeP6RuUkg9ZK4w1c2NhxFsO3GPhrWd6NS8cpafM28qu/Xx6YdVOHHpCVtP3MPTrTTKXJx5OSshIQYY5tFyb3l1noLAwMDgtf82JKkVQgjxWjQRQSRfPkjy7dPotBoirJpy56kBZmYKjMp6YPnqIgq8B6EpE+7Vsc+fm4iTJ0/SoEEDbG1TWiA6dOjA6tWrCQsL0+8TGhqKg4MDNjY2xMTEoNFoUCqV+u0ADg4OhIWFUbx4cdRqNTExMVhZWeHg4EBoaChlypRJU9brkO7HWSuMXUtzg9RD5gpT3cQlJDNn7TmszFV0fd+ZsLCYHD/H69ZHhyblWLL9CtuO3KKpS4kcjyc7tFptnnQL/q91P9Zqtel+F17V/fidT/nfkSHD/1ny/ydEwZIYsJ24LT4k3fYnxMKTy6XmEFquL/b2OkxN8zu6nPMwNJxiBgbYO+bPRVWpUoVTp04RFxeHTqfjyJEj1K9fH5VKxfnzKcu37dixgyZNmmBkZISbmxv79u1Lsx3A09OTHTt2ALBv3z7c3NwwMjLC09OTnTt3Ailjc1UqlXQ9FkJkSKfT8dOBm0REJTLo4+qYmhSMBcXrVLKnUsmi7Dhxj/hEdX6HI/LZO91Sa2hoTGxsFGZmlrnSRULkLp1OR2xsFIaGhWx2GSHeITp1Euo7Z1CWqEYsdjxLrk6SjTFhVu9jbmOOeSEcL/sqCUnJPH0RSUMjHSb5sJQPQKNGjbh27RodOnTAyMiImjVrMmDAAFq0aIGPjw+xsbFUq1aNXr16AfDVV18xceJEli1bhqOjI999lzJPwYgRI5g4cSLe3t5YWFgwb948AHr27MnUqVPx9vbG2NiYuXPn5st1CiEKvhOXggm4EUKnps44lyia3+HoKRQKPm1WkelrA9h35iEdPTNfRka8+97p2Y81GjXPn4eiViflU1SFj4GBAVptweneYGhojLW1PUpl4Xv+Upi6FRV0Upc5J7t1qY2PIvn6UZKuHIaEKMJKdOaeSRuMjcHCAt7l54Q3g59y4NIVhjpoqF/XLsN9cnv248JAuh9nTT63Ukg9ZK4w1M3j0Bi+XhtApZJFGfWpCwa5+OH/pvWxcvdVzt0IZdYAd+yKFsmFyDInsx/nDpn9+F+USkPs7BzzO4xCpTB8wAohco9OpyPx1AaSb5wATTLR5rUIcmyN2qoqdv+R5QAfhoWjUiioWkJ6iQgh/rsSkzUs33mVIsZK+n1ULVcT2rfR0dOZgJuhbDt+jwFtq+d3OAXGq5bkbNTIjZMnAzI9Pjj4MWvXrmbSpKncuHGNHTu2MnHilNwK962900mtEEKIV9PpdGjDHqK0L0tiooLoF2pizBsSbNESIzsnVCrIn064eU+n0/EgNCzflvIRQoiC4tfDt3kcFsvoT2tT1LzgfgvYWJrwYf1S7Dn1kGZuJXF2KjhdpAuzp0+f8PhxEABVqlRj4sRq+RxR1vIlqd25cyd+fn4ANGnShAkTJnD9+nV8fHyIiYnBzc0NX19f/Zp7Qgghcp5Oo0Z915+kywfRhj8ipM7XPIgsh8Lkc6wceSfHy75KSFQ08UnJVDfPn6V8hBCiIDh3I4RjF4Np5VGaGuUK/jqwrdzLcOLSE349fIdJPer8J+fS0el0LF78PX/9dRI7Ozu0Wi2urnXZv38Pv/22Ca1WR+XKVRg9egIq1f8eUsTFxfHdd3O4d+8uWq2W7t170aJFS374YR7BwY+ZP38O77/fjDVr/Fi82I9Hjx4yd+5MoqOjMDEpwsiRY6latTozZ07DzMycmzevExoaQt++/fH2bktAwFmWLl2IQqHAwsKCadNmYWVllePXn+dZY3x8PDNnzuTAgQNYWlrStWtXTp06xaxZs5gxYwYuLi5MnjyZzZs3061bt7wOTwgh3nm6pHiSrh0m+cohdHEvSDQpwWO7z4mKccLGRsdrLlf6TnmYupSPw3/vhkgIIQDCXsTz0/4blHeypH3j8vkdTrYUURnSoUl5ftp/g/M3Q3Gr8npLlOWEqFN/EXnyRK6Ube3pibn7e1nuc+zYYW7dusmGDZuJjo6mT58uxMfHc/jw7yxbtgaVSsXy5YvZtGk9ffr00x+3du1qKleuio+PL7GxMQwa9BnVqtVgxIixrFnjx5gxE/j77/91U54+fQo9evTB09OLK1cu4+MzgU2btgEQEvKMpUtXce/eXYYNG4i3d1vWrl3NuHGTqFq1Or/99gu3bt2gfn2PHK+jPE9qNRoN7ctllAAAIABJREFUWq2W+Ph4TE1NUavVGBoakpCQgIuLC5CyHt/ChQslqRVCiByk+/9J89RqHYl/7yHWpAJBxfuTbFsDM3PFO7G+7Nt6EBqOg4EBDo4m+R2KEELkObVGy4pdVwEdA9tWx1BZeJ5yNqrpyKGAQDYfvUPtCnYYGRae2HPChQvn8fR8H0NDQ6ytrfHwaAjoCAoKZODAvgCo1clUqlQlzXEBAWdJTExg795dACQkJHD//j1MM1inLy4ujqCgIDw9vQCoUaMmlpaWPHr0EID69d1RKBSUL+9MVFQkAI0aNWHy5HE0buxJ48ae1KuX8wkt5ENSa25uzv+xd+fhUdVn4//fsyQzSSbLZDLZgYQQAiGQEMIuoGILVNQW16JFrVvbp7WPrTyPj6LWqt3cqPWn4lKxfIuKqKDIosgWQPYlOyEhCSH7nkyS2c/vDyrVqpjATCbL/bquXled5HzOPYfMnHOf8/nc969//WsWLFiAXq9nypQp+Pn5YTabz/2O2Wymrq6uV+OerxqW6B2zOdjXIQwaciw9R47lhVEUBWtlAW37P8LR2sipy5+i9FQUytgXCTKFEiW52zldNju1rW3MCVITMyL8/L/sL0WkhBCDz7rsMkqr2/n5D9Mwh/VtJeGLpVaruPHyZJ555xifHT7D/KnD+3T/ITNmEjJjplfG7kn1Y5VK9ZVOMBqNBpfLzeWXX8F///dS4GxS6nK5vrKd2+3i4YcfJyXlbLLb3NxESEgoOTnHvrYPRXHzn41zFIVzY/r7687F8oUbb7yZmTNns3dvNi+++DyXXprPrbfe0dO33mN9ntQWFRXx3nvvsX37doKDg7n//vvZs2fP136vt3Phv6mlj+g9qX7sOXIsPUeOZe8pbifOU4fOrpdtKMOlNVAXPJeuYidulR1toJbu7k66u30daf9RXFOHAowLctDS0nne31XrnUgpEiHEYJJX1sTGfRXMyYhlsg+m73rCuMRwJiSZ+GhvOTPHRxMcOHRuQGZlTWH16lX88IfXYrVa2b//cxISRrJr1w5uvfUOwsKMPPPMH4mNjeeOO+45t11m5mTWrVvL//7vMhobG7n99sW8/PLf0Wi0X0uAg4IMxMXFs3PntnPTj5ubmxg58tt7BN91160sXfp/3HDDYoKDQ9i9e6dX3n+fJ7W7d+9m+vTpmExnF50vWrSI119/ncbGxnO/09DQQGTkwPwwCSFEf+AoPYRt+8vY9dFURdxGq3EmwWE6TCY/Wlqkd/c3KW9oPNvKJ2boXAQJIQRAW6ed1zYUEhcRxE1zk30dzkW5/rJRPPr6AT7cXc7N3x/t63D6zKxZl1JYWMCSJTcSHm4iIWEkBoOB22+/i3vv/RmKopCcnMItt9z2le1++tO7eOaZP/OTn9yA2+3mF7+4l7i4eAwGAxZLB48//jBXXnnNud9/5JHHeeqpP/D66yvw8/PnySf/gp+f37fGdc89/8WTTz6GRqNBp9OxdOn/eeX9q5T/fIbsZbt37+app57irbfeIiAggEcffRSz2cyWLVt47LHHmDRpEsuWLSMhIYE777zzuwf8F3lS6xnyRMxz5Fh6jhzL7+Zur8ee9ykER9MWeQXlpS40jfnYTeMJMvx7XZHRGPSdTyGHIkVReG1bNsOcdh64LOQ7Kx+r9TpCL5naR9H1T01NFkwmg3w2v4V8b50lx+Hb9Zdj41YUnltznOLKVh6+NYt4s2+W9HnyeKzacoKdx6p5/M4pxJi8156ttraC6OgRXhv/Cz2ZfjyYfNNxVatV511u2udPai+55BIKCgpYtGgRfn5+jB8/nrvvvpvvfe97LFu2jM7OTlJTU1myZElfhyaEEAOKoii460qw527BWX4YBTV1xvlU1KkJDlbhNyKdb793Kr6sob2DLrudVGnlI4QYYrbsP01+WTNL5qf4LKH1tGsuSWRfQS1rtpXw6+vTfR2O6AM+aQR79913c/fdd3/ltTFjxrB27VpfhCOEEAOS7fPVOPI+xa0NpC70SurCvkegyYhZK7NWequ8sQmASdLKRwgxhJRWt/H+rlNkjYlkTnqsr8PxmJAgfxZOT+DdHaUUlDeTmvAdxf/EgDe0al0LIcQApti7seduwWVpobUVKlyTKTct4XjCcjqTbyAkyojWJ7cqB76KhibMajWRUV9vYSCEEINRl9XBivX5hBl03DY/pddFWvu7K7LiiQjV8862ElmiOATI5Y8QQvRzbksT9rxPcRTuBEc3Naf9OK37HgEBYwhKGkPo4LoO6XM2h4Oallam+ynow6THkRBi8FMUhZWbT9DcbuP/bskkUD/4Fqv4aTVcd2kSL6/PZ09uDbO89CRaUZRBd0PAly603JMktUII0U8pihvr9ldxlu5HAVqCp1ITOR9MiZgD5K6zp5xubEYB0sPkmAohhoZdx6s5VFTPdZcmkRQ3eBuUTR4TyacHK3l/1ykmj41E7+/Z1Eer9aezs52goBBJbD1AURQ6O9vRanvfhUCSWiGE6EcUtxtX3Um0MSl0damxdOloD51PXej30ZvCCRh8N9N97otWPqmxcnCFEINfVYOFt7aeZFyCkflTh/s6HK9SqVTcNDeZJ1cdZvP+0/xw1kiPjm80mmlpacBiafXouP9JrVbjdg+N6sdarT9Go7n323khFiGEEL2kOKw4TmRjz/0EpaOBMxP+QJVlGFrDTwmJhWApyOsViqJQ0dDECDUYIrzX9kEIIfoDu8PFy+vz0ftruHNhKuoh8HQxKS6UKWMj2bz/NLPTYwkP8dwyE41GS0REjMfG+zb9pf1TfyaFooQQwocUqwXb/jVY/vkbbHv/SacrlJORv6TJEUNEhILRCBpJaL2mscNCp91OaoBLWvkIIQa9tz87SVVjJ3delUqoQefTWOwuB6sK1nCqrcLr+7puThJuBd7fdcrr+xK+IUmtEEL4gOKwAmB3gC1/Oy26NAriHqFs1CNoEqcQHKJhCNxA97nyhn+18omS06EQYnA7WFTPjmPVLJg6nLREk09jcStuXsr5O/tqD/HS8b/TYbd4dX8RYQF8b3I8e/NqKa9t9+q+hG/IWVwIIfqIorhxVhyj66M/YVn/J0pOwp6DoRyJX07dqF+ijx9FQICvoxxaKhoaiVCriYqUAy+EGLwaW7tZuamIkbEh/Gi2Z9eVXoi3it6nuKWUuKAYup1WXstbdcFVb3vqymkJBAf68c5nJV7fl+h7ktQKIYSXKU4b9oJtdK55kO4ty7E11XNaNZ2qKoWwMAVjVAD+vS/0Jy6SzeGkuqWVZI1LWvkIIQYtp8vNig/zAYV7rh6HVuPby/9PK3awt+YAcUHRTIhIZVRYIiWtZXxQsNmr+w3Ua/nhJYmcqGzl6MlGr+5L9D1JaoUQwsvsJ/dj2/0PuhwBnIz8BXkjn8aRtIAwo0bWy/rQ6aYmFCBDWvkIIQaxddlllFa3c+v8MZjDfDsr5Wh9LutKN2LUhTEhYhwqlYpRoYkYdWGsyf+IsrbTXt3/7IxYYkyBrNlegtM1NKoJDxWS1AohhIe5mirp3vEa3bnbqK5WcahhJgUxyziV9Ds0idMICdXKetl+oKKhCX9p5SOEGMTyy5rZtK+C2emxTBkb5dNYytsqeSN/NUHaQCZHZZzr66pSqcg0j0er1rIidyXdTqvXYtCo1dx4eTL1Ld1sP1Lltf2IvidJrRBCeICiKDgrc+j6+Cm63nsYR8kBKoq7KChQ4x/gh374aAICJZPtLxRFobyhiREqhaCIQF+HI4QQHtfWaefVDQXERATx4yuSfRpLc3cLLxx/FY1KzZSoTLTqr3YV1Wl1TI2fSIfdwhv5q7265nX8yHDGJRj5cE8Zlm6H1/Yj+pYktUII4QHWna/TvelZbA1VVIbfwPHEv9KVeDVmsyLrZfuhxg4LnTYbqYFutP7Ssl0IMbi4FYXXNhTQbXPys2vGofPz3VqXbqeV546+jN1lZ3LURAL8vrmGQZTBTGLICPKbith5Zo/X4lGpVNx4eTJdNicf7Sn32n5E35IzuRBCXAB3dzuOgm1oUi6lzWakVpmFwzwOi2kqhhAtIfJQtl+r+Fcrn0yz/EMJIQafLQdOk1/WzJJ5KcSbDT6Lw+V28cKx12ixtpBpTidUF3Le3x9jHEWztZn3SzaQbEwizhDjlbjiIw3MmhDLtiNnuDwzjqhwmbEz0MmTWiGE6AVXSxXWXX+nc/VvsB9ex4mdhRw9qqZdPxbVyJkEy3rZAaG8sRGTWk1MtFzICCEGl9LqNt7feYqsFDNzMmJ9FoeiKKzMf4vy9tOMMY4mKsj8nducXV+bjgo1Lx9/A7vLe9ODfzQrEa1Wzbs7Sr22D9F3JKkVQogeUNwuujY9S9e7D2Ev/pwGw2yOx/+FrsiZmM0KgZIbDRg2h5OaljaS1dLKRwgxuHRZHaxYn0+YQcdtC8acK8bkCx+d2sKRhhxGBMeTGDq8x9sF+OmZEJFKs62VVYVrvBZfqEHHD6aN4EhxAydOt3htP6JvSFIrhBDfQnE5cJ7JA8DSqaHNYeJM+LUcT/grrcm3ERwXLetlB6DKpmbcikK6UVr5CCEGD0VReHPzCZrbbdxzzTgC9b6r7P55zSG2VGzDHGAiNTyl19tHB0UyzBDHkfrj7Ks55IUIz5o3eRjhITre/qwEtxeLUwnvkzW1QgjxHxSrBXvBNhz5n6F0t1Ge9hT13VH4hd1OcDCEyO3AAa2i8Wwrn7RoOQUKIQaP7JwaDhbVc+2ckYyKC/VZHCdaSlldtJZgPwOZ5gkX/LR4nCmFFlsrb514n5GhI4gM/O7py73l76fh2jlJvPpRAfvya5mR5p01vML75NJMCCH+xd3VijX7TSz//A32Q+/TrhlOUfT/0KFEEhEBoaGglm/NAU1RFMrrGxmuUgiKDPJ1OEII4RFVDRZWf1pMaoKRBdNG+CyOus56Xs55A3+1H1OiMtGoL7zqslqlZlJkOoqi8NLxlTjdTg9G+m9TU6NIjAnmvZ2nsDlcXtmH8D65PBNCDGmKoqBYLQDY7GpsJ/fTaJhOTvwfODNqKf4j0ggySOWnwaLJ0onFZmOctPIRQgwSdoeLl9fno/fXcNfCVNQ+WkfbYbew/OgK3G4XU6Iy0Wkvfn1OkF8gaaYx1Hc38M6JdR6I8uvU/2rx09JhY8uB017Zh/A+SWqFEEOS4nbiOLmXrg9+h2XjcoqKVOw9bOTYiOdpHnUHhrh4dDpfRyk8raKhEYDMiP5/o2Lbtm0sWrSI+fPn88QTTwCwd+9errrqKr7//e/z3HPPnfvdwsJCrr32WubNm8dDDz2E03n2iUZ1dTU333wz8+fP5+c//zmdnZ0AtLe3c/fdd7NgwQJuvvlmGhoa+v4NCiE84u1tJVQ1dnLnVamEGnxz4nK4HDx/9BU67BYyI9Mx+HtuJkycIYbYoCj21hzgaH2ux8b9stHDwpiUYmbTvtO0Wmxe2YfwLklqhRBDimLrxHbsYzrfWop1+ytYLXZOMZv6OoXwcIWwCH+08gBv0CpvaPpXK58AX4dyXpWVlTz66KO8+OKLfPTRRxQUFLBz504efPBBXnzxRTZu3EheXh47d+4EYOnSpTz88MNs2bIFRVFYs+ZsxdDHHnuMxYsXs3nzZtLS0njxxRcBWL58OVlZWWzatInrr7+eJ5980mfvVQhx4Q4V1bPjaBULpg4nLdHkkxjciptXcv9BdWctaaaxRASEe3wf402pBGoDWFX4Di3WVo+PD3D9pUk4XW4+2HXKK+ML75KkVggxpNhO7sd+4F06iKEo6n5OJP0Rki4lNEwt62UHObvTSXVLK6PULvTG/p3Ufvrpp/zgBz8gOjoaPz8/nnvuOQICAhgxYgTDhg1Dq9Vy1VVXsXnzZqqqqrBarWRkZACwaNEiNm/ejMPh4ODBg8ybN+8rrwPs2LGDq666CoCFCxeya9cuHA7v9YMUQnheY2s3b2wqYmRsCD+aPdJncawt/pCC5hMkhSQwLNg7fXE1ag2TItNxuJy8nLMSt+L2+D4ijYHMnRTP7pwaTtd1eHx84V1yCSeEGLQURcFZe5LuT/5GV+4OKipUHGiYTW78E5we9QD+CRMIMsjX4FBxrpVPmOcvhjytoqICl8vFHXfcwdVXX83q1aupr6/HbP539c/IyEjq6uq+9rrZbKauro6WlhYMBgPaf009+OJ14CvbaLVaDAYDzc3NffgOhRAXw+lys+KjfEDhnqvHodX45ly2o3I3O6v2EhMYxWhjklf3FexvYGx4Mmcs1XxQ8rFX9nHVzAQC9Vre2VaCIi1+BhSZZCeEGHQUtwtn2SHsOVtwN5zCrQ2i0jKO+jA1oaH+aMN73gReDB7lDU34qVSkRfuud2NPuVwuDh06xKpVqwgMDOQXv/gFAQFff7qsUqm+8cLrfK9/G3UvpyqYTAYAzObgXm03lMixOUuOw7e70GPzj40FlFa18z+3ZJGaHOnhqHrmUFUOa09+RHhAGNMTMlGrLj6xDgw6/5rgMYFJNNtb2FaZzfSRGaRHp170Pr/MDNw8fyyvrMulorGLyanRHh3/Ysjn6PwkqRVCDDrdW1/EVX4Yuz6aqojbaDXOJDhMh0ktd12HKkVRKG9oZLgKDFH9v5VPREQE06dPJzz87Nq0uXPnsnnzZjSaf7fHqK+vJzIykqioKBobG8+93tDQQGRkJOHh4VgsFlwuFxqN5tzrcPYpb2NjI9HR0TidTiwWC2FhYb2KsanJgslkoKFBpul9E7M5WI4NchzO50KPTX5ZM2s/O8ns9FjGxIf45PhWtlfx7JFXCdDqmWTOwNp18csXAoN0dHV+d5Gm8eGptHS38eye13hk6v2E6Dyb7GUlm1gfHsir63KJDw/w2VPwL5PPEajVqnM3U7/x530YixBCeIW7owHr52/htLRTV6eiRP19TkTdR9HIP+EeeTmh4TpZLzvENVs6sVhtjAt0DYhWPpdddhm7d++mvb0dl8tFdnY28+fPp6ys7NzU5A0bNjB79mzi4uLQ6XQcPnwYgHXr1jF79mz8/PzIyspi48aNX3kdYM6cOaxbd7Y9xsaNG8nKysLPr/8/wRZiqGvrtPPqhgJiIoL48RXJPomhxdrG3469igoVU6Iy8VP37XeqVq0lM3ICVqeVV/P+4fFpwlqNmhsuS6KmqYudx6o9Orbwnv5/ZhdCiG/hqivBnrsFZ9khFNScaB5Doz4LQ/BY9GaQS3TxhYrGJgAyTf2/lQ9Aeno6d955J4sXL8bhcDBz5kx+/OMfM3LkSH71q19hs9mYM2cO8+fPB+Dpp59m2bJldHZ2kpqaypIlSwB49NFHeeCBB3jppZeIiYnh2WefBeDXv/41DzzwAFdeeSXBwcE8/fTTPnuvQoiecSsKr28ooNvm5P6bMtD5ab57Iw+zOm389ejLdLusTIueRKCfb4ruhelCGR02khOtpWws38qVid/z6PgZoyIYMzyM9bvLmD4uikC9XFH0dyrFB6ugt23bxgsvvEBXVxeXXHIJy5YtY+/evfzxj3/EZrOxYMEC7rvvvl6N2dRkwe2WqYUXS6Y3eI4cS8/5z2OpuBx0b/gLrrqTuDWB1AVfRl3Y9wkINyIPm87PaAyipaXT12H0ufcPHKGzpZU/TfH3SOVjtV5H6CVTPRDZwCXTj89PzgFnyXH4dr09Npv2V/Du9lKWzEvh0olxXozsm7kVN8uPrKC0rYyJ5gnEBHl2LW9Ppx9/QVEU9tcdocXayn2ZPycpLMGj8VTUdvD7lQeZN3U4N1w2yqNj95Z8jr57+nGfP6n9ovfeu+++i8lk4tZbb2Xnzp08+uijrFq1ipiYGO655x527tzJnDlz+jo8IUQ/pdi7cdWcQDM8g7YOPyzKcFpNU2k2zsYQpiek729YC6C6pZWTtXW+DuM7VTW3kKVxowvV+zoUIYTotdLqNt7feYqsFDNzMrzTNud8FEVhVcEaStvKSAkb5fGE9kKoVComRownu3ofr+S+ye+m/w8BWs89OR4RHcyM8dFsPVTJpRPjiAzr363ghro+T2q/3HsP4LnnnqOiouJc7z3gXO89SWqFEG5LM005H2A58gmK3crJsc/RYjOhj7gVgwFCB8Zs0kGp227nw0PHcDqd9Pd7CjqVimlGBZVa/mCEEANLl9XJivX5hBl03LZgzHmrmHvL5vLPOFB3hGGGWI8/Eb0YOq0/GebxHKg7zOu5/4//yrjTo8dn0ewkDhbVs3ZHKb/4YZrHxhWe1+dJbUVFBX5+ftxxxx00NDRw2WWXkZyc/I2993rjfI+jRe9IyXDPkWN54Rxt9bRsX01n4V5QFDrDp9MYsRBD5DDMcrP0ohiNnqn+u/dgKXankweHa0lP613lXF/wD/RD4++h9Nvf3zPjCCHEeSiKwpubi2hut/HALZk+Wdt5sPYoG8o+waQPJ800ts/3/10iAowkhSRQ2HKSbZXZzB0+22NjG4N1LJg6gvW7yyg508ao+FCPjS08q8+T2t703usNWVPrGTJn33PkWPaeorhRujtQB4bS2WTDWXyM+pDv0xV/NQ7/IPz8wGXtxGr1daQDl6fW1DZ1WDh0sox0rYqRiXqsissD0XmXtdMFHlpOrNY7kUsbIYS3ZefUcLConmvnjGRUXN9/65S2lrGq8B0MfkFkRab75ClxT4w2JtFka2Zd6UZGhyUxLMRza47nTxnOzmNVvPXZSR5aMgl1Pz0GQ12fN7n4cu89vV7P3Llz2bNnz1d67H3Re08IMTQoDhv2/M/ofOf/sGx5kZwcFZ8fNXE88a90jPoxxvhIKQDVjyiKwq6iYvxUaq4bpuAXKP84QgjhaVWNnaz+tJjUBCMLpo3o8/03dDXy4vG/o1VrmRKViUbdfxeaqFQqMs0T0KjUrMhdid1l99jYOn8Ni2YnUVbTzoHC/l9DYqjq86S2N733hBCDm7uzBduBtVhW/wbbnlV0OYMoUV1BextERCiEGrVo+u85dMgqa2jkdGMzs/2cxI+S55VCCOFpdoeLl9fnoffXcNfC1D5/Otjl6Gb50RU43E6mRGWi1+r6dP8XQq/Vkx4xjhZbG28WvOPRsWeMj2Z4lIH3dpRid/T/mUlDUZ9PP+5t7z0hxOCjKAoqlQrbyYM4jn1MW/AkqkwLwDSKgEAVshK5/3K53WQXniRMreGaFDVqbZ/fGxVCiEHv7W0lVDV08psb0gk19G1C6XQ7ef7oCtps7WRFphPsP3Dq1kQGmhkeHM+xhlz2Vh9gRuwUj4yrVqm48fJknnrrKJ8equTK6QkeGVd4Tp8ntQDXXXcd11133Vdemz59Oh9++KEvwhFC9AFFceOqzMGeswX38GnUBV3KmabL0AzPRGeKJEDq7gwIxysqae3q4pZgF6HxEb4ORwghBp1DRfXsOFrFgqnDSRtp6tN9K4rC63n/pNJSTVr4GMyBA+97PjV8NC3WVt458QFJoQlEeaj90NgRRjJGRfDx5xVcMiGW0CC5cOlP5Ba7EMKrFKcde8F2utY8SPfm5dgaaykp9aeyUk2wUUdwTKQUkh0gumx29peUMUKjZvY4z1RQFkII8W+Nrd28samIxJgQfjR7ZJ/v/4OSj8lpzCcxZDjDQ+L7fP+eoFapmRSZjgK8lPMGDrfTY2Nff1kSDqeb9dmnPDam8IweP6k9fvw42dnZ56YMT5nimcf5QojBrXvL87iq8rAGJFAV+TM6wqcQHKLFqJJq5QPNvpOlOJxOrot0oTfKWtoLYbFYaG9vJzY21tehCCH6GafLzYqP8gGFe64Zh1bTt8+esqv28VnlLiIDzIwxJvfpvj0t0C+A8aYxHGvM5+0T7/OTsTd4ZNwYUxCXTYzjsyNnmDspnjjzwJmaPdj16NOybt067r33Xtra2ujs7OS3v/0ta9as8XZsQogByNVchXXXSuwWC9XVKk5oryY/+kFKkh5DlTiDkFAtUg1/4Glo7yCvsoqJWhWpYyWh7Y1PP/2Uxx9/HIvFwtVXX80111zDm2++6euwhBD9zPrdZZRWtXPr/DFEhvVtQ/bCpmLeOfEBof7BZEaO77ete3oj1hBDXFA0+2oOcbjuuMfGvfqSRAL8tbyzvcRjY4qL16OkduXKlbz77rs89NBDLFu2jLVr1/KPf/zD27EJIQYIRVFwnsmja+PTdK19CHvxXnJ3nqawUI0zfAwBI8YQGDTwT5BD1RctfPxVaq4dLi18emvFihXccMMNfPLJJ2RkZLB9+3apISGE+Ir88mY2fl7B7PQYpoyN6tN9V1tqWZH7JnqNjilRmahVg2d1YlpEKoHaQP5f4Rqau1s8MqYhwI+rZiaQd6qZvFNNHhlTXLwe/dW63e6v9I2NiopCrR48f/BCiAunOGx0rX2Y7o1PY68/Q2X49RxLWI4qJo2ICEXWyw4Cp+obONPUwhw/J3FJ8pS2txRFISUlhb179zJ79mwMBgOKItPvhRBntXXaee2jAqJNgfz4itF9uu92Wwd/PboCBYUp0Zn4aQbXTUuNSk1WZDpOxcVLOW/gcnumHc/lmfFEhgXwzrYSXG63R8YUF6dHmWlYWBhbt249999bt24lNFQubIQYqtzd7ThOHcTthuZ2PU3aVEoj7yZv5LPYR11FqNkg/WUHCafrbAsfo0bDVSlaaeFzAdRqNRs3bmT37t3MnDmTnTt3+jokIUQ/4VYUXt9QQJfNyc+vSUPn13cnT7vLwfKjK+hydJEVmUGQX2Cf7bsvGfyDSA0fTXVnLe+XbPDImH5aNddflkRVYyfZOTUeGVNcnB4Vinr44Yf5xS9+weOPP46iKPj7+/PCCy94OzYhRD/jaqnGkfsJjpN7UNxuClNS6bCHEBR9C4GBSH/ZQeh4xWnauru5NdhJaLzZ1+EMSP/7v//LCy+8wH333YfZbOall15i2bJlvg5LCNEPfHKgkryyZpbMSyE+su+KDrkVNy/nvEFdVz3pEeMI14dt8hbbAAAgAElEQVT12b59YXhwPA1dTew4s4fU8BTGRYy56DEzR5sZHR/Kul2nmDo2igCdTzqlin/p0dFPTk5m8+bNlJeX43a7SUxMRKuVfzghhgp3ay3Wz1fjqsxBUfvRGDyTmuD5+AcEYw6VaZSDVafNxv6SMhI1amaOkwqPFyorK4uVK1fS3t4OwNtvv+3jiIQQ/UHx6Rbe21nKpBQzczL6tiL620UfcKKlhOSwkcQZYvp0376Sbk5jV9Xn/D1/NY9OW0qI7uJuxatUKm6cm8zjbx5i474Krp2T5KFIxYU4b2b66quvctddd/H4449/YxU0udMsxOCluJwo1g7UQUY67f646iqpMS6iMexygkwhGGR68aD3eXEpTpeLa6Pc0sLnIpw6dYpf/epXtLe3s3btWm677TZeeOEFkpLkAkiIoarL6uQvqw4RZtBx24IxfVpt+NOKHeyp2U9cUDSjQhP7bL++plVrmBQ5gb01B1mR+ya/nfSLiy6KlRgTwvRxUWw5UMmcjFgiQvu2arX4t/P+SwYHn72DYTQaCQsL+9r/hBCDj2K1YDu6gc637qfj09c4ckTN/hwzOYnPYR31Q0IiQ2S97BBQ39ZO/plqMrWQmioJ7cV44oknePDBBzGZTERFRXHLLbfwyCOP+DosIYSPKIrCP7YU0dDazT3XjCNI33fFmY7W57KudCNGXRgTIsYNitY9vRGqCyHFmER5+2k+PvWpR8a8dk4SKhW8v/OUR8YTF+a8T2pvuukmAMLDw1m8ePFXfvbKK694LyohRJ9zt9Vhz/0ER3E2OO10GNKoUi/AYQWzWaGHdeXEIKAoCrsKi9Gr1Fw33IVWL8tNLkZrayszZ87kqaeeAuDmm2+WXu9CDGHZOTUcKKxnyQ/GMiqu724alrdV8kb+aoK0gUyOyhhyCe0XEkNG0NDdxJaKbYwNT2aUceRFjRceomfelGFs2FvBFVnDGBkb4qFIRW+c90rlrbfewmq1snLlSmw227nXHQ4Hq1at4u677/Z6gEII7znbVkRBpVLTffIwrsKdNIXMoCZ4PtqIeHQ6kI48Q09JbT1VLa0s0LmJSTL6OpxBwWaznbuAbGhowC0tIIQYkqoaO1n9aTGpCUauvSyZpiZLn+y3ubuFF46/ikalZkpUJlr10L1ZqVKpyDRPYGf157ySt4rfTfsfAv0ubtrwgqkj2HW8hre3neT/bs4csjcMfOm8f9FarZbi4mKsVivFxcXnXtdoNDz88MNeD04I4R2K24nz1CHsuVtwJ11Blf8l1LZegSZhFoHhoQQN3XPdkOd0udh94iQmtZofjNFICx8PWLx4MXfccQdNTU0888wzfPzxx9x5552+DksI0cfsDhcvr89D76/hroWpqNV9k/h0O60sP/oyNped6dFZBPjp+2S//Zmfxo9M83j21x7m1bx/cG/G3ReViAbotCyaPZKVm4o4fKKBrDGRHoxW9MR5L12vv/56rr/+erZu3coVV1zRVzEJIbxEsXXiKNqJPW8rSmczdn0M5WUBWMJUhJr0qNVyohvqjpafpr3byu0hTkJjpYWPJ1x33XUMHz6cnTt34nQ6+f3vf88ll1zi67CEEH2ksa2b3Tk17Mmtoandxm9uSCfUoOuTfbvcLl449hrN1hYyzemE6mRq7BfC9UaSQhMpbill6+mdfG/EpRc13iXjY9h6qJI120tIHxWBn9wU7lM9eh6TmZnJypUr6ezsRFEU3G43FRUVPPPMM96OTwjhQV2bnsNdX4IlaCxV0bdjN40nyKBGyr4JgE6rjYOl5YzUqJk+TroOe1JGRgYpKSn/mvJ/dp2tFFwUYvByON0cPdlA9vFqCspbAEhNDOeW76eQNtLUJzEoisLKgrcobz/NWONoooLkRuV/Sg4bSZO1mQ9PbSbFOIrhIfEXPJZareLGy5N55p1jfHb4DPOnDvdgpOK79Cip/e///m/0ej0lJSXMmDGDvXv3MmnSJG/HJoS4CIqi4K4rwV7wGarJS6hvCaJBdyO2OD2aiBHo9dB39RbFQLCnuASXy8X10S70YVLx2FPefPNNnnnmGRwOB3D2s6lSqSgsLPRxZEIITztTb2FXTjX78uuwdDswhei5+pJEZo6P7vN2LxtOfcKR+hyGB8eTGCoJ1jdRqVRMNE8gu3ofL+es5JFpS9FrL/wp+rjEcCYkmfhobzkzx0cTHCiVSfpKj5La6upqtm7dyu9+9ztuuukmfvWrX3Hvvfd6OzYhxAVQ3C6cZYex527GXX8KtzaIE93VWAJSCI1MkfWy4hvVtbVTWFXDFC2kjJUniJ60atUq3nrrLcaNG+frUIQQXtBldXKgsI7snGrKajrQalRkjjYza0IsYxOMqH1QNOjzmkNsrvgMc4CJceEpfb7/gUSv1ZEeMY5D9cd4s+At7plw20WNd/1lo3j09QN8uLucm78/2jNBiu/Uo8vbiIgIABISEiguLubqq6/G6XR6NTAhRO8ptk4633sExdKEQxdJlWkJLcZZBBt1mNSKr8MT/ZSiKOwsPEGAWs21CW5p4eNhZrNZElohBhlFUSiubCU7p4ZDRfXYnW7izUH8eG4y09OiMQT4bi5UcUspq4vWEuxnINM8QSrx9kBkYAQjgoeR01hA9pl9zIqfdsFjxUUEMScjlu1Hq7h8UhwxpiAPRiq+TY+uXEwmE6+99hoZGRn87W9/w2AwYLH0TQlyIcT5uS1NuGpPok6cRmOrgS79ZJqDUrCaJhJkUBMq5zLxHU7W1lHT0sbCADfRSeG+DmfQmTlzJqtXr2bu3LnodP+e1iZraoUYeFotNvbk1rA7p4a6lm4CdBpmpEUzKz2WhOhgnyeQdZ31vJTzBn5qP6ZETUSj1vg0noFkbHgyLbYW3j25nmRjItFBURc81jWXJLKvoJZ3t5dy73UTPBil+DY9Smp///vf8/HHH5OVlUVaWhrPP/88S5cu9XZsQojzcNWfwp6zGWfZIRSVlpzKidjcgQTFLiYgAAy+DlAMCE6Xi91FJZg0ahaM0aDqoxYTQ8krr7yC3W7n97///bnXZE2tEAOHy+0mp7SJ7OM15JQ24VYURg8LY+GMBLLGRKLz6x+JY4fdwvKjK3C7XcyImYLuItaGDkVqlZpMczq7q/fx0vE3WDb1t/hpLuyJe0iQPwunJ/DujlIKyptJTZAbxt7W4ye1S5YsAWDp0qUsXbqUPXv2eDUwIcQ3czWdxrbn/+GqLcatCaQudD71od9HHxxAsJ9MMRa9c7isgg6rlTtDnQRHS2VMb8jJyfF1CEKIC1Db3EV2TjV7c2tp67QTGuTP/KnDmTUhhqjwQF+H9xUOl4Pnj75Ch93C5KiJGPxlyuuFCPQLYHxEKkcbcvln0XvcNu6mCx7riqx4th+t4p1tJTx62+Q+60s8VJ03qc3Ly+OJJ54gLCyMP/zhD4SHh1NdXc0f/vAHdu3aJSdqIfqI4rCiWC2oDBFYbAG4WtuoMt1Ci3EWhrAAgvvHTWIxwFisVg6VljNKo2JKmvQu9LT169dzzTXX8MYbb3zjz2+//fY+jkgI8V1sdheHTtSTfbya4jNtqFUqJiSZmJUew4QkExp1/+s96lbcvJr7D6o7axlvSiUiQJ4KXoyYoCgaupo4WHeEcaYUJkdPvKBx/LQarrs0iZfX57Mnt4ZZ6bEejlR82XmT2scee4wFCxZQXV3NSy+9xMSJE3nooYeYOHEi69ev76sYhRiy3JZmHPlbsRfuwGUcRXHM/bS3R6NP/AuGYJWslxUXZc+JEhS3wvWxTvQhMk3N0yoqKgAoLi7+2s86OzslqRWin1AUhbKaDrJzqtlfUIfV7iLKGMB1lyYxIy2aMEP//n5ce/JD8ptPkBSSwLBgSZw8IS1iDC32Vv5ZtJaRoSMwXeCNgsljIvn0YCXvZ59i8thI9P5SiNFbzntkOzo6+OlPf4rL5WLevHls2rSJJ554giuvvLKv4hNiSHI1ncZ+fBPO0gMoipvW4MnUaOejuMFsVgDJZsXFqWlpo6i6lmlaSJYWPl7xReu7P/7xj1/7WWZmZo/H+fOf/0xLSwt/+tOfKCwsZNmyZVgsFrKysnjsscfQarVUV1ezdOlSmpqaSExM5OmnnyYoKIj29nbuv/9+KisrCQ8PZ/ny5ZjNZux2Ow899BB5eXno9XqefvppkpKSPPbehRgILN0OPs+rJTunmjMNnfhr1UweE8ms9FiS40N9XvSpJ3ZU7mbnmb1EB0Yy2iifYU9Rq9RMMqezu2Y/L+W8wf9N/u8LKrqlUqm4aW4yT646zOb9p/nhrJFeiFYAnHcORUDA2SbRGo0Gm83Gq6++KgmtEF6iKG4UtxuA7tLj2MuOUhtyBceHP03dqF+ijx9FQN/2bReDlKIo7Co8QaBazaJE0Mqd4z6nKD1b//7555/zwQcfnPvvpUuX8vDDD7NlyxYURWHNmjXA2ZlVixcvZvPmzaSlpfHiiy8CsHz5crKysti0aRPXX389Tz75JHC2d25AQACbNm3iwQcf5IEHHvDwOxSif3IrCnllTby0Lo/fvLCbtz47iZ9WzZJ5KTz7y0u4Y2Eqo4eFDYiENrexkLUnPyJMF0KGOW1AxDyQGPyDGBeeQk1nHWtPfnjB4yTFhTJlbCSb95+mud3qwQjFl503qf3ySTc8PJyxY8d6PSAhhhrFacNesI3ONQ/Snn+AvDwVB9vmkZv4HB3JNxMSY8bf39dRisHkRHUttW3tXKFzEDVSntL6Qk8uPltbW3nuuef42c9+BkBVVRVWq5WMjAwAFi1axObNm3E4HBw8eJB58+Z95XWAHTt2cNVVVwGwcOFCdu3ahcPhYMeOHVx99dUATJ48mZaWFqqrqz3+PoXoLxrbulmXfYr/fWkvz75znILyZi6dGMfvfzqFh2+dzKUT4wgcQD26K9ureC1vFQFaPZOjMlGr+t9a38FgWHAc0YGR7Kr6nNyGC69Yf92cJNwKfLDrlAejE1923k+v2+2mra0NRVFQFOXc//+C9NgT4sK5u1px5H+GvWA72Cx0ByZSXhaCzajCaNYjN1yFN9idTnafKMGs0TBPWvj0a4888gj33XcfNTU1ANTX12M2/7tCtdlspq6ujpaWFgwGA1qt9iuv/+c2Wq0Wg8FAc3PzN45VW1tLbGzP1+OZTIZ/bRt8cW90EJNjc5avjoPD6WJfbi2fHKjg+MkGADKSzdxxzQimpUXjp/V9lcULOTbNXa28sPs11CoVcxKnEeTfvyoxX4zAoP63fnnaiEw+KdnJm4VvsTzhUYyBvc9/zOZgrpk9kve2l3DdFSmMGnZhY4hvd96ktri4mGnTpp1LZKdOnXruZ9JjT4iL07XxWdzNlbQFT6LKNB9MyQQEquh/X+diMNlTUEKnzcbiMGnh420TJ078xieyiqJgtZ5/Ctq7775LTEwM06dP5/333z+33X9SqVTf+vq3UX9L9dZve/3bNDVZMJkMNDR09Gq7ocJsDpZjg2+Ow5l6C7tyqtmXX4el24EpRMfVMxOZOT6aiNCz63haW7r6NKZvciHHxuay88cDy+lydDMtehIqh4Yuh81LEfatwCAdXZ39871MjBjP5zUHeXLH/8fSrF9e0JPxy9Jj+WR/BS+/d5z/WfzN54dvI98noFarzt1M/SbnTWqLioo8HtAXelL4QojBQlHcuCrzcBTtQJl2N7WNATQF3oo1KBR9RCQBMr14wKtpaaOty/cXSefjUhT2FJxktEbF5HHSwsfbNmzYcMHbbty4kYaGBq655hra2tro6upCpVLR2Nh47ncaGhqIjIwkPDwci8WCy+VCo9Gcex0gMjKSxsZGoqOjcTqdWCwWwsLCiIyMpKGhgREjRnxlLCEGqi6rkwOFdWTnVFNW04FWo2JisplZ6TGkjggfFD1C3YqbF4+9TkN3IxPN4wnThfo6pCEjVBdCijGZwpZiPjq1hWuSFvR6jEC9lh9eksiqT4o5erKRzNFyY9mTfJI5flH44tJLLwXOFr544oknyMjI4MEHH2TNmjUsXrzYF6EJ4VGK046j5HMcOVtwt1bj9A+jaHc9tsBEQqKTCfH9zCfhAaV19Ww4MjD6duvUaq6LckkLnz4QFxd3wdt+ubft+++/z4EDB/jjH//IwoULOXz4MJMmTWLdunXMnj0bPz8/srKy2LhxI1ddddW51wHmzJnDunXr+NnPfsbGjRvJysrCz8+POXPmsH79erKysjh06BA6na5XU4+F6A8URaG4spXsnBoOFdVjd7qJNwfx47nJTE+LxhDg5+sQPWpV4RpK2spICRtFTFCUr8MZchJChtHQ3cSnFTsYEz6alAuoNj07I5ath8/w7vYSJiSZ0GpkLbSn9HlS++XCF0VFRd9Y+OL555+XpFYMeE5LK5bVvwVrB9aA4VRF3oMlfCqGEC2Bqp5VPhX9X2OHhS3H84nUaLgj2o5fYP+eZRIXHoC/Qe6mDFRPP/00y5Yto7Ozk9TUVJYsWQLAo48+ygMPPMBLL71ETEwMzz77LAC//vWveeCBB7jyyisJDg7m6aefBuAnP/kJjzzyCFdeeSX+/v785S9/8dl7EqK32iw29uTVkn28mrqWbvT+GqanRTM7PZaE6OBBWQV4U9lnHKg9QrwhlqSwBF+HMySpVComRo5nV9VeXs9dxSPTlmLwD+rVGBq1mhsvT2b5u8fZfqSK700e5qVoh54+v/rqaeGL3jrfHGvRO7IQ/cLZGyqx1ZSgH3sZtXXQZppLW2AG6qg0wg0qLqx1twAwGnt34ugLXTY7H+/KwR/47SgNqZNG+Dok0Vf6sCT5okWLWLRoEQBjxoxh7dq1X/uduLg4Vq1a9bXXw8LCePnll7/2uk6n489//rPngxXCS1xuNzmlTWQfryGntAm3ojA6PpSFMxLISolE5z94b9YdrD3KhrItmPRGxpukE4kv+am1ZJonsK/2MK/lreLXE+/p9U2U8SPDGZdg5MM9ZYNyRoGv9GlS25vCF73V1GTB7ZanXxdLFqL3nqIouKryseduwVWZi1sbxLHCDIJCjCiRi85e+zq6sLf4OtKBy2gMoqWl09dhfIXL7WbdoaO0d3ZzV4iD6ISIfhfjN+mPx3IgUuudyGo2IbyvtrmL7Jxq9ubW0tZpJzTIn3lThzFrQizR4YOn6u+3KW0tZ1XhOxj8gsiKzBiUT6EHGqM+jFFhiZxsPcWWiu3MT7i8V9urVCpuvDyZR984wIa95dw0N9lLkQ4tfZrU9qbwhRADgauuBGv2StzNZ3D5hVITfi2NYXMJDvMnIgJaJJEdtLKLTnKmqYWr9S6mTjZKaxwhhPAQm93FoRP1ZB+vpvhMG2qViglJJmalxzB+5NBZh9jQ1ciLx19Ho9YyJWoiGvXgfRo90IwKTaSxu5mPT21hjDGZhNDeTSOOjzQwa0Isnx0+w2UT44gaAjdovK1Pk9reFL4Qor9SrBYUezeqYDNttmDc3WqqzHfRET6NoBA/QobGuXZIy6us4nhFJZO0KhZOCkbr37/X0QohRH+nKArltR1kH69mX0EdVruLKGMA184ZyczxMYQZhlZxuy5HN8uPrsDhdjIjZjJ6rd7XIYkvUalUZJrHs6t6HytyV/LotKW9/jf60axE9hfW8e6OUn65aLyXIh06+sWV2LcVvhCiP3G31mDP/QRH8R6cEeMoNP+Grq5YAkc+TpBBhaxEHhqqW1rZnl/EMI2a28appIqwEEJcBEu3g8/zasnOqeZMQyf+WjVZYyKZNSGG0cPChuR0W6fbyd+OvUKbrZ2syHSC/aVuTH+k0+rIMKdxsO4ob+Sv5mcTbu/V32uoQccPpo3gg12nOHG6hZThRi9GO/j5LKntSeELIfoDZ+1J7Mc+xnX6GIpKS1PITKr95uGnVThb42zonXCHqo5uKx8fySFYpebnw12ExsoJSAghesutKBSUN5N9vIajJxtwuhQSY4JZMi+FKWOjCNT3i2cuPqEoCq/n/ZPTHVWMC0/BHBjh65DEeZgDTCSGDCevqYhdVXuZEz+zV9vPmzyMnceqeHtbCQ/fmoV6CN7E8ZSh+60hxHkoLieo1KjUarrL8nHVlFJr/BH1oXMJMoVgkE/OkON0udhw5DgOh4N7TA7ix0jTdCGE6I3Gtm5259SwJ7eGpnYbQXotl06MY/aEWOIj5WkkwAelH5PTmE9iyHBGhEi7l4FgjDGZZmsL753cQHJYErGG6B5v6++n4do5Sbz6UQH78muZkRbjxUgHN7k0F+JLFFsn9sLtOPK24ki/hQrXFJo7foBf4kIMof6EynrZIUlRFD7NLaC+vYMfB7mYkC7NmYQQoiccThcHCuvIzqmhoKwZgNQEI9dfNoqJyWb8tHJi/UJ21T4+O72LyAAzY4xSEXegOLu+Np3s6n28lPMGD0+9H39Nz9v0TE2NYuuhSt7beYpJKZHo/KQg2IWQpFYIwN1ejz13C44T2eC0YzGMo7zciGICU5QUZxjqDp+qoLimjtn+MHdSCJpB3A9RCCE84Uy9hV051ewvqKejy44pRMdVMxO4ZEIMEaEBvg6v3ylsKuadEx8Q6h9MZuT4IbmWeCAL8NMzISKVIw05/LPwXW5PW9zjbdX/avHzp38e4ZMDp7lqZqIXIx28JKkVQ56iKHRufBalo4Hm4OlUh8xHGzGcQKn/I4Cy+kb2FJcwWqtm8QQt/kH+vg5JCCH6JUu3gwOFdezJraGspgOtRsW0tBimjDGTOiIctbQ++0Y1nXWsyH0TnUbH5MiJqFXy9Hogig6KZFh3HIfqjzGuJoUpMZN6vO3oYWFMSjGzcd9pZqXHDrlq354gSa0YchS3C2fZIRwnsnHN+BVVtXraQu7BHh5BoCmUIPlUiH9ptnSy+XguERoN9yS5CTQH+TokIYToV5wuN3llzezNreFYSSNOl0K8OYib5iYzfVwUI0eYaGjo8HWY/Va7rYO/HlmBgsLUqEz8tXLjdCAbZ0qhxdbK6hPvkRA6gsheFPq6/tIkjp1s5INdp7j9B2O9GOXgJJfvYshQ7F04inZiz9uKYmnCrouiaG8LzqBYQmKS0MuNUfElVoeDjw4fR+NWuCfagXmkVKAUQogvnK7rYG9eLfvya2nvchAc6MelE+OYmRbD8CiDTJ/tAbvTzl+PrqDT0cmU6EkE+Qf6OiRxkdQqNZPM6eyu2c/LOSt5aMp9aNQ9W7IUaQxk7qR4Pj1YydxJ8QyPkmaRvSFJrRgS3JYmOt99CBxWOoPGciZqCTZTOoZgyWTF17kVhc3H8mjv6uKnIU5Gj5eEVggh2jvt7MuvZU9eLZX1FjRqFRmjIpgxPprxI01oNXJO7Sm34ubP2S9R21VPesQ4wvVhvg5JeEiQfyDjTCnkNBbwTvE6Fo+5tsfbXjUzgT25NbyzrYT7b8qQm0O9IEmtGLRcdSW4WqpQEudQ12TCGjKfRv0kNBEj0Ouh53XpxFCz58RJKhqb+IHOzYxJRlSyDkwIMUQ5nG6OlzSyJ7eG3FPNuJWzPWVv/t5opqZGYQiQs+mFeLvoA3Lri0gOG0mcQdq4DDbxhlgaupvYU72fcaYU0s1pPdouSO/HNZcksnrrSXJKm0gfJTfVe0qSWjGoKG43zvLD2HO34K4rweUfxtGq2SgqLSEjFhEk517xHQqrqjlSdpoMrYofZgah1cvXpBBiaFEUhbKaDvbk1XCgoI5Oq5Mwgz/zpgxjxvgY4iKkvsDF2Hp6J3tq9jM8NJZRoVLpdrCaYEqlzdbOmwXv8PDUYRj1oT3a7tKJcXx2pIo120tITTDip5WOCz0hV2ti0HBWFWDd9QZKRwMOXSRVpp/QYpxFmFGDWq34OjwxANS2tvFZXiFxGg13jFXQh0k7JyHE0NHSYWNvXg1782qpaerCT6smc7SZmWnRpCZI9WJPOFqfywclH2PUhZEVl461y+HrkISXaNQaJkWms6f6ACty3uB/Jt/bo8rWWo2aH88dxfJ3c3jqrWP86trxmPsg3oFOkloxoLktTeB2oQRF0mI14naHcSbqx1hNmQQZ1ITK+Vf0kMVqY8OR4wSi5mfxTkLjw30dkhBCeJ3N4eJocQN7cmsoKG9BAUbFh3Lr/BQmj4kiUGareITVaeVwXQ5rij8gUBvI5KgMad0zBAT7Gxgbnkx+8wnWlW5k0aiFPdpuQlIEv/hhGq9uKODJfxzm9z+bgdTFPj/5phIDkquhDHvOFpynDmCPmkR+2L3Y7fEYkh4mIAAMvg5QDChOl4sNR45jszv4L6OD4WNkDYsQYvBSFIWTZ9rYk1vDwaJ6rHYXphA9C2ckMGN8NFFGqcLrCV2OLg7VHedQ3VHK2k/jVtzoNTqmRmWiVcsl+FAxPDie+u4mPju9i1TjaMaYRvdou6wxkRiDdTz/Xg5Ln9/Ff/1oPKOHSUGxbyOfKDGgOCtzsR/bgKvmBG6NnvrQedTpvk9goEJoz5YqCPEViqKwLb+IurZ2bghwkTExXApDCSEGpYbWbvbm1bI3r4aGVis6Pw1ZY8zMTIth9PAw1FJp9aJ12C0crD3CobrjnO44g4KCv9qPmMBI4gyxmPRGqWg7xKhUKiaa09hV9Tmv5/+TR6YuJVjXs8cvSXGhPLQkixfez+Xpt4/y0x+MZdq4aC9HPDBJUiv6PcVhA60foKar/ASupkaqwhfTEj6HoLAAgmX9vLgIR8tPU1hVw0wtfG9SMBp/+YMSQgwe3TYnh07Usze3lhOVraiAMSOMXD0zkUkpZvT+cil4sVptbRyoOcLh+mOcsdQAoNP4E2eIYZghljBdqCSyQ5xWrSUzcgKf1xzi1bx/cF/mz3v8NxEZFsBTv5rFY69+zisfFdDQ2s3CGQnyN/Uf5JtM9FvuzhYc+Z9hL9yOfeJdnLJl0tF9Df5J12II1hAin2VxkSoamthddJIkjZqb0zXognW+DkkIIS6a261QeLqFvbk1HC5uwO5wE7XxNHAAACAASURBVGUM4EezRzJjXDSmUCmCd7Eau5rYV3uYo/U51HbVAxCg0TMiOJ54Qywh/sGSdIivCNOFkhw2kuLWUjaXf8aCxCt6vK0h0J/f3JjBGxuL+CC7jPrWbm6dP0Z6Q3+JJLWi33E1VmDP3YKzdD+K201b8CQqKyNQm8AUJUmH8IyWzi42Hcvl/2fvzOOjKu/9/z5n9jWZTPaFAEkIhH1RWUS0LhUUFRFrXeiirWtte39al2uv1632tlx3UdveLoptXapYN9xBZN9DIBACYUsyWWYmmcx+5pzz+yMQiQgCTvbzfr3ymuRkzjPPfHNO5vk8382l03HTUBl7pha/rqGh0bep94YOhRd78LfFsJj0TB2ZzdTRORTlOjWR9S3xhBpZXb+eTU1baY54AbDqLQxxDiLfnovDqFX00Dg+RSmDaY76eLfmI4a7ShiSWnjC5+p1IjdcPIJMl4W3vqjBF4hx65xRWM1av0rQRK1GL0NVFcIfPI0SbaPJeS4e5wWY3JnYtJJvGkkkJiV4Z8MWkBV+mpUgq9jd01PS0NDQOCVCUYm1lY2s3FrP7roAggCjhrj53neKGV+SrvW4/BaoqkptsJ5V9evZ0lSBP9YCgN1goyhlCHm2bOxGrWevxokjCALj00ezvG4VL1T8jfsn/wqL/sQjJwRB4NIzh5CZauHP71XyyEsb+MW8sWSkWrpw1n0DTdRq9ChqIoZUtZLE7tUkzvx/7K81EUz9GZI5C5vLquXLdiGBSJR3N24hEI709FS+EUEUUJXk9RqWVZWELPMDh8zw0VrrHg0Njb6FrChU7PGxosLD5l3NJGSFvAwbV55TzOSRWaTataimU0VVVfYFDrCqfj3lzdsIxNsAcBjsDEstIteWjdWgCQiNU8ekNzIuYzRrGzby54qXuWXsj086imLKqGzSnCaeeWMrj7y4ntuvGMvQXGcXzbhvoIlajR5BCbcgbf+U+LZPIRYkahnMzjUBVFsmjrwhWLQIqS4lKkm8tW4TwXCEUbpEr6/2azDokCQ5qWMOt8qcNcmFqNfyUTQ0NPoGBxqDrNhaz+rtDQRCcewWA2ePy2Xa6BwGZdm18OJTRFEVdrfUsKp+Pdu8OwhKIQBSjA6Gu0rIs2dj0mkbBRrJI92SxlBnIdt9O1l6cAXnFJx50mOUDnJx73UTeeK1Lfzu7xv5yeyRTCzN6ILZ9g00UavR7SgtHkKv34eqyATs46lzz0ROG4bDpn0YdwcJWebtDVtoCYX5gSPBjMlpvV7YuVw2/P5QT09DQ0NDo9sJhOOs2dbAiop69jcE0YkCY4rcTBudw5git1Yo5hSRFZkq/25W169jm28nkUQUAYEUk5OytFJybVkYdVruU9JRFAyNLRjqmjDVNWNqaiWS6yZ02giwDSxBVuoqxhv182b1O5SkDiXfkXvSY+S4bfzn/Ek8/Xo5C9/cyrxzivnu6QUDcoNLE7UaXY6qqsi121DampEHn029N5u46zK81tMxpmdj0j4zug1VVfmgfBt1/hYut8hMPz211wtaDQ0NjYFGQlbYUt3Miq0etu7xIisqhdkOrj6vhDPKsnBYtQ/OUyGhJNjm3ckazwZ2+KqIyXFERFLNKRSnDCHbloVB1JbGSUNVMfjaMNQ1YahtwuLxYW0MoEsoAChmI0JWBvZNe8jYsJu24iyUicOJDs2FASDKBEFgYuYYPq9bzfPlf+W/Jt9xShspTquRO78/nj+9W8mrn1XT1BLh6vNL0IkDa32n3bkaXYaaiJOoXk186wco/lokSzabas9BpxdxDrkEu5Yv262oqsrnlVVUexo51wizTnOi1/oTamhoaPQKVFVlr6eNFVvrWbO9gVA0QYrNyPmTCpg6Opv8DK2y7qkQlyW2Nm9nTf0Gqlp2IykSoiCSZkpluCuHLGsmelFbkHxrVBV9WwRDbROGukbM9T5sDa3oYwkAFL0OIT8H05mjcRQNwzF0GIbMTARBQPL78Hy6BL74Aucry4i4bPgnlhAZW4Ji6t+Vfc16M2PdI9nQtIUXt7/KDaOvPaVxjAYdN106kn+lmnl/9X6aW6PcdOlILKaBs84bOO9Uo1tJ7NtEZNlfIBogZingYOZPCaZNxu0UEITkFfzROHE27d3P5n0HmKgX+N4EE0atpLSGhoZGj+Nvi7F6m4cVFR7qmkPodSIThqUzdVQOI4e4Bpy3JRlEEzG2NFWwxrOB6pYaZFVGJ+hwm13k2XPItKSj04Tst0IMxzDWNWOoa8RU58XW0IoxFANAFQXUnEyMEyfiGFqMo6gUU24egu7rbW5wpVEw92rc1/+Yne9+gPfjD8j9eDPysq34Rg0ieFoZifT+23Yvy5bBoGg+m5rKWVW3jim5p53SOKIgMO/sYjJTLbz0QRW/fXkjv5g3FpdjYOSDa6JWI2nI/joEUYdiy8IbzSChH0JtzkwSaSOw2QUcPT3BAczOOg/Ld+yiRC/y4zEiZpdWuVFDQ0Ojp4hLMht3NbFyq4dte32oKhTnpTD/wlJOH56p9Z08BcJShI2NW1jn2cSewD4UVcEg6smwuMm355JuSUMUtA2CU0GIS5jqfehrGzHVNWNtaMHc2t45QQXUjDQMI8qwDS3GWTQM86BCRMPJb5yLBgPp02aQPm0GoT3VeD58l/RNW8jYVENrYTptk4YTKcmHfrjRU5Y2DH+0hX/ufJOhqYPJsp56fvGMcXm4nWYWLq7g4RfX8/MrxjAoq/+vwgVVVfuF28zrDaIkseXHQCUjw0FTU9sJP19VVeS6SuLlS5APlBPLmco2581IEqSkqBgHsDOwtxQ3OuD1sXjdJrJFkTtLVdyFfW+3s7fYsj+g2TI5iGYTKWee0dPT6FG83iBut/2kPjMGEl/9PFVVlV0HW1lZUc+6HY1EYjJup4kpo3KYNiqbrDRrD8626zjZdcXJEIyHWNewkfUNm9kXOIiKikE0HBKyObjNab26YI7VZiJ8yLvZWxASMoYGP4a6Jox1zVg9fiy+IIeD7JRUB7pBg7AOLSJlaCmWIUPRWZKzUf5110oiEKDh0yW0LVuG2BYimmLBP6GY8LhSFEv/WmSGpTDL69bgNrv4zzP+A/0R+d2nch8daAzyxGtbCMcS3HzpSMYUpSd7yt2KKAq43cdOw9A8tRqnjLRnLfFNb6N4DyAbnHjSLqfJfC4Oh8oxIkw0upmmQBvvbNxCiiBy2yAZd6Grp6ekoaGhMaBobo2wssLDygoPjf4IJoOOiaUZTBuVTWmhC7EXi67eSGsswFpPu5A9GKwDwKQzkmfPId+ei8uU0quFbK9CUTA0BzDUNmKsa8bi8WNtbkOUDxVyslkQC/KxnD6UlOJSrEOK0Tu7txeq3ukk77IrUS++HN/6NTR/vIScz7YiL9+Oryyf0OllSJn9Y21jNVgZ7R7O5uZt/HPnm1w7Yt63Gq8g08598yfx5OtbePL1cq49fxjnTMhP0mx7H5qo1Tgp1FgIjFZAILS/hkRIoTb9egJpU7CnGHH2v4iQPksgEuWt9ZsxKHBrdpz84X17h05DY6DxzDPP8P777wMwY8YMfvWrX7Fy5UoeffRRYrEYM2fO5Je//CUAlZWV3HfffQSDQSZNmsQDDzyAXq+nrq6OO++8E6/Xy5AhQ1iwYAE2m41AIMAdd9zBgQMHSEtL44knniAjY2C10+hKovEEH6/dzweratixvwWA4YNSmT11MBNLMzBrRfpOCm/ExxrPBjY2lFMfbgDArDMzyJFPvj2XFKNDE7LfhKqibwkdqkTciKXeh60xgO5QD3jFaEAoyMU8ajzOolLsRSXo09y9xq6CXo978jTck6cR2beX+o/ewb1+Exnl+wgUpBGYNIJIaUGfD03OtefQGPGyqn4dI93DGZ85+luN53KYuPuaCbzw1jZe+rCKxpYI884p7pebaVr4sUYnjhXeoLR6iG/9EKnqC6Kn3U51cAzhNgmLTY/N3v9ujGTQk2GeUUnitdXraQtFuMUVZ8Jp6Qhi3/07aSGzyUOzZXLo6vDjlStX8tRTT/Hiiy8iCAI33HAD8+bNY8GCBbz00kvk5ORw4403Mn/+fGbMmMHFF1/Mww8/zLhx47j33nsZNWoUV199NTfeeCOXXHIJF110Ec8++yzhcJg777yTBx98kOzsbH7605+yePFili5dyhNPPHFSc9TCjzsTk2R217ayssLD+p2NxCWFTJeFaaOymTIqm/SUgVnL4FTDjxtCTaz2rGdT41aaIs0AWPUWMq0Z5NtycJr6fo5gV4Yf64KR9kJOtV8WcjJEJQBUnQ5yszAOHtxRidiYnY3Qw4LwZK8VORikYemHBJZ+htjSRsxhxj++mNCEYShWcxfOtGuRFZnldatJKAl+UHYV4zJHf+swfkVR+cfHu/hk40EmDsvghtllmAx9K6yyV4Yfn8zus0bPoaoqsqcKqXwJiX2bUQUdXudU6g5mYnBDepZWyKI3kpBl3tmwhZZgiPm2BOMnuvu0oNXQGIhkZGRw9913YzxUmKCoqIi9e/dSWFhIQUEBALNnz2bJkiUUFxcTjUYZN24cAJdffjlPPfUU8+bNY926dTz77LMdx6+99lruvPNOli5dyssvvwzAxRdfzIMPPogkSRgM2v/146GqKi3BOB5viHpfGI833PHoDUQBsJh0TC7L5qLpQ0m3GXqNp6u3o6oqdUEPqzzr2NK0DV/UD4DNYKXIOZg8ew52o62HZ9k7EaNxjHXN6OuaMNc1Y/W0Ygq2X4+qIKBmpWMcN7a9kNPQUsz5BQj6vh8toLPbyb34cnJmXUbLpnU0ffQ+2Z9XoKzYjm9EHqHTyojnuHt6mieNTtQxKXMcaxs28ceKlxiaMpjbp/4AA6d+/YuiwDUXDCPTZeGfn+zid3/fxO1XjCGlH3XC6PYreuXKlXzxxRe8+eabHbvP77zzzlG7z8uWLWPGjBndPT2NI1FlIh8/jyxJeFyX0pR6Lta0FOx9//9gv0VVVT4s30atv4XLLTJnTXYh6vt2KI6GxkCkpKSk4/u9e/fy3nvvcd1113UKEc7MzKShoYHGxsZOxzMyMmhoaMDv92O329EfWrwePg50Okev12O32/H5fGRlZZ3wHA/vmGdk9H2P2VeJSzJ1zSEONrZxsDFIbWOQg41t1DYFicTkjueZjTryMu2MKkonL9PO4BwHE4Zn9TkPSFdzrGtEVVV2+/bxWc1K1teW44+2ApBiclCWMYzC1Dxsxv5ZQOswVtvJtVsR4gn09V7EffXoDzZjrvdh9h0RfZPhwjyyjJRhw0gvG4mjqAidqe+0dDnV/yeZF57HsAvPI7hvH7sXv4GwfDXpFQcI5acRnjKS+OihoOs76yGrzcSslHOobN5FVfMe/mPJQ5xfNJ1rxs7BrD/1v+fVs8oYOsjFgpc38OjLG7n/+jMYlN29edJdRbfLk5PZfdZEbfeixkK0rPqEUMUK4tPvZd8BI+G0/0fCmo0jVcuX7Qt8vqOKXZ5GvmOEWac50Wt5WxoafZpdu3Zx4403ctddd6HX66mpqen0e0EQ+LosouMdPxbiSYYe9vXwY1VVaQ3FO3lb632hdq9ra5Qjred2mshOszJ1VA7ZaVZy3Fay06y4HKajbBpoCQNdW/W3L/FVOyiqQk3rPlbVr2NrcyVBqV2QpRgdDHcVk2vL+XLRLkFY6l3VgZPJN4YfywrGxvZKxIZDlYitzUGEQ/e24rShG1SAZVoRKUWlWIcUobN96c2TAF8gDsS79o0kiaTcM9Y0cq++Afmy79O47BOUzz7B9tpy4u+txTduKKGJpSj2vrNRMsQ2mGxjNjtaq/igehnL965lbslszsieeMpRIEVZdn71/fE8+Xo5dzy1nNvmjGLE4LQkzzz59Lrw45PZfdboHpRAI/GKj5B2fA6JGEHbCHatC6F3GrHlD+rp6WmcIBtr9rF57wEm6AW+N96IsR+FlGhoDEQ2bNjA7bffzr333stFF13E2rVraW5u7vh9Y2MjmZmZZGVldTre1NREZmYmaWlpBINBZFlGp9N1HIf2z9nm5mays7NJJBIEg0FSU1O7/T12B1JCpsEf6SRePb4QHl+4k9fVaBDJdlkZmutk6qhsctw2stPaxavJqHlevw2KqlDl281qz3oqvDuIJCIICKSYnJS5hpFrz8aoG+CfWaqK3hvAWNeEobYJi8ePrTHwZSViiwmxIA/zhDNwDi3FXlSMPrV/VP3tCnRWGzkzLyH7uxfTWr6Jxo/eI3tFJcqqHfhLcwmeVkY8v28Ux7MYzEwrPI0DzfVs9VbyUuWrfHbgC64bcSX5jtxTGnNIjpP75k/kydfKeezVLfxw5nCmjc5J8sy7lx5z45zI7vPJcDzlrnFsYp4aal+5GxBoc59Js/tizDlDGdx38+t7FS5X9+T/bN17kOU7djHCZOA/plpJyel/4YDdZcuBgGbLJNDFTbjr6+u59dZbefzxx5kyZQoAY8eOpaamhn379pGfn88777zD3LlzycvLw2QysWHDBiZOnMjixYs566yzMBgMTJo0iffee4/Zs2d3HIf2ehaLFy/mpptu4r333mPSpEl9Op9WVVUCYalTrqvHF6beG6K5NcqRTmuXo93rOmVk9iGva7t4dTlN/bIiaE8hKzLbfDvZXLWFzfXbickxBARcphSKUwaTbcvCIA7QaCJVRR8IY9xTi35PHeZ6H7aGVvTxBACKQY+Qn4PprDE4hg7DUTQMQ0aGlp99CgiiSOq4iaSOm0i0vhbPx+/jWrUGd2UtwewUWieVEikbgqrv/RtXbksaM/KmUhPYx66WGn677klOz57AFSWXYDWcfCG69BQL91w7kYWLt/J/71bS6I9w2fQhffY665Hqx1+3+7xw4UL++te/ArB48WLWrFnDo48+esJjatWPTwxVkUnUbECNthEvPJfagyBvW4LXMQWr20VGhlYZNVl0V5XZA14fi9dtIlsUubNUxV2Y0uWv2d1oFXuTh2bL5NDV1Y8ffvhh/vWvfzFo0JfRMldddRWDBw/uKKo4Y8YM7rnnHgRBYMeOHdx3332EQiHKysp49NFHMRqN1NbWcvfdd+P1esnJyeGxxx4jJSWFlpYW7r77bg4cOIDD4WDBggXk559c/8KeCD9OyEqH19VzKFT4sIgNxxIdzzPoxQ4v6+FQ4Ry3jaw0S7e10xlo4ceqqtIc8VHp20mFdydV/mokRUIniLhMqeTac8i2ZqDvr0JWltFFJcRoHDEWR4jEEKJxxOjhx/YvQyDUXok43B4SrIoiak4mxsLCQ5WISzDl5iHoer/ISjbddc8o0QhNyz/D/+nHiE0+4lYj/rFDCE0cjuzsnZu+Xw1VjyVibPdVUR9uwKwzccnQC5mePwVROPlcwYSs8NIHO1leXs/ksix+NGsEhl5Yj+Wbwo+7XdTW19czZ86cTrvPsViMCy64gBdffJH8/HxuvPFG5s6dy8yZM094XE3UHh81HkHa8Tnxig9Rg15itsFsznwQgxGczi/bemkL3uTRHbZsbgvy2up12BS4s1Amf3j/DEXSrsvkodkyOXS1qO0LdJWoVVWVtojUydt6WLw2t0RRjli2pNqNnbythwVsWoq5x72u/V3UKqpCbVs923ztAnZ/20EiifaKu3pBR5rZRZ4th8EZecQiiW8YrRdwWJTG4ohRCTEag2gcMRJFiMYRYnF0kXbBqotK6GIS+lgCXaz9e11COe7wqiigWsxgt2EYNAjb0CIGTRxP1JGOaBjgodeH6O57RlVV2raV0/Dheyjbd6KKAi3F2QRPLyNWkAm9yGN5rPxrf7SVrd7tBKUQWdZMrh1+BUNTB5/0+Kqq8t7qffxr2R6G5adw29wx2C29K3qn14nak919PlE0UXtspOrVRJf/FaQoIVsptfaZRN3jsNnFo+5XbcGbPLralm2RKK+sWocqSfwiS2L4uPQue62eRrsuk4dmy+SgidpvL2oTskJTy5e5rvXe9jxXjzdMKPqlCNLrRLLTLO2eV7eNnDQr2YfEq8XUe71+/U3USrJETWAf27w7qfbvoTZUj6S0/52MohGn0UG6xUW62Y3DaO9Yw3VlL9ZOyAq6Q17SY4rSwx7TmIQ+JqGLJdBH2x91Cfm4w6uigGo2oVrMCBYzosWCYLWis1gRrVb0Vht6mw2D1Y7B5kBvs6Oz2dBZrYgWK4LReNS6tr9dI9+WnrRHvLGB+o/fI7JyFWI0TijDSeukYYRHDUU19Pz/mePdR6qqsr+tlqqWaiQlwbiMUXxv2JxT6uW8ZnsD//duJW6niV9cOZYsV+8pqtXrRG1XoYnazsiNexBMNhKWLJqr9iFVLKHecSG4h2A5Tti9tuBNHl1py5gk8drq9QRCEW52xZh4Wka/7kWrXZfJQ7NlctBE7YmL2mBE6uRt/dLrGkE+4nM7xWbs8LRmH+F5dTvNiH3w/1tfFyxhKUJVy24qvVXsbqmhIdyEQrs30qI3k2J0kmFxk25xY9EfuxDHCYtaWenwgoqxOGIkjhCNQTR2KHS3PYxXF5MOidcE+sPiNHoColQQUC0mMJvAYjkkSi3torNDlNrRW+0YD4tSqw3RakVn/XpR+m3p69dIsukN9lBiMZpXLMP3yYeIDc1IZgP+MYMJThqBnNpz9XtO5D6SlASVvp3UBuvRiwZmDT6PcwedhU48uVD2XQdbePpfWwG4fe4YivN7R1qbJmoHEKqikNi3CWnrB8ieKsI5M9huvwFVBadT5URqgGgL3uTRVbZMyDKL12+i3tfCfLvM2VPS+n0vWu26TB6aLZODJmo7i9qErNDcGu3wttYfCh32eMMEI1LHOXqdQJbrsHA9HC7cLmCt5p73hiST3rBAPxlaYq1Uequo9FVRE9iPL+oHQEDAZrCSakohw+zGbXEdu1KxqrbnjbaGECIxxEgMoyKTCISPI0oPhfBKJyBKzUawmOGwp9TSLjhF2yFRarWjt9kx2OwYrO2eUvHQcwTT0e2Xepq+do10Nb3JHqqqEtyxHc9H76JsrQRUWoqyCJ5WRnRwdreHJp9MxENbPEh583Za4wHSzC6uGX4Fw9NKvvnEI2jwh3ni1S14AzFuuHgEp4848R7mXYUmagcI8R3LiG9+FzXQiGRKp9Z+IX7XdOypFk6m1oC24E0eXWFLVVV5f3MFuzwNzLHIzJ6cir6fLQS/Du26TB6aLZODJmrhpXe3sa8hyH5PgEZ/Z6+r02ro5G09/JieYumTXtdToTct0L+Kqqp4Qg1U+naxw7+LfYEDHf1iRUHEYbCTZk4l3eImzZTa2dOjKOgDYQy+NkR/Kzp/G4aWNowtYcytYfSxr8+f7RClZhNYLR2iVLRa0Fms6Gx29FZbuyC12TFYHehs1i89pSZzrxOl35befI30BL3VHvHmZjyfvE94xQrEcJSw207LxGGExxSjdlPhuZMN41dVlbqQh0rfLuJKnLK0YVw9/Apc5hNv3RaMSDzzr3KqDrYyd8ZQZk0u7NF7UBO1/Rgl3IJgSUFVBVo//Ttxzx5q7TMJuydgd+hOaRNJW/Amj66w5eeVVWzau59zjHDNZNuA6UWrXZfJQ7NlctBELfz0Nx+h14tkpFg6iddstxWbuXcVGOkJetMCXVZk9rUdZLt3J7v8uzkQrCUmt1ffNYh6HEYHbrOLDLMbp8mBPi6jbwmi8wXQ+VrRt7RhbAliao1gDEQQj1hvqaKI6nIipqdjyMjAlJmFJSMHkzMFndVGRkEG/oiKaDIhiP07quhk6U3XSG+gt9tDkeL4Vn2B95MPEGobSJj0+EYVEjptBIk0Z5e+9qnmpsuKzE5/NfvaDiIKIucNOouZQ84/4XZaUkLhL+9Vsnp7A9PH5HDdd0vR63rmPtZEbT9Ebt5LvPwDErvXEjztDqrbRiHFEljtuuPmy54IA3HBG4hEqDhQi6wcv3LhyWIyGYjFpG9+4gkSjsXZUedhgl7gxolGLGm9J3m/qxmI12VXodkyOWiiFpqaAmRkOHv1IrQn6ckFekyOs7ulpr2oU8se6kMNyGp7eK9JZyLFYCdPtpMTNWINxND7A+hbghhbQphbIxgi8U7jKRYTuF3o09MxZmRhzsrGmpWLKTMbvct1XLHa24VKT6LZpjN9xR6qqhKurqL+w3eQt1SAotI6NJO2ScOJFuV1SWjyty24FoqH2eqrxBf14zQ6uGrYHMZmjjqhc1VV5c3lNbyzci9lg13cctnoHkkX+SZR2//jFvsJqqog799CvPwD5PodKDozjc7z8DTkYE5TcToHXj+zZLCnsYkPt2wjnkgk/2YQBEjyntFIvcD1Y3QDStBqaGj0TkTN69ZraIsHqfLvZrtvJ3ta9tIU8aJLKDiDMpkRHVPDOtxhEUebhCXgw9Raiyh/uZGrCgJqqgPR7UY/pN3bas3KwZyZgzEzE521d/bu1NDoCQRBwFZSSnFJKZLfh+fTJajLvyD11c+JuGz4J5YQGVuCYuo9ESs2o5XJ2RPxhBrZ7tvJHypepChlMNeMmEeWNeO45wqCwOVnDSUj1cyLS3by6KIN/HzeGNJTvqUnLclontpejqqqCIKAKku0vXwHsqKjzvFdfK4Z2FKtJ5UveyIMFC+Ooiis3LWbDXv2ka7T8eP0OMVDbEndXUt12WhJsi1FgzhgQo6PZKBcl92BZsvkoHlqu65PbX+hq7xOqqrijfqp9O5k98Ft+Otq0PnbSA3KpARlUkMqKUEZa7hzbqtiNIDbhS49HWNGBubMHKxZOZiycjCkpSHou8bP0Ve8bz2BZpvO9GV7qIkEvjUraP7kQ4T9tcgGHb5RgwieVkYi/dtXD05mayxFVahuqWFPYB8A0/Mmc1nRrGMXgDuCyr0+nnmzAqNe5OfzxjA4u2vDro9ECz/uoyjhFqRtn5DYX074zPvZu19PvKkWxZ6Fw6nvsqJrA2HBG4zGeH/zVur8LYzRC/ygGDKGnHji/IkyKIShVAAAIABJREFUEGzZXWi2TB6aLZODJmo1UftNJGOBriYSxJoaaThYRd2+StrqD4DPj70tjjOoYEx0XvfITlu7tzUjA1NmJpasXKyZORgzsxDt9h4p8tKXhUpXo9mmM/3FHqE9u/F8+A6JTVsQZIXWwnTaJg0nUpIPpxjh0hX9niNSlArfDpoizdj0VuaWzOb07Anf+H+itjnEE69uoS0S58bZIxk/7Pie3mShido+huw9QHzrBySqV6MqMq2OiexO+xHmFMe3zpc9Efr7gnd/s5clWyqQpAQXmxPMGmfHnHrs/nrfhv5uy+5Es2Xy0GyZHDRRq4nab+JEFuiqqqKEQkhNjUhNTcSbGok1NRCsP4jU1Ig+EObI5WVCJxB2msGdijUrF1fuEGxZuZizstGnpyMael8kT38RKl2BZpvO9Dd7JAIBGj77gLZlSxEDIaIpFvwTigmPK0WxnNy92hWi9jDNER8V3krCiQgFjjyuG3Elefac457TGorz1Otb2FvfxlXnlnD+aQVdMrcj0URtH0JuqCb81sOoopFGxwzqnd/FnJ6JsRs/o/rrgldRVdZV17C6eg8unY4fumKMGZuGzth1ucj91ZY9gWbL5KHZMjloolYTtd/E4QW6KsskfD6k5ibijY2HBGy7iJWaGlEikU7nhc0iLXYdrXYRyWrEZHGQ5sqmYPBocsomYnB2X7hfMuhvQiWZaLbpTH+1h5pI4N+wlqaP30eoOYCs1+Eryyd0ehlSpuuExuhKUQvtIck1rfupbq1BURXOyJ7IFcNmY9Ef26MWk2T++PZ2NlY1ce7EfL5/bkmXtmzTCkX1YtREHKl6FcgS0YLzONBUhJL+A1pSpmBzWdFqPyWHcCzOB+UV7G/2MVwncP1Qmazi7gmV0NDQ0NDo36iqihxsI+H1IXmbOwRrY6uPUG0dktcLR1bXFwUki4mgVaQpV4fHaafVoaPNpsOsN5MvuihyDGJa7khSM/PQOVO0VjgaGn0YQa8n7YyppJ0xlci+vdR/9A7u9ZvIKN9HoCCNwKQRREoLTjk0ORmIgkhR6mDy7Tls8+1ktWc9m5u2cknRTKbnTUYUjp6byaDjlstG8drSaj5Ye4Dmlgg3XjoSczf17v0qmqe2B1AiAaRtnxDf/ilE2wg5yqhIvwuTWcDh6JJK4CdMf/Pi1PpaeH/zVqLxON81ylw6ztJtlYP7my17Es2WyUOzZXLQPLUDx1OrxOPtnlafl4TP++X3Xh+Sr5mEz4cqdW7fJhiN6C0WVL2emM2E1yFQm6Ky2yVT51BQRQGDIpAfs1BIKsWuoQzNLcOeno1os/VI7mtX0V+9b8lAs01nBpI95GCQhqUfElj6GWJLGzGHGf/4YkIThqFYj06L62pP7VfxR1vY6q0kKIXItmZy7fB5DEktPObzP914kJc/qmJQpoPbrxiDy2FK+py08ONeRrxyKbGVi0BOELCPo9YxE9k9HKutd3yA9ZcFr6qqbKzZz4qqXTgEHfNTokyYkIa+G3eP+ostewOaLZOHZsvkoIna/iFqVUVBDgSOKVglrxclGDzqPMFsRjQaEQwGRJMZwWxCNJrAaKQpVc+BFJk6a4waoZWQ2F6F2CrrKIhZGKJLpzi9hMKcUszpGYjm3tUWI9kMJKFysmi26cxAtIeqKLRsWkfTR+9D9V4UnYhvRB6h08qI57g7ntfdohba19L72w5S1bIbSUkwPmM03xs2B4fp64Xllupmnn9rGzaLnl9cMZb8zGML0FNBE7U9jKqqyLXbEB0ZxIxZNO/cg7TzC+qdF2JMz+7WfNkToT8seKOSxEfl29jT2EyxTuSGQpnc0rRun0d/sGVvQbNl8tBsmRw0Uds3RK0SjSL5fCR8XiSfF8nbTNzbLlYTPi9KSwsc0a8VQNHrSFgMxE16YmY9MauBiEVH2KojbDUQtOmRDCCJIAkqCUFGQkFCISwmkIT28dISBvJjVoaYsijJGkFuVhEGtxvR0Ht6V3YHA1GonCiabToz0O0ROXgAz0fvElu7HlFK0JaTSrAkj/jQfPRFOYQj8R6Zl6QkqPTu5GCoHoNoYNbg8zh30FnoxKPzJPc3tPHEa1uIxmVuuWwUo4a6v2bEU0MTtT2EKkskqlcT3/oBiu8gbTnns8M2H0GAlBQ16f1lk0VfX/A2tAZ4b1M5wUiUc40Kc8eYsWb0TNP4vm7L3oRmy+Sh2TI5aKI2eaJWURUSSoK4IiHJEnE5TlxJIClxYok4UTlGNBEjrsSJyTHicpyYHCcmxRDagogtbegDQQyBCMZABHNbDEswjjUoYYrLnV9LgJBFpM2mo8361UcdAZtI3CAclQekVwQMqoBBFdEffjx8DBEDOgzosAgGCs3ZlOSUUTpiJK2yHqG3fuB3EwNdqBwPzTad0ezRjhwO0fT5p7SsWI5Y3whAwmIkMCid6NBcYkV5JJzdv7YNxNrY6q2kNR7AbXZx9fArGJ5WctTzfIEoT75eTm1TiGu/O4yzx+Ul5fU1UdsDxMuXEN/yHmokQMxSQK39QtrSJmNzGnoyB/yE6KsLXlVVKd9/kM8rq7AKItc6Ypw+wYXe3HO10PqqLXsjmi2Th2bL5KCJWvhsxxoS+jje1kC7yDwkOA+L0rgcR1IkJEUiLksklMShnxMklAQJRUZW27++DmNcwRFWcIRkHGEZR+jw9+2P9oiC+JWP/ZhRJGI3ErUYiJv1SFYDksWIbDW156lZrRj1Rgw6A0adEaPOiEFvxGQwY9QbMerMGPRGjHojJoMJo86IXm9Gp28Xp4JOB4ceBZ0ORPGYRZy0BXo7mh2OjWabzmj2OJpEawutFVsIV1UQ2rINMRgGIJJmo60wk1hRPrHCbFRT90SAqKpKbaieHb5dxBWJsrRSrh4+F5c5tdPzIrEEz71VQcUeHzMnD2LujCLEb1krQKt+3E0ogSYERzqyLNDmaSKmG8zB7Jkk3GXY7AKOnp5gPyaeSPBJRSVV9Q0M1oncUCBTUJqO0IVlxTU0NDQGOq/v+jdNYd9Rx0VB/PKLw98L6AQRUdBhFA1YRRO2Q2LVGpQOfcWxhNo9rJZgHMNXvKyqKCA77YiuVPSFaRjT0jGlZ2BxZ2NKz0CfloauOxq6a2hoaHQT+pRU3NNmMPyyi2lsDBCrPYhv60ao2Ippaw3iphoUUSCY4yI8JJt4UT6xnLQuq6QsCAL59lxyrFns8FdT6avi/lX/w/mDzmbmkHPRi+3S0mLS8/MrxvDyR7t4f/V+mlqi3HDRCIyGrotc0UTtt0BVVeT6HcTLlyDv30LrafeyOzACSZiPs1jAZILk1/7SOJLmtiDvbiqnNRRmhhGuHCXiyErp6WlpaGho9HsmZY1H1cnEo3K7YBV1iAgIgBiJYwiEEFuDiK1BdK1B9IEwxkAYU1sUQzDKV7cdFasZUp3ocnIxuNMwujMwp2dhdmdiTHdrrW00NDQGNIIgYM4vIDe/AGZeiiLFCe2qwl++ESq34fxiO3yxnYTJQKDQTXRIHtGhuciu5LvWdKKOke5SBjsK2OrbzpJ9n7Cqfh3fK53D2IyRh54jct0Fw8hMtfDaZ9X426L8bO4YnNauKSikidpTQFUSJHavJV7+AYp3H7LBgcc1h6bmPOxpKnq95iHsDrYfrOOzbTswAj90JDhzQioG68AqwKGhoaHRU6Q3hJG9jchNLehaQxgCYUxtEYyBKLrEV7ysOh1qqgPRlYquoN3Lak5vF62m9Az0rjREk7YNrJFcjEYR6B2paV9HLBbDaNTWjIfR7HFsvtY2RhPmcaNxjxsNgCpJxFv8SH4fcmsrgiSBCnJQj2Q3ozisyHYrqi65m4NnDz2DiBSlJd6KrMaobqtiaEohZr0ZELhkeiHTxmRRdbCFfQ0Byga7sJhOXoIKgkAikUCv//pzNVF7EqiqgiCIqLJCZMU/kEQ7B9N/TCBtKvYUI05tA7lbkGSZpdt2sL22nnydjp/kJRg8Qgs31tDQ0OhOcv69klhjEwCK3QKpqejyMzGkuTG6M7CkZ2FOz8CQ5kbncGheVo0eQKXx0DXaG7HZTIS6uU1Lb0azx7E5cdsIkOqGVDdyWxuRRg/x2npUnx9BUVCBuMOMlGpHdjlJOK1HFcY7VVRVxRf1szO2i3VsotCZT6mrGJ3QHnKcZk6wtrKWiqr9nD4iE7fz6H68x31ngkBBQY4mar8NSmsD8YoPSdRVEZr+IHtqzEiZ96NPScdmF3H29ASTQFSSWL97Lw2BAInE1xft6C2EYjHaojGm6uGa0eDI7v52PRoaGhoDnazrbyS9MJuAakQ09LL+dBoaGhoDHJ3Dgd3hgKISUBTifh/RhnpoasR4oBnhQDOKTiSWYiHhcpBIdaJYjKcscgVBwG1JI8XooDHSzN7AAeqCHsrSSsm1Z+NymJg+JpfV2xtYWeFhfEk6+RnJ62WridpjoKoqcsMupPIlJPZuQhVEfI4p7C2PYUmxkpKX2dNTTAqyolC+7yBrd+8hKiUYpNdhlnu3qLUKMMchc9Z4J0abtpDS0NDQ6AmsJSVY3HaCWrVSDQ0Njd6NKGJ0p2N0pwOgxuPEmhuJNngQmpsQfB7AQ8KkJ5Zqa/fipjpQT6Gwk15nINeeQ1gK0xhpZnNzBTWBfYxJH4nDbGf6mBzW7WhkY1UzoWiC0oLUbx70RF43KaP0Q+Ta7UTe+z2K3obHNZvGlPOxulNw9ROLqapKtaeRL3ZWE4hEKNCJXJ4hc9aUTEKS1NPT09DQ0NDQ0NDQ0NDoAgSjEXNuPubcfADkYJBoowcaG9A1+xAaWlEByW4ifmSo8kmkkVgNVgr1BbTEWvFF/XxRt5p8ey4j0oYxeWQWW6q97NzfQjiaYGyx+1u3/OknEu3bo8bDSDuWoYoGwnnnsb95JGrGTwikno4j1dSv8mXr/C0s37ELT0sraaLIfEeCaWU2LGkpGO1GQn5N1GpoaGhoaGj0L8rLt/D88wtZuPAFFi16kZycXM4997xue/1YLMZzzz3Lrl1VqKpCWVkZP/nJzZhMJmpq9rBw4TNEo1EEQWD+/B8yadJpfPLJxyxe/EbHGOFwiObmZv7610W4XK5O4y9f/jmvvPJ3AJzOFG699Xby8vIA+PnPbyMej3fkI5599jnMnTuPSCTCk08+xv79+1FVlfPPv4DLL78iae/5gQf+i2nTzuS88y7odLy1tZVrrvke77yzJCnjfZW2tjaef34hBw7sJx6PceWVV/Gd77T/rd9++y3eeeffGI0mCgoKuPnm23A4jq4Q3NjYyHPPPYPX60WWZX784xuYOHESqqqyaNHfWLlyJQAlJcO45ZbbMJvNXW7PcDjE/ff/mlAoyDXXzGfatDNP6nyPx8Of//xH7r33152O6+x2bPZibEOLQVGQWvxEGuqhqQnDQS/CQS+KKBBLsbaHKrscKBZTR6jyR28tYf0Xa1BkhYnTTuO7l1+EIAgIgoDLnIrDaOcff1nEvqoaBATMehMBfys2Ryqz599LJJbgtOGZbC3fxF/+8n88/fTCk7bNgBe1SlsT8YqPkXYsAylKa8pp7PB8F4tFxT50Ov2pOYw/FGbFzl3sbmjCKorMNstcOEKHIzujp6emoaGhoaGhodFtXHvt/G5/zVdf/QeyLPP00wtRVZUnn/xfXnvtFa69dj7/+7+/45pr5jNlylT27t3LnXf+kr///VXOPfe8DuGdSCS46647uOKKK48StH6/n2effYqnn36OjIwM3n773zz//LM89NBviEajeDz1vPzyK0cV2XnjjdcxGk0sXPgC4XCIW265kVGjRjNsWGm32aUrePzxBRQUDOLOO++iubmJW2+9mTFjxlJXV8frr7/G//7v46SnZ/Dppx/z9NNPcu+99x01xoMP3s+sWRcxa9bF7N5dzb333s2iRX9n3bq1bNy4kaeeeha9Xs9vf/sI//73Yq688qout+eePXtoafHzxz/+5ZTOb2xs4ODBg8d/kihiSHNjSHPDiPaqyrHmJqIN9QjNzQh7DoUqG/XEU61sqa9ny5oN/MdDdyOKIi/87mmy1uQwfvLEjiH1op7rrv8h0USUhnATzU3NvPXEK9z2y1txu9NZv72O3z72GlvXLyU9Pf2U3tuAFrXx8veJrXkVFRG/YzJ1WRciugvJMPfe8u+nQiQeZ011DVv3H0QEphvg0iEKGUPcWsVgDQ0NDQ0NjQHH448voLBwMJdffgVz5szmiiuuZPPmTfh8Xi655DIuvXQOAB9+uIR3330HVVVxOBzcdNOtFBQUsG1bBX/60x9QFAVBEJg373tMm3YmL7ywkIqKik6vZTAYeOyxJxk5cjTf+U4W4qEQzuLiYqqr9wDw5JPPdhz3eOqx2WwdPx/m9ddfJTU1lZkzLzrq/bhcLhYt+id6vR5ZlmlqasDpbC9lWlW1E7PZwgMP/Bc+n49x48Yzf/4PMZlMKIpCJBJGlmXicQlFUdHrj98eUVEU/vjHF9i5cweRSARVVbn99l9QVjYSr9fL448vwOfzkpGRRWtrS8d5K1d+wYsv/g2TyURJybBOY3744RLef/9dZFnpZOdjjRcMBrnnnl8dNbczz5zOrFkXs3nzJn71q3sASE/P4LHHnsBud1BdvYtx48aRnt7u0Jk69UyeeupJJEnCYPjyfe/Zs5tgsI1Zsy4GoKiomN/9bgGCIDJ16pmcfvpk9Ho94XCI1tbWDlufij3Ly7fwhz88j9lsJhqN8vjjT7Jx40ZeeeUfJBIJTCYTt956C3q9iSeffAyv18vPfnYLCxY8zltvvcmqVSuRpDjRaIwf//gGpk6dhizL/PnPf2LdurXodDpGjBjBzTffytNPP4HX6+XXv76Xhx76DatWreQf/3gZRZGxWq1cf/2NlJaW8vLLL7FjRyV+v4/Bg4dQWDiYzz9fdvgCQEkkUBMSd118MdtXrOPMnALSt+8jnmpj8qTxbFixppOoPYxZb6bQkc+/n3mNsd+ZSK25CUUVMUmNRGNRZlx0HeWr3juuvY7FgBK1qqKQ2LsBXXohEV0mjbESpJSLaEg9H0uai/7W4jQhy2zee4B1u2uQZJkxeoHLcxUGlaSgM5584reGhoaGhoaGRn9DkiSczhR+//vHqK7exZ13/gczZ17Ezp07+OSTj/mf/1mA2Wxm48YN/OY3D/Hcc3/g5ZcXcdlllzNjxtnU1OxhyZL3mDbtTG688ZZjvs6ECV8u8hsbG/jXv/7FrbfeDoBOp0NVVW644Uc0Njbwk5/chE735VqttbWVN998gyeffOaY4+v1enbtquKBB+4nHo/xwAMPAxCJhBkzZgw333wrer2BBQv+h7/97S/89Kc3MXfuPO65507mz7+acDjMRRfNZujQoce1186dO/D5vCxY8DiiKPLaa6/w2muvcv/9D/Dcc89SWjqc6677AXV1ddx+e7s9/H4/Tz75OL///WMMGlTIq6/+s2O8rVvL+eSTj3nqqaeQZaGTnY81nt1uP2aI6s6dO3G50li8+A02bFiPJEnMmTOXvLx8hg0r5e2336KxsYHMzCw++uhDEgmJtrYAaWnujjFqa2vJzMzij398gcrK7eh0Oq655loKCwd32Prtt//NokV/w+12M2XKVIBTsifA/v37+NOf/kJmZha1tbW8+OJfefTR3+F0Otm3by+//vW9/OEPf+ZnP/sFzz+/kKefXkhjYwObN2/it7/9PSaTiWXLlvLyyy8xdeo03n33bXbvrubppxdiMBj4/e9/y/Lln3ec/9BDv+HAgQM8++zTLFjwGNnZOWzZspmHH/5vXnjhTwA0NTXy7LMvdFyH8+Z97+iJqyqBtesYXVKCwWDCUOcjzx/liwMebFt3I6U6kNOcyNYvQ5Ury7cT9Ldx6SVz8EZ91Icb0JWIXDtuLquX7iEcS1DvDZHjtn2j3Y5kQIhaNR5B2rmceMVHqG1N+HNnU2W+Er1+GM7iYTj7mb5TVZWddR5WVFUTjMYYohO5PFOmbEQKhv6m3DU0NDQ0NDQ0viWTJ08B2j1ykiQRjUZZt24t9fV13Hnnf3Q8r62tjba2NqZPn87zzz/L2rVrDnk+fwRwXE/tYaqrd/HIIw9y2WWXcfrpZ3QcFwSBP/3pL3g8Hu666w4GDRrE2LHjAPjgg/eZPHky2dnZx30fJSXDWLToH2zYsJ4HHvgv/vSnv3LGGVM444wpHc+58sqreOSRh/jpT2/iueeeZfz4icyf/0NaWvzcd989rFhRdtxczREjynA4HCxZ8h719fVs3VqOxWIBYMuWTVx//Q0A5ObmMmbMWAC2b99GYeFgBg0qBGDmzIt48cW/AnTY+bbbbkNR1E52PtZ4x/PUjh49hoYGD1arld///jHq6uq4667/R15eHqNGjeb737+GRx55EEEQOf/8C3A4HEd5UxOJBJWV27n88iv4yU9uZOfOnfz3f9/HM888j9vdLn5nz76Eiy+ezaJFf+PRRx/ht7/9/SnZEyA9PZ3MzCwANm/eiN/v4z//8+6O34uiSF1dXadzMjOz+OUv72Dp0k+pr69nx45KotHIoTE2cc4538FkMgFw1133Au1e4cOUl29m7NhxZGfnADB27DhSUlKprq4GoLR0eIegfe21V7701B7BI4/8FvR6LDl5uKefjZpIYP58KbrNGzHFFcx7G2BvA7JBRyzVRsLlYNl7H3Pu7AvQ6/Rk2TJJTaTQEGmiJrgHncuDKKqs29FE2eAExXknngja70VtbN2/iFd8DFKEsLWEg1lXE00dT7pdTVav4V7FAa+PL3bsojHQRoao43spMmeUWTCn9qfsYA0NDQ0NDQ2N5GE0trcIFA4tDlVVRVEUzjnnXH70o+uB9tBSn8+L3W5n5syLOP30yWzatIENGzbw978v4plnnjuupxZg2bKlPPfcM9x0061cdNGFhEIxJEli5coVTJ9+FqIokp2dzbhx49i9e3eHqP3882XceOPNxxzX6/Wyd28NEydOAmDixElYrVY8nnq8Xi82m41Ro0Z3vDe9vl2srFq1gmeeeR5RFElLczNt2nTKy7ccV4StW7eGP/zheebMmcsZZ0whPz+fzz779NBvBdQjsvgOi6J2s6pHHP8ytPqwnW+77RZCoVgnOx9rvON5aj0eDwDnnXc+0C6Gy8pGUlW1k9zcPEaNGsMFF1wItHuQFy168ahCUW63G5vN1rHZUVpaSnZ2NjU1e2htbUVVFYqKihEEgQsuuJB///utU7YngNls6WSPsWPHdQhRgFCoFbPZzrZtX26YVFfv4uGHH+DSS+cwfvwERo0azcKFT3fYSThC6Pj9flRV6fSahzcQjkRVVRKJxFFzmjfve1/vqQUyMjLw+30ACHo9bapKZkEh7nO/ixKJEG2sJ9bYgOj10ba/gf27arj9nHMRdtcipzkRnDYG2fMISEHqlYOoYhxrVhPb9ycIRxOMHprW6b0ci15V0/ftt99m1qxZnH/++bz88sunPI7srwUgHoe25iB+8xgqcu+npvjX6AdPxO4Q+52g9bYF+ff6zbyxdiPhYIg5Fpn/HicwY2o65lRzT09PQ0NDQ6OXkazPXA2N/sr48RP4/POl+HxeAN5//90O79kdd/ySPXuqOe+8C/jZz24nFAoSDB6/Z/MXXyznD394joce+g1nn31Ox3GDwcCiRX/r8IR5vV7Ky8sZPbpdhAaDbdTX1zFiRNkxx5akOL/73aMd3rzy8i3IskJ+fgFebzP/939/JBaLIcsyixe/wfTpM4B2z/Ty5e2vG41G2bhxA8OHDz/u+9i0aROnnz6ZWbMuZtiwYaxevQpFaRdMEydOZMmS9pzIxsZGysvLARg5chT79u1nz572HOKPP/7oKDt7vUfb+VjjHY/s7GyKior55JOPgXZBV1lZSXFxCT6fl3vu+RXhcAiAf/7z75x11tlHiaYRI0ZgNBpZs2Y1AAcOHKC+vp7Bg4ewd+8ennjiMaLRKACffvpJhwf5VOz5VcaMGcvGjRs5cOAA0O7Jvv7665G+0nJz27YKiouHMWfOXEaNGs3q1Ss7/g7jxo1n6dKlSFIcRVFYuPBpli1bik6nQ5bbRevYsWPZtGkjHk89AFu2bKa5uYnS0pOb7xlnTGHp0s+IRqNIUpyPP/6IKVPaNwNEiwVr4VBcp03B/d1ZHHSnUVRYiMPuxFbvJ6ViL67V27Ft3UN6Q5hcMRVR0CHpWzFk72N/cD+rt3tIyMo3zKIXeWobGhp4/PHHeeONNzAajVx11VWcccYZFBcXn9D5qqIg7d2EtPUD5PqdeCf+FzWBElTjj3CW0O/yZQ8TisX+P3v3HRXF2bYB/FpgAaVJE4g9mGChiBqVaDQaFQUBRaNgD3ZEDJYIij12jRFs0ZDYC6KABU2ifr6JYoxdYu8gKk2UJmV35/vDl31FQIrA7sL1O4dz2Kn3czPs7L3zzDP4++4DXI+Lh1gkQlexABdLAcaNOAgUEREV7UPPuUQ1QZs2bdG//9cIDJwJNTURateujZkzZ0MkEuGbb0Zh06aN2L59G0QiETw9h8DM7P1dg7du/RWCAAQF/QgAUFMToVmz5pgwwQezZs3Bhg3rsH//PqipieDlNVo+mNLTp09hZGRUaOTiu3fvICjoRwQHr4e5uQV8ff2wePFCiEQi6OjoYM6cedDW1kavXk54/vwZJk/2gVQqha2tHTw9BwMApkyZhg0b1uHkyRMQiUT44ovO6Nr1q0Kxnzt3FlFRUZg/fyF693bGypVL4eMzHmpq6mjZ0hrR0Wcgk8kwYcJE/PjjDxg/fgxMTEzk95MaGNTB9OkzsHLlMojFGrC2ti2U52nTpgFAgTwXt72S5Ofz6NEjkMkEeHoOlo9APGDAQEyZ8u1/H6vUEuPHTyyUT7FYEwsWLMLGjeuxdeubkYYnT54CExMTdOvWHc+ePYOfny/U1dXQsGEj+Pr6lZjPoKDVaNr0Uzg5FR7o622NGjXGpEm+WL6AX5PBAAAgAElEQVR8CQRBgLq6OhYtWgRt7YIXqTp3/hJnzpzGhAljoaGhATu7VkhPT0dWVhZ69XJCQkICJk+eBECAjY0tXF374vXrLKipqcPPzxc//LAG3t4TsWjRQkilUmhpaWH27PnQ0Snbvazt23fA48ePMGWKL/LyJOjQwUH++KSoqCO4d+/Om/yIREh4+QofNfkYRp2+hCCVIjc5CdmJbx4dpP0oEfpPn0IrWwLrpyIk6whIqZ2EF0jDqZtp+MLq/ecnkSAISjHUb3h4OM6fP4/FixcDANatWwdBEODj41Oq9e//PBOihNt4rWGIO5pfIK5We2jqaJflGcEqJzEtHZcePoZEKoW9hgjuDQTU+1j/gwaBMjTUQWpqZgVGWXMxlxWHuaw4zGXFUNPWgkGn9iUvqKQ+9JwLACkpGTA21kVS0vuvTtVUpqZ6zA0UmwdNTRESE5MUsu/S0NHRQmZmzgdtY9682Zg3b2EFRVQ8qVSKxYsXYvbseZW2j4rIx4eqzHxevnwJjx8/Qt++7mVeVxlyU9lk2dnITnyOnMTnkCWnQJT35opylpYaXumqIcfABH1795PfJ/wupblSm5iYCFPT/z0vtW7duqXqYpAvIQM4mt4Z1/IaQgY1ALcrIUrl84kaMEDvJaw+0YO6lgaQlQ5kfcgWcyF6+UEbIDnmsuIwlxWHuawIotq1Sl5IiX3oOZeIFC85ORnOzn2qZF9xcbEYMGBglexLUSo7n2lpaejZ07HStq/q1LS1UbthY9Ru2BgQBEjS0vA68RlqJSZA+8VLiLPT3ru+0hS1RV0wLs1Nwfk06/dDX/Mc9K3IoJRcbQ0RmjTWh3adiv1wVadCt1azMZcVh7msOMxlBdBQmtNnuXzoORcAjI11Aby5EkdFY27eUFQecnJyoKNT9FUdZfEh8eno1EOjRvUqMJritWxZtvssy0uRf6/KzqeT04cVtMp+LFc4XW0YfFQXACCTSJD938GoiqM0Z2UzMzNcuHBB/joxMRF169Yt9fpNujYvchSv6i79vz8Vhd2lKg5zWXGYy4rDXFYMNTURjEteTGl96DkXYPfjkvB/7Q1Fdz9W5i6bNaFLaVkwH8VjbgCRzvu/kleaovbzzz9HcHAwXrx4gVq1auH333/HwoWVf48AERFRTcNzLtUMItSta1ryYgqirS2Gjk5eyQvWEMxH8ZibN72J8h/pVBSlKWrNzMzg5+eH4cOHIy8vDwMGDICtrW3JKxIREVGZ8JxLNUFubsmPAVEkAwMtpKXlKjoMpcF8FI+5AdTUUGgE8LcpTVELAC4uLnBxcVF0GERERNUez7lERFRdVOMH3hAREREREVF1x6KWiIiIiIiIVBaLWiIiIiIiIlJZSnVP7YdQUyvb8/WoeMxlxWEuKw5zWXGYyw/HHP4vB8xF8ZibN5iH4jE3BTEfxavpuSmp/SKhqCewExEREREREakAdj8mIiIiIiIilcWiloiIiIiIiFQWi1oiIiIiIiJSWSxqiYiIiIiISGWxqCUiIiIiIiKVxaKWiIiIiIiIVBaLWiIiIiIiIlJZLGqJiIiIiIhIZbGoJSIiIiIiIpWl8kXtoUOH4OTkhB49emDnzp2KDkelrF27Fs7OznB2dsby5csBANHR0XBxcUHPnj2xevVqBUeoepYtWwZ/f38AwM2bN9G/f384Ojpi1qxZkEgkCo5ONZw8eRLu7u7o1asXvv/+ewA8Lj9EZGSk/P982bJlAHhslkVGRgb69OmDJ0+eACj+WFSlnJblvf/48eNwc3ODq6srvL298erVKwDA06dPMWTIEPTq1QsTJkxAZmZmkfsqKS9r1qxBcHBwJbW07JQhNxcvXkT//v3h5uaGESNGID4+vpJbXZgy5OHChQtwd3eHi4sLxo8fL9+uoilDbvLduHED1tbWldTS0lGGfERERKBTp05wc3ODm5ub0nxOUIbcJCYmYuzYsejbty88PDzk57JqSVBhz58/F7p27SqkpqYKmZmZgouLi3D37l1Fh6USzpw5IwwaNEjIyckRcnNzheHDhwuHDh0SunTpIsTGxgp5eXmCl5eXcOrUKUWHqjKio6OF9u3bCzNmzBAEQRCcnZ2Fy5cvC4IgCAEBAcLOnTsVGZ5KiI2NFTp16iQ8e/ZMyM3NFTw9PYVTp07xuCynrKws4bPPPhNSUlKEvLw8YcCAAcKZM2d4bJbSlStXhD59+ggtW7YU4uLihNevXxd7LKpKTsvy3p+eni507NhReP78uSAIgvDjjz8KCxcuFARBEMaOHSscPnxYEARBWLt2rbB8+fIi91dcXtLS0oSAgADB1tZWCAoKquxml4qy5KZr167CzZs3BUEQhH379gnjx4+v1Ha/S1ny0L17d/lnuhUrVgirVq2q1HaXhrLkRhDevL8PGjRI+PTTTyuzye+lLPlYsGCBcOjQocpubpkoS25GjBgh7Nq1SxAEQdi1a5cwefLkSm23Iqn0ldro6Gh06NABderUQe3ateHo6Ihjx44pOiyVYGpqCn9/f2hqakIsFsPS0hKPHj1Co0aN0KBBA2hoaMDFxYX5LKWXL19i9erVGD9+PAAgPj4e2dnZaNWqFQDA3d2duSyFP/74A05OTjA3N4dYLMbq1atRq1YtHpflJJVKIZPJ8Pr1a0gkEkgkEmhoaPDYLKXQ0FDMnTsXdevWBQBcu3atyGNRlf7fy/Len5eXh3nz5sHMzAwAYGVlhWfPniEvLw/nz5+Ho6MjgOLb+768nDhxAo0bN8Y333xTRS0vmTLkJjc3F5MnT0azZs0KbLcqKUMeACAqKgpNmzZFXl4eEhISoK+vX0UZKJ6y5AYAli5dipEjR1Z+o99DWfIRExODiIgIuLq6Ytq0aUpxVV8ZcvPixQvcunULHh4eAID+/fvj22+/raIMVD2VLmoTExNhamoqf123bl0kJCQoMCLV8cknn8gP/kePHiEqKgoikYj5LKc5c+bAz89PftJ999g0NTVlLkvh8ePHkEqlGDVqFFxdXbFr1y7+n38AXV1dTJ48Gb1790bnzp1Rr149iMViHpultGjRIrRt21b+urhjUZX+38vy3m9oaIju3bsDALKzs7Fp0yZ0794dqamp0NXVhYaGBoDi2/u+vPTt2xdjx46Furp6pbW1rJQhN5qamnBzcwMAyGQyrF27Vr6fqqIMeQAAsViM27dvo0uXLjh37hycnZ0rrc2lpSy5OXHiBLKzs9GrV69Ka2tpKEs+TE1NMWnSJERGRsLCwgILFiyotDaXljLkJi4uDh999BEWL14MV1dX+Pr6QiwWV2azFUqli1pBEApNE4lECohEdd29exdeXl6YMWMGGjZsWGg+81myffv2wcLCAg4ODvJpPDbLRyqV4uzZs1ixYgVCQ0MRExNT5P0fzGXp3Lp1C/v378f//d//4fTp01BTU8OZM2cKLcd8lk5x/9eq+P9elvf+9PR0jBkzBs2aNUO/fv1K3V5VzAugHLnJzc3FtGnTIJFIMG7cuPI25YMoQx6srKwQHR0Nb29v+Pn5lbcpFU6RuUlKSsKGDRswe/bsD2xFxVH0sbJu3TrY2dlBJBJh9OjR+PPPPz+kORVKkbmRSCS4ceMGPv/8cxw8eBBfffWVfNyX6kili1ozMzMkJyfLXycmJsq7iVHJLl68iJEjR2Lq1Kno168f81lOUVFROHPmDNzc3BAUFISTJ09i3759BXKZlJTEXJaCiYkJHBwcYGRkBG1tbXz11Vc4c+YMj8tyOn36NBwcHGBsbAxNTU24u7vj3LlzPDbLqbj3yHenK3tOy/Len5iYiMGDB6NZs2ZYtGgRAMDIyAgZGRmQSqUA/tfehIQE+UAtY8aMUbm8AMqRm8zMTIwePRoSiQQbNmxQyJUVRechJycHx48fl093dXXF7du3q6LpJVJ0bk6dOoWXL19iyJAh8qv6bm5uyMjIqKoUFKDofKSnp2PLli3y6YIgyK9sKpqic2NqagodHR107doVANCnTx9cu3atqppf9ar8Lt4KlD9QVEpKipCVlSW4uroKV69eVXRYKuHp06dC+/bthejoaPm07OxsoXPnzsKjR48EiUQijBo1SoiKilJglKpn//79BQaKunDhgiAIgjBr1ixh8+bNigxNJVy5ckVwdHQUXr16JUgkEmHcuHHCjh07eFyW019//SW4uroKmZmZgkwmE2bPni0EBQXx2Cyjrl27CnFxce99j1SVnJblvV8ikQj9+vUT1q1bV2g7Y8aMEQ4ePCgIgiCsX79emDdvXpH7KykvQUFBSjNQlLLkZsKECUJgYKAgk8kquomlogx5kEgkQqdOnYSYmBhBEAQhLCxM8PLyquimlpky5OZdihwoShnyIZFIhI4dOwpXrlwRBEEQgoODhdmzZ1d0U8tMGXIjCILQu3dv+YCGR44cETw9PSu0ncpEJAhFXLNWIYcOHcJPP/2EvLw8DBgwAGPGjFF0SCrh+++/x/79+wt0hfDw8EDjxo2xZMkS5OTkoEuXLggICFCJ7mLK4sCBA/jnn3+wdOlS3Lp1C4GBgcjMzESLFi2wZMkSaGpqKjpEpRcWFoYtW7YgLy8PHTt2RGBgIM6dO8fjspw2bdqEAwcOQCwWw8bGBnPnzsXDhw95bJZBt27dsG3bNtSvXx9nz54t8lhUlf/3srz3Hz9+HJMmTYKVlZV8WWtrayxatAjx8fHw9/dHSkoKLCws8MMPP8DAwKDQ/krKS/7jfCZNmlSJrS4dZcjNvXv30K9fPzRt2lR+talu3brYvHlz5Sfgv5QhD5qamrhw4QIWL14MqVQKMzMzLFiwAObm5lWSg+IoS27eZmVlpbCr2MqSjwsXLmDRokXIzs5G48aNsXz5cujp6VVJDoqjLLl58OAB5s6dK78/d+nSpWjcuHFVpKDKqXxRS0RERERERDWXSt9TS0RERERERDUbi1oiIiIiIiJSWSxqiYiIiIiISGWxqCUiIiIiIiKVxaKWiIiIiIiIVBaLWiIl9eTJEzRv3lz+gG03Nze4uroiLCxMoXF5eXnhxYsXAIAxY8bg3r17OHfuHPr06aPQuIiIiIioZtJQdABEVDxtbW1ERkbKXyckJKBPnz6wtrZGs2bNFBLTmTNn5L/nPzsxJSVFIbEQEREREbGoJVIhZmZmaNSoER49eoSYmBjs3r0bMpkMderUwezZs2FpaQl/f3+8fPkScXFx+PLLL+Ht7Y3vv/8ely5dgrq6Orp37w4/Pz/k5eVh5cqVOH/+PKRSKVq0aIHAwEDo6uqiW7du6NevH86ePYtnz56hd+/e+O677xAQEAAAGDFiBDZt2oQhQ4ZgzZo1BWLMzc0tdrtERERERBWN3Y+JVMjly5cRGxsLIyMjREREYOfOnYiIiMDo0aMxadIk+XLZ2dk4cuQIpk+fjqCgIOTk5CAqKgoRERG4dOkS/vnnH2zatAnq6uo4cOAADh48iLp162LlypXybWRlZWHXrl3Ys2cPduzYgbi4OCxZsgQAsHXrVlhYWBQZY0nbJSIiIiKqSLxSS6TEsrOz4ebmBgCQSqUwNDTEihUrcOrUKTx+/BgeHh7yZV+9eoWXL18CANq0aSOfHh0djYCAAKirq0NdXR07duwAAKxYsQLp6emIjo4GAOTl5cHY2Fi+3ldffQXgzdVhY2NjvHr1Cg0aNCgx5lOnTr13u0REREREFYlFLZESe/ee2nxnz56Fm5sbpk+fDgCQyWRITEyEgYEBAKB27dryZTU0NCASieSvnz17Bm1tbchkMsycORNdunQBAGRmZiInJ0e+nJaWlvx3kUgEQRBKFXNJ2yUiIiIiqkjsfkykgjp27IgjR44gMTERALB7926MGDGiyGUdHBwQHh4OmUyG3Nxc+Pr64vz58+jUqRN27tyJ3NxcyGQyzJ49Gz/88EOJ+1ZXV4dEIil2fnm3S0RERERUHixqiVTQF198gTFjxsDLywsuLi44fPgw1q5dW+CKbD4fHx+IxWK4ubmhb9++6NKlC3r27Alvb2/Uq1cP/fr1g5OTEwRBgL+/f4n77tGjBwYPHow7d+4UOb+82yUiIiIiKg+RUNo+hURERERERERKhldqiYiIiIiISGWxqCUiIiIiIiKVxaKWiIiIiIiIVBaLWiIiIiIiIlJZLGqJiIiIiIhIZbGoJSIiIiIiIpXFopaIiIiIiIhUFotaIiIiIiIiUlksaomIiIiIiEhlsaglIiIiIiIilcWiloiIiIiIiFQWi1oiIiIiIiJSWSxqiYiIiIiISGWxqCUiIiIiIiKVxaKWiIiIiIiIVBaLWiIiIiIiIlJZLGqJiIiIiIhIZbGoJSIiIiIiIpXFopaIiIiIiIhUFotaIiIiIiIiUlksaomIiIiIiEhlsaglIiIiIiIilcWiloiIiIiIiFQWi1oiIiIiIiJSWSxqiYiIiIiISGWxqCUiIiIiIiKVxaKWiIiIiIiIVBaLWiIiIiIiIlJZLGqJiIiIiIhIZbGoJSIiIiIiIpXFopaIiIiIiIhUFotaIiIiIiIiUlksakllDRs2DLNmzSpy3siRI+Hv7w8AePLkCaysrHDhwoVSbffUqVO4d+9ehcWpSN9//z3s7e3Rpk0bJCcnF7lMRkYGfvzxR/Tu3Rt2dnbo3Lkzpk6diocPH37Qvp8/fw4rKyucO3fug7aTkZGBNWvWoFevXrCxsUHHjh3h4+ODy5cvl3lbly9fxsWLFz8onndVp+OFiKg4w4YNQ7t27ZCYmFhonr+/P0aOHFmp+7eyskJkZGSl7qM04uPjMWDAAFhbW2Py5MnFLhcSEgIrKyts2rSpzPt49uwZjhw58iFhFlDaz0ELFiyAlZUVoqKiyrWfd8+xyvI3o5qBRS1VexYWFjh9+jTs7OxKXDYhIQHjxo1DSkpKFURWue7du4ft27djxowZiIyMhImJSaFlkpOT4e7ujlOnTmHq1Kk4fPgw1qxZg/T0dHh4eODu3bsKiPx/Xr58CQ8PD0RFRcHX1xdHjx7Fhg0bUKdOHQwdOhT79+8v0/aGDh2Kx48fV1h81el4ISIqyatXrzB//nxFh6FQO3fuxLNnzxAZGYk5c+YUu1xERAQaN26MsLAwCIJQpn3MnDkTf/3114eGWia5ubk4cuQIGjdujL1795ZrGxV9jiUqCxa1VO2pq6vD1NQUYrG4xGXLeuJRZq9evQIAdOzYEfXr1y9ymXnz5kEQBOzYsQPdu3dHgwYNYG9vj3Xr1sHMzAzLli2rypALWbhwITIzM7F37144OTmhfv36sLW1xffff48xY8Zg/vz5ePToUam3V9F/3+p0vBARlaR+/fo4fvx4hV5FVDVpaWlo0qQJLC0tYWxsXOQyMTExuHPnDqZPn47Hjx/j77//LtM+FHFuOXnyJLKysuDr64tz586VqzjlOZEUiUUtVXvvdru5cuUKPDw80KpVK7Rv3x7Tp0/Hy5cvAQBdunQBAAwfPlzeffnhw4fw8vJCq1at0K1bN0RERKBFixbybrXDhg3DnDlz4O7ujs8++wwnT57Ey5cvERAQgE6dOqFly5bo1KkTli1bBplMBgAIDg7GqFGjEBQUBAcHB9jb22PevHl4+vQpxowZAzs7Ozg6OuLPP/8stl0SiQSbN29Gz549YWNjAxcXF3mXoQMHDmDw4MEAgO7du8vb8rakpCScOHECI0aMgK6uboF5YrEYq1atQmBgoHzanTt3MGbMGHz22Wdo164dvvvuO7x48UI+Pz4+HmPHjoW9vT26detW5LfMoaGhcHR0hK2tLVxcXBAeHl5s+168eIGjR49ixIgRqFOnTqH53t7eEIvFCA0Nlee0R48eBZZ5e1q3bt0glUoREBCAYcOGAXjTNWrPnj1wd3eHra0t3N3dcf78efn6RXWpe3taUccLEVF11aFDB/Tp0wcLFy4s8P7/rqK6nb49zd/fHwEBAZg/fz7atm2L9u3bY926dbh79y48PDxga2sLNzc3xMTEFNjGvXv38PXXX8Pa2hpubm44c+ZMgfnvO8ecO3cONjY2WL9+Pdq1ayc/D7zr6dOn8PPzQ4cOHWBvbw9vb2/ExcUBeHO+37dvH86fP//e22vCw8NRv359dO/eHY0aNSryyue1a9cwbNgwtGrVCp06dcLy5cshkUjg7++Ps2fPIjw8HFZWVvL9vnu71bvTdu/ejT59+sDGxgb29vbw8vIqU2EaHh4Oe3t7dO/eHbVq1ZKfW/OV5xwLAPfv38ewYcNgY2ODbt26ISwsrMA2wsLC0KdPH9ja2qJHjx7YsWOHfN6BAwfg6OiIefPmoU2bNvjuu++QlZWFgIAAfP7557CxscHAgQNx9uzZUreTqi8WtVSjSKVSTJgwAQ4ODjh8+DA2bdqEmJgY+RXJ/BNgcHAwZs2ahaysLHzzzTfQ1NREaGgoFi5ciKCgIEil0gLb3bdvH8aOHYvt27ejXbt2mDFjBu7fv48NGzbg2LFjmDBhAn799VecPHlSvs65c+cQGxuLXbt2ITAwELt378bAgQPh4uKCAwcOoEmTJggICCi2LUuXLkVISAimTJmCgwcPwtnZGVOmTMFvv/0GJycnrF+/Xh5bUfce37x5EzKZrNhu2Z988gkaN24M4M0XA56enjAwMMDOnTuxfv163Lp1C15eXpBKpcjLy8Po0aPx+vVr7N69G4sXLy50H9GuXbuwevVq+Pn54fDhwxg9ejQWLVpUbGEbExMDqVSK1q1bFzlfU1MTrVq1KvW9tWFhYVBXV8fMmTMRHBwsn758+XJ4eHggPDwc1tbWGDVqlPwDTEnePV6IiKq7wMBAqKmpYeHChR+0nUOHDkFbWxsHDhzAiBEjEBQUhIkTJ2LcuHHYt28fxGIxFixYUGCdbdu2wcPDA5GRkWjTpg0mTJiA58+fAyjdOSY3Nxfnzp3Dvn37Cnxpmy8jIwOenp549eoVQkJCsH37dqSnp2Po0KFIT09HcHAw+vTpA3t7e5w+fRr29vaFtpHfjbdnz54AgN69e+P48eMFvgSIi4vD8OHD0ahRI4SFhWHFihU4ePCg/FzStm1b9O7dG6dPny5VLo8dO4YlS5bA29sbx44dw08//YT4+PhS97ZKSkrC6dOn4ejoCC0tLXTr1g3h4eHIy8sr1fpA8efYnTt3wtPTE1FRUejWrRtmz54tP8f++uuvWLhwIUaMGIGDBw9i1KhRWL58OX755Rf5+o8ePUJGRgYiIiIwbtw4BAUF4d69ewgJCUFUVBSaN28OHx8fZGVllTpWqp5Y1JJKi4iIgL29faGff/75p8jl09PTkZqaChMTE9SrVw92dnZYt24dRowYAQAwMjICABgYGEBPTw9Hjx5FWloaVqxYgU8//RQdO3bE7NmzC23X1tYWvXr1QrNmzaCrq4svvvgCixYtgo2NDRo0aIAhQ4bAwsICt2/fLrDeggUL0KRJE/Tv3x+Ghobo2LEjXF1dYWlpCU9PTyQnJxf5bXhGRgZ2794NPz8/9OrVC02aNMH48ePRq1cvbNq0Cdra2jAwMJC3SU9Pr9A20tLSAAD6+vol5nnXrl3Q19fHkiVL8Omnn6Jt27ZYvXo1bt68ib/++gvR0dF4+PAhli1bhmbNmqFDhw6FPjBs3LgRPj4+6NWrFxo2bAg3NzeMGjUKGzduLHKf+fEVdZU2X506dd57teBt+X9bPT29AtscOHAgBg4cCEtLS8ydOxempqaFvqEuaZv5xwsRUXVnaGiI2bNnIyoqCsePHy/3doyMjPDdd9+hYcOG8t4vffr0QdeuXWFlZQV3d/dC4zoMGzYM/fv3h6WlJQIDA2FmZobdu3cDKP05ZvTo0WjUqJH8KujbIiMjkZaWhh9++AEtW7aEtbU11qxZg1evXuHgwYOoU6cOtLW1IRaLYWpqCk1NzULbyO+t1bt3bwCAk5MT8vLycODAAfkyoaGhMDExwfz589G0aVM4ODhg4cKFqFu3LvT09CAWi6GtrQ1TU9NS53Lx4sVwcnJCvXr10K5dOzg7O+POnTulWv/gwYOQyWTyQtzZ2RkpKSll+vsWd44dOnQonJyc0KBBA0yaNAkymQw3b96EIAj4+eefMWLECHz99ddo3LgxPDw8MGzYMPz8888FujJ7e3ujQYMGsLS0xOPHj6Gjo4P69eujQYMGmDFjBoKDg6Gurl7qWKl60lB0ABUpIyMDHh4e2LhxY7H3EL7P06dP4ezsjIYNGwIATExMEBISUtFhUgXq3r07pkyZUmj6jBkzily+Tp06+Oabb7BgwQIEBwejY8eO6Nq1KxwdHYtc/saNG7C0tCxQsLRp06bQcu8eb56enjhx4gT27duHR48e4fbt23j+/Lm8+zEAmJqaonbt2vLXtWvXRoMGDeSvtbW1Abz51vddDx48gEQiKfQtcX7359IwNDQE8L97b9/n7t27sLGxKXBfsqWlJQwNDXHnzh2oqanB0NAQH330kXz+21eAX7x4gYSEBCxbtgwrV66UT5dIJJBKpcjNzS304SD/pJiRkVFsXOnp6fITaXl99tln8t/V1dVhbW1d6g8CREQ1Ue/evREVFYV58+YVeA8ti4YNG0IkEgGA/FyY//kLeHMOfPf89/Y5T01NDS1atMDdu3dLdY7J9/Z59l13795FkyZNChRlRkZGsLS0LPV5ITw8HPXq1YOtrS2AN92uLS0tsW/fPowaNQoikQh37txBy5YtCxRiXbt2LdX2i9KuXTvcuXMHa9euxYMHD/Dw4UPcuXMHZmZmpVo/IiICbdu2lRfRnTp1gr6+Pvbu3Ssvzssrv8cXAPmX7dnZ2Xjx4gWSk5OL/Bzz888/ywdgFIlEBT5jjRo1Ct7e3vJbt7744gu4urpCS0vrg+Ik1VdtitqrV68iMDCwTIPGvCsmJgYuLi6FuruQ8tLV1UWjRo0KTc8vCIsyY8YMDBkyBP/5z39w+vRpBAQEIDQ0FNu2bSu0rLq6eoFCtO0fRJkAACAASURBVDhv708mk2Hs2LF4+PAhXFxc4ObmBltbW/nV4HwaGoX//dTUStd5org3b6lUWuR2i2JtbQ0NDQ1cuXJFfvJ926FDh3DixAksW7as2HzKZDKIxWLIZLJCA0S8XQDn/z579my0a9eu0HaKitnW1hZisRgXL15EixYtCs3Pzc3F1atX4e7uXmwbJRJJsfOK27dMJpN/0CrvNomIqru5c+fC2dkZS5YsKXHZot43i3rff997L4BCV+MEQYCmpmaZzjHv+3xQ0rmuJPndeKVSaYHzVv458u+//4aDg0Opz9Pv83ZOIyIiEBgYCFdXV7Rt2xZDhw7Fn3/+iYMHD5a4nfxBrUQiUYGYpVIp/v77b8TGxhb4sqG4GIpT1OcaQRDe+zkG+N/fTE1NrcCX3m3btpV/fjt9+jR27tyJDRs2IDQ0FJ988kmJ8VD1VW26H4eGhmLu3LmoW7eufFpERAT69esHNzc3zJw5Ezk5Oe/dRv4/tru7O4YPH16oqyipvtjYWHkX0yFDhmDDhg1YtmwZzp07h5SUlEInVCsrKzx48ADp6enyaVevXn3vPm7cuIHTp08jODgYfn5+cHZ2hqGhIZKSkipsZMBGjRpBLBbj0qVLBaZfvHgRTZs2LdU2DAwM0KNHD2zduhWZmZkF5uXk5GDz5s14+fIltLS0YGlpiZiYmAL319y7dw+vXr2CpaUlmjdvjtTU1AJfKv3777/y3/X09GBmZoYnT56gUaNG8p/o6GiEhIQUedIzMDBAv379EBISgtTU1ELzQ0JCkJWVhYEDBwJ4Uzi/2453B8ko6gPT23FKJBL8+++/8hO7WCwudKX47W2W9AGMiKi6MjExQUBAAMLDwws9//Td986KeszLjRs35L/n5eUhJiYGTZs2Ldc5piiWlpZ4+PChfPBI4E1Po4cPH8LS0rLE9Q8ePAiJRIKff/4ZERER8p9du3YVGNjQ0tJSPq5Fvr1798q/pH333PJuPmUyWYGxH0JCQuDh4YHFixdj8ODBaN26NWJjY0v1mSM8PBza2trYt29fgZjXr18PQRDkMZf3HFscXV1dmJubF/k5xtTUVH5V911r167FpUuX0KNHD8yfPx+///47xGIxTp06Vep9U/VUbYraRYsWoW3btvLXd+/eRWhoKPbs2YPIyEgYGxuX2JVYS0sLffv2xYEDBzBq1ChMnDixyK6fpLoMDQ1x9OhRzJs3D/fv38f9+/dx9OhRNGzYEIaGhtDR0QEA3L59G6mpqejTpw/09fUxY8YM3LlzB3///bd8cIzi3rxNTU2hoaGBo0eP4smTJ7h8+TK8vb2Rm5tbYceTtrY2vvnmG/z44484duwYHj16hE2bNuH333/HN998U+rt+Pv7QxAEDBkyBCdPnkRcXBz+/vtvjB49GgkJCfJn8OUPkhEQEIC7d+/iwoULmDZtGpo1awYHBwe0b98eLVu2xPTp0xETE4NLly7h+++/L7CvCRMmYMuWLdi7dy9iY2Nx6NAhLF269L33DM2YMQN169aFh4cHjh49ivj4eNy8eRPff/891q1bh7lz56JJkyYAgFatWiElJQVbtmzBkydPsGvXrkKjR+vo6ODevXsFniv766+/4ujRo7h//z7mzJmDtLQ0DBo0SL7NGzdu4MiRI4iLi8PatWsLdEF793ghIqpJ+vbtiy+//LLQ4HqtWrVCaGgobt26hevXr2Pu3LlF3n9aVj///DMOHz6M+/fvY/bs2cjIyJCP9F+ec8y7XF1dYWRkhClTpuD69eu4fv06pkyZAn19fTg7O5e4fkREBBwcHNCpUyd8+umn8p/WrVvDyckJf/zxB168eIEhQ4YgKSkJCxcuxP3793HmzBkEBwfLR9TX0dHBkydPEB8fD+BNPv/66y/89ddfePToEebPny8fdwIAzM3NcfHiRdy6dQuPHj3C2rVrERUVVeJnjvxBrfJHTX475q+++gpt27aVDxhV3nPs+0yYMAHbtm3Dvn378PjxY4SGhmLHjh0YOXJksZ+x4uPjMX/+fJw7dw7x8fE4ePAg0tPTix30kmqOalPUviv/GVsDBw6Em5sbTpw4gQcPHuD58+fo3LlzoZ9Xr15h0qRJ8PDwAPDmUR21a9fGgwcPFNwSqkh6enrYvHkz4uLiMHDgQAwYMAC5ubnYtGkT1NTUoKuri2HDhmHlypUIDAyElpYWNm/ejLS0NPTv3x8zZ84scGWwKGZmZli8eDGOHTuG3r17Y/r06bCzs4Orq2uhxxN8CF9fXwwaNAiLFy+WP87nhx9+KNP9L+bm5ti7dy/atm2LxYsXw9nZGf7+/rCwsMC+ffvw8ccfA3jzjfwvv/yChIQE9O/fHxMnTkTz5s3x66+/QiwWQ11dHZs3b4aFhQWGDx+OyZMnF3oUjqenJ6ZMmYKQkBA4OTnhxx9/hLe3N3x8fIqNT1dXF9u3b8eAAQOwfv16ODk5YdSoUUhMTMSOHTswYMAA+bIdOnTApEmTsHnzZjg7O+Ps2bPw9fUtsL0xY8Zg9+7dGDVqlHzawIEDsXHjRvTr1w+xsbHYtm2b/D4kV1dXDB48GPPnz4ebmxuePXtWoBv5u8cLEVFNs2DBgkID5c2bNw96enr4+uuv4evri4EDB8Lc3PyD9+Xt7Y3NmzfDzc0NDx8+REhIiHxchfKcY96lpaWFkJAQaGpqYujQoRgxYgT09PSwc+fOEgdVzO/tN2TIkCLnjxw5Enl5eQgPD4eZmRk2b96Mmzdvom/fvpg5cyYGDBggj3XIkCF4+PAhnJyckJSUBC8vL3Tr1k1+3tfV1S1QZM+ePRt6enrw8PCAp6cnYmJisGDBAqSkpODp06fFxpw/qNX7Yk5OTsaJEyfKfY59Hw8PD/j5+eGnn36Cs7Mzfv31V/j7+2P06NHFrhMYGIgOHTpg6tSpcHR0xJYtW7BkyZIiu51TzSISqtmTkrt164Zt27bhxIkTiIuLk3/QzMzMhFQqfe+b0vbt29GnTx/5ADouLi5YvXp1qbtzUvUTHx+P2NhYODg4yKdduXIFgwYNwqlTp2BhYaHA6OhDWVlZYfny5XBzc1N0KERERERUTtX2Sm379u3xxx9/ICUlBYIgYN68edi6det71zl//rz8odD//PMPZDKZ/EoV1UzZ2dnw8vLCzp078eTJE1y7dg1Lly7FZ599xoKWiIiIiEgJVJvRj9/VrFkz+Pj4YMSIEZDJZGjevDnGjh373nVmzZoFf39/REZGQktLC6tWrSr14AJUPVlaWmLVqlXYuHEjli1bhtq1a6Nr166YPn26okMjIiIiIiJUw+7HREREREREVHPwMiQRERERERGpLBa1REREREREpLKqzT21qamZkMmqR09qY2NdpKRklLygimB7lFd1agvA9ii76tQeNTURDA11FB2GQqWmZsLQUKfa/E0rWnU63j8E81A85qYg5qN4zE3J591qU9TKZEK1KWoBVKu2AGyPMqtObQHYHmVX3dpTk+X/Lfk3LR5z8wbzUDzmpiDmo3jMzfux+zERERERERGpLBa1REREREREpLJY1BIREREREZHKqjb31BIRERERUc0klUqQmpoEiSRX0aFUuMRENchkMkWHUSU0NDRhaGgKdfWylaksaomIiIiISKWlpiZBW7s2dHTMIRKJFB1OhdLQUINEUv2LWkEQkJmZhtTUJJiYWJRpXXY/JiIiUgHfffedokMgKpU7cak4cvYR7sW/UnQoVINIJLnQ0dGvdgVtTSISiaCjo1+uq+28UktERKQCbt26BUEQ+IGNlNqh6EcI//MBAECsoYbFdWrDWEes4KiopuD7o+or79+QRa2SkMiAnDwJAEB4kYWsHImCIyobLbEGNHjdn4io0piamsLZ2Rl2dnbQ0fnfA+gDAwMVGBXR/9x/+gqRpx/KX+dJZLh2Lxld7crWjZCIqKxY1CqJnDwJzt9MAADo6WojPSNbwRGVzWfNzaChxcOJiKiy2Nvbw97eXtFhEBUp+eVrBIddg35tMTKzJcj77/1/95+8RBdbc6jxChpRpUtOTsLSpQuxcmUQTp/+E0+exMLDY+gHbTMjIwOLFs3FkiWr8OzZU0yaNA5hYYcqKOKKwyqEiIhIBfj4+CAzMxPXr1+HRCKBra0tdHV1FR0WEbKyJVgTdg0SqYBZQ1ojM1uCW49TEZ+cieiYZ5BJZfBybg4NdXbpIuV0L/4VbsemwqqhIZrWM1B0OOVmYmKKlSuDAAC3b9+skG2mp6fh7t07FbKtysSiloiISAVcu3YN3t7eMDExgVQqRUJCAjZu3IjWrVsrOjSqwaQyGTZG/ovnL7IwZaAdLIzfdI3PLwysGhthW9RNZOVIMKGvNbTE6ooMl2qQMzHPcPrasxKXe50jQVxSBgQBEImABqa6qFVC78NOthboaFN8t3pBELBhQzD+/PMUNDTU4erqjoEDPREb+xjLly9CenoatLVr4dtvp6F585ZYtGgetLVr4dq1K8jISIev71T89lsU7t27gy+++BJ+flMRFXUI0dGnkZychMTEBAwc6ImEhARcunQe+voGWLkyCC9epGDSpHFYsWINIiMPAADMzS3g7Owqjy0pKRFLlixERkY6UlKS0b27IyZMmISoqEO4fPkiZs2aBwDw8RkLL6+x2Lt3J5KTkxAQMA2+vlOQk5ODuXMD8ODBfejp6WPJkpUwMKiDM2f+wubNGyAIMnz0UT1Mnz4TRkbGGDDABS1aWOPu3dtYvXo9Vq1agpSUFACAl9cYdOrUpcS/UWnwKzMiIiIVsGzZMqxcuRIRERE4dOgQ1qxZg6VLlyo6LKrBBEHArj/u4t+HLzDM0QrNGxsVWubrrz7FcEcrxNxPweq9V5CVrVpjhlD1l5UjgSC8+V0QUCHj2vzf/51ATMxVbNu2B5s2bUVU1CGkpCRj4cLZ+PprD2zdugeTJk1BYOAM5Oa+Gek3OTkJW7fuxujR47FkyXxMnx6ALVt24dChCGRkpAMAbt68jlWrgrB+/c9Yu/ZHdOjwObZu3QMA+Oefs/L9N2nyMdzc3OHm5l6goAWAP/74DT16OGLTpi3YunUPwsPD8PLly2Lb8u2302FiYoolS1YCAF6+TMWgQUOwfXsojIyMcPz470hNfYEVKxZjyZKV2Lp1D2xs7PDDD8vl2+jQ4XPs3n0Aly9fgLn5R/jllx2YM2chrl698sG5zscrtURERCogIyMDHTp0kL92cHDA4sWLFRgR1XTHLzzB/12OR+8ODdHZ7qNil/vSvh5qa2tg86EbWL77EqYMbAV9Hc0qjJRqoo4277+amu9e/Cus2H0ZUqkM6upqGOva8oO7IF+5chHduvWApqYmNDU1sWXLLmRlZeHJkyfo0qUbAMDa2gb6+vqIjX0M4E3hBwBmZuZo0sQShoZvviTS19dHWtqbotbGxg46OrrQ0Xlz60mbNp8BeHM1Nj09vVSxDR48DJcuXcCuXdvx8OF9SCR5yM5+Xeq2mZiYokULawBAkyaWePXqJW7cuI7mzVvCwuLN+4Crqzu2b98iXyd/eWtrW/z00zokJyfCwaETRo4cVer9loRXaomIiFSAmpoa4uPj5a+fPHkCdXV25STFuHI3GXtO3EWbT03Rv4tlicu3a24G3wG2eJ6ShSU7LyHllWoNiEnVV9N6BpjuaY9+nT/GdE/7CrmnVkOj4HXDZ8+eQiaTQci/JPxfggBIpVIAgFj8v0dfFffe/vYyRe2nNIKDV2Pfvj0wN7fAiBGjYGBQR/64uLfjk0qLvmL9bmyCIEAQZIWm5bcLALS0tAAADRo0xK5dYejRozeuXr2MMWNGFMpJebGoJSIiUgETJ07EoEGDMH36dEybNg0DBw7EhAkTFB0W1UCxCen46eB1NDLXw2iXFqUe2djmY2NM9WiFtMxcLN5xEc9SMis5UqLSaVrPAM4OjStskCg7u9b4z39OQiKRIDs7G1OnTsKLFymoV68+/vOfkwCAf/+NwYsXKfj445K/FCoPdXX1AoVlvgsXzmHw4GHo1q07EhMTkJSUCJlMBgODOnj8+CEEQcDTp/G4d+/ee7fzthYtrHHjRgyePXsKADh48ABat25TaLn9+/ciJOQndOvWHVOn+iM1NRUZGRkV0Fp2PyYiIlIJ3bt3x8cff4y///4bgiBgwoQJsLQs+cPQmjVr8Ntvv0EkEmHAgAH45ptvEB0djSVLliAnJwe9e/eGn59fFbSAqoPU9BysCbsGnVoa8B1gW+aBnz6pXwczBtvjh9CrWLLjEqYMskNjc/1KipZIMbp06Ypbt27Ay2sIZDIBX3/tiYYNG2HOnIVYsWIxQkJ+glisiUWLlhe6+lpRWrVqjUWL5sHIyAgDBnjIpw8dOhILF86Brq4ejIyM0KxZCzx9Go+2bdvhyJFIeHr2R6NGjWBr2woAYGRkDDMzc0yaNA4zZ84tcl9GRsaYPn0WZs6chrw8CczNzeHvP6fQcr16OWPevFkYPnwQNDQ04OU1Fnp6ehXSXpFQUdd8FSwlJQMymeo2JTNH9Z9Tq1PMSHGmpnpISipdP39VUJ3aU53aArA9yq46tUdNTQRj46p/nM7Fixfx4sWLAt21evbsWezy//zzD1avXo3t27dDIpHAyckJ69evx/jx47F9+3ZYWFhg3LhxGD58OLp0KdsIlCkpGTA21q02f9OKVp2O93w5uVIs3XkJz1OzMHNoGzSoW/L/QHF5SEjNwqo9V5DxOg++/W3RrJFhZYSs1KrjMfIhPjQfz58/hrl5owqMSHloaKhBIpGVvGA1UdTfsqTzLq/UEhERqYBZs2bhzz//ROPGjeXTRCLRe4vadu3aYdu2bdDQ0EBCQgKkUinS0tLQqFEjNGjQAADg4uKCY8eOlbmopZpFJhOw6dB1xCamw7e/bakK2vcxM6yNgKFtsGrvFfwQehXefa3R6hOTCoqWiGoaFrVEREQq4OzZs/jjjz+gra1dpvXEYjGCgoLwyy+/oFevXkhMTISpqal8ft26dZGQkFDR4VI1E3bqPi7fTcbg7p/ArmnFFJ+GelrwH9Iaq0OvYO2BGIxybg4Ha/MK2TYR1SwsaomIiFSAsbFxmQvafL6+vhgzZgzGjx+PR48eFZovKuVAPwXjeXOlztS0Yu6Hqo6qS26OnX2EY//Eok/HJvDs3aLM678vD6YAlvp8gUW//oPNh29ApKEOly8+Ln+wKqa6HCMV5UPykZioBg2N6jsGbnVu27vU1NTKfCywqCUiIlJiv//+OwCgSZMm8PHxgZOTU4HHOLyv+/H9+/eRm5uL5s2bo1atWujZsyeOHTtW4JEMiYmJqFu3bpnj4j2171dd7pe8/ugFNuy/BpuPjeHWsVGZ21TaPEzs2xIbI69jU0QMEpIz4Nqxcbm+bFEl1eUYqSgfmg+ZTFZt7zutaffUymSyQscC76klIiJSYdu3by/wevfu3fLfS7qn9smTJwgKCpKvc+LECXh4eGD58uV4/Pgx6tevj8OHD6N///6VEzyptPjkTKwP/xcfmdTGeLeWUFervCtFYg11ePezxpajtxB5+iEyX+fBo/snpX5cEBHVbCxqiYiIlFh+UXvt2jXY2toWmBcdHf3edbt06YKrV6+ib9++UFdXR8+ePeHs7AwjIyNMmjQJOTk56NKlC3r16lVp8ZNqSsvMxZp9VyHWUMPkAXaoVcwTDiqSupoavnFqDh1tMX4/H4esHAm+cWpWqcU0EVUPLGqJiIiU2I0bNyAIAmbMmIFVq1bJH+cjkUgQGBiIkydPvnd9X19f+Pr6Fpjm4OCAgwcPVlrMpNryJFIEH7iGtMxczBjSGsYG5buXuzzURCIM6tYUOrXECP/zAbKyJZjQtyXEGmV7Hi4R1SwsaomIiJTY7t27cebMGSQmJsLHx0c+XUNDA46OjgqMjKojQRAQcuQm7senwbuvNZpY6Fd5DCKRCC6fN4aOtgZ2/n4Hq0OvYlJ/2yq5WkxEqonvDkREREps4cKFAIDVq1fDz89PwdFQdRd5+iH+uZmIAV9aom2zsg8gVpG6ta6P2loaCDlyEyt2X4bfQDvo1dZUaExEpXXp0gVs2/YLBAF4+vQJvvzyK+jo6OCvv/4DQRCwcuUa3LlzGyEhGyGRSGBhUQ8zZsyCgUEdnDx5HHv27EBOTg5ycnIwa9Zs2NjYw8dnLFq0aImrV6/g5ctUfPvtdDg4dFR0U5UCi1oiIiIV0LNnT1y/fr3Q9JYtWyogGqqOzv77HAfPPEInWwv0bt9Q0eEAADq0NEctLQ2sj/gXS3dewtRBrWCkX3XdoUm1ZR1aUmiaxsftoNnyKwiSHLw++kOh+eJPO0Fs9QVk2enI/mNt4fktukFs2b5U+79x4zq2b98LA4M6cHHpgYkTv0VIyHYsXjwfERH78eefpxAUtBH6+vqIiNiPDRuC8d13sxAZuR/Ll/+IOnXq4PDhSOzYsQ3LltkDAPLyJPjpp19x+vSf2Lx5A4va/2JRS0REpAImTZok/z0vLw9JSUmwtrZGWFiYAqOi6uJO3Ev8evQmmjWsg+GOVkr1OB27piaYMtAOQfuvYcmOi5jqYQ9zo9qKDouoRB9/bAkzM3MAgIFBHbRt2w4AYGZmjjNn/kJCwnP4+o4HAMhkUujrG0BNTQ2LF6/AmTN/ITb2MS5fvljgMWzt2zvIt52enlbFLVJeLGqJiIhUwLsDQl25coUFLVWIhNQsrD0QAxODWpjobgMNdeUbbdiqoSG+82yNH0KvYOmOi5gyqBUamukpOixScrVdAoqdJ9LQeu98NW29984vjbefKQ6gQHEqk0lha2uHZctWAwBycnKQlZWFrKwsjB49HI6OTrCzs4elZVMcOLBPvp6m5psu+CKRSD5wIAFV9q61du1aODs7w9nZGcuXLy80/+bNm+jfvz8cHR0xa9YsSCSSqgqNiIhI5bRq1arI7shEZZGZnYcf910DAEz+2hY62mIFR1S8RuZ6CBjaBhoaali26xLuxL1UdEhE5daihTWuX49BbOxjAMCWLT9j/fo1iIuLhZqaGoYP90KbNp/h77+jIZNJFRyt8quSojY6OhqnT59GeHg4IiIicP36dfzxxx8Flpk+fTpmz56N3377DYIgIDQ0tCpCIyIiUgnXr1+X//z777/Ys2cPsrOzFR0WqTCJVIZ1B2KQ8uo1fNxtYGao/F16zY1qY+bQNqijq4VVe6/g2v1kRYdEVC5GRsbw95+DOXMCMHz4INy5cxs+Pt+iadNP0LTppxg8eAC8vIaiVq3aeP78uaLDVXpV0v3Y1NQU/v7+8svllpaWePr0qXx+fHw8srOz0apVKwCAu7s7goKCMHjw4KoIj4iISOm9fU+tSCSCkZER5s2bp7iASKUJgoBtv93GrdiXGNOnBT5tUEfRIZWakb42ZgxpjdWhVxG8Pwaj+jRHhxbmig6LqIDWrduideu28tdhYYfkv48aNU7+e6dOnQutO3/+4gKvp037DhKJDGvXbpJPs7D4qMA2a7oqKWo/+eQT+e+PHj1CVFQU9uzZI5+WmJgIU1NT+WtTU1MkJCSUaR/GxrofHqgCCS+yoKf7v9H83v5dFdSurQXT9wzaYGpave57qU7tqU5tAdgeZVfd2lOV3r2nluhDHDsXi9PXnsG1Y2M4WKteQahfWxPfedojKOwaNh+8gdfZEnRtXV/RYRGRglTpQFF3797FuHHjMGPGDDRu3Fg+vaibnMs66l5KSgZkMtW9WTorR4L0jDfdyPR0teW/q4qsrBwkSYvu729qqoekpPQqjqjyVKf2VKe2AGyPsqtO7VFTE1X5l6kvXrzAwYMHkZmZCUEQIJPJ8PjxY6xatapK4yDVd/F2Ivaduo/2Lczg1qmJosMpt1paGvAbaIeNkdex/fc7yMiWoI9DI6UauZmIqkaVDRR18eJFjBw5ElOnTkW/fv0KzDMzM0Ny8v/uiUhKSkLduop94DcREZEy+fbbbxEdHY39+/fj+fPniIiIgJqa8o1SS8rt4bM0bD50A5b19OHl1EzlC0BNsTq8+1nDoaUZwv98gL0n73FEWKIaqErOhs+ePcPEiROxcuVKODs7F5pfr149aGlp4eLFiwCAiIgIdO5cuH85ERFRTfX06VNs2rQJnTt3xtChQ7F7927ExsYqOixSISmvshEUdg36OpqY5G4LsYZ6ySupAA11NYzq0wJftamP38/H4deoW5DKZIoOixSAX2iovvL+Dauk+3FISAhycnKwdOlS+TQPDw+cPHkSvr6+sLGxwcqVKxEYGIjMzEy0aNECw4cPr4rQiIiIVIKJiQkAoHHjxrhz5w5cXV35+Dsqtdc5EqwJu4pciRTTPO2hr6Op6JAqlJpIhMHdP4FuLTEiTz/E6xwJxrq2hFiDvRlqCg0NTWRmpkFHR1/leyDUVIIgIDMzDRoaZX9/qpKiNjAwEIGBgYWme3p6yn9v1qwZHyJPRERUDGNjY/z8889o1aoVgoODoauri4yMDEWHRSpAKpNhY+R1PE3Ogt9AO9Qz0VF0SJVCJBLBrVMT1NbSwO4Td/HjvqvwcbdBLa0qHUKGFMTQ0BSpqUnIyKh+zy9WU1ODrIb0PtDQ0IShoWnJC767XiXEQkRERBVswYIFOHLkCNq2bQtra2sEBQVh2rRpig6LVMCeE/cQ8yAFw3tZoWUTI0WHU+l6fNYAtbU18GvULazccwV+A+2gW0us6LCokqmra8DExELRYVSK6jTQYmVhnwwiIiIVYGxsLL81Z/r06YiIiECPHj0UHBUpu+MX4nDi4hM4tmuAL1vVU3Q4VaajjQUmulsjLjEDy3ZeQmp6jqJDIqJKxKKWiIiIqBq6dj8Zu0/chf0nJvj6y6aKDqfK2X9iCr+BdkhOy8aSHReRkJql6JCIqJKwqCUiIiKqZuISM7Ah8joa1NXFWJeWUFOrmQPnNG9kiO887ZGdK8WSHZcQl8j70ImqIxa1Bp0kwAAAIABJREFURERERNXIy4wcrAm7itpaGpg8wA5amtXj0T3l1cRCH/5DWkNdTYRlOy/h3pNXig6JiCoYB4oiIiJSYmvXrn3vfB8fnyqKhFRBTp4UQWHXkPlaAv8hrWGop6XokJTCRyY6CBjaGqv2XMHKvZfh088G1h8bKzosIqogvFJLRESkxFJTU5GamoqLFy8iLCwML1++REZGBiIjI3Hr1i1Fh0f/z96dh0dVnv8ff8+WSSb7Mtk3SAhhzcISoiAChYiAgAuya1u3ulWtBaVYK78qVq1+rUtV2rpiVaCiskQsKIgQlgQCIWEnO9kTQvaZzPz+SBtN2UMyZzK5X9eVi8ycnHM+58mQmfuc5zyPHbFYrfztq2zySs5y702DiAh0VzqSXfHzdOGJ+cMI9Dbw6uoD7M4pVTqSEKKLyJVaIYQQwo499dRTACxcuJB//etf+Pi0Tcnyq1/9ivvvv/+S67/++uts3LgRgLFjx7Jo0SKefPJJ0tPTcXFxAdqu9spIyj3fmq0nSD9azuwJ/Yjv56d0HLvk6erEormJvLo6k7e/OERjs5mxvWhUaCEclRS1QgghRA9QXl7eXtACeHh4UFlZedF1duzYwfbt2/n8889RqVTcddddfPPNN2RlZfHRRx/h7+/f3bGFjWzLLGZjWj7jEkKYODxU6Th2zeCs5bHb4/nr2izeTz1CfZOZG0dFKB1LCHEVpKgVQggheoD+/fvz5JNPMn36dKxWK6tXryYuLu6i6xiNRp544gmcnJwAiIqKori4mOLiYp566imKi4uZOHEiDz74IGq13JHUU+XkVvHh10cY1MeHuRP7oVL1zpGOr4Rep+HBm4fw9/U5rP7uBPWNJm69PkrazoasVis7D5VwrOAM4QFuDOrnT02NTLsEUFReR3FlA0kDA4gO8VQ6To8gRa0QQgjRA/zxj3/kL3/5C88++ywqlYoxY8bw0EMPXXSdfv36tX+fm5vLhg0b+Pjjj9m9ezfLli3DYDBw7733snr1ambNmtXdhyC6wenKet74PItAHwO/mj4YjZycuGxajZq7pw3EoNeycVc+9U1mFqb077XTH9lKUUU9u7JL2H7gNDV1LT8u2HRUuVB2auv+IhbNTcRolPvjL0WKWiGEEKIHcHNz47HHHiM3N5eYmBhaWlpwdna+rHWPHTvGvffey+LFi+nbty9vvPFG+7IFCxawdu3aKy5qfX3dAOTD1kV0d9ucqWvmtX+l4aTT8My91xDgY+jW/XWWvb9GHp03DH9fVz7991Fagd/MTUSntc00SPbeNl2lrKqBbfuL2JpRSO7pWtQqCPB15UxdC1ZApYIJI8IZmyD3N2/dV8TmPflYrWButVJY2Xb1ure8VjpLilohhBCiB9i/fz8PPvggWq2WTz75hOnTp/PXv/6VxMTEi66Xnp7Oww8/zJIlS5gyZQpHjhwhNzeXlJQUoK0LoFZ75R8HKivr8PV1o7z8bKeOx9EZje7d2jYms4UXP9lH5ZkmFs1JQN3aape/i+5uh66SMjwULBY+3XKcmtomHpw5pNvn9+0pbdNZtfUt7Dlcxq6c0va5gaNCPJj7s36MGBBAeU0jL/5zH62tFjQaNSlJEfi66hROrbyR/Y1szSjEZLYAoLFaARz6tXI51GpV+8nU85GiVgghhOgBXnjhBd577z0ef/xxAgMDeeGFF3j22WdZs2bNBdc5ffo0DzzwAK+88grJyclAWxH73HPPMWrUKAwGA59++ikzZ8601WGILmC1Wnl3Yw7HC89w3/RBRMk9d10iZWQ4Br2W91IP89Kn+3jktjhcnaXIuhKNzWYyjpazK7uU7NxqLFYrIUZXbhnbl5EDAjB6ubT/rKerE7+dk8CR/Gr6h3sTG+nT6ws3gOgQT347J4Gsk5V8vbuAzBOV3PwzpVPZPylqhRBCiB6gqamJ6Ojo9sdjx47llVdeueg6f//732lubub5559vf2727Nncc889zJkzB7PZzKRJk5g6dWq35RZd76sfckk7VMrM69oKBdF1xsQFY3DW8vaXh/jTygweuz0eLze90rHsmsncSubxSnbllJJ5vBJzqwU/T2cmjwonaUAAof4XvroWHeIpAyGdx3/bxWqFr3bkkl9Si4tG7vW+GClqhRBCiB5Aq9Vy5syZ9tFZT548ecl1li5dytKlS8+7bN68eV2aT9hG2qES1m4/xTWDA5maLNPQdIdh/f155DYtr605yPMfZfCb2fEdrjAKaLVYyMmrZtehUjKOldPY3IqHqxNj44MZNTCAvsEeMpJ0F5g4IoxNewpYveUYCybGKB3HrklRK4QQQvQA9913H/Pnz6eiooLHHnuMH374gWXLlikdS9jQscIa/rEhh5gwL+64IVaKhm40MNKHx+fE83+fZfLcR+n85vZ4Qo0XvuLYG1itVk4U1bIru5Q9h0upbTDhotcwLMafpIEBxEZ4yejbXczNRcfY+GD+nV7IDSPC5OTKRUhRK4QQQvQA48ePJyoqih9++AGLxcL999/foTuycGxlNY28tuYgPh7OPHjzEHRaKR66W1SwJ0/MS+TPn+7nTyszeGRWHFHBvaurrNVqpbC8nl3ZpezKLqWytgmdVk1clC9JAwMZGuVjs5Gie6uUkeFsySgidVc+C1L6Kx3HbklRK4QQQvQAS5Ys4bnnniMi4scupw899BCvvfaagqmELTQ0mXh1VSZWq5VHbovDzUUGL7KVEKMbT84fxp8/2c9L/9zPg7cMYVCkj9Kxul1ZTSO7skvZnV1KUUU9apWKgX28mXldHxL6GXHRSwlhK97ueiaMCGPzngKmXRsp93hfgLwihRBCCDv29NNPU1paSnp6OlVVVe3Pm83my7qvVvRs5lYLb67Noqy6kcdnxxNop3PROjKjlwtPzk/kz59m8uqqTO69aRDD+vsrHavLnalrZndO2xQ8J4trAegX6sn8STEMj/XHw+CkcMLe65Zx/di0K49NewqYNU566JyPFLVCCCGEHbv11ls5duwYR44caZ9bFkCj0ZCQkKBgMtHdrFYrH206SnZuNb+4cQD9w72VjtRrebrpWTwvgVdXHeDNtVnceUMsY+KClY511RqaTKQfKSctu5TD+dVYrRDu78Zt10cxckAAvp7OSkcUQJCfK0kDAvh2XxE3joqQ3hrnIUWtEEIIYceGDBnCkCFD0Gg0TJ8+vcOytWvXEhYWplAy0d2+3l3AtsxipiRHMHpokNJxej1XZx2/uT2eNz4/yLsbD1PfZOaGpHClY12xZlMrmccr2JVdysGTlZhbrfh7uTA1OZKkgQEE+7kqHVGcx43JEaRll7I5vZDpo/soHcfuSFErhBBC2LEtW7ZgNpt57bXXMBgMWK1WoK378SuvvMKMGTMUTii6Q8bRclZ9e5zhsf7MvK6v0nHEf+idNDx861BWfJXNZ98ep77JxM3X9bX7kajNrRayc6vYlV1KxrEKmlta8XRzYlxCKKMGBRAZ6G73x9DbhRrdSOjnx7/3FjBpRJjc1/w/pDWEEEIIO5aTk0NaWhqVlZV88MEH7c9rtVp++ctfKphMdJfcklre+eoQfYI9uGvKANRSbNgVrUbNvTcNwuCsZf3OPBqazMybFGN3vyeL1cqxghp25ZSx93AZdY0mDHotSQP8SRoYSP8wL9Rq+8osLu7G5Aj2Hatg6/7iHtlLoDtJUSuEEELYsQceeIAHHniAlStXMm/ePKXjiG5WVdvEq6sP4O7ixEO3DMVJJ9Ol2CO1WsXClP4YnLVsTMunvsnEXVMHotUoO9WS1Wolv7SubQqenFKqzzbjpFMTH+1H0sAABvfxlemgerCoYE8GRHjz9Z58JgwLkemUfkKKWiGEEMKOffHFF0yfPp3m5mbefffdc5b//Oc/VyCV6A5NLWZeXX2A5pZWfrMgHk9XGW3WnqlUKm67Pho3Zx2rvjtBY3Mr988cjF6BExElVQ3tc8mWVDWgUasY3MeH266PIr6fH85O8pHfUUxNjuDFT/bzw8ESrk8IUTqO3ZBXuBBCCGHH8vLyADh27JjCSUR3slisvP3FIYrK63nktqGEGt2UjiQu0+RRERictXyQeoSXP93Pr28disG5+0enrT7bzO6cUtKyS8krOYsKiAnzYtLIMIb395cRch1UbIQ3fYM92JCWx5i4IDRqufIOUtQKIYQQdu3hhx8GYPny5QonEd3p0y3HyTxRyYJJMQzu66t0HHGFxsaHYHDW8c6Xh3jh4308env3XGmvazSx90gZu7NLOZJfgxWICHRn1rhoRg7wx8dDpuBxdCqViinJEby25iC7s8tIHhyodCS7IEWtEEII0QPs2rWLd955hzNnznR4fvXq1QolEl1lS0Yh3+wtYOLwMMYlhiodR3TSiFh/XJw0vP75QZZ/lM7js+Px83S56u02tZjZf6xtCp6sU1W0WqwE+hi4aXQfkgYGEOhj6IL0oieJi/YjxOjK+rQ8kgYF2N0gZUqQolYIIYToAZYuXcqCBQsID5cRLx3JwZOVfPzNMeKifLl9fLTSccRVGtzXl8dnJ/B/n2Wy/KMMfnN7fKfmfTW3Wsg6WUVadgn7j1fQYrLg7a5n4vAwkgYGEB7gJlPw9GLq/1ytfefLbPYfqyAxxqh0JMVJUSuEEEL0AL6+vixcuFDpGKILFZbX8de1WYQaXbl3+iCZXsVBRId4snheIi9/up/nV2bw6Kw4+gR5XHI9i8XKkYIadmWXkn6kjPomM67OWq4ZFEjSwAD6hXnJFTnRbkSsP2u3nWL9zlwS+vn1+pMcUtQKIYQQPcD48eNZuXIlY8aMQav98e07ODhYwVSis87UNfPqqkz0ThoevnWojE7rYML83XhyfiIvfbKfF/65j4dvGcqACO9zfs5qtZJbcrZ9Cp4zdS3odRoSYvxIGhDAoD4+ik8TJOyTRq1m8qhw3k89QnZeNYMifZSOpCj5CyqEEEL0AFVVVbz88su4uPx4j55KpSIjI0PBVKIzWkyt/GXNQc42mnhiXqIM7uOg/L0NPDl/GC9/up9XPstkxuhIrED/cG8Mei1fpxfy7d4Cyqob0ahVDI3yJWlgAHHRfopMCyR6nmsGB/HF9lOs35ErRa0td1ZXV8fs2bN56623CA3tOBDC66+/zpo1a/DwaOueMWvWLJlkXgghhPiP1NRUtm/fjp+fn9JRxFWwWK38bX0OuadreeDmIUQGXrpbqui5vN31LJ6XyPMr01m99SQAKsAKqFQQG+7NjaMiGNbfiKsNpgESjkWnVXPDyHA+2XKc40VniA7xVDqSYmxW1GZmZrJ06VJyc3PPuzwrK4uXX36ZhISETm2/1QINzearSKgsi1XpBEIIIeyZr68vPj69+0y8I/h820n2Hi5j1rhoGdyll3Bz0TE81p8vt+cCbQXt0ChffjN/OK3NJkWziZ7vuvhg1u3MY/2OXH59W5zScRRjs6L2s88+4+mnn2bRokXnXZ6VlcWKFSsoKChgxIgRLF68GL1ef9nbbzG3sientKvi2lycvLEJIYS4iCFDhjB37lzGjRuHk9OP81/+/Oc/VzCVuBLbD5xm/c48rosLJmVkmNJxhA0N7uPLxrR8zK0WtBo1U6+JxMfDmfJyKWrF1XF20vKz4aGs/f4U+aVnCQ9wVzqSImx25/mzzz7L8OHDz7usvr6eAQMGsHjxYj7//HNqa2t58803bRVNCCGEsHvNzc306dOH3Nxcjh492v4leobDedW8n3qYARHezJ8U0+tHKu1tokM8+e2cBG6+ri+/nZPQq7uJiq43YVgozk4aNqTlKR1FMXYxUJSrqysrVqxof/yLX/yCJUuW8Oijj172NpxdnHB367kDLeh02g75e9qxGAx6jBeZ/NtodKyzRo50PI50LCDHY+8c7Xhsafny5Z1a7/XXX2fjxo0AjB07lkWLFrFjxw6WL19Oc3MzkydPvqL3W3HlSqoaeOPzg/h7u/DAzMEymm0vFR3iKcWs6BauzjrGJYaQuiufmWMaCLjIZ3JHZRdFbXFxMTt27ODWW28F2oY3/+l0BZejqbGFs3VN3RHPJkwmc3t+dzfnHncsDQ3NlLe2nneZ0ehOeflZGyfqPo50PI50LCDHY+8c6XjUahW+vm423eeuXbt45513OHPmTIfnV69efcF1duzYwfbt2/n8889RqVTcddddrFu3jpdeeokPP/yQoKAg7r33XrZu3crYsWO7+xB6pbpGE/+3KhOVSsWvb4vDIIMBCSG6waQR4fx7byEbd+Vx5+QBSsexObsoap2dnXnxxRdJSkoiNDSUlStXMnHiRKVjCSGEEHZj6dKlLFiwgPDw8Mtex2g08sQTT7TfgxsVFUVubi4RERGEhbXd0zlt2jRSU1OlqO0GJrOF19ccoKq2mUVzEvD3crn0SkII0Qmerk6MGRrE1v3F3HRtn143VZiiRe3dd9/Nww8/zJAhQ1i2bBm/+tWvMJlMJCYmysAXQgghxE/4+vqycOHCK1qnX79+7d/n5uayYcMGFixYgNH44+CE/v7+lJZe+UCL/71SLV3Kz89qtfLpdyc4WniGx+cNIzkh9NIrOSh5jVyYtE1H0h4XdjltM2/yQLbuL2ZbVgl3Tx9ig1T2w+ZF7ZYtW9q//+l9tCkpKaSkpNg6jhBCCNEjjB8/npUrVzJmzJgOt+gEBwdfct1jx45x7733snjxYrRaLadOneqwvDODFlVW1uHr6+YwXcq72pbMYrbsLWDG6D4MDPPste3kSLcddDVpm46kPS7scttGBYwaGEDqzlzGxwfjYXC61Co9xqVu+7GL7sdCCCGEuLiqqipefvllXFx+7MKqUqnIyMi46Hrp6ek8/PDDLFmyhClTprB7924qKiral5eVleHv73/FeRo3v0lxcx0mU9sc8dq+I3EaNAGruZnGjS+f8/O6mNHo+o/B0nSWpm9eP3f5wPHoopKw1FXS9O075yx3GnoD2ogELDWnafr+vXOXJ9yENnQQrRV5NO/8+Jzl+hG3ognsR2vJMZr3nHsfsj55Lhq/CMyFh2jZ9+U5y53H3InaKwhz3j5aDqSeu3zcPajdfDGd2EXl3q/xqqjnd4FOBFbvouErcJ74IGpnd0xHvsd0dPs567tMfgyVVk/Loc2YT+4+Z7lh2pMAtGRuxJy/v+NCrROGyb8BoDnjC1qLsjssVundcJn0UNvy3atoLT3ecbmrDy7j7wWgacdKLJX5HZarPQNxvq6tB13TtnexnCnpuNw3HOdr5gHQuOVtrPVV7cuKdVosPpHoR97WtnzTa1ib6zqsrwkZiD5xOgANG/8M5paOhxcej1Pc5LblX507YFpPfe0V67SYTOYufe2Zsrecu7yHvPYqNv2DhsKOy6/mtQegCYh2iNdeS2URDV+9ce7y87z2bjZZSHSupXrNt7hOnG+zv3vd/dqzVuXCrPNPDQtS1AohhBA9QmpqKtu3b8fPz++y1zl9+jQPPPAAr7zyCsnJyQDExcVx6tQp8vLyCA0NZd26ddxyyy3dFbvXKalqoKKyHldnLQE+BmTiHiGELel1atwMOmrqmvFracVV6UA2orJarValQ3SFwtNn2Jl1WukYnRYXYyTzaDnQM0c/HjEgAFf9+c+ROFp3Ekc6Hkc6FpDjsXeOdDxKjH58yy23sGrVKtTqy58O5o9//CNr1qzpMLjU7NmziYyMbJ/SZ+zYsTz55JNX3AVZuh93dLzoDBlHytmWWYSri45XHr2elsaWS6/o4Bzp/31Xk7bpSNrjwq60bfJKzvLMe3u4ZWxfpiRHdl8wG5Lux0IIIYQDGDJkCHPnzmXcuHHtoxkDFx1YcenSpSxduvS8y7788tyuZqJzjhed4cV/7sNktgCwIKU/nm56yqWoFUIoICLQncF9fdi0p4CfDQ9Dr9MoHanbyezfQgghRA/Q3NxMnz59yM3N5ejRo+1fQjlWq5VjhTV8kHq4vaBVqaDiTM/qbSWEcDxTkyM522Bi+4Ge25P1SsiVWiGEEKIHWL68bZCSoqIizGYzERERCifqvVpMrezKLmVzeiH5ZXU46TSoVSqsWNFq1PQP91Y6ohCil4sJ86JfqCcbd+UxNj4Yrcaxr2VKUduDWK1Wmk0WzGYLplYL5lYLJnPbvzqtGhcnLc56DXqdplPTMwghhLBfeXl53H///ZSVlWGxWPD29ubtt98mKipK6Wi9RnlNI9/uK+L7zGLqm8yEGF1ZeEN/kgcGUlBex5H8avqHexMd4ql0VCGEYEpyJP+3KpO0Q6WMHhqkdJxuJUWtnWpttVBd10L12Saqa5upPttMdV0zLSbLJddVAXonDe4GHX6eLhi9XTB6OePqrOv+4EIIIbrFsmXLuOuuu5g5cyYAa9as4ZlnnuGDDz5QOJljs1it5ORWszm9kMzjFahUKhJj/JgwLJSYMK/2k8jRIZ5SzAoh7MqQvj6E+7uxPi2PawYHolY77kUvKWrtSHNLK4XldZyuLCG/tBZza9vA1FqNCi83PZGB7ni4OuGk1aDVqtFp1Gi1KrRqNSazhcYWM00trW1fzWbO1LdwtKCGnLxqAAx6LUZvF8ID3Ajzd3P4bghCCOFIKisr2wtaaBsN+b333lMukINrbDbzw8HTbMkooqSqAQ+DjinXRHJ9fDA+Hs5KxxNCiEtSqVRMuSaSv67NIv1oOSNir3xO8p5CilqFNZtaOVVcy46sEk4UncFqBVdnLVEhngT6GPB21+Nu0HW6O3GrxUr12WbKaxopr2mktKqBvJKzaDUqwgPc6RPkQZCvwaHP3AghhCNobW2lpqYGLy8vAKqqqhRO5JiKKurZklHIjqwSmltaiQr24O5pAxne3x+dVk4GCyF6lmExRgJ9DKzfkcvw/kaHvUVRilqF1Na3kJNXzYmiM5hbrQR4uzCoT1sXgcgQL+rqm7tkPxq1Cj9PZ/w8nRkQ4Y3FaqWsqpGTp2vJLznLyeJanJ00RIV4MCDCB4OzvCSEEMIezZ8/n9tvv53JkycDsHHjRu644w6FUzmGVouFzOOVbE4vJCevGq1GTdIAf8YPC6VPkIfS8YQQotPUahU3jorgHxtyOHiyiqFRvkpH6hZSwdiQ1WrldGUDOXnVFJXXo1ap6BPsTmyEN+OHh5F5tBygW8+gqFUqAn0NBPoaSBroT1F5PSeLa8k+VU1OXg3RIZ4M6uONu8Hp0hsTQghhM7fffjsRERF8//33WCwW/vCHP5CcnKx0rB7tbEML2zKL+W5fEZW1zfh46LllbF/GxAXjIe+DQggHMWpQAF9sP8n6nblS1IqrU1LVQPrhciprm3B20hAX7UtMmBcueuV+BRq1mvAAd8ID3Kmtb+HQqSqOF9ZwrLCGPkEeDO7rg5ebXrF8QgghflRaWkpqaip/+MMfOHnyJC+99BLR0dEYjUalo/U4uSW1bE4vZFd2GeZWCwMivJk9IYb4fr5o1NLFWAjhWLQaNTckRbDym6McLaghJsxL6UhdTorabnamrpn0oxUUltVhcNaSPDiAvsEedvem6eHqRPLgQOKifTl0qppjhTWcKq6lX5gXCf380DtplI4ohBC92uLFixk/fjwAISEhjBw5kiVLlrBixQqFk/UMJrOFvUfK2JJeyIniWvQ6DWPighifGEqIn6vS8YQQoluNGRrEVz+cYt3OXB4Li1c6TpeTorabNDabOXCikqMFNWjVahJi/BgQ4W33Iw4bnHWMGODPkCgfDhyv5Eh+DbkltST0M9IvzBO1g95cLoQQ9q66upqFCxcCoNfrufPOO1m7dq3CqexfVW0T3+0vZtv+ImobTAT4GJj7s35cMzhIxpEQQvQaTjoNE0eEsWbrSXJLaokMdKzxAuSveRezWq0cLzzD3iPlmFstxIR5MTTKV9Fuxp3h7KRl5MAA+oV5sTu7lF3ZpRwrrGHkAH/8vQ1KxxNCiF6ntbWV0tJSAgICAKioqMBqtSqcyj5ZrVaOFtSwOaOIjCPlWK1W4qL9GD8shIGRPnKCVgjRK41LCGVDWj7rd+bxwMwhSsfpUj2r0rJzdY0mdmaVcLqygQBvF0YNCsCzh9+T6u2uZ9LIMHJLzpJ+pJzUXQX0C/VkeKxMbSCEELZ05513MmPGDMaMGYNKpWLHjh0sWrRI6Vh2pbmllZ3ZJWxJL6SwvB5XZy2TRoQxLjEEo5eL0vGEEEJRBmctE4aFsn5HLsUV9QQ70K0XUtR2gf+eEU4/0jZ68ciB/vQP83KYeaBUKhV9gjwINbqRebyC7NxqSqoaGD0kCKO3fEgQQghbuPXWWxk8eDBpaWloNBp++ctfEhMTo3Qsu1BW3cCWjCK+P3CaxmYzYf5u3Dk5lqSBAeh1MiaEEEL818ThoWzak8/GtDx+OXWg0nG6jBS1V6mu0cSOgyWUVDUQ5GsgeVAgbgad0rG6hU6rZnisP2H+bmw/cJrUXfkM7uvD0Gg/paMJIUSvEBsbS2xsrNIx7ILFaiXrZBVbMgo5eKIStVrFsP5GJgwLJTrE02FOLAshRFdyNzgxNi6EzemFTB/dBz8H6cUiRe1VKCir44cDp7Fa2+Z/6hfaO95EA3wMTBsdyZ6cMg6erKKoop6IQHeigz2VjiaEEMLBNTSZ2H7gNFv2FVFW3YinqxPTro1kbHwI3u49+5YfIYSwhZSRYWzJKCR1dz7zJ/VXOk6XkKK2EywWKxlHy8nOrcbHQ8/Y+GDce9kk7U5aDdcOCSLM342dWaW8+PE+7pwcS/KgQKWjCSGEcECFZXVsyShkx6ESWkwWokM9mTmmL8P6G+1+ZgEhhLAnPh7OXDskkG2Zp5l2TWSPHwMIpKi9YvWNJrZlFlNe00T/cC+G9zei6cVvpuEB7vh5urDvWDkrvsrmZFEtt0+Ilg8YQgjRRe666y7+9re/8f7773PHHXcoHcemzK0W9h+rYHN6IUcKatBp1SQNDGBCYigRge5KxxNCiB5rclIE3x84zaZBlG2hAAAgAElEQVS9Bdx2fbTSca6aFLVXoKi8ju0HSrBYrIyJC6JPkGPN79RZBmctD90ylA0789i0p4Dc0lrunzFEuoEJIUQXOHHiBF999RUffvghQUFB5yyfNGmSAqm6V219C1szi/luXxHVZ5vx83TmtnFRjBkajJuLY45bIYQQthTgY2BErD/fZhRx46gIXJ179t9WKWovg9VqJTu3mvQj5Xi7t3U39nDtXd2NL0WjUTN7Qj/6Bnvw7obDPPPubu6bPpjYCG+lowkhRI/28MMPs3r1aiorK/nwww87LFOpVA5V1J4oPsOW9EL2HC7D3GplUB8fFkzqz9AoX9Rqxx+zQgghbGlKciS7c8rYnF7ITdf2UTrOVZGi9hJaLVbSDpVwoqiWiAA3rh0aJF1rL2LkgABCjG688a+DvPTJfuZN7MeslAFKxxJCiB5r5syZzJw5k+XLl/Pkk08qHafLmcyt7M4pY0tGIadOn8XZScPY+BDGJ4YQ5Os4cygKIYS9CfN3Iz7aj2/2FDBpRBjOTj23NOy5yW2gqcXMd/uKKatuZGiUL3HRvr1idOOrFeLnylN3DOftLw/x4aaj1Da1Mm1UuJxlF0KIq7Bo0SJWrFjBtm3bMJvNXHvttdx3331otZd+K6+rq2P27Nm89dZbhIaG8uSTT5Keno6LS9tUDg8++CATJ07s7kPooPJME9/tL2Lr/mLqGk0E+RqYPymG5EGBuOjl44kQQtjClOQInv2wgm37i5k0MlzpOJ0m7xoXUFPXzJb0IhqazYwZGkSfYLl/9kq46LU8fMtQPtl8jC+2nSCv+Az33DSwR58BEkIIJb3yyivk5ORwxx13YLFY+PTTT3nhhRdYsmTJRdfLzMxk6dKl5Obmtj+XlZXFRx99hL+/fzen7shqtXI4r5rNGUXsO1YOQHy0HxOGhTIgwltOHAshhI1FhXgSG+5F6u58xiWGotP2zB6pUmGcR3FFPVv3F6PVqEgZGYbRQSYltjW1WsXciTFEhXvzztqDPL8yg1/fGicDSAkhRCds27aNNWvWoNO1DeZx/fXXc9NNN12yqP3ss894+umnWbRoEQANDQ0UFxfz1FNPUVxczMSJE3nwwQdRq7vvg0xTi5mdWSVsziiiuKIeNxcdk5MiuD4hGD9PeY8VQgglTbkmkj9/sp8dWacZGx+idJxOkaL2f5w6XcsPB07j4erEhGGhuMooi1dt6ui+OGtUvPXlIf74wV5+fetQwgNkKgYhhLgSVqu1vaAFcHJy6vD4Qp599tkOjysrKxk1ahTLli3DYDBw7733snr1ambNmnVFeXx93QAwGi/897yovI71P5xi8558GprMRId68sjsBMbEh+Ck01zR/nqii7VNbyLtcGHSNh1Je1xYd7bNWD83vvwhl017Cpk5PqZHTlcqRe1PZOdWsfdwOQHeLoxL7B1vuLYSF+3Hk/MSeXX1Af70cQYP3TxURkYWQogrEBsby3PPPcf8+fMBWLlyJTExMVe8nbCwMN544432xwsWLGDt2rVXXNRWVtbh6+tGefnZDs9bLFYOnKxkS3ohWaeq0KhVjBjgz4TEUPoGe6BSqThT03DFuXsao9H9nLbpjaQdLkzapiNpjwuzRdukjAjj9X8dZMP3Jxg1KLBb99UZarWq/WTqeZfbMIvdslqtpB8pZ+/hcsID3PjZ8FApaLtBeIA7v1swDC83PS9/tp/0I2VKRxJCiB7j6aefpra2ltmzZ3P77bdTVVXFU089dcXbOXLkCF9//XX7Y6vVelmDTV1KXaOJ1F35PPH2Tv6y+gCF5XXMGNOHlx64lnumDSIqxFPumRVCCDsV38+PED9X1qflYbFalY5zxXr9lVqLxcqOrBJOFtcSE+bJyIEBqOVNt9v4eDjz5PxhvLo6kzfXZrEgpT/X99C++0IIYUtubm48//zzV70dq9XKc889x6hRozAYDHz66afMnDmz09vLLz3L5vRC0rJLMZktxIR5cdu4aBL6+ckUeEII0UOoVSpuTI5gxVfZZB6vIKGfUelIV6RXF7WtrRa+219MUXk98dG+DImSKXtswc1Fx+OzE/jr2iw+SD1CbX0L066JlLYXQggbiI2N5Z577mHOnDmYzWYmTZrE1KlTr3g7/04vIONoBdmnqnDSqrlmcCDjE0MJ879w9zAhhBD2a+QAfz7fdpJ1O/KIj/brUZ/NbVbU/u8ceT+Vk5PD0qVLqaurY/jw4TzzzDNd0hXqYkxmC99mFFFS1UDSwAD6h3t16/5ER3qdhgdvHsJ7Gw+z9vtTnK03MWdiP7lKLoQQ3WTLli3t38+bN4958+Zd1fY27S7A3Grh9vHRjB4ahKuzDKwohBA9mUat5sZREXzw9REO51UzINJH6UiXzSb9gjIzM5kzZ06HOfJ+6re//S1PPfUUX3/9NVarlc8++6xb87SYWvn33gJKqxq4dkigFLQK0WrU/GLKAFJGhrE5o5C/r8uh1WJROpYQQtilQ4cOKR2hAxUw5dq+pIwMl4JWCCEcxLVDAvF0c2Ldzjylo1wRmxS1/50j73yTvBcVFdHU1ER8fDwAN998M6mpqd2WpamllW/2FFB5ponr4oOJCvHstn2JS1OrVMwaF83MMX3YeaiEt9YewmSWwlYIIf7X448/rnSEDjRaNUOj/ZSOIYQQogvptBpSRoSTk1fNieIzSse5bDYpap999lmGDx9+3mVlZWUYjT/eiGw0GiktLe2WHI3NZjbtzqe6roXrE0OICJS5sOyBSqVi2rV9mD2hH+lHy3ntXwdoNrUqHUsIIexK//79+eqrryguLqampqb9Syn33jSI2B7UNU0IIcTluT4hGFdnLet39JyrtYoPFGU9z5DRnbkp2dnFCXc35wsur2s0sWnPKeobzUwb3YdQf/sqaHU6bYf8FzsWe2Qw6DH6GC64/HImjJ5340D8fFx5Y/V+3libxVO/SMJgp13aHGlycEc6FpDjsXeOdjy2tHnz5nN6MqlUKnJychTJExEgv0shhHBEzk5afjY8jC+2n6KwrI7QHjAAoOJFbUBAABUVFe2Py8vLz9tN+VKaGls4W9d03mX1TSY27S6gqbmVnw0PwdOgu+DPKsVkMrdncndztrt8l9LQ0Ex56/mvrl7JhNGJUT7cPW0gf/sqhyde386js+Jwc7GvwtaRJgd3pGMBOR5750jHc6lJ4LvDwYMHbbo/IYQQvdeEYaGk7s5nQ1oe99w0SOk4l6T4BHIhISHo9XrS09MBWLt2Ldddd12Xbb++8T8FbUsrPxsRir/3ha8mCvswamAgD9w8mIKys7z4z33UNrQoHUkIIRRnsVj4+9//zhNPPEFdXR1vv/02rRc4mSiEEEJcDTcXHeMSQtiVU0pZdYPScS5JsaL27rvvbj/r/NJLL7F8+XImT55MY2MjCxcu7JJ91DWa+Pq/Be3wUIxeLl2yXdH9EvoZefjWoZRUNfDix/s4U9esdCQhhFDUCy+8wJEjR8jMzMRqtfL999+zfPlypWMJIYRwUJNGhKFRq9m4K1/pKJdk0+7HP50jb8WKFe3fx8bGsnr16i7dV91/rtA2m1qZODwUPyloe5zBfXx55LY4Xl2dyfMf72PRnAS83fVKxxJCCEXs3LmTzz//nJtvvhl3d3f+8Y9/MH36dKVjCSGEcFBebnrGDA3i+wPF3HRtH7v+HK549+Pu0KGgHSEFbU82IMKbx2bFc6aumedXplNxplHpSEIIoQitVota/ePbtpOTE1qt4kNjCCGEcGA3JIVjscDXu+37aq3DFbX/vYe2xdTKxBFh+HlKQdvTxYR58ZvZ8dQ1mvnTyowe0a9fCCG6WkxMDCtXrqS1tZWTJ0/y+9//ntjYWKVjCSGEcGBGLxeSBgbw3f4i6hpNSse5IIcqahuaTGza03aF9mcjQvHz7FnT4ogLiwr2ZNGcBJpaWvnTx/soqZLCVgjRu/zud7/j0KFDVFZWMmfOHOrr61myZInSsYQQQji4G5MjaDFZ+GZPgdJRLshh+i3VNrT8OG3PiFC5QuuAIgLdWTQ3kZc+2cefVmbw+JwEQvxclY4lhBA24ebmxnPPPad0DCGEEL1MiJ8rw2KMbE4v5IakcFz09ldCOsyV2vc35tDQbGbC8BAZ5diBhfm7sWhuIgAvfJxBYVmdwomEEMI2Kisreeyxx0hKSmL06NEsWbKE2tpapWMJIYToBW5MjqCh2cx3+4qUjnJeDlPUnqlrYcIwmYe2Nwjxc2XxvES0GjUv/HMfeSVnlY4khBDdbunSpYSFhbF69WpWrlyJp6cnv//975WOJYQQohfoE+TBoD4+fL2nbewie+MwRe3cSf0J8JGCtrcI9DGweG4Cep2aF/+5j1On5WqFEMKxFRUV8eijjxIWFkZERASLFy/mxIkTSscSQgjRS0xNjqC2voXtB08rHeUcDlPU9g3yUDqCsDF/bwOL5yVicNby0if7OF54RulIQgjRbfz9/Sko+HGQjpKSEoxGo4KJhBBC9CYxYV5Eh3iyMS0fc6tF6TgdOExRK3onP08XnpiXiIfBiT9/up+cvGqlIwkhRJe67777uO+++6iqqmLGjBk89NBD/PrXv2bGjBm0ttpfFzAhhBCOSaVSMSU5gsraJnZllyodpwP7G7pKiCvk4+HME/MSeemT/fzfqkwevHkIQ/r6Kh1LCCG6REpKynmfv/76620bRAghRK83NMqXMH83NqTlkTw4ELVKpXQkQIpa4SA83fQsmpvAnz/Zz19WH+D+GYNJiJFueUKInm/mzJkdHjc2NiqURAghRG/336u1b31xiIwj5QyP9Vc6EiDdj4UDcTc48du5CUQEuvPm2ix259hXtwghhLga7733HnFxcSQmJpKYmEhCQgKJiYlKxxJCCNHLDO/vT4C3C+t35mG1WpWOA8iVWuFgXJ11/Ob2eF5dlcnbXx6ixWRh9NAgpWMJIcRVe/fdd/n0008JDw9XOooQQoheTK1WceOoCN7deJhDp6oYbAe3/cmVWuFwXPRaHp0Vz8AIb/6xIYdv9hRceiUhhLBzERERxMbGYjAYOnxdjrq6OqZOnUphYSEAO3bsYNq0aUyaNIlXXnmlO2MLIYRwQMmDA/F217NuZ57SUQApaoWD0jtpePjWOIbFGPnn5mOs/f6k3XSPEEKIzpg/fz6PPPIIq1atYu3ate1fl5KZmcmcOXPIzc0FoKmpiSVLlvDmm2+yYcMGsrKy2Lp1azenF0II4Ui0GjU3JIVztKCGowU1SseR7seia6jUKuqbzeddZq1qoOECy7rbgsmx6HRqvvwhlzP1Ldx8fdQFR2nT67Roe/BpHrMFmk1X1s5K/m7+V09vfyG628qVK6msrKSpqanD8zNmzLjoep999hlPP/00ixYtAuDAgQNEREQQFhYGwLRp00hNTWXs2LHdE1wIIYRDui4umK9+yGVDWh4xYV6KZpGiVnSJZlMrmUfLz7vM3c2Zs3VN511mC/1CPTlT18LW/cUUlNVxzeBA1OpzC9sRAwLQ6nvuf4lmk5k9Vzg4ltK/m5/q6e0vRHc7ffo0mzZtuuL1nn322Q6Py8rKMBp/HB3e39+f0tIrH1jP19cNAKPR/YrX7S2kbdpIO1yYtE1H0h4XZq9tM/P6aD7cmENtcytRocoVtvIJUjg8lUrF8Fgjep2a/ccrMZktjIkLQquRy4JCiJ4jJCSE0tJSAgICrmo757sVQ9WJeQYrK+vw9XWjvPzsVeVxVEaju7QN0g4XI23TkbTHhdlz2yT192P1Fg0fbczh/hmDu20/arWq/WTq+UhRK3oFlUrF0Gg/nHQadueU8c2eQsYnhqB30igdTQghLoter2fatGkMGTIEnU7X/vxbb711RdsJCAigoqKi/XFZWRn+/vYxz6AQQoiexeCsY3xiKBt25nG6sp4gX1dFckhRK3qV2AhvnJ00bD9QQuqufCYMD8XNRXfpFYUQQmEpKSmkpKRc9Xbi4uI4deoUeXl5hIaGsm7dOm655ZYuSCiEEKI3mjg8jG/2FLAxLZ9fTBmgSAYpakWvExnkgbNey7cZRWxMy2PCsFB8PJyVjiWEEBc1c+bMLtmOXq/n+eef56GHHqK5uZmxY8dyww03dMm2hRBC9D4erk5cFxfMt/uKmD66D76etv9cLUWt6JUCfQzckBTO5vRCvt5VwPWJwUpHEkKIi0pISDjvva8ZGRmXtf6WLVvav09OTubLL7/ssmxCCCF6txuSwvl2XxGpu/OZNzHG5vuXolb0Wt7ueiaPCmfz3kI27y0kwMeV8QkhSscSQojzWrduXfv3JpOJTZs2odHIuABCCCGU5+PhTPLgQLZlFjPtmkg8XJ1sun8Z/lX0aq7OOm5ICsff28BHXx9h1XfHsZxnZFAhhFBaSEhI+1dkZCT33HMPqampSscSQgghALhxVARms4Vv9hbYfN9S1Ipez0mn4WfDQ7l2SCAb0/J5418HaWoxKx1LCCEu6sSJE1RWViodQwghhADabu8bHuvPloxCGppMNt23dD8Wgra5r26f0I/wAHc+2XyM5R9l8PAtQxW50V0IIc7np/fUWq1WTCYTv/3tbxVOJYQQQvxoSnIEew6XsTmjiGnXRNpsv1LUCvEfKpWKicPDCPQx8NYXWfy/9/fw4C1DiQ7xVDqaEEJ0uKdWpVLh4eGBm9uFJ6IXQgghbC08wJ2hUb58s6eAScPD0DvZZuwH6X4sxP8Y0teX3y0Yjt5Jw59WZrA5vRCr3GcrhFBYSEgIRUVFZGdnk5WVxY4dO9i0aZPSsYQQQogOpiZHUtdoYltmsc32KVdqhTiPYD9XnrpjBH9bl83Kb45yvOgMd9zQH2cn+S8jhFDGokWLSEtLIyIiov05lUrFpEmTFEwlhBBCdBQd6kn/MC9Sd+czLjEErab7r6PKJ3QhLsDNRcfDtw5l/c481m47SUFZHQ/MHIzR6K50NCFEL7R37142btyIq6ur0lGEEEKIi5pyTQQvf5rJjqwSrosL7vb9SfdjIS5CrVIx7ZpIHpsdz9mGFpa9v5fv9xcpHUsI0QsFBwdLQSuEEKJHGBTpQ0SgOxvS8rBYuv82PilqhbgMgyJ9ePrOEYQZ3Xjhw728uyGHxmaZ9kcIYTuJiYk8+uijfPnll2zatKn9SwghhLA3KpWKqckRlFU3sudwWbfvT7ofC3GZfDycWTQ3gU3pRazZcozD+dXcNXUg/UK9lI4mhOgF9u3bB8CqVavan5N7aoUQQtirhBgjQb4G1u/MZeQA//Zp6bqDFLVCXAGtRs0dUwYSHeTO39Zl8/zKDG4cFcH00X1schO8EKL3+vDDD5WOIIQQQlw2tUrFlOQI/rYuh8wTlcRH+3Xfvrpty//jq6++4sYbb2TixImsXLnynOWvv/4648aNY/r06UyfPv28PyOEvYgJ8+KZX4zk2iFBrN+Zxx8/2EtheZ3SsYQQQgghhLAbIwcE4OfpzPodud06RaZNrtSWlpbyyiuv8K9//QsnJydmz55NUlIS0dHR7T+TlZXFyy+/TEJCgi0iCXHVXPRafnHjAOKj/Xhv42GeeXcPKSPDmXZtJHqdbSaaFkIIIYQQwl5pNWomJ4Xz4aajHMmvITbCu1v2Y5MrtTt27GDUqFF4eXlhMBhISUkhNTW1w89kZWWxYsUKpk2bxrJly2hubrZFNCGuWmKMkT/encSoQQFsSMvjqb/tIvN4hdKxhBBCCCGEUNzooUF4uDqxfmdut+3DJldqy8rKMBqN7Y/9/f05cOBA++P6+noGDBjA4sWLCQkJ4YknnuDNN9/k0Ucfvex9OLs44e7m3KW5bUmn03bI39OO5X/z/6+ecDwGgx6jj+GyfvZ/56o1Ak/c6cvBExX8dU0mr64+wDVDg7h7+hD8vFy6Ie25rFUNnWpne/ndXEn7X4yjzSMsxyN+KjU1lZycHO677z42b97M1KlTlY4khBBCXJROqyFlZBirvj3BqdO19Any6PJ92KSoPV//6Z+OfuXq6sqKFSvaH//iF79gyZIlV1TUNjW2cLau6eqCKshkMrfnd3dz7nHH8tP8/6unHE9DQzPlra2X/Dmj0Z3y8rPnXRbooeephcP5enc+X/2QS3pOGZNGhHFDUjgu+u7979bQfOHfwYXY0+/mctv/Yi72u+mJ5Hjsl1qtwtfXzab7fOedd/jhhx8oKSnhzjvv5PXXXycvL48HHnjApjmEEEKIK3V9fAgbduaxbkcuD90ytMu3b5PuxwEBAVRU/Ngds6ysDH9///bHxcXFrF69uv2x1WpFq5WBmUXPpNWomZIcyf+7K4m4aF++2pHL4rd28s2eAkxmi9LxhBA91Pr161mxYgUuLi54e3vz2WefsW7dOqVjCSGEEJfkotcyYVgo+45VUNQNg6vapKi95ppr2LlzJ1VVVTQ2NrJp0yauu+669uXOzs68+OKLFBQUYLVaWblyJRMnTrRFNCG6jdHLhfumD+b3dw4nzN+Nf24+xu9WpLEzqwSLpftGfxNCOCatVouTk1P7Yw8PDzkBLIQQosf42fAw9DoNG9LyunzbNrtS++ijj7Jw4UJmzJjB1KlTGTp0KHfffTcHDx7Ex8eHZcuW8atf/YobbrgBq9XKz3/+c1tEE6LbRQZ68Ns5Cfzm9nhcnXWsWJfNkhVpfLuviBbT1XW3FUL0HkFBQXz33XeoVCpaWlr461//SkhIiNKxhBBCiMvi5qLj+oRgdmWXUVbT2KXbttkp3mnTpjFt2rQOz/30PtqUlBRSUlJsFUcImxvUx4cBkd5kHCln4648Pvz6CGu/P8mExFDGJYbgbnC69EaEEL3WU089xaJFizhy5Ajx8fHExcXx0ksvKR1LCCGEuGyTRoSzOb2Q1F35LEzp32XblX5LQtiQWqVieKw/w/obOVpQQ+qufNZuP8WGtDySBwcyemgQfYM8OgykJoQQ0Nbr6f3336exsZHW1lbc3K5+oKqFCxdSWVnZ3o152bJlxMXFXfV2hRBCiPPxdtczekgQ2w8Uc9O1kXi56btku1LUCqEAlUpF/3Bv+od7U1RRz6bd+ezMKmHr/mKC/Vy5dkgg1wwKxLOL/qMLIXq+d99997zPd/Z2HavVysmTJ/nuu+/k3lwhhBA2c8OoCLZmFrNpdwGzxkd3yTblXUwIhYX4ufLzGwcwe0I/9hwuY/uB06z69gRrvjvJ4L4+DIsxEhfth4erdE8Wojc7evRo+/ctLS2kp6eTlJTU6e2dPHkSlUrF3XffTWVlJbNmzWL+/PldEVUIIYS4IH8vF5IGBvDtviJuTI7AzUV31duUolYIO+Gi13JdXDDXxQVzurKeHw6WkJZdwoETlaiAqBBPEvr5Ed/Pj0Afg3RRFqKXWb58eYfHVVVVLFq0qNPbq62tJTk5mT/84Q80NTWxcOFC+vTpw7XXXntZ6/93nl6j0b3TGRydtE0baYcLk7bpSNrjwhytbeZPHkjaoW/ZmVPG3JTYq96eFLVC2KEgX1duvT6KW8b2Jb+0jv3HK9h/rIJV351g1Xcn8PHQExPqRUyYF/3CvAj2NSgdWQhhYz4+PhQVFXV6/YSEBBISEgAwGAzceuutbN269bKL2srKOnx93SgvP9vpDI7MaHSXtkHa4WKkbTqS9rgwR2wbg1ZFQj8/vtx2gtGDAnDRX7wsVatV7SdTz0eKWiHsmEqlIiLQnYhAd6aP7kNVbROZxys4nF9DTl41admlQNsQ6ZFB7qhVKrzd9Xi763E36ORqrhAO5Kf31FqtVrKysvD19e309vbu3YvJZCI5Obl9m3JvrRBCCFuZkhzJvmN72bq/mBuSwq9qW/LuJUQP4uPhzLjEUMYlhmK1WimraeRoQQ1HC2o4UVRLaVUD1v/8rFajwstNj4erE+4GHe4GJ9xddLgZdDg7aaTgFaKH+ek9tdA2b+3VdD8+e/Ysf/nLX/jkk08wmUx8/vnnPPPMM1cbUwghhLgsfYM9GBjpzde785kwLASdVtPpbUlRK0QPpVKpCPA2EOBtYMzQYOqbzezMOk1NXQvVZ5upPttE9dlmSqoaOFls7rCuRq3CRa/F3aDDSavGRa/FRa9F76RBr9P8+K9Og5NOjUatkiJYCIX97z21V2vcuHFkZmYyY8YMLBYLc+fObe+OLIQQQtjClORIXvznPrYfLGFcQkintyNFrRAORKtR4+fpjJ+nM+DZ/nxrq4W6RhNnG9q+6ptMNDabaTFbqa5robiyAZPZcsHtqlXgpNOg06px0qrRadu+//Hxf7/XoNO1Pffj9xr0Tmo0arUNWkAIx7VgwYKLnlz64IMPrnibjzzyCI888sjVxBJCCCE6LTbci6hgDzam5XFdXFCnPy9KUStEL6DRqPF0058z7627mzNn65oAMLdaaG5ppdn036+2xy3mVlpMFkzmVlrMFkxmCy0mC00NLZjMFkytbc9Zrefb84+0GlWHq8Auei3OTpr/XCXW4G5wItjXgI+7M3qnznc/EcJRDR48mBMnTjBr1ix0Oh1ffPEFZrOZKVOmKB1NCCGE6BSVSsWU5Ej+suYAu7PLSB4c2KntSFErhADarvJqXdS4dmKuMKvVirnV2lbwmlsxmSy0/Of7lp8UyO0Fc0srZxsaaWw202ppq4a3Hyhp356rsxZfD2d8PJzx9XQmwNsFf28DAT4u+Hk6y1Vf0StlZGTw8ccfo9G0nfQZM2YMs2bNIiUlReFkQgghROcNjfYl1OjK+rQ8kgYFoO7ELW9S1AohrppKpUKnVaHTqjFcwZ8Vq7WtEG5qaSU80J2mJjOVtU1U1TZTWdtExZlGcvKraW5pbV9Ho1bh5+lMsJ8rIUZXQvzcCDG6Eugj0xoJx1ZVVUVLSwsuLi4A1NfX09TUpHAqIYQQ4uqo/3O19u0vD7HvaAXD+huveBtS1AohFKNSqXDSaXDSaegX6oXreeYos1qt1Na3UFrdSGl1A2XVjZRUNdTFRV4AACAASURBVFBcUU/m8Uos/+n3rFGrCAtwJ8TX0D4NUri/u+JdmZ2c1MAl+mZfQHNzM05OjjNAlyMdj0qlwmw223QKnKlTpzJr1iwmTpyI1Wpl48aNLFy40Gb7F0L0TlfzPnYlHOk9oqv1hrYZHReIwaBBq1ad91gv9b4rRa0Qwq6pVKr2+4Fjwrw6LDOZLZRUNVBUUUdReT2lNU0cPFXFD1ltXZlVQJCfK32DPYgO8SQq2IMgP9dOdWvpPCtlZeWdWtPVVU99fXMX51GOIx2PSqUiLCzIpkXtr3/9awYOHEhaWhp6vZ5ly5YxcuRIm+1fCNFbdf597Eo40ntEV+stbWNtPsue45VoLQ0YvVw6LLvU+64UtUKIHkunVRPm70aYvxsARqM7ZWW11NS1kFdyltySWnJLzrLvaDnbD5wGwEWvpW+wB/1CPekf5kXfYI+rmhdNiO524sQJoqKiOHToEMHBwdx8883tyw4dOsSgQYMUTCeEEEJ0jVCjG4fzazhWeOacovZSpKgVQjgUlUqFt7seb3c98f38gLYuzKXVjZwoOsOJojMcLzrDF9+fwkrbqMx9gzyICfemf7gX0SGe6HVS5Ar78cILL/D222/z0EMPnbNMpVKxefNmBVIJIYQQ/7+9Ow+P6WwfOP6dJZPIvkiCUGuL2FK1lFpqJ0RsfVGllkZpERS1b7XU1tTSKtpXt9BWi4jiVVVVy09RLUHUvkZ22ZOZzJzfH6mpkQTRbFP357pyMWfO8tz3M8mZ+zxnKVxqtYoaPi5EXE4gMTkLN2fbhy/0FylqhRD/eiqVinLu9pRzt+eFeuUBSM0wcOFGEn9ev8O564nsOHyV7YeuoNXk/EGtVdkN38ruVCnvhFYjd1sWJWfNmjUA7N27t4RbIoQQ1ik1NZXJkycQHDyep59+BoBLly6xevUq0tLSsLe3Z+DAV2nQwC/XsllZWfz3vx9z9uwZMjMz6dSpM717v1Qo7UpLS2P+/LksWLAIgG7dOhMa+jUuLi6Fsv77XblyhQ0bviQ4eJzFdkePfoOFCxfj6OhYJNsFCAlZSuXKVejVq0++86SlpbFu5Xxa93iTP2/coamv9yOvX4paIcQTybGMDX5PlzWP5mZkZXP+RhKRVxM5czWBsF8us/WXy9jqNNSq5Eqdqu7UqepOOXd7VMV6Te7DrV79Ac7OzgwYMJBZs2YwbNhrPPVU5ULfzp9/nmPdujVkZmZiMpno0+cl2rRpB0B4eBjbt29Dp7OlUqVKjBw5Cicnp1zr+OWX/Wzc+CVqtYayZcvyxhuj8PLyxmAwsGbNh5w+fRqA555rxJAhw8yPrwFITU0hOHg0Q4YMo0WLlrnWPXnyRLp1607t2r4sXDiPpUtDCj0HJSk2Npavv/6aO3fuWEyfPn16CbVICCFKv6NHf2XdujXExERbTJ83bzb9+79Chw4dSUxMYPLkibz77hLc3Nwt5vv0009ITU0hJGQFmZmZjB49kjp16lKrVu1/3LbU1BT+/PPcP17Pozpy5DDNmjXLtd2VKz8stjY8SGpqCufPn2NIBWcir90hOU2Ps4PukZaVolYIIci51rZ+dQ/qV/cAckZyI68mcvZqIqevJPDHxXgAPJxt8a2SU+D6VnHH8TGe61uU5sx5p0jWqygKCxfOIzh4HH5+DYmLiyU4eBTPPFOL+Pg4vv12E8uWhVC2rCd79+5h5crlTJ1qWWzduHGDVatWsGjREqpUqUpExCkWLpxHSMhKtm/fRlJSEh988BGKojBp0lscOLCf1q3bmLe/bNlS0tPTHtpWDw+Pf11BCzB27FicnZ3x9fUtdQdWhBBPhpMn/+Czz9bj4eHBtWtXsbW15eWXBxIeHsbNmzdo3rwFQUGvA3DkyP/x9dcbyc7OxtbWlqFDX6N2bV8SExP54IMVJCYmcudOIp6eXkyePA0HB2+GDh1Eu3Yd+OOP34mNjaVly1YMHfparnZMmDCOrCzLGyf5+voycuSoXPOGh4cxbtxbLFnyrnlaUlIScXFxtG2bc2DWzc2dKlWqcvz4Mdq372ieT1EU9u7dS0jICjQaDQ4ODixYkPeIZmTkWdav/wSDwUBiYgJ+fs8SHDye6OjbvPnmCL79diuAxev3338PvV7P6NFv8P77KwHYsOELIiMjSUlJplevPnTr1h2AjRtD2b//ZzQaNRUqVGTkyDdwc3Nn8uSJODk5cePGDfz9u+Lh4cHXX29EpVKjVqsZOvQ16tatB+QU+LNnz2X+/HcsthsY2JXQ0K85evQIBw8eRK/PIiYmGk9PL7p2DWD79m3cvHmTHj160atXbwB2797F999vR1EUnJycGDHiTSpVqsTp0xF8/PFaTCYTKpWKl17qywsvtLDIVUREBOvXf0xWViZarQ0DB77Kc881MudjxaKpdH55IudvJD3y432kqBVCiDw4lrGhUS0vGtXyAiDmTgZnLidw+nICx87F8svJKFQqqFbemTpV3albzYNq5Z1Rqx9cbBTkC8GhQ4f47LPPc30hSE9PY8WK97l8+RJubu5oNBp8fXNuFjR06CCmTJlO9eo1WLduDefORZKRkYGiKIwZMxZf3zqEhCzF3t6eK1euEBcXS8WKlZg0aQpGo5EpUyblanOLFi3p2bM3/fsPwM+vIQBly3ri7OxCfHwcFy6cx8/Pj7Jlc3Y8zZu3YMWK5RgMBmxs/i76L168SNWqValSpSoAdevWIzo6hujo2/Ts2ZuAgEDUajWJiYmkpaXh6Pj3SO9XX22gatWqZGSkP7Tv7v3CEBr6BTEx0SQkJBAbG4Ozswtvvz0VDw8P4uLi+OijD4mNjcFozKZVqxf5z3/6YTQa+eijDzlz5jRarZZy5coxduxb5ufDlpSEhARCQ0NLtA1CCHH+/J+88cYKqlevwaxZ09m06WsWLlxMeno6r746gF69+pCZmcnnn3/KwoWLcXZ25urVK0yfPoV169azf//P1KpVmz59/oOiKMyePZO9e39k4MCXAcjMzGTx4mXExcUxfPhQ/P27Ua5cOYs2FOTA5dy583NNc3Fxwdvbmx9/3EPHjp24fTuK06dPU716DYv5kpKSyMhI5/ffT7BiRQhpaWm0b9+RwMAeuda5bdtWBgwYSP36DcjIyGDYsMFcuHA+z7OW7ho7djxvvjnCYqTU27s8I0eO4uLFC0yYMI7Onf3ZuXMvx48fIyRkBXZ2doSGfkFIyDJzbI6OjqxevRaA114bwoQJk6hVqza//XacU6dOUrduPeLi4rC1tcXR0SnP7d515kwEq1Z9hIeHB6NGjWD//n3Mn/8uV65cYcKEsfTo0ZPTpyP48cc9LFq0FDs7O3777TgLFrzD6tVrCQ39kh49etG69YtcvnyJXbt2WBS1ycnJvPvuPGbMmE3NmrW4evUKU6ZM4r33VpjbtWrVh5y9msj5G0nUfMr1kQYQpKgVQohH4OVaBq9nfXjxWR+MJhOXo1KIuBRPxOUEwg9eYdvBKzjYaaldxZ16fxW5bk553+DgUb8QfPzxx8yfvyjXF4LQ0C/R6Wz56KOPSU5OIjh4lLmovevcuUgSEuJZujQEtVrNpk1fs2nTN8yaNQeACxcusGDBu6hUat56K5gDB36hQ4eODzwFqWPHzub/79q1g8zMDGrWrIVarSY8PIyYmGi8vLz54YfdZGcbSElJxt3dw7xMjRo1uHbtKpcuXaRateocOfJ/pKQkk5CQgLd3ObRaLZ9++l+2b99GjRpPU6dOXQB+++04ERGnmDt3PtOmTS5w350+fZoVK1Zhb+/A3Lmz2LVrBwMGDOS995YQGNiTpk2fR6/XM3v2DMqXr4CbmzunTp1k9eq1qFQq1q//hCtXLlO7tm+Bt12YKlSoQHp6Ovb29iXaDiHEk83bu5y5+CtXrjwODg7Y2Njg4uKCvb09KSkpnD59isTEBIu/2SqVmlu3bhEY2IOIiAi2bPmOW7duce3aFWrWrGmer2nTZgCULVsWFxdXUlNTAMuitiAjtfmZMWM2n3zyMWFhW6hatSqNGzdGq7UsnrKzszGZTNy+fYsFCxaRlJTElCmT8PLyolmz5hbzjhs3gWPHjvLNN19x/fp1srIyycjIeGBRm5cXX3wRgGrVqmMwGEhPT+fIkSO0b98ROzs7AAIDezBgQD8MBgOAeX8J0KpVa+bPn0vjxk3w82tovv73yJHDNG36/EO3//TTz+DpmXOQ2tu7HM8++xxqtZry5cuj1+vJysri6NFfiYq6xcSJ483LpaSkkJKSQsuWLfnoow/49dcj+Pk9y6BBQyzWf+5cJOXLV6BmzVoAVK5chdq1fTl16iT169c3z1etgjMXbyVz4WYSfjXKPrTdUtQKIUQBadRqavi4UMPHhR4tq5GaYeDMlQQiLiUQcTmeY5ExAPh4OjCypy9pyRl4ONuZR3Ef9QtBfHx8nl8Ifv/9BEFBr+c8w9fFNdeOFaB2bV+cnJzYtWsHUVFRnDp10mKksWHD57CxyblOpXLlqqSkpJCamprvSG3fvv3Nrzdt+ppt27YyZ858bG1tqVu3Hv37D2D+/LmoVGo6dOiIk5NTri8HPj4+BAeP44MPVmIwGGja9HmqVq1mMZo7ePBQXnllECtXvs+HH67ilVcG8ckn65g3b4HF9bUFUa9ePeztHQCoXr0GKSkpZGZmEhFxipSUFL788nMAMjMzuHTpIr17N0StVjN+fDANGz5H8+YtLL5wlRQvLy969OhBkyZNzF9sQK6pFUIUr3v/ZgN5/m02mUw0aODH229PNU+LjY3F3d2d9es/4c8/z9GhQ0fq12+A0ZiNoijm+Wxt/76GUqXC4r27CuMSE5NJYebM2eb2z5o13VxQ3+Xi4oJWq6VNm3ao1Wrc3Nxo0qQJkZFnc+173357AlWrVuO5556jRYtW/PlnJIqioFKpLGLIzs5+YLs0mpzy7O5lJoqi5MqByaRgNBqBnOl2dn/v3wcNGkzHjp347bff+PHHH/j22294//2VHDnyf4waNeahebm/f7V5PPbQZDLRpk07hgwZZn6dkBCPo6MjXbp0pUmT5zlx4jjHjx9nw4YvWbVqtXnZvPozJx7LvNjaaKjs7cSV28nUrOSKvd2DR2ulqBVCiH/IsYwNTWp706S2N4qicDM2jYjLCZy6FE9UXDqnz0ejUavwcLEjKT4NzX0PDs/vC0HDhg2ZMOHvovbuF4Kc/dzfOwW1OvfyR48eYe3aj+jZszdNmzajYsWK/PTT33fPtbX9exT57vocHR0fOFJrMOgJCVnGtWvXWLo0BG/vnCPn6enp1K1b3zySm5iYyJdffp7r6LRer6d8+QosW/Y+AEajkW3btuLtXY4zZ07j4uKCj09FtFot7dp1YM2a1Rw48AtZWVnMnJlTuEVF3WL9+o9JTk7G379rvm29l05nOWKuKAomkxFFUViy5D1zgZiUlIROp6NMmTKsXPkhZ8+e4Y8/fmfx4gUEBATSo0evvFZfbHx8fPDx8SnRNgghxKOoX78BX375BdevX6dSpUocPforS5cu5rPPvuS3344zYMBAnn++GXFxcZw4ccJ8bWtxWrVqOT169KJFi5acPXuGq1ev4Of3rMU8NjY2NGnSlL17f2TYsGpkZGRw4sQJiwO9kHODowsXzjN37jwcHZ04deokUVFRmEwmHBwcyc7O5tq1qzz1VGUOHz5kXk6j0WAymczFb34aN27M99/v4MUX22BnZ0d4eBh169YzH5y+y2g0EhQ0hJkz5+Dv35WGDZ9j5MggMjIySEtLw8vLu0Dbzc+zzzZk1arlBAb2wN3dg507v2fbtq189NHHTJgwjr59+9G+fUeaN3+BwYMH/jXanqNmzVrcvHmDc+fOUbNmTa5evcLp06d47bWgXO2q7uPMldvJXLyVTL1qHg9okRS1QghRqFQqFRW9HKno5Ujnpk+h1YKTTRYxdzKJSczgclQKaRnZ7D56HU/XMqRmGHBwyH3Usn79BoSG5v2FoGHDRuze/T/q1/cjPT2NI0f+jzZt2losf+LECZo0eR5//27o9Xq+++4bTCbTP4pt4cL5mEwmli4NsRgpTEiIZ9q0KaxevQZ7ewe++moDrVq9mGtHaTAYmDjxLVatWo2npydbt27G17cOTk5O/PHH75w7F8mMGbNRqVTs2/cT9es3oFev3uabUsDfdzjO6+7HBWFv70DNmrXYunUz/fq9TGpqKpMmjadfvwE4ODiwefO3zJ//rvnmGpcvX/pH2ysMo0Y9+ml1QghRkipXrsLo0WNYvHghiqKg0WiYMWM2dnZ29O//Mp98so6NG0PN94SIirpV7G0cNSqYlSvfZ+PGUOzs7Jg+fZZ53zZr1gz8/f1p2rQZo0ePZe3a1YwcORyTyUjr1m1y7YMcHZ146aX/EBw8CicnZ5ydnald25eoqFv4+T3LkCHDmDVrBq6uLrzwQivzcm5u7lSvXoORI4NYvPi9fNvq7+/PzZtRjB8/BpNJoXz5CkyYkPvMKo1GQ1DQCJYsWYRWq0GlUhMcPJ5jx47y3HONCrzd/Dz3XCN6936J6dOnolarsLe3Z+rUGahUKoYMGcbatR/xxRefo1Kp6N9/gPkgOOSMfk+ePI01az4kKysLlUrF2LHj8fGpiNFotGiXs7MzFT0duXo7hZpPuT6wTSolrzFgK3QjKonDEVEl3YzH1uAZT/74MxYAJ0c7UlIzS7hFBXNv++9nLfE0ru2Ng+3Dj/N4ejoRG5vy0PmKW1pWNkfPRj98xnuUpr551Pw/SGnsG51ORUzM378bx47/xpo1q3n9rQXE3sngp+9DsbN3pE2nXni5luGdKa8zb/4ialSvxtGjh/n88y/MXwiCgkZQt25dMjMz+eCDlZw7F4mrqwvOzi5UrVqNAQMGmm8UZWdXhqVL38VoNKJWa6hTpy6HDh1k/frPWb78PYtnxT3Ks+POnDnNpElv4ePjYzHyOXjwUJ57rhHh4dv4/vtwFMWEr28dRox4E1tbW86f/5MVK95n5coPcXCw5X//28OGDV9iMpmoVKkSo0YF4+LigsFgYN26NUREnESlUuPrW4dhw4Isimd4cFF7972nn37a4kZRycnJjBz5JoDF6+jo23z00YfExMSQnW2gVasXGTBgIEajkbVrV/PHH79jZ1cGR0dHRo8Ottgpq1QqKlUqbzHiXVQGDhxocYBAo9Hg6upK69at6dEj981Kikt8fCoeHo6l7neutCiNf49KguQhf9aSm/v3Y0XFwcGWtLSsh8/4BHqSc5OaYWDvbzepW82dF5vWyne/K0VtKSFFbcmTorZkPSlF7b0UIDlVT2xSBrF3MohPysL0159kV0cd5cs64OKgw93JFq1GXYytLlyzZ89g9ux3/lU75eIsav/3v/9ZvDaZTMTHx7Nlyxa6devGkCFD8lmyaElR+2Cl8e9RSZA85M9aciNFbcl70nNz7FwsKRl6Xu3eON/9rpx+LIQQJUQFuDjqcHHUUcPHBZNJISEli7ikDOKSMjl3NRGT8vd8Hs52uDvb4e5si63N4900qbjFxcXRtWu3km6GVevUqVOe0wMCAhg4cGCJFbVCCCFEcfB2K0NS6oOLeilqhRCilFCrVZR1saOsS84pt7a2NtyITiY+OZP4pCwuR6Vw8VYykHNzKncnW9ycbXFztMXJXsdj3OuhyJUtW5ayZR9+K35RcC4uLo91gw8hhBDCmmTqH3zHaJCiVgghSi2tVo2naxk8XXNu1W8yKdxJzSI+OZOE5CxuJ6RzLSYVAI1GhZujLW5Otrg66nBxsMXeTv7E/5spivLQR0MIIYQQ1q6sSxluxKY9cB75xiOEEFZCrVb9dfrx3zdPSss0kJiSRUJKFonJWVy4mcTdOyXotGpcHHOKXGcHHc72OhzLaGV0z8rcuXMnz2lffPEFfn5+JdAiIYQQovi4OdnyXE3PB84jRa0QQhQpFV5eD/5DnB87OxscHAwPna/qPf83KQrpmdmkZhjMP+mZ2aQbIT0F1KkmythpcbCzwd5WSxlbLfa2Gmx1miIvdh81HmugUqnyfL5wUXj++edRqVTmB9arVCrc3d1p1aoVU6dOfez1hoeHs3r1agwGA4MHD2bAgAGF1WQhxL/K4+/HCuLftI8obJIb8H7IfleKWiGEKEJ6/eM/G9bFxZbkZH2Bl9NptLg7anF3zDltOdtoIio+nRsxqVyPTTX/m5T697q1GhXe7vaUc7PHy63MXz/2eLmWwc3ZFnUhFLyPG09ppFaDVls8u9DIyMhCX2d0dDQhISFs3rwZnU5Hv379aNq0KTVq1Cj0bQkhrNs/2Y8VxL9pH1HYJDcP3+9KUSuEEP9yWo2aSl6OVPJypNk909MzDUTFp3MrPo3b8elExadzMy6NPy7GkW1U7llehbuTHR4udng4//2vm7Mtro62uDnqKGMrpzVbk0OHDvH888/j6przMPtOnTqxa9cuRo0aVcItE0IIIQqu2Irah53mdPbsWaZPn05qaiqNGjVizpw5xXYUXAghnkT2djZU93Ghuo+LxfScRwtlEpOYQcydDGITM/66A3MmEZfjSUrVc/8DznU2alwdbXF10OH01/W7TvY2ODvocLLX4WinJdVgQp+hx8HOBp2NWorgEhQTE4On59+nE3p5eXHy5MkCrcPDwxHIedamyJvkJofkIX+SG0uSj/xJbh6sWKrGRznNaeLEicybNw8/Pz+mTp3KN998w8svv1wczRNCCHGPnEcLlaGsSxl883jfkG0iMSWTxJQs7qTquZOaxZ3ULBJTskhK1RMVn865a3dIyzDkKn7v0mpU2NvZUEanoYz52t6cf211Guz++rG1ybne19ZGg06rQWejRmejQadVY2P+0WCjUWGjVaPVSLH8KO5en3uvguYtPj4VDw9HYmNTCqtZ/yqenk6SGyQPDyK5sST5yJ/kJue7yd2DqXkplqL2Yac53bx5k8zMTPNdHHv16sWKFSsKVNRq/vqCZK20GrW5/WVstRizrSuWe9t/P2uJR6tRo1Y/2pe6R52vOD2oD/JTmvqmIPl/kNLYN/9EaYzHVqehnIcD5TwcHjifyWQiLctIarqB9EwDGhstMfGpOTeuyswmQ59Npt5Ihj6bLL2RTL2R5DsZ6PVGsgxGTHkUXg9jY6NmVM/6lPewf9zwHklp7JeC8Pb25tixY+bXMTExeHl5FWgdd3Ng7bkoSpKbHJKH/EluLEk+8vek5+Zh8RdLUfuw05zuf9/T05Po6OgCbaO8lzNdvZz/eWNLULWKbiXdhH/E2ttfEA86UlSSKpZ3efhM/3KltW8el7XHk/t+md4l0Apxv+bNm7Ny5UoSEhIoU6YMu3fv5p133inQOtzccg5qWPtntChJbnJIHvInubEk+cif5ObBiqWofdhpToVxGpQQQgghHo23tzfjxo1j0KBBGAwG+vTpQ/369Uu6WUIIIcRjKZai9mGnOXl7exMXF2d+HRsbW+DToIQQQgjx6AICAggICCjpZgghhBD/mLo4NtK8eXMOHz5MQkICGRkZ7N69m1atWpnf9/HxwdbWluPHjwOwdetWi/eFEEIIIYQQQoi8qJS8zv0tAuHh4axZs8Z8mlNQUBBBQUGMGTOGevXqERkZyfTp00lLS8PX15eFCxei0+mKo2lCCCGEEEIIIaxUsRW1QgghhBBCCCFEYSuW04+FEEIIIYQQQoiiIEWtEEIIIYQQQgirJUWtEEIIIYQQQgirJUWtEEIIIYQQQgirZfVFbXh4OP7+/nTo0IHQ0NCSbo6FVatW0bVrV7p27crixYsBOHToEAEBAXTs2JGQkBDzvGfPnqV379506tSJadOmkZ2dDcCtW7cYMGAAnTt3ZuTIkaSlpQGQnJzM8OHD6dKlCwMGDCA2NrbY4lq0aBGTJ08u1Hbr9XomTpxIly5d6NmzJxcvXizyOPbu3UuvXr3o3Lkz8+bNA6y7f8LCwsyft0WLFhVqu4urf1JTU+nWrRs3btwAir4/ijqu++P5+uuv6datGwEBAUyZMgW9Xm/V8dwVGhrKwIEDza8Lq92KorBo0SI6d+6Mv7+/+bFvRRXPiRMn+M9//kPXrl0ZP3681fXPoyrIvmnPnj0EBgbSvXt33njjDZKSkoD8Y79ffrm7a/ny5axcubKIIi240pCb48eP07t3bwIDA3n11Ve5efNmEUedW2nIw7Fjx+jVqxcBAQGMGDHCvN6SVhpyc9eZM2eoW7duEUX6aEpDPrZu3UqLFi0IDAwkMDDQYpslqTTkJiYmhuHDh9OjRw/69euXa//9r6JYsdu3bytt2rRREhMTlbS0NCUgIEA5f/58STdLURRFOXjwoNK3b18lKytL0ev1yqBBg5Tw8HCldevWyrVr1xSDwaAMHTpU2bdvn6IoitK1a1flxIkTiqIoypQpU5TQ0FBFURRl+PDhyvbt2xVFUZRVq1YpixcvVhRFUebMmaOsWbNGURRF2bJlixIcHFwscR06dEhp2rSp8vbbbxdquz/++GNlxowZiqIoyq+//qr06dOnSOO4du2a0qJFCyUqKkrR6/VK//79lX379llt/6SnpyuNGzdW4uPjFYPBoPTp00c5ePCgVfXP77//rnTr1k2pU6eOcv36dSUjI6PI+6Mo47o/nkuXLikdOnRQUlJSFJPJpEyaNElZv3691cZz1/nz55WWLVsqr7zyinlaYbV7586dSlBQkGI0GpVLly4p7du3VwwGQ5HEk5KSorzwwgvK2bNnFUVRlHHjxpn7wRr651EVZN90Nye3b99WFEVR3n//feWdd95RFCX/2O+XX+6Sk5OVKVOmKPXr11dWrFhR1GE/ktKSmzZt2pg/h5s2bVJGjBhRpHHfr7TkoX379ubvdEuWLFGWLVtWpHE/itKSG0XJ2e/37dtXeeaZZ4oy5AcqLfmYO3euEh4eXtThFkhpyc2rr76qbNiwQVEURdmwYUOx1QslV5JRcQAAEgVJREFUwapHag8dOsTzzz+Pq6sr9vb2dOrUiV27dpV0swDw9PRk8uTJ6HQ6bGxsqF69OleuXKFy5cpUqlQJrVZLQEAAu3bt4ubNm2RmZuLn5wdAr1692LVrFwaDgaNHj9KpUyeL6QD79u0jICAAgG7durF//34MBkORxnTnzh1CQkIYMWIEQKG2e9++fXTv3h2Axo0bk5iYyK1bt4oslh9++AF/f3/KlSuHjY0NISEhlClTxmr7x2g0YjKZyMjIIDs7m+zsbLRarVX1zzfffMOsWbPw8vIC4OTJk0XeH0UZ1/3x6HQ6Zs+ejaOjIyqVimeeeYZbt25ZbTyQM/I4c+ZMgoODzdMKs90///wz/v7+qNVqqlatSoUKFThx4kSRxHPw4EH8/PyoVasWANOnT6dDhw5W0z+PqiD7JoPBwOzZs/H29gagZs2aREVFPTD2e+WXO4Aff/yRKlWqMGTIkGKK/OFKQ270ej3BwcHmz+Hd9Ran0pAHgB07dlCjRg0MBgPR0dE4OzsXUwbyV1pyA/Duu+8yePDgog/6AUpLPk6dOsXWrVvp3r07EyZMKBWj+qUhNwkJCURGRtKvXz8AevfuzdixY4spA8XPqovamJgYPD09za+9vLyIjo4uwRb97emnnzZ/uK5cucKOHTtQqVR5tvf+ODw9PYmOjiYxMRFHR0e0Wq3FdLCMXavV4ujoSEJCQpHGNHPmTMaNG2fesRRmu/Na1+3bt4sslqtXr2I0Ghk2bBjdu3dnw4YN+X6erKF/HB0dCQ4OpkuXLrRq1QofHx9sbGysqn/mz59Po0aNzK+Loz+KMq774/Hx8aF58+YAJCQkEBoaSrt27aw2HoBly5bRu3dvKlasaJ5WmO2OiYmxKKKLMp6rV69ib2/Pm2++SUBAACtXrsTZ2dlq+udRFWTf5ObmRvv27QHIzMxk7dq1tG/f/oGx3yu/3AH06NGD4cOHo9FoiizWgioNudHpdAQGBgJgMplYtWqVeTvFpTTkAcDGxoZz587RunVrjhw5QteuXYss5kdVWnLz448/kpmZSefOnYss1kdRWvLh6enJ6NGjCQsLo3z58sydO7fIYn5UpSE3169fp0KFCixYsIDu3bszZswYbGxsijLsEmXVRa2iKLmmqVSqEmhJ/s6fP8/QoUN5++23eeqpp3K9r1Kp8o2joPGp1UXXnZs2baJ8+fI0a9bMPK2o212U8RiNRg4fPsySJUv45ptvOHXqVJ7XGVhL/0RGRvLdd9/x008/ceDAAdRqNQcPHsyzfdbQP1Dwz5e1xBUdHc2rr75K7969adq0qdXGc/DgQaKioujdu7fF9MJsd17rKqp4jEYjBw4cYPLkyWzdupWMjAzWrl1rtf3zMI+yb7orJSWFoKAgatWqRc+ePR85dmvYR+elNORGr9czYcIEsrOzef311x83lH+kNOShZs2aHDp0iDfeeINx48Y9biiFriRzExsby+rVq5kxY8Y/jKLwlPRn5YMPPqBBgwaoVCpee+019u/f/0/CKVQlmZvs7GzOnDlD8+bN2bZtG+3atTPfE+ffyKqLWm9vb+Li4syv7z+qX9KOHz/O4MGDeeutt+jZs2e+7b1/emxsLF5eXri7u5OamorRaLSYDjlHd+4uk52dTWpqKq6urkUWy44dOzh48CCBgYGsWLGCvXv3smnTpkJrt5eXl8XNlO5dpiiULVuWZs2a4e7ujp2dHe3atePgwYNW2z8HDhygWbNmeHh4oNPp6NWrF0eOHLHa/oH8f78Lsz+KO66LFy/Sv39/evbsyZtvvplnnNYSz/bt2zl//jyBgYFMnz6diIgIxo4dW6jt9vb2LrZ4ypYtS4MGDahUqRIajYYuXbpw8uRJq+2fB3nUfdPd/7/88svUqlWL+fPnA+Qbe3R0tPlGLUFBQfnmrjQrDblJS0vjtddeIzs7m9WrV5fIyEpJ5yErK4s9e/aYp3fv3p1z584VR+gPVdK52bdvH3fu3GHAgAHmUf3AwEBSU1OLKwUWSjofKSkpfPrpp+bpiqKYRzZLWknnxtPTEwcHB9q0aQPkXA5z8uTJ4gq/2Fl1Udu8eXMOHz5MQkICGRkZ7N69m1atWpV0swCIiorizTffZOnSpeZTZho0aMDly5fNp75u377dfKqora2t+c6eW7dupVWrVtjY2NCoUSN27NhhMR2gdevWbN26FcgpOBs1alSkO77169ezfft2wsLCGDNmDG3btmXhwoWF1u7WrVsTFhYG5Nzx0NbWlgoVKhRZPG3atOHAgQMkJydjNBr55Zdf6Ny5s9X2T61atTh06BDp6ekoisLevXtp0qSJ1fYPFM/vS3HGlZqayrBhwwgODmbo0KHm6dYaz8KFC9m5cydhYWHMmzePunXr8v777xdqu1u1akV4eDhGo5GrV69y5coV6tWrVyTxtGjRgtOnT5uvX/zpp5+oU6eO1fZPfgqybzIajYwYMYIuXbowbdo08whBfrF7e3sTFhZGWFgY69atyzd3pVVpyc3EiROpXLkyy5cvR6fTPZF50Gq1zJkzh4iICAB27txJw4YNiz0X9ysNuXnppZfYs2ePeV7IefqBo6PjE5kPe3t7Pv74Y/744w8AvvzySzp06FDsubhfacjNU089hbe3Nz///DPw937t30ql5DVmbUXCw8NZs2YNBoOBPn36EBQUVNJNAmDevHl89913Fqca9OvXjypVqrBw4UKysrJo3bo1U6ZMQaVSERkZyfTp00lLS8PX15eFCxei0+m4efMmkydPJj4+nvLly/Pee+/h4uLCnTt3mDx5MtevX8fJyYmlS5daXNdWlDZv3syvv/7Ku+++W2jtzsrKYubMmURERKDT6Zg3b16R/+J9++23fPrppxgMBl544QWmT5/OkSNHrLZ/1q5dy+bNm7GxsaFevXrMmjWLy5cvW13/tG3bls8//5yKFSty+PDhIu2P4ojrbjx79uxh6dKlVK9e3eK94OBgq4zn3s/zkSNHWLVqFV988QVAobVbURQWL15sPpVsypQptGjRosji2bdvHyEhIWRlZVG7dm0WLFhAmTJlrKp/HqYg+6Y9e/YwevRoatasaZ63bt26zJ8/P9/Y75df7u66+zif0aNHF2HUj6Y05ObChQv07NmTGjVqmEebvLy8WLduXdEn4C+lIQ86nY5jx46xYMECjEYj3t7ezJ07l3LlyhVLDvJTWnJzr5o1a5bYKHZpycexY8eYP38+mZmZVKlShcWLF+Pk5FQsOchPacnNpUuXmDVrlvn63HfffZcqVaoURwqKndUXtUIIIYQQQgghnlxWffqxEEIIIYQQQognmxS1QgghhBBCCCGslhS1QgghhBBCCCGslhS1QgghhBBCCCGslhS1QgghhBBCCCGslhS14ollNBpZv349vXr1IjAwEH9/f5YsWYJery/QehISEixuw14QGzdupHv37vj7+9O1a1cmTpzIrVu3HmnZ6dOnm5/h9zhSUlIYNGhQnu8tX77c/JzNgvr2228ZMWJEvu+3bdsWPz8/0tLSLKZv2bKFmjVrsmvXrsfaLsC+fftYvnw5kPPoqddff/2x1yWEEEIIIayDFLXiiTV79mxOnDjBZ599RlhYGN9++y2XL19m2rRpxbL9RYsWsXv3btasWcOOHTsIDw/nhRdeoG/fvty+ffuhyx86dIh/8kSupKQkTp06led7wcHB9OjRo0Dru3PnDjNnzmTevHkPbZebmxs//PCDxbQtW7ZQtmzZAm3zfqdOnSIpKekfrUMIIYQQQlgXbUk3QIiScP36dcLDwzlw4ACOjo4A2NvbM2fOHE6cOAHkjGTOmTOHyMhIVCoVLVu2ZPz48Wi1Wnbv3k1ISAhlypShbt26FuvetGkTGzduxGQy4erqyowZM6hevbrFPLdv3+arr75i37595odoq9VqevToQUREBGvWrGHWrFm0bduW5cuXU69ePQDz6z179hATE8OECRNYvHgxS5cupXr16kRERJCYmEhgYCBjxozhxo0bBAQEmGO69/WUKVPIzMwkMDCQzZs3o9FozO2bPHkyTz/9NMOGDaNevXoMHz6cgwcPEhMTw6BBgxg8eHCunO7cuRMvLy8mTZrEzz///MD8d+/enW3btpkL55s3b5Kenk61atXM8xw7dozFixeTkZGBjY0NY8eOpVWrVmzevJkffvgBtVrN1atXsbGxYdGiRWRkZPDVV19hNBpxcnKicuXKxMbGMnz4cKKiotBoNCxbtozq1auze/duVq9ejUqlQqPRMGnSJBo3bvzQz40QQgghhCh9ZKRWPJHOnDlDjRo1zAXtXZ6ennTs2BGAefPm4erqSnh4ON999x3nzp3jv//9L3FxcUydOpWVK1eyefNmfHx8zMv/+uuvbN26ldDQULZu3cprr73G6NGjc23/jz/+oFq1auaC9l7Nmzfn+PHjD2z/uHHj8PLyYunSpTRo0ACAW7dusXHjRrZs2cKOHTv46aefHriOhQsXYmdnR1hYmEVBez+9Xo+bmxtfffUVK1asYNmyZWRlZeWar3///owaNQo7O7sHbhegdevWnD17lpiYGADCwsIsRoYTExMZM2YM06ZNIzw8nEWLFjFx4kSuX78OwNGjR5kxYwbbt2+nYcOGfPLJJzRo0IB+/frh7+/PuHHjgJyDF3fX0ahRIz755BMAFi9ezKxZs9i8eTPBwcEcOXLkoW0WQgghhBClkxS14omkVqsxmUwPnGf//v288sorqFQqdDod/fr1Y//+/Rw/fpxnnnmGGjVqANC3b1/zMvv27ePq1av069ePwMBAlixZQlJSEnfu3Mm1/uzs7Dy3q9frUalUBY6pb9++2NjY4OzsTOfOnTlw4ECB15Gfdu3aAVCnTh30ej3p6en/aH02NjZ07tyZ7du3A7Bjxw66detmfv/kyZM89dRT5oL96aefpmHDhvz666/mdpQrVw4AX1/ffE85rl+/PpUrVwagdu3aJCQkANC1a1dGjRrFtGnTSE5OJigo6B/FI4QQQgghSo4UteKJVL9+fS5dukRqaqrF9OjoaIYPH05mZmauotdkMpGdnY1KpbK4ZlSr1VrMExgYSFhYGGFhYWzZsoXvvvsu14isn58fV69eJTY2Nlfbjhw5wrPPPmt+fe+2HnQTq3vboSgKarU6V1sNBkO+yz+Ira0tgLnY/ifX8t7Vo0cPtm3bxm+//Ua1atVwdXU1v5fXAQdFUcwHAu4dDb4/xnvdm5N75xs3bhwbN26kbt26bN68mb59+z70IIcQQgghhCidpKgVTyRvb28CAgKYOnWqubBNTU1l9uzZuLq6YmdnR4sWLQgNDUVRFPR6Pd988w3NmzenUaNGXLhwgcjISCDnLrt3vfDCC3z//ffm02o3btzIq6++muf2Bw4cyPjx44mOjjZP/+6779i9e7d55NDd3d18h+Pff//dogjWaDQWo73btm3DZDKRlJTEzp07adu2Lc7OzhgMBi5cuABgcXMmrVaL0WgslAL1cTRo0IDMzExCQkLo2bNnrvcuX77MyZMnATh//jxHjx6lSZMmD1zn/TnJS3Z2Nm3btiU9PZ3+/fsza9YsLl68+NDlhBBCCCFE6SQ3ihJPrFmzZvHhhx/Sr18/NBoNer2e9u3bm6+BnT59OvPmzSMgIACDwUDLli0ZMWIEOp2OpUuXMmHCBGxsbCxuMNSyZUuCgoIYOnQoKpUKR0dHVq1alefpxG+99RabNm1i5MiR6PV69Ho99erV46uvvjJfpzthwgRmz57N119/TZ06dahTp455+fbt2zNu3DjmzZsHQGZmJn369CEtLY2XX36ZZs2aATBx4kSCgoJwd3enc+fO5uU9PT3x9fWlS5cubNy4ETc3t8JP8kMEBgYSGhpKy5YtLaa7u7uzfPly3nnnHTIzM1GpVCxcuJCqVauab3qVl2bNmjF69GhsbGwscnUvrVbL1KlTmTBhAlqtFpVKxYIFC9DpdIUamxBCCCGEKB4qpaSGaYQQhWbgwIEMGDDAomgVQgghhBDiSSCnHwshhBBCCCGEsFoyUiuEEEIIIYQQwmrJSK0QQgghhBBCCKslRa0QQgghhBBCCKslRa0QQgghhBBCCKslRa0QQgghhBBCCKslRa0QQgghhBBCCKslRa0QQgghhBBCCKv1/04UxDPTtwsgAAAAAElFTkSuQmCC\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "gilot.plot(df_csv,timeslot=\"1M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37664bitb87193a5a712411bbdb7b45f827bcc44",
   "display_name": "Python 3.7.6 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

================================================
FILE: setup.cfg
================================================
[flake8]
ignore = E203,E231,W503
max-line-length = 99
statistics = True
exclude = venv,build


================================================
FILE: src/gilot/__init__.py
================================================

from .core import from_csv, from_csvs, from_dir  # NOQA
from .hotgraph import plot_hotgraph  # NOQA
from .hotspot import get_hotspots  # NOQA
from .plotter import info, plot ,authors  # NOQA


================================================
FILE: src/gilot/app.py
================================================
from gilot.core import Duration
import argparse
import json
import logging
import sys
from fnmatch import fnmatch
from logging import getLogger
from typing import Callable, List, Optional

import gilot

logger = getLogger(__name__)

parser = argparse.ArgumentParser(description="""
gilot is a tool for analyzing and visualizing git logs

1) simple way (one liner using pipe)
! gilot log REPO_DIR | gilot plot

2) 2-phase way
! gilot log REPO_DIR > repo.csv
! gilot plot -i repo.csv -o graph.png


""", formatter_class=argparse.RawDescriptionHelpFormatter)  # type:ignore


def init_logger(args):
    root_logger = logging.getLogger()
    log_level = max(logging.NOTSET,logging.WARNING - args.verbose * 10)
    root_logger.setLevel(logging.NOTSET)
    handler = logging.StreamHandler()
    handler.setLevel(log_level)
    handler.setFormatter(logging.Formatter("%(levelname)-8s: %(message)s"))
    root_logger.addHandler(handler)


def add_logging_option(parser):
    parser.add_argument("-v","--verbose",action="count",default=0,help="increase log level")


def compose_filter(allow: Optional[List[str]], deny: Optional[List[str]]) -> Callable[[str], bool]:
    allow_list = allow or ["*"]
    deny_list = deny or []

    def match(file_name: str) -> bool:
        # いずれかのallow条件にmatchするか
        is_allowed = any(fnmatch(file_name, p) for p in allow_list)
        # いずれかのdeny条件にmatchするか
        is_denyed = any(fnmatch(file_name, p) for p in deny_list)
        # 許可されており、拒否リストに含まれていない。
        return (is_allowed and not is_denyed)

    return match


def args_to_duration(args) -> Duration:

    if (args.since and args.until):
        return Duration.range(args.since, args.until)
    if (args.since and args.month):
        return Duration.months(int(args.month),since=args.since)
    if (args.since):
        return Duration.months(6,since=args.since)
    if (args.month):
        return Duration.months(int(args.month))
    return Duration.months(6)


def handle_log(args) -> None:
    init_logger(args)
    duration = args_to_duration(args)
    df = gilot.from_dir(
        args.repo,
        branch=args.branch,
        duration=duration,
        full=args.full
    )
    df.to_csv(args.output)


def handle_plot(args) -> None:
    init_logger(args)
    df = gilot.from_csvs(args.input)
    if (args.allow_files or args.ignore_files):
        df = df.filter_files(compose_filter(allow=args.allow_files,deny=args.ignore_files))
    if len(df) == 0:
        logger.warning("No data to plot")
        return
    gilot.plot(df, output=args.output, name=args.name, timeslot=args.timeslot)


def _load_df(args):
    init_logger(args)
    df = gilot.from_csvs(args.input)
    if (args.allow_files or args.ignore_files):
        df = df.filter_files(compose_filter(allow=args.allow_files, deny=args.ignore_files))
    return df


def handle_info(args) -> None:
    df = _load_df(args)
    if len(df) == 0:
        logger.warning("No data to analyze")
        print(json.dumps({
            "lines": 0,
            "added": 0,
            "refactor": 0,
            "gini": 0,
            "since": "",
            "until": "",
            "timeslot": "2W"
        }, indent=4, sort_keys=False))
        return
    print(json.dumps(gilot.info(df), indent=4, sort_keys=False))


def handle_hotspot(args) -> None:
    init_logger(args)
    df = gilot.from_csvs(args.input)
    result = gilot.get_hotspots(
        df.expand_files(
            compose_filter(
                allow=args.allow_files,
                deny=args.ignore_files)))
    if(args.csv) :
        result.to_csv(args.output)
    else :
        pretty_print_hotspot(result[:args.num])


def handle_author(args) -> None:
    init_logger(args)
    df = gilot.from_csvs(args.input)
    if (args.allow_files or args.ignore_files):
        df = df.filter_files(compose_filter(allow=args.allow_files,deny=args.ignore_files))

    gilot.authors(df, output=args.output, name=args.name,top=args.top,only=args.only)


def handle_hotgraph(args) -> None:
    init_logger(args)
    df = gilot.from_csvs(args.input)
    is_match = compose_filter(allow=args.allow_files,deny=args.ignore_files)
    epanded_df = df.expand_files(is_match)
    gilot.plot_hotgraph(
        epanded_df,
        output_file_name=args.output,
        rank=args.rank,
        stop_retry=args.stop_retry,
        k=args.k,
        font_size=args.font_size,
        newline=args.newline)


def pretty_print_hotspot(df) -> None:
    print("""
------------------------------------------------------------
    gilot hotspot ( https://github.com/hirokidaichi/gilot )
------------------------------------------------------------
    """)
    targets = ["hotspot","commits","authors","file_name"]
    columns_text = " ".join([f"{t:>8}" for t in targets])
    print(columns_text)
    for k,v in df.iterrows():

        hotspot = "{:.2f}".format(v["hotspot"])
        commits = "{:6d}".format(int(v["commits"]))
        authors = "{:6d}".format(int(v["authors"]))
        row = [hotspot,commits,authors,k]
        print(" ".join([f"{c:>8}" for c in row]))


def add_file_filter_option(parser):
    parser.add_argument(
        "--allow-files",
        nargs="*",
        help="""
        Specify the files to allow.
        You can specify more than one like 'src/*' '*.rb'. Only data with the --full flag is valid.""")

    parser.add_argument(
        "--ignore-files",
        nargs="*",
        help="""
        Specifies files to ignore.
        You can specify more than one like 'dist/*' '*.gen.java'. Only data with the --full flag is valid.""")


def add_author_filter_option(parser):
    parser.add_argument(
        "--allow-authors",
        nargs="*",
        help="""
        Specify the files to allow.
        You can specify more than one like 'src/*' '*.rb'. Only data with the --full flag is valid.""")

    parser.add_argument(
        "--ignore-authors",
        nargs="*",
        help="""
        Specifies files to ignore.
        You can specify more than one like 'dist/*' '*.gen.java'. Only data with the --full flag is valid.""")


def add_log_option(parser):
    """
        gilot log コマンドのオプション
    """
    parser.add_argument(
        'repo',
        help='REPO must be a root dir of git repository')

    parser.add_argument(
        "-b", "--branch",
        help="target branch name. default 'origin/HEAD' ",
        default="origin/HEAD")

    parser.add_argument(
        "-o", "--output",
        default=sys.__stdout__)

    parser.add_argument(
        "--since",
        help="SINCE must be ISO format like 2020-01-01.")

    parser.add_argument(
        "--until",
        help="UNTIL must be ISO format like 2020-06-01.")

    parser.add_argument(
        "--month",
        help="MONTH is how many months of log data to output. default is 6")

    parser.add_argument(
        "--full",
        action="store_true",
        help="If this flag is enabled, detailed data including the commuted file name will be output.")

    parser.set_defaults(handler=handle_log)
    return parser


def add_plot_option(parser):
    """
        gilot plot コマンドのオプション
    """
    parser.add_argument(
        '-i', "--input",
        nargs="*",
        default=[sys.__stdin__])

    parser.add_argument(
        '-t', "--timeslot",
        help="resample period like 2W or 7D or 1M ",
        default="2W")

    parser.add_argument(
        '-o', "--output",
        default=False,
        help="OUTPUT FILE ")

    parser.add_argument(
        "-n", "--name",
        default="GIT LOG REPORT",
        help="name")

    add_file_filter_option(parser)
    parser.set_defaults(handler=handle_plot)
    return parser


def add_info_option(parser):
    """
        gilot info コマンドのオプション
    """
    parser.add_argument(
        '-i', "--input",
        nargs="*",
        default=[sys.__stdin__])

    parser.add_argument(
        '-t', "--timeslot",
        help="resample period like 2W or 7D or 1M ",
        default="2W")

    add_file_filter_option(parser)

    parser.set_defaults(handler=handle_info)
    return parser


def add_hotspot_option(parser):
    """
        gilot hotspot コマンドのオプション
    """
    parser.add_argument(
        '-i', "--input",
        nargs="*",
        default=[sys.__stdin__])

    parser.add_argument(
        '-n', "--num",
        type=int,
        default=30)

    parser.add_argument(
        "--csv",
        action="store_true",
        help="dump csv")

    parser.add_argument(
        "-o", "--output",
        default=sys.__stdout__)

    add_file_filter_option(parser)

    parser.set_defaults(handler=handle_hotspot)
    return parser


def add_hotgraph_option(parser):
    """
        gilot hotgraph コマンドのオプション
    """
    parser.add_argument(
        '-i', "--input",
        nargs="*",
        default=[sys.__stdin__])

    parser.add_argument(
        '-r', "--rank",
        type=int,
        default=70)

    # 10秒以上かかる処理だった場合に自動的に閾値を引き上げてリトライする。
    parser.add_argument(
        "--stop-retry",
        action="store_true")

    parser.add_argument(
        "--csv",
        action="store_true",
        help="dump csv")

    parser.add_argument(
        "-o", "--output",
        default=False)

    # 反発力
    parser.add_argument(
        "-k",
        type=float,
        default=0.6)

    parser.add_argument(
        "--font-size",
        type=int,
        default=10)

    # "/" で改行する場合に指定する
    parser.add_argument(
        "--newline",
        action="store_true")

    add_file_filter_option(parser)

    parser.set_defaults(handler=handle_hotgraph)
    return parser


def add_author_option(parser):
    """
        gilot authors コマンドのオプション
    """
    parser.add_argument(
        '-i', "--input",
        nargs="*",
        default=[sys.__stdin__])

    parser.add_argument(
        '-o', "--output",
        default=False,
        help="OUTPUT FILE ")

    parser.add_argument(
        '-t', "--top",
        type=int,
        default=10)

    parser.add_argument(
        '-n',"--name",
        default="--",
        help="name")

    parser.add_argument(
        "--only",
        nargs="*")

    add_file_filter_option(parser)

    parser.set_defaults(handler=handle_author)
    return parser


"""
    gilot コマンドのオプション
"""

subparsers = parser.add_subparsers()


subparsers_list = [
    add_log_option(subparsers.add_parser(
        'log', help='make git log csv data/ see `log -h`')),
    add_plot_option(subparsers.add_parser(
        'plot', help='plot graph using the csv file see `plot -h`')),
    add_info_option(subparsers.add_parser(
        'info', help='info graph using the csv file see `info -h`')),
    add_hotspot_option(subparsers.add_parser(
        'hotspot', help='search hotpost files `hotspot -h`')),
    add_hotgraph_option(subparsers.add_parser(
        'hotgraph', help='plot hotpost network `hotgraph -h`')),
    add_author_option(subparsers.add_parser(
        'author', help='author hotpost network `author -h`')),
]

for p in subparsers_list:
    add_logging_option(p)


def main():
    # コマンドライン引数をパースして対応するハンドラ関数を実行
    args = parser.parse_args()
    if hasattr(args, 'handler'):
        args.handler(args)
    else:
        # 未知のサブコマンドの場合はヘルプを表示
        parser.print_help()


if __name__ == "__main__":
    main()


================================================
FILE: src/gilot/core.py
================================================
from __future__ import annotations
from .filetracker import FileTracker

import git
import datetime
import json
import pandas as pd
from git.objects import Commit
from typing import Any, Iterator,Type,List,Optional,Callable,cast
from dataclasses import dataclass,asdict,fields
from dateutil.relativedelta import relativedelta
from logging import getLogger

logger = getLogger(__name__)


def text_to_date(date_text:str) -> datetime.date:
    return datetime.date.fromisoformat(date_text)


def date_to_text(date: datetime.date) -> str:
    return date.isoformat()


@dataclass
class Duration:

    since: datetime.date
    until: Optional[datetime.date]

    def since_text(self) -> str:
        return date_to_text(self.since)

    def __str__(self) -> str:
        since_text = self.since_text()
        until_text = self.until_text()
        return f"({since_text} - {until_text})"

    def until_text(self) -> str:
        if (self.until):
            return date_to_text(self.until)
        return "now"

    def delta(self) -> datetime.timedelta:
        if(self.until and self.since):
            return self.until - self.since
        return (datetime.date.today()) - self.since

    @classmethod
    def months(cls:Type[Duration], months: int,*,since:Optional[str] = None) -> Duration:
        if(since is None):
            # 今からnヶ月前　から　今　までの期間
            delta = relativedelta(months=-int(months))
            return cls(until=None,since=datetime.date.today() + delta)
        # 指定時刻からnヶ月間
        delta = relativedelta(months=int(months))
        since_date = text_to_date(since)
        until = since_date + delta

        return cls(until=until,since=since_date)

    @classmethod
    def range(cls:Type[Duration],since: str, until: str) -> Duration:
        return cls(since=text_to_date(since),until=text_to_date(until))


DEFAULT_DURATION = Duration.months(6)


@dataclass
class Repo:
    repo: git.Repo
    branch : str

    @classmethod
    def from_dir(
            cls,
            repo_dir:str,
            branch: str) -> Repo:
        return cls(repo=git.Repo(repo_dir), branch=branch)

    def commits(self, duration: Duration) -> Iterator[Commit]:

        return self.repo.iter_commits(self.branch,since=duration.since_text(),until=duration.until_text())


def timestamp_to_date_text(timestamp: int) -> str:
    return datetime.datetime.fromtimestamp(timestamp).strftime("%Y-%m-%d %H:%M:%S")


def is_merge(commit: Commit) -> bool:
    ret = (1 < len(commit.parents))
    if ret :
        logger.info(f"merge commit should be treated as 0 {commit}")
    return ret


@ dataclass
class CommitRecord:
    date: str
    hexsha : str
    author : str
    insertions : int
    deletions :int
    lines:int
    files: int
    files_json : Optional[str]

    @ classmethod
    def compose(cls, commit: Commit, full: bool = False) -> CommitRecord:
        if is_merge(commit):
            total = dict(insertions=0, deletions=0, lines=0, files=0)
            file_json = json.dumps(dict()) if full else None
        else:
            total = commit.stats.total
            file_json = json.dumps(commit.stats.files) if full else None

        return cls(
            date=timestamp_to_date_text(commit.committed_date),
            hexsha=commit.hexsha,
            author=commit.author.name,
            insertions=total["insertions"],
            deletions=total["deletions"],
            lines=total["lines"],
            files=total["files"],
            files_json=file_json
        )

    def to_dict(self) -> dict:
        return asdict(self)

    def expand(self) -> List[dict]:
        if (not self.files_json):
            return [self.to_dict()]

        date = self.date
        hexsha = self.hexsha
        author = self.author
        file_info = json.loads(self.files_json)

        return [dict(date=date,hexsha=hexsha,author=author,file_name=str(k),**v)
                for k,v in file_info.items()]

    def filter_files(self,is_match_file:Callable[[str],bool]) -> Optional[CommitRecord]:
        if (not self.files_json):
            return self

        file_info = json.loads(self.files_json)
        insertions = 0
        deletions = 0
        lines = 0
        files = 0

        for k,v in file_info.items():
            if (is_match_file(str(k))):
                insertions += v["insertions"]
                deletions += v["deletions"]
                lines += v["lines"]
                files += 1

        if(files == 0) :
            return None

        self.insertions = insertions
        self.deletions = deletions
        self.lines = lines
        self.files = files
        self.files_json = None
        return self


class CommitDataFrame(pd.DataFrame):
    _metadata = ['name']

    @property
    def _constructor(self):
        return CommitDataFrame

    @property
    def _constructor_sliced(self):
        return pd.Series

    @property
    def _constructor_expanddim(self):
        return pd.DataFrame

    DF_NULL = pd.DataFrame([], columns=[i.name for i in fields(CommitRecord)])
    DF_NULL["date"] = pd.to_datetime([])
    DF_NULL.set_index("date", inplace=True)

    @classmethod
    def from_dataframe(cls, df: pd.DataFrame) -> CommitDataFrame:
        if len(df) == 0:
            return cls(cls.DF_NULL.copy())
        return cls.up(df)

    @classmethod
    def up(cls, df: pd.DataFrame) -> CommitDataFrame:
        s = cls(df)
        if "date" in s.columns:
            s["date"] = pd.to_datetime(s["date"])
            s.set_index("date", inplace=True)
        s.sort_index()
        s = s.astype({"insertions":"int64","files":"int64","deletions":"int64","lines":"int64"})
        return CommitDataFrame(s)

    @classmethod
    def from_records(cls, commits: List[CommitRecord]) -> CommitDataFrame:
        if not commits:
            return cls(cls.DF_NULL.copy())
        return cls.from_dataframe(pd.DataFrame.from_records(
            [commit.to_dict() for commit in commits if commit is not None]))

    @classmethod
    def from_commits(cls, commits: List[git.Commit], *, full: bool = False) -> CommitDataFrame:
        if not commits:
            return cls(cls.DF_NULL.copy())
        return cls.from_records(
            [CommitRecord.compose(c, full=full) for c in commits])

    def expand_files(self, filter_func=None):
        if len(self) == 0:
            # 空のDataFrameの場合、最低限必要なカラムを持つDataFrameを返す
            df = pd.DataFrame(columns=["date", "hexsha", "author", "file_name", "insertions", "deletions", "lines"])
            df.set_index("date", inplace=True)
            return CommitDataFrame(df)

        records = []
        for index, row in self.iterrows():
            files = json.loads(str(row["files_json"]))
            for file_name, file_info in files.items():
                if filter_func and not filter_func(file_name):
                    continue
                record = {
                    "date": index,
                    "hexsha": row["hexsha"],
                    "author": row["author"],
                    "file_name": file_name,
                    "insertions": file_info["insertions"],
                    "deletions": file_info["deletions"],
                    "lines": file_info["lines"]
                }
                records.append(record)
        
        if not records:
            # フィルタリング後にレコードがない場合も同様の空のDataFrameを返す
            df = pd.DataFrame(columns=["date", "hexsha", "author", "file_name", "insertions", "deletions", "lines"])
            df.set_index("date", inplace=True)
            return CommitDataFrame(df)
        
        df = pd.DataFrame.from_records(records)
        df.set_index("date", inplace=True)
        return CommitDataFrame(df)

    def filter_files(self, is_match: Callable[[str], bool]) -> CommitDataFrame:
        if len(self) == 0:
            empty_df = pd.DataFrame([], columns=[i.name for i in fields(CommitRecord)])
            empty_df.index = pd.DatetimeIndex([])
            return CommitDataFrame(empty_df)
        records = [cr.filter_files(is_match) for cr in self.to_records()]
        filtered = [r for r in records if r is not None]
        if not filtered:
            empty_df = pd.DataFrame([], columns=[i.name for i in fields(CommitRecord)])
            empty_df.index = pd.DatetimeIndex([])
            return CommitDataFrame(empty_df)
        return CommitDataFrame.from_records(filtered)

    def to_records(self) -> List[CommitRecord]:
        def convert(index, row):
            return CommitRecord(date=str(index),**row.to_dict())
        return [convert(index, row) for index, row in self.iterrows()]


def from_csv(csvFileName: str) -> CommitDataFrame:
    return CommitDataFrame.from_dataframe(pd.read_csv(csvFileName))


def from_csvs(csvFileNames: List[str]) -> CommitDataFrame:
    df = pd.concat([pd.read_csv(i) for i in csvFileNames])
    return CommitDataFrame.from_dataframe(df.drop_duplicates(subset="hexsha"))


def from_dir(
        dirName: str = "./",*,
        branch: str = "origin/HEAD",
        duration: Duration = DEFAULT_DURATION,
        full : bool = False) -> CommitDataFrame:
    commits = Repo.from_dir(dirName, branch=branch).commits(duration)
    return CommitDataFrame.from_commits(commits,full=full)


================================================
FILE: src/gilot/filetracker.py
================================================

from __future__ import annotations

import re
from typing import Dict,List,Tuple,Optional
from dataclasses import dataclass
from logging import getLogger

logger = getLogger(__name__)

OPEN = "{"
CLOSE = "}"
TRANS = "=>"
FRAGMENT = "([^{}=>]*)"
SP = " "
BEGIN = "^"
END = "$"
OR = "|"
TRANSOPS = SP + TRANS + SP
SIMPLE = BEGIN + FRAGMENT + TRANSOPS + FRAGMENT + END
SEPARETE = BEGIN + FRAGMENT + OPEN + FRAGMENT + TRANSOPS + FRAGMENT + CLOSE + FRAGMENT + END

simple = re.compile(SIMPLE)
separete = re.compile(SEPARETE)


def track_name(before:str,after:str,begin:str = "",end:str = "") -> Tuple[str,str]:
    before_name = begin + before + end
    after_name = begin + after + end
    return (before_name,after_name)


def match(str:str) -> Optional[Tuple[str,str]]:
    mo = simple.match(str)
    if mo:
        return track_name(mo.group(1),mo.group(2))
    mo = separete.match(str)
    if mo:
        return track_name(mo.group(2),mo.group(3),mo.group(1),mo.group(4))
    return None


@dataclass
class FileTracker():
    track_map : Dict[str,str]

    def _search_recursive(self, file_name: str) -> str:
        if (file_name not in self.track_map):
            return file_name
        return self._search_recursive(self.track_map[file_name])

    def newest_name(self, file_expression: str) -> str:
        result = match(file_expression)
        name = file_expression if not result else result[1]
        newest_name = self._search_recursive(name)
        logger.debug(f"newestname {newest_name}")
        return newest_name

    @classmethod
    def create(cls, file_expressions: List[str]) -> FileTracker:
        track_map = dict()
        for fe in reversed(file_expressions):
            result = match(fe)
            if result:
                before = result[0]
                after = result[1]
                logger.debug(f"expression: {fe}")
                logger.debug(f"before {before} => after {after}")

                track_map[before] = after
                # A -> B ( A => B)
                # B -> C ( A => B, B => C )
                # C -> A ( [A => B], B => C, C => A ) delete A -> B
                if after in track_map:
                    logger.info(f"delete avoiding cyclic rename:{after}")
                    del track_map[after]

        return cls(track_map=track_map)


================================================
FILE: src/gilot/hotgraph.py
================================================
import os
from itertools import combinations
from logging import getLogger
from typing import List

import community
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
import timeout_decorator
from pyfpgrowth import find_frequent_patterns

from gilot.hotspot import get_hotspots

logger = getLogger(__name__)


def all_commit_list(df: pd.DataFrame) -> List[List[str]]:
    return df.groupby("hexsha").agg({"file_name": list})["file_name"].tolist()


def search_threshold(df,rank=70) -> int:
    vc = df["file_name"].value_counts()
    if (len(vc) > rank):
        v = vc.values[rank]
        return max(v, 3)
    return vc.values[-1]


def short_name(path:str, newline:bool) -> str:
    subdirname = os.path.basename(os.path.dirname(path))
    filepath = subdirname + "/" + os.path.basename(path)
    return filepath.replace("/", "/\n") if newline else filepath


def add_edge_with_weight(graph,pair,weight=1) :
    if(graph.has_edge(*pair)):
        graph.edges[pair[0],pair[1]]["weight"] += 1
    else:
        graph.add_edge(*pair, weight=weight)


def gen_commit_to_pattern(stop_retry: bool, timeout=10, max_retry=5, auto_increase_rate=1.3):

    if (stop_retry):
        logger.info(f"stop-retry {stop_retry}")
        return find_frequent_patterns
    logger.info(
        f"retry-info timeout {timeout }sec,max_retry {max_retry},auto_increasing_rate:{auto_increase_rate}")

    @timeout_decorator.timeout(timeout, timeout_exception=StopIteration)
    def commit_to_pattern(edge_info, th):
        return find_frequent_patterns(edge_info, th)

    def retry_commit_to_pattern(edge_info, th):
        nonlocal max_retry
        max_retry -= 1
        if (max_retry == 0):
            return None

        try:
            logger.info(f"trial threshold:{th}")
            return commit_to_pattern(edge_info, th)
        except StopIteration:
            retry_th = int(th * auto_increase_rate)
            logger.warning(f"timedout : retrying  {retry_th}")
            return retry_commit_to_pattern(edge_info, retry_th)

    return retry_commit_to_pattern


def set_hotspot_point(g, df):
    h_df = get_hotspots(df)
    for (n, d) in g.nodes(data=True):
        d["hotspot"] = h_df.loc[n, "hotspot"] if n in h_df.index else 0


def set_page_rank(g):
    pagerank = nx.pagerank(g)
    for (n, d) in g.nodes(data=True):
        d["pagerank"] = pagerank[n]


def set_partition_number(g):
    partition_map = community.best_partition(g)
    for (n, d) in g.nodes(data=True):
        d["partition_id"] = partition_map[n]


def graph_edge_size(g):
    return [min(d['weight'] * 0.5,5) for (a,b,d) in g.edges(data=True)]


def graph_label_name(g, newline):
    return dict([(n,short_name(n, newline)) for (n,d) in g.nodes(data=True)])


def graph_node_size(g):
    return [d["hotspot"] * 300 + d["pagerank"] * 5000 for (n, d) in g.nodes(data=True)]


def graph_node_color(g):
    return [d["partition_id"] for (n, d) in g.nodes(data=True)]


def plot_hotgraph(df: pd.DataFrame, *,
                  output_file_name=None,
                  rank=70,
                  stop_retry=False,
                  k=0.6,
                  font_size=10,
                  newline=False
                  ) -> None:

    commit_to_pattern = gen_commit_to_pattern(stop_retry)
    commit_list = all_commit_list(df)
    th = search_threshold(df,rank=rank)
    patterns = commit_to_pattern(commit_list, th)

    g = nx.Graph()
    for edge in patterns.keys():
        for a, b in combinations(edge, 2):
            add_edge_with_weight(g, (a, b))

    set_hotspot_point(g,df)
    set_page_rank(g)
    set_partition_number(g)

    edge_width = graph_edge_size(g)
    labels = graph_label_name(g, newline)
    node_size = graph_node_size(g)
    node_color = graph_node_color(g)

    plt.figure(figsize=(16,9))
    pos = nx.spring_layout(g, k=k, seed=2020)

    nx.draw_networkx_nodes(
        g,
        pos,
        node_color=node_color,
        cmap=plt.cm.Set3,
        edgecolors="blue",
        alpha=0.8,
        node_size=node_size)

    nx.draw_networkx_labels(
        g,
        pos,
        labels=labels,
        font_size=font_size,
        font_color="#333",
        font_weight="bold")

    nx.draw_networkx_edges(
        g,
        pos,
        alpha=0.3,
        edge_color='#05e',
        width=edge_width)

    plt.tight_layout(pad=0.1, w_pad=0)
    plt.axis("off")

    if (output_file_name):
        logger.info(output_file_name)
        plt.savefig(output_file_name, dpi=150, bbox_inches='tight')
    else:
        plt.show()


================================================
FILE: src/gilot/hotspot.py
================================================
import datetime

import numpy as np
import pandas as pd
from dateutil.relativedelta import relativedelta


def remove_outer_lines(df : pd.DataFrame) -> pd.DataFrame:
    outer_sup = np.percentile(df["lines"].values,99.5)
    outer_sub = np.percentile(df["lines"].values,0.5)
    return df[(outer_sub < df["lines"]) & (df["lines"] < outer_sup)].copy()


def get_hotspots(df : pd.DataFrame) -> pd.DataFrame:
    df = remove_outer_lines(df)
    now = datetime.datetime.now()
    a_year = relativedelta(months=-12)
    last = now + a_year
    oldest = last - now

    df["ntd"] = 1 - (df.index - now) / oldest
    df.loc[df['ntd'] < 0,'ntd'] = 0
    score = 1 / (1 + np.exp((-12 * df["ntd"]) + 12))

    df["hotspot"] = score * np.log10(df["lines"])
    by_file = df.groupby("file_name").sum()
    by_file = by_file.drop(columns=["ntd"])
    by_file["authors"] = df.groupby("file_name").nunique()["author"]
    by_file["commits"] = df.groupby("file_name").nunique()["hexsha"]
    by_file["edit_rate"] = by_file["insertions"] / by_file["lines"]
    result = by_file.sort_values("hotspot",ascending=False)
    return result.loc[:, ['hotspot', 'commits', 'authors', 'edit_rate', "lines"]].copy()


================================================
FILE: src/gilot/plotter.py
================================================
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


TITLE_SIZE = 15


def gini(x):
    # Mean absolute difference
    mad = np.abs(np.subtract.outer(x, x)).mean()
    rmad = mad / np.mean(x)
    # Gini coefficient
    return 0.5 * rmad


def lorenz(v):
    x = np.linspace(0., 100., 21)
    total = np.sum(v, dtype=np.float64)
    y = []
    for xi in x:
        yi_vals = v[v <= np.percentile(v, xi)]
        yi = (np.sum(yi_vals) / total) * 100.0
        y.append(yi)
    return x, y


def _ts_to_string(ts):
    pattern = r"(\d+)([a-zA-Z])"
    r = re.match(pattern, ts)
    if r == None:
        return ""

    MAP = dict(W="Weeks", D="Days", M="Months", Y="Years")
    unit = MAP[r.group(2).upper()]
    count = r.group(1)
    return f"{count} {unit}"


def _in_sprint(df, timeslot="2W"):
    if len(df) == 0:
        empty_df = pd.DataFrame([], columns=df.columns)
        empty_df["date"] = pd.to_datetime([])
        empty_df.set_index("date", inplace=True)
        empty_df["authors"] = 0
        empty_df["addedlines"] = 0
        return empty_df
    df_resampled = df.resample(timeslot).sum()

    df_resampled["authors"] = df["author"].resample(timeslot).nunique()

    df_resampled["addedlines"] = df_resampled["insertions"] - df_resampled["deletions"]
    return df_resampled


def _plot_gini(df, plt):
    v = df.lines.values
    bins, result = lorenz(v)
    gi = gini(v)
    plt.plot(bins, result, label="commit")
    plt.plot(bins, bins, '--', label="perfect equality")
    plt.xlabel("Percentile")
    plt.ylabel("Ratio")
    plt.xlim(0, 100)
    plt.fill_between(bins, bins, result, color="blue", alpha=0.2)
    plt.fill_between(bins, result, color="red", alpha=0.2)
    title_label = f"GINI COEFFICIENT: {gi:.1%}"
    plt.title(title_label, fontsize=TITLE_SIZE)
    plt.legend()
    pass


def _plot_hist(df, plt, ts):
    v = df.lines.values
    median = np.median(v)
    timeslot = _ts_to_string(ts)
    # 非推奨API回避: pandas+matplotlibでヒストグラム描画
    plt.hist(v, bins='auto', color='C0', alpha=0.7, edgecolor='black')
    plt.xlim(0,)

    plt.title("Histgram of Code Output", fontsize=TITLE_SIZE)
    _plot_text(plt, f"median={int(median) :,d} lines")
    plt.ylabel("")
    plt.xlabel(f"Code Output in {timeslot}")


def _plot_text(plt, text):
    ax = plt.gca()
    plt.text(0.99, 0.1, text,
             horizontalalignment='right',
             verticalalignment='top',
             bbox=dict(facecolor='#cccccc', alpha=0.5),
             transform=ax.transAxes)


def _plot_authors(df, plt):
    date = df.index.values
    authors = df["authors"]
    mean = authors.mean()
    plt.title(" Number of Actual Authors", fontsize=TITLE_SIZE)
    plt.plot(date, authors, marker=".", label="commit authors")
    plt.plot(date, np.ones(len(authors)) * mean, "--", label="mean")
    _plot_text(plt, f"mean = {mean :.1f} authors/timeslot")
    if len(date) > 1 :
        plt.xlim(date[0], date[-1])
    plt.ylabel("Unique number of committed author")
    plt.legend()


def _plot_code(df, plt):
    date = df.index.values
    total_change = df.lines.sum()
    total_added = (df.insertions - df.deletions).sum()
    refactor = 1 - total_added / total_change
    plt.title("Code Output and Productivity", fontsize=TITLE_SIZE)
    plt.plot(date, df.lines, label="lines")
    plt.plot(date, df.insertions, color="g", label="insertions")
    plt.plot(date, df.deletions, color="r", label="deletions")
    _plot_text(
        plt,
        f"lines={total_change:,d} ,added={total_added:,d}, refactor={refactor:.2f}")

    if len(date) != 1:
        plt.xlim(date[0], date[-1])
    plt.ylabel("Lines")
    plt.fill_between(
        date,
        df.insertions,
        df.deletions,
        where=df.insertions >= df.deletions,
        color="g",
        alpha=0.5,
        interpolate=True)
    plt.fill_between(
        date,
        df.insertions,
        df.deletions,
        where=df.insertions < df.deletions,
        color="r",
        alpha=0.5,
        interpolate=True)
    plt.legend()
    _plot_productivity(df,plt)


def _plot_productivity(df,plt):
    time = 0.5  # 2 weeks mostly 0.5 months
    beta = 0.3
    df["effort"] = df["authors"] * time
    effort = df["effort"]
    s_time = time ** (4 / 3)
    s_effort = (effort / beta)**(1 / 3)
    s_size = np.maximum(df["insertions"] - df["deletions"],df["lines"] / 2) * 0.7
    prod = s_size / (s_effort * s_time)
    level = prod_to_level(prod)
    max_level = int(level.max() + 3)
    df["level"] = level.rolling(3,center=True).mean()
    ax = plt.gca()
    ax2 = ax.twinx()
    ax2.grid(False)
    date = df.index.values
    ax2.plot(date,df["level"],color="gray",linestyle="--",marker="x")
    ax2.set_ylim([0, max_level])


def prod_to_level(prod) :
    a = 600.7669
    b = 1.272067
    return np.maximum(np.log(prod / a) / np.log(b),0)


def plot(df, timeslot='2W', output=False, name="[This Graph]"):
    sns.set()
    suptitle = f"{name} : created by 'gilot'"
    dfs = _in_sprint(df, timeslot=timeslot)
    plt.figure(figsize=(16, 9))
    plt.tight_layout(pad=0.05, w_pad=0)
    plt.suptitle(suptitle, fontsize=13, y=0.95, x=0.8)
    plt.subplots_adjust(wspace=0.15, hspace=0.4)
    # PLOT GINI / LORENTZ
    plt.subplot(2, 2, 1)
    _plot_gini(dfs, plt)

    # PlOT HIST
    plt.subplot(2, 2, 3)
    _plot_hist(dfs, plt, timeslot)

    # PLOT CODE
    plt.subplot(2, 2, 2)
    _plot_code(dfs, plt)

    # PLOT AUTHORS
    plt.subplot(2, 2, 4)
    _plot_authors(dfs, plt)
    if(output):
        plt.savefig(output, dpi=150, bbox_inches='tight')
    else:
        plt.show()


def info(df, timeslot="2W"):
    rdf = _in_sprint(df, timeslot)
    desc = rdf.describe().drop("count")
    dic = desc.to_dict()
    sdf = df.sum()
    lines = int(sdf.lines)
    added = int(sdf.insertions - sdf.deletions)
    # 0による除算を防ぐ
    if lines == 0:
        refactor = 0
    else:
        refactor = 1 - added / lines

    # 期間情報
    if len(df) > 0 and hasattr(df.index, 'min'):
        since = str(df.index.min())
        until = str(df.index.max())
    else:
        since = ""
        until = ""

    # gini計算
    gini_value = float(gini(rdf.lines.values)) if len(rdf) > 0 and hasattr(rdf, 'lines') else 0.0

    # timeslot文字列
    timeslot_str = _ts_to_string(timeslot)

    # 詳細な統計情報
    stats = {}
    for key in ['insertions', 'deletions', 'lines', 'files', 'authors', 'addedlines']:
        if key in dic:
            stats[key] = {
                'mean': float(dic[key].get('mean', 0)),
                'std': float(dic[key].get('std', 0)),
                'min': float(dic[key].get('min', 0)),
                '25%': float(dic[key].get('25%', 0)),
                '50%': float(dic[key].get('50%', 0)),
                '75%': float(dic[key].get('75%', 0)),
                'max': float(dic[key].get('max', 0)),
            }

    output = dict(
        gini=gini_value,
        output=dict(lines=lines, added=added, refactor=refactor),
        since=since,
        until=until,
        timeslot=timeslot_str,
        **stats
    )
    return output


def _top_authors(df,num):
    return df.author.value_counts()[:num].index.tolist()


def _count_commits(df, top=15, only=None):
    authors = only if only else _top_authors(df,top)
    index = df.resample("1W").sum().index
    result = pd.DataFrame([],index=index , columns=[*authors,'Others'])

    for a in authors:
        result[a] = df["hexsha"][df.author == a].resample("1W").nunique()

    result["Others"] = df["hexsha"][~df.author.isin(authors)].resample("1W").nunique()

    result.fillna(value=0,inplace=True)
    return result


def _commit_ratio(df):
    def ratio(s):
        total = np.sum(s)
        if total == 0:
            return s * 0.0
        return s / total
    return df.apply(ratio, axis=1)


def authors(df, output=False, top=None, name="--", only=None):
    sns.set_style("darkgrid")
    #plt.rcParams["font.family"] = "IPAGothic"
    result = _count_commits(df, top=top, only=only)

    fig = plt.figure(figsize=(16, 9))
    plt.suptitle(f"GIT LOG {name} AUTHORS REPORT created by gilot", fontsize=13, y=0.95, x=0.7)
    gs = fig.add_gridspec(10, 10)

    plt.tight_layout(pad=0.05, w_pad=0)

    plt.subplot(gs[0:5,0:8])
    plt.ylabel("number of commits")
    plt.title("Changes in the number of commits (stack graph by author)")
    result.plot.area(ax=plt.gca(), colormap="Spectral")
    plt.legend(loc='upper left', bbox_to_anchor=(1.0, 1.01))
    plt.xlabel("")
    plt.subplot(gs[5:10,0:8])
    plt.ylabel("commit ratio")
    ratio = _commit_ratio(result)
    ratio.plot.area(ax=plt.gca(), colormap="Spectral",legend=None)
    plt.ylim(0,1)
    if(output):
        plt.savefig(output, dpi=150, bbox_inches='tight')
    else:
        plt.show()


================================================
FILE: tests/__init__.py
================================================


================================================
FILE: tests/conftest.py
================================================
import sys
sys.path.insert(0,"./src")


================================================
FILE: tests/test_app.py
================================================
import sys
import os
import shutil
import pytest
from gilot.app import parser,args_to_duration
import json
import subprocess


@pytest.fixture
def tempdir():
    os.makedirs("./temp/", exist_ok=True)
    yield
    shutil.rmtree("./temp/")


def test_log_repo():
    a = parser.parse_args(["log","./"])
    assert a.repo == "./"


def test_log_branch():
    a = parser.parse_args(["log","./","-b","master"])
    assert a.repo == "./"
    assert a.branch == "master"
    b = parser.parse_args(["log","./","--branch","master"])
    assert b.repo == "./"
    assert b.branch == "master"
    c = parser.parse_args(["log","./"])
    assert c.repo == "./"
    assert c.branch == "origin/HEAD"


def test_log_output():
    a = parser.parse_args(["log","./","--output","target.file"])
    assert a.output == "target.file"
    b = parser.parse_args(["log","./"])
    assert b.output == sys.__stdout__


def test_log_duration():
    d = args_to_duration(parser.parse_args(["log","./"]))
    assert d.until_text() == "now"
    a = args_to_duration(parser.parse_args(
        ["log","./","--since","2019-01-20","--until","2020-01-20"]))
    assert a.since_text() == "2019-01-20"
    assert a.until_text() == "2020-01-20"

    b = args_to_duration(parser.parse_args(
        ["log","./","--since","2019-01-20","--month","10"]))
    assert b.since_text() == "2019-01-20"
    assert b.until_text() == "2019-11-20"

    c = args_to_duration(parser.parse_args(
        ["log","./","--month","10"]))
    assert c.until_text() == "now"

    d = args_to_duration(parser.parse_args(
        ["log","./","--since","2019-01-01","--month","10"]))
    assert d.since_text() == "2019-01-01"
    assert d.until_text() == "2019-11-01"

    d = args_to_duration(parser.parse_args(
        ["log","./","--since","2019-01-01"]))
    assert d.since_text() == "2019-01-01"
    assert d.until_text() == "2019-07-01"


def test_hotspot_option():
    a = parser.parse_args(["hotspot","--ignore-files","*.rb"])
    assert a.ignore_files == ["*.rb"]
    b = parser.parse_args(["hotspot","--allow-files","*.rb"])
    assert b.allow_files == ["*.rb"]
    c = parser.parse_args(["hotspot","-n","10","-i","input.csv"])
    assert c.num == 10
    assert c.input == ["input.csv"]


def test_handlers(tempdir):
    # log をえて、出力
    log = parser.parse_args(["log", "./", "--full", "--output", "temp/_test.csv", "--month", "60"])
    assert log.handler
    log.handler(log)
    plot = parser.parse_args(
        ["plot", "-i", "./temp/_test.csv", "--allow-files", "*.py", "--output", "temp/_test.png"])
    assert plot.handler

    plot.handler(plot)
    info = parser.parse_args(
        ["info", "-i", "./temp/_test.csv", "--allow-files", "*.py"])
    assert info.handler
    info.handler(info)

    hotspot = parser.parse_args(
        ["hotspot", "-i", "./temp/_test.csv", "--allow-files", "*.py"])
    assert hotspot.handler
    hotspot.handler(hotspot)

    hotspot2 = parser.parse_args(
        ["hotspot", "-i", "./temp/_test.csv", "--ignore-files", "*.lock","--csv"])
    assert hotspot2.handler
    hotspot2.handler(hotspot2)

    hotgraph = parser.parse_args(["hotgraph",
                                  "-i",
                                  "./temp/_test.csv",
                                  "--ignore-files",
                                  "*.lock",
                                  "--output",
                                  "./temp/hoge.png"])
    assert hotgraph.handler

    hotgraph.handler(hotgraph)
    hotgraph = parser.parse_args(["hotgraph",
                                  "-i",
                                  "./temp/_test.csv",
                                  "--ignore-files",
                                  "*.lock",
                                  "--rank","1",
                                  "--output",
                                  "./temp/hoge.png"])
    assert hotgraph.handler

    author = parser.parse_args(["author",
                                "-i",
                                "./temp/_test.csv",
                                "--ignore-files",
                                "*.lock",
                                "--top","1",
                                "--output",
                                "./temp/hoge.png"])
    assert author.handler
    author.handler(author)


def test_gilot_info_react_csv_matches_readme():
    # sample/react.csvをinputにgilot infoを実行
    csv_path = os.path.join(os.path.dirname(__file__), '../sample/react.csv')
    # gilot info -i sample/react.csv をサブプロセスで実行
    result = subprocess.run([
        sys.executable, '-m', 'gilot.app', 'info', '-i', csv_path
    ], capture_output=True, text=True)
    assert result.returncode == 0, f"gilot info failed: {result.stderr}"
    output = json.loads(result.stdout)

    # READMEの期待値
    expected = {
        "gini": 0.41398343573957913,
        "output": {
            "lines": 230004,
            "added": 66676,
            "refactor": 0.7101093894019235
        },
        "since": "2020-01-02 00:58:47",
        "until": "2020-06-19 10:18:56",
        "timeslot": "2 Weeks",
        "insertions": {
            "mean": 11410.76923076923,
            "std": 10912.175548088828,
            "min": 471.0,
            "25%": 3723.0,
            "50%": 7371.0,
            "75%": 17712.0,
            "max": 39681.0
        },
        "deletions": {
            "mean": 6281.846153846154,
            "std": 4380.664938989549,
            "min": 181.0,
            "25%": 3466.0,
            "50%": 5009.0,
            "75%": 9850.0,
            "max": 13477.0
        },
        "lines": {
            "mean": 17692.615384615383,
            "std": 14508.378898292196,
            "min": 652.0,
            "25%": 7369.0,
            "50%": 10780.0,
            "75%": 26834.0,
            "max": 52914.0
        },
        "files": {
            "mean": 361.61538461538464,
            "std": 262.79635286077144,
            "min": 35.0,
            "25%": 179.0,
            "50%": 359.0,
            "75%": 447.0,
            "max": 1062.0
        },
        "authors": {
            "mean": 13.615384615384615,
            "std": 4.8740548064635325,
            "min": 3.0,
            "25%": 10.0,
            "50%": 15.0,
            "75%": 16.0,
            "max": 21.0
        },
        "addedlines": {
            "mean": 5128.923076923077,
            "std": 8126.4102003030675,
            "min": -1337.0,
            "25%": -88.0,
            "50%": 2193.0,
            "75%": 6065.0,
            "max": 26448.0
        }
    }
    # 数値は小数点誤差を許容して比較
    def approx_equal(a, b, tol=1e-6):
        if isinstance(a, float) and isinstance(b, float):
            return abs(a - b) < tol
        return a == b

    def recursive_compare(d1, d2):
        assert d1.keys() == d2.keys(), f"Keys mismatch: {d1.keys()} vs {d2.keys()}"
        for k in d1:
            if isinstance(d1[k], dict):
                recursive_compare(d1[k], d2[k])
            else:
                assert approx_equal(d1[k], d2[k]), f"Mismatch at {k}: {d1[k]} vs {d2[k]}"

    recursive_compare(expected, output)


================================================
FILE: tests/test_core.py
================================================
import gilot.core
import pytest
import os
import shutil
from fnmatch import fnmatch
from gilot.core import Duration


@pytest.fixture
def tempdir():
    os.makedirs("./temp/", exist_ok=True)
    yield
    shutil.rmtree("./temp/")


def echo(p):
    assert isinstance(p,Duration)
    return p


def test_duration_constructor():
    assert echo(Duration.range("2012-10-10", "2020-10-10"))
    assert echo(Duration.months(5))


def test_commit_record(tempdir):
    df = gilot.core.from_dir("./")
    assert isinstance(df, gilot.core.CommitDataFrame) ,"i"
    df.to_csv("./temp/self.csv")
    csv_df = gilot.core.from_csv("./temp/self.csv")
    assert isinstance(csv_df, gilot.core.CommitDataFrame)
    df.to_csv("./temp/self2.csv")
    csv_df2 = gilot.core.from_csvs(["./temp/self.csv","./temp/self2.csv"])
    assert isinstance(csv_df2, gilot.core.CommitDataFrame)
    assert(len(csv_df2["hexsha"].value_counts()) == len(csv_df2))


def test_expander():
    df = gilot.core.from_dir("./", full=True, duration=Duration.months(60))
    import json
    for v in df["files_json"].values:
        obj = json.loads(str(v))
        assert isinstance(obj, dict)

    edf = df.expand_files()

    assert (len(edf["file_name"]) > 0)


def test_expander_with_filter():
    def is_match(file_name):
        return fnmatch(file_name,"*app.py")
    df = gilot.core.from_dir("./", full=True, duration=Duration.months(60))
    import json
    for v in df["files_json"].values:
        assert isinstance(json.loads(str(v)),dict)

    edf = df.expand_files(is_match)
    odf = df.expand_files()

    assert (len(edf["file_name"]) > 0)
    assert (len(odf["file_name"]) > 0)
    assert (len(odf["file_name"]) > len(edf["file_name"]))